Prasad V Krishna, Ortiz Ariel, Stinner Ben, McCartney David, Parker Jason, Hudgins Deana, Hoy Casey, Moore Richard
Agroecosystem Management Program, Ohio State University, USA.
Environ Monit Assess. 2005 Nov;110(1-3):141-69. doi: 10.1007/s10661-005-6688-9.
Ohio is typical among the Midwestern and Eastern United States with high levels of water pollutants, the main sources being from agriculture. In this study, we used a digital elevation model in conjunction with hydrological indices to determine the role of landscape complexity affecting the spatial and temporal variation in pollutant levels, in one of the most impaired headwater streams in Ohio. More than eighty five percent of the study area is dominated by agriculture. Spatial distribution of slope (S), altitude and wetness index along with other watershed parameters such as flow direction, flow accumulation, stream networks, flow stream orders and erosion index were used within a Geographic Information Systems framework to quantify variation in nitrate and phosphate loads to headwater streams. Stream monitoring data for nutrient loads were used to correlate the observed spatial and temporal patterns with hydrological parameters using multiple linear regressions. Results from the wetness index calculated from a digital elevation model suggested a range of 0.10-16.39, with more than 35% having values less than 4.0. A Revised Universal Soil Loss Equation (RUSLE) predicted soil loss in the range of 0.01-4.0 t/ha/yr. Nitrate nitrogen levels in the study area paralleled precipitation patterns over time, with higher nitrate levels corresponding to high precipitation. Atmospheric deposition through precipitation could explain approximately 35% of total nitrate levels observed in streams. Among the different topographic variables and hydrological indices, results from the step-wise multiple regression suggested the following best predictors, (1) elevation range and upstream flow length for nitrate, (2) flow direction and upstream flow length for ammonia-nitrogen and slope, and (3) elevation range for phosphate levels. Differences in the landscape models observed for nitrate, phosphate and ammonia-nitrogen in the surface waters were attributed partly to differences in the chemical activity and source strengths of the different forms of these nutrients through agricultural management practices. The results identify geomorphologic and landscape characteristics that influence pollutant levels in the study area.
俄亥俄州在美国中西部和东部地区中具有代表性，水体污染物含量很高，主要来源是农业。在本研究中，我们使用数字高程模型并结合水文指数，以确定景观复杂性对俄亥俄州一条受损最严重的源头溪流中污染物水平的时空变化的影响。研究区域超过85%由农业主导。在地理信息系统框架内，利用坡度（S）、海拔和湿度指数的空间分布以及其他流域参数，如水流方向、水流累积量、河网、水流等级和侵蚀指数，来量化源头溪流中硝酸盐和磷酸盐负荷的变化。利用多元线性回归，将营养负荷的溪流监测数据用于将观测到的时空模式与水文参数相关联。根据数字高程模型计算出的湿度指数结果显示范围为0.10 - 16.39，超过35%的值小于4.0。修订后的通用土壤流失方程（RUSLE）预测土壤流失量在0.01 - 4.0吨/公顷/年范围内。研究区域内硝酸盐氮水平随时间与降水模式平行，硝酸盐水平较高对应高降水量。通过降水的大气沉降可解释溪流中观测到的总硝酸盐水平的约35%。在不同的地形变量和水文指数中，逐步多元回归的结果表明以下最佳预测因子：（1）硝酸盐的海拔范围和上游水流长度，（2）氨氮和坡度的水流方向和上游水流长度，以及（3）磷酸盐水平的海拔范围。地表水中硝酸盐、磷酸盐和氨氮的景观模型差异部分归因于这些营养物质不同形式通过农业管理实践在化学活性和源强度方面的差异。研究结果确定了影响研究区域污染物水平的地貌和景观特征。
