The Mediterranean Basin is a significant area will be affected by drought and water scarcity in future. In this context, Bursa urban area, the fourth largest city in terms of population in Türkiye was used for quantification. A high-resolution global climate model of MPI-ESM-MR based RCP4.5 and 8.5, and population projections based on arithmetic and exponential growth models until 2100 was utilised. Support Vector Machine (SVM) regression was established between observed precipitation, evapotranspiration, runoff and reservoir volume for the reference period. Climate model outputs like precipitation and derived outputs such as evapotranspiration based on Penman-Monteith, runoff from SCS-Curve Number were used for SVM future dam volume prediction. Reference (observed data) and near and distant future (projected) dam volumes were converted to the Standardized Reservoir Index (SRI), and water scarcity as water per capita was also calculated. As a result, increased droughts and extreme conditions are identified in the near and distant future compared to the reference period. In addition, decrease in water per capita was determined with respect to the reference period. Therefore, results demonstrate that water scarcity is worsened by both semi-arid climate and population in urban area. Hence, water management in urban areas should address climatic variability and economic processes together.

中文翻译：

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通过支持向量机揭示城市水资源短缺和干旱的未来复杂性——以半干旱布尔萨市区为例


地中海盆地是一个重要的地区，未来将受到干旱和水资源短缺的影响。在这种情况下，土耳其人口第四大城市布尔萨市区被用于量化。使用了基于 MPI-ESM-MR 的 RCP4.5 和 8.5 的高分辨率全球气候模型，以及基于 2100 年之前基于算术和指数增长模型的人口预测。在参考期观测到的降水、蒸散、径流和水库体积之间建立支持向量机 （SVM） 回归。气候模型输出（如降水）和派生输出（如基于 Penman-Monteith 的蒸散）和 SCS 曲线数的径流用于 SVM 未来大坝体积预测。参考（观测数据）和近期和遥远的将来（预测）大坝体积被转换为标准化水库指数 （SRI），并且还计算了作为人均用水量的缺水情况。因此，与参考期相比，在不久的将来和遥远的将来可以确定更多的干旱和极端条件。此外，根据参考期确定了人均用水量的减少。因此，结果表明，半干旱气候和城市地区人口加剧了水资源短缺。因此，城市地区的水资源管理应同时解决气候变化和经济过程问题。