TY - JOUR
T1 - Hyperresolution hydrologic modeling in a regional watershed and its interpretation using empirical orthogonal functions
AU - Mascaro, Giuseppe
AU - Vivoni, Enrique
AU - Méndez-Barroso, Luis A.
N1 - Funding Information:
We thank three anonymous reviewers for their comments that helped to improve the quality of the manuscript. We would also like to thank funding from the NOAA Climate Program Office (grants GC07-19 and NA10OAR4310165 ), NSF IRES Program (grants OISE 0809946 and OISE 0553852 ) and the U.S. Army Research Office (grant 56059-EV-PCS ). The bi-national NAME-SMEX04 project gave an important impetus for field observations and modeling activities in the study region. We acknowledge the computing facilities from the ASU Advanced Computing Center. We also thank Christopher J. Watts, Julio C. Rodríguez, Juan A. Saíz-Hernandez, Enrico A. Yépez, Jaime Garatuza-Payan, Agustin Robles-Morua and Tiantian Xiang who contributed to the data collection and processing efforts. Field data sets are available through data catalogs at http://catalog.eol.ucar.edu/name/ and http://vivoni.asu.edu/sonora/www/pages/hydromet.html .
Publisher Copyright:
© 2015 Elsevier Ltd.
PY - 2015/9/1
Y1 - 2015/9/1
N2 - Hyperresolution (<1km) hydrologic modeling of regional watersheds is expected to support a broad range of terrestrial water cycle studies, but its feasibility is still challenging due to process, data and computational constraints, as well as difficulties in interpreting the high-dimensional dataset of spatiotemporal model forcings and outputs. We address some of these modeling challenges by extending the application of a physically-based, distributed hydrologic model to the Río San Miguel watershed (3796km2) in Mexico based on prior efforts that demonstrated the process fidelity at smaller spatiotemporal scales. Long-term (7year) simulations are conducted at a hyperresolution (~78m) over the large domain using parallel simulation capabilities. To address data sparseness, we develop strategies to integrate ground, remotely-sensed and reanalysis data for setting up, forcing and parameterizing the model. Complementary tests with observations at individual stations and remotely-sensed spatial patterns reveal a robust model performance. After building confidence in the model, we interpret the spatiotemporal model forcings and outputs using empirical orthogonal functions (EOFs) analyses. For all model outputs, a large portion (58-80%) of the spatiotemporal variability can be explained by two dominant EOFs, which are related to model forcings and basin properties. Terrain controls on soil water accumulation have a marked impact on the spatial distribution of most hydrologic variables during the wet season. In addition, soil properties affect soil moisture patterns, while vegetation and elevation distributions influence evapotranspiration and runoff fields. Given the large outputs from long-term hyperresolution simulations, EOF analyses provide a promising avenue for extracting meaningful hydrologic information within complex, regional watersheds.
AB - Hyperresolution (<1km) hydrologic modeling of regional watersheds is expected to support a broad range of terrestrial water cycle studies, but its feasibility is still challenging due to process, data and computational constraints, as well as difficulties in interpreting the high-dimensional dataset of spatiotemporal model forcings and outputs. We address some of these modeling challenges by extending the application of a physically-based, distributed hydrologic model to the Río San Miguel watershed (3796km2) in Mexico based on prior efforts that demonstrated the process fidelity at smaller spatiotemporal scales. Long-term (7year) simulations are conducted at a hyperresolution (~78m) over the large domain using parallel simulation capabilities. To address data sparseness, we develop strategies to integrate ground, remotely-sensed and reanalysis data for setting up, forcing and parameterizing the model. Complementary tests with observations at individual stations and remotely-sensed spatial patterns reveal a robust model performance. After building confidence in the model, we interpret the spatiotemporal model forcings and outputs using empirical orthogonal functions (EOFs) analyses. For all model outputs, a large portion (58-80%) of the spatiotemporal variability can be explained by two dominant EOFs, which are related to model forcings and basin properties. Terrain controls on soil water accumulation have a marked impact on the spatial distribution of most hydrologic variables during the wet season. In addition, soil properties affect soil moisture patterns, while vegetation and elevation distributions influence evapotranspiration and runoff fields. Given the large outputs from long-term hyperresolution simulations, EOF analyses provide a promising avenue for extracting meaningful hydrologic information within complex, regional watersheds.
KW - Catchment hydrology
KW - Distributed hydrologic model
KW - High performance computing
KW - North American monsoon
KW - Principal component analysis
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U2 - 10.1016/j.advwatres.2015.05.023
DO - 10.1016/j.advwatres.2015.05.023
M3 - Article
AN - SCOPUS:84934957899
SN - 0309-1708
VL - 83
SP - 190
EP - 206
JO - Advances in Water Resources
JF - Advances in Water Resources
ER -