Climate projections in Lake Maggiore watershed using statistical downscaling model

2020 ◽  
Vol 81 ◽  
pp. 113-130
Author(s):  
H Saidi ◽  
C Dresti ◽  
D Manca ◽  
M Ciampittiello

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.

2021 ◽  
Author(s):  
Salah SAHABI ABED

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.


2002 ◽  
Vol 33 (5) ◽  
pp. 415-424 ◽  
Author(s):  
Cintia B. Uvo ◽  
Ronny Berndtsson

Climate variability and climate change are of great concern to economists and energy producers as well as environmentalists as both affect the precipitation and temperature in many regions of the world. Among those affected by climate variability is the Scandinavian Peninsula. Particularly, its winter precipitation and temperature are affected by the variations of the so-called North Atlantic Oscillation (NAO). The objective of this paper is to analyze the spatial distribution of the influence of NAO over Scandinavia. This analysis is a first step to establishing a predictive model, driven by a climatic indicator such as NAO, for the available water resources of different regions in Scandinavia. Such a tool would be valuable for predicting potential of hydropower production one or more seasons in advance.


Author(s):  
Sudeep Pokhrel ◽  
Saraswati Thapa

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.


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