| Autor: |
Roy, Dibyandu, Aithal, Bharath H., Dhar, Anirban, Desai, Venkappayya R. |
| Předmět: |
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| Zdroj: |
Journal of Hydrologic Engineering; Jun2024, Vol. 29 Issue 3, p1-18, 18p |
| Abstrakt: |
Hydrological extremes and land use are intimately connected, as land use alterations frequently exacerbate or mitigate the consequences of extreme events. Therefore, assessment and prediction of land use dynamics retain great significance in projected scenarios of hydrological extremes. This study aimed to quantify the impact of land use changes on future landslide-prone sites, flash flood vulnerable zones (FFVZs), and monsoon runoff. In this context, a Markov chain–cellular automata (MC-CA) model integrated with a multilayer perceptron–neural network (MLP-NN) model was designed to predict near-future land use/land cover changes (LULCC) using high-resolution existing land use/land cover (LULC) information. Uncorrected finer-scale Coupled Model Intercomparison Project Phase 6 (CMIP6) meteorological data sets and predicted LULC data were used to simulate daily runoff with the help of the Soil and Water Assessment Tool (SWAT). Statistical evaluation matrices were used to assess the efficacy of using uncorrected CMIP6 data sets for hydrological modeling. The methodology was evaluated for suitability in the ungauged high-altitude Ranikhola watershed. The prediction results showed that the watershed can be expected to be more susceptible to landslides and flash floods (9%). The most challenging discovery from this analysis was an anticipated 32.50% increase in average monsoon runoff from 2021 to 2032 compared to the observed period, 2015–2020. In addition, the runoff peak magnitude is projected to rise by 26.00%. Consequently, it is imperative to develop strategies and approaches for sustainable watershed management to address future challenges. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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