Utility Programs:SERVES
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Soil-moisture Estimation of Root Zone through Vegetation-Index Based Evapotranspiration-Fraction and Soil-Properties (SERVES) model
SERVES (Pradhan 2019, 2021) was developed to improve the estimation of root-zone soil moisture distribution at a fine spatial resolution through globally available, remotely sensed digital data and soil physical properties. It is a useful method, especially in the arid and semiarid climatic regions where the topography is less dominant in the distribution of soil moisture as compared to that in humid catchments (Pradhan 2019, 2021).
The SERVES method is simple and computationally straightforward, which bypasses the
complexity of ground-based auxiliary measurements, especially in an ungauged environment. The
method’s application and verification are shown in Pradhan (2019). Pradhan et al. (2020) shows
the effect of input initial soil moisture resolution on hydrological modeling. Pradhan and Floyd
(2021) applied the SERVES method to estimate pre-fire and post-fire soil moisture in Southern
California.
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Example Case
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References
Pradhan, N. 2019. “Estimating Growing-Season Root Zone Soil Moisture from Vegetation Index-Based Evapotranspiration Fraction and Soil Properties in the Northwest Mountain Region, USA.” Hydrological Science Journal 64:771–788.
Pradhan, N. 2021. Estimating Growing-Season Root Zone Soil Moisture from Vegetation Index-Based Evapotranspiration Fraction and Soil Properties in the Northwest Mountain Region, USA. ERDC/CHL MP-21- 6. Vicksburg, MS: US Army Engineer Research and Development Center, Coastal and Hydraulics Laboratory.
Pradhan, N. R., and I. Floyd. 2021. “Event Based Post-Fire Hydrological Modeling of the Upper Arroyo Seco Watershed in Southern California.” Water 13 (16): 2303. https://doi.org/10.3390/w13162303.
Pradhan, N. R., I. Floyd, and S. Brown. 2020. “Satellite Imagery-Based SERVES Soil Moisture for the Analysis of Soil Moisture Initialization Input Scale Effects on Physics-Based Distributed Watershed Hydrologic Modelling.” Remote Sensing 12 (13): 2108.
