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  • 1
    In: Sustainability, MDPI AG, Vol. 12, No. 20 ( 2020-10-14), p. 8449-
    Kurzfassung: Watershed management plays a dynamic role in water resource engineering. Estimating surface runoff is an essential process of hydrology, since understanding the fundamental relationship between rainfall and runoff is useful for sustainable water resource management. To facilitate the assessment of this process, the Natural Resource Conservation Service-Curve Number (NRCS-CN) and Geographic Information Systems (GIS) were integrated. Furthermore, land use and soil maps were incorporated to estimate the temporal variability in surface runoff potential. The present study was performed on the Haridwar city, Uttarakhand, India for the years 1995, 2010 and 2018. In a context of climate change, the spatiotemporal analysis of hydro meteorological parameters is essential for estimating water availability. The study suggested that runoff increased approximately 48% from 1995 to 2010 and decreased nearly 71% from 2010 to 2018. In turn, the weighted curve number was found to be 69.24, 70.96 and 71.24 for 1995, 2010 and 2018, respectively. Additionally, a validation process with an annual water yield model was carried out to understand spatiotemporal variations and similarities. The study recommends adopting water harvesting techniques and strategies to fulfill regional water demands, since effective and sustainable approaches like these may assist in the simultaneous mitigation of disasters such as floods and droughts.
    Materialart: Online-Ressource
    ISSN: 2071-1050
    Sprache: Englisch
    Verlag: MDPI AG
    Publikationsdatum: 2020
    ZDB Id: 2518383-7
    Standort Signatur Einschränkungen Verfügbarkeit
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  • 2
    In: Sustainability, MDPI AG, Vol. 15, No. 10 ( 2023-05-19), p. 8285-
    Kurzfassung: Slopy agricultural lands are more susceptible to soil erosion and hence are priority sites for the application of protective soil management practices. A conservation agriculture field experiment was established at a 6% field slope in 2011 at the ICAR-IISWC Research Farm, Dehradun, Uttarakhand, which is situated in the Northwestern Himalayan Region, India. The objective of this study was to experimentally determine the long-term effects of tillage practices on runoff and soil erosion. The tillage practices opted for were conventional tillage (CT), minimum tillage (MT), and zero tillage (ZT). Event-based runoff and soil loss were monitored during three monsoon seasons (June to September) from 2018 to 2020. Results showed lower runoff and soil loss in the ZT plot than in CT and MT plots. CT produced 1.51 and 2.53 times higher runoff than MT and ZT, respectively. Moreover, this increased runoff generated 1.84 and 5.10 times higher soil erosion in CT than in MT and ZT, respectively. The extreme rainfall events being less than 10% generated 54.93%, 57.35%, and 63.43% of the total runoff volume which resulted in 82.08%, 85.49%, and 91.00% of the total soil loss in CT, MT, and ZT plots, respectively. For the same amount of rainfall, the reduction in soil loss was 39% and 68% in the CT and ZT plots, respectively, at the highest growth stage in comparison to the initial crop growth stage. The values of runoff reduction benefit (RRB) and sediment reduction benefit (SRB) showed a reduction in runoff (63.53%) and soil loss (80.39%) in the CT. Results concluded that conservation tillage reduced runoff and soil loss significantly even in extreme rainfall events.
    Materialart: Online-Ressource
    ISSN: 2071-1050
    Sprache: Englisch
    Verlag: MDPI AG
    Publikationsdatum: 2023
    ZDB Id: 2518383-7
    Standort Signatur Einschränkungen Verfügbarkeit
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  • 3
    Online-Ressource
    Online-Ressource
    MDPI AG ; 2023
    In:  J Vol. 6, No. 4 ( 2023-10-07), p. 544-563
    In: J, MDPI AG, Vol. 6, No. 4 ( 2023-10-07), p. 544-563
    Kurzfassung: The friction factor is a widely used parameter in characterizing flow resistance in pipes and open channels. Recently, the application of machine learning and artificial intelligence (AI) has found several applications in water resource engineering. With this in view, the application of artificial intelligence techniques on Moody’s diagram for predicting the friction factor in pipe flow for both transition and turbulent flow regions has been considered in the present study. Various AI methods, like Random Forest (RF), Random Tree (RT), Support Vector Machine (SVM), M5 tree (M5), M5Rules, and REPTree models, are applied to predict the friction factor. While performing the statistical analysis (root-mean-square error (RMSE), mean absolute error (MAE), squared correlation coefficient (R2), and Nash–Sutcliffe efficiency (NSE)), it was revealed that the predictions made by the Random Forest model were the most reliable when compared to other AI tools. The main objective of this study was to highlight the limitations of artificial intelligence (AI) techniques when attempting to effectively capture the characteristics and patterns of the friction curve in certain regions of turbulent flow. To further substantiate this behavior, the conventional algebraic equation was used as a benchmark to test how well the current AI tools work. The friction factor estimates using the algebraic equation were found to be even more accurate than the Random Forest model, within a relative error of ≤±1%, in those regions where the AI models failed to capture the nature and variation in the friction factor.
    Materialart: Online-Ressource
    ISSN: 2571-8800
    Sprache: Englisch
    Verlag: MDPI AG
    Publikationsdatum: 2023
    ZDB Id: 2962863-5
    Standort Signatur Einschränkungen Verfügbarkeit
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