南方小流域典型水污染调查研究

某河流的污染状况在南方小流域中具有普遍性。本研究通过传统调查分析＋卫星遥感监测的技术手段,摸清掌握该河流各主要断面水质污染状况及流域内各类污染源的排放情况,在此基础上对水质污染程度、污染特征及污染成因进行了分析评价。调查表明,该流域水质污染程度与降水量密切相关,生活污水是水体污染的主要来源,治理的关键在于截污,同时要解决一定的补水问题。研究结果可为该流域环境综合整治提供必要的技术支持,研究方法可为今后开展类似小流域污染调查提供可行借鉴。     

九龙江流域氮的源汇时空模式与机理初探

综合运用定位监测、野外试验、模型模拟与GIS技术等手段和方法,定量研究了南亚热带地区九龙江流域和五川小流域氮的大气沉降、河流输送(地表径流)、淋失、反硝化和氨挥发等输入输出(源汇)时空模式与机理.结果表明,九龙江流域氮"源"以化肥与饲料输入为主(占总输入125.6kg·hm-2的86%).氮"汇"以氨挥发和河流输送为主(占总输出72.9kg·hm-2的82%).氮输入后50%以上进入大气和水环境,14.5%通过河流输送至河口与近海.大气氮沉降通量为14.9kg·hm-2,其中干沉降占34%,湿沉降占66%,形成1:2的干湿沉降结构;源于化肥施用与畜禽养殖引起的强烈氨挥发,氮沉降集中在春夏两季(占全年80%),且以铵态氮为主(39%以上).氮的径流输出及河流输送受人为氮输入与水文条件的双重控制,2004年九龙江向厦门海域输送无机氮11.5kg·hm-2,其中90%发生在春夏秋季(同期流量占全年89%);五川小流域总氯径流输出负荷为67.1kg·hm-2,其中85%发生在施肥量大、降雨集中的春夏两季(作物生长期);基流与降雨径流分别贡献25%和75%.总氮淋失负荷为27.5kg·hm-2,占总输入的9%;pH<5的酸性土壤带正电荷导致氮淋失以铵态氮为主(约占40%).九龙江流域反硝化通量为7.7 kg.hm-2,而氮挥发高达42.1kg·hm-2,氨挥发主要来自化肥施用与畜禽养殖(分别贡献50%和39%).减少春夏时期肥料氮的输入(养分管理),有效截留雨季的降雨径流(水文控制)是该流域氮素管理的关键.     

Maximizing Returns from Payments for Water‐Based Ecosystem Services: Incorporating Externality Effects of Land Management

Given the expansion of payments for water‐based ecosystem services (PWES) worldwide, two relevant issues are as follows: (1) determination of efficient allocations of payments among land managers, and (2) how this might change when paying one manager to implement a best management practice (BMP) to enhance an ecosystem service impacts the cost‐effectiveness of BMPs considered by other land managers not currently involved in PWES. Such externalities may be negative if diminishing returns dominate, or positive if mechanisms such as “social diffusion” dominate. We analyze how a planner should optimally allocate payments, depending on whether the expected externalities are negligible, negative, or positive. We employ (1) an optimal control model to gain insights on the problem’s dynamics, and (2) stochastic dynamic programming to determine optimal funding strategies using a specific application. The study contributes to the literature by identifying dynamically optimal PWES payment patterns, and illustrates how they should change when one accounts for externalities induced by the program. Because such impacts have not been addressed previously in a rigorous way, this treatment provides useful value added for PWES design and implementation.     

Modeling Flow,Nutrient, and Sediment Delivery from a Large International Watershed Using a Field‐Scale SWAT Model

A large international watershed, the St. Clair‐Detroit River System, containing both extensive urban and agricultural areas, was modeled using the Soil and Water Assessment Tool (SWAT) model. The watershed, located in southeastern Michigan, United States, and southwestern Ontario, Canada, encompasses the St. Clair, Clinton, Detroit (DT), Sydenham (SY), Upper, and Lower Thames subwatersheds. The SWAT input data and model resolution (i.e., hydrologic response units, HRUs), were established to mimic farm boundaries, the first time this has been done for a watershed of this size. The model was calibrated (2007–2015) and validated (2001–2006) with a mix of manual and automatic methods at six locations for flow and water quality at various time scales. The model was evaluated using Nash–Sutcliffe efficiency and percent bias and was used to explore major water quality issues. We showed the importance of allowing key parameters to vary among subwatersheds to improve goodness of fit, and the resulting parameters were consistent with subwatershed characteristics. Agricultural sources in the Thames and SY subwatersheds and point sources from DT subwatershed were major contributors of phosphorus. Spatial distribution of phosphorus yields at HRU and subbasin levels identified locations for potential management targeting for both point and nonpoint sources and revealed that in some subwatersheds nonpoint sources are dominated by urban sources.     

Evaluation of SWAT Impoundment Modeling Methods in Water and Sediment Simulations

Worldwide studies show 80%–90% of all sediments eroded from watersheds is trapped within river networks such as reservoirs, ponds, and wetlands. To represent the impact of impoundments on sediment routing in watershed modeling, Soil and Water Assessment Tool (SWAT) developers recommend to model reservoirs, ponds, and wetlands using impoundment tools (ITs). This study evaluates performance of SWAT ITs in the modeling of a small, agricultural watershed dominated by lakes and wetlands. The study demonstrates how to incorporate impoundments into the SWAT model, and discusses and evaluates involved parameters. The study then recommends an appropriate calibration sequence, i.e., landscape parameters calibration, followed by pond/wetlands calibration, then channel parameter calibrations, and lastly, reservoir parameter calibration. Results of this study demonstrate not following SWAT recommendation regarding modeling water land use as an impoundment depreciates SWAT performance, and may lead to misplaced calibration efforts and model over‐calibration. Further, the chosen method to model impoundments’ outflow significantly impacts sediment loads in the watershed, while streamflow simulation is not very sensitive. This study also allowed calculation of mass accumulation rates in modeled impoundments where the annual mass accumulation rate in wetlands (2.3 T/ha/yr) was 39% higher than mass accumulation rate in reservoirs (1.4 T/ha/yr).     

Does Wetland Location Matter When Managing Wetlands for Watershed‐Scale Flood and Drought Resilience?

Wetland protection and restoration strategies that are designed to promote hydrologic resilience do not incorporate the location of wetlands relative to the main stream network. This is primarily attributed to the lack of knowledge on the effects of wetland location on wetland hydrologic function (e.g., flood and drought mitigation). Here, we combined a watershed‐scale, surface–subsurface, fully distributed, physically based hydrologic model with historical, existing, and lost (drained) wetland maps in the Nose Creek watershed in the Prairie Pothole Region of North America to (1) estimate the hydrologic functions of lost wetlands and (2) estimate the hydrologic functions of wetlands located at different distances from the main stream network. Modeling results showed wetland loss altered streamflow, decreasing baseflow and increasing stream peakflow during the period of the precipitation events that led to major flooding in the watershed and downstream cities. In addition, we found that wetlands closer to the main stream network played a disproportionately important role in attenuating peakflow, while wetland location was not important for regulating baseflow. The findings of this study provide information for watershed managers that can help to prioritize wetland restoration efforts for flood or drought risk mitigation.     

国内流域生态系统土壤动物群落结构研究

土壤动物与其生存环境息息相关.土壤质地优良,食物种类丰富多样,则土壤动物个体数、类群数、多样性显著增多.目前,国内学者多侧重研究农田、森林、草原等生态系统土壤动物群落结构,而流域生态系统土壤动物研究较少涉及.对此,从土壤动物的水平分布、垂直分布和群落多样性三个方面来综述流域生态系统相关因素对土壤动物的影响具有代表性.在此基础上借鉴其他生态系统土壤动物相关研究并且结合流域生态系统自身特点拓展土壤动物研究方向,为研究区土地利用方向提供依据.研究流域中不同环境因素对土壤动物区系的影响,了解国内外土壤动物的研究和流域生态系统对土壤动物影响的进展.对于生态环境的恢复与重建具有重大意义.     

滇池流域入湖河流丰水期大型底栖动物群落特征及其与水环境因子的关系

研究了滇池流域入湖河流丰水期大型底栖动物群落特征及其与水环境因子的关系.在滇池流域29条入湖河流2009年7～8月进行大型底栖动物群落调查,并在2008年9月～2009年8月进行逐月17项水环境指标监测,目的是阐明滇池流域入湖河流丰水期大型底栖动物群落特征,识别影响大型底栖动物群落特征的主要水环境因子,比较大型底栖动物群落Shannon-Weaver多样性指数与水环境质量评价空间分布格局的特点.滇池流域入湖河流丰水期共检出大型底栖动物3门7科8属(其中环节动物门4科5属,软体动物门2科2属,节肢动物门1科1属),群落结构以环节动物门的水丝蚓属(耐污生物)为优势属;TN、 NH⁺₄-N、 TP和DO是影响大型底栖动物群落特征的主要水环境因子,分别为2.03～32.00、 0.34～26.66、 0.09～3.20、 0.10～6.80 mg/L;大型底栖动物群落Shannon-Weaver多样性指数与水环境质量评价的空间分布格局一致,均为流域北部入湖河流(王家堆渠、新运粮河、老运粮河、乌龙河、大观河、西坝河、船房河、采莲河、金家河、盘龙江、大青河、海河、六甲宝象河、小清河、五甲宝象河、虾坝河、老宝象河、新宝象河和马料河)污染状况严重程度>流域南部入湖河流(淤泥河、老柴河、白鱼河、茨巷河、东大河、中河和古城河)>流域东部入湖河流(洛龙河、捞鱼河和南冲河).     

煤层瓦斯压力探测新技术在寺河煤矿瓦斯防治中的应用

寺河矿属于高瓦斯矿井,是煤层气综合开发的主要矿井之一,其煤层气开发主要分两部分,即地面煤层气井抽采与井下煤层钻孔抽采.抽采环节中对煤矿瓦斯的防治是煤矿安全生产的重要前提.本文探讨了煤层瓦斯压力探测新技术--煤层瓦斯原位探测仪在该煤矿瓦斯防治中的具体运用,该探测技术可以高效地检验瓦斯抽放效果,从而为预防瓦斯突出提供科学依...     

基于DEM地表水文特征和汇水子流域的提取研究

文章以天津于桥水库上游流域为研究对象,以DEM(数字高程模型)为基础数据,对DEM生成方法、DEM精度、流域地形地貌特征及阈值的选取三个方面分别作了详细研究。试验结果表明,从于桥水库上游流域的水文特征所提取的河网分布与河流与实际分布情况基本吻合,淋河、沙河、黎河三大流域的面积误差范围分别为-3.44%、2.28%和7.63%。