scholarly journals A improved SCS-CN method incorporating slope, soil moisture and storm duration factors for runoff prediction

Authorea ◽  
2019 ◽  
Author(s):  
Wenhai Shi ◽  
NI Wang
Keyword(s):  
Soil Moisture ◽  
Runoff Prediction ◽  
Storm Duration ◽  
Scs Cn

scholarly journals An Improved SCS-CN Method Incorporating Slope, Soil Moisture, and Storm Duration Factors for Runoff Prediction

Water ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 1335 ◽  
Author(s):  
Wenhai Shi ◽  
Ni Wang
Keyword(s):  
Soil Moisture ◽  
Loess Plateau ◽  
Prediction Method ◽  
Slope Gradient ◽  
The Loess Plateau ◽  
Calibration And Validation ◽  
Runoff Prediction ◽  
Storm Duration ◽  
Scs Cn ◽  
Runoff Plots

Soil Conservation Service Curve Number (SCS-CN) is a popular surface runoff prediction method because it is simple in principle, convenient in application, and easy to accept. However, the method still has several limitations, such as lack of a land slope factor, discounting the storm duration, and the absence of guidance on antecedent moisture conditions. In this study, an equation was developed to improve the SCS-CN method by combining the CN value with the tabulated CN2 value and three introduced factors (slope gradient, soil moisture, and storm duration). The proposed method was tested for calibration and validation with a dataset from three runoff plots in a watershed of the Loess Plateau. The results showed the model efficiencies of the proposed method were improved to 80.58% and 80.44% during the calibration and validation period, respectively, which was better than the standard SCS-CN and the other two modified SCS-CN methods where only a single factor of soil moisture or slope gradient was considered, respectively. Using the parameters calibrated and validated by dataset of the initial three runoff plots, the proposed method was then applied to runoff estimation of the remaining three runoff plots in another watershed. The proposed method reduced the root-mean-square error between the observed and estimated runoff values from 5.53 to 2.01 mm. Furthermore, the parameters of soil moisture (b1 and b2) is the most sensitive, followed by parameters in storm duration (c) and slope equations (a1 and a2), and the least sensitive parameter is the initial abstraction ratio λ on the basis of the proposed method sensitivity analysis. Conclusions can be drawn from the above results that the proposed method incorporating the three factors in the SCS method may estimate runoff more accurately in the Loess Plateau of China.

scholarly journals Improved SMA-based SCS-CN method incorporating storm duration for runoff prediction on the Loess Plateau, China

2020 ◽  
Vol 51 (3) ◽  
pp. 443-455
Author(s):  
Wenhai Shi ◽  
Ni Wang
Keyword(s):  
Loess Plateau ◽  
Curve Number ◽  
The Loess Plateau ◽  
Moisture Condition ◽  
Soil Moisture Accounting ◽  
Antecedent Moisture ◽  
Storm Duration ◽  
Service Curve ◽  
Scs Cn ◽  
Runoff Events

Abstract In the Soil Conservation Service Curve Number (SCS-CN) method for estimating runoff, three antecedent moisture condition (AMC) levels produce a discrete relation between the curve number (CN) and soil water content, which results in corresponding sudden jumps in estimated runoff. An improved soil moisture accounting (SMA)-based SCS-CN method that incorporates a continuous function for the AMC was developed to obviate sudden jumps in estimated runoff. However, this method ignores the effect of storm duration on surface runoff, yet this is an important component of rainfall-runoff processes. In this study, the SMA-based method for runoff estimation was modified by incorporating storm duration and a revised SMA procedure. Then, the performance of the proposed method was compared to both the original SCS-CN and SMA-based methods by applying them in three experimental watersheds located on the Loess Plateau, China. The results indicate that the SCS-CN method underestimates large runoff events and overestimates small runoff events, yielding an efficiency of 0.626 in calibration and 0.051 in validation; the SMA-based method has improved runoff estimation in both calibration (efficiency = 0.702) and validation (efficiency = 0.481). However, the proposed method performed significantly better than both, yielding model efficiencies of 0.810 and 0.779 in calibration and validation, respectively.

Geospatial estimation of soil moisture in rain-fed paddy fields using SCS-CN-based model

2008 ◽  
Vol 95 (4) ◽  
pp. 447-457 ◽  
Author(s):  
T.V. Reshmidevi ◽  
R. Jana ◽  
T.I. Eldho
Keyword(s):  
Soil Moisture ◽  
Paddy Fields ◽  
Scs Cn

scholarly journals Improving runoff prediction through the assimilation of the ASCAT soil moisture product

2010 ◽  
Vol 7 (4) ◽  
pp. 4113-4144 ◽  
Author(s):  
L. Brocca ◽  
F. Melone ◽  
T. Moramarco ◽  
W. Wagner ◽  
V. Naeimi ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Central Italy ◽  
Model Performance ◽  
Remotely Sensed ◽  
Model Parameters ◽  
Rainfall Runoff ◽  
Catchment Scale ◽  
Runoff Prediction ◽  
Initial Soil ◽  
Soil Wetness

Abstract. The role and the importance of soil moisture for meteorological, agricultural and hydrological applications is widely known. Remote sensing offers the unique capability to monitor soil moisture over large areas (catchment scale) with, nowadays, a temporal resolution suitable for hydrological purposes. However, the accuracy of the remotely sensed soil moisture estimates has to be carefully checked. The validation of these estimates with in-situ measurements is not straightforward due the well-known problems related to the spatial mismatch and the measurement accuracy. The analysis of the effects deriving from assimilating remotely sensed soil moisture data into hydrological or meteorological models could represent a more valuable method to test their reliability. In particular, the assimilation of satellite-derived soil moisture estimates into rainfall-runoff models at different scales and over different regions represents an important scientific and operational issue. In this study, the soil wetness index (SWI) product derived from the Advanced SCATterometer (ASCAT) sensor onboard of the Metop satellite was tested. The SWI was firstly compared with the soil moisture temporal pattern derived from a continuous rainfall-runoff model (MISDc) to assess its relationship with modeled data. Then, by using a simple data assimilation technique, the linearly rescaled SWI that matches the range of variability of modelled data (denoted as SWI*) was assimilated into MISDc and the model performance on flood estimation was analyzed. Moreover, three synthetic experiments considering errors on rainfall, model parameters and initial soil wetness conditions were carried out. These experiments allowed to further investigate the SWI potential when uncertain conditions take place. The most significant flood events, which occurred in the period 2000–2009 on five subcatchments of the Upper Tiber River in Central Italy, ranging in extension between 100 and 650 km2, were used as case studies. Results reveal that the SWI derived from the ASCAT sensor can be conveniently adopted to improve runoff prediction in the study area, mainly if the initial soil wetness conditions are unknown.

scholarly journals A new data assimilation approach for improving runoff prediction using remotely-sensed soil moisture retrievals

2009 ◽  
Vol 13 (1) ◽  
pp. 1-16 ◽  
Author(s):  
W. T. Crow ◽  
D. Ryu
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Surface Runoff ◽  
Surface Soil ◽  
Remotely Sensed ◽  
Hydrologic Model ◽  
Surface Soil Moisture ◽  
Antecedent Soil Moisture ◽  
Runoff Prediction ◽  
Moisture Conditions

Abstract. A number of recent studies have focused on enhancing runoff prediction via the assimilation of remotely-sensed surface soil moisture retrievals into a hydrologic model. The majority of these approaches have viewed the problem from purely a state or parameter estimation perspective in which remotely-sensed soil moisture estimates are assimilated to improve the characterization of pre-storm soil moisture conditions in a hydrologic model, and consequently, its simulation of runoff response to subsequent rainfall. However, recent work has demonstrated that soil moisture retrievals can also be used to filter errors present in satellite-based rainfall accumulation products. This result implies that soil moisture retrievals have potential benefit for characterizing both antecedent moisture conditions (required to estimate sub-surface flow intensities and subsequent surface runoff efficiencies) and storm-scale rainfall totals (required to estimate the total surface runoff volume). In response, this work presents a new sequential data assimilation system that exploits remotely-sensed surface soil moisture retrievals to simultaneously improve estimates of both pre-storm soil moisture conditions and storm-scale rainfall accumulations. Preliminary testing of the system, via a synthetic twin data assimilation experiment based on the Sacramento hydrologic model and data collected from the Model Parameterization Experiment, suggests that the new approach is more efficient at improving stream flow predictions than data assimilation techniques focusing solely on the constraint of antecedent soil moisture conditions.

An approach to accommodate and estimate antecedent moisture in runoff curve number methodology- An experimental study

2020 ◽  
Author(s):  
Surendra Kumar Mishra ◽  
Ishan Sharma ◽  
Ashish Pandey ◽  
Shailendra Kumar Kumre
Keyword(s):  
Soil Moisture ◽  
Water Conservation ◽  
Curve Number ◽  
Yield Potential ◽  
Water Yield ◽  
Absolute Maximum ◽  
Rainfall Runoff ◽  
Antecedent Moisture ◽  
Scs Cn

<p>Modelling of the event-based rainfall-runoff process has considerable importance in Hydrology, especially for assessment of water yield potential of a watershed, planning of soil and water conservation measures, reducing sedimentation, and flooding hazards downstream. Antecedent moisture (M) plays a significant role in governing the rainfall-runoff modelling process. It has been the focal point of research in the last decade for improving the Soil Conservation Service Curve Number (SCS-CN) method (also known as NRCS-CN method) for surface runoff computation. In this study, an innovative procedure is proposed to accommodate M in the basic structure of the SCS-CN methodology which otherwise was incorporated externally; to compute M using rainfall-runoff data and verify its applicability by comparing M with the in-situ soil moisture.</p><p>Natural rainfall, runoff, and soil moisture data from 6 small experimental farms with different land-use viz. Maize, Finger Millet, and Fallow land, located at Roorkee, India, are utilized. The M is computed by optimizing two parameters, i.e., absolute maximum potential retention (S<sub>abs</sub>) and initial abstraction ratio (λ), and the optimization is accomplished by minimizing the root mean square error (RMSE). Results show that there exists a good correlation between theoretical M and measured in-situ moisture. Also, the optimized value of λ has the less error in computing M than the other standard values of λ (λ = 0.2; λ= 0.03). This study not only improves the SCS-CN method but also widens its application horizon in soil moisture studies.</p>

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