Breaking walls towards fully open source hydrological modeling
- Authors: Rahman K.1,2, Ray N.1,3, Giuliani G.1,3, Maringanti C.4, George C.5, Lehmann A.1
- 
							Affiliations: 
							- Institute for Environmental Sciences, enviroSPACE Lab., Battelle
- Environmental Earth System Science
- Risk Modeling Unit
- United Nations Environment Programme, Division of Early Warning and Assessment
- Centre for Software Certification
 
- Issue: Vol 44, No 1 (2017)
- Pages: 23-30
- Section: Water Resources and the Regime of Water Bodies
- URL: https://journals.rcsi.science/0097-8078/article/view/174114
- DOI: https://doi.org/10.1134/S0097807817010067
- ID: 174114
Cite item
Abstract
Hydrological models are powerful mathematical tools to address environmental problems and are often used for watershed management and planning. Hydrological models are data driven and the lack of data availability often limits model development. In this paper, we address several challenges in building and running a hydrological model for streamflow simulations based solely on freely available data and open source software. The Soil and Water Assessment Tool (SWAT) hydrological modeling software has been used in the Map Window Geographic Information System (GIS). All spatial and non-spatial data used in this study were obtained from various free of charge online sources. Model calibration and validation represent major challenges following the initial model construction since they involve several trial and error processes to reach acceptable model performances. These critical steps were programmed here as automated scripts in the R open source statistical package. The challenges of model building are described step by step through video tutorials. Using a case study in the Mendoza watershed in Argentina, we show that simulated streamflow exhibits sound agreement with the observed streamflow considering daily time steps (NSE = 0.69, R2 = 0.72 and Percent bias = +9%). The workflow demonstrated in this study can be applied for other watersheds, especially in data-sparse regions that may lack key regional or local data sets.
Keywords
About the authors
Kazi Rahman
Institute for Environmental Sciences, enviroSPACE Lab., Battelle; Environmental Earth System Science
							Author for correspondence.
							Email: krahman@stanford.edu
				                					                																			                												                	Switzerland, 							Building D, 7 route de Drize, Carouge, CH-1227; California						
Nicolas Ray
Institute for Environmental Sciences, enviroSPACE Lab., Battelle; Risk Modeling Unit
														Email: krahman@stanford.edu
				                					                																			                												                	Switzerland, 							Building D, 7 route de Drize, Carouge, CH-1227; Zurich						
Grégory Giuliani
Institute for Environmental Sciences, enviroSPACE Lab., Battelle; Risk Modeling Unit
														Email: krahman@stanford.edu
				                					                																			                												                	Switzerland, 							Building D, 7 route de Drize, Carouge, CH-1227; Zurich						
Chetan Maringanti
United Nations Environment Programme, Division of Early Warning and Assessment
														Email: krahman@stanford.edu
				                					                																			                												                	Switzerland, 							11 chemin des Anémones, Châtelaine, CH-1219						
Chris George
Centre for Software Certification
														Email: krahman@stanford.edu
				                					                																			                												                	Canada, 							Information Technology Building 101 1280 Main Street West Hamilton, Ontario, L8S 4K1						
Anthony Lehmann
Institute for Environmental Sciences, enviroSPACE Lab., Battelle
														Email: krahman@stanford.edu
				                					                																			                												                	Switzerland, 							Building D, 7 route de Drize, Carouge, CH-1227						
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