Seasonal drought prediction for semiarid northeast Brazil
Seasonal drought prediction for semiarid northeast Brazil
复制标题
巴西东北部半干旱地区的季节性干旱预测
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
A. Bronstert
中科院分区：
文献类型：
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作者：
T. Pilz;J. Delgado;Sebastian Voss;K. Vormoor;T. Francke;A. Costa;E. Martins;A. Bronstert
巴西东北部的半干旱地区是世界上人口最稠密的干旱地区之一，经常受到严重干旱的影响。因此，可靠的径流和水库蓄水季节预报对水资源管理人员具有很高的价值。这种预报可以通过应用代表基本过程的水文模型或利用气象和水文变量之间的相关性的统计关系来产生。这项工作评估和比较了基于过程的水文模型和统计方法得出的季节性水库蓄水预报的性能。
在观测的推动下，这两个模型实现了相似的模拟精度。然而，在后播实验中，使用基于过程的水文模型估计区域水库蓄水量的精度相当低，而两种方法对干旱事件预测的分辨率和可靠性相似。进一步研究了基于过程的模式的不足之处，发现前期湿度条件和模式预报性能对降雨预报质量具有较高的敏感性。
在本研究的范围内，统计模型被证明是在区域和每月聚合尺度上预测水库水位和干旱事件的更直接的方法。然而，对于更精细的空间和时间尺度的预测或对潜在过程的调查，基于过程的模型的成本高昂的初始化和应用可能是值得的。此外，应用创新的数据产品，如遥感数据和业务模型校正方法，如数据同化，可加强利用基于过程的水文模型的先进能力。
The semiarid northeast of Brazil is one of the most densely populated dryland regions in the world and recurrently affected by severe droughts. Thus, reliable seasonal forecasts of streamflow and reservoir storage are of high value for water managers. Such forecasts can be generated by applying either hydrological models representing underlying processes or statistical relationships exploiting correlations among meteorological and hydrological variables. This work evaluates and compares the performances of seasonal reservoir storage forecasts derived by a process-based hydrological model and a statistical approach.
Driven by observations, both models achieve similar simulation accuracies. In a hindcast experiment, however, the accuracy of estimating regional reservoir storages was considerably lower using the process-based hydrological model, whereas the resolution and reliability of drought event predictions were similar by both approaches. Further investigations regarding the deficiencies of the process-based model revealed a significant influence of antecedent wetness conditions and a higher sensitivity of model prediction performance to rainfall forecast quality.
Within the scope of this study, the statistical model proved to be the more straightforward approach for predictions of reservoir level and drought events at regionally and monthly aggregated scales. However, for forecasts at finer scales of space and time or for the investigation of underlying processes, the costly initialisation and application of a process-based model can be worthwhile. Furthermore, the application of innovative data products, such as remote sensing data, and operational model correction methods, like data assimilation, may allow for an enhanced exploitation of the advanced capabilities of process-based hydrological models.