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Basin-Scale Streamflow Projections for Greater Pamba River Basin, India Integrating GCM Ensemble Modelling and Flow Accumulation-Weighted LULC Overlay in Deep Learning Environment
Accurate prediction of future streamflow in flood-prone regions is crucial for effective flood management and disaster mitigation. This study presents an innovative approach for streamflow projections in deep learning (DL) environment by integrating the quantitative Land-Use Land-Cover (LULC) overlaid with flow accumulation values and the various Global Climate Model (GCM) simulated data. Firstly, the Long Short Term Memory (LSTM) model was developed for the streamflow prediction of Greater Pamba River Basin (GPRB) in Kerala, India for 1985 to 2015 period, considering the climatic inputs. Then, the flow accumulation-weighted LULC integration was considered in modelling, which substantially improves the accuracy of streamflow predictions including the extremes of all the three stations, as the model accounts for the geographical variety of land cover types towards the streamflow at the sub-basin outlets. Subsequently, Reliability Ensemble Averaging (REA) technique was used to create an ensemble of three candidate GCM products to illustrate the spectrum of uncertainty associated with climate projections. Future LULC changes are accounted in regional scale based on the sub-basin approach by means of Cellular-Automata Markov Model and used for integrating with the climatic indices. The basin-scale streamflow projection is done under three climate scenarios of SSP126, SSP245 and SSP585 respectively for lowest, moderate and highest emission conditions. This work is a novel approach of integrating quantified LULC with flow accumulation and other climatic inputs in a DL environment against the conventional techniques of hydrological modelling. The DL model can adapt and account for shifting hydrological responses induced by changes in climatic and LULC inputs. The integration of flow accumulation with changes in LULC was successful in capturing the flow dynamics in long-term. It also identifies regions that are more likely to experience increased flooding in the near future under changing climate scenarios and supports decision-making for sustainable water management of the Greater Pamba Basin which was the worst affected region in Kerala during the mega floods of 2018.
Basin-Scale Streamflow Projections for Greater Pamba River Basin, India Integrating GCM Ensemble Modelling and Flow Accumulation-Weighted LULC Overlay in Deep Learning Environment
Accurate prediction of future streamflow in flood-prone regions is crucial for effective flood management and disaster mitigation. This study presents an innovative approach for streamflow projections in deep learning (DL) environment by integrating the quantitative Land-Use Land-Cover (LULC) overlaid with flow accumulation values and the various Global Climate Model (GCM) simulated data. Firstly, the Long Short Term Memory (LSTM) model was developed for the streamflow prediction of Greater Pamba River Basin (GPRB) in Kerala, India for 1985 to 2015 period, considering the climatic inputs. Then, the flow accumulation-weighted LULC integration was considered in modelling, which substantially improves the accuracy of streamflow predictions including the extremes of all the three stations, as the model accounts for the geographical variety of land cover types towards the streamflow at the sub-basin outlets. Subsequently, Reliability Ensemble Averaging (REA) technique was used to create an ensemble of three candidate GCM products to illustrate the spectrum of uncertainty associated with climate projections. Future LULC changes are accounted in regional scale based on the sub-basin approach by means of Cellular-Automata Markov Model and used for integrating with the climatic indices. The basin-scale streamflow projection is done under three climate scenarios of SSP126, SSP245 and SSP585 respectively for lowest, moderate and highest emission conditions. This work is a novel approach of integrating quantified LULC with flow accumulation and other climatic inputs in a DL environment against the conventional techniques of hydrological modelling. The DL model can adapt and account for shifting hydrological responses induced by changes in climatic and LULC inputs. The integration of flow accumulation with changes in LULC was successful in capturing the flow dynamics in long-term. It also identifies regions that are more likely to experience increased flooding in the near future under changing climate scenarios and supports decision-making for sustainable water management of the Greater Pamba Basin which was the worst affected region in Kerala during the mega floods of 2018.
Basin-Scale Streamflow Projections for Greater Pamba River Basin, India Integrating GCM Ensemble Modelling and Flow Accumulation-Weighted LULC Overlay in Deep Learning Environment
Arathy Nair Geetha Raveendran Nair (author) / Shamla Dilama Shamsudeen (author) / Meera Geetha Mohan (author) / Adarsh Sankaran (author)
2023
Article (Journal)
Electronic Resource
Unknown
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