Dam-induced hydrological alterations in the upper Cauvery river basin, India

Ekka, Anjana; Keshav, Saket; Pande, Saket; Zaag, Pieter van der; Jiang, Yong

doi:10.1016/j.ejrh.2022.101231

Cited by 9 publications

(5 citation statements)

References 70 publications

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“…Reservoirs have significant effect on the flow regime characteristics and thus influences the ecological processes (Ekka et al, 2022). Hence, it is imperative to include reservoirs in modeling managed catchment.…”

Section: Semi-distributed Simhyd With Reservoirmentioning

confidence: 99%

Leveraging Contextual Cues from a Conceptual Model with Predictive Skills of Machine Learning for Improved Predictability and Interpretability in the Hydrological Processes

Bhasme

Bhatia

2023

Preprint

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In recent years, Machine Learning (ML) techniques have gained the attention of the hydrological community for their better predictive skills. Specifically, ML models are widely applied for streamflow predictions. However, limited interpretability in the ML models indicates space for improvement. Leveraging domain knowledge from conceptual models can aid in overcoming interpretability issues in ML models. Here, we have developed the Physics Informed Machine Learning (PIML) model at daily timestep, which accounts for memory in the hydrological processes and provides an interpretable model structure. We demonstrated three model cases, including lumped model and semi-distributed model structures with and without reservoir. We evaluate the first two model structures on three catchments in India, and the applicability of the third model structure is shown on the two United States catchments. Also, we compared the result of the PIML model with the conceptual model (SIMHYD), which is used as the parent model to derive contextual cues. Our results show that the PIML model outperforms simple ML model in target variable (streamflow) prediction and SIMHYD model in predicting target variable and intermediate variables (for example, evapotranspiration, reservoir storage) while being mindful of physical constraints. The water balance and runoff coefficient analysis reveals that the PIML model provides physically consistent outputs. The PIML modeling approach can make a conceptual model more modular such that it can be applied irrespective of the region for which it is developed. The successful application of PIML in different climatic as well as geographical regions shows its generalizability.

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Section: Semi-distributed Simhyd With Reservoirmentioning

confidence: 99%

Leveraging Contextual Cues from a Conceptual Model with Predictive Skills of Machine Learning for Improved Predictability and Interpretability in the Hydrological Processes

Bhasme

Bhatia

2023

Preprint

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“…After calibrating the reservoir operations model and integrating it with the FLEX-Topo models for the areas upstream and downstream of a reservoir within the corresponding sub-basin, the FLEX-Topo parameters (within the ranges shown in Table S.1) are optimized to ensure the representation of the hydrological processes for various sub-basins (four sub-basins, one for each of the four reservoirs) in the Upper Cauvery basin, which is predominantly covered by field crops, plantation crops, and evergreen forests. For more details readers are referred to Ekka et al (2022).…”

Section: The Flex-topo Modelmentioning

confidence: 99%

“…FLEX-Topo model parameters ranges. These define the feasible range within which parameters are calibrated and are discussed in detail in Ekka et al (2022).…”

Section: The Flex-topo Modelmentioning

confidence: 99%

“…Parameter setting for NSGA II optimization of the model. Source:Ekka et al, 2022 TableS.3 presents the calibration results of both reservoir operations models and FLEX-Topo parameters after integrating the reservoir operations models for each of the sub-basins, obtained using the NSGA II algorithm.Negative Nash-Sutcliffe Efficiency (NSE) values are reported instead of positive NSE (due to its use to maximize as an objective in NSGA-II), and the Pareto front ranges for both -NSE (note the negative sign in front of NSE) and Mean Absolute Error (MAE) are shown within the parentheses. The MAE values range from 0.71 to 2.92 (10 6 m 3 day -1 ), falling within an acceptable range.…”

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confidence: 99%

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Supplementary material to "How economically and environmentally viable are multiple dams in the Upper Cauvery basin, India? A hydro-economic analysis using a landscape-based hydrological model"

Ekka,

Jiang,

Pande

et al. 2023

Preprint

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“…Reservoirs constructed for irrigation, flood control, water supply, hydropower generation, etc., have had a pronounced effect on river flow regimes and terrestrial hydrology (Ekka et al., 2022; Haddeland et al., 2014; Kåresdotter et al., 2022; Poff et al., 1997). Consequently, the development of reservoir operation models that can be integrated with rainfall‐runoff process simulation in hydrologic models has received significant attention (Chen et al., 2022; Coerver et al., 2018; Dang et al., 2020; Döll et al., 2003; Hanasaki et al., 2006; Haddeland et al., 2006; Meigh et al., 1999; Solander et al., 2016; Turner, Steyaert, et al., 2021; Van Beek et al., 2011; Wisser et al., 2010; Yang et al., 2019; Yassin et al., 2019; G. Zhao et al., 2016).…”

Section: Introductionmentioning

confidence: 99%

Coupling Reservoir Operation and Rainfall‐Runoff Processes for Streamflow Simulation in Watersheds

Vora,

Cai,

Chen

et al. 2024

Water Resources Research

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We assess the overall watershed system representation via fully coupling a generic reservoir operation model with a conceptual rainfall‐runoff model. The performance of the coupled model is evaluated comprehensively by examining watershed outflow simulations, model parameter values, and a key internal flux of the watershed model (here reservoir inflow). Five published generic reservoir operation models are coupled with a watershed rainfall‐runoff model, and results are compared across the coupled models and one additional model called ResIgnore that ignores reservoir operation. Traditional loosely coupled watershed hydrologic models (where calibrated inflow is routed through reservoir operation models) are used as baselines to examine the differences in simulation performance and parameterization obtained from the fully coupled models. We find that fully coupling the Generic Data‐Driven Reservoir Operation Model (GDROM) and the Dynamically Zoned Target Release (DZTR) reservoir operation models with the rainfall‐runoff model obtains robust simulations of watershed outflow with realistic parameterization, suggesting that they can be reliably integrated into large‐scale hydrological models for simulating streamflow in heavily dammed watersheds. Our results also show that compared to ResIgnore, the fully coupled watershed models more accurately simulate the entire distribution of watershed outflow, obtain more realistic values of model parameters, and simulate reservoir inflow with higher accuracy. Finally, we note that the prediction intervals of watershed outflow obtained from the GDROM‐ and DZTR‐based fully coupled models consistently envelop observed watershed outflow across the study watersheds, indicating that GDROM and DZTR can be suitable reservoir components of large‐scale hydrology models.

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Dam-induced hydrological alterations in the upper Cauvery river basin, India

Cited by 9 publications

References 70 publications

Leveraging Contextual Cues from a Conceptual Model with Predictive Skills of Machine Learning for Improved Predictability and Interpretability in the Hydrological Processes

Leveraging Contextual Cues from a Conceptual Model with Predictive Skills of Machine Learning for Improved Predictability and Interpretability in the Hydrological Processes

Supplementary material to "How economically and environmentally viable are multiple dams in the Upper Cauvery basin, India? A hydro-economic analysis using a landscape-based hydrological model"

Coupling Reservoir Operation and Rainfall‐Runoff Processes for Streamflow Simulation in Watersheds

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