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Seasonal forecasts from dynamical models are expected to be useful for drought predictions in many regions. This study investigated the usefulness of the Climate Forecast System version 2 (CFSv2) in improving meteorological drought prediction in China based on its 25-year reforecast. The six-month standard precipitation index (SPI6) was used as the drought indicator, and its persistence forecast served as the benchmark against which CFSv2 forecasts were evaluated. The analysis found that the SPI6 persistence forecast shows good skills in all regions at short lead times, and CFSv2 forecast can further improve those skills in most regions. The improvement is particularly pronounced at longer lead times and over the humid regions in the southeast. This study also examined the seasonality and regionality of persistence forecast skills and CFSv2 contributions, and reveals regions where CFSv2 forecast shows no or sometimes even negative contributions.
Yang Lang; Lifeng Luo; Aizhong Ye; Qingyun Duan. Do CFSv2 Seasonal Forecasts Help Improve the Forecast of Meteorological Drought over Mainland China? Water 2020, 12, 2010 .
AMA StyleYang Lang, Lifeng Luo, Aizhong Ye, Qingyun Duan. Do CFSv2 Seasonal Forecasts Help Improve the Forecast of Meteorological Drought over Mainland China? Water. 2020; 12 (7):2010.
Chicago/Turabian StyleYang Lang; Lifeng Luo; Aizhong Ye; Qingyun Duan. 2020. "Do CFSv2 Seasonal Forecasts Help Improve the Forecast of Meteorological Drought over Mainland China?" Water 12, no. 7: 2010.
Seasonal predictions of precipitation and surface air temperature from the Climate Forecast System, version 2 (CFSv2), are evaluated against gridded daily observations from 1982 to 2007 over 17 hydroclimatic regions in China. The seasonal predictive skill is quantified with skill scores including correlation coefficient, RMSE, and mean bias for spatially averaged seasonal precipitation and temperature forecasts for each region. The evaluation focuses on identifying regions and seasons where significant skill exists, thus potentially contributing to skill in hydrological prediction. The authors find that the predictive skill of CFSv2 precipitation and temperature forecasts has a stronger dependence on seasons and regions than on lead times. Both temperature and precipitation forecasts show higher skill from late summer [July–September (JAS)] to late autumn [October–December (OND)] and from winter [December–February (DJF)] to spring [March–May (MAM)]. The skill of CFSv2 precipitation forecasts is low during summer [June–August (JJA)] and winter (DJF) over all of China because of low potential predictability of the East Asian summer monsoon and the East Asian winter monsoon for China. As expected, temperature predictive skill is much higher than precipitation predictive skill in all regions. As observed precipitation shows significant correlation with the Oceanic Niño index over western, southwestern, and central China, the authors found that CFSv2 precipitation forecasts generally show similar correlation pattern, suggesting that CFSv2 precipitation forecasts can capture ENSO signals. This evaluation suggests that using CFSv2 forecasts for seasonal hydrological prediction over China is promising and challenging.
Yang Lang; Aizhong Ye; Wei Gong; Chiyuan Miao; Zhenhua Di; Jing Xu; Yu Liu; Lifeng Luo; Qingyun Duan. Evaluating Skill of Seasonal Precipitation and Temperature Predictions of NCEP CFSv2 Forecasts over 17 Hydroclimatic Regions in China. Journal of Hydrometeorology 2014, 15, 1546 -1559.
AMA StyleYang Lang, Aizhong Ye, Wei Gong, Chiyuan Miao, Zhenhua Di, Jing Xu, Yu Liu, Lifeng Luo, Qingyun Duan. Evaluating Skill of Seasonal Precipitation and Temperature Predictions of NCEP CFSv2 Forecasts over 17 Hydroclimatic Regions in China. Journal of Hydrometeorology. 2014; 15 (4):1546-1559.
Chicago/Turabian StyleYang Lang; Aizhong Ye; Wei Gong; Chiyuan Miao; Zhenhua Di; Jing Xu; Yu Liu; Lifeng Luo; Qingyun Duan. 2014. "Evaluating Skill of Seasonal Precipitation and Temperature Predictions of NCEP CFSv2 Forecasts over 17 Hydroclimatic Regions in China." Journal of Hydrometeorology 15, no. 4: 1546-1559.
Agriculture has been identified as the major contributor of non-point source pollution of Hainan water resources. In this study, we coupled the Xinanjiang model and SWAT to a new model, EcoHAT, to assess the non-point source pollution in Hainan. The study site is located around the Songtao reservoir, Hainan Island, China, which is regarding as the most important water source system for Hainan. EcoHAT, including algorithms for the hydrological cycle, nutrient cycle, and plant growth cycle, simulated the non-point source pollution for the watershed in calculated grid cell units based on remote sensing data. Remote sensing data were used to interpret the spatial land surface information and derive the model parameters. Besides the remote sensing data, other essential databases such as the meteorological databases, soil chemical and physical databases, and plant nutrients databases were also used in this study. The EcoHAT model was calibrated and validated with 5 years of monitored water quantity and quality data in the Songtao reservoir watershed. The study results indicated that the EcoHAT model has simulated the hydrologic pollutant adequately. After the calibration and validation, the parameters were applied to simulate the nutrient and sediment transport in the Songtao watershed during 2003–2007. In the end, the effects of several specific scenarios of changes in the land covers or management practices on the local watershed nutrients transport were also simulated. The results revealed that: (1) the model has predicted the runoff volume within a range of acceptable accuracy which was reflected by a large coefficient of determination; (2) regression analysis between the observed and simulated values resulted in high values of coefficient of determination (R2) during the calibration and validation period. The high values of Nash–Sutcliffe simulation efficiency were achieved with a close agreement between the observed and simulated pollutants concentrations in the runoff. It also indicated that the model simulated the NO3–N, NH4+–N, and P concentrations in the runoff for the Songtao watershed with considerable accuracy; (3) the sediment loads, TN, and TP, experienced temporal and spatial variations, with strong correlations existing between the parameters and the land use as well as the precipitation; (4) the scenario analysis showed that with 40% fertilization reduction, 7.51% and 7.76% reduction on TN and TP loads respectively could be reached. Besides, the conservation measures are more effective in the study area to reduce the sediment loads than in other areas.
Shengtian Yang; Guotao Dong; Donghai Zheng; Honglin Xiao; Yunfei Gao; Yang Lang. Coupling Xinanjiang model and SWAT to simulate agricultural non-point source pollution in Songtao watershed of Hainan, China. Ecological Modelling 2011, 222, 3701 -3717.
AMA StyleShengtian Yang, Guotao Dong, Donghai Zheng, Honglin Xiao, Yunfei Gao, Yang Lang. Coupling Xinanjiang model and SWAT to simulate agricultural non-point source pollution in Songtao watershed of Hainan, China. Ecological Modelling. 2011; 222 (20-22):3701-3717.
Chicago/Turabian StyleShengtian Yang; Guotao Dong; Donghai Zheng; Honglin Xiao; Yunfei Gao; Yang Lang. 2011. "Coupling Xinanjiang model and SWAT to simulate agricultural non-point source pollution in Songtao watershed of Hainan, China." Ecological Modelling 222, no. 20-22: 3701-3717.