Comparison of the Performance of Statistical Models in Forecasting Monthly Total Dissolved Solids in the Rio Grande¹

Abstract

Abudu, S., J.P. King, Z. Sheng, 2011. Comparison of the Performance of Statistical Models in Forecasting Monthly Total Dissolved Solids in the Rio Grande. Journal of the American Water Resources Association (JAWRA) 48(1): 10‐23. DOI: 10.1111/j.1752‐1688.2011.00587.x Abstract: This paper presents the application of autoregressive integrated moving average (ARIMA), transfer function‐noise (TFN), and artificial neural networks (ANNs) modeling approaches in forecasting monthly total dissolved solids (TDS) of water in the Rio Grande at El Paso, Texas. Predictability analysis was performed between the precipitation, temperature, streamflow rates at the site, releases from upstream reservoirs, and monthly TDS using cross‐correlation statistical tests. The chi‐square test results indicated that the average monthly temperature and precipitation did not show significant predictability on monthly TDS series. The performances of one‐ to three‐month‐ahead model forecasts for the testing period of 1984‐1994 showed that the TFN model that incorporated the streamflow rates at the site and Caballo Reservoir release improved monthly TDS forecasts slightly better than the ARIMA models. Except for one‐month‐ahead forecasts, the ANN models using the streamflow rates at the site as inputs resulted in no significant improvements over the TFN models at two‐month‐ahead and three‐month‐ahead forecasts. For three‐month‐ahead forecasts, the simple ARIMA showed similar performance compared to all other models. The results of this study suggested that simple deseasonalized ARIMA models could be used in one‐ to three‐month‐ahead TDS forecasting at the study site with a simple, explicit model structure and similar model performance as the TFN and ANN models for better water management in the Basin.

Long-term generation scheduling of Xiluodu and Xiangjiaba cascade hydro plants considering monthly streamflow forecasting error

“…ANN model provides a powerful tool for modeling the complex nonlinear relationship between inputs and outputs [26]. It has been demonstrated that ANN model can provide better forecasting accuracy than statistical-based methods [27].…”

High-Resolution Reconstruction of the Maximum Snow Water Equivalent Based on Remote Sensing Data in a Mountainous Area

“…The northern Xinjiang is one of the areas exhibiting frequent snowmelt flood disasters in Xinjiang [5]. Therefore, the assessment of maximum snowpack volumes plays an important role in regional water resource management and flood prevention [6][7][8].The snow water equivalent (SWE), as an important variable in the earth system, reflects the amount of water resources in the form of snow. The SWE is also the main factor influencing river runoff, the regional water resources supply, and flood safety during the snowmelt period.…”

“…The SWE is also the main factor influencing river runoff, the regional water resources supply, and flood safety during the snowmelt period. Therefore, accurate acquisition of the snow water equivalent is of great significance for studies on hydrology, meteorology, the water cycle, and global climate change [8,9]. However, accurate estimation of the watershed SWE is the major problem to be solved in the study of mountain hydrology [10].…”

“…The northern Xinjiang is one of the areas exhibiting frequent snowmelt flood disasters in Xinjiang [5]. Therefore, the assessment of maximum snowpack volumes plays an important role in regional water resource management and flood prevention [6][7][8].…”

Incorporação Da Incerteza Dos Parâmetros Do Modelo Estocástico De Vazões Na Política Operativa Do Despacho Hidrotérmico

“…Como aplicações, pode-se citar o controle de cheias, gerenciamento de irrigação e operação de reservatórios de abastecimento de água, bem como aspectos legais e institucionais relacionados ao gerenciamento e planejamento dos recursos hídricos [54].…”