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.

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Water management is recognized as one of the most important factors in regulating nitrous oxide (N 2 O) emissions from paddy fields. In China, controlled irrigation (CI) is widely applied because it has been proved highly effective in saving water. During the rice-growing season, the soil in CI paddy fields remains dry 60-80% of the time compared with soil irrigated by traditional methods. This study aims to assess N 2 O emissions from paddy fields under CI, with traditional irrigation (TI) as the control. The cumulative N 2 O emission from CI paddy fields was 2.5 kg N ha -1 , which was significantly greater than that from TI paddy fields (1.0 kg N ha -1 ) (P \ 0.05). Soil drying caused substantial N 2 O emissions. The majority (73.9%) of the cumulative N 2 O emission from CI paddy fields was observed during the drying phase, whereas no substantial N 2 O emissions were observed when the soil was re-wetted after the drying phase. More and significantly higher peaks of N 2 O emissions from CI paddy fields (P \ 0.05) were also detected. These peaks were observed *8 days after fertilizer application at water-filled pore spaces (WFPS) ranging from 78.0 to 83.5%, soil temperature ranging from 29.1 to 29.4°C, and soil redox potential (Eh) values ranging from ?207.5 to ?256.7 mV. The highest N 2 O emission was measured 8 days after the application of base fertilizer at a WFPS of 79.0%, soil temperature of 29.1°C, and soil Eh value of ?207.5 mV. These results suggest that N 2 O emissions may be reduced obviously by keeping the WFPS higher than 83.5% within 10 days after each fertilizer application, especially when the soil temperature is suitable.