ABSTRACT:

This repository contains data for HUC 8 priority watersheds in the Mississippi River Basin core states (Arkansas, Illinois, Indiana, Iowa, Kentucky, Louisiana, Minnesota, Mississippi, Missouri, Ohio, Tennessee, Wisconsin). Nitrate monitoring data in receiving streams and rivers in each priority watershed from 2000-2015 was collected from Water Quality Portal. Watershed metadata are included in the PriorityWatershed_Metadata file. Results of time series analysis (Seasonal Mann-Kendall test) for watersheds with sufficient data are detailed in SMK_results, SMKmonth_results, and SMKmonth_Trend_results files. All analyses were conducted in R within the attached R script file.

ABSTRACT:

Physical Properties of Rivers: Calculating Recurrence Interval and Exceedance Probability

This lesson was adapted from educational material written by Dr. Kateri Salk for her Fall 2019 Hydrologic Data Analysis course at Duke University. This is the first part of a two-part exercise focusing on the physical properties of rivers.

Introduction

Rivers are bodies of freshwater flowing from higher elevations to lower elevations due to the force of gravity. One of the most important physical characteristics of a stream or river is discharge, the volume of water moving through the river or stream over a given amount of time. This exercise will introduce the concepts of Recurrence Intervals and Exceedance Probability for the prediction of streamflow discharge.

Learning Objectives

After successfully completing this exercise, you will be able to:

1. Execute queries to pull a variety of National Water Information System (NWIS) and Water Quality Portal (WQP) data into R.
2. Calculate recurrence interval and exceedance probability from daily discharge data.

ABSTRACT:

Chemical Properties of Rivers: Impacts of Mining on Specific Conductance and pH

This lesson was adapted from educational material written by Dr. Kateri Salk and Cathy Chamberlin for the Fall 2019 Hydrologic Data Analysis course at Duke University.

Introduction

The hydrologic impacts on mining can cause cause damage to a landscape in an area much larger than the mining site itself. Water-pollution problems caused by mining include acid mine drainage, metal contamination, and increased sediment levels. The devastating effects of mining impact fisheries, swimming, domestic water supply, irrigation, and other uses of streams. For more information on the environmental impacts of mining, please visit http://www.pollutionissues.com/Li-Na/Mining.html#ixzz6jGlfrX9m

Learning Objectives

After successfully completing this exercise, you will be able to:

1. Execute queries to pull a variety of National Water Information System (NWIS) and Water Quality Portal (WQP) data into R.
2. Analyze inorganic aspects of water quality following a watershed disturbance such as mining.

ABSTRACT:

Physical Properties of Rivers: Querying Metadata and Discharge Data

This lesson was adapted from educational material written by Dr. Kateri Salk for her Fall 2019 Hydrologic Data Analysis course at Duke University. This is the second part of a two-part exercise focusing on the physical properties of rivers.

Introduction

Rivers are bodies of freshwater flowing from higher elevations to lower elevations due to the force of gravity. One of the most important physical characteristics of a stream or river is discharge, the volume of water moving through the river or stream over a given amount of time. Discharge can be measured directly by measuring the velocity of flow in several spots in a stream and multiplying the flow velocity over the cross-sectional area of the stream. However, this method is effort-intensive. This exercise will demonstrate how to approximate discharge by developing a rating curve for a stream at a given sampling point. You will also learn to query metadata from and compare discharge patterns in climatically different regions of the United States.

Learning Objectives

After successfully completing this exercise, you will be able to:

1. Execute queries to pull a variety of National Water Information System (NWIS) and Water Quality Portal (WQP) data into R.
2. Analyze seasonal and interannual characteristics of stream discharge and compare discharge patterns in different regions of the United States

ABSTRACT:

Chemical Properties of Lakes: Introduction to the Lake Multi-Scaled Geospatial and Temporal Database (LAGOSNE)

This lesson was adapted from educational material written by Dr. Kateri Salk for her Fall 2019 Hydrologic Data Analysis course at Duke University.

Introduction
Trophic states are based on lake fertility. The root “trophy” means nutrients; therefore, lakes are classified based on the amount of available nutrients for organisms. More fertile lakes have more nutrients and therefore more plants and algae. There are four lake trophic states:

“Oligo” means very little; therefore, oligotrophic means very little nutrients (Phosphorus and Nitrogen). In oligotrophic lakes, oxygen is found at high levels throughout the water column. Cold water can hold more dissolved oxygen than warm water, and the deep region of oligotrophic lakes stays very cold. In addition, low algal concentration allows deeper light penetration and less decomposition.

“Meso” means middle or mid; therefore, mesotrophic means a medium amount of nutrients (Phosphorus and Nitrogen). Mesotrophic lakes behave differently than oligotrophic lakes in that they stratify, meaning they separate into layers in the summer (more on lake stratification). The top layer of water becomes warm from the sun and contains algae. Since the by-product of photosynthesis is oxygen, oxygen concentration remains high at the surface of the lake. The bottom layer remains cooler and can become anoxic in mid-summer.

“Eu” means true; therefore, eutrophic literally means true nutrients or truly nutrient rich (Phosphorus and Nitrogen). Eutrophic lakes are found in southern Minnesota where the soils are more fertile and where there is a lot of farmland. Eutrophic lakes are shallow and have murky water and mucky, soft bottoms.

Hypereutrophic lakes are at the extreme end of the eutrophic range with exceedingly
high nutrient concentrations and associated biomass production. In temperate regions
the fish communities are dominated by roach and bream. Anoxia or complete loss of oxygen often occurs
in the hypolimnion during summer stratification.

For more information on lake trophic states, please visit http://www.lake.wateratlas.usf.edu/library/learn-more/learnmore.aspx?toolsection=lm_tsi and http://www.manitowoccountylakesassociation.org/oligotrophic-vs-mesotrophic-vs-eutrophic/.

Learning Objectives

After successfully completing this exercise, you will be able to:

1. Navigate and explore the LAGOSNE database and R package
2. Evaluate lake water quality using the trophic state index
3. Analyze spatial and temporal patterns of water quality across the northeast U.S.

ABSTRACT:

This lesson was adapted from educational material written by Dr. Kateri Salk for her Fall 2019 Hydrologic Data Analysis course at Duke University. This is the first part of a two-part exercise focusing on time series analysis.

Introduction

Time series are a special class of dataset, where a response variable is tracked over time. The frequency of measurement and the timespan of the dataset can vary widely. At its most simple, a time series model includes an explanatory time component and a response variable. Mixed models can include additional explanatory variables (check out the `nlme` and `lme4` R packages). We will be covering a few simple applications of time series analysis in these lessons.

Opportunities

Analysis of time series presents several opportunities. In aquatic sciences, some of the most common questions we can answer with time series modeling are:

* Has there been an increasing or decreasing trend in the response variable over time?
* Can we forecast conditions in the future?

Challenges

Time series datasets come with several caveats, which need to be addressed in order to effectively model the system. A few common challenges that arise (and can occur together within a single dataset) are:

* Autocorrelation: Data points are not independent from one another (i.e., the measurement at a given time point is dependent on previous time point(s)).

* Data gaps: Data are not collected at regular intervals, necessitating *interpolation* between measurements. There are often gaps between monitoring periods. For many time series analyses, we need equally spaced points.

* Seasonality: Cyclic patterns in variables occur at regular intervals, impeding clear interpretation of a monotonic (unidirectional) trend. Ex. We can assume that summer temperatures are higher.

* Heteroscedasticity: The variance of the time series is not constant over time.

* Covariance: the covariance of the time series is not constant over time. Many of these models assume that the variance and covariance are similar over the time-->heteroschedasticity.

Learning Objectives

After successfully completing this notebook, you will be able to:

1. Choose appropriate time series analyses for trend detection and forecasting

2. Discuss the influence of seasonality on time series analysis

3. Interpret and communicate results of time series analyses

ABSTRACT:

Trend Detection and Forecasting

This lesson was adapted from educational material written by Dr. Kateri Salk for her Fall 2019 Hydrologic Data Analysis course at Duke University. This is the second part of a two-part exercise focusing on time series analysis.

Introduction

Time series are a special class of dataset, where a response variable is tracked over time. Time series analysis is a powerful technique that can be used to understand the various temporal patterns in our data by decomposing data into different cyclic trends. Time series analysis can also be used to predict how levels of a variable will change in the future, taking into account what has happened in the past.

Learning Objectives

1. Choose appropriate time series analyses for trend detection and forecasting
2. Discuss the influence of seasonality on time series analysis
3. Interpret and communicate results of time series analyses

ABSTRACT:

High Frequency Flow Data: Introduction to Dygraphs

This lesson was adapted from educational material written by Dr. Kateri Salk and teaching assistant Cathy Chamberlin for her Fall 2019 Hydrologic Data Analysis course at Duke University. This is the first part of a two-part exercise focusing on high frequency flow data.

Introduction

High frequency data is usually defined as frequencies significantly lower than daily (e.g. 5-minute, 15-minute, 1 hr etc). The large amount of data allows us to distinguish between different models (model validation) with a higher statistical precision. Baseflow is a portion of streamflow that is not directly generated from the excess rainfall during a storm event. In other words, this is the flow that would exist in the stream without the contribution of direct runoff from the rainfall. It should not be confused with groundwater flow. Quickflow is the part of a storm rainfall which moves quickly to a stream channel via surface runoff or overland flow, and forms a flood wave in the channel. What types of hydrological and biological processes happen on this timescale that we might want to investigate?

Learning Objectives

After successfully completing this notebook, you will be able to:
1. Determine stormflow and baseflow from high frequency flow data
2. Communicate findings with peers through oral, visual, and written modes

ABSTRACT:

High Frequency Flow Data: Flashiness Index Value and Hysteresis Plots

This lesson was adapted from educational material written by Dr. Kateri Salk for her Fall 2019 Hydrologic Data Analysis course at Duke University. This is the second part of a two-part exercise focusing on high frequency flow data.

Introduction

Flashiness is how responsive a stream is to precipitation. Flashiness is an important characteristic of the stream hydrologic regime. A "flashy" stream is one that experiences a rapid increase in flow shortly after onset of a precipitation event, and an equally rapid return to base conditions shortly after the end of the precipitation event. A "flashy" stream will thus increase in stormflow much faster following a precipitation event.

Flashiness in a stream can be increased or decreased by a variety of land use/land cover changes in the stream's watershed. For example, an increase in impervious surfaces (urbanization) or agricultural land typically leads to an increase in flashiness. Land management practices that increase infiltration of precipitation into the soil, such as restoration of native vegetation, or implementation of best management practices like rain gardens, grass swales, and forested riparian buffers, typically decrease stream flashiness. Streams that experience an increase in flashiness will undergo a period of channel adjustment to accommodate the increased peak flows. This may include incision (downcutting) and widening of the stream channel, which affects in-stream and near-stream infrastructure as well as stream-adjacent lands.

Learning Objectives

After successfully completing this notebook, you will be able to:
1. Calculate the Flashiness Index Value of a river
2. Use a hysteresis plot to understand watershed dynamics
3. Communicate findings with peers through oral, visual, and written modes