稀疏数据下复杂流域的水质模拟:以赣江为例

以赣江流域为例,对稀疏数据条件下复杂流域水质模型的建立和参数识别进行了探讨．采用了结构相对比较简单的CSTR模型,将其参数划分为水文参数和水质参数,并分别进行识别．水文参数采用回归方法进行识别,对水文参数的扰动实验表明,水质对于水文参数在可能的取值范围内的变化不敏感,因此在随后的水质参数的识别中,将其固定在回归方法求出的最优值上．水质参数的确定则采用了模型方法和资料方法相结合的手段,综合考虑了赣江流域可获得的数据信息、文献资料中对于参数的经验取值和模拟者在大量数学建模中的经验,最终根据河流的不同类型确定了赣江流域的水质参数,并对模型进行了验证。     

Urea Fluctuations in Stream Baseflow across Land Cover Gradients and Seasons in a Coastal Plain River System

Urea‐N is a component of bioavailable dissolved organic nitrogen (DON) that contributes to coastal eutrophication. In this study, we assessed urea‐N in baseflow across land cover gradients and seasons in the Manokin River Basin on the Delmarva Peninsula. From March 2010 to June 2011, we conducted monthly sampling of 11 streams (4 tidal and 7 nontidal), 2 wastewater treatment plants, an agricultural drainage ditch, and groundwater underlying a cropped field. At each site, we measured urea‐N, DON, dissolved organic carbon (DOC), total dissolved nitrogen (TDN), NO₃^?‐N, and NH₄⁺‐N. In general, urea‐N comprised between 1% and 6% of TDN, with the highest urea‐N levels in drainage ditches (0.054 mg N/L) and wetland‐dominated streams (0.035–0.045 mg N/L). While urea‐N did not vary seasonally in tidal rivers, nontidal streams saw distinct urea‐N peaks in summer (0.038 mg N/L) that occurred several months after cropland fertilization in spring. Notably, the proportion of wetlands explained 78% of the variance in baseflow urea‐N levels across the Manokin watershed. In wetland‐dominated basins, we found urea‐N was positively related to water temperature and negatively related to DOC:DON ratios, indicating short‐term urea‐N dynamics at baseflow were more likely influenced by instream and wetland‐driven processes than by recent agricultural urea‐N inputs. Findings demonstrate important controls of wetlands on baseflow urea‐N concentrations in mixed land‐use basins.     

Developing Subseasonal to Seasonal Climate Forecast Products for Hydrology and Water Management

We describe a new effort to enhance climate forecast relevance and usability through the development of a system for evaluating and displaying real‐time subseasonal to seasonal (S2S) climate forecasts on a watershed scale. Water managers may not use climate forecasts to their full potential due to perceived low skill, mismatched spatial and temporal resolutions, or lack of knowledge or tools to ingest data. Most forecasts are disseminated as large‐domain maps or gridded datasets and may be systematically biased relative to watershed climatologies. Forecasts presented on a watershed scale allow water managers to view forecasts for their specific basins, thereby increasing the usability and relevance of climate forecasts. This paper describes the formulation of S2S climate forecast products based on the Climate Forecast System version 2 (CFSv2) and the North American Multi‐Model Ensemble (NMME). Forecast products include bi‐weekly CFSv2 forecasts, and monthly and seasonal NMME forecasts. Precipitation and temperature forecasts are aggregated spatially to a United States Geological Survey (USGS) hydrologic unit code 4 (HUC‐4) watershed scale. Forecast verification reveals appreciable skill in the first two bi‐weekly periods (Weeks 1–2 and 2–3) from CFSv2, and usable skill in NMME Month 1 forecast with varying skills at longer lead times dependent on the season. Application of a bias‐correction technique (quantile mapping) eliminates forecast bias in the CFSv2 reforecasts, without adding significantly to correlation skill.     

Adaptive Management and Climate Change Adaptation: Two Mutually Beneficial Areas of Practice

Adaptive management (AM) is a rigorous approach to implementing, monitoring, and evaluating actions, so as to learn and adjust those actions. Existing AM projects are at risk from climate change, and current AM guidance does not provide adequate methods to deal with this risk. Climate change adaptation (CCA) is an approach to plan and implement actions to reduce risks from climate variability and climate change, and to exploit beneficial opportunities. AM projects could be made more resilient to extreme climate events by applying the principles and procedures of CCA. To test this idea, we analyze the effects of extreme climatic events on five existing AM projects focused on ecosystem restoration and species recovery, in the Russian, Trinity, Okanagan, Platte, and Missouri River Basins. We examine these five case studies together to generate insights on how integrating CCA principles and practices into their design and implementation could improve their sustainability, despite significant technical and institutional challenges, particularly at larger scales. Although climate change brings substantial risks to AM projects, it may also provide opportunities, including creating new habitats, increasing the ability to quickly test flow‐habitat hypotheses, stimulating improvements in watershed management and water conservation, expanding the use of real‐time tools for flow management, and catalyzing creative application of CCA principles and procedures.     

土地利用转型背景下的乡村景观格局演变响应——基于草堂溪流域的样带分析

研究山区土地利用转型背景下乡村景观格局演变与转型,对山区现代化农业可持续发展和景观格局优化具有一定的指导意义。以三峡库区草堂溪流域为研究对象,利用ArcGIS 10.2和Fragstats 4.2软件,采用样带梯度分析结合景观格局分析方法,基于地形和社会经济因素,分别在河谷地区和山地丘陵地区设置5条样带,对研究区2000—2018年不同方向样带上景观格局梯度演变和驱动机制进行分析比较。结果表明：（1）景观类型整体变化幅度大致可分为较稳定型、逐年递减型和波动递增型,且景观转型的重点主要体现在耕地收缩、果园扩张和林地恢复性增长。（2）2000—2018年,研究区河谷地区的景观类型逐渐多样化,土地利用集聚,呈现空间集约化;而山地丘陵地区景观类型逐渐单一化,林地恢复。整体上由生产型景观格局转型成生态经济型、生态调节型为主的景观格局。（3）社会经济发展和政府政策导向等因素导致研究区内土地利用发生转型,在土地利用转型背景下的乡村景观格局也发生了相应的转变。研究结果可为类似山区土地资源的合理利用、生态治理和乡村振兴提供借鉴价值。     

太湖流域湖荡湿地水生植物的分布特征

2018年在太湖流域内的87个湖荡湿地调查中共发现64种水生植物,隶属于33科51属.总体来看,太湖流域水生植物种类、数量以及覆盖面积与历史上比较呈下降趋势.根据流域上不同地理区域分区（东、西、南、北4个）,分析太湖流域水生植物的分类和功能多样性,发现太湖流域四个区域的分类α多样性指数分别为4.33、5.58、5.01和3.46, β多样性指数分别为3.46、4.23、1.63和7.02,且北部和西部、北部和南部、东部和西部、东部和南部、西部和南部湖荡湿地的分类α多样性均有显著差异,同时太湖流域北部和东部、北部和西部、北部和南部、西部和南部湖荡湿地间的分类β多样性有显著差异.功能多样性方面,四个区域的β多样性指数分别为0.18、0.14、0.09、0.30,太湖流域各分区部分之间水生植物的功能β多样性均有显著差异.CCA结果表明环境变量对太湖流域湖荡水生植物组成的解释度为21.21%,水体总氮和总磷贡献最大,水体富营养化可能是影响太湖流域水生植物衰退的一个重要原因.     

SWAT模型在我国流域水环境模拟应用中的评估验证过程评价

模型评估验证是模型开发和应用过程中的重要环节,是管控模型应用于管理决策风险的重要手段.近年来,以SWAT模型为代表的流域水环境模型在我国流域水环境管理中得到广泛应用,但模型评估验证过程尚缺乏规范性指导.结合SWAT模型建模过程和模型评估验证的基本步骤,梳理总结SWAT模型评估验证过程的评价方法,针对确定研究或决策目标、获取输入数据、构建模型、模型率定验证、展示模型结果等5个阶段分别设计评价指标.筛选中国知网收录的2015—2017年发表的以SWAT模型为主题的428篇学术论文作为研究对象,评价现有研究的模型评估验证过程,分析我国SWAT模型评估验证的现状以及存在的问题和不足.评价结果表明,我国现有的SWAT模型应用研究总体比较重视模型评估验证过程,能够清晰定义研究区域、建模目标和期望产出,说明输入数据来源和质量以及模型概化、参数灵敏度分析、参数识别、不确定性分析的方法和结果,模型模拟效果较好.但是,针对SWAT模型评估验证方法的研究较少,在模型参数识别方面还存在参数选取未考虑研究区域特征、参数识别结果不合理等问题.建议在SWAT模型应用研究中关注参数灵敏度分析和参数识别,结合研究区域特征建立本地化应用数据库,加强模型评估验证方法研究.当模型应用于流域水环境管理决策时,应完整地开展模型评估验证,并分析模型不确定性对决策的影响.      

长江上游典型山地农业小流域浅层地下水硝态氮时空变异特征及影响因素

长江上游山区以浅层地下水作为主要供水水源,但其极易受到农业生产等活动所导致的硝态氮（NO₃^--N）污染.本文选取长江上游典型山区农业小流域作为研究对象,对土地利用与管理强度和水文地质条件等进行了野外调查,阐明其浅层地下水NO₃^--N时空变异特征并分析其影响因素.结果表明,研究小流域地下水中NO₃^--N质量浓度变化范围为0.40~12.51 mg ·L^-1,超标率近30%.受降雨和管理强度影响,丰水期降雨量和施肥量增加,土壤中氮素在降雨驱动下淋溶流失进入浅层地下水,呈现出丰水期NO₃^--N质量浓度（6.73 mg ·L^-1）高于枯水期NO₃^--N质量浓度（6.28 mg ·L^-1）的时间变异特征.在空间上,小流域地下水中NO₃^--N质量浓度呈现坡耕地和居民区集中分布的截留和大兴子流域中地下水NO₃^--N质量浓度（截留子流域：6.58mg ·L^-1;大兴子流域：6.34 mg ·L^-1）高于苏荣子流域（5.20 mg ·L^-1）的特征,主要由不同子流域地下水埋深和土地利用类型的空间分异特征导致.此外,浅层地下水NO₃^--N质量浓度与Cl^-、NH₄⁺-N、DOC和SO₄^2-质量浓度呈正相关,而与pH值呈负相关,表明地下水化学因子亦是其不可忽略的影响因素.因此,加强山地农业小流域浅层地下水NO₃^--N时空变异特征及其影响因素研究对防控山区农村浅层地下水硝态氮污染和保障饮用水安全十分必要.     

基于SWAT模型的泗河流域除草剂迁移模拟

泗河流域农药污染对南四湖湖泊乃至南水北调工程具有重要的影响,为了解流域农药迁移过程并对其采取治理措施,本研究在泗河流域通过实地采样与调查、室内实验分析、数据统计等手段,借助SWAT模型对流域除草剂阿特拉津及其代谢产物进行迁移模拟.结果表明,除草剂输出与流域径流量有很高的相关性,输出时间以7~8月份为主,输出量占全年的69%以上.且受河道长度、耕地分布等因素的影响,阿特拉津的输出量以东部上游地区和中部地区为主,流域出水口处的阿特拉津输出量居中等水平;阿特拉津代谢产物DEA和DIA的输出量的空间分布相似,以下游流域出口处和中部地区为主.本研究可为流域除草剂迁移治理提供理论支持.