Most of China's wetland areas are located in Sanjiang Plain (Three River Basin). It's area has 207×104 hm2 of wet and low-lying farmland, of which 59% is cropped. During 1970s and 1980s, the Chinese gover...Most of China's wetland areas are located in Sanjiang Plain (Three River Basin). It's area has 207×104 hm2 of wet and low-lying farmland, of which 59% is cropped. During 1970s and 1980s, the Chinese government organized intensive scientific research into potential changes to existing natural resources conditions for this farmland. The aim was to make the water resources regime beneficial to crop production. Arterial drainage, field drainage and appropriate sub-soil treatments were suggested. The Four Waters converting mechanism and the estimation of the Four Water converting amount in wet and low-lying farmland were discussed in the paper.展开更多
Most of China's wetland areas are located in the Sanjiang Plain.This area has 207×10 4 hm 2 of wet and low lying farmland,of which 59% is cropped.During the 1970s and 1980s,the Chinese government organize...Most of China's wetland areas are located in the Sanjiang Plain.This area has 207×10 4 hm 2 of wet and low lying farmland,of which 59% is cropped.During the 1970s and 1980s,the Chinese government organized intensive scientific research into potential changes to existing natural resources conditions for these farmlands.The aim was to change the water resources regime to one that was beneficial to crop production.Arterial drainage,field drainage and appropriate sub soil treatments were required.The relation between plant products industry and the Four Waters distribution,also the main measures of the Four Waters converting in wet and low lying farmland were discussed in the paper.展开更多
利用2017—2022年汛期(4—9月)湖南省1912个地面观测站降水实况和欧洲中心中期天气预报一体化预报系统(European Centre for Medium-Range Weather Forecasts-Integrated Forecasting System,ECMWFIFS)最优因子集,在一种U型语义分割网络...利用2017—2022年汛期(4—9月)湖南省1912个地面观测站降水实况和欧洲中心中期天气预报一体化预报系统(European Centre for Medium-Range Weather Forecasts-Integrated Forecasting System,ECMWFIFS)最优因子集,在一种U型语义分割网络(U-Net模型)基础上结合残差网络和注意力机制网络,构建了逐时降水订正预报模型(SARU),并将模型2023年汛期预报结果与最优TS评分订正法(OTS)以及中国气象局上海数值预报模式系统(CMA-SH9)进行对比。(1)SARU模型整体的晴雨准确率、相关系数(COR)、平均绝对误差(MAE)、偏差(BIAS)分别为0.87、0.17、0.35、0.73,皆优于OTS模型和CMA-SH9模式,尤其在湘中地区,SARU模型对于预报趋势和量级有明显优势,其空报率和漏报率比例基本相当,OTS模型漏报率远超空报率,CMA-SH9则正好相反。(2)SARU模型对于分级降水频次的预报较OTS模型和CMA-SH9模式更接近实况,尤其是20mm以上降水频次,预报偏少27.29%,远优于OTS模型预报的偏少85.54%和CMA-SH9模式的偏多95.50%。(3)对于小时雨量[5,10)、[10,20)和≥20 mm这三个级别的降水,SARU模型TS、命中率(POD)、空报率(FAR)、漏报率(MAR)皆最优,尤其短时强降水,SARU模型较CMA-SH9模式有明显优势,而OTS模型的预报能力则明显不足。(4)湖南存在明显的夜雨特征,夜间时段(北京时02—08时)短时强降水频次明显高于其他时段。SARU模型很好地把握了夜间短时强降水特征,TS在夜间明显升高,尤其是在北京时05时达到峰值(0.07左右),明显优于CMA-SH9模式和OTS模型。展开更多
为探究21世纪10年代以来海河流域年降水量增多的原因,基于实测降水、旱涝等级以及再分析资料等多源数据,利用小波分析、水汽平衡、WRF模型(Weather Research and Forecast Model)数值模拟等方法,分析了海河流域降水丰枯周期特征、外来...为探究21世纪10年代以来海河流域年降水量增多的原因,基于实测降水、旱涝等级以及再分析资料等多源数据,利用小波分析、水汽平衡、WRF模型(Weather Research and Forecast Model)数值模拟等方法,分析了海河流域降水丰枯周期特征、外来水汽收支变化以及外调水对降水的影响。结果表明:海河流域存在33 a的降水周期,最新一轮周期的丰水段从2013年开始,年降水量较上一轮周期枯水段(1997—2012年)增多80.0 mm;2013—2022年较1997—2012年夏季西北通道、东南通道和西南通道水汽输入分别增加0.35、0.29和0.26 kg/(m·s),导致年降水量增加76.7 mm,占降水增量的95.9%;2015—2022年南水北调中线一期工程输入水量导致流域年降水量增加3.3 mm,占降水增量的4.1%。本研究对掌握海河流域降水演变规律、保障流域长期水资源安全具有重要意义。展开更多
文摘Most of China's wetland areas are located in Sanjiang Plain (Three River Basin). It's area has 207×104 hm2 of wet and low-lying farmland, of which 59% is cropped. During 1970s and 1980s, the Chinese government organized intensive scientific research into potential changes to existing natural resources conditions for this farmland. The aim was to make the water resources regime beneficial to crop production. Arterial drainage, field drainage and appropriate sub-soil treatments were suggested. The Four Waters converting mechanism and the estimation of the Four Water converting amount in wet and low-lying farmland were discussed in the paper.
文摘Most of China's wetland areas are located in the Sanjiang Plain.This area has 207×10 4 hm 2 of wet and low lying farmland,of which 59% is cropped.During the 1970s and 1980s,the Chinese government organized intensive scientific research into potential changes to existing natural resources conditions for these farmlands.The aim was to change the water resources regime to one that was beneficial to crop production.Arterial drainage,field drainage and appropriate sub soil treatments were required.The relation between plant products industry and the Four Waters distribution,also the main measures of the Four Waters converting in wet and low lying farmland were discussed in the paper.
文摘利用2017—2022年汛期(4—9月)湖南省1912个地面观测站降水实况和欧洲中心中期天气预报一体化预报系统(European Centre for Medium-Range Weather Forecasts-Integrated Forecasting System,ECMWFIFS)最优因子集,在一种U型语义分割网络(U-Net模型)基础上结合残差网络和注意力机制网络,构建了逐时降水订正预报模型(SARU),并将模型2023年汛期预报结果与最优TS评分订正法(OTS)以及中国气象局上海数值预报模式系统(CMA-SH9)进行对比。(1)SARU模型整体的晴雨准确率、相关系数(COR)、平均绝对误差(MAE)、偏差(BIAS)分别为0.87、0.17、0.35、0.73,皆优于OTS模型和CMA-SH9模式,尤其在湘中地区,SARU模型对于预报趋势和量级有明显优势,其空报率和漏报率比例基本相当,OTS模型漏报率远超空报率,CMA-SH9则正好相反。(2)SARU模型对于分级降水频次的预报较OTS模型和CMA-SH9模式更接近实况,尤其是20mm以上降水频次,预报偏少27.29%,远优于OTS模型预报的偏少85.54%和CMA-SH9模式的偏多95.50%。(3)对于小时雨量[5,10)、[10,20)和≥20 mm这三个级别的降水,SARU模型TS、命中率(POD)、空报率(FAR)、漏报率(MAR)皆最优,尤其短时强降水,SARU模型较CMA-SH9模式有明显优势,而OTS模型的预报能力则明显不足。(4)湖南存在明显的夜雨特征,夜间时段(北京时02—08时)短时强降水频次明显高于其他时段。SARU模型很好地把握了夜间短时强降水特征,TS在夜间明显升高,尤其是在北京时05时达到峰值(0.07左右),明显优于CMA-SH9模式和OTS模型。
文摘为探究21世纪10年代以来海河流域年降水量增多的原因,基于实测降水、旱涝等级以及再分析资料等多源数据,利用小波分析、水汽平衡、WRF模型(Weather Research and Forecast Model)数值模拟等方法,分析了海河流域降水丰枯周期特征、外来水汽收支变化以及外调水对降水的影响。结果表明:海河流域存在33 a的降水周期,最新一轮周期的丰水段从2013年开始,年降水量较上一轮周期枯水段(1997—2012年)增多80.0 mm;2013—2022年较1997—2012年夏季西北通道、东南通道和西南通道水汽输入分别增加0.35、0.29和0.26 kg/(m·s),导致年降水量增加76.7 mm,占降水增量的95.9%;2015—2022年南水北调中线一期工程输入水量导致流域年降水量增加3.3 mm,占降水增量的4.1%。本研究对掌握海河流域降水演变规律、保障流域长期水资源安全具有重要意义。
