Sensitivity of LISEM predicted catchment discharge to initial soil moisture content of soil profile

Sheikh, Vahedberdi; Loon, E. Emiel van; Hessel, Rudi; Jetten, Victor

doi:10.1016/j.jhydrol.2010.08.016

Cited by 32 publications

(22 citation statements)

References 50 publications

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“…4b). Small sensitivity indexes were observed for the parameters θ r , α, and n. These results confirm the necessity of considering the spatial variability of soil parameters when hydrological processes are calculated at field scale, which has been reported by several studies (Merz and Plate, 1997;Sheikh et al, 2010). The total effect sensitivity indexes of all four sensitive parameters except c 0 were generally slightly greater than the first-order sensitivity indexes, indicating limited interactions between parameters on NO 3 -N leaching.…”

Section: Global Sensitivity Analysissupporting

confidence: 82%

“…Deep percolation was most susceptible to initial soil water content (θ 0 ) since the S Tθ0 was as high as 0.86. The importance of initial soil moisture on the water cycle in soils was also reported by Merz and Plate (1997) and Sheikh et al (2010). The S Tθs and S TKs were 0.37 and 0.12, respectively, indicating that the effects of θ s and K s on deep percolation were relatively less than θ 0 .…”

Section: Global Sensitivity Analysissupporting

confidence: 63%

See 1 more Smart Citation

Assessing the Effects of Drip Irrigation System Uniformity and Spatial Variability in Soil on Nitrate Leaching through Simulation

2016

Trans.ASABE

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Section: Global Sensitivity Analysissupporting

confidence: 82%

Section: Global Sensitivity Analysissupporting

confidence: 63%

Assessing the Effects of Drip Irrigation System Uniformity and Spatial Variability in Soil on Nitrate Leaching through Simulation

2016

Trans.ASABE

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“…There are two alternative functions of SCS method (antecedent soil moisture and plant evapotranspiration). In this study, the antecedent soil moisture method was used because of its suitability in semi-humid and humid regions (this study area belong to the semi-humid region with annual precipitation of 739 mm) [56][57][58][59][60]. The model offers three options for estimating potential evapotranspiration: the Hargreaves [61], Priestley-Taylor [62] and Penman-Monteith methods [63].…”

Section: The Swat Hydrological Modelmentioning

confidence: 99%

Investigating Alternative Climate Data Sources for Hydrological Simulations in the Upstream of the Amu Darya River

Sidike

Chen

et al. 2016

Water

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Abstract:The main objective of this study is to investigate alternative climate data sources for long-term hydrological modeling. To accomplish this goal, one weather station data set (WSD) and three grid-based data sets including three types of precipitation data and two types of temperature data were selected according to their spatial and temporal details. An accuracy assessment of the grid-based data sets was performed using WSD. Then, the performances of corrected data combination and non-corrected grid-based precipitation and temperature data combinations from multiple sources on simulating river flow in the upstream portion of the Amu Darya River Basin (ADRB) were analyzed using a Soil and Water Assessment Tool (SWAT) model. The results of the accuracy assessments indicated that all the grid-based data sets underestimated precipitation. The Asian Precipitation Highly Resolved Observational Data Integration Towards the Evaluation of Water Resources (APHRODITE) precipitation data provided the highest accuracy (correlation coefficients (CF) > 0.89, root mean square error (RMSE) < 41.6 mm), followed by the CRUNCEP reanalysis data (a combination of the CRU TS.3.2 data and the National Centers for Environmental Prediction (NCEP) reanalysis data) (CF > 0.5, RMSE < 58.1 mm) and Princeton's Global Meteorological Forcing Dataset (PGMFD) precipitation data (CF > 0.46, RMSE < 62.8 mm). The PGMFD temperature data exhibited a higher accuracy (CF > 0.98, RMSE < 7.1 • C) than the CRUNCEP temperature data (CF > 0.97, RMSE < 4.9 • C). In terms of the simulation performance, the corrected APHRODITE precipitation and PGMFD temperature data provided the best performance. The CF and Nash-Sutcliffe (NSE) coefficients in the calibration and validation periods were 0.96 and 0.92 and 0.93 and 0.83, respectively. In addition, the combinations of PGMFD temperature data and APHRODITE, PGMFD and CRUNCEP precipitation data produced good results, with NSE ≥ 0.70 and CF ≥ 0.89. The combination of CRUNCEP temperature data and APHRODITE precipitation produced a satisfactory result, with NSE = 0.58 and CF = 0.82. The combinations of CRUNCEP temperature data and PGMFD and CRUNCEP precipitation data produced poor results.

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“…Several studies have examined the effect of antecedent soil moisture on runoff simulation in physically based models (Goodrich et al, 1994;Castillo et al, 2003;Zehe and Blöschl, 2004;Brocca et al, 2008;Sheikh et al, 2010). Goodrich et al (1994) found that runoff volumes and peak rates were not particularly different using antecedent soil moisture as calculated with spatially averaged remotely sensed data vs. a simple daily soil moisture model at the small (4.4 ha) and medium (631 ha) watershed scales in semiarid southeastern Arizona, USA.…”

Section: Introductionmentioning

confidence: 99%

Effects of antecedent soil moisture on runoff modeling in small semiarid watersheds of southeastern Arizona

Zhang

Wei

Nearing

2011

Hydrol. Earth Syst. Sci.

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Abstract. This study presents unique data on the effects of antecedent soil moisture on runoff generation in a semi-arid environment, with implications for process-based modeling of runoff. The data were collected from four small watersheds measured continuously from 2002 through 2010 in an environment where evapo-transpiration approaches 100 % of the infiltrated water on the hillslopes. Storm events were generally intense and of short duration, and antecedent volumetric moisture conditions were dry, with an average in the upper 5 cm soil layer over the nine year period of 8 % and a standard deviation of 3 %. Sensitivity analysis of the model showed an average of 0.05 mm change in runoff for each 1 % change in soil moisture, indicating an approximate 0.15 mm average variation in runoff accounted for by the 3 % stan-

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Sensitivity of LISEM predicted catchment discharge to initial soil moisture content of soil profile

Cited by 32 publications

References 50 publications

Assessing the Effects of Drip Irrigation System Uniformity and Spatial Variability in Soil on Nitrate Leaching through Simulation

Assessing the Effects of Drip Irrigation System Uniformity and Spatial Variability in Soil on Nitrate Leaching through Simulation

Investigating Alternative Climate Data Sources for Hydrological Simulations in the Upstream of the Amu Darya River

Effects of antecedent soil moisture on runoff modeling in small semiarid watersheds of southeastern Arizona

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