Assessing climate change impacts on river hydrology – A case study in the Western Ghats of India

Sharannya, Thalli Mani; Mudbhatkal, Amogh; Mahesha, A.

doi:10.1007/s12040-018-0979-3

Cited by 31 publications

(18 citation statements)

References 10 publications

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“…For example, for Chameliya, Karnali, Bheri, Kaligandaki, Indrawati, Tamakoshi, Arun and Tamor basins of Nepal, NSE (and PBIAS) values are respectively 0.75 (+5.1%), 0.84 (−14.2%), 0.70 (−4.4%), 0.78 (−4.0%), 0.72 (-), 0.76 (−1.7%), 0.81 (−6.8%) and 0.85 (+4.3%) for calibration period while these values are 0.65 (-9.3%), 0.84 (−15.4%), 0.71 (−8.9%), 0.8 (+9.6%), 0.87 (-), 0.84 (+5.2%), 0.58 (+24.6%) and 0.89 (+5.5%) respectively for validation period [25,40,45,47,51,[85][86][87]. Similarly, NSE (and PBIAS) values of Gilgelabay basin of Ethiopia, Gurupura basin of India and Tizinafu basin of Western China were found to be respectively 0.69 (+4.8%), 0.83 (+17.5%) and 0.71 (+5.79%) for calibration period while these values for validation period were 0.68 (+4.9%), 0.85 (−3.9%) and 0.64 (−18.0%), respectively [88][89][90]. Moreover, [42] used SWAT model to simulate five glacierized mountain river basins of the world that includes the Narayani (Nepal), Vakhsh (Central Asia), Rhone (Switzerland), Mendoza (Central Andes, Argentina), and Chile (Central Dry Andes, Chile).…”

Section: Discussionmentioning

confidence: 95%

Application of SWAT in Hydrological Simulation of Complex Mountainous River Basin (Part I: Model Development)

Marahatta

Devkota

Aryal

2021

Water

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The soil and water assessment tool (SWAT) hydrological model has been used extensively by the scientific community to simulate varying hydro-climatic conditions and geo-physical environment. This study used SWAT to characterize the rainfall-runoff behaviour of a complex mountainous basin, the Budhigandaki River Basin (BRB), in central Nepal. The specific objectives of this research were to: (i) assess the applicability of SWAT model in data scarce and complex mountainous river basin using well-established performance indicators; and (ii) generate spatially distributed flows and evaluate the water balance at the sub-basin level. The BRB was discretised into 16 sub-basins and 344 hydrological response units (HRUs) and calibration and validation was carried out at Arughat using daily flow data of 20 years and 10 years, respectively. Moreover, this study carried out additional validation at three supplementary points at which the study team collected primary river flow data. Four statistical indicators: Nash–Sutcliffe efficiency (NSE), percent bias (PBIAS), ratio of the root mean square error to the standard deviation of measured data (RSR) and Kling Gupta efficiency (KGE) have been used for the model evaluation. Calibration and validation results rank the model performance as “very good”. This study estimated the mean annual flow at BRB outlet to be 240 m3/s and annual precipitation 1528 mm with distinct seasonal variability. Snowmelt contributes 20% of the total flow at the basin outlet during the pre-monsoon and 8% in the post monsoon period. The 90%, 40% and 10% exceedance flows were calculated to be 39, 126 and 453 m3/s respectively. This study provides additional evidence to the SWAT diaspora of its applicability to simulate the rainfall-runoff characteristics of such a complex mountainous catchment. The findings will be useful for hydrologists and planners in general to utilize the available water rationally in the times to come and particularly, to harness the hydroelectric potential of the basin.

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Section: Discussionmentioning

confidence: 95%

Application of SWAT in Hydrological Simulation of Complex Mountainous River Basin (Part I: Model Development)

Marahatta

Devkota

Aryal

2021

Water

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“…However, GCMs limit the accurate simulations of regional climatology due to the inability in accurately simulating features of local climate including topography, cloudiness, orography, and land use due to the inherent coarse resolution ranging between 100 and 250 km [40][41][42]. Hence, increased tendency is witnessed in applications of regional climate models (RCMs) combined with hydrological models to examine the impact of climate change on hydrology [43]. e resolution of these RCMs is in the range of 12 to 50 km, in proximity of the watershed scale.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Future Climate and Potential Impact on Streamflow in the Upper Nan River Basin of Northern Thailand

Gunathilake

Amaratunga

Perera

et al. 2020

Advances in Meteorology

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Water resources in Northern Thailand have been less explored with regard to the impact on hydrology that the future climate would have. For this study, three regional climate models (RCMs) from the Coordinated Regional Downscaling Experiment (CORDEX) of Coupled Model Intercomparison Project 5 (CMIP5) were used to project future climate of the upper Nan River basin. Future climate data of ACCESS_CCAM, MPI_ESM_CCAM, and CNRM_CCAM under Representation Concentration Pathways RCP4.5 and RCP8.5 were bias-corrected by the linear scaling method and subsequently drove the Hydrological Engineering Center-Hydrological Modeling System (HEC-HMS) to simulate future streamflow. This study compared baseline (1988–2005) climate and streamflow values with future time scales during 2020–2039 (2030s), 2040–2069 (2050s), and 2070–2099 (2080s). The upper Nan River basin will become warmer in future with highest increases in the maximum temperature of 3.8°C/year for MPI_ESM and minimum temperature of 3.6°C/year for ACCESS_CCAM under RCP8.5 during 2080s. The magnitude of changes and directions in mean monthly precipitation varies, with the highest increase of 109 mm for ACESSS_CCAM under RCP 4.5 in September and highest decrease of 77 mm in July for CNRM, during 2080s. Average of RCM combinations shows that decreases will be in ranges of −5.5 to −48.9% for annual flows, −31 to −47% for rainy season flows, and −47 to −67% for winter season flows. Increases in summer seasonal flows will be between 14 and 58%. Projection of future temperature levels indicates that higher increases will be during the latter part of the 20th century, and in general, the increases in the minimum temperature will be higher than those in the maximum temperature. The results of this study will be useful for river basin planners and government agencies to develop sustainable water management strategies and adaptation options to offset negative impacts of future changes in climate. In addition, the results will also be valuable for agriculturists and hydropower planners.

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“…The Gurupura river is one of the west-flowing rivers originating in the WG of India, at an elevation of 1880 m above mean sea level. The catchment has two river gauging stations at Addoor and Polali, which are maintained by the Central Water Commission, Government of India and the Public Works Department of the Government of Karnataka [34], respectively. For the present investigation, the Addoor gauging station was selected, which drains out an area of about 840 km 2 (Figure 1).…”

Section: Study Areamentioning

confidence: 99%

“…The deep convection of this cloud system is due to latent heat release. The infrared satellite sensors may not detect precipitation from warm clouds and may lose the capture of ice loft, thereby detecting only a portion of rain from deep convection [6,11,34,60]. This process may be the reason for the lower performance of the satellite data-driven model than the IMD data-driven model.…”

Section: Evaluation Of Streamflow Generationmentioning

confidence: 99%

Evaluation of Satellite Precipitation Products in Simulating Streamflow in a Humid Tropical Catchment of India Using a Semi-Distributed Hydrological Model

Sharannya

Al‐Ansari

Barma

et al. 2020

Water

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Precipitation obtained from rain gauges is an essential input for hydrological modelling. It is often sparse in highly topographically varying terrain, exhibiting a certain amount of uncertainty in hydrological modelling. Hence, satellite rainfall estimates have been used as an alternative or as a supplement to station observations. In this study, an attempt was made to evaluate the Tropical Rainfall Measuring Mission (TRMM) and Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), employing a semi-distributed hydrological model, i.e., Soil and Water Assessment Tool (SWAT), for simulating streamflow and validating them against the flows generated by the India Meteorological Department (IMD) rainfall dataset in the Gurupura river catchment of India. Distinct testing scenarios for simulating streamflow were made to check the suitability of these satellite precipitation data. The TRMM was able to better estimate rainfall than CHIRPS after performing categorical and continuous statistical results with respect to IMD rainfall data. While comparing the performance of model simulations, the IMD rainfall-driven streamflow emerged as the best followed by the TRMM, CHIRPS-0.05, and CHIRPS-0.25. The coefficient of determination (R2), Nash–Sutcliffe efficiency (NSE), and percent bias (PBIAS) were in the range 0.63 to 0.86, 0.62 to 0.86, and −14.98 to 0.87, respectively. Further, an attempt was made to examine the spatial distribution of key hydrological signature, i.e., flow duration curve (FDC) in the 30–95 percentile range of non-exceedance probability. It was observed that TRMM underestimated the flow for agricultural water availability corresponding to 30 percent, even though it showed a good performance compared to the other satellite rainfall-driven model outputs.

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Assessing climate change impacts on river hydrology – A case study in the Western Ghats of India

Cited by 31 publications

References 10 publications

Application of SWAT in Hydrological Simulation of Complex Mountainous River Basin (Part I: Model Development)

Application of SWAT in Hydrological Simulation of Complex Mountainous River Basin (Part I: Model Development)

Evaluation of Future Climate and Potential Impact on Streamflow in the Upper Nan River Basin of Northern Thailand

Evaluation of Satellite Precipitation Products in Simulating Streamflow in a Humid Tropical Catchment of India Using a Semi-Distributed Hydrological Model

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