气候变化领域集成服务门户(CCPortal): Seasonal drought prediction for semiarid northeast Brazil: What is the added value of a process-based hydrological model?

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DOI	10.5194/hess-23-1951-2019
	Seasonal drought prediction for semiarid northeast Brazil: What is the added value of a process-based hydrological model?
	Pilz T.; Delgado J.M.; Voss S.; Vormoor K.; Francke T.; Cunha Costa A.; Martins E.; Bronstert A.
发表日期	2019
ISSN	1027-5606
起始页码	1951
结束页码	1971
卷号	23 期号:4
英文摘要	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. © 2019. This work is distributed under the Creative Commons Attribution 4.0 License.
语种	英语
scopus关键词	Climate models; Digital storage; Drought; Remote sensing; Weather forecasting; Hydrological modeling; Hydrological models; Hydrological variables; Process-based modeling; Remote sensing data; Statistical approach; Statistical modeling; Statistical relationship; Reservoirs (water); data assimilation; drought stress; hindcasting; hydrological modeling; precipitation intensity; remote sensing; semiarid region; streamflow; weather forecasting; Brazil
来源期刊	Hydrology and Earth System Sciences
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/159702
作者单位	Pilz, T., Institute of Environmental Science and Geography, University of Potsdam, Potsdam, Germany; Delgado, J.M., Institute of Environmental Science and Geography, University of Potsdam, Potsdam, Germany; Voss, S., Institute of Environmental Science and Geography, University of Potsdam, Potsdam, Germany; Vormoor, K., Institute of Environmental Science and Geography, University of Potsdam, Potsdam, Germany; Francke, T., Institute of Environmental Science and Geography, University of Potsdam, Potsdam, Germany; Cunha Costa, A., Institute of Engineering and Sustainable Development, University of International Integration of the Afro-Brazilian Lusophony (UNILAB), Acarape, Ceara, Brazil; Martins, E., Research Institute for Meteorology and Water Resources - FUNCEME, Fortaleza, Ceara, Brazil; Bronstert, A., Institute of Environmental Science and Geography, University of Potsdam, Potsdam, Germany
推荐引用方式 GB/T 7714	Pilz T.,Delgado J.M.,Voss S.,et al. Seasonal drought prediction for semiarid northeast Brazil: What is the added value of a process-based hydrological model?[J],2019,23(4).
APA	Pilz T..,Delgado J.M..,Voss S..,Vormoor K..,Francke T..,...&Bronstert A..(2019).Seasonal drought prediction for semiarid northeast Brazil: What is the added value of a process-based hydrological model?.Hydrology and Earth System Sciences,23(4).
MLA	Pilz T.,et al."Seasonal drought prediction for semiarid northeast Brazil: What is the added value of a process-based hydrological model?".Hydrology and Earth System Sciences 23.4(2019).