Development and testing of updated curve number models for efficient runoff estimation in steep-slope watersheds

Ajmal, Muhammad; Waseem, Muhammad; Jehanzaib, Muhammad; Kim, Tae‐Woong

doi:10.1016/j.jhydrol.2022.129049

Cited by 4 publications

(1 citation statement)

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“…The Soil Conservation Service-Curve Number (SCS-CN) model has been frequently employed for computation of the potential of this runoff from a given amount of rainfall (Mishra et al, 2006, Singh et al, 2010. It is the most straightforward, extensively studied model that takes into consideration via its parameter Curve Number (CN) dependent on the key factors affecting runoff in the watershed, such as soil type, land use-land cover (LULC), and antecedent soil moisture status (AMC) (Mishra and Singh 2003, Mishra et al 2004, Aragaw and Mishra 2022a, Sharma et al 2022, Ajmal et al 2023. Several hydrological processes, including meteorological forcing (precipitation and temperature), morphologic basin characteristics (slope, elevation), geologic characteristics of the underground system, and anthropogenic activities (construction of reservoirs and dams, changes in land use, and excessive water exploitation) all contribute to streamflow (Gupta et al 2023).…”

Section: Introductionmentioning

confidence: 99%

Integrated Influence of Changing LULC and Aridity on Runoff Curve Numbers

Prashant,

Mishra,

Lohani

2024

Preprint

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The popular Soil Conservation Service-Curve Number (SCS-CN) method is widely used for direct surface runoff estimation from a given amount of rainfall in a watershed. The present urban sprawl, socioeconomic anthropogenic activities, and environmental changes all have affected the cosmic extent of land use-land cover (LULC) complex and climate, both spatially and temporally, which directly affect the parameter curve number (CN) and, in turn, the direct surface runoff. Therefore, the study propels the disparity of representative CNs of SCS-CN methodology, which is usually derived from NEH-4 tables based on land use and soil type (CNLU−ST) and from the observed rainfall(P)-runoff(Q) events (CNP−Q). The annual series of CNP−Q and CNLU−ST (from 1980 to 2020) showed the existence of trends and the inconsistency between CNP−Q and CNLU−ST for the Ong River basin (India). The land use and land cover (LULC) alteration analysis utilized the supervised machine learning algorithm and indicated two major LULC classes as the contributing factors for increasing CNs. Furthermore, the study attributes the implications of shifting LULC dynamics (~ 70%) and climate variations (~ 30%) to the watershed. Employing Aridity Index (AI), as a parameter in solving the disparity for representative CNs for annual/decadal values revealed strong evidence with a fit of high R2 range (0.72, 0.99) of LULC and aridity influencing CNs.

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Section: Introductionmentioning

confidence: 99%

Integrated Influence of Changing LULC and Aridity on Runoff Curve Numbers

Prashant,

Mishra,

Lohani

2024

Preprint

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An integrated land change modeler and distributed hydrological model approach for quantifying future urban runoff dynamics

Muhammad,

Muhammad

et al. 2024

Sci. China Earth Sci.

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Assessing the impacts of dam/weir operation on streamflow predictions using LSTM across South Korea

Kwon

Cha

Park

et al. 2023

Sci Rep

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Recently, weather data have been applied to one of deep learning techniques known as “long short-term memory (LSTM)” to predict streamflow in rainfall-runoff relationships. However, this approach may not be suitable for regions with artificial water management structures such as dams and weirs. Therefore, this study aims to evaluate the prediction accuracy of LSTM for streamflow depending on the availability of dam/weir operational data across South Korea. Four scenarios were prepared for 25 streamflow stations. Scenarios #1 and #2 used weather data and weather and dam/weir operational data, respectively, with the same LSTM model conditions for all stations. Scenarios #3 and #4 used weather data and weather and dam/weir operational data, respectively, with the different LSTM models for individual stations. The Nash–Sutcliffe efficiency (NSE) and the root mean squared error (RMSE) were adopted to assess the LSTM’s performance. The results indicated that the mean values of NSE and RMSE were 0.277 and 292.6 (Scenario #1), 0.482 and 214.3 (Scenario #2), 0.410 and 260.7 (Scenario #3), and 0.592 and 181.1 (Scenario #4), respectively. Overall, the model performance was improved by the addition of dam/weir operational data, with an increase in NSE values of 0.182–0.206 and a decrease in RMSE values of 78.2–79.6. Surprisingly, the degree of performance improvement varied according to the operational characteristics of the dam/weir, and the performance tended to increase when the dam/weir with high frequency and great amount of water discharge was included. Our findings showed that the overall LSTM prediction of streamflow was improved by the inclusion of dam/weir operational data. When using dam/weir operational data to predict streamflow using LSTM, understanding of their operational characteristics is important to obtain reliable streamflow predictions.

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Development and testing of updated curve number models for efficient runoff estimation in steep-slope watersheds

Cited by 4 publications

References 72 publications

Integrated Influence of Changing LULC and Aridity on Runoff Curve Numbers

Integrated Influence of Changing LULC and Aridity on Runoff Curve Numbers

An integrated land change modeler and distributed hydrological model approach for quantifying future urban runoff dynamics

Assessing the impacts of dam/weir operation on streamflow predictions using LSTM across South Korea

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