Estimation of baseflow nitrate loads by a recursive tracing source algorithm in a rainy agricultural watershed

Baseflow has become an important source of nitrate nonpoint source pollution in many intensive agricultural watersheds. Uncertainties in baseflow nutrient load separation are caused by the effects of hydrometeorological factors on both baseflow recession and baseflow nutrient load recession. These uncertainties have not been addressed well in the existing separating algorithms, which are based on simple baseflow rate-load relationships. In the present study, a recursive tracing source algorithm (RTSA) was developed based on a nonlinear reservoir algorithm and hydrometeorology-corrected baseflow nutrient load recession parameter. This approach was used to reduce the uncertainty of baseflow nitrate load estimation caused by variations in different load recessions under varying climate conditions. RTSA validation in a typical rainy agricultural watershed yielded Nash-Sutcliffe efficiency, root mean square error-observation standard deviation ratio, and R-2 values of 0.91, 0.30, and 0.91, respectively. The baseflow nitrate-nitrogen (NNO3-) loads from 2003 to 2012 in the Changle River watershed of eastern China were estimated with the RTSA. The results indicated that baseflow nitrate export accounted for 62.0% of the mean total annual NNO3- loads (18.0 kg/ha). The total baseflow NNO3- export was highest in spring (3.6 kg/ha), followed by summer (3.2 kg/ha), winter (2.3 kg/ha), and autumn (2.1 kg/ha). The contribution of baseflow to total nitrate in the stream decreased in the order of winter (69.88%) >spring (66.59%) >autumn (60.36%) >summer (54.04%). The monthly baseflow NNO3- loads and flow-weighted concentrations greatly increased during the research period (Mann-Kendall test, Z(s) > 2.56, p < .01). Without proper countermeasures, baseflow nitrate may represent a serious long-term risk for water surfaces in the future.