Projected Temperature and Precipitation changes using the LARS‐WG statistical downscaling model in the Shire River Basin, Malawi

Climate change is a global issue that draws widespread attention from the international society. As an important component of the climate system, the water cycle is directly affected by climate change. Thus, it is very important to study the influences of climate change on the basin water cycle with respect to maintenance of healthy rivers, sustainable use of water resources, and sustainable socioeconomic development in the basin. In this study, by assessing the suitability of multiple General Circulation Models (GCMs) recommended by the Intergovernmental Panel on Climate Change, Statistical Downscaling Model (SDSM) and Automated Statistical Downscaling model (ASD) were used to generate future climate change scenarios. These were then used to drive distributed hydrologic models (Variable Infiltration Capacity, Soil and Water Assessment Tool) for hydrological simulation of the Yangtze River and Yellow River basins, thereby quantifying the effects of climate change on the basin water cycle. The results showed that suitability assessment adopted in this study could effectively reduce the uncertainty of GCMs, and that statistical downscaling was able to greatly improve precipitation and temperature outputs in global climate mode. Compared to a baseline period (1961–1990), projected future periods (2046–2065 and 2081–2100) had a slightly decreasing tendency of runoff in the lower reaches of the Yangtze River basin. In particular, a significant increase in runoff was observed during flood seasons in the southeast part. However, runoff of the upper Yellow River basin decreased continuously. The results provide a reference for studying climate change in major river basins of China.

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Climate change projection using the statistical downscaling model in Modjo watershed, upper Awash River Basin, Ethiopia

International Journal of Environmental Science and Technology ◽

10.1007/s13762-021-03752-x ◽

2021 ◽

Author(s):

M. A. Gurara ◽

N. B. Jilo ◽

A. D. Tolche ◽

A. K. Kassa

Keyword(s):

Climate Change ◽

River Basin ◽

Statistical Downscaling ◽

Statistical Downscaling Model ◽

Climate Change Projection ◽

Awash River Basin

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Statistical Downscaling Based On Multiple Linear Regression Analysis for Temperature and Precipitation inLidder River Basin of India

IOSR Journal of Engineering ◽

10.9790/3021-0706015560 ◽

2017 ◽

Vol 07 (06) ◽

pp. 55-60

Author(s):

Mehnaza Akhter

Keyword(s):

Regression Analysis ◽

Linear Regression ◽

Multiple Linear Regression ◽

River Basin ◽

Statistical Downscaling ◽

Linear Regression Analysis ◽

Multiple Linear Regression Analysis ◽

Temperature And Precipitation

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Projections of Future Climate Change in the Vu Gia Thu Bon River Basin, Vietnam by Using Statistical DownScaling Model (SDSM)

Water ◽

10.3390/w12030755 ◽

2020 ◽

Vol 12 (3) ◽

pp. 755

Author(s):

Dang Nguyen Dong Phuong ◽

Trung Q. Duong ◽

Nguyen Duy Liem ◽

Vo Ngoc Quynh Tram ◽

Dang Kien Cuong ◽

...

Keyword(s):

Climate Change ◽

Trend Analysis ◽

River Basin ◽

Statistical Downscaling ◽

Minimum Temperature ◽

Model Performance ◽

Future Climate Change ◽

Extreme Temperatures ◽

Emission Scenarios ◽

Statistical Downscaling Model

Future projections of anthropogenic climate change play a pivotal role in devising viable countermeasures to address climate-related risks. This study strove to construct future daily rainfall and maximum and minimum temperature scenarios in Vu Gia Thu Bon river basin by employing the Statistical DownScaling Model (SDSM). The model performance was evaluated by utilizing a Taylor diagram with dimensioned and dimensionless statistics. During validation, all model-performance measures show good ability in simulating extreme temperatures and reasonable ability for rainfall. Subsequently, a set of predictors derived from HadCM3 and CanESM2 was selected to generate ensembles of each climatic variables up to the end of 21st century. The generated outcomes exhibit a consistent increase in both extreme temperatures under all emission scenarios. The greatest changes in maximum and minimum temperature were predicted to increase by 2.67–3.9 °C and 1.24–1.96 °C between the 2080s and reference period for the worst-case scenarios. Conversely, there are several discrepancies in the projections of rainfall under different emission scenarios as well as among considered stations. The predicted outcomes indicate a significant decrease in rainfall by approximately 11.57%–17.68% at most stations by 2099. Moreover, all ensemble means were subjected to the overall and partial trend analysis by applying the Innovative-Şen trend analysis method. The results exhibit similar trend patterns, thereby indicating high stability and applicability of the SDSM. Generally, it is expected that these findings will contribute numerous valuable foundations to establish a framework for the assessment of climate change impacts at the river basin scale.

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Statistical downscaling and projection of future temperature and precipitation change in middle catchment of Sutlej River Basin, India

Journal of Earth System Science ◽

10.1007/s12040-015-0575-8 ◽

2015 ◽

Vol 124 (4) ◽

pp. 843-860 ◽

Cited By ~ 12

Author(s):

Dharmaveer Singh ◽

Sanjay K Jain ◽

R D Gupta

Keyword(s):

River Basin ◽

Statistical Downscaling ◽

Precipitation Change ◽

Temperature And Precipitation

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Temporal and Spatial Variation Characteristics of Precipitation in the Haihe River Basin under the Influence of Climate Change

Water ◽

10.3390/w13121664 ◽

2021 ◽

Vol 13 (12) ◽

pp. 1664

Author(s):

Yuhang Han ◽

Bin Liu ◽

Dan Xu ◽

Chaoguo Yuan ◽

Yanan Xu ◽

...

Keyword(s):

Climate Change ◽

River Basin ◽

Haihe River Basin ◽

Haihe River ◽

Temperature And Precipitation ◽

Increasing Trend ◽

Temporal And Spatial ◽

The Haihe River Basin ◽

Precipitation Changes ◽

Spearman’S Correlation

The impact of global climate change on the temporal and spatial variations of precipitation is significant. In this study, daily temperature and precipitation data from 258 meteorological stations in the Haihe River Basin, for the period 1960–2020, were used to determine the trend and significance of temperature and precipitation changes at interannual and interseasonal scales. The Mann–Kendall test and Spearman’s correlation analysis were employed, and significant change trends and correlations were determined. At more than 90% of the selected stations, the results showed a significant increase in temperature, at both interannual and interseasonal scales, and the increasing trend was more significant in spring than in other seasons. Precipitation predominantly showed a decreasing trend at an interannual scale; however, the change trend was not significant. In terms of the interseasonal scale, the precipitation changes in spring and autumn showed an overall increasing trend, those in summer showed a 1:1 distribution ratio of increasing and decreasing trends, and those in winter showed an overall decreasing trend. Furthermore, the Spearman’s correlation analysis showed a negative correlation between temperature and precipitation in the entire Haihe River Basin, at both interannual and interseasonal scales; however, most of the correlations were weak.

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