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

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

scholarly journals Prediction of Future Climate Change for Rainfall in the Upper Kurau River Basin, Perak Using Statistical Downscaling Model (SDSM)

2019 ◽  
Vol 7 (6A) ◽  
pp. 33-42
Author(s):  
Nuramidah Hamidon ◽  
Sobri Harun ◽  
Norshuhaila Mohamed Sunar ◽  
Nor Hazren A.Hamid ◽  
Mimi Suliza Muhamad ◽  
...  
Keyword(s):  
Climate Change ◽  
River Basin ◽  
Statistical Downscaling ◽  
Future Climate ◽  
Future Climate Change ◽  
Statistical Downscaling Model

scholarly journals Hydrological Implications of Climate Change on River Basin Water Cycle: Case Studies of the Yangtze River and Yellow River Basins, China

2016 ◽  
Author(s):  
Liu Liu ◽  
Zongxue Xu ◽  
Rong Li ◽  
Youzhi Wang
Keyword(s):  
Climate Change ◽  
River Basin ◽  
Statistical Downscaling ◽  
Yangtze River ◽  
Water Cycle ◽  
Yellow River ◽  
River Basins ◽  
The Yangtze River ◽  
Statistical Downscaling Model ◽  
Basin Water

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.

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

Water ◽  
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.

scholarly journals Analysis of Long-Term Trends of Annual and Seasonal Rainfall in the Awash River Basin, Ethiopia

Water ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 1498 ◽  
Author(s):  
Solomon Mulugeta ◽  
Clifford Fedler ◽  
Mekonen Ayana
Keyword(s):  
Climate Change ◽  
River Basin ◽  
Summer Rainfall ◽  
Seasonal Rainfall ◽  
Winter Rainfall ◽  
Adaptation Measures ◽  
Awash River Basin ◽  
Long Term Trends ◽  
Increasing Trends

With climate change prevailing around the world, understanding the changes in long-term annual and seasonal rainfall at local scales is very important in planning for required adaptation measures. This is especially true for areas such as the Awash River basin where there is very high dependence on rain- fed agriculture characterized by frequent droughts and subsequent famines. The aim of the study is to analyze long-term trends of annual and seasonal rainfall in the Awash River Basin, Ethiopia. Monthly rainfall data extracted from Climatic Research Unit (CRU 4.01) dataset for 54 grid points representing the entire basin were aggregated to find the respective areal annual and seasonal rainfall time series for the entire basin and its seven sub-basins. The Mann-Kendall (MK) test and Sen Slope estimator were applied to the time series for detecting the trends and for estimating the rate of change, respectively. The Statistical software package R version 3.5.2 was used for data extraction, data analyses, and plotting. Geographic information system (GIS) package was also used for grid making, site selection, and mapping. The results showed that no significant trend (at α = 0.05) was identified in annual rainfall in all sub-basins and over the entire basin in the period (1902 to 2016). However, the results for seasonal rainfall are mixed across the study areas. The summer rainfall (June through September) showed significant decreasing trend (at α ≤ 0.1) over five of the seven sub-basins at a rate varying from 4 to 7.4 mm per decade but it showed no trend over the two sub-basins. The autumn rainfall (October through January) showed no significant trends over four of the seven sub-basins but showed increasing trends over three sub-basins at a rate varying from 2 to 5 mm per decade. The winter rainfall (February through May) showed no significant trends over four sub-basins but showed significant increasing trends (at α ≤ 0.1) over three sub-basins at a rate varying from 0.6 to 2.7 mm per decade. At the basin level, the summer rainfall showed a significant decreasing trend (at α = 0.05) while the autumn and winter rainfall showed no significant trends. In addition, shift in some amount of summer rainfall to winter and autumn season was noticed. It is evident that climate change has shown pronounced effects on the trends and patterns of seasonal rainfall. Thus, the study contribute to better understanding of climate change in the basin and the information from the study can be used in planning for adaptation measures against a changing climate.

scholarly journals Climate change assessment for Mediterranean agricultural areas by statistical downscaling

2010 ◽  
Vol 10 (7) ◽  
pp. 1647-1661 ◽  
Author(s):  
L. Palatella ◽  
M. M. Miglietta ◽  
P. Paradisi ◽  
P. Lionello
Keyword(s):  
Climate Change ◽  
River Basin ◽  
Statistical Downscaling ◽  
Principal Component ◽  
Summer Precipitation ◽  
Ebro River ◽  
Po Valley ◽  
Apulia Region ◽  
Climate Change Assessment ◽  
Agricultural Areas

Abstract. In this paper we produce projections of seasonal precipitation for four Mediterranean areas: Apulia region (Italy), Ebro river basin (Spain), Po valley (Italy) and Antalya province (Turkey). We performed the statistical downscaling using Canonical Correlation Analysis (CCA) in two versions: in one case Principal Component Analysis (PCA) filter is applied only to predictor and in the other to both predictor and predictand. After performing a validation test, CCA after PCA filter on both predictor and predictand has been chosen. Sea level pressure (SLP) is used as predictor. Downscaling has been carried out for the scenarios A2 and B2 on the basis of three GCM's: the CCCma-GCM2, the Csiro-MK2 and HadCM3. Three consecutive 30-year periods have been considered. For Summer precipitation in Apulia region we also use the 500 hPa temperature (T500) as predictor, obtaining comparable results. Results show different climate change signals in the four areas and confirm the need of an analysis that is capable of resolving internal differences within the Mediterranean region. The most robust signal is the reduction of Summer precipitation in the Ebro river basin. Other significative results are the increase of precipitation over Apulia in Summer, the reduction over the Po-valley in Spring and Autumn and the increase over the Antalya province in Summer and Autumn.

Prediction of climate change in Brunei Darussalam using statistical downscaling model

2017 ◽  
Vol 133 (1-2) ◽  
pp. 343-360 ◽  
Author(s):  
Dk. Siti Nurul Ain binti Pg. Ali Hasan ◽  
Uditha Ratnayake ◽  
Shahriar Shams ◽  
Zuliana Binti Hj Nayan ◽  
Ena Kartina Abdul Rahman
Keyword(s):  
Climate Change ◽  
Statistical Downscaling ◽  
Statistical Downscaling Model ◽  
Brunei Darussalam

scholarly journals Climate change projection of snowfall in the Colorado River Basin using dynamical downscaling

2012 ◽  
Vol 48 (5) ◽  
Author(s):  
Sungwook Wi ◽  
Francina Dominguez ◽  
Matej Durcik ◽  
Juan Valdes ◽  
Henry F. Diaz ◽  
...  
Keyword(s):  
Climate Change ◽  
River Basin ◽  
Colorado River ◽  
Dynamical Downscaling ◽  
Colorado River Basin ◽  
Climate Change Projection
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