A modified NRCS-CN method for eliminating abrupt runoff changes induced by the categorical antecedent moisture conditions

The popular Natural Resources Conservation Service Curve Number (NRCS-CN) (earlier known as Soil Conservation Service Curve Number (SCS-CN) method of rainfall-runoff modeling has often faced the criticism of exhibiting quantum jumps in runoff computations because of the sudden jumps appearing in CN-values derived from NEH-4 tables for three antecedent moisture conditions (AMC), viz., AMC-I, AMC-II, and AMC-III valid for dry, normal, and wet conditions, respectively. The variability of antecedent soil moisture within an AMC category is responsible for the abrupt jump and other deficiencies in the CN method for runoff estimation. This paper suggests a novel procedure to account for the antecedent moisture (M), preventing quantum jumps and eliminating deficiencies in determination of CN and, in turn, estimation of direct runoff. Its validity was verified utilizing the observed rainfall (P)-runoff (Q) events from 36 US watersheds, four sub-catchments of the Godavari basin, and small agricultural plots at Roorkee, India. The performance of the proposed model (M5) for runoff prediction was compared with the existing NRCS-CN (M1), Mishra and Singh (2002) (M2), Singh et al. (2015) (M3), and Verma et al. (2021) (M4) model using various performance indices. Using the CNs derived from observed events, model M5 was seen to have performed better than M1-M4 in terms of Nash Sutcliffe Efficiency (NSE), Root Mean Square Error (RMSE), and Percent Bias (PBIAS) for the data of US watersheds, and CN-P correlation improved as the coefficient of determination (R²) enhanced. Similarly, using the RS & GIS-based CNs on natural watersheds of the Godavari basin and considering AMC-I, the performance of M5 was again better than M1-M4 in terms of RMSE, Mean Bias Error (mBIAS), Mean Absolute Error (MAE), and Normalized-Nash Sutcliffe Efficiency (NNSE). Interestingly, there existed a significant (p < 0.05) relationship between the in-situ water content (w) measured for the experimental plots of Roorkee and the model input variable antecedent moisture (M), offering a physical touch to the conceptual model.     

怒江流域云南区段降雨时空变化分析

利用怒江流域云南区段及其毗邻地区77个站点1980~2009年雨量资料,以及怒江干流道街坝站1957~2015年和南汀河支流姑老河站1960~2015年降雨、径流资料,研究该流域降雨区域分异特征及变化趋势。结果表明:怒江流域云南区段多年平均面雨量达1 336.7 mm,远大于全省平均降雨量;怒江干流云南区段上游贡山地区和下游支流苏帕河流域,以及支流南汀河流域下游多年平均降雨量达1 500 mm以上,而干流的中游怒江坝干热河谷不足1 000 mm,降水空间分布格局与西南季风强弱及纵向岭谷地形密切相关;怒江流域云南区段降水量呈下降趋势,与怒江上游西藏区域相反,支流南汀河流域面雨量减少速率(56 mm/10 a)大于怒江干流云南区段(29 mm/10 a);怒江流域云南区段夏季降水占全年降水量的51%,怒江干流云南区段与支流南汀河流域降水量差异主要表现在夏季;怒江流域云南区段春季降水量呈增加趋势,夏季、秋季和冬季降水量呈减少趋势;怒江干流道街坝站、支流南汀河姑老河站降雨量长周期分别为36 a和33 a左右,从降雨变化长周期来看,2002年以来的少水期将持续到2018~2020年。怒江干流云南区段降水量变化的Hurst指数值为0.69,表明其未来趋势与过去一致,仍将延续降水量减少的变化趋势。     

新形势下做好长江流域水土保持监测工作的思考

基于长江流域水土保持的监测调研工作,结合我国当前社会、经济发展新形势的总体要求,针对开展好流域水土保持监测工作这一课题,从完善水土流失动态监测工作、构建应急监测体系、强化机构职能定位、推进先进技术和设施设备应用、优化流域网络体系顶层设计、探究水土流失监测综合性指标设置等方面开展了一些有益的探索,以利于更好地开展新形势下的水土保持监测工作。     

水土流失遥感调查中植被覆盖度因子提取研究

植被覆盖度是水土流失的主要影响因子,而利用遥感影像判读可以快速有效地获得大范围的植被覆盖度。简要介绍了水土流失遥感调查中植被覆盖度因子提取的方法。基于遥感与实地野外调查数据,采用MODIS时序数据融合高分遥感数据的方法,以湖南省蓝山县为试验区,对植被覆盖度因子进行了提取。结果表明,该方法提取植被覆盖度因子的结果较理想。提出的植被覆盖度因子提取方法对今后的水土流失动态监测研究具有重要的理论意义和实践意义。     

大凌河流域初始水权分配实践评价

结合大凌河流域初始水权分配实践,从分配范围、分配机制、分配原则、分配模型等方面,对现有研究成果进行全面梳理。在此基础上,结合大凌河流域特点,借鉴我国流域水资源配置评价指标体系,从公平性、效率性、可持续发展、政府宏观调控4个维度,设计了一套水权分配评价指标体系,进而构建了初始水权分配实践效果的耦合评价模型,并应用于大凌河流域。结果表明,在政府和大凌河流域水行政管理部门的宏观调控作用下,通过加强大凌河流域所在区域的政治民主协商,流域内各区域水权分配结果充分体现了与其社会经济发展之间的匹配性,且各区域之间的耦合协同发展效度均达到较高水平。     

Estimation of future water resources of Xiangjiang River Basin with VIC model under multiple climate scenarios

Variation trends of water resources in the Xiangjiang River Basin over the coming decades have been investigated using the variable infiltration capacity(VIC) model and 14 general circulation models'(GCMs') projections under the representative concentration pathway(RCP4.5) scenario. Results show that the Xiangjiang River Basin will probably experience temperature rises during the period from 2021 to2050, with precipitation decrease in the 2020 s and increase in the 2030 s. The VIC model performs well for monthly discharge simulations with better performance for hydrometric stations on the main stream of the Xiangjiang River than for tributary catchments. The simulated annual discharges are significantly correlated to the recorded annual discharges for all the eight selected target stations. The Xiangjiang River Basin may experience water shortages induced by climate change. Annual water resources of the Xiangjiang River Basin over the period from 2021 to 2050 are projected to decrease by 2.76% on average within the range from-7.81% to 7.40%. It is essential to consider the potential impact of climate change on water resources in future planning for sustainable utilization of water resources.     

基于SOM和 PCA的闽江流域地表水水质综合评价

根据2014年1月至2015年11月闽江流域19个断面的水质月均值监测数据,采用自组织特征映射网络(SOM)和主成分分析(PCA)法研究了闽江流域水质时空变化特征,并用水质指数对闽江水质进行了综合评价。SOM分析将水质样本分为3个空间群组,其中水质变化周期分2个阶段:4—11月、12月至次年3月。PCA法分析表明,春冬季沙溪和富屯溪支流以及闽江下游福州城区河段营养盐水平偏高,在春夏季上游部分河段和下游闽江口有机污染水平偏高。水质指数评价结果显示,闽江流域整体水质较好,其中三大子流域及闽江下游水质评价从优至劣顺序为:富屯溪流域、建溪流域、沙溪流域、闽江下游流域。     

阳宗海流域冬季典型农村污水污染特征及水质评价

为揭示阳宗海流域不同类型农村污水各污染指标的影响因素和分布特征,选取云南省的海晏村(传统型)和大营村(集镇型)的污水进行水样采集,通过方差分析研究其不同污染指标的浓度变化以及不同类型农村污水水质的差异性,并运用主成分分析法对污水进行水质评价。结果表明:阳宗海流域海晏村污水中的TN、COD均明显超过了城镇污水处理厂污染物排放二级标准,大营村的TP、TN、COD均明显超过了二级标准;pH值与氧化还原电位都是农村污水中污染物浓度的重要影响因子;两村的水质具有显著的差异性;传统型农村的水质比集镇型农村的要好,说明对阳宗海流域造成污染风险更大的是集镇型农村污水。     

海河流域典型河流沉积物粒度特征及分布规律

为了解海河流域典型河流沉积物粒度参数空间分布规律,对海河典型河流的表层沉积物进行采集并利用Mstersize2000激光粒度仪进行粒度分析。结果表明,海河典型河流沉积物平均粒径差异显著,分选状况总体较差;山区水系与平原水系相比,沉积物平均粒径减小,呈集中分布,水动力减弱;山区自北向南变化明显,水系沉积物平均粒径减小,分布集中化,分选状况变差,由极正偏-正偏,向对称方向变化,峰度向平坦方向趋近,水动力减弱,符合粒径自然变化规律;平原区水系沉积物粒径参数表明南北变化不明显,因受较多支流、河流-湖泊-洼淀湿地等复杂系统和人为干扰强烈的影响,南北无显著变化规律。总的来说,海河流域河流受人为干扰强度较大,河流沉积物源较为复杂,栖息地物理完整性差。     

灞河流域气候因子对水沙变化的影响

利用灞河流域蓝田气象站和马渡王水文站1960—2012年的气象、水文实测资料,分析灞河流域气候及水沙变化规律,同时运用相关性分析、灰色关联分析、多元线性回归模型等多种方法探讨了该流域水沙变化与气候变化的关系。结果表明:灞河流域降雨量、蒸发量、径流量和输沙量皆呈显著下降趋势,而气温呈上升趋势;降雨量与水沙都有重要的相关关系,1960—1990年影响径流量的气候因子敏感度由大至小依次为降雨量、气温、蒸发量,而1991—2012年则为降雨量、蒸发量、气温,当气温和蒸发量不变时,降雨量每增加1 mm,两阶段的年径流量分别增加0.14亿m3和0.08亿m3;1960—2012年影响输沙量的气候因子敏感度由大至小依次为降雨量、气温、蒸发量,当气温和蒸发量不变时,降雨量每增加1 mm,年输沙量增加0.668万t。