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首页> 外文会议>nternational Conference on Water Resources Engineering >Stochastically downscaled rainfall projections and modelled hydrological response for the Mount Lofty Ranges, South Australia
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Stochastically downscaled rainfall projections and modelled hydrological response for the Mount Lofty Ranges, South Australia

机译：随机较低的降雨预测和南澳大利亚山区崇高范围的模型水文反应

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The catchments of the Mount Lofty Ranges (MLR), South Australia (SA), provide approximately 60% of Adelaide's water supply. Recent years of low rainfall, combined with climate change projections of a continuing drying trend, highlight significant challenges to water resource management for this region. Regional rainfall simulated by general circulation models (GCMs) is not suitable input for hydrological models due to its coarse spatial resolution. Thus stochastic downscaling is used to simulate multi-site, daily rainfall conditional on large-scale atmospheric forcing. Stochastic downscaling is shown to reproduce the 1958-2003 observed rainfall variability and trends for a network of 20 stations across the MLR. These stations were selected based on their data quality and relevance to hydrological modelling. Driving the stochastic downscaling model with atmospheric predictors from the CSIRO Mk3.0 GCM, for A2 and B2 scenarios, provides multiple realisations of projected multi-site, daily rainfall. These are assessed in terms of changes to regional rainfall patterns, timing and amount. When linked to a hydrological model this modelling framework allows the quantification of the potential impacts of projected climate change on streamflow. Results for the River Torrens catchment in the MLR highlight the non-linear response of runoff processes to these projected rainfall changes. Sensitivity to changes in evaporation and evapotranspiration is also assessed. Future work will apply this framework to multiple regions across SA to comprehensively assess hydrological response to projected climate change.
机译：南澳大利亚（SA）山山山口（MLR）的集水区提供了约60％的阿德莱德供水。近年来较低的降雨量，结合气候变化预测持续干燥趋势，凸显了该地区水资源管理的重大挑战。通过通用循环模型（GCMS）模拟的区域降雨不是由于其粗糙空间分辨率而适用于水文模型的输入。因此，随机缩小用于模拟大型大气强制上的多场，日降雨条件。随机缩小显示，繁殖1958 - 2003年观察到的降雨变量和跨越MLR网络的网络的趋势。根据其数据质量和与水文建模相关性选择这些站。从CSIRO MK3.0 GCM驾驶随机缩小模型，用于A2和B2场景，提供了一定程度的多网站，每日降雨量的多次实现。这些在区域降雨模式，时序和金额的变化方面评估。当与水文模型相关联该建模框架允许定量预计气候变化对流流的潜在影响。结果在MLR中的奶酪集水区突出了径流过程对这些预计的降雨变化的非线性响应。还评估了对蒸发和蒸发蒸腾的变化的敏感性。未来的工作将在SA跨越多个地区将此框架应用于全面评估对预计气候变化的水文反应。

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