基于SWAT模型的太湖西北部30a来氮磷的输出特征

随着工业的迅速发展和农业生产方式的转变,使得太湖富营养化现象日趋严重,对流域内生态环境构成极大的威胁。以研究区6个时期土地利用数据和30a逐日降雨数据为模型的主要输入变量,利用SWAT模型分别对研究区内6个不同时期营养盐输出进行模拟研究,得到研究区内30a时间尺度(1984~2013年)营养盐输出情况。根据模型输出结果探究研究区内营养盐输出与降雨量、径流量以及土地利用变化的关系。以2009~2013年宜兴站径流数据和水质数据作为模型的率定和验证数据,总氮(TN)和总磷(TP)在模型率定期确定系数R2为0.76和0.92,纳什效率系数Ens为0.76和0.79,验证期确定系数R2为0.66和0.95,纳什效率系数Ens为0.6和0.54,模拟结果较好。结果表明:营养盐输出与降雨在时间是呈现较强的相关性,但是在空间上降雨与营养盐输出相关性不明显;土地利用类型与营养盐输出密切相关,耕地和建设用地是研究区主要的营养盐输出源,土地利用类型空间分布与TN、TP空间分布相关系数分别为0.74和0.73。将为太湖流域非点源污染控制和治理提供理论支撑及数据基础。     

重金属面源污染模拟及其不确定性分析——以湘江株洲段镉污染为例

基于我国现有的环境数据条件,将复杂的分布式水文、泥沙模拟（采用SWAT模型）与简化的输出系数方法进行有效整合,建立了可实际应用的流域重金属面源污染负荷模型。应用所建模型对湘江株洲段所属汇水区域的镉面源污染进行动态模拟,并进行不确定性分析。模拟结果显示,研究区镉面源污染负荷总量约为2 435kg/a,受污染土壤对于镉面源负荷量贡献率达657%。土壤侵蚀过程对研究区的面源污染过程起控制作用,因此,土壤污染防治和水土保持是该区域镉面源污染管理的关键。土壤污染程度高且易侵蚀的地区（如清水塘工业区及其邻近地区）是防治的重点区域,而每年的4月和5月是防治的重点时期。不确定性分析结果显示,超过50%的概率下,研究区镉面源负荷总量处于2 000～3 000 kg/a这一范围,但仍有可能低于1 500 kg/a或高于4 000 kg/a     

基于SWAT模型的金属矿区雨水径流中锰污染负荷分析

通过改进林玉环的汞迁移转化一维模型,并在一定的基本假设下,得到了锰的一维迁移转化动力学模型,利用Fortran语言编程嵌入SWAT模型源程序,在参数敏感性分析后进行改进模型的率定与校正以及敏感性参数的调节分析后,得到金属矿区雨水径流中锰污染负荷分析的SWAT改进模型。运行结果表明,SWAT改进模型能够对雨水径流中锰污染负荷进行较高精度的模拟,其中回归系数(r2)、Nash-Sutcliffe效率系数(Ens)分别为0.88、0.91,符合回归系数和Nash-Sutcliffe系数的评价标准(r2≥0.6,Ens≥0.5),获得锰矿区的污染关键区,与实际矿区污染相符合,为锰矿区及其他金属矿区雨水径流中重金属污染的评测和控制、治理与修复等提供科学依据。     

Validation of Satellite Precipitation Adjustment Methodology From Seven Basins in the Continental United States1

Tobin, Kenneth J. and Marvin E. Bennett, 2012. Validation of Satellite Precipitation Adjustment Methodology From Seven Basins in the Continental United States. Journal of the American Water Resources Association (JAWRA) 48(2): 221‐234. DOI: 10.1111/j.1752‐1688.2011.00604.x Abstract: The precipitation science community has expressed concern regarding the ability of satellite‐based precipitation products to accurately capture rainfall values over land. There has been some work that has focused on addressing the deficiencies of satellite precipitation products, particularly on the adjustment of bias. This article outlines a methodology that adjusts satellite products utilizing ground‐based precipitation data. The approach is not a simple bias adjustment, but is a three‐step process that transforms a satellite product based on a ground‐based precipitation product (NEXRAD‐derived Multisensor Precipitation Estimator [MPE] product or rain‐gauge data). The developed methodology was successfully applied to seven moderate‐to‐large sized watersheds from continental United States (CONUS) and northern Mexico over a spectrum of climatic regimes ranging from dry to humid settings. Methodology validation is based on comparison of observed and simulated streamflow generated with SWAT (Soil and Water Assessment Tool) model using unadjusted and adjusted precipitation products as input. Streamflow comparison is based on mass balance error and Nash‐Sutcliffe efficiency coefficient. Finally, the contribution of how adjustment to correct misses, false alarms, and bias impacts adjusted datasets and the potential impact that the adjustment methodology can have on hydrological applications such as water resource monitoring and flood prediction are explored.     

Assessment of Future Climate Change Impact on Water Quality of Chungju Lake,South Korea,Using WASP Coupled with SWAT

This study is to evaluate the future potential impact of climate change on the water quality of Chungju Lake using the Water Quality Analysis Simulation Program (WASP). The lake has a storage capacity of 2.75 Gm³, maximum water surface of 65.7 km², and forest‐dominant watershed of 6,642 km². The impact on the lake from the watershed was evaluated by the Soil and Water Assessment Tool (SWAT). The WASP and SWAT were calibrated and validated using the monthly water temperatures from 1998 to 2003, lake water quality data (dissolved oxygen, total nitrogen [T‐N], total phosphorus [T‐P], and chlorophyll‐a [chl‐a]) and daily dam inflow, and monthly stream water quality (sediment, T‐N, and T‐P) data. For the future climate change scenario, the MIROC3.2 HiRes A1B was downscaled for 2020s, 2050s, and 2080s using the Change Factor statistical method. The 2080s temperature and precipitation showed an increase of +4.8°C and +34.4%, respectively, based on a 2000 baseline. For the 2080s watershed T‐N and T‐P loads of up to +87.3 and +19.6%, the 2080s lake T‐N and T‐P concentrations were projected to be 4.00 and 0.030 mg/l from 2.60 and 0.016 mg/l in 2000, respectively. The 2080s chl‐a concentration in the epilimnion and the maximum were 13.97 and 52.45 μg/l compared to 8.64 and 33.48 μg/l in 2000, respectively. The results show that the Chungju Lake will change from its mesotrophic state of 2000 to a eutrophic state by T‐P in the 2020s and by chl‐a in the 2080s. Editor's note: This paper is part of a featured series on Korean Hydrology. The series addresses the need for a new paradigm of river and watershed management for Korea due to climate and land use changes.     

Climate Change and Agricultural Development: Adapting Polish Agriculture to Reduce Future Nutrient Loads in a Coastal Watershed

Currently, there is a major concern about the future of nutrient loads discharged into the Baltic Sea from Polish rivers because they are main contributors to its eutrophication. To date, no watershed-scale studies have properly addressed this issue. This paper fills this gap by using a scenario-modeling framework applied in the Reda watershed, a small (482 km²) agricultural coastal area in northern Poland. We used the SWAT model to quantify the effects of future climate, land cover, and management changes under multiple scenarios up to the 2050s. The combined effect of climate and land use change on N-NO₃ and P-PO₄ loads is an increase by 20–60 and 24–31 %, respectively, depending on the intensity of future agricultural usage. Using a scenario that assumes a major shift toward a more intensive agriculture following the Danish model would bring significantly higher crop yields but cause a great deterioration of water quality. Using vegetative cover in winter and spring (VC) would be a very efficient way to reduce future P-PO₄ loads so that they are lower than levels observed at present. However, even the best combination of measures (VC, buffer zones, reduced fertilization, and constructed wetlands) would not help to remediate heavily increased N-NO₃ loads due to climate change and agricultural intensification.     

降雨时间分辨率对SWAT水文模拟的影响

分别基于13个雨量站的日降雨量和小时降雨量,在率水流域建立SWAT（Soil and Water Assessment Tool）水文模型,对基于不同降雨时间分辨率的SWAT模型进行独立的参数率定和验证,在此基础上分析降雨时间分辨率对率水流域SWAT模拟性能的影响.结果表明,降雨输入的时间分辨率对率水流域SWAT模型的水文模拟性能有显著影响.小时降雨输入SWAT模型的纳什系数（NSE）和决定系数（R²）在率定期分别为0.89、0.90,在验证期分别为0.86、0.88,均显著高于日降雨输入SWAT模型对应的模型评价统计量值.究其原因,小时降雨输入SWAT模型对于峰值流量的模拟要明显优于日降雨输入SWAT模型.建议应在不同气候与自然环境条件的流域内开展类似的降雨时间分辨率对水文模型模拟性能的影响研究.     

京津冀生态屏障区景观格局变化及其对水源涵养的影响

选择京津冀重要水源涵养区--滦河,潮河和白河流域,通过分析2010与2015年土地利用的变化及景观格局演变,研究该研究区的土地利用转移与景观格局特征的时空分布与变化特点;运用SWAT水文模型模拟分析该时段内研究区的水源涵养量分布与变化;并和降水分布特征进行对比分析.利用偏相关分析法,重点分析了景观格局的变化对水源涵养量的影响.结果表明水源涵养量与PLAND指数相关性为-0.846;与NP指数相关性为-0.635;与LPI指数相关性为0.468;与LSI指数相关性为0.523;与COHESION指数显著相关,相关性为0.918.景观格局和水源涵养量存在以下关系：①景观多样性越高,聚集程度越高,景观形状越复杂,越趋向不规则且优势度越高,越有利于水源涵养;②景观的破碎化程度越高,水源涵养量越低.     

基于分布式水文模型的徒骇河河流生态需水量预测研究

流域的气候、植被和土地利用方式等变化对河流生态需水具有决定性作用.以徒骇河刘桥闸控制区域为例,定量估算了现状河流生态需水量,并以流域分布式水文模型SWAT为工具,预测了各水平年不同保证率下不同生态恢复目标的各月河流生态需水量.结果表明,现状年河流生态需水量为81.09×106m3,其中自净需水是河流生态需水的主要组成部分,因此保证徒骇河生态需水的关键是污染源的治理.拟通过减少点源排放、减少灌溉水量、减少农药化肥施用及增加干流两边的缓冲带等措施,使污染源得到进一步控制,预测2015年、2020年、2030年河流生态需水量不断减少,丰水年份河流天然径流量能满足河流生态需水要求,但枯水年份仍存在较为严重的生态缺水问题,需要对河流进行生态补水.     

基于土地利用的厦门市生态水文调节服务评估

生态系统具有重要的水文调节服务,通过对降水截留、过滤、吸收等手段,能够起到削峰补枯、缓和地表径流、增加地下径流的作用.以厦门市为研究对象,采用SWAT（soil and water assessment tool）水文模型评估了2015年厦门市生态系统水文调节服务;以土地利用为驱动变量,对2010年厦门市生态水文调节服务进行了评估,分析近年来厦门市加快城市绿化和海绵城市建设背景下土地利用变化对厦门市生态水文调节服务的影响.结果表明:2015年厦门市生态水文调节量为5.43×108 m3,时空差异较为明显.从各辖区来看,位于北部山区的同安区调节量（2.91×108 m3）最高,单位面积生态水文调节量（44.61×104 m3/km2）亦最高;建成区内的湖里区调节量和单位面积调节量均最低.从年内分布来看,5—9月生态水文调节量占全年的84.96%,生态系统有效减缓了径流输出,起到削峰的作用;10—11月,生态系统通过拦蓄降水,将其转换为土壤水和地下径流,使得该时期的实际径流大于潜在径流,起到补充枯期径流的作用.在相同气象条件下,2010年和2015年厦门市生态水文调节能力的空间分布整体一致,相比于2010年,2015年厦门市生态水文调节能力有明显提高,各辖区调节量和单位面积调节量均有所增加.研究显示,厦门市生态水文调节服务受气象条件和土地利用的共同作用,降水量越大,生态水文调节量越大,并且北部高植被覆盖区的生态水文调节能力高于南部建成区.