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 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 Future Climate Projections in Algeria Using Statistical DownScaling Model

2021 ◽  
Author(s):  
Salah SAHABI ABED
Keyword(s):  
Statistical Downscaling ◽  
Minimum Temperature ◽  
Radiative Forcing ◽  
Maximum Temperature ◽  
Climate Projections ◽  
Reference Period ◽  
Extreme Temperatures ◽  
Emission Scenarios ◽  
Statistical Downscaling Model ◽  
Worst Case

Abstract In this study, we perform a statistical downscaling to investigate projected future changes in minimum temperature (T-min), maximum temperature (T-max), and precipitation (PRCP) for the three periods the 2020s (2011–2040), the 2050s (2041–2070), and the 2080s (2071–2100), with respect to the reference period 1981–2010 over Algeria by applying the Statistical DownScaling Model (SDSM). The NCEP reanalysis data and CanESM2 predictors of three future scenarios, RCP2.6, RCP4.5 and RCP8.5 are used for model calibration and future projection, respectively. In order to get realistic results, bias correction was also applied to the climate variables. The evaluation of the SDSM performance indicated that model accuracy for simulating temperatures and precipitation was statistically acceptable. The predicted outcomes exhibit strong warming for both extreme temperatures under the worst-case scenario (RCP 8.5), it is more pronounced for the maximum temperature and over the Sahara region. The results indicate that the highest changes are expected to increase by 3.6 to 5.0°C for the minimum temperature and 5.0 to 8.0°C for the maximum temperature for the strong radiative forcing pathway (RCP8.5) by the end of the century as compared to the reference period. Under the optimistic scenario (RCP2.6), the strength of the warming is projected to increase up to 2.0°C for both extreme temperatures. For the precipitation, the projections indicate for all scenarios a significant decrease in rainfall by approximately 20% over the northwest region and central Sahara, while non-significant change is expected for the center and eastern coastal regions. Conversely, the projections of rainfall under different emission scenarios exhibit an increase (~10–40%) at the central and eastern high plateaus in the north and the extreme west and south of the Sahara. The study reveals several discrepancies among considered stations in the projections of seasonal rainfall under different emission scenarios where most of them exhibit a significant increase of precipitation in summer. Our findings corroborate previous studies by demonstrating that Algeria’s climate will warm further in the future. The results might be beneficial for policymakers for planning strategies and may help to mitigate the risks linked to climate change.

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 Incorporation of Long Term Climate Changes in Hydrological Modelling

2019 ◽  
Vol 8 (10) ◽  
pp. 4521-4526
Keyword(s):  
Climate Change ◽  
Statistical Downscaling ◽  
General Circulation ◽  
Global Climate ◽  
Circulation Model ◽  
Cmip5 Models ◽  
Future Climate Change ◽  
Statistical Downscaling Model ◽  
Intergovernmental Panel ◽  
Statistical Relationships

One of climate change's most important concerns at the moment is its impact on hydrology as it has direct links with agriculture, vegetation, and livelihood. This study tries to analyze potential future climate change in the Kumaradhara river basin. This study involved three steps: (1) acquiring and using general circulation model (GCM) to project future global climate scenarios; (2) establishing statistical relationships between GCM data and observed data using Statistical Downscaling Model (SDSM); (3) downscaling the second generation Canadian Earth system Model (CanESM2)GCM output based on the established statistical relationship. The statistical downscaling is carried out for three scenarios used in the fifth evaluation report of the recent Intergovernmental Panel on Climate Change (IPCC) viz., Representative Concentration Pathways (RCPs) 2.6, 4.5 and 8.5. The statistical downscaling Model (SDSM) results showed that the mean annual daily precipitation is altered in the basin under all the scenarios but it will be different in different time periods depending on scenarios and the basin will experience the reduced precipitation levels in summer. Also the precipitation will marginally rise in all the time slices with reference to baseline data. We can conclude from the results that this region's climate will affect future farming as the availability of water is bound to change. This study should, however, be followed up by a larger study incorporating multiple CMIP5 models such that changes in hydrological-regimes can be examined appropriately.

scholarly journals Future climate change scenario in hot semi-arid climate of Saurashtra, Gujarat by using statistical downscaling by LARS-WG model

MAUSAM ◽  
2021 ◽  
Vol 68 (4) ◽  
pp. 589-596
Author(s):  
JAYANTA SARKAR ◽  
J. R. CHICHOLIKAR
Keyword(s):  
Climate Change ◽  
Statistical Downscaling ◽  
Minimum Temperature ◽  
Daily Rainfall ◽  
Arid Climate ◽  
Weather Data ◽  
Maximum Temperature ◽  
Future Climate Change ◽  
Semi Arid ◽  
Far Future

Climate change is considered to be the greatest challenge faced by mankind in the twenty first century which can lead to severe impacts on different major sectors of the world such as water resources, agriculture, energy and tourism and are likely to alter trends and timing of precipitation and other weather drivers. Analyses and prediction of change in critical climatic variables like rainfall and temperature are, therefore, extremely important. Keeping this in mind, this study aims to verify the skills of LARS-WG (Long Ashton Research - Weather Generator), a statistical downscaling model, in simulating weather data in hot semi-arid climate of Saurashtra and analyze the future changes of temperature (maximum and minimum) and precipitation downscaled by LARS-WG based on IPCC SRA2 scenario generated by seven GCMs' projections for the near (2011-2030), medium (2046-2065) and far (2080-2099) future periods. Rajkot (22.3° N, 70.78° E) observatory of IMD, representing hot semi-arid climate of Saurashtra, Gujarat state was chosen for this purpose. Daily rainfall, maximum and minimum temperature data for the period of 1969-2013 have been utilized.             LARS-WG is found to show reasonably good skill in downscaling daily rainfall and excellent skill in downscaling maximum and minimum temperature. The downscaled rainfall indicated no coherent change trends among various GCMs’ projections of rainfall during near, medium and far future periods. Contrary to rainfall projections, simulations from the seven GCMs have coherent results for both the maximum and minimum temperatures. Based on the ensemble mean of seven GCMs, projected rainfall at Rajkot in monsoon season (JJAS) showed an increase in near future, i.e., 2011-2030, medium future (2046-2065) and far future (2080-2099) periods to the tune of 2, 11 and 14% respectively compared to the baseline value. Model studies indicating tropospheric warming leading to enhancement of atmospheric moisture content could be the reason for this increasing trend. Further, at the study site summer (MAM) maximum temperature is projected to increase by 0.5, 1.7 and 3.3°C during 2011-2030, 2046-2065 and 2080-2099 respectively and winter (DJF) minimum temperature is projected to increase by 0.8, 2.2 and 4.5 °C during 2011-2030, 2046-2065 and 2080-2099 respectively.  

scholarly journals Spatiotemporal climate and vegetation greenness changes and their nexus for Dhidhessa River Basin, Ethiopia

2019 ◽  
Vol 8 (1) ◽  
Author(s):  
Gizachew Kabite Wedajo ◽  
Misgana K. Muleta ◽  
Berhan Gessesse ◽  
Sifan A. Koriche
Keyword(s):  
Climate Change ◽  
Trend Analysis ◽  
River Basin ◽  
Climate Change Adaptation ◽  
Spatial Relationship ◽  
Local Scale ◽  
Climate Variables ◽  
Climatic Zones ◽  
Vegetation Greenness ◽  
Climate Change Adaptation Strategies

Abstract Background Understanding spatiotemporal climate and vegetation changes and their nexus is key for designing climate change adaptation strategies at a local scale. However, such a study is lacking in many basins of Ethiopia. The objectives of this study were (i) to analyze temperature, rainfall and vegetation greenness trends and (ii) determine the spatial relationship of climate variables and vegetation greenness, characterized using Normalized Difference in Vegetation Index (NDVI), for the Dhidhessa River Basin (DRB). Quality checked high spatial resolution satellite datasets were used for the study. Mann–Kendall test and Sen’s slope method were used for the trend analysis. The spatial relationship between climate change and NDVI was analyzed using geographically weighted regression (GWR) technique. Results According to the study, past and future climate trend analysis generally showed wetting and warming for the DRB where the degree of trends varies for the different time and spatial scales. A seasonal shift in rainfall was also observed for the basin. These findings informed that there will be a negative impact on rain-fed agriculture and water availability in the basin. Besides, NDVI trends analysis generally showed greening for most climatic zones for the annual and main rainy season timescales. However, no NDVI trends were observed in all timescales for cool sub-humid, tepid humid and warm humid climatic zones. The increasing NDVI trends could be attributed to agroforestry practices but do not necessarily indicate improved forest coverage for the basin. The change in NDVI was positively correlated to rainfall (r2 = 0.62) and negatively correlated to the minimum (r2 = 0.58) and maximum (r2 = 0.45) temperature. The study revealed a strong interaction between the climate variables and vegetation greenness for the basin that further influences the biophysical processes of the land surface like the hydrologic responses of a basin. Conclusion The study concluded that the trend in climate and vegetation greenness varies spatiotemporally for the DRB. Besides, the climate change and its strong relationship with vegetation greenness observed in this study will further affect the biophysical and environmental processes in the study area; mostly negatively on agricultural and water resource sectors. Thus, this study provides helpful information to device climate change adaptation strategies at a local scale.

scholarly journals Prediction of extreme floods based on CMIP5 climate models: a case study in the Beijiang River basin, South China

2015 ◽  
Vol 19 (3) ◽  
pp. 1385-1399 ◽  
Author(s):  
C. H. Wu ◽  
G. R. Huang ◽  
H. J. Yu
Keyword(s):  
Climate Change ◽  
River Basin ◽  
South China ◽  
Climate Models ◽  
Annual Maximum ◽  
Emission Scenarios ◽  
Vic Model ◽  
Extreme Floods ◽  
Beijiang River ◽  
Increasing Trends

Abstract. The occurrence of climate warming is unequivocal, and is expected to be experienced through increases in the magnitude and frequency of extreme events, including flooding. This paper presents an analysis of the implications of climate change on the future flood hazard in the Beijiang River basin in South China, using a variable infiltration capacity (VIC) model. Uncertainty is considered by employing five global climate models (GCMs), three emission scenarios (representative concentration pathway (RCP) 2.6, RCP4.5, and RCP8.5), 10 downscaling simulations for each emission scenario, and two stages of future periods (2020–2050, 2050–2080). Credibility of the projected changes in floods is described using an uncertainty expression approach, as recommended by the Fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC). The results suggest that the VIC model shows a good performance in simulating extreme floods, with a daily runoff Nash–Sutcliffe efficiency coefficient (NSE) of 0.91. The GCMs and emission scenarios are a large source of uncertainty in predictions of future floods over the study region, although the overall uncertainty range for changes in historical extreme precipitation and flood magnitudes are well represented by the five GCMs. During the periods 2020–2050 and 2050–2080, annual maximum 1-day discharges (AMX1d) and annual maximum 7-day flood volumes (AMX7fv) are expected to show very similar trends, with the largest possibility of increasing trends occurring under the RCP2.6 scenario, and the smallest possibility of increasing trends under the RCP4.5 scenario. The projected ranges of AMX1d and AMX7fv show relatively large variability under different future scenarios in the five GCMs, but most project an increase during the two future periods (relative to the baseline period 1970–2000).

Impact of climate change on streamflow and water quality in the upper Dong Nai river basin, Vietnam

2018 ◽  
pp. 70-79 ◽  
Author(s):  
Le Viet Thang ◽  
Dao Nguyen Khoi ◽  
Ho Long Phi
Keyword(s):  
Climate Change ◽  
Water Quality ◽  
River Basin ◽  
Sediment Load ◽  
Swat Model ◽  
Future Climate Change ◽  
Climate Change Scenarios ◽  
Nutrient Load ◽  
Impact Of Climate Change ◽  
The Impact

In this study, we investigated the impact of climate change on streamflow and water quality (TSS, T-N, and T-P loads) in the upper Dong Nai River Basin using the Soil and Water Assessment Tool (SWAT) hydrological model. The calibration and validation results indicated that the SWAT model is a reasonable tool for simulating streamflow and water quality for this basin. Based on the well-calibrated SWAT model, the responses of streamflow, sediment load, and nutrient load to climate change were simulated. Climate change scenarios (RCP 4.5 and RCP 8.5) were developed from five GCM simulations (CanESM2, CNRM-CM5, HadGEM2-AO, IPSL-CM5A-LR, and MPI-ESM-MR) using the delta change method. The results indicated that climate in the study area would become warmer and wetter in the future. Climate change leads to increases in streamflow, sediment load, T-N load, and T-P load. Besides that, the impacts of climate change would exacerbate serious problems related to water shortage in the dry season and soil erosion and degradation in the wet season. In addition, it is indicated that changes in sediment yield and nutrient load due to climate change are larger than the corresponding changes in streamflow.

Sign in / Sign up

Export Citation Format

Share Document