scholarly journals A new probability density function for spatial distribution of soil water storage capacity leads to SCS curve number method

2018 ◽  
Author(s):  
Dingbao Wang
Keyword(s):  
Distribution Function ◽  
Storage Capacity ◽  
Water Storage ◽  
Soil Water Storage ◽  
Water Storage Capacity ◽  
Soil Storage ◽  
Soil Water Storage Capacity ◽  
Soil Wetting ◽  
Scs Cn ◽  
New Distribution

Abstract. Following the Budyko framework, soil wetting ratio (the ratio between soil wetting and precipitation) as a function of soil storage index (the ratio between soil wetting capacity and precipitation) is derived from the SCS-CN method and the VIC type of model. For the SCS-CN method, soil wetting ratio approaches one when soil storage index approaches infinity, due to the limitation of the SCS-CN method in which the initial soil moisture condition is not explicitly represented. However, for the VIC type of model, soil wetting ratio equals soil storage index when soil storage index is lower than a certain value, due to the finite upper bound of the power distribution function of storage capacity. In this paper, a new distribution function, supported on a semi-infinite interval x ∈ [0, ∞), is proposed for describing the spatial distribution of storage capacity. From this new distribution function, an equation is derived for the relationship between soil wetting ratio and storage index. In the derived equation, soil wetting ratio approaches zero as storage index approaches zero; when storage index tends to infinity, soil wetting ratio approaches a certain value (≤ 1) depending on the initial storage. Moreover, the derived equation leads to the exact SCS-CN method when initial water storage is zero. Therefore, the new distribution function for soil water storage capacity explains the SCS-CN method as a saturation excess runoff model and unifies the surface runoff modeling of SCS-CN method and VIC type of model.

scholarly journals A new probability density function for spatial distribution of soil water storage capacity leads to the SCS curve number method

2018 ◽  
Vol 22 (12) ◽  
pp. 6567-6578 ◽  
Author(s):  
Dingbao Wang
Keyword(s):  
Distribution Function ◽  
Storage Capacity ◽  
Water Storage ◽  
Curve Number ◽  
Soil Water Storage ◽  
Water Storage Capacity ◽  
Soil Storage ◽  
Soil Wetting ◽  
Scs Cn ◽  
New Distribution

Abstract. Following the Budyko framework, the soil wetting ratio (the ratio between soil wetting and precipitation) as a function of the soil storage index (the ratio between soil wetting capacity and precipitation) is derived from the Soil Conservation Service Curve Number (SCS-CN) method and the variable infiltration capacity (VIC) type of model. For the SCS-CN method, the soil wetting ratio approaches 1 when the soil storage index approaches ∞, due to the limitation of the SCS-CN method in which the initial soil moisture condition is not explicitly represented. However, for the VIC type of model, the soil wetting ratio equals the soil storage index when the soil storage index is lower than a certain value, due to the finite upper bound of the generalized Pareto distribution function of storage capacity. In this paper, a new distribution function, supported on a semi-infinite interval x∈[0,∞), is proposed for describing the spatial distribution of storage capacity. From this new distribution function, an equation is derived for the relationship between the soil wetting ratio and the storage index. In the derived equation, the soil wetting ratio approaches 0 as the storage index approaches 0; when the storage index tends to infinity, the soil wetting ratio approaches a certain value (≤1) depending on the initial storage. Moreover, the derived equation leads to the exact SCS-CN method when initial water storage is 0. Therefore, the new distribution function for soil water storage capacity explains the SCS-CN method as a saturation excess runoff model and unifies the surface runoff modeling of the SCS-CN method and the VIC type of model.

scholarly journals Diagnosis toward predicting mean annual runoff in ungauged basins

2021 ◽  
Vol 25 (2) ◽  
pp. 945-956
Author(s):  
Yuan Gao ◽  
Lili Yao ◽  
Ni-Bin Chang ◽  
Dingbao Wang
Keyword(s):  
Water Balance ◽  
Soil Water ◽  
Storage Capacity ◽  
Water Storage ◽  
Annual Runoff ◽  
Balance Model ◽  
Soil Water Storage ◽  
Ungauged Basins ◽  
Water Storage Capacity ◽  
Soil Water Storage Capacity

Abstract. Prediction of mean annual runoff is of great interest but still poses a challenge in ungauged basins. The present work diagnoses the prediction in mean annual runoff affected by the uncertainty in estimated distribution of soil water storage capacity. Based on a distribution function, a water balance model for estimating mean annual runoff is developed, in which the effects of climate variability and the distribution of soil water storage capacity are explicitly represented. As such, the two parameters in the model have explicit physical meanings, and relationships between the parameters and controlling factors on mean annual runoff are established. The estimated parameters from the existing data of watershed characteristics are applied to 35 watersheds. The results showed that the model could capture 88.2 % of the actual mean annual runoff on average across the study watersheds, indicating that the proposed new water balance model is promising for estimating mean annual runoff in ungauged watersheds. The underestimation of mean annual runoff is mainly caused by the underestimation of the area percentage of low soil water storage capacity due to neglecting the effect of land surface and bedrock topography. Higher spatial variability of soil water storage capacity estimated through the height above the nearest drainage (HAND) and topographic wetness index (TWI) indicated that topography plays a crucial role in determining the actual soil water storage capacity. The performance of mean annual runoff prediction in ungauged basins can be improved by employing better estimation of soil water storage capacity including the effects of soil, topography, and bedrock. It leads to better diagnosis of the data requirement for predicting mean annual runoff in ungauged basins based on a newly developed process-based model finally.

scholarly journals A stochastic approach for the description of the water balance dynamics in a river basin

2008 ◽  
Vol 12 (5) ◽  
pp. 1189-1200 ◽  
Author(s):  
S. Manfreda ◽  
M. Fiorentino
Keyword(s):  
Water Balance ◽  
Probability Density ◽  
Soil Water ◽  
Storage Capacity ◽  
Water Storage ◽  
Soil Water Balance ◽  
Soil Water Storage ◽  
Water Storage Capacity ◽  
Soil Water Storage Capacity ◽  
Balance Dynamics

Abstract. The present paper introduces an analytical approach for the description of the soil water balance dynamics over a schematic river basin. The model is based on a stochastic differential equation where the rainfall forcing is interpreted as an additive noise in the soil water balance. This equation can be solved assuming known the spatial distribution of the soil moisture over the basin transforming the two-dimensional problem in space in a one dimensional one. This assumption is particularly true in the case of humid and semihumid environments, where spatial redistribution becomes dominant producing a well defined soil moisture pattern. The model allowed to derive the probability density function of the saturated portion of a basin and of its relative saturation. This theory is based on the assumption that the soil water storage capacity varies across the basin following a parabolic distribution and the basin has homogeneous soil texture and vegetation cover. The methodology outlined the role played by the soil water storage capacity distribution of the basin on soil water balance. In particular, the resulting probability density functions of the relative basin saturation were found to be strongly controlled by the maximum water storage capacity of the basin, while the probability density functions of the relative saturated portion of the basin are strongly influenced by the spatial heterogeneity of the soil water storage capacity. Moreover, the saturated areas reach their maximum variability when the mean rainfall rate is almost equal to the soil water loss coefficient given by the sum of the maximum rate of evapotranspiration and leakage loss in the soil water balance. The model was tested using the results of a continuous numerical simulation performed with a semi-distributed model in order to validate the proposed theoretical distributions.

The hydrological response of soil water storage capacity to human activities: A case study in the upper Yangtze River Basin, China

2020 ◽  
Author(s):  
Xiaojing Zhang ◽  
Pan Liu ◽  
Chong-Yu Xu
Keyword(s):  
Soil Water ◽  
Human Activities ◽  
Storage Capacity ◽  
Yangtze River Basin ◽  
Water Storage ◽  
Soil Water Storage ◽  
Water Storage Capacity ◽  
Upper Yangtze River ◽  
Soil Water Storage Capacity ◽  
Reservoir Construction

<p>The intensification of climate change and human activities can lead to non-stationarity of hydrological model parameters, which in turn affects the correctness of model simulation results. Previous studies mainly focus on impacts of climate change, while catchment hydrological responses to human activities require detailed investigation for sustainable water management. This study evaluates anthropogenic impacts on soil water storage capacity of the upper Yangtze River Basin by representing hydrological parameters as functions of human activity indicators. The Xinanjiang (XAJ) model is used since its parameter WM accounts for soil water storage capacity. In this study, time-variations of WM are identified by the split-sample calibration based on dynamic programming (SSC-DP). The variations are further related to ten indicators of human activities from five aspects: population, gross domestic product, farming, irrigation and reservoir construction. Then, the proposed WM functional form is selected by comparing the performance of a set of parameter functions of the identified human activity indicators during the validation period. The study shows that WM increases in 1976-2000, while a relatively high relationship is detected between WM and some indicators such as agricultural acreage, population and reservoir construction. It is further demonstrated that agricultural population has the greatest impact on soil water storage capacity and its linear functional form for WM is validated to be effective in 2001-2010 with best streamflow simulation, especially for low streamflow. These results can help understand the hydrological response to the increasing human development and contribute to adaptive development strategies for future water resource management.</p>

The relationship between soil water storage capacity and plant species diversity in high alpine vegetation

2013 ◽  
Vol 6 (3-4) ◽  
pp. 457-466 ◽  
Author(s):  
Peter M. Kammer ◽  
Christian Schöb ◽  
Gabriel Eberhard ◽  
Renzo Gallina ◽  
Remo Meyer ◽  
...  
Keyword(s):  
Species Diversity ◽  
Soil Water ◽  
Plant Species ◽  
Storage Capacity ◽  
Water Storage ◽  
Soil Water Storage ◽  
Alpine Vegetation ◽  
Water Storage Capacity ◽  
Soil Water Storage Capacity ◽  
The Relationship

scholarly journals Diagnosis toward predicting mean annual runoff in ungauged basins

2020 ◽  
Author(s):  
Yuan Gao ◽  
Lili Yao ◽  
Ni-Bin Chang ◽  
Dingbao Wang
Keyword(s):  
Soil Water ◽  
Storage Capacity ◽  
Water Storage ◽  
Annual Runoff ◽  
Balance Model ◽  
Soil Water Storage ◽  
Ungauged Basins ◽  
Water Storage Capacity ◽  
Soil Water Storage Capacity ◽  
Soil Storage Capacity

Abstract. The present work diagnoses the prediction in mean annual runoff affected by the uncertainty in estimated distribution of soil water storage capacity. Based on a distribution function, a water balance model for estimating mean annual runoff is developed, in which the effects of climate variability and the distribution of soil water storage capacity are explicitly represented. As such, the two parameters in the model have explicit physical meanings, and relationships between the parameters and controlling factors on mean annual runoff are established. The estimated parameters from the existing data of watershed characteristics are applied to 35 watersheds. The results showed that the model could capture 88.2 % of the actual runoff on average, indicating that the proposed new water balance model is promising for estimating mean annual runoff in ungauged watersheds. The underestimation of runoff is mainly caused by the underestimation of the spatial heterogeneity of soil storage capacity due to neglecting the effect of land surface and bedrock topography. A higher spatial variability of soil storage capacity estimated through the Height Above the Nearest Drainage (HAND) indicated that topography plays a crucial role in determining the actual soil water storage capacity. The performance of mean annual runoff prediction in ungauged basins can be improved by employing better estimation of soil water storage capacity including the effects of soil, topography and bedrock. The purpose of this study is to diagnose the data requirement for predicting mean annual runoff in ungauged basins based on a newly developed process-based model.

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