The application of conceptual modelling to assess the impacts of future climate change on the hydrological response of the Harvey River catchment

Abstract The source region of the Yangtze River (SRYR) is located in the hinterland of the Tibetan Plateau (TP). The natural environment is hash, and the hydrological and meteorological stations are less distributed, making the observed data are relatively scarce. In order to overcome the impact of lack of data, the China Meteorological Forcing Dataset (CMFD) was used to correct the meteorological data, to make the data more closer to the real distribution on the SRYR surface. This paper used the Soil and Water Assessment Tool (SWAT) to verify interpolation effect. Since the SRYR is an important water resource protection area, have a great significance to study the hydrological response under future climate change. The Back Propagation (BP) neural network algorithm was used to integrate data extracted from the six Global Climate Models (GCMs), and then the SWAT model was used to predict runoff changes in the future status. The results show that the CMFD data set has a high precision in the SRYR, and can be used for meteorological data correction. After the meteorological data correction, the Nash-Sutcliffe efficiency increased from 0.64 to 0.70. Under the future climate change, the runoff in the SRYR shows a decreasing trend, and the distribution of runoff during the year changes greatly. This reflects the amount of water resources in the SRYR will be decreased, which will brings challenges to water resources management in the SRYR.

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Hydrological response to future climate change in the Agano River basin, Japan

Hydrological Research Letters ◽

10.3178/hrl.4.25 ◽

2010 ◽

Vol 4 ◽

pp. 25-29 ◽

Cited By ~ 27

Author(s):

Xieyao Ma ◽

Takao Yoshikane ◽

Masayuki Hara ◽

Yasutaka Wakazuki ◽

Hiroshi G Takahashi ◽

...

Keyword(s):

Climate Change ◽

River Basin ◽

Future Climate ◽

Future Climate Change ◽

Hydrological Response

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Assessment of future climate change impacts on hydrological behavior of Richmond River Catchment

Water Science and Engineering ◽

10.1016/j.wse.2017.05.004 ◽

2017 ◽

Vol 10 (3) ◽

pp. 197-208 ◽

Cited By ~ 9

Author(s):

Hashim Isam Jameel Al-Safi ◽

Priyantha Ranjan Sarukkalige

Keyword(s):

Climate Change ◽

Climate Change Impacts ◽

Future Climate ◽

Future Climate Change ◽

River Catchment

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Hydrological Responses to the Future Climate Change in a Data Scarce Region, Northwest China: Application of Machine Learning Models

Water ◽

10.3390/w11081588 ◽

2019 ◽

Vol 11 (8) ◽

pp. 1588 ◽

Cited By ~ 2

Author(s):

Zhu ◽

Yang ◽

Liu ◽

Wen ◽

Zhang ◽

...

Keyword(s):

Climate Change ◽

Artificial Intelligence ◽

Northwest China ◽

Future Climate ◽

Future Climate Change ◽

Hydrological Response ◽

Hydrological Responses ◽

Mountainous Watershed ◽

The Future ◽

Rcp 8.5

Forecasting the potential hydrological response to future climate change is an effective way of assessing the adverse effects of future climate change on water resources. Data-driven models based on machine learning algorithms have great application prospects for hydrological response forecasting as they require less developmental time, minimal input, and are relatively simple compared to dynamic or physical models, especially for data scarce regions. In this study, we employed an ensemble of eight General Circulation Models (GCMs) and two artificial intelligence-based methods (Support Vector Regression, SVR, and Extreme Learning Machine, ELM) to establish the historical streamflow response to climate change and to forecast the future response under Representative Concentration Pathway (RCP) scenarios 4.5 and 8.5 in a mountainous watershed in northwest China. We found that the artificial-intelligence-based SVR and ELM methods showed very good performances in the projection of future hydrological responses. The ensemble of GCM outputs derived very close historical hydrological hindcasts but had great uncertainty in future hydrological projections. Using the variables of GCM outputs as inputs to SVR can reduce intermediate downscaling links between variables and decrease the cumulative effect of bias in projecting future hydrological responses. Future precipitation in the study area will increase in the future under both scenarios, and this increasing trend is more significant under RCP 8.5 than under scenario 4.5. The results also indicate the streamflow change will be more sensitive to temperature (precipitation) under the RCP 8.5 (4.5) scenario. The findings and approach have important implications for hydrological response studies and the evaluation of impacts on localized regions similar to the mountainous watershed in this study.

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Quantifying the Impact of Future Climate Change on Runoff in the Amur River Basin Using a Distributed Hydrological Model and CMIP6 GCM Projections

Atmosphere ◽

10.3390/atmos12121560 ◽

2021 ◽

Vol 12 (12) ◽

pp. 1560

Author(s):

Ke Wen ◽

Bing Gao ◽

Mingliang Li

Keyword(s):

Climate Change ◽

Climate Changes ◽

Hydrological Model ◽

Future Climate ◽

Future Climate Change ◽

Amur River Basin ◽

Distributed Hydrological Model ◽

Hydrological Response ◽

Amur River ◽

The Amur River

The Amur River is one of the top ten longest rivers in the world, and its hydrological response to future climate change has been rarely investigated. In this study, the outputs of four GCMs in the Coupled Model Intercomparison Project Phase 6 (CMIP6) were corrected and downscaled to drive a distributed hydrological model. Then, the spatial variations of runoff changes under the future climate conditions in the Amur River Basin were quantified. The results suggest that runoffs will tend to increase in the future period (2021–2070) compared with the baseline period (1961–2010), particularly in August and September. Differences were also found among different GCMs and scenarios. The ensemble mean of the GCMs suggests that the basin-averaged annual precipitation will increase by 14.6% and 15.2% under the SSP2-4.5 and SSP5-8.5 scenarios, respectively. The increase in the annual runoff under the SSP2-4.5 scenario (22.5%) is projected to be larger than that under the SSP5-8.5 scenario (19.2%) at the lower reach of the main channel. Future climate changes also tend to enhance the flood peak and flood volume. The findings of this study bring new understandings of the hydrological response to future climate changes and are helpful for water resource management in Eurasia.

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