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Assessment of climate change impacts on climate variables using probabilistic ensemble modeling and trend analysis

机译：使用概率集成模型和趋势分析评估气候变化对气候变量的影响

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Water resources in snow-dependent regions have undergone significant changes due to climate change. Snow measurements in these regions have revealed alarming declines in snowfall over the past few years. The Zayandeh-Rud River in central Iran chiefly depends on winter falls as snow for supplying water from wet regions in high Zagrous Mountains to the downstream, (semi-)arid, low-lying lands. In this study, the historical records (baseline: 1971-2000) of climate variables (temperature and precipitation) in the wet region were chosen to construct a probabilistic ensemble model using 15 GCMs in order to forecast future trends and changes while the Long Ashton Research Station Weather Generator (LARS-WG) was utilized to project climate variables under two A2 and B1 scenarios to a future period (2015-2044). Since future snow water equivalent (SWE) forecasts by GCMs were not available for the study area, an artificial neural network (ANN) was implemented to build a relationship between climate variables and snow water equivalent for the baseline period to estimate future snowfall amounts. As a last step, homogeneity and trend tests were performed to evaluate the robustness of the data series and changes were examined to detect past and future variations. Results indicate different characteristics of the climate variables at upstream stations. A shift is observed in the type of precipitation from snow to rain as well as in its quantities across the subregions. The key role in these shifts and the subsequent side effects such as water losses is played by temperature.
机译：由于气候变化，雪域依赖地区的水资源发生了重大变化。这些地区的降雪量显示，过去几年降雪量惊人。伊朗中部的Zayandeh-Rud河主要依靠冬季的降雪，因为积雪从高Zagrous山区的潮湿地区向下游（半干旱）低洼土地供水。在这项研究中，选择了湿润地区气候变量（温度和降水）的历史记录（基线：1971-2000年），以使用15个GCM构建概率集合模型，以预测未来趋势和变化，而Long Ashton研究利用台站天气发生器（LARS-WG）来预测两种A2和B1情景下的气候变量到未来一段时间（2015-2044）。由于研究区域尚无法提供GCM的未来雪水当量（SWE）预测，因此实施了人工神经网络（ANN）在基准期内建立气候变量和雪水当量之间的关系，以估算未来降雪量。作为最后一步，进行了均质性和趋势测试以评估数据系列的稳健性，并检查变化以检测过去和将来的变化。结果表明上游站的气候变量具有不同的特征。从下雪到下雨的降水类型以及该次区域的降水量都有所变化。在这些变化和随后的副作用（如水分流失）中，关键作用在于温度。

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