Evaluation of NRCS curve number and MODIS time-series proxies for antecedent moisture condition

2009 ◽  
Vol 26 (1) ◽  
pp. 85-101 ◽  
Author(s):  
B. P. Weissling ◽  
H. Xie ◽  
K. Murray
Keyword(s):  
Time Series ◽  
Curve Number ◽  
Moisture Condition ◽  
Antecedent Moisture ◽  
Antecedent Moisture Condition

scholarly journals Evaluation of SCS - unit hydrograph model to estimate peak flows in watersheds of Norte de Santander

Respuestas ◽  
2019 ◽  
Vol 24 (1) ◽  
pp. 6-15
Author(s):  
Leonardo Vargas Garay ◽  
Oscar David Torres Goyeneche ◽  
Gustavo Adolfo Carrillo Soto
Keyword(s):  
Weather Conditions ◽  
Curve Number ◽  
Moisture Condition ◽  
Extreme Flows ◽  
Antecedent Moisture ◽  
Aster Gdem ◽  
Antecedent Moisture Condition ◽  
Digital Elevation ◽  
Scs Model ◽  
Elevation Model

In several studies it is necessary to determine the magnitude of extreme flows in a river. Having an adequate register of observed discharge it is possible to adjust a probability density function (fdp) that allows estimating events associated with a high return period (i.e. 100 years). In ungauged basins, such as the majority of basins in the world are, other methodologies are used, such as the Synthetic Unity Hydrograph proposed by the United State Soil Conservation Service (UH-SCS). The UH-SCS model was evaluated in watersheds of the Norte de Santander department (COL), in its capacity to estimate extreme flows, and to explore its possible regionalization. The evaluation is done by comparing estimates of Q100, using the Frequency Factors method and the UH-SCS model. Discharge and precipitation time series were obtained from the IDEAM network, selecting 19 basins based on their drainage area, climatological stations density and records length. Geomorphology was characterized using ArcMap™ on the ASTER-GDEM digital elevation model. Using information available on geology, soils, vegetation cover, and assuming a wet antecedent moisture condition (AMC-III), values of the median of relative Q100 error (ε-Q100) of + 507% and + 406% were obtained for the fdp Gumbel and Log-Pearson. Using dry antecedent moisture condition (AMC-I) ε-Q100 low to + 36% and + 17%. It was possible to minimize ε-Q100 by calibrating the Curve Number (CN) parameter. A satisfactory regionalization function for CN was not found. Applying SCS-HU under AMC-III condition, Q100 is greatly overestimated. It is possible to minimize the error by considering AMC-I and reduce CN, a counter-intuitive situation since extreme flows are associated with wet weather conditions (i.e. Año Niña). Improvements in the characterization of rainfall and soils in Norte de Santander should be investigated.

scholarly journals Rainfall-Runoff Modeling Using NRCS-CN method and GIS approach

2020 ◽  
Author(s):  
Rekha Verma ◽  
Azhar Husain ◽  
Mohammed Sharif
Keyword(s):  
Hydrological Modeling ◽  
Curve Number ◽  
Geographical Information ◽  
Rainfall Runoff ◽  
Moisture Condition ◽  
Natural Resource Conservation ◽  
Antecedent Moisture ◽  
Natural Resource Conservation Service ◽  
Runoff Modeling ◽  
Runoff Depth

Rainfall-Runoff modeling is a hydrological modeling which is extremely important for water resources planning, development, and management. In this paper, Natural Resource Conservation Service-Curve Number (NRCS-CN) method along with Geographical Information System (GIS) approach was used to evaluate the runoff resulting from the rainfall of four stations, namely, Bilodra, Kathlal, Navavas and Rellawada of Sabarmati River basin. The rainfall data were taken for 10 years (2005-2014). The curve number which is the function of land use, soil and antecedent moisture condition (AMC) was generated in GIS platform. The CN value generated for AMC- I, II and III were 57.29, 75.39 and 87.77 respectively. Using NRCS-CN method, runoff depth was calculated for all the four stations. The runoff depth calculated with respect to the rainfall for Bilodra, Kathlal, Navavas and Rellawada shows a good correlation of 0.96. The computed runoff was compared with the observed runoff which depicted a good correlation of 0.73, 0.70, 0.76 and 0.65 for the four stations. This method results in speedy and precise estimation of runoff from a watershed.

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.

scholarly journals Formulation of Parsimonious Urban Flash Flood Predictive Model with Inferential Statistics

Mathematics ◽  
2022 ◽  
Vol 10 (2) ◽  
pp. 175
Author(s):  
Lloyd Ling ◽  
Sai Hin Lai ◽  
Zulkifli Yusop ◽  
Ren Jie Chin ◽  
Joan Lucille Ling
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Flash Flood ◽  
Curve Number ◽  
Runoff Coefficient ◽  
Rainfall Runoff ◽  
Inferential Statistics ◽  
Moisture Condition ◽  
Antecedent Moisture ◽  
Runoff Model

The curve number (CN) rainfall–runoff model is widely adopted. However, it had been reported to repeatedly fail in consistently predicting runoff results worldwide. Unlike the existing antecedent moisture condition concept, this study preserved its parsimonious model structure for calibration according to different ground saturation conditions under guidance from inferential statistics. The existing CN model was not statistically significant without calibration. The calibrated model did not rely on the return period data and included rainfall depths less than 25.4 mm to formulate statistically significant urban runoff predictive models, and it derived CN directly. Contrarily, the linear regression runoff model and the asymptotic fitting method failed to model hydrological conditions when runoff coefficient was greater than 50%. Although the land-use and land cover remained the same throughout this study, the calculated CN value of this urban watershed increased from 93.35 to 96.50 as the watershed became more saturated. On average, a 3.4% increase in CN value would affect runoff by 44% (178,000 m3). This proves that the CN value cannot be selected according to the land-use and land cover of the watershed only. Urban flash flood modelling should be formulated with rainfall–runoff data pairs with a runoff coefficient > 50%.

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