Integrating reservoirs in a landscape-based hydrological model to understand the impact of the reservoir on flow regime in the Cauvery river basin, India

Author(s):  
Anjana Ekka ◽  
Saket Kesav ◽  
Saket Pande ◽  
Pieter van der Zaag ◽  
Yong Jiang

<p>As economic development continues to expand, rivers resources are exploited for power generation, flood control, and irrigation, which substantially impacts the river hydrology and surrounding ecosystem.  Reservoir construction is one of the major contributors to such changes.  Around the world, the long free-flowing rivers are impaired due to reservoirs and their downstream propagation of fragmentation and flow regulation, which impacts the structural and functional connectivities of the entire basin. The extent of interdependence and interactions of biophysical, social, and economic characteristics determine hydrological behaviour and thus define the sustainability of the river ecosystem. In this regard, the topography driven rainfall-runoff modeling (Flex-Topo model) approximates the river landscape hydrological behaviour by delineating the catchment into three functional hydrological units (HRUs).  However, these HRUs are natural and do not take anthropogenic factors into account. Therefore, the present study aims to understand the effects of the integration of reservoirs into a Flex-Topo model to assess model transferability in predicting the river flow regime in ungauged basins.</p><p>The Cauvery river basin in India is chosen as a case study. The construction of reservoirs in the Cauvery basin helped to expand irrigated areas, securing water availability during water stress conditions. Nevertheless, it aggravates the water allocation between upstream and downstream states leading to conflict among states sharing the river basin. Based on size and storage capacity, four large reservoirs are selected for the study. At first, the watershed area is delineated based on the gauge location. For adding reservoirs, two different flex-models are created for the watershed’s areas upstream and downstream of the reservoirs. A separate reservoir model is created for each reservoir. The reservoir model is integrated into the flex-model following operation rule curves to simulate the reservoir based on different reservoir yield. It is assumed that the response of the upstream catchment will serve as an input to the reservoir, and the outflow of the reservoir will be an input to the downstream catchment. These three subunits are connected, and river flow is simulated at the gauge station located at the downstream of the reservoir. Three different procedures are adopted to calibrate the model. First, the integrated flex reservoir model is calibrated using the downstream gauging station. In the second calibration method the reservoir is calibrated first, then keeping the parameters of the reservoir fixed the integrated model is calibrated using downstream gauging station. Third, both the reservoir model and flex model are calibrated separately. The modelled runoff from each parameter sets are compared using Nash-Sutcliffe Model Efficiency and Mean Absolute Error with the observed.</p><p>Results indicate that the second calibration method performed the best and improved the overall performance of the Flex-Topo model. Further, results are compared across the four reservoirs in order to develop a generalized understanding of transferring a integrated flex model to basins where data on reservoirs is unavailable. The proposed method therefore provides a way to simulate both biophysical constraint and anthropogenic modifications simultaneously in river landscape and enhance understanding of impact of reservoirs on river flow regime.</p>

2021 ◽  
Author(s):  
Gowri Reghunath ◽  
Pradeep Mujumdar

<p>The hydrological cycle is governed by a number of complex processes which occur at different spatial and temporal scales. Hydrological modelling plays an integral role in enhancing the understanding of hydrological behaviour and process complexities at a range of scales. Different hydrological models have various strengths in the representation of hydrological processes. The performance and applicability of each hydrological model can differ between catchments due to several catchment characteristics and dominant hydrological processes. With a wide variety of model structures, it is important to evaluate how different hydrological models capture the process dynamics in various catchments. This study aims at a comprehensive evaluation of the performance of two widely used hydrological models, namely, the HEC-Hydrologic Modeling System (HEC-HMS) and the Variable Infiltration Capacity (VIC) model, in simulating various water balance components in the sub-catchments of the Cauvery River Basin which is a major river basin in Peninsular India. The basin is characterized by extensive regional variability in land use patterns, water availability, and water demands. The chosen models differ in their model structure complexities, methods adopted for simulation of water balance components, and the representation of geographical information, meteorological and physiographical inputs. The models are calibrated with respect to the observed streamflow at various gauge locations, and the simulated water balance components such as evapotranspiration and baseflow are assessed at annual and seasonal time scales. Also, the impact of the representation of the spatial distribution of input variables and model parameters (lumped versus distributed) are evaluated among the models. This work provides valuable insights into the applicability of various hydrological models in simulating hydrological processes in catchments with high regional complexities. Also, this work aids in the identification of effective models and model parameters which can be useful for hydrological data transfers between catchments as well as predictions in ungauged basins.</p>


Hydrology ◽  
2018 ◽  
Vol 6 (1) ◽  
pp. 2 ◽  
Author(s):  
Kinati Chimdessa ◽  
Shoeb Quraishi ◽  
Asfaw Kebede ◽  
Tena Alamirew

In the Didessa river basin, which is found in Ethiopia, the human population number is increasing at an alarming rate. The conversion of forests, shrub and grasslands into cropland has increased in parallel with the population increase. The land use/land cover change (LULCC) that has been undertaken in the river basin combined with climate change may have affected the Didessa river flow and soil loss. Therefore, this study was designed to assess the impact of LULCC on the Didessa river flow and soil loss under historical and future climates. Land use/land cover (LULC) of the years 1986, 2001 and 2015 were independently combined with the historical climate to assess their individual impacts on river flow and soil loss. Further, the impact of future climates under Representative Concentration Pathways (RCP2.6, RCP4.5 and RCP8.5) scenarios on river flow and soil loss was assessed by combining the pathways with the 2015 LULC. A physically based Soil and Water Assessment Tool (SWAT2012) model in the ArcGIS 10.4.1 interface was used to realize the purpose. Results of the study revealed that LULCC that occurred between 1986 and 2015 resulted in increased average sediment yield by 20.9 t ha−1 yr−1. Climate change under RCP2.6, RCP4.5 and RCP8.5 combined with 2015 LULC increased annual average soil losses by 31.3, 50.9 and 83.5 t ha−1 yr−1 compared with the 2015 LULC under historical climate data. It was also found that 13.4%, 47.1% and 87.0% of the total area may experience high soil loss under RCP2.6, RCP4.5 and RCP8.5, respectively. Annual soil losses of five top-priority sub catchments range from 62.8 to 57.7 per hectare. Nash Stuncliffe Simulation efficiency (NSE) and R2 values during model calibration and validation indicated good agreement between observed and simulated values both for flow and sediment yield.


2019 ◽  
Vol 23 (4) ◽  
pp. 1833-1865 ◽  
Author(s):  
Jonathan D. Mackay ◽  
Nicholas E. Barrand ◽  
David M. Hannah ◽  
Stefan Krause ◽  
Christopher R. Jackson ◽  
...  

Abstract. The flow regimes of glacier-fed rivers are sensitive to climate change due to strong climate–cryosphere–hydrosphere interactions. Previous modelling studies have projected changes in annual and seasonal flow magnitude but neglect other changes in river flow regime that also have socio-economic and environmental impacts. This study employs a signature-based analysis of climate change impacts on the river flow regime for the deglaciating Virkisá river basin in southern Iceland. Twenty-five metrics (signatures) are derived from 21st century projections of river flow time series to evaluate changes in different characteristics (magnitude, timing and variability) of river flow regime over sub-daily to decadal timescales. The projections are produced by a model chain that links numerical models of climate and glacio-hydrology. Five components of the model chain are perturbed to represent their uncertainty including the emission scenario, numerical climate model, downscaling procedure, snow/ice melt model and runoff-routing model. The results show that the magnitude, timing and variability of glacier-fed river flows over a range of timescales will change in response to climate change. For most signatures there is high confidence in the direction of change, but the magnitude is uncertain. A decomposition of the projection uncertainties using analysis of variance (ANOVA) shows that all five perturbed model chain components contribute to projection uncertainty, but their relative contributions vary across the signatures of river flow. For example, the numerical climate model is the dominant source of uncertainty for projections of high-magnitude, quick-release flows, while the runoff-routing model is most important for signatures related to low-magnitude, slow-release flows. The emission scenario dominates mean monthly flow projection uncertainty, but during the transition from the cold to melt season (April and May) the snow/ice melt model contributes up to 23 % of projection uncertainty. Signature-based decompositions of projection uncertainty can be used to better design impact studies to provide more robust projections.


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