ABSTRACT:

The Lower Nooksack Water Budget provides an estimate of the land components of the water cycle in the Lower Nooksack Subbasin’s sixteen drainages, as they vary seasonally throughout the year. It is intended to provide a common body of factual information to support water resources professionals and their salmon recovery partners working with the WRIA 1 Joint Board, in water supply planning and instream flow negotiations. This Collection links to a list of Collections relating to each Chapter in the Lower Nooksack Water Budget report.

ABSTRACT:

This overview introduces and summarizes the Lower Nooksack Water Budget, full technical report, which can be accessed at the Water Resource Inventory Area 1 (WRIA 1) Watershed Management website document library (http://wria1project.whatcomcounty.org/Home/Water-Budget/97.aspx).

This resource is a subset of the LNWB Ch01 Public Processes Collection Resource.

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Overview:
Topnet-WM refers to the Water Management version of Topnet developed as a work product for the Utah State University WRIA 1 Watershed Management Project (Tarboton, 2007). This version of the model evolved from the Topnet Model developed in a collaboration between NIWA New Zealand and Utah State University (Bandaragoda et al., 2004; Ibbitt and Woods, 2004) that combines TOPMODEL concepts (Beven and Kirkby, 1979; Beven et al., 1995a) for the simulation of relatively small drainages combined with channel routing. This approach provides a modeling system that can be applied over large watersheds using smaller drainages within the large watershed as model elements.

In Topnet-WM, spatial variability is represented by subdividing the watershed domain into model elements at the scale of drainages. Within drainages, the modeling is essentially lumped but includes parameterization of some subgrid variability, notably (1) the wetness index, used to parameterize the variability of soil moisture, (2) a depletion curve, used to parameterize the variability of snow water equivalent, (3) the fraction of area that is irrigated, and (4) areas with artificial drainage. Surface runoff and baseflow can be designated as model outputs at multiple nodes within a drainage. The model may thus be classified as semi-distributed.

Topnet-WM includes many enhancements beyond the original Beven and Kirkby TOPMODEL, such as: (1) calculation of reference evapotranspiration using the ASCE standardized Penman-Monteith method (Allen et al., 2005; Jensen et al., 1990); (2) calculation of snowmelt using the Utah Energy Balance Snowmelt model (Tarboton et al., 1995a); (3) the partition of model elements into separate components representing irrigated and non-irrigated areas; (4) artificial drainage to represent the effect of ditch and tile drained areas on the runoff response; (5) the partition of the model elements into pervious and impervious areas to allow representation of urbanization; (6) options for the diversion and storage of water under different management options; and (7) components to calculate water use and implement water right rules.

The Lower Nooksack Water Budget will be estimated based on the distributed hydrologic model, Topnet-WM. The Lower Nooksack Water Budget included updating the data inputs and model calibration, which requires a thorough understanding of how the model represents physical hydrologic processes. In order to guide the development of model inputs and analysis of model outputs, the project team has edited and reviewed portions of the WRIA 1 Water Management Project Phase III Task 4.1 report (Tarboton, 2007) to include in the general description of the Topnet-WM model that follows. This chapter provides reference to the details of the model processes used by Topnet-WM to convert data inputs into model outputs.

This resource is a subset of the LNWB Ch02 Model Processes Collection Resource.

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Overview:
The Lower Nooksack Water Budget Project involved assembling a wide range of existing data related to WRIA 1 and specifically the Lower Nooksack Subbasin, updating existing data sets and generating new data sets. This Data Management Plan provides an overview of the data sets, formats and collaboration environment that was used to develop the project. Use of a plan during development of the technical work products provided a forum for the data development and management to be conducted with transparent methods and processes. At project completion, the Data Management Plan provides an accessible archive of the data resources used and supporting information on the data storage, intended access, sharing and re-use guidelines.

One goal of the Lower Nooksack Water Budget project is to make this “usable technical information” as accessible as possible across technical, policy and general public users. The project data, analyses and documents will be made available through the WRIA 1 Watershed Management Project website http://wria1project.org. This information is intended for use by the WRIA 1 Joint Board and partners working to achieve the adopted goals and priorities of the WRIA 1 Watershed Management Plan.

Model outputs for the Lower Nooksack Water Budget are summarized by sub-watersheds (drainages) and point locations (nodes). In general, due to changes in land use over time and changes to available streamflow and climate data, the water budget for any watershed needs to be updated periodically. Further detailed information about data sources is provided in review packets developed for specific technical components including climate, streamflow and groundwater level, soils and land cover, and water use.

Purpose:
This project involves assembling a wide range of existing data related to the WRIA 1 and specifically the Lower Nooksack Subbasin, updating existing data sets and generating new data sets. Data will be used as input to various hydrologic, climatic and geomorphic components of the Topnet-Water Management (WM) model, but will also be available to support other modeling efforts in WRIA 1. Much of the data used as input to the Topnet model is publicly available and maintained by others, (i.e., USGS DEMs and streamflow data, SSURGO soils data, University of Washington gridded meteorological data). Pre-processing is performed to convert these existing data into a format that can be used as input to the Topnet model. Post-processing of Topnet model ASCII-text file outputs is subsequently combined with spatial data to generate GIS data that can be used to create maps and illustrations of the spatial distribution of water information. Other products generated during this project will include documentation of methods, input by WRIA 1 Joint Board Staff Team during review and comment periods, communication tools developed for public engagement and public comment on the project.

In order to maintain an organized system of developing and distributing data, Lower Nooksack Water Budget project collaborators should be familiar with standards for data management described in this document, and the following issues related to generating and distributing data:
1. Standards for metadata and data formats
2. Plans for short-term storage and data management (i.e., file formats, local storage and back up procedures and security)
3. Legal and ethical issues (i.e., intellectual property, confidentiality of study participants)
4. Access policies and provisions (i.e., how the data will be made available to others, any restrictions needed)
5. Provisions for long-term archiving and preservation (i.e., establishment of a new data archive or utilization of an existing archive)
6. Assigned data management responsibilities (i.e., persons responsible for ensuring data Management, monitoring compliance with the Data Management Plan)

This resource is a subset of the LNWB Ch03 Data Processes Collection Resource.

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Overview:
The model of watershed hydrology and water management used for the Lower Nooksack Water Budget is Topnet-WM, developed for Water Resources Inventory Area 1 (WRIA 1) in an effort led by researchers from Utah State University, as reported in peer-reviewed publications (Bandaragoda et al., 2004; Ibbitt and Woods, 2004; Tarboton, 2007). The model has also been applied, at finer spatial resolution, to the Fishtrap Creek and Bertrand Creek watersheds (Bandaragoda, 2008; Bandaragoda and Greenberg, 2009). The model processes of Topnet-WM are described in detail in Chapter 2 Model Processes. The daily meteorological variables required by Topnet-WM are precipitation, temperature (minimum and maximum), and wind speed.

Prior to the Lower Nooksack Water Budget project, WRIA 1 Topnet-WM used interpolated climate data (1946-2006) from 19 weather stations located within or near the WRIA 1 boundary. A significant component of the Lower Nooksack Water Budget Project was to update Topnet-WM to use the high resolution (1/8 lat/long degree; approximately one data point every 8 miles) gridded climate dataset that is updated and distributed, on an ongoing basis, by the University of Washington (UW) Land Surface Hydrology Research Group1 , following methods described in Maurer et. al. (2002) and Hamlet and Lettenmaier (2005). This dataset includes daily precipitation, wind speed, and daily maximum and minimum temperatures over the 1915 through 2011 water years (October 1 through September 30).

Figure 1 shows the distribution of the updated mean annual precipitation distribution derived from the Lower Nooksack Water Budget Topnet-WM gridded climate data for the 172 drainages (black dots) in WRIA 1. The lowest annual precipitation values are around Lummi Island and Bellingham (31-38 inches per year) and the highest precipitation values are near Mount Baker (121-207 inches per year). The increase in annual precipitation follows a gradient of increase from the west coast of the watershed to the eastern mountains, reflecting the role of orographic uplift of moist oceanic air masses in generating precipitation in this region.

Purpose:
The purpose for updating climate data used for watershed model inputs is to use the most current and up to date datasets. For the Lower Nooksack Water Budget Topnet-WM model, this includes new Snotel stations, an additional 8 years of daily climate data, and a higher resolution data product, compared to the initially developed Topnet-WM (Tarboton, 2007), which was populated with climate data ending in 2004. Updated climate data helps build our knowledge of the watershed system, since we have more information about when and where water is input to the system as rain and/or snow.

This resource is a subset of the Lower Nooksack Water Budget (LNWB) Collection Resource.

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TOPNET Water Management Model Inputs : Climate and Precipitation data inputs.

This resource is a subset of the LNWB Ch04 Climate Data Collection Resource.

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Matlab code to process Climate data.

This resource is a subset of the LNWB Ch04 Climate Data Collection Resource.

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Example Topnet-WM model climate output text files

This resource is a subset of the LNWB Ch04 Climate Data Collection Resource.

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Geodatabase of GIS files of grid points and drainage centroids for WRIA 1

This resource is a subset of the LNWB Ch04 Climate Data Collection Resource.

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Overview:
Land cover mapping represents the coverage of vegetation, bare, wet and built surfaces (developed and natural surfaces) at a given point in time. The existing land cover map was developed by Whatcom County Planning and Development Services (PDS) during spring of 2012 for the Lower Nooksack Water Budget. The dataset represents ground conditions between 2006 and 2010. The project team created the existing condition land cover dataset by combining local and regional datasets to get the most accurate and current data for the U.S. and Canadian portions of WRIA 1. The development of the existing land cover map includes 14 land cover categories; each has a unique impact on the water balance. The agricultural land cover class was further classified into crop types.

Land cover and crop types influence evapotranspiration and infiltration, playing an important role in determining the watershed’s water balance. Land cover data provides information used to parameterize the water movement through the vegetation canopy and water demand of plant evapotranspiration in the estimation of the water budget by the hydrology model.

Land cover changes over time, as exemplified by comparing the existing and historic land cover data in WRIA 1, displayed in Figure 1 and Figure 2. Historic land cover mapping developed by Utah State University (Winkelaar, 2004) as part of the WRIA 1 Watershed Management Project was used to represent land cover/land use for the undepleted flow simulations. This work was done using a suite of studies and ancillary datasets, including turn of the century GLO maps and NRCS soils data. Methods and sources more thoroughly described in Mapping Methodology and Data Sources for Historic Conditions Landuse/ Landcover Within Water Resource Inventory Area 1 (WRIA1) Washington, U.S.A. The historic land cover map includes 10 land cover classes.

Purpose:
Within the Topnet-WM hydrologic model used to estimate the Lower Nooksack Water Budget, the local land cover type is used to parameterize the water movement through the vegetation canopy and water demand for plant evapotranspiration, as described in detail in Chapter 2: Water Budget Model. Water input to the canopy comes from rainfall, snowmelt, and irrigation. The process of some water retention by the canopy is known as interception. Potential evapotranspiration is first satisfied from the canopy interception storage. Water that passes through the canopy to the soil becomes input to the vadose zone soil storage. The vadose zone is the unsaturated soil region above the water table. Potential evapotranspiration not satisfied from the interception storage becomes potential evapotranspiration from the vadose zone soil storage. The model calculates crop evapotranspiration using the Penman-Monteith method. Irrigation requirements are calculated using potential crop evapotranspiration and irrigation efficiency. Land cover mapping also identifies impervious surfaces where water directly runs off, as well as lakes and wetlands where water is stored and evaporates.

This resource is a subset of the Lower Nooksack Water Budget (LNWB) Collection Resource.

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Matlab code to convert raster, lookup tables, and shapefile data to area averaged parameter values.

This resource is a subset of the LNWB Ch05 Land Cover Collection Resource.

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This resource contains two files to recognize Geospatial organizations. Lulccharts_wria1.xlsx contains the spatial extents, land cover codes, tables, and charts for the WRIA1 region. Lulc_charts_lowerNookonly.xlsx contains only the spatial extents for the Lower Nooksack Subbasin of the WRIA1 region.

This resource is a subset of the LNWB Ch05 Land Cover Collection Resource.

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The GIS data contains Whatcom County, Washington Agricultural land cover analysis and land cover shapes.

This resource is a subset of the LNWB Ch05 Land Cover Collection Resource.

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wria1_lulc_water_budget.mdb is an ArcGIS geodatabase meant to generate estimate land cover model inputs, and all other layer files (.lyr) are meant to provide land-use classifications in the Whatcom County, Washington region.

This resource is a subset of the LNWB Ch05 Land Cover Collection Resource.

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Overview:
Water, in its many forms is one of Whatcom County’s signature features from snow-capped mountains,to our rainy climate, salmon-bearing streams, wetlands, lakes, marine waters, and marine shorelines. Five distinct hydrologic components control the storage and movement of water through the canopy and soils: canopy interception store (green trees), upper soil zone (vadose zone, brown soil fill) store, groundwater saturated zone (gray soil fill), channel flow (blue), and artificial drainage (blue line from agriculture to channel). Surface water inputs from direct precipitation, throughfall through the vegetation canopy, and irrigation are taken as input to the unsaturated, or vadose zone soil store. The unsaturated portion of the upper soil layer (brown), or vadose zone, is shown with recharge water (blue downward line) infiltrating the surface layer of soils, draining through the unsaturated zone (brown), to recharge the saturated zone (gray). The thickness of the vadose zone changes as the water table level (hashed gray and brown interface) shifts up and down, depending on the water held in the saturated zone. Based on the input and storage in the vadose zone, recharge to groundwater (gray, saturated zone) and surface water runoff is calculated. The vadose zone soil store is decreased by artificial drainage, representing ditch and tile drains that remove water directly from the vadose zone soil store to channels. The vadose zone soil store calculation also accounts for potential upwelling from groundwater where the water table is shallow. The groundwater saturated zone calculations account for recharge, upwelling and groundwater pumping and produce baseflow as an output. In the Lower Nooksack Water Budget, baseflow is defined as the outflow from the saturated zone and referred to as groundwater contribution; and baseflow and surface runoff are combined to calculate channel flow.

Purpose:
The baseflow in streams is supported by the gradual drainage of groundwater in shallow aquifer systems. The rate of this drainage depends on the amount of water stored in shallow aquifers (depth to water table) and the hydraulic properties of the aquifer, specifically the lateral hydraulic conductivity, or its depth integral, transmissivity. The amount of water stored depends on recharge, the vertical movement of water through unsaturated soils from the surface into the shallow groundwater. The rate of recharge is determined by the supply of water above. This is a function of whether surface water input is retained in the soil zone where it is taken up by plant roots and becomes evapotranspiration, or whether it infiltrates beyond the root zone and percolates to aquifers. These processes depend on the properties of the soils, such as porosity, field capacity, and hydraulic conductivity. The representation of the hydrologic processes of recharge and drainage to baseflow on a drainage scale is done using estimates based on measured data at point locations, as well as soil texture information. As more data is collected, information about subsurface processes can be incorporated into the model representation.

For the Lower Nooksack Water Budget soils parameters, soils data was compiled from both local and federal datasets. Using data available from the Natural Resource Conversation Service (NRCS – formerly the Soil Conservation Service) soils databases (NRCS; SSURGO and STATSGO (www.soilsdatamart.gov)), we have used estimates of averaged soils parameter values over each drainage area as data inputs for the hydrology model compiled in previous work (Tarboton, 2007). These soil parameters include plant available soil moisture, soil depth, hydraulic conductivity, and wetting front suction. Earlier calibrations of Topnet-WM showed that the most sensitive and therefore important soil parameters controlling baseflow movement are saturated soil store sensitivity (f) and soil profile lateral conductivity or transmissivity (To). The Lower Nooksack transmissivity parameters were derived from aquifer hydraulic conductivity values for specific wells, completed within shallow near surface aquifers, as described in the U.S. Geological Survey (USGS) Lynden-Everson-Nooksack-Sumas (LENS) Study (Cox and Kahle, 1999). Although the variability in well data is high given the heterogeneity of glacial and alluvial deposits, interpolating available well data to derive drainage average values captures the drainage level heterogeneity. Here changes in average depth to water table described in the Department of Ecology Study, Nooksack Watershed Surficial Aquifer Characterization (Tooley and Erickson, 1996), were used. Water movement through the surficial aquifer is assumed to decrease exponentially as the depth to the water table increases based on the Topmodel algorithm (Beven, et al., 1995a).

This resource is a subset of the Lower Nooksack Water Budget (LNWB) Collection Resource.

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Soil data from the NRCS lower resolution State Soil Geographic (STATSGO) Database.

The SSURGO data are generally not available in uninhabited landscapes with dense vegetation, such as National Forest lands. SSURGO data are available everywhere in the Lower Nooksack Subbasin. Where SSURGO data are not available in WRIA 1, STATSGO soils datasets were merged to parameterize Topnet-WM (see Tarboton, 2007, this same data layer will be used in this Lower Nooksack Water Budget project. The soils data generally pertains to the upper 80 inches of surficial material, with data in WRIA 1 ranging from 0.03 inches to 2 feet. Soils parameterization in the 2012 work is derived using information from the soils database using data extraction and depth averaging of publicly available soils data accessible through the USDA-NRCS Soils Data Mart1 . A search for soils data for Canada was completed during earlier work (Tarboton, 2007), but adequate data available in electronic form was not found at that time

This resource is a subset of the LNWB Ch06 Soil Processes and Inputs Collection Resource.

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GIS raster grids of soils layers for WRIA 1 and Bertrand Creek and Fishtrap Creek spatial extent, including intermediate files.

This resource is a subset of the LNWB Ch06 Soil Processes and Inputs Collection Resource.

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Soil data from the NRCS high resolution Soil Survey Geographic (SSURGO) Database.

The SSURGO data are generally not available in uninhabited landscapes with dense vegetation, such as National Forest lands. SSURGO data are available everywhere in the Lower Nooksack Subbasin. Where SSURGO data are not available in WRIA 1, STATSGO soils datasets were merged to parameterize Topnet-WM (see Tarboton, 2007, this same data layer will be used in this Lower Nooksack Water Budget project. The soils data generally pertains to the upper 80 inches of surficial material, with data in WRIA 1 ranging from 0.03 inches to 2 feet. Soils parameterization in the 2012 work is derived using information from the soils database using data extraction and depth averaging of publicly available soils data accessible through the USDA-NRCS Soils Data Mart1 . A search for soils data for Canada was completed during earlier work (Tarboton, 2007), but adequate data available in electronic form was not found at that time

This resource is a subset of the LNWB Ch06 Soil Processes and Inputs Collection Resource.

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This resource contains parameter grids (Ascii files) and two Excel spreadsheets which are the Land Cover Model Parameter Lookup Tables (i.e., lulc_existing.xls and lulc_historic.xls). The lulcExisting.xls lookup table separates the monthly crop coefficients according to WRIA1 land cover class. lulcHistoric.xls contains some historic land cover classes that were not used within the 2012 Lower Nooksack Water Budget.

This resource is a subset of the LNWB Ch05 Land Cover Collection Resource.

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The Lower Nooksack Subbasin is comprised of a variety of land and water uses both agricultural and nonagricultural. The water uses, described in this chapter include municipal, industrial, residential, and commercial. The following analysis of nonagricultural water use is divided into three sections: municipal/industrial, residential, and commercial/industrial.

The utilities that serve municipal/industrial customers in the Lower Nooksack Subbasin include the City of Bellingham, the City of Everson, the City of Ferndale, the City of Lynden, and the PUD #1 of Whatcom County. For these utilities, large municipal user water system records were obtained for years 2007 through 2011. Averages for this five year period were used in the model for water use, return flows, and interbasin transfers. The average annual diversion by the large utilities municipalities in our analysis totals nearly 25 cfs, 82% of which are transferred out of the Nooksack basin. The majority of that water serves Cherry Point industries; a small amount serves out-of-basin irrigators.

Residential, or domestic, water use was estimated according to population. Population and per capita water use rates are the foundation for calculating an average daily residential water use for each drainage in the Lower Nooksack Subbasin. 2010 Census geospatial census data were used to calculate the population for each drainage. The 2010 population for the Lower Nooksack Subbasin was 46,204, up 8,345 since the 2000 census. A per capita demand rate of 88 gallons per capita per day was agreed upon by the WRIA 1 Joint Board Staff Team to use as input along with the new population numbers. Seasonal demand factors were also determined to distribute monthly water use such that maximum demand occurred in the summer months and minimum demand in the winter.

Businesses and other operations that are not supplied water by a municipality are either served by a smaller public water system or are self-supplied. For commercial users not within the service area of the municipalities listed above, water use was estimated based on average use rates agreed on in 2003 with the WRIA 1 Water Quantity Technical Team. Commercial use was based on the type of operation identified in the Whatcom County Assessor records.

This resource is a subset of the LNWB Ch08 Water Management - industrial, residential, and commercial water use Collection Resource.

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Overview:
Artificial drainage is used throughout WRIA 1 to aid in the flow of water on top of or through the soil, sometimes to slow it down, other times to direct it to a specific location at any given depth of the landscape. Some of these systems are critical to the farm operations that make Whatcom County one of Washington’s top agricultural producers, others help riverside and lowland communities alleviate the impacts of high flowing rivers and streams. In high precipitation events, increased numbers of flow pathways provided by artificial drains may increase the peak stormwater quantities and contribute to flood impacts. Some artificial drains may even offer opportunities to improve low instream flows in the late season.

Purpose:
The model feature that represents artificial drainage has been incorporated into WRIA 1 Topnet Water Management (Topnet-WM) because of the assumption that agricultural drainage installed during development of agriculture in WRIA 1 has altered the runoff processes to a large enough degree that these alterations should be part of the simulation. Calculating how the ditches and tiles influence the drainage of the soils was done based on a drainage coefficient from NRCS technical guides. The Lower Nooksack Water Budget project team used previously existing (2007 Topnet-WM model) compiled information, data and maps of the many ditches and tile drains that exist in the Lower Nooksack study area in order to develop the artificial drainage inputs for the 2012 work conducted on the Lower Nooksack Water Budget.

This resource is a subset of the Lower Nooksack Water Budget (LNWB) Collection Resource.

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Correspondence between David Tarboton and Becky Peterson on drainage procedures, and two memos describing PhaseIII Task4 work.

Within the Rainfall-Runoff Transformation of Topnet-WM there are five subcomponents: canopy interception store, vadose zone soil store, groundwater saturated zone, channel flow, and artificial drainage. Surface water input to the canopy interception store comprises rainfall and snow as well as sprinkler irrigation. Throughfall is computed based upon the canopy interception capacity, surface water input, and water in canopy storage and is taken as input to the vadose zone soil store. Potential evapotranspiration not satisfied from the interception store becomes potential evapotranspiration from the vadose zone soil store. Drip irrigation is also an input to the vadose zone soil store. Based on the input and storage in the vadose zone soil storage, recharge to groundwater and surface runoff is calculated. The vadose zone soil storage is depleted for areas with artificial drainage, representing ditch and tile drains that remove water directly from the vadose zone soil storage to stream channels. The vadose zone soil store calculation also accounts for potential upwelling from groundwater where the water table is shallow. The groundwater saturated zone calculations account for recharge, upwelling and groundwater pumping and produce baseflow as an output. Baseflow and surface runoff from the vadose zone soil store are combined to calculate channel flow.

This resource is a subset of the LNWB Ch09 Artificial Drainage Collection Resource.

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Geodatabase of Lower Nooksack and WRIA 1 ditches and tile drain areas.

This resource is a subset of the LNWB Ch09 Artificial Drainage Collection Resource.

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Overview:
Streamflow is part of the dynamic hydrologic system that supports a range of water dependent activities in Whatcom County, including farming, fishing and recreation. The relationship between streamflow, groundwater recharge and groundwater discharge, precipitation, fish habitat and crop production is critical for understanding how best to manage water to meet those needs. Streamflow is the best characterized, and most easily measured, component of the dynamic hydrologic system, and as such, is the primary metric used in modeling the water budget. For example, facilitating development of water management options that improve streamflow in the late summer is one of the reasons for developing the Lower Nooksack Water Budget.

The Lower Nooksack Water Budget project included a review of available streamflow measurements made available since WRIA 1 Water Management Project Phase III Task 1 (2002). For the streamflow database update (Lower Nooksack Water Budget Project, Task 2), the project team compiled available information, data, and maps of the stream data collected in the Lower Nooksack study area, as well as WRIA 1 upstream boundary conditions, and updated the database of measured streamflow from 1999 to 2011. The updated streamflow database will be used to calibrate and validate the Topnet-WM hydrologic model, and to calculate water budgets for each of the Lower Nooksack drainages; data will be available in an ASCII format for all other drainages but will not be formally summarized.

The following section begins with the list of streamflow gages in WRIA 1, followed by an explanation of how the data were used for model inputs. This information was used along with other Lower Nooksack Water Budget technical components to calculate the hydrologic model outputs working with the WRIA 1 Joint Board’s existing hydrologic model and supporting technical tools.

This resource is a subset of the Lower Nooksack Water Budget (LNWB) Collection Resource.

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Matlab code for processing stream flow data into model inputs.

This resource is a subset of the LNWB Ch10 Stream Flow Collection Resource.

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Geodatabase of streamflow data.

This resource is a subset of the LNWB Ch10 Stream Flow Collection Resource.

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Access Database of streamflow records.

This resource is a subset of the LNWB Ch10 Stream Flow Collection Resource.

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Analysis and charts with boundary flow relationship development and data outputs.

This resource is a subset of the LNWB Ch10 Stream Flow Collection Resource.

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Overview:
The Lower Nooksack Water Budget is calculated using a numerical simulation model called Topnet-WM. This hydrologic model uses data distributed in space and time to determine the flow of water between various locations and points in time on a daily time step. The modeling of the watershed is limited by the representation of hydrologic processes built into the model, the spatial data used to parameterize the model, and the climate time series data which provides the daily water inputs to the model. To address the limits of our data and knowledge of the system, parameters are used to control the relationships among hydrologic processes and the data used to represent them. In model calibration, parameters are changed within a range of expected values so that the model representation results in modeled streamflow that closely matches observed streamflow. The calibration parameters used include: saturated soil store sensitivity, hydraulic conductivity, overland flow velocity, transmissivity, and impervious surface fraction. The saturated soil store sensitivity, or f parameter, is the most sensitive parameter in this model. It is a measure of the sensitivity of lateral groundwater flow to changes in groundwater level.

The process of model calibration is complex because of limitations in models, input and output data, mathematical structure of the models, and quantitative methods used to fit the model to the data, as well as imperfect knowledge of basin characteristics (Schaake, 2003). In a world of perfect understanding of hydrologic processes, perfect input data, and no scale discrepancy between modeled and measured data, it might be possible to avoid hydrologic model calibration. An important result from the National Weather Service Distributed Model Intercomparison Project (DMIP; Smith et al., 2004a) experiment was the acknowledgement that uncalibrated models do not have the benefit of accounting for the known biases in the rainfall archives over the calibration period. Only in the absence of precipitation and other data input biases, might uncalibrated models be able to outperform calibrated models (Reed et al., 2004). Errors in input data cannot be ignored (Gupta et al., 1998), and therefore model calibration cannot be avoided.

Past work by the Lower Nooksack Water Budget Project Team has examined ways to improve the use of streamflow data that are available within a watershed and that can be used for model calibration, especially to improve the model performance where streamflow data are not available (Bandaragoda et al., 2004; Bandaragoda, 2007; Bandaragoda and Greenberg, 2009; Bandaragoda, 2008; Bandaragoda and Nielson, 2011, Neilson et al., 2010, Tarboton, 2007). The primary calibration locations in this project focused on Fishtrap Creek, Bertrand Creek and Tenmile Creek, with verification at Nooksack River locations at Cedarville and Ferndale.

In hydrologic model calibration, streamflow prediction statistics can be used as a measure of model performance, but the calibration must also address issues relevant to understanding the heterogeneity of the hydrologic system and the unique locations that are modeled. Implemented carefully, automatic calibration techniques that employ multiple objectives and estimates of distributions of watershed parameters may be a step towards both improving models and understanding hydrologic processes.

As calibration is used to conduct diagnostic model analysis and interactive learning about watersheds, our understanding of how to best model the movement of water can increase, and lead to an improvement in our existing models. As the existing models develop, the reliance on calibration will decrease, development of new models will increase, and our predictions of streamflow will improve.

This resource is a subset of the Lower Nooksack Water Budget (LNWB) Collection Resource.

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Matlab code for reading model outputs and plotting figures.

This resource is a subset of the LNWB Ch11 Model Calibration Collection Resource.

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Plotted figures for the Topnet-WM model calibration outputs.

This resource is a subset of the LNWB Ch11 Model Calibration Collection Resource.

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Overview:
The availability of updated climate data, streamflow data, updated water use estimates and the incorporation of the Topnet Water Management (Topnet-WM) components provides the opportunity to build watershed knowledge by better understanding the climate, watershed hydrology and water budget.

Purpose:
The Lower Nooksack Water Budget project analysis focused on the 16 drainages of the Lower Nooksack Subbasin and for each drainage considers precipitation, evapotranspiration, storage, streamflow and user withdrawals. Storage includes canopy storage, unsaturated soil storage, and subsurface storage. Total streamflow includes baseflow, surface runoff, and artificial drainage. User withdrawals include irrigation, dairy, municipal/industrial water supply, and residential and commercial water use served by small public water systems or private wells. The sum of user withdrawals for each drainage is partitioned into groundwater and surface water withdrawals. In addition to streamflow prediction at each drainage, streamflow is calculated at multiple locations of interest (nodes) within each drainage. Total streamflow at nodes is partitioned into surface runoff and baseflow.

Model calculations are conducted on a daily timestep from 1952-2011. In future work, further summaries of daily information and other various components can be done at multiple time scales (daily, monthly, seasonal, annual) and multiple spatial scales (WRIA 1, Upper or Lower Nooksack, individual drainage).

This resource is a subset of the LNWB Ch12-13 Existing and Historic Model Outputs Collection Resource.

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This resource is a subset of the LNWB Ch11 Model Calibration Collection Resource.

ABSTRACT:

Overview:
Agricultural water use includes the irrigation of croplands and the water needs of dairy farms. Irrigated agriculture is an important component of the Lower Nooksack Water Budget due to the high demand for water during the relatively dry summer seasons. Along with out-of-basin industrial water use, irrigation is the largest use of water in the Lower Nooksack Subbasin and has a commensurate effect on the water budget. The highest demand for irrigation water occurs during the month of July when streamflows are low. Dairy water use equals the amount of water the cows drink plus the water used for washdown. The dairy demand is small relative to irrigation.

Since measured diversion or withdrawal records are not available, an estimate of crop irrigation requirements was developed using an empirically derived calculation of water demand. Drainage-wide irrigation estimates are based on the acres irrigated, type of crop, method of irrigation, and soil types.

Recent crop data from Washington Department of Agriculture and previous studies are summarized by surface water drainage area. The U.S. portion of the Lower Nooksack Subbasin contains approximately 54,044 acres of which 28,140 acres are irrigated or approximately 80% of the countywide irrigation total. Major crops include grass hay, pastureland, field corn, raspberries, and potatoes which comprise 97% of the crops grown in the Subbasin.

Within the 16 Lower Nooksack Subbasin drainages, the irrigated agriculture areas have the highest percent of total drainage area in Scott, Fourmile, Kamm, and Wiser Lake/Cougar Creek. Bertrand and Fishtrap Creek percentages include the irrigated area on both the US and Canadian sides. By volume alone, Bertrand and Fishtrap Creek drainages use the highest amount of irrigation water. Irrigation water use rates for each drainage can be found in Chapter 11 Existing Scenario, Water Budget Model Outputs for Lower Nooksack Drainages.

Crop evapotranspiration is an integral component of the hydrologic cycle and is calculated internally in the Topnet Water Management (Topnet-WM) model. The Topnet model uses the Penman Monteith method (adopted and standardized by the American Society of Civil Engineers (2005)) for calculating evapotranspiration of a reference crop [ETr] (short cut grass or tall alfalfa). Tall alfalfa is the reference crop integrated in the WRIA 1 Topnet model. The difference between potential and actual evapotranspiration is the amount of crop water demand that must be satisfied by irrigation.

Purpose:
This section defines the model inputs necessary for calculating the irrigation demand outputs. Results summarizing the irrigation demand on a monthly basis can be found in Chapter 11 Existing Conditions, Water Budget Model Outputs for Lower Nooksack Drainages.

Crop evapotranspiration is an integral component of the hydrologic cycle and is calculated, along with the other components of the water budget. The inputs defined in this chapter include crop type, number of acres, monthly crop coefficients, type of irrigation application (drip or spray) and irrigation efficiencies. Inputs were developed for the 16 Lower Nooksack Subbasin drainages only.

This resource is a subset of the LNWB Ch07 Water Management - Agricultural water use Collection Resource.

ABSTRACT:

The Lower Nooksack Water Budget provides an estimate of the water cycle components in the Lower Nooksack Subbasin’s 16 drainages, as they vary seasonally throughout the year. It is intended to provide a common body of factual information to support water resource professionals and their salmon recovery partners working with the WRIA 1 Joint Board on water supply planning and instream flow negotiations. This overview introduces and summarizes the Lower Nooksack Water Budget, full technical report, which can be accessed at the Water Resource Inventory Area 1 (WRIA 1) Watershed Management website document library (http://wria1project.whatcomcounty.org/Home/Water-Budget/97.aspx).

The work was produced in three stages, for three audiences: technical work, usability testing and public accessibility, with the review audience widening with each stage.

Stage 1 - WRIA 1 Joint Board Watershed Management Team and related Technical Teams reviewed and commented on technical products (data inputs, outputs, and model analysis).

Stage 2 - WRIA 1 Joint Board Management Team, Water Resource and Salmon Recovery Staff Teams and respective constituents provided feedback on the usability of the technical work.

Stage 3 - The draft final report, public overview and work products were then presented to the WRIA 1 Joint Board’s policy and technical bodies, their stakeholders and public audiences in a series of 2012 presentations at public meetings; with work also posted on the WRIA 1 Project public website.

This resource is a subset of the Lower Nooksack Water Budget (LNWB) Collection Resource.

ABSTRACT:

Overview:
Topnet-WM refers to the Water Management version of Topnet developed as a work product for the Utah State University WRIA 1 Watershed Management Project (Tarboton, 2007). This version of the model evolved from the Topnet Model developed in a collaboration between NIWA New Zealand and Utah State University (Bandaragoda et al., 2004; Ibbitt and Woods, 2004) that combines TOPMODEL concepts (Beven and Kirkby, 1979; Beven et al., 1995a) for the simulation of relatively small drainages combined with channel routing. This approach provides a modeling system that can be applied over large watersheds using smaller drainages within the large watershed as model elements.

In Topnet-WM, spatial variability is represented by subdividing the watershed domain into model elements at the scale of drainages. Within drainages, the modeling is essentially lumped but includes parameterization of some subgrid variability, notably (1) the wetness index, used to parameterize the variability of soil moisture, (2) a depletion curve, used to parameterize the variability of snow water equivalent, (3) the fraction of area that is irrigated, and (4) areas with artificial drainage. Surface runoff and baseflow can be designated as model outputs at multiple nodes within a drainage. The model may thus be classified as semi-distributed.

Topnet-WM includes many enhancements beyond the original Beven and Kirkby TOPMODEL, such as: (1) calculation of reference evapotranspiration using the ASCE standardized Penman-Monteith method (Allen et al., 2005; Jensen et al., 1990); (2) calculation of snowmelt using the Utah Energy Balance Snowmelt model (Tarboton et al., 1995a); (3) the partition of model elements into separate components representing irrigated and non-irrigated areas; (4) artificial drainage to represent the effect of ditch and tile drained areas on the runoff response; (5) the partition of the model elements into pervious and impervious areas to allow representation of urbanization; (6) options for the diversion and storage of water under different management options; and (7) components to calculate water use and implement water right rules.

The Lower Nooksack Water Budget will be estimated based on the distributed hydrologic model, Topnet-WM. The Lower Nooksack Water Budget included updating the data inputs and model calibration, which requires a thorough understanding of how the model represents physical hydrologic processes. In order to guide the development of model inputs and analysis of model outputs, the project team has edited and reviewed portions of the WRIA 1 Water Management Project Phase III Task 4.1 report (Tarboton, 2007) to include in the general description of the Topnet-WM model that follows. This chapter provides reference to the details of the model processes used by Topnet-WM to convert data inputs into model outputs.

This resource is a subset of the Lower Nooksack Water Budget (LNWB) Collection Resource.

ABSTRACT:

Overview:
The Lower Nooksack Water Budget Project involved assembling a wide range of existing data related to WRIA 1 and specifically the Lower Nooksack Subbasin, updating existing data sets and generating new data sets. This Data Management Plan provides an overview of the data sets, formats and collaboration environment that was used to develop the project. Use of a plan during development of the technical work products provided a forum for the data development and management to be conducted with transparent methods and processes. At project completion, the Data Management Plan provides an accessible archive of the data resources used and supporting information on the data storage, intended access, sharing and re-use guidelines.

One goal of the Lower Nooksack Water Budget project is to make this “usable technical information” as accessible as possible across technical, policy and general public users. The project data, analyses and documents will be made available through the WRIA 1 Watershed Management Project website http://wria1project.org. This information is intended for use by the WRIA 1 Joint Board and partners working to achieve the adopted goals and priorities of the WRIA 1 Watershed Management Plan.

Model outputs for the Lower Nooksack Water Budget are summarized by sub-watersheds (drainages) and point locations (nodes). In general, due to changes in land use over time and changes to available streamflow and climate data, the water budget for any watershed needs to be updated periodically. Further detailed information about data sources is provided in review packets developed for specific technical components including climate, streamflow and groundwater level, soils and land cover, and water use.

Purpose:
This project involves assembling a wide range of existing data related to the WRIA 1 and specifically the Lower Nooksack Subbasin, updating existing data sets and generating new data sets. Data will be used as input to various hydrologic, climatic and geomorphic components of the Topnet-Water Management (WM) model, but will also be available to support other modeling efforts in WRIA 1. Much of the data used as input to the Topnet model is publicly available and maintained by others, (i.e., USGS DEMs and streamflow data, SSURGO soils data, University of Washington gridded meteorological data). Pre-processing is performed to convert these existing data into a format that can be used as input to the Topnet model. Post-processing of Topnet model ASCII-text file outputs is subsequently combined with spatial data to generate GIS data that can be used to create maps and illustrations of the spatial distribution of water information. Other products generated during this project will include documentation of methods, input by WRIA 1 Joint Board Staff Team during review and comment periods, communication tools developed for public engagement and public comment on the project.

In order to maintain an organized system of developing and distributing data, Lower Nooksack Water Budget project collaborators should be familiar with standards for data management described in this document, and the following issues related to generating and distributing data:
1. Standards for metadata and data formats
2. Plans for short-term storage and data management (i.e., file formats, local storage and back up procedures and security)
3. Legal and ethical issues (i.e., intellectual property, confidentiality of study participants)
4. Access policies and provisions (i.e., how the data will be made available to others, any restrictions needed)
5. Provisions for long-term archiving and preservation (i.e., establishment of a new data archive or utilization of an existing archive)
6. Assigned data management responsibilities (i.e., persons responsible for ensuring data Management, monitoring compliance with the Data Management Plan)

This resource is a subset of the Lower Nooksack Water Budget (LNWB) Collection Resource.

ABSTRACT:

Overview:
Agricultural water use includes the irrigation of croplands and the water needs of dairy farms. Irrigated agriculture is an important component of the Lower Nooksack Water Budget due to the high demand for water during the relatively dry summer seasons. Along with out-of-basin industrial water use, irrigation is the largest use of water in the Lower Nooksack Subbasin and has a commensurate effect on the water budget. The highest demand for irrigation water occurs during the month of July when streamflows are low. Dairy water use equals the amount of water the cows drink plus the water used for washdown. The dairy demand is small relative to irrigation.

Since measured diversion or withdrawal records are not available, an estimate of crop irrigation requirements was developed using an empirically derived calculation of water demand. Drainage-wide irrigation estimates are based on the acres irrigated, type of crop, method of irrigation, and soil types.

Recent crop data from Washington Department of Agriculture and previous studies are summarized by surface water drainage area. The U.S. portion of the Lower Nooksack Subbasin contains approximately 54,044 acres of which 28,140 acres are irrigated or approximately 80% of the countywide irrigation total. Major crops include grass hay, pastureland, field corn, raspberries, and potatoes which comprise 97% of the crops grown in the Subbasin.

Within the 16 Lower Nooksack Subbasin drainages, the irrigated agriculture areas have the highest percent of total drainage area in Scott, Fourmile, Kamm, and Wiser Lake/Cougar Creek. Bertrand and Fishtrap Creek percentages include the irrigated area on both the US and Canadian sides. By volume alone, Bertrand and Fishtrap Creek drainages use the highest amount of irrigation water. Irrigation water use rates for each drainage can be found in Chapter 11 Existing Scenario, Water Budget Model Outputs for Lower Nooksack Drainages.

Crop evapotranspiration is an integral component of the hydrologic cycle and is calculated internally in the Topnet Water Management (Topnet-WM) model. The Topnet model uses the Penman Monteith method (adopted and standardized by the American Society of Civil Engineers (2005)) for calculating evapotranspiration of a reference crop [ETr] (short cut grass or tall alfalfa). Tall alfalfa is the reference crop integrated in the WRIA 1 Topnet model. The difference between potential and actual evapotranspiration is the amount of crop water demand that must be satisfied by irrigation.

Purpose:
This section defines the model inputs necessary for calculating the irrigation demand outputs. Results summarizing the irrigation demand on a monthly basis can be found in Chapter 11 Existing Conditions, Water Budget Model Outputs for Lower Nooksack Drainages.

Crop evapotranspiration is an integral component of the hydrologic cycle and is calculated, along with the other components of the water budget. The inputs defined in this chapter include crop type, number of acres, monthly crop coefficients, type of irrigation application (drip or spray) and irrigation efficiencies. Inputs were developed for the 16 Lower Nooksack Subbasin drainages only.

This resource is a subset of the Lower Nooksack Water Budget (LNWB) Collection Resource.

ABSTRACT:

The Lower Nooksack Subbasin is comprised of a variety of land and water uses both agricultural and nonagricultural. The water uses, described in this chapter include municipal, industrial, residential, and commercial. The following analysis of nonagricultural water use is divided into three sections: municipal/industrial, residential, and commercial/industrial.

The utilities that serve municipal/industrial customers in the Lower Nooksack Subbasin include the City of Bellingham, the City of Everson, the City of Ferndale, the City of Lynden, and the PUD #1 of Whatcom County. For these utilities, large municipal user water system records were obtained for years 2007 through 2011. Averages for this five year period were used in the model for water use, return flows, and interbasin transfers. The average annual diversion by the large utilities municipalities in our analysis totals nearly 25 cfs, 82% of which are transferred out of the Nooksack basin. The majority of that water serves Cherry Point industries; a small amount serves out-of-basin irrigators.

Residential, or domestic, water use was estimated according to population. Population and per capita water use rates are the foundation for calculating an average daily residential water use for each drainage in the Lower Nooksack Subbasin. 2010 Census geospatial census data were used to calculate the population for each drainage. The 2010 population for the Lower Nooksack Subbasin was 46,204, up 8,345 since the 2000 census. A per capita demand rate of 88 gallons per capita per day was agreed upon by the WRIA 1 Joint Board Staff Team to use as input along with the new population numbers. Seasonal demand factors were also determined to distribute monthly water use such that maximum demand occurred in the summer months and minimum demand in the winter.

Businesses and other operations that are not supplied water by a municipality are either served by a smaller public water system or are self-supplied. For commercial users not within the service area of the municipalities listed above, water use was estimated based on average use rates agreed on in 2003 with the WRIA 1 Water Quantity Technical Team. Commercial use was based on the type of operation identified in the Whatcom County Assessor records.

This resource is a subset of the Lower Nooksack Water Budget (LNWB) Collection Resource.

ABSTRACT:

Overview:
The availability of updated climate data, streamflow data, updated water use estimates and the incorporation of the Topnet Water Management (Topnet-WM) components provides the opportunity to build watershed knowledge by better understanding the climate, watershed hydrology and water budget.

Purpose:
The Lower Nooksack Water Budget project analysis focused on the 16 drainages of the Lower Nooksack Subbasin and for each drainage considers precipitation, evapotranspiration, storage, streamflow and user withdrawals. Storage includes canopy storage, unsaturated soil storage, and subsurface storage. Total streamflow includes baseflow, surface runoff, and artificial drainage. User withdrawals include irrigation, dairy, municipal/industrial water supply, and residential and commercial water use served by small public water systems or private wells. The sum of user withdrawals for each drainage is partitioned into groundwater and surface water withdrawals. In addition to streamflow prediction at each drainage, streamflow is calculated at multiple locations of interest (nodes) within each drainage. Total streamflow at nodes is partitioned into surface runoff and baseflow.

Model calculations are conducted on a daily timestep from 1952-2011. In future work, further summaries of daily information and other various components can be done at multiple time scales (daily, monthly, seasonal, annual) and multiple spatial scales (WRIA 1, Upper or Lower Nooksack, individual drainage).

This resource is a subset of the Lower Nooksack Water Budget (LNWB) Collection Resource.

ABSTRACT:

Compressed GIS files used in PhaseIIITask4 to create ditch and tile drain layers.

Spatial data files specifying the areas with ditch and tile drainage were developed in 2004 by the USDA, Natural Resources Conservation Service (NRCS; Resource Conservationist, John Gillies, and Agricultural Engineer, Dean Renner). Drainage coefficients from NRCS technical guides are shown in Table 2. The drained areas were estimated using a geographic information system (GIS) to identify hydric soils on agricultural land use areas, based on the assumption that if a hydric soil was cleared and in agricultural use, then there was a drainage system in place to manage water using sub-surface (tile) and surface (open ditch) practices. For the Lower Nooksack Water Budget these are areas used to determine the tile drained and ditch drained fraction of each drainage model element as well as to assign drainage coefficients.

This resource is a subset of the LNWB Ch09 Artificial Drainage Collection Resource.

ABSTRACT:

Compressed GIS files used in PhaseIIITask4 to create ditch and tile drain layers.

Spatial data files specifying the areas with ditch and tile drainage were developed in 2004 by the USDA, Natural Resources Conservation Service (NRCS; Resource Conservationist, John Gillies, and Agricultural Engineer, Dean Renner). Drainage coefficients from NRCS technical guides are shown in Table 2. The drained areas were estimated using a geographic information system (GIS) to identify hydric soils on agricultural land use areas, based on the assumption that if a hydric soil was cleared and in agricultural use, then there was a drainage system in place to manage water using sub-surface (tile) and surface (open ditch) practices. For the Lower Nooksack Water Budget these are areas used to determine the tile drained and ditch drained fraction of each drainage model element as well as to assign drainage coefficients.

This resource is a subset of the LNWB Ch09 Artificial Drainage Collection Resource.

ABSTRACT:

Compressed GIS files used in PhaseIIITask4 to create ditch and tile drain layers.

Spatial data files specifying the areas with ditch and tile drainage were developed in 2004 by the USDA, Natural Resources Conservation Service (NRCS; Resource Conservationist, John Gillies, and Agricultural Engineer, Dean Renner). Drainage coefficients from NRCS technical guides are shown in Table 2. The drained areas were estimated using a geographic information system (GIS) to identify hydric soils on agricultural land use areas, based on the assumption that if a hydric soil was cleared and in agricultural use, then there was a drainage system in place to manage water using sub-surface (tile) and surface (open ditch) practices. For the Lower Nooksack Water Budget these are areas used to determine the tile drained and ditch drained fraction of each drainage model element as well as to assign drainage coefficients.

This resource is a subset of the LNWB Ch09 Artificial Drainage Collection Resource.

ABSTRACT:

Compressed GIS files used in PhaseIIITask4 to create ditch and tile drain layers.

Spatial data files specifying the areas with ditch and tile drainage were developed in 2004 by the USDA, Natural Resources Conservation Service (NRCS; Resource Conservationist, John Gillies, and Agricultural Engineer, Dean Renner). Drainage coefficients from NRCS technical guides are shown in Table 2. The drained areas were estimated using a geographic information system (GIS) to identify hydric soils on agricultural land use areas, based on the assumption that if a hydric soil was cleared and in agricultural use, then there was a drainage system in place to manage water using sub-surface (tile) and surface (open ditch) practices. For the Lower Nooksack Water Budget these are areas used to determine the tile drained and ditch drained fraction of each drainage model element as well as to assign drainage coefficients.

This resource is a subset of the LNWB Ch09 Artificial Drainage Collection Resource.

ABSTRACT:

Compressed GIS files used in PhaseIIITask4 to create ditch and tile drain layers.

Spatial data files specifying the areas with ditch and tile drainage were developed in 2004 by the USDA, Natural Resources Conservation Service (NRCS; Resource Conservationist, John Gillies, and Agricultural Engineer, Dean Renner). Drainage coefficients from NRCS technical guides are shown in Table 2. The drained areas were estimated using a geographic information system (GIS) to identify hydric soils on agricultural land use areas, based on the assumption that if a hydric soil was cleared and in agricultural use, then there was a drainage system in place to manage water using sub-surface (tile) and surface (open ditch) practices. For the Lower Nooksack Water Budget these are areas used to determine the tile drained and ditch drained fraction of each drainage model element as well as to assign drainage coefficients.

This resource is a subset of the LNWB Ch09 Artificial Drainage Collection Resource.

ABSTRACT:

Compressed GIS files used in PhaseIIITask4 to create ditch and tile drain layers.

Spatial data files specifying the areas with ditch and tile drainage were developed in 2004 by the USDA, Natural Resources Conservation Service (NRCS; Resource Conservationist, John Gillies, and Agricultural Engineer, Dean Renner). Drainage coefficients from NRCS technical guides are shown in Table 2. The drained areas were estimated using a geographic information system (GIS) to identify hydric soils on agricultural land use areas, based on the assumption that if a hydric soil was cleared and in agricultural use, then there was a drainage system in place to manage water using sub-surface (tile) and surface (open ditch) practices. For the Lower Nooksack Water Budget these are areas used to determine the tile drained and ditch drained fraction of each drainage model element as well as to assign drainage coefficients.

This resource is a subset of the LNWB Ch09 Artificial Drainage Collection Resource.

ABSTRACT:

Seaber, P.R., Kapinos, F.P., and Knapp, G.L., 1987, Hydrologic Unit Maps: U.S. Geological Survey Water-Supply Paper 2294, 63 p.

https://www.nrcs.usda.gov/wps/portal/nrcs/main/national/water/watersheds/dataset/
The Watershed Boundary Dataset (WBD) - is a nationally consistent watershed dataset that is subdivided into 6 levels (12-digit hucs) and is available from the USGS and USDA-NRCS-National Cartographic and Geospatial Center's (NCGC). The new 8-digit WBD (130 megabytes) and the new 12-digit WBD (980 megabytes) are available as geodatabases for download, along with the metadata. The WBD contains the most current, the highest resolution and the most detailed delineation of the watershed boundaries.

ABSTRACT:

Testing
Testing

[Modified in JupyterHub on 2017-02-10 01:56:05.915793]
This is a demo of the HydroShare Landlab Watershed Dynamics Notebook

ABSTRACT:

Watershed Dynamics Model Demo #1

ABSTRACT:

This is a collection of step by step demonstrations on how to use HydroShare Apps.

ABSTRACT:

This is a step-by-step demonstration of how to Add Images, PDFs, and Videos to digital maps using the HydroShare GIS App using an example from this related HydroShare resource: Ames, D. (2016). Algae Growth in Utah Lake Time-lapse, HydroShare, http://www.hydroshare.org/resource/4c8ecb05a72647339df0df6e9a87718f

ABSTRACT:

This is a step-by-step demonstration of how to view and download forecasts from any stream in the National Hydrography Dataset with the National Water Model App.

ABSTRACT:

This iPython notebook demonstrates the workflow for obtaining and processing gridded meteorology data files with the Observatory for Gridded Hydrometeorology Python library.

Using the Sauk-Suiattle, Elwha, and Upper Rio Salado watersheds as the study sites of interest, each Jupyter notebook will guide the user through assembling the datasets and analyses from each of seven gridded data product.

In Usecase 1, users may inspect their study site of interest given in the form of summary spatial visualizations. The treatgeoself() function will yield a mapping file per study site, which reduces the gridded cell centroids to the subset that intersects with the study area (i.e., within the watershed). Within treatgeoself(), the user may determine the amount of buffer space to include outside of the study site (default is 0.06-degree buffer region).

In Usecase 2, each of the mapping files are used to guide data retrieval from each of the gridded data products. A series of _get_ functions then downloads the files to designated subfolders. The resulting file paths are cataloged into the mapping file, which can be summarized for data availability according to the elevation gradient using the mappingfileSummary() function. These downloaded files are compressed into tar.gz files, then migrated with their respective mapping files as content files within a new HydroShare resource, for ease of collaborative use.

In Usecase 3, the downloaded files from Usecase 2 are processed in to spatial and temporal summary statistics. The gridclim_dict() function compiles and computes daily, monthly, annual, and monthly-yearly average values for each variable described in the gridded data product metadata (e.g., the ogh_meta class dictionary). Monthly averages are then visualized as time-series plots, while spatial averages are visualized as spatial heatmaps. Finally, the dictionary of dataframes (the product of the spatial-temporal analyses) is saved into a json file and migrated out as a content file within a new HydroShare resource.

ABSTRACT:

This ESRI shape file was obtained from the US Census Bureau TIGER/Line to represent the administrative boundaries between States (accessed from TIGER/Line in February 2017).

Please refer to the US Census Bureau TIGER/Line data portal for inquiries regarding shape boundaries.
https://www.census.gov/cgi-bin/geo/shapefiles/index.php

ABSTRACT:

Recovery efforts from natural disasters can be more efficient with data-driven information on current needs and future risks. We aim to advance open-source software infrastructure to support scientific investigation and data-driven decision making with a prototype system using a water quality assessment developed to investigate post-Hurricane Maria drinking water contamination in Puerto Rico. The widespread disruption of water treatment processes and uncertain drinking water quality within distribution systems in Puerto Rico poses risk to human health. However, there is no existing digital infrastructure to scientifically determine the impacts of the hurricane. After every natural disaster, it is difficult to answer elementary questions on how to provide high quality water supplies and health services. This project will archive and make accessible data on environmental variables unique to Puerto Rico, damage caused by Hurricane Maria, and will begin to address time sensitive needs of citizens. The initial focus is to work directly with public utilities to collect and archive samples of biological and inorganic drinking water quality. Our goal is to advance understanding of how the severity of a hazard to human health (e.g., no access to safe culinary water) is related to the sophistication, connectivity, and operations of the physical and related digital infrastructure systems. By rapidly collecting data in the early stages of recovery, we will test the design of an integrated cyberinfrastructure system to for usability of environmental and health data to understand the impacts from natural disasters. We will test and stress the CUAHSI HydroShare data publication mechanisms and capabilities to (1) assess the spatial and temporal presence of waterborne pathogens in public water systems impacted by a natural disaster, (2) demonstrate usability of HydroShare as a clearinghouse to centralize selected datasets related to Hurricane Maria, and (3) develop a prototype cyberinfrastructure to assess environmental conditions and public health impacted by natural disasters. The project thus serves to not only document post-disaster conditions, but develops a process to track the impact of recovery over time, as monitored through health, power availability and water quality.

PLAIN LANGUAGE SUMMARY
There is an urgent need to understand the impacts of infrastructure damage on public health after natural disasters. One limitation to effective disaster response is easy and rapid access to diverse information about available resources and maps of community resource needs and risks. We aim to expand access to diverse datasets useful for understanding disaster related environmental conditions, with a focus on drinking water quality information. The research products will be made publicly available using a collaborative, online sharing platform – HydroShare. Curating a central repository of assembled data has the potential to greatly facilitate coordinated disaster responses of all types, with opportunities to improve the monitoring of the recovery process. We will prototype this system with an assessment of drinking water, environmental, and public health concerns unique to Puerto Rico in the aftermath of Hurricane Maria. By working directly with public water utilities, we intend to characterize and map the severity of impaired water resources and distribution systems in Puerto Rico. Developing cyber and social infrastructure to understand the dynamics of drinking water contamination after natural disasters will improve disaster preparedness and response, and contribute to efforts across the nation and the world to build for a resilient future.

Poster presented at AGU Fall Meeting New Orleans Ernest N. Morial Convention Center
Session: NH23E Late-Breaking Research Related to the 2017 Hurricane Season in the Americas (Harvey, Irma, Jose, Maria): Poster Contributions
Program: Natural Hazards
Day: Tuesday, 12 December 2017

ABSTRACT:

Overview: There is urgent need to characterize the severely impaired water resources and distribution systems in Puerto Rico and inform the community about how they can protect themselves against hazards in their water. The situation is also an important opportunity to engage the public in collecting samples and create a rich dataset to not only better understand the impacts of Hurricane Maria, but build preparedness towards future water crises. Hurricane Maria may be one of the most complex disasters in human history - we need to have all available data strategically archived and integrated for use in further research. In this moment in time, in this one special place, the uncertainties and stress of where to find clean drinking water and how to restore basic services is beyond human comprehension. The current situation has been generated by a unique culmination of pre-existing conditions, natural disaster, disaster response, and lack of infrastructure. The current widespread disruption of drinking water distribution systems in Puerto Rico may pose risks to human health, but there is no existing digital infrastructure to scientifically determine the impacts of baseline environmental conditions, the hurricane event, and response to the crisis within a framework of understanding impacts to population health. We propose to provide drinking water test kits and analyze for biological, inorganic chemicals, and organic compounds. One month after Hurricane Maria, elementary questions on how to provide needed water quantity and quality and how to support basic human health care cannot be answered. With this project funding, we can soon archive and make accessible data on environmental variables unique to Puerto Rico and Hurricane Maria, unique damage caused by the storm (lack of electricity, blocked transportation corridors), and begin to address time sensitive needs of victims limited by the natural water resources of the island.

Intellectual Merit: Hurricane, environmental, water quality and health data integrated in one infrastructure system will be a resource for researchers to examine all aspects of how natural-human coupled systems respond to extreme weather events. We will have a unique dataset to allow us to generate testable hypotheses on how the severity of a hazard to human health and well-being is related to the sophistication, connectivity, and operations of the physical and digital infrastructure systems. In the short term, we plan to test the design of an integrated cyberinfrastructure system to increase the accessibility of environmental and health data for understanding the impacts from hurricane-related natural disasters. Conceptually, it is well understood that the severity of the disaster is a function of the sophistication of the physical and digital infrastructure. This work will develop a prototype of a synthesized system to advance our understanding of how infrastructure and data-driven information can reduce the impacts of natural disaster, and serve as a platform for future research.

Broader Impacts: Hurricanes Maria, Irma and Harvey are high profile events that have had catastrophic societal impacts. This will be a community-led activity coordinated through CUAHSI to ensure that the data are assembled to be broadly accessible to the research community. Research that deepens our understanding of these events, which will be greatly facilitated by the assembled data, will have broad impact in not only the affected areas but also in other parts of the country subject to hurricane flooding. CUAHSI membership includes over includes over 130 institutions and having this information centrally available through CUAHSI data services would provide a common point of access, in a consistent and documented format, with tools already developed. This will facilitate readiness in advance of disasters, to prepare to collect post-disaster data, as well as facilitate broad and unanticipated use of this data when it is available and easily accessible for research on HydroShare. This RAPID grant targeted at Maria, will expand our capacity to understand and support communities around the world who need to develop information collection and sharing infrastructure towards fostering self-resiliency.

ABSTRACT:

This work improves the Skagit Climate Science Consortium to model the Skagit (SC2DHSVM2015) calibration in the Sauk-Suiattle Basin. We use the spatially-distributed DHSVM glacio-hydrology model (Frans, 2015; Naz et al. 2014) for predicting
hydrologic states (glaciers, snow, soil moisture, streamflow) within modeled basins for historical and future climate conditions.  This work builds on the DHSVM-glacier model supported by a 2014-2015 collaboration managed by the Skagit Climate Science Consortium to model the Skagit (SC2DHSVM2015).

This work is intended to by used as an input for streamflow temperatures that are modeled and mapped at the scale of stream links using the DHSVM- RBM glacier model, and a sediment module driven by hydrology using the concept of sediment transport capacity, and/or rating curves to give a sediment load associated with each flood.

Use this resource to launch Jupyter notebooks that use the latest versions of OGH library (current version included).

ABSTRACT:

This output is a bias correction test to generate a hybrid gridded meteorology product. This dataset was generated December 21, 2017 using Observatory code from https://github.com/ChristinaB/Observatory.

ABSTRACT:

Data and scripts used to prepare forcing data for PREEVENTS project

ABSTRACT:

The US Department of Homeland Security, Homeland Infrastructure Foundations - Level Data (HIFLD) provided geographic shapefiles for United States hospitals. This feature class/shapefile contains locations of Hospitals for US territories of Puerto Rico. The dataset only includes hospital facilities based on data acquired from various state departments or federal sources which has been referenced in the SOURCE field. Hospital facilities which do not occur in these sources will be not present in the database. The source data was available in a variety of formats (pdfs, tables, webpages, etc.) which was cleaned and geocoded and then converted into a spatial database. The database does not contain nursing homes or health centers. Hospitals have been categorized into children, chronic disease, critical access, general acute care, long term care, military, psychiatric, rehabilitation, special, and women based on the range of the available values from the various sources after removing similarities. This feature class/shapefile contains Hospitals derived from various sources (refer SOURCE field) for the Homeland Infrastructure Foundation-Level Data (HIFLD) database. This feature class/shape file has a one-to-many relationship class (HospitalsToTrauma) relate with the “Trauma_Levels” table. This table captures the relationship between Hospitals and the state trauma level designations. “Hospitals” is the origin using STATE as the primary key. “Trauma_Levels” table is the destination using STATE as the foreign key.
This dataset is based on information from the period 20120605-20170329.

The complete dataset for 50 States can be obtained from the HIFLD website: https://hifld-dhs-gii.opendata.arcgis.com/datasets/5eafb083e43a457b9810c36b2414d3d3_0
The shape file metadata can be obtained from: https://www.arcgis.com/sharing/rest/content/items/5eafb083e43a457b9810c36b2414d3d3/info/metadata/metadata.xml?format=default&output=html

ABSTRACT:

In 2010, the US Census Bureau released data about the US population and demographics by tabulated census blocks. Shape files could be obtained through query by State. The files contained here are the tabulated census blocks for Puerto Rico, using the 2010 released version.

The original file can be found at the US Census bureau 2017 TIGER/LINE shape files. Please refer to US Census Bureau TIGER/Line for the query tool and for any necessary updates to the tabulated block information: https://www.census.gov/cgi-bin/geo/shapefiles/index.php?year=2017&layergroup=Blocks+%282010%29

ABSTRACT:

The US Department of Homeland Security, Homeland Infrastructure Foundations - Level Data (HIFLD) provided geographic shapefiles for United States Urgent Care Facilities. This feature class/shapefile contains Urgent Care Facilities recognized by the US Department of Homeland Security within the US territories of Puerto Rico. Urgent Care Facilities Urgent care is defined as the delivery of ambulatory medical care outside of a hospital emergency department on a walk-in basis without a scheduled appointment. (Source: Urgent Care Association of America) The Urgent Care dataset consists of any location that is capable of providing emergency medical care and must provide emergency medical treatment beyond what can normally be provided by an EMS unit, must be able to perform surgery, or must be able to provide recuperative care beyond what is normally provided by a doctor's office. In times of emergency, the facility must be able to accept patients from the general population or patients from a significant subset of the general population (e.g., children). Although all Urgent Care facilities are intended to be included in this dataset, the newest facilities may not be included. This data set includes "mobile" urgent care center that provides urgent care to private residences, which is plotted at its administrative building. Entities that are excluded from this dataset are administrative offices, physician offices, workman compensation facilities, free standing emergency rooms, and hospitals. Urgent Care facilities that are operated by and co-located with a hospital are also excluded because the locations are included in the hospital dataset.

Records with "-DOD" appended to the end of the [NAME] value are located on a military base, as defined by the Defense Installation Spatial Data Infrastructure (DISDI) military installations and military range boundaries. At the request of NGA, text fields in this dataset have been set to all upper case to facilitate consistent database engine search results. At the request of NGA, all diacritics (e.g., the German umlaut or the Spanish tilde) have been replaced with their closest equivalent English character to facilitate use with database systems that may not support diacritics. The records within this dataset were compiled between 2004-11-22 through 2009-07-17.

The complete dataset for 50 States can be obtained from the HIFLD website: https://hifld-dhs-gii.opendata.arcgis.com/datasets/0d748999f5eb4e76a7e0389442381af6_0
The shape file metadata: https://www.arcgis.com/sharing/rest/content/items/0d748999f5eb4e76a7e0389442381af6/info/metadata/metadata.xml?format=default&output=html

ABSTRACT:

The US Department of Homeland Security, Homeland Infrastructure Foundations - Level Data (HIFLD) published spatial data set describing the locations of pharmacies and medical supplies in the United States. This feature class/shapefile contains pharmacies found in the US territories of Puerto Rico. A pharmacy is a facility whose primary function is to store, prepare and legally dispense prescription drugs under the professional supervision of a licensed pharmacist. It meets any licensing or certification standards set forth by the jurisdiction where it is located. The tabular data was gathered from the National Plan and Provider Enumeration System (NPPES) dataset. Pharmacies that were verified to service only animal populations were excluded from the dataset. The records within this dataset was compiled between 2010-03-30 through 2010-10-25.

The complete dataset for the United States and its territories can be obtained from the HIFLD website: https://hifld-dhs-gii.opendata.arcgis.com/datasets/19145a0e403a4af4b2e4b76a6f2ec0ee_0
The shape file metadata: https://www.arcgis.com/sharing/rest/content/items/19145a0e403a4af4b2e4b76a6f2ec0ee/info/metadata/metadata.xml?format=default&output=html

ABSTRACT:

This shapefile describes the Census 2010 published population estimates by US County-equivalent boundaries for the United States Territory of Puerto Rico.

The original Census 2010 County-equivalent shapefile with Selected Demographic and Economics Data was obtained from the US Census Bureau TIGER/Line data:
http://www2.census.gov/geo/tiger/TIGER2010DP1/County_2010Census_DP1.zip

Other TIGER/Line Selected Demographic and Economics Data shapefiles can be found at the US Census Bureau TIGER/Line web portal:
https://www.census.gov/geo/maps-data/data/tiger-data.html

ABSTRACT:

The US Department of Homeland Security, Homeland Infrastructure Foundation - Level Data (HIFLD) has compiled a number of ESRI shapefiles to describe their Health Services facilities. This collections refers to the subset of spatial information reported for Hospitals, Urgent Care Facilities, and Pharmacies available for civilian access to care within Puerto Rico.

ABSTRACT:

Considering the number of people in an area can be a difficult task. The US Census Bureau provides a data product which takes the baseline values from the most recent Census (i.e., Census 2010), then adjusts for increases from new births, decreases from registered deaths, and whether migration has net increased or decreased the number of residents in the area-of-interest. The US Census Bureau Fact Finder provides the Annual population estimates values modeled from April 2010 to July 2017 for Nation, States, Counties, and Puerto Rico.

This file in this resource is the population estimate at the county/municipio spatial scale. The file uses LATIN1 encoding and decoding for the names of the municipios. These estimates can provide the baseline population measurement for economic considerations 2-3 months prior to Hurricane Irma and Maria making landfall.

These files are data products authored from the US Census Bureau. Please direct inquiries regarding the modeling method and retrieval of new annual population estimates from the US Census Bureau:
https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=bkmk

ABSTRACT:

This shapefile describes the Census 2010 published population estimates by US County-equivalent boundaries for the United States.

The original Census 2010 County-equivalent shapefile with Selected Demographic and Economics Data was obtained from the US Census Bureau TIGER/Line data:
http://www2.census.gov/geo/tiger/TIGER2010DP1/County_2010Census_DP1.zip

Other TIGER/Line Selected Demographic and Economics Data shapefiles can be found at the US Census Bureau TIGER/Line web portal:
https://www.census.gov/geo/maps-data/data/tiger-data.html

ABSTRACT:

The US Census Bureau TIGER/Line released this shape file as the 2017 version for state-level boundaries. This ESRI shape file describes the state-level boundary for the US Territory of Puerto Rico for the 2017 version.

Please refer to the US Census Bureau TIGER/Line for the inquiries regarding shape boundaries and updates on polygon information:
https://www.census.gov/geo/maps-data/data/tiger-line.html

ABSTRACT:

This shapefile describes the Census 2010 published population estimates by US County-equivalent boundaries for the United States Territory of Puerto Rico.

The original Census 2010 County-equivalent shapefile with Selected Demographic and Economics Data was obtained from the US Census Bureau TIGER/Line data:
http://www2.census.gov/geo/tiger/TIGER2010DP1/County_2010Census_DP1.zip

Other TIGER/Line Selected Demographic and Economics Data shapefiles can be found at the US Census Bureau TIGER/Line web portal:
https://www.census.gov/geo/maps-data/data/tiger-data.html

ABSTRACT:

The Henry J. Kaiser Family Foundation (KRR) has conducted a number of interviews with Puerto Rico residents in November 2017, two months after Hurricane Maria crossed Puerto Rico. A series of short video interviews identify key social, economic, recovery, and issues of well-being that remain.

DISCLAIMER: While we may own this hydroshare resource, we did not author the reports herein. Please refer to Kaiser Family Foundation with further inquiries and updates on their ongoing research works.

Voices from Puerto Rico: Reflections Two Months After Maria (Video by Samantha Artiga and Barbara Lyons):
https://www.kff.org/disparities-policy/video/voices-from-puerto-rico-reflections-two-months-after-maria/

Voices from Puerto Rico: Reflections Two Months After Maria (clips and reports):
https://www.kff.org/report-section/voices-from-puerto-rico-reflections-two-months-after-maria-report-issue-brief/

Interview appendix with map of Puerto Rico municipalities where focus group interviews were conducted:
https://www.kff.org/report-section/voices-from-puerto-rico-reflections-two-months-after-maria-report-appendix/

KFF newsroom report:
https://www.kff.org/disparities-policy/press-release/report-and-video-highlight-challenges-facing-hurricane-marias-survivors-in-puerto-rico/

ABSTRACT:

The Henry J. Kaiser Family Foundation (KFF) conducted a broad assessment of Puerto Rico and US Virgin Islands with regards to Population health, health care insurance status, and categories of public health concern as a result of Hurricane Irma and Maria in 2017. The links below and the content files in this resource were the health fact sheets made publicly available by the Kaiser Family Foundation.

DISCLAIMER: While we may own this hydroshare resource, we did not author the reports herein. Please refer to Kaiser Family Foundation with further inquiries and updates on their ongoing research works.

Public Health assessment in November 2017 (summary report by Josh Michaud and Jennifer Kates):
https://www.kff.org/other/issue-brief/public-health-in-puerto-rico-after-hurricane-maria/

Puerto Rico:Fast-facts in October 2017 (fact sheet - attached):
https://www.kff.org/disparities-policy/fact-sheet/puerto-rico-fast-facts/

Puerto Rico and Virgin Islands health news in 2017 (news hub):
https://www.kff.org/other/kaiser-health-news-coverage-puerto-rico-virgin-islands/

8 Question and Answers about Puerto Rico Population Health in September 2016 (fact sheet - attached):
https://www.kff.org/disparities-policy/fact-sheet/8-questions-and-answers-about-puerto-rico/

ABSTRACT:

The Henry J. Kaiser Family Foundation (KFF) has conducted a number of interviews with Puerto Rico and US Virgin Islands residents in February 2018, six months after Hurricane Maria crossed Puerto Rico. A panel of experts involved in federal, territory, or facility health care coordination were present to discuss the issues experienced and the ongoing works to prepare for the next Hurricane season. The panel discussed issues of the electrical power source, the hospital operations and status, and the financial needs from federal funding programs like Medicaid for Puerto Rico and the US Virgin Islands.

DISCLAIMER: While we may own this hydroshare resource, we did not author the reports herein. Please refer to Kaiser Family Foundation with further inquiries and updates on their ongoing research works.

"Health Care: Puerto Rico and the U.S. Virgin Islands Six Months After the Storms"(Interview video):
https://www.kff.org/medicaid/video/health-care-puerto-rico-and-the-u-s-virgin-islands-six-months-after-the-storms-video/

Health center operation status on March 2018 (report):
https://www.kff.org/medicaid/fact-sheet/health-centers-in-puerto-rico-operational-status-after-hurricane-maria/

Kaiser Family Foundation panel discussion event on March 2018 (video):
https://www.kff.org/medicaid/event/march-19-event-health-care-in-puerto-rico-and-the-u-s-virgin-islands-a-six-month-check-up-after-the-storms/

ABSTRACT:

This is a Collection of public health summaries and documents related to Puerto Rico. The Henry J. Kaiser Family Foundation (KRR) has led a series of population health interviews and analyses to describe Puerto Rico and US Virgin Islands' population health status before and after Hurricane Maria. This includes information from 2016-2017 ( before Hurricane Maria) and reports through 2018 (following Hurricane Maria).

ABSTRACT:

Demographic and legislative boundaries for Puerto Rico.

ABSTRACT:

Studies of earth surface and environmental systems are becoming increasingly complex with integration of knowledge across multiple domains, enabled by technological advances to provide the collection of massive quantities of data, but requiring data science advances to improve usability of the largest of these datasets - spatially distributed time series of precipitation, temperature, and related atmospheric forcing data (hydrometeorology) used to drive hydrologic processes in models. Increasing the efficiency of using gridded, hydrometeorology data by scientists can be achieved by 1) increasing access to the latest research products such that 2) there is a decrease in effort spent on data processing and 3) an increase in time spent analyzing spatial and temporal characteristics which impact earth surface and environmental modeling experiments. The development of digital land-based Observatories supports ongoing improvements of knowledge at the watershed scale, critical for local decision-making, policy development, and natural disaster planning. The Observatory Gridded Hydrometerology (OGH) python library is designed as an open source software tool for environmental scientists and modelers to easily download and access time series from within regional, continental or global scale hydrometeorology products. This is especially useful for scientists and modelers who are not trained to select the most appropriate climate forcing data for their modeling study, do not have software tools for downloading and processing large datasets for watershed scale applications, and want to publish and run models in a cloud environment. We demonstrate the the use of this library with examples from three publicly available climate research products generated from interpolated gridded observations, hydrologic model and atmospheric model generated climate forcings. Our use cases include download, subset, and generation of statistics useful for for hydrologic and geomorphology modelers who study processes requiring time series of precipitation and temperature data for long term (+50 year) modeling studies. The OGH library is available on publicly accessible Github repository to encourage use in model research studies, and to expand the number of hydrometeorology products supported by this software with future contributions by researchers and software developers.

ABSTRACT:

This is shape file was obtained from the US Census Bureau for the Census 2010 Demographic profiles. The shape polygons represented are the Zip Code Tabulated Areas (ZCTA), where spatial information about the population are described using US Postal service zipcode areas.

The original Census 2010 ZCTA shapefile with Selected Demographic and Economics Data was obtained from the US Census Bureau TIGER/Line data:
https://www2.census.gov/geo/tiger/TIGER2010DP1/ZCTA_2010Census_DP1.zip

Please refer to the US Census bureau as the source data providers for this shapefile resource. The original shapefile and other TIGER/Line Selected Demographic and Economics Data shapefiles can be found at the US Census Bureau TIGER/Line web portal: [https://www.census.gov/geo/maps-data/data/tiger-data.html]

ABSTRACT:

The US Census Bureau TIGER/Line released this shape file as the 2017 version for Census tract boundaries. The shape file contains the census tract areas for the Territory of Puerto Rico, which is 945 shape polygons.

Please refer to the US Census Bureau TIGER/Line for the inquiries regarding census tract and updates on polygon boundaries:
https://www.census.gov/cgi-bin/geo/shapefiles/index.php

ABSTRACT:

This resource contains a data-table from the Center for Disease Control and Prevention (CDC) Behavioral Risk Factor Surveillance System (BRFSS) survey program Selected Metropolitan Area Risk Trends (SMART) MMSA prevalence data. The data were retrieved in 2018 after the 2017 sampling data had been released.

See source data access portal for the BRFSS SMART MMSA Prevalence data (2011 to 2017):
https://chronicdata.cdc.gov/Behavioral-Risk-Factors/Behavioral-Risk-Factors-Selected-Metropolitan-Area/j32a-sa6u/data

ABSTRACT:

Suppose that you are provided with two items, a mapping file describing your study site of interest and a zipped folder of files that you will need to use. The mapping file describes the gridded cell centroids for each 1/16th longitude-latitude gridded cell within the study site, and the files within the folder follow the same gridded cell centroid schema in their naming conventions. The folder may contain more files than is necessary, so the mapping file will need to be used to isolate the files of interest after they have been unzipped.

These two files are intended as a tutorial materials. The Observatory for Gridded Hydrometeorology (OGH) python library contains a function called 'remapCatalog', which will be used to construct a new column in the mapping file. The column will be filled with the file path for the files that correspond to each gridded cell centroid. Files that are missing will be filled with NA.

ABSTRACT:

This assessment of blocked roads in the days after Hurricane Maria made landfall was conducted and published by FEMA. The extent of the blocked roads assessment included the affected zones in Puerto Rico and the US Virgin Islands.

Please refer to the FEMA data services for the original data files (published 2017-09-26) and methodology: [https://data.femadata.com/NationalDisasters/HurricaneMaria/Data/Transportation/]

ABSTRACT:

Scope - GIS inputs, products, and final outputs for 1km source areas in PR. Jupyter Notebooks with Landlab code. This is a Discoverable resource with private geo locations. Users must request access and sign the responsible use agreement managed by Graciela Ramirez-Toro.

Overall project Objective: Develop a prototype cyberinfrastructure to assess conditions of environmental resources (including drinking water quality and landscape conditions) and population health impacted by natural disasters like Hurricane Maria. We have adapted existing cyberinfrastructure components to foster streamlined disaster preparedness, recovery, and population health research.

Community Surface Dynamics Modeling System (CSDMS)
Eric Hutton is the Senior Software Engineer with experience in multi-language codes, sediment transport and geophysical model development, and model coupling. Eric is overseeing and coordinating the software development necessary to ingest and use the identified data into the Landlab framework. Greg Tucker is the Director of CSDMS and PI on the NSF Landlab project. Lynn McCready is supporting the organization of planning and user testing workshops.

Original types of data - some is in other resources
Geospatial Data includes roads and road closure information, Safe Drinking Water Information System point locations of Community and Public Water Systems, locations of 69 hospitals in Puerto Rico, mudslide locations and landslide hazards obtained from the USGS, and storm deforestation rasters generated from satellite data. An effort will be taken to acquire information about electrical power availability in relation to hospitals and drinking water treatment and distribution systems, it is unknown at this time if these data may become available. Population health data publicly available from the Institute for Health Metrics and Evaluation (IHME) will be used at the highest resolution available (5 x 5 km) to map the Local Burden of Disease for Puerto Rico.

ABSTRACT:

In 2018, The George Washington University (GWU) Milken Institute School of Public Health, in collaboration with the Center for Disease Control and Prevention (CDC) and a number of institutions in Puerto Rico, conducted a modeling effort to approximate the estimate excess mortality due to the devastation left by Hurricane Maria. As a follow-up to the controversial official death count of 64 deaths in Puerto Rico after Hurricane Maria, this report summarizes the methods used and the interpretations thereof for the final estimate of approximately 3000 deaths.

Please refer to the George Washington University website for the original report or any inquiries thereof:
https://publichealth.gwu.edu/sites/default/files/downloads/projects/PRstudy/Acertainment%20of%20the%20Estimated%20Excess%20Mortality%20from%20Hurricane%20Maria%20in%20Puerto%20Rico.pdf

ABSTRACT:

In an effort to improve access and use of data archived across multiple hydroshare resources, this Jupyter notebook was developed to use the HydroShare REST API client python library utilities to simplify data access steps.

ABSTRACT:

By December 2017, the official death toll in Puerto Rico due to Hurricane Maria was set at 64 excess deaths. To verify the validity of this death count, a study by Kishore et al used a community-based survey sampling method to compute an empirical measurement of the death count. The study sampled from approximately 3000 households, then compared the estimated deaths with the vital statistics data from 2016 through the end of December 2017. The study method estimated 4645 excess deaths with a 95% confidence interval from 793 to 8498 potential excess deaths. These estimated excess mortality shows a markedly high estimate with a wide confidence interval, but despite these issues the estimates do indicate that the official death tool is a significantly underestimate of the realistic excess deaths in the population.

Due to copy-right permissions, the article should be accessed at the source website. Please use the following reference citation and doi to redirect there:
Kishore N, Marqués D, Mahmud A, Kiang MV, Rodriguez I, Fuller A, Ebner P, Sorensen C, Racy F, Lemery J, Maas L. Mortality in Puerto Rico after Hurricane Maria. New England journal of medicine. 2018 Jul 12;379(2):162-70. http://dx.doi.org/10.1056/NEJMsa1803972

ABSTRACT:

Almost three weeks after Hurricane Maria made landfall in Puerto Rico, Shultz and Galea published this perspective article on the research gaps learned from Hurricane Harvey, Irma, and Maria. The article remarks about the common practice in generalizing hurricanes and their potential outcomes. The Hurricanes of 2017 were devastating not just because of the economic damage sowed, but the variations in climatological hazards (i.e., heavy inundation floods, wind storm, land coverage by the storm) where some health systems were afflicted less dramatically than others. Several key preparation issues arose from each affected zone, such as 1) the delays in emergency response to geographically isolated areas due to loss of communication, 2) the concerns about not having pre-disaster baseline data and the difficulties that causes with measuring disaster consequences, 3) the gaps in preparation due to the use of outdated flood hazard information, and 4) the differences in care-seeking behavior within affected communities due to socioeconomic disparities. The article was published at New England Journal of Medicine with an informational interview with Dr. Carmen Zorrilla, who spoke about the population health and health system issues incurred after Hurricane Maria.

Due to copy-right permissions, the perspective article and the interview should be accessed at the source website. Please use the following reference citation and doi to redirect there:
Shultz, JM, Galea, S. Preparing for the next Harvey, Irma, or Maria—addressing research gaps. N Engl J Med. 2017;377(19):1804-1806. http://dx.doi.org/10.1056/NEJMp1712854

ABSTRACT:

On September 20, 2017, Hurricane Maria made landfall in Puerto Rico, leaving widespread destruction in its path. The official death count for Puerto Rico after Hurricane Maria was 64 excess deaths, but that controversial death toll has been debated by a number of academic and independent researcher journalists. With the loss of electrical power and telecommunication systems for much of the island, it was unclear how many deaths in Puerto Rico were an immediate result of Hurricane Maria's destruction as opposed to the access to care conditions that prolonged. Santos-Burgoa et al. applied a time-series analysis of the Puerto Rico Vital Statistics data to estimate the death count over time. To consider how many people died as opposed to emigrated away from Puerto Rico, two counterfactual assumptions were used, a Census-based scenario and a Displacement-based scenario for expected population change. Under the Census scenario and the Displacement scenario, the estimated death counts in Puerto Rico was approximately 1200 deaths and 3000 deaths, respectively, where the Displacement scenario was acclaimed as the preferred model.

Due to copy-right issues, the article and supplementary materials should be accessed at the source website. Please use the following reference citation and doi to redirect there:
Santos-Burgoa C, Sandberg J, Suárez E, Goldman-Hawes A, Zeger S, Garcia-Meza A, Pérez CM, Estrada-Merly N, Colón-Ramos U, Nazario CM, Andrade E. Differential and persistent risk of excess mortality from Hurricane Maria in Puerto Rico: a time-series analysis. The Lancet Planetary Health. 2018 Nov 1;2(11):e478-88. http://dx.doi.org/10.1016/S2542-5196(18)30209-2

ABSTRACT:

This is shape file was obtained from the US Census Bureau for the Census 2010 Demographic profiles, then clipped for the Territory of Puerto Rico. The shape polygons represented are the Zip Code Tabulated Areas (ZCTA), where spatial information about the population are described using US Postal service zipcode areas.

The original Census 2010 ZCTA shapefile with Selected Demographic and Economics Data was obtained from the US Census Bureau TIGER/Line data:
https://www2.census.gov/geo/tiger/TIGER2010DP1/ZCTA_2010Census_DP1.zip

Please refer to the US Census bureau as the source data providers for this shapefile resource. The original shapefile and other TIGER/Line Selected Demographic and Economics Data shapefiles can be found at the US Census Bureau TIGER/Line web portal: [https://www.census.gov/geo/maps-data/data/tiger-data.html]

ABSTRACT:

Days after Hurricane Maria made landfall in Puerto Rico on September 20, 2017, the Federal Emergency Management Agency (FEMA) responded by assessing the extent of flooding hazard in Puerto Rico and the US Virgin Islands. Two mapping systems for remote sensing were used, Copernicus EMS and the NASA NASA MSFC SPoRT. The resulting raster images for flooding extent represented central and eastern Puerto Rico, but did not include the western segment of the island which was Hurricane Maria's exit trajectory.

The imagery files were taken from the FEMA data services. Please refer to the FEMA website for the original files and inference methods:
https://data.femadata.com/NationalDisasters/HurricaneMaria/Data/RemoteSensing/FEMA_FloodDetectionMaps/

ABSTRACT:

This shape file contains the primary and secondary roads for transportation infrastructure within the Territory of Puerto Rico. The file was obtained from the US Census Bureau TIGER/Line using the 2017 released version. Although there are many more roadways possible, this file contains 4233 line geometries representing primary and secondary roads and thoroughfares.

Please refer to the US Census Bureau TIGER/Line for inquiries regarding road geometries and for updates on infrastructure information:
https://www.census.gov/cgi-bin/geo/shapefiles/index.php?year=2017&layergroup=Roads

ABSTRACT:

This resource contains a data-table from the Center for Disease Control and Prevention (CDC) Behavioral Risk Factor Surveillance System (BRFSS) survey program. This data is summarized for each of the demographic categories within the BRFSS questionnaires, and incapsulates data from 2011 through 2017 sampled from Puerto Rico.

This data was obtained from the BRFSS Prevalence data access portal with a filter for Puerto Rico locations. Please refer to the US Center For Disease Control and Prevention guidelines for inquiries regarding sampling method and updates to information:
https://chronicdata.cdc.gov/Behavioral-Risk-Factors/Behavioral-Risk-Factor-Surveillance-System-BRFSS-P/dttw-5yxu/data

ABSTRACT:

Punta Santiago, Puerto Rico was the entry point for when Hurricane Maria first made landfall in Puerto Rico, but aid was slow to be received being a coastal town. This study conducted by Ferré et al. sought to assess the cross-sectional community health needs and recovery conditions of the Punta Santiago population 6 months post-hurricane, looking particularly to assess the prevalence and severity of major depressive disorders, post-traumatic stress, and generalized anxiety. The results indicate that the economic disruption had not subsided. Household drinking water samples show signs of ongoing contamination that are unsafe for consumption, there was 10-50% prevalence of food insufficiency and subsistence upon food stamp, and many of the sampled households still displayed heightened signs of depressive disorders concerns.

Due to copy-right permissions, the article should be accessed at the source website. Please use the following reference citation and doi to redirect there:
Ferré IM, Negrón S, Shultz JM, Schwartz SJ, Kossin JP, Pantin H. Hurricane Maria’s Impact on Punta Santiago, Puerto Rico: Community Needs and Mental Health Assessment Six Months Postimpact. Disaster medicine and public health preparedness. 2018 Nov 5:1-6. https://doi.org/10.1017/dmp.2018.103

ABSTRACT:

Prior to Hurricane Maria, Puerto Rico was not in a great state of economics. The territory economy was in a notable deficit and public debt. The aged road and water infrastructure were prone to contamination issues when periods of high rainfall occurred. The territory electrical utilities had declared bankruptcy just a few months pre-Maria. Irma and Maria revealed the severity of the situation by calling attention to the deficits in preparation and risk mitigating actions. This perspective piece discussed the various areas of engineering need vulnerable to hurricane devastation, and what Puerto Rico needs to consider to build resilience to future hurricanes.

Due to copy-right permissions, the article should be accessed at the source website. Please use the following reference citation and doi to redirect there:
Torres B. After María, Resilience in Puerto Rico: Why María had such a devastating impact—and how to mitigate future climate disaster. NACLA Report on the Americas. 2018 Jan 2;50(1):11-4. https://doi.org/10.1080/10714839.2018.1448583

ABSTRACT:

In the wake of Hurricane Maria, the Federal Emergency Management Agency (FEMA) was called in to conduct damage assessments. The resulting data collection was a series of geodatabases for sections of the affected zone surveyed. Here, the geodatabases have been transformed into ESRI shape files for ease of use.

The original data files can be found at the FEMA data services webportal. Please refer inquiries about the survey data and the data collection methods therein to FEMA:
https://data.femadata.com/NationalDisasters/HurricaneMaria/Data/DamageAssessments/Visual/

ABSTRACT:

This is shape file was obtained from the US Census Bureau for the Census 2010 Demographic profiles, then clipped for the Territory of Puerto Rico. The shape polygons represented are the Census tract areas, where spatial information about the population are described using Census tract polygons.

The original Census 2010 tract shape file with Selected Demographic and Economics Data was obtained from the US Census Bureau TIGER/Line data:
https://www2.census.gov/geo/tiger/TIGER2010DP1/Tract_2010Census_DP1.zip

Please refer to the US Census bureau as the source data providers for this shapefile resource. The original shapefile and other TIGER/Line Selected Demographic and Economics Data shapefiles can be found at the US Census Bureau TIGER/Line web portal: [https://www.census.gov/geo/maps-data/data/tiger-data.html]

ABSTRACT:

The US Census Bureau provides a large collection of data files, some of which are encoded separately or do not have an obvious means to integrate. Suppose that the files are located and need to be integrated to make some data-driven decisions using Census population estimates. The resultant files may be very useful to explore, but the user wants to get into visual representation and start considering things spatially and temporally. In this resource, the Jupyter notebook walks through a set of operations created to integrate Census population estimates with the known ESRI shapefile for the equivalent county-scales.

ABSTRACT:

Hydrological and meteorological information can help inform the conditions and risk factors related to the environment and their inhabitants. Due to the limitations of observation sampling, gridded data sets provide the modeled information for areas where data collection are infeasible using observations collected and known process relations. Although available, data users are faced with barriers to use, challenges like how to access, acquire, then analyze data for small watershed areas, when these datasets were produced for large, continental scale processes. In this tutorial, we introduce Observatory for Gridded Hydrometeorology (OGH) to resolve such hurdles in a use-case that incorporates NetCDF gridded data sets processes developed to interpret the findings and apply secondary modeling frameworks (landlab).

LEARNING OBJECTIVES
- Familiarize with data management, metadata management, and analyses with gridded data
- Inspecting and problem solving with Python libraries
- Explore data architecture and processes
- Learn about OGH Python Library
- Discuss conceptual data engineering and science operations

Use-case operations:
1. Prepare computing environment
2. Get list of grid cells
3. NetCDF retrieval and clipping to a spatial extent
4. Extract NetCDF metadata and convert NetCDFs to 1D ASCII time-series files
5. Visualize the average monthly total precipitations
6. Apply summary values as modeling inputs
7. Visualize modeling outputs
8. Save results in a new HydroShare resource

For inquiries, issues, or contribute to the developments, please refer to https://github.com/freshwater-initiative/Observatory