Facilitating Reproduction of the Components of A Complex Hydrologic Modeling Study: "Hydrologic Model Sensitivity to Temporal Aggregation of Meteorological Forcing Data: A Case Study for the Contiguous United States"


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Created: Apr 06, 2021 at 3:10 a.m.
Last updated: Apr 13, 2023 at 1:13 p.m.
DOI: 10.4211/hs.c0e8de47aee744d088db7019d78c2b3f
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Abstract

The overall goal of this collection is to use the basic strategy and architecture presented by Choi et al. (2021) to make components of a modern and complex hydrologic modeling study (VB study; Van Beusekom et al., 2022) easier to reproduce. The design and implemention of the developed cyberinfrastructure to achieve this goal are fully explained by Maghami et al. (2023).

In VB study, hydrological outputs from the SUMMA model for the 671 CAMELS catchments across the contiguous United States (CONUS) and a 60-month actual simulation period are investigated to understand their dependence on input forcing behavior across CONUS. VB study layes out a simple methodology that can be applied to understand the relative importance of seven model forcings (precipitation rate, air temperature, longwave radiation, specific humidity, shortwave radiation, wind speed, and air pressure).

Choi et al. (2021) integrated three components through seamless data transfers for a reproducible research: (1) online data and model repositories; (2) computational environments leveraging containerization and self-documented computational notebooks; and (3) Application Programming Interfaces (APIs) that provide programmatic control of complex computational models.

Therefore, Maghami et al. (2023), integrated the following three components through seamless data transfers to make components of a modern and complex hydrologic study (VB study) easier to reproduce:
(1) HydroShare as online data and model repository;
(2) CyberGIS-Jupyter for Water for self-documented computational notebooks as computational environment (with and without HPC notebooks);
(3) pySUMMA as Application Programming Interfaces (APIs) that provide programmatic control of complex computational models.

This collection includes three resources:

1- First resource, provides the entire NLDAS forcing datasets used in the VB study.
2- Second resource provides an end-to-end workflow of CAMELS basin modeling with SUMMA for the paper simulations configured for execution in connected JupyterHub compute platforms. This resource is well-suited for a smaller scale exploration: it is preconfigured to explore one example CAMELS site and a period of 60-month actual simulation to demonstrate the capabilities of the notebooks. Users still can change the CAMELS site, the number of sites being explored or even the simulation period. To quickly assess the capabilities of the notebooks in this resource, we even recommend running an actual simulation period as short as 12 months.
3- Third resource, however, uses HPC (High-Performance Computing) through CyberGIS Computing Service. The HPC enables a high-speed running of simulations which makes it suitable for running larger simulations (even as large as the entire 671 CAMELS sites and the whole 60-month actual simulation period used in the VB study) practical and much faster than the second resource. This resource is preconfigured to explore four example CAMELS site and a period of 60-month actual simulation to only demonstrate the capabilities of the notebooks. Users still can change the CAMELS sites, the number of sites being explored or even the simulation period.

Greater details can be found in each resource.

Subject Keywords

Coverage

Spatial

Coordinate System/Geographic Projection:
WGS84 EPSG:4326
Coordinate Units:
['Decimal degrees']
North Latitude
49.1506°
East Longitude
-67.6906°
South Latitude
26.9700°
West Longitude
-124.6032°

Temporal

Start Date:
End Date:

Collection Contents


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Related Resources

This resource is referenced by Van Beusekom A.E, Hay L.E, Bennett A.R, Choi YD, Clark M.P, Goodall J.L, Li Z., Maghami I., Nijssen B., Wood A.W., 2022. "Hydrologic Model Sensitivity to Temporal Aggregation of Meteorological Forcing Data: A Case Study for the Contiguous United States". Journal of Hydrometeorology, 23(2), pp.167-183. https://doi.org/10.1175/JHM-D-21-0111.1
This resource is referenced by Maghami, I., Van Beusekom, A., Hay, L., Li, Z., Bennett, A., Choi, Y., Nijssen, B., Wang, S., Tarboton, D. and Goodall, J.L., 2023. "Building cyberinfrastructure for the reuse and reproducibility of complex hydrologic modeling studies." Environmental Modelling & Software, p.105689. https://doi.org/10.1016/j.envsoft.2023.105689

Credits

Funding Agencies

This resource was created using funding from the following sources:
Agency Name Award Title Award Number
National Science Foundation Collaborative Research: SI2-SSI: Cyberinfrastructure for Advancing Hydrologic Knowledge through Collaborative Integration of Data Science, Modeling and Analysis OAC-1664061, OAC-1664018, OAC-1664119
National Science Foundation HDR Institute: Geospatial Understanding through an Integrative Discovery Environment OAC-2118329
National Science Foundation EarthCube Data Capabilities: Collaborative Research: Integration of Reproducibility into Community CyberInfrastructure RISE-1928369

How to Cite

Choi, Y., I. Maghami, A. Van Beusekom, Z. Li, B. Nijssen, L. Hay, A. Bennett, D. Tarboton, J. Goodall, M. P. Clark, S. Wang (2023). Facilitating Reproduction of the Components of A Complex Hydrologic Modeling Study: "Hydrologic Model Sensitivity to Temporal Aggregation of Meteorological Forcing Data: A Case Study for the Contiguous United States", HydroShare, https://doi.org/10.4211/hs.c0e8de47aee744d088db7019d78c2b3f

This resource is shared under the Creative Commons Attribution CC BY.

http://creativecommons.org/licenses/by/4.0/
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