Simulation of the Potential Impacts of Projected Climate Change on Streamflow in the Vakhsh River Basin in Central Asia under CMIP5 RCP Scenarios

Gulakhmadov, Aminjon; Chen, Xi; Gulahmadov, Nekruz; Li, Shipeng; Anjum, Muhammad Naveed; Rizwan, Muhammad

doi:10.3390/w12051426

Cited by 29 publications

(18 citation statements)

References 92 publications

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“…In addition, studies investigating the impacts of current and future climate extremes (Lins and Slack 2005;Milly et al 2002;Sharma et al 2018) indicate that the U.S. population is becoming alarmingly vulnerable to flood-associated risks (Naz et al 2016;Wing et al 2018). These trends are particular not only to CONUS but have been observed in other parts of the world, including Europe (Jarosińska and Pierzga 2017;Teuling et al 2019), Central Asia (Gulakhmadov et al 2020) and South America (Lara et al 2017). Under various growth scenarios for future climate as prescribed by global climate models, and, despite local-and regional-scale complexities, this continual, upward intensification of median and high flows remains a consistent find in these studies.…”

Section: Introductionsupporting

confidence: 74%

Advancing flood warning procedures in ungauged basins with machine learning.

Rasheed¹,

Aravamudan²,

Sefidmazgi³

et al. 2021

Preprint

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Flood prediction across scales and more specifically in ungauged areas remains still a great challenge that limits the efficiency of flood risk mitigation strategies and disaster preparedness.Building upon the recent success of Machine Learning (ML) models on streamflow prediction, this work presents a prototype ML-based framework for flood warning and flood peak prediction. The fundamental elements of the proposed system consist of a) a LSTM model for classifying storm events to threat/no-threat given a threshold based on the 90th flow percentile and b) the flood peak prediction models. The selected ML-models for flood peak prediction are the Histogram based Gradient Boosting Regressor and the Random Forest. One of the strengths, and reason for selection, of these decision-tree models is their degree of interpretability. This is exploited in the study to help us spatially disentangle the role of both the static and dynamic drivers of flood peak response. Our analysis is presented for 18 distinct hydroclimatic regions across the contiguous US. Results reveal a significant regional dependence on both predictive performance and dominant flood predictors, which emphasize the variability in the complexity of a catchment's hydrologic behavior as well as its impact on modeling flood response. Evaluation of the drivers of flood peaks noted distinct dependencies among the dynamic and static predictors considered in our models for flood peaks of different severity. Specifically, low-moderate flood events showed a clear preponderance for the static catchment attributes over dynamic predictors like precipitation whereas precipitation was the dominant driver for the high severity flood peaks. The proposed flood peak prediction models were compared against a state-of-the-art LSTM model and were shown to consistently outperform in ungauged basins. Overall, the proposed system classified storms correctly for >85% in all cases and exhibited a percent relative difference in flood peak estimates of <30% in most cases.

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Section: Introductionsupporting

confidence: 74%

Advancing flood warning procedures in ungauged basins with machine learning.

Rasheed¹,

Aravamudan²,

Sefidmazgi³

et al. 2021

Preprint

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“…Our result of the trend analysis showed that annual runoff significantly increased in upstream and downstream areas of the KRB. In our previous study, we predicted a significant upward trend in the high flow of the Vakhsh River [32], which is adjacent to the Kofarnikhon River in Tajikistan. The upward trend in river runoff can be explained by the rapid melting of snow and ice caused by the constant rise in air temperature.…”

Section: Discussionmentioning

confidence: 86%

“…In recent years, due to the variations in water resources and the frequent reoccurrence of natural disasters, studies estimating the hydrological response to climate variability and Our previous study in which the SWAT model was applied in the mountainous Vakhsh River Basin (VRB) in Central Asia [32] showed that the hydrologic response to climate variations had significant effects on runoff changes. It was found that global warming accelerated the process of snow/glacier melting, which induced an increasing runoff in the VRB in the past recent decades [32]. In this study, the variability in runoff to climate change and anthropogenic disturbances were separated, which significantly varied from basin to basin [36,37].…”

Section: Introductionmentioning

confidence: 99%

Quantifying the Relative Contribution of Climate Change and Anthropogenic Activities on Runoff Variations in the Central Part of Tajikistan in Central Asia

Gulahmadov

Chen

Gulakhmadov

et al. 2021

Land

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Quantifying the relative contribution of climate change and anthropogenic activities to runoff alterations are essential for the sustainable management of water resources in Central Asian countries. In the Kofarnihon River Basin (KRB) in Central Asia, both changing climate conditions and anthropogenic activities are known to have caused changes to the hydrological cycle. Therefore, quantifying the net influence of anthropogenic contribution to the runoff changes is a challenge. This study applied the original and modified Mann–Kendall trend test, including the Sen’s slope test, Pettitt’s test, double cumulative curve, and elasticity methods. These methods were applied to determine the historical trends, magnitude changes and change points of the temperature, precipitation, potential evapotranspiration, and runoff from 1950 to 2016. In addition, the contributions of climate change and anthropogenic activities to runoff changes in the KRB were evaluated. The trend analysis showed a significant increasing trend in annual temperature and potential evapotranspiration, while the annual precipitation trend showed an insignificant decreasing trend during the 1950–2016 time period. The change point in runoff occurred in 1986 in the upstream region and 1991 in the downstream region. Further, the time series (1950–2016) is separated into the prior impacted period (1950–1986 and 1950–1991) and post impacted period (1987–2016 and 1992–2016) for the upstream and downstream regions, respectively. During the post impacted period, climate change and anthropogenic activities contributed to 87.96% and 12.04% in the upstream region and 7.53% and 92.47% in the downstream region of the KRB. The results showed that in runoff changes, the anthropogenic activities played a dominant role in the downstream (97.78%) and the climate change impacts played a dominant factor in the upstream region (87.96%). In the land-use type changes, the dominant role was played by construction land, which showed that the area from 248.63 km2 in 1990 increased to 685.45 km2 (175.69%) in 2015. These findings suggest that it is essential to adopt effective steps for the sustainable development of the ecological, hydrological, and social order in the KRB in Central Asia.

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“…As in our previous study, Gulakhmadov et al presented the hydrological model calibration results they obtained with the SWAT-CUP tool before and after the EB approach. The application of EB had a positive impact on the modeling of river flow in a mountain watershed [61].…”

Section: Methodsmentioning

confidence: 99%

Evaluation of the CRU TS3.1, APHRODITE_V1101, and CFSR Datasets in Assessing Water Balance Components in the Upper Vakhsh River Basin in Central Asia

et al. 2021

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In this study, the applicability of three gridded datasets was evaluated (Climatic Research Unit (CRU) Time Series (TS) 3.1, “Asian Precipitation—Highly Resolved Observational Data Integration Toward the Evaluation of Water Resources” (APHRODITE)_V1101, and the climate forecast system reanalysis dataset (CFSR)) in different combinations against observational data for predicting the hydrology of the Upper Vakhsh River Basin (UVRB) in Central Asia. Water balance components were computed, the results calibrated with the SUFI-2 approach using the calibration of soil and water assessment tool models (SWAT–CUP) program, and the performance of the model was evaluated. Streamflow simulation using the SWAT model in the UVRB was more sensitive to five parameters (ALPHA_BF, SOL_BD, CN2, CH_K2, and RCHRG_DP). The simulation for calibration, validation, and overall scales showed an acceptable correlation between the observed and simulated monthly streamflow for all combination datasets. The coefficient of determination (R2) and Nash–Sutcliffe efficiency (NSE) showed “excellent” and “good” values for all datasets. Based on the R2 and NSE from the “excellent” down to “good” datasets, the values were 0.91 and 0.92 using the observational datasets, CRU TS3.1 (0.90 and 0.90), APHRODITE_V1101+CRU TS3.1 (0.74 and 0.76), APHRODITE_V1101+CFSR (0.72 and 0.78), and CFSR (0.67 and 0.74) for the overall scale (1982–2006). The mean annual evapotranspiration values from the UVRB were about 9.93% (APHRODITE_V1101+CFSR), 25.52% (APHRODITE_V1101+CRU TS3.1), 2.9% (CFSR), 21.08% (CRU TS3.1), and 27.28% (observational datasets) of annual precipitation (186.3 mm, 315.7 mm, 72.1 mm, 256.4 mm, and 299.7 mm, out of 1875.9 mm, 1236.9 mm, 2479 mm, 1215.9 mm, and 1098.5 mm). The contributions of the snowmelt to annual runoff were about 81.06% (APHRODITE_V1101+CFSR), 63.12% (APHRODITE_V1101+CRU TS3.1), 82.79% (CFSR), 81.66% (CRU TS3.1), and 67.67% (observational datasets), and the contributions of rain to the annual flow were about 18.94%, 36.88%, 17.21%, 18.34%, and 32.33%, respectively, for the overall scale. We found that gridded climate datasets can be used as an alternative source for hydrological modeling in the Upper Vakhsh River Basin in Central Asia, especially in scarce-observation regions. Water balance components, simulated by the SWAT model, provided a baseline understanding of the hydrological processes through which water management issues can be dealt with in the basin.

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Simulation of the Potential Impacts of Projected Climate Change on Streamflow in the Vakhsh River Basin in Central Asia under CMIP5 RCP Scenarios

Cited by 29 publications

References 92 publications

Advancing flood warning procedures in ungauged basins with machine learning.

Advancing flood warning procedures in ungauged basins with machine learning.

Quantifying the Relative Contribution of Climate Change and Anthropogenic Activities on Runoff Variations in the Central Part of Tajikistan in Central Asia

Evaluation of the CRU TS3.1, APHRODITE_V1101, and CFSR Datasets in Assessing Water Balance Components in the Upper Vakhsh River Basin in Central Asia

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