Assessment of Time Series Models for Mean Discharge Modeling and Forecasting in a Sub-Basin of the Paranaíba River, Brazil

IF 3.1 Q2 WATER RESOURCES
Gabriela Emiliana de Melo e Costa, Frederico Carlos M. de Menezes Filho, Fausto A. Canales, Maria Clara Fava, Abderraman R. Amorim Brandão, Rafael Pedrollo de Paes
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Abstract

Stochastic modeling to forecast hydrological variables under changing climatic conditions is essential for water resource management and adaptation planning. This study explores the applicability of stochastic models, specifically SARIMA and SARIMAX, to forecast monthly average river discharge in a sub-basin of the Paranaíba River near Patos de Minas, MG, Brazil. The Paranaíba River is a vital water source for the Alto Paranaíba region, serving industrial supply, drinking water effluent dilution for urban communities, agriculture, fishing, and tourism. The study evaluates the performance of SARIMA and SARIMAX models in long-term discharge modeling and forecasting, demonstrating the SARIMAX model’s superior performance in various metrics, including the Nash–Sutcliffe coefficient (NSE), the root mean square error (RMSE), and the mean absolute percentage error (MAPE). The inclusion of precipitation as a regressor variable considerably improves the forecasting accuracy, and can be attributed to the multivariate structure of the SARIMAX model. While stochastic models like SARIMAX offer valuable decision-making tools for water resource management, the study underscores the significance of employing long-term time series encompassing flood and drought periods and including model uncertainty analysis to enhance the robustness of forecasts. In this study, the SARIMAX model provides a better fit for extreme values, overestimating peaks by around 11.6% and troughs by about 5.0%, compared with the SARIMA model, which tends to underestimate peaks by an average of 6.5% and overestimate troughs by approximately 76.0%. The findings contribute to the literature on water management strategies and mitigating risks associated with extreme hydrological events.
巴西Paranaíba河子流域平均流量建模与预报的时间序列模型评价
利用随机模型预测气候变化条件下的水文变量对水资源管理和适应性规划至关重要。本研究探讨了随机模型的适用性,特别是SARIMA和SARIMAX,以预测巴西MG州帕托斯德米纳斯附近Paranaíba河子流域的月平均河流流量。Paranaíba河是阿尔托Paranaíba地区的重要水源,为工业供应、城市社区的饮用水废水稀释、农业、渔业和旅游业提供服务。研究对SARIMA和SARIMAX模型在长期流量建模和预测中的性能进行了评价,结果表明SARIMAX模型在Nash-Sutcliffe系数(NSE)、均方根误差(RMSE)和平均绝对百分比误差(MAPE)等指标上表现优异。将降水作为回归变量显著提高了预测精度,这可归因于SARIMAX模型的多元结构。虽然像SARIMAX这样的随机模型为水资源管理提供了有价值的决策工具,但该研究强调了采用包括洪涝和干旱时期在内的长期时间序列并包括模型不确定性分析以增强预测稳健性的重要性。在本研究中,SARIMAX模型提供了更好的极值拟合,峰值高估约11.6%,低谷高估约5.0%,而SARIMA模型倾向于平均低估峰值6.5%,高估低谷约76.0%。这些发现有助于水资源管理策略和减轻与极端水文事件相关的风险的文献。
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来源期刊
Hydrology
Hydrology Earth and Planetary Sciences-Earth-Surface Processes
CiteScore
4.90
自引率
21.90%
发文量
192
审稿时长
6 weeks
期刊介绍: Journal of Hydrology publishes original research papers and comprehensive reviews in all the subfields of the hydrological sciences, including water based management and policy issues that impact on economics and society. These comprise, but are not limited to the physical, chemical, biogeochemical, stochastic and systems aspects of surface and groundwater hydrology, hydrometeorology, hydrogeology and hydrogeophysics. Relevant topics incorporating the insights and methodologies of disciplines such as climatology, water resource systems, ecohydrology, geomorphology, soil science, instrumentation and remote sensing, data and information sciences, civil and environmental engineering are within scope. Social science perspectives on hydrological problems such as resource and ecological economics, sociology, psychology and behavioural science, management and policy analysis are also invited. Multi-and interdisciplinary analyses of hydrological problems are within scope. The science published in the Journal of Hydrology is relevant to catchment scales rather than exclusively to a local scale or site. Studies focused on urban hydrological issues are included.
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