Using Statistical Downscaling to Quantify the GCM-Related Uncertainty in Regional Climate Change Scenarios: A Case Study of Swedish Precipitation

There are a number of sources of uncertainty in regional climate change scenarios. When statistical downscaling is used to obtain regional climate change scenarios, the uncertainty may originate from the uncertainties in the global climate models used, the skill of the statistical model, and the forcing scenarios applied to the global climate model. The uncertainty associated with global climate models can be evaluated by examining the differences in the predictors and in the downscaled climate change scenarios based on a set of different global climate models. When standardized global climate model simulations such as the second phase of the Coupled Model Intercomparison Project （CMIP2） are used, the difference in the downscaled variables mainly reflects differences in the climate models and the natural variability in the simulated climates. It is proposed that the spread of the estimates can be taken as a measure of the uncertainty associated with global climate models. The proposed method is applied to the estimation of global-climate-model-related uncertainty in regional precipitation change scenarios in Sweden. Results from statistical downscaling based on 17 global climate models show that there is an overall increase in annual precipitation all over Sweden although a considerable spread of the changes in the precipitation exists. The general increase can be attributed to the increased large-scale precipitation and the enhanced westerly wind. The estimated uncertainty is nearly independent of region. However, there is a seasonal dependence. The estimates for winter show the highest level of confidence, while the estimates for summer show the least.

Prediction of the potential distribution and analysis of the freezing injury risk of winter wheat on the Loess Plateau under climate change

Determining the suitable areas for winter wheat under climate change and assessing the risk of freezing injury are crucial for the cultivation of winter wheat.We used an optimized Maximum Entropy(MaxEnt)Model to predict the potential distribution of winter wheat in the current period(1970-2020)and the future period(2021-2100)under four shared socioeconomic pathway scenarios(SSPs).We applied statistical downscaling methods to downscale future climate data,established a scientific and practical freezing injury index(FII)by considering the growth period of winter wheat,and analyzed the characteristics of abrupt changes in winter wheat freezing injury by using the Mann-Kendall(M-K)test.The results showed that the prediction accuracy AUC value of the MaxEnt Model reached 0.976.The minimum temperature in the coldest month,precipitation in the wettest season and annual precipitation were the main factors affecting the spatial distribution of winter wheat.The total suitable area of winter wheat was approximately 4.40×10^(7)ha in the current period.In the 2070s,the moderately suitable areas had the greatest increase by 9.02×10^(5)ha under SSP245 and the least increase by 6.53×10^(5)ha under SSP370.The centroid coordinates of the total suitable areas tended to move northward.The potential risks of freezing injury in the high-latitude and-altitude areas of the Loess Plateau,China increased significantly.The northern areas of Xinzhou in Shanxi Province,China suffered the most serious freezing injury,and the southern areas of the Loess Plateau suffered the least.Environmental factors such as temperature,precipitation and geographical location had important impacts on the suitable area distribution and freezing injury risk of winter wheat.In the future,greater attention should be paid to the northward boundaries of both the winter wheat planting areas and the areas of freezing injury risk to provide the early warning of freezing injury and implement corresponding management strategies.

The Potential Scenarios of the Impacts of Climate Change on Egyptian Resources and Agricultural Plant Production

The emissions of greenhouse gasses in Egypt are about 0.58% of the total emissions of the world in the year 2015, although Egypt is one of the countries most affected by the impacts of climate change. By assessment and analysis of the expected economic impacts of climate change by the year 2030, the Egyptian cultivated area will be reduced to about 0.949 million acres, equal to about 8.22% of the Egyptian cultivated area compared with the case of no sinking part of the Delta land, thus reducing crop area in Egypt to about 1.406 million acres, approximately to about 6.25% of crop area compared with the case of no sinking part of the Delta land, in addition to surplus in the Egyptian balance water to about 2.48 billion m3. In this case value of the Egyptian agriculture production will decrease by about 6.19 billion dollars, equal to about 6.19% compared with presumably no sinking of the Delta land. In the case of sinking 15% of Delta lands, with the change of the productivity and water consumption of most crops, the result will be a reduction in the cultivated area to about 0.94 million acres. In addition to decreasing the Egyptian crop area to about 1.39 million acres, with a deficit in the Egyptian balance water to about 4.74 billion m3 compared to the case of no sinking part of the Delta land, the cultivated area will decrease to about 8.17%, and the crop area will decrease 6.18%. Also, the value of the Egyptian agriculture production will decrease by about 12.51%. While compared to sinking part of the Delta land to about 15% of the total Delta area without the other impacts of climate change, the cultivated area will increase by about 0.06%;the crop area will increase by about 0.08%;also, the value of the Egyptian agriculture production will decrease by about 5.57%.

Modelling the biological invasion of Prosopis juliflora using geostatistical-based bioclimatic variables under climate change in arid zones of southwestern Iran

Invasive species have been the focus of ecologists due to their undesired impacts on the environment.The extent and rapid increase in invasive plant species is recognized as a natural cause of global-biodiversity loss and degrading ecosystem services.Biological invasions can affect ecosystems across a wide spectrum of bioclimatic conditions.Understanding the impact of climate change on species invasion is crucial for sustainable biodiversity conservation.In this study,the possibility of mapping the distribution of invasive Prosopis juliflora(Swartz)DC.was shown using present background data in Khuzestan Province,Iran.After removing the spatial bias of background data by creating weighted sampling bias grids for the occurrence dataset,we applied six modelling algorithms(generalized additive model(GAM),classification tree analysis(CTA),random forest(RF),multivariate adaptive regression splines(MARS),maximum entropy(Max Ent)and ensemble model)to predict invasion distribution of the species under current and future climate conditions for both optimistic(RCP2.6)and pessimistic(RCP8.5)scenarios for the years 2050 and 2070,respectively.Predictor variables including weighted mean of CHELSA(climatologies at high resolution for the Earth’s land surface areas)-bioclimatic variables and geostatistical-based bioclimatic variables(1979–2020),physiographic variables extracted from shuttle radar topography mission(SRTM)and some human factors were used in modelling process.To avoid causing a biased selection of predictors or model coefficients,we resolved the spatial autocorrelation of presence points and multi-collinearity of the predictors.As in a conventional receiver operating characteristic(ROC),the area under curve(AUC)is calculated using presence and absence observations to measure the probability and the two error components are weighted equally.All models were evaluated using partial ROC at different thresholds and other statistical indices derived from confusion matrix.Sensitivity analysis showed that mean diurnal range(Bio2)and annual precipitation(Bio12)explained more than 50% of the changes in the invasion distribution and played a pivotal role in mapping habitat suitability of P.juliflora.At all thresholds,the ensemble model showed a significant difference in comparison with single model.However,Max Ent and RF outperformed the others models.Under climate change scenarios,it is predicted that suitable areas for this invasive species will increase in Khuzestan Province,and increasing climatically suitable areas for the species in future will facilitate its future distribution.These findings can support the conservation planning and management efforts in ecological engineering and be used in formulating preventive measures.

Modelling the impact of climate change on rangeland forage production using a generalized regression neural network:a case study in Isfahan Province,Central Iran

Monitoring of rangeland forage production at specified spatial and temporal scales is necessary for grazing management and also for implementation of rehabilitation projects in rangelands. This study focused on the capability of a generalized regression neural network（GRNN） model combined with GIS techniques to explore the impact of climate change on rangeland forage production. Specifically, a dataset of 115 monitored records of forage production were collected from 16 rangeland sites during the period 1998–2007 in Isfahan Province, Central Iran. Neural network models were designed using the monitored forage production values and available environmental data（including climate and topography data）, and the performance of each network model was assessed using the mean estimation error（MEE）, model efficiency factor（MEF）, and correlation coefficient（r）. The best neural network model was then selected and further applied to predict the forage production of rangelands in the future（in 2030 and 2080） under A1 B climate change scenario using Hadley Centre coupled model. The present and future forage production maps were also produced. Rangeland forage production exhibited strong correlations with environmental factors, such as slope, elevation, aspect and annual temperature. The present forage production in the study area varied from 25.6 to 574.1 kg/hm^2. Under climate change scenario, the annual temperature was predicted to increase and the annual precipitation was predicted to decrease. The prediction maps of forage production in the future indicated that the area with low level of forage production（0–100 kg/hm^2） will increase while the areas with moderate, moderately high and high levels of forage production（≥100 kg/hm^2） will decrease both in 2030 and in 2080, which may be attributable to the increasing annual temperature and decreasing annual precipitation. It was predicted that forage production of rangelands will decrease in the next couple of decades, especially in the western and southern parts of Isfahan Province. These changes are more pronounced in elevations between 2200 and 2900 m. Therefore, rangeland managers have to cope with these changes by holistic management approaches through mitigation and human adaptations.