Future Climate Projections in Algeria Using Statistical DownScaling Model

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.

IMPACTS OF SNOWMELT AND CLIMATE CHANGE ON HIMALAYAN RIVERS: THE CASE OF DATA-SCARCE DUDHKOSHI RIVER IN EASTERN NEPAL

Water from snow-melt is crucial to provide ecosystem services in downstream of the Himalayas. To study the fate of snow hydrology, an integrated modeling system has been developed coupling Statistical Downscaling Model (SDSM) outputs with Snowmelt Runoff Model (SRM) in the Dudhkoshi Basin, Nepal. The SRM model is well-calibrated in 2011 and validated in 2012 and 2014 using MODIS satellite data. The annual average observed and simulated discharges for the calibration year are 177.89 m3 /s and 181.47 m3 /s respectively. To assess future climate projections for the periods 2020s, 2050s, and 2080s, the SDSM model is used for downscaling precipitation, maximum temperature, and minimum temperature from the Canadian GCM model (CanESM2) under three different scenarios RCP2.6, RCP4.5 and RCP8.5. All considered scenarios are significant in predicting increasing trends of maximumminimum temperature and precipitation and the storehouse of freshwater in the mountains is expected to deplete rapidly if global warming continues.

Climate projections in Lake Maggiore watershed using statistical downscaling model

Precipitation and temperature over the Lake Maggiore watershed greatly influence its water balance. Local communities from both Italy and Switzerland rely on the watershed for agriculture, tourism and hydropower production. Accurate climate projections in this area are vital in dealing with their impacts and yet are still lacking. Future climate was assessed by applying the Statistical DownScaling Model (SDSM) and using CanESM2 predictors. Three scenarios defined by RCP2.6, RCP4.5 and RCP8.5 were adopted. Based on our results, SDSM is to a certain degree applicable for simulating precipitation and temperature in an Alpine area. Results indicate that warming from now until the end of the century will be about 2 to 3 times greater without global mitigation. Temperature is estimated to increase throughout the 21st century, with a stronger warming trend in the northeastern part of the region than in the southwestern part. The strength of the warming at the end of the century highly depends on the scenario considered, with an increase up to 1.7°C for the mitigation scenario RCP2.6 compared to 4.2°C for the unmitigated scenario RCP8.5. Seasonal precipitation is expected to change depending on the future scenarios. Most of the region is expected to display a seasonally positive precipitation change during the cold season and vice versa, resulting in a shift in the peak rainy season from autumn to winter. These findings suggest that the area might be vulnerable to global change and will provide useful insight to develop a better strategy for the management of water resources and to study the adoptive measures to manage flood disasters.

Seasonal Forecast of Early Summer Rainfall at Stations in South China Using a Statistical Downscaling Model

Current dynamic models are not able to provide reliable seasonal forecasts of regional/local rainfall. This study aims to improve the seasonal forecast of early summer rainfall at stations in South China through statistical downscaling. A statistical downscaling model was built with the canonical correlation analysis method using 850-hPa zonal wind and relative humidity from the ERA-Interim reanalysis data. An anomalous southwesterly wind that carries sufficient water vapor encounters an anomalous northeasterly wind from the Yangtze River, resulting in a wet anomaly over all of South China. This model provided good agreement with observations in both the training and independent test periods. In an independent test, the average temporal correlation coefficient (TCC) at 14 stations was 0.52, and the average root-mean-square error was 21%. Then, the statistical downscaling model was applied to the Climate Forecast System, version 2 (CFSv2), outputs to produce seasonal forecasts of rainfall for 1982–2018. A statistical downscaling model improved CFSv2 forecasts of station rainfall in South China with the average TCC increasing from 0.14 to 0.31. Forecasts of South China regionally averaged rainfall were also improved with the TCC increasing from 0.11 to 0.53. The dependence of forecast skill for regional average rainfall on ENSO events was examined. Forecast error was reduced, but not statistically significant, when it followed an El Niño event in both CFSv2 and the downscaling model. While when it followed an EP-type El Niño, the significantly reduced forecast error (at the 0.1 level) could be seen in the downscaling model and CFSv2.

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

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.

MODEL DEBIT DAERAH ALIRAN SUNGAI JANGKOK BERDASARKAN HASIL PREDIKSI MODEL STATISTICAL DOWNSCALING NONPARAMETRIK KERNEL CURAH HUJAN DAN TEMPERATUR

River water discharge is important information for water resources management planning, so it is necessary to develop river water discharge model as basis of its predictions. In order to get the result of predictions of river water discharge with high accuracy, it is developed a model of river water discharge based on the predictions of local climate (local rainfall and temperature) that are influenced by global climate conditions. Prediction of local climate is based on the Kernel nonparametric statistical downscaling model by utilizing GCM data. GCM data is a high dimensional global data, so data pre-processing is needed to reduce data dimension. It is done by CART algorithm. Statistical downscaling model is used to predict local rainfall and temperature. The prediction results are quite good with relatively small RMSE value. They are used to develop model of river water discharge. Modeling river water discharge is carried out using the Kernel nonparametric approach. The model of river water discharge produced is quite good because it can be used to predict river water discharge with relatively small RMSE.