SCS Runoff Equation Revisited for Variable-Source Runoff Areas

Steenhuis, Tammo S.; Winchell, Michael; Rossing, Jane; Zollweg, James A.; Walter, Michael

doi:10.1061/(asce)0733-9437(1995)121:3(234)

Cited by 209 publications

(180 citation statements)

References 6 publications

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“…The curve number indicates the amount of runoff or infiltration that will occur during a precipitation event (SIUG, Section 2.2.2). 107 Soil loss resulting from erosion was calculated using the Revised Universal Soil Loss Equation (RUSLE), which accounts for precipitation amount, soil erodibility, regional slope, cover management, and support practices (SIUG, Section 2.2.3). Dissolution of ENMs in soil water was assumed to occur at the same rate as in groundwater studies (SIUG, Section 2.2.6).…”

Section: −99mentioning

confidence: 99%

Assessing the Risk of Engineered Nanomaterials in the Environment: Development and Application of the nanoFate Model

Garner

Suh

Keller

2017

Environ. Sci. Technol.

213

166

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We developed a dynamic multimedia fate and transport model (nanoFate) to predict the time-dependent accumulation of metallic engineered nanomaterials (ENMs) across environmental media. nanoFate considers a wider range of processes and environmental subcompartments than most previous models and considers ENM releases to compartments (e.g., urban, agriculture) in a manner that reflects their different patterns of use and disposal. As an example, we simulated ten years of release of nano CeO 2 , CuO, TiO 2 , and ZnO in the San Francisco Bay area. Results show that even soluble metal oxide ENMs may accumulate as nanoparticles in the environment in sufficient concentrations to exceed the minimum toxic threshold in freshwater and some soils, though this is more likely with high-production ENMs such as TiO 2 and ZnO. Fluctuations in weather and release scenario may lead to circumstances where predicted ENM concentrations approach acute toxic concentrations. The fate of these ENMs is to mostly remain either aggregated or dissolved in agricultural lands receiving biosolids and in freshwater or marine sediments. Comparison to previous studies indicates the importance of some key model aspects including climatic and temporal variations, how ENMs may be released into the environment, and the effect of compartment composition on predicted concentrations.

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Section: −99mentioning

confidence: 99%

Assessing the Risk of Engineered Nanomaterials in the Environment: Development and Application of the nanoFate Model

Garner

Suh

Keller

2017

Environ. Sci. Technol.

213

166

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“…Even though specification of CN2 can be subjective to certain extent, the variability of these values have consensus in the literature and have a sound scientific basis. Therefore we can be more certain about them than pure calibration parameters (Dahlke et al, 2012;Steenhuis et al, 1995). Other examples of derived parameters include Manning's roughness coefficients for tributary and main channels (CH_N1 and CH_N2) and others shown in Fig.…”

Section: Derived Parameters (Stage 2)mentioning

confidence: 99%

Multiple domain evaluation of watershed hydrology models

Kumarasamy

Belmont

2017

Preprint

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Abstract. Watershed scale models simulating hydrology and water quality have advanced rapidly in sophistication, process 5 representation, flexibility in model structure, and input data. Given the importance of these models to support decision-making for a wide range of environmental issues, the hydrology community is compelled to improve the metrics used to evaluate model performance. More targeted and comprehensive metrics will facilitate better and more efficient calibration and will help demonstrate that the model is useful for the intended purpose. Here we introduce a suite of new tools for model evaluation,packaged as an open-source Hydrologic Model Evaluation (HydroME) Toolbox. Specifically, we demonstrate the use of box 10 plots to illustrate the full distribution of common model performance metrics, such as R 2 , use of Euclidian distance, empirical Quantile-Quantile (Q-Q) plots and flow duration curves as simple metrics to identify and localize errors in model simulations.Further, we demonstrate the use of magnitude squared coherence to compare the frequency content between observed and modeled streamflow and wavelet coherence to localize frequency mismatches in time. We provide a rationale for a hierarchical selection of parameters to adjust during calibration and recommend that modelers progress from parameters with the most 15 uncertainty to the least uncertainty, namely starting with pure calibration parameters, followed by derived parameters, and finally measured parameters. We apply these techniques in the calibration and evaluation of models of two watersheds, the Le Sueur River Basin (2880 km 2 ) and Root River Basin (4300 km 2 ) in southern Minnesota, USA.

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“…Soil water content was measured by the oven dry method. [57][58][59] . The specified daily CN values in AquaCrop was determined by the antecedent moisture class (AMC) which was derived from the rainfall of the preceding 5 days, and the CN value varied with the land use, management practices, and saturated hydraulic conductivity of the top soil layer [31] .…”

Section: Soil Water Balance Parameters Descriptionmentioning

confidence: 99%

Quantitative analysis of yield and soil water balance for summer maize on the piedmont of the North China Plain using AquaCrop

Wang¹,

Huang²,

Li³

2015

Front. Agr. Sci. Eng.

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The North China Plain (NCP) is a major grain production area in China, but the current winter wheatsummer maize system has resulted in a large water deficit. This water-shortage necessitates the improvement of crop water productivity in the NCP. A crop water model, AquaCrop, was adopted to investigate yield and water productivity (WP) for rain-fed summer maize on the piedmont of the NCP. The data sets to calibrate and validate the model were obtained from a 3-year (2011-2013) field experiment conducted on the Yanshan piedmont of the NCP. The range of root mean square error (RMSE) between the simulated and measured biomass was 0.67-1.25 t$hm -2 , and that of relative error (RE) was 9.4%-15.4%, the coefficient of determination (R 2 ) ranged from 0.992 to 0.994. The RMSE between the simulated and measured soil water storage at depth of 0-100 cm ranged from 4.09 to 4.39 mm; and RE and R 2 in the range of 1.07%-1.20% and 0.880-0.997, respectively. The WP as measured by crop yield per unit evapotranspiration was 2.50-2.66 kg$m . The simulated impact of long-term climate (i.e., 1980-2010) and groundwater depth on crop yield and WP revealed that the higher yield and WP could be obtained in dry years in areas with capillary recharge from groundwater, and much lower values elsewhere. The simulation also suggested that supplementary irrigation in areas without capillary groundwater would not result in groundwater over-tapping since the precipitation can meet the water required by both maize and ecosystem, thus a beneficial outcome for both food and ecosystem security can be assured.

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SCS Runoff Equation Revisited for Variable-Source Runoff Areas

Cited by 209 publications

References 6 publications

Assessing the Risk of Engineered Nanomaterials in the Environment: Development and Application of the nanoFate Model

Assessing the Risk of Engineered Nanomaterials in the Environment: Development and Application of the nanoFate Model

Multiple domain evaluation of watershed hydrology models

Quantitative analysis of yield and soil water balance for summer maize on the piedmont of the North China Plain using AquaCrop

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