干旱区生态环境知识资源中心(Arid): Monthly and seasonal streamflow forecasting of large dryland catchments in Brazil

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	Monthly and seasonal streamflow forecasting of large dryland catchments in Brazil
	Costa Alexandre C; Estacio Alvson B S; Souza Filho Francisco De A De; Lima Neto Iran E
来源期刊	干旱区科学
ISSN	1674-6767
出版年	2021
卷号	13 期号:3 页码:1674-6767
英文摘要	Streamflow forecasting in drylands is challenging. Data is scarce, catchments are highly humanmodified and streamflow exhibits strong nonlinear responses to rainfall. The goal of this study was to evaluate the monthly and seasonal streamflow forecasting in two large catchments in the Jaguaribe River Basin in the Brazilian semi-arid area. We adopted four different lead times: one month ahead for monthly scale and two, three and four months ahead for seasonal scale. The gaps of the historic streamflow series were filled up by using rainfall-runoff modelling. Then, time series model techniques were applied, i.e., the locally constant, the locally averaged, the k-nearest-neighbours algorithm (k-NN) and the autoregressive model (AR). The criterion of reliability of the validation results is that the forecast is more skillful than streamflow climatology. Our approach outperformed the streamflow climatology for all monthly streamflows. On average, the former was 25% better than the latter. The seasonal streamflow forecasting (SSF) was also reliable (on average, 20% better than the climatology), failing slightly only for the high flow season of one catchment (6% worse than the climatology). Considering an uncertainty envelope (probabilistic forecasting), which was considerably narrower than the data standard deviation, the streamflow forecasting performance increased by about 50% at both scales. The forecast errors were mainly driven by the streamflow intra-seasonality at monthly scale, while they were by the forecast lead time at seasonal scale. The best-fit and worst-fit time series model were the k-NN approach and the AR model, respectively. The rainfall-runoff modelling outputs played an important role in improving streamflow forecasting for one streamgauge that showed 35% of data gaps. The developed data-driven approach is mathematical and computationally very simple, demands few resources to accomplish its operational implementation and is applicable to other dryland watersheds. Our findings may be part of drought forecasting systems and potentially help allocating water months in advance. Moreover, the developed strategy can serve as a baseline for more complex streamflow forecast systems.
英文关键词	nonlinear time series analysis probabilistic streamflow forecasting reconstructed streamflow data dryland hydrology rainfall-runoff modelling stochastic dynamical systems
类型	Article
语种	英语
国家	中国
收录类别	CSCD
WOS类目	Geology
CSCD记录号	CSCD:6955760
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/365036
作者单位	Costa Alexandre C, Institute of Engineering and Sustainable Development, University of International Integration of the Afro-Brazilian Lusophony, Redencao, CEP 62.790-970, Brazil.; Estacio Alvson B S, Department of Hydraulic Engineering and Environment, Federal University of Ceara, Fortaleza, Ceara CEP 60.451-970, Brazil.; Souza Filho Francisco De A De, Department of Hydraulic Engineering and Environment, Federal University of Ceara, Fortaleza, Ceara CEP 60.451-970, Brazil.; Lima Neto Iran E, Department of Hydraulic Engineering and Environment, Federal University of Ceara, Fortaleza, Ceara CEP 60.451-970, Brazil.
推荐引用方式 GB/T 7714	Costa Alexandre C,Estacio Alvson B S,Souza Filho Francisco De A De,et al. Monthly and seasonal streamflow forecasting of large dryland catchments in Brazil[J],2021,13(3):1674-6767.
APA	Costa Alexandre C,Estacio Alvson B S,Souza Filho Francisco De A De,&Lima Neto Iran E.(2021).Monthly and seasonal streamflow forecasting of large dryland catchments in Brazil.干旱区科学,13(3),1674-6767.
MLA	Costa Alexandre C,et al."Monthly and seasonal streamflow forecasting of large dryland catchments in Brazil".干旱区科学 13.3(2021):1674-6767.