Libing Zhang

Inference of reference conditions for nutrient concentrations of Chaohu Lake based on model extrapolation

In the mid-eastern China, there are few or no lakes which are in the absence of anthropogenic disturbances, or their sediments remain undisturbed. As a result, the reference lakes distribution and paleolimnological reconstruction approaches usually are inappropriate to estimate lake reference conditions for nutrients. This yields the necessity of using the extrapolation methods to estimate the lake reference conditions for nutrients within those regions. The lake reference conditions for nutrients could be inferred inversely from the law of mass conservation, current lake nutrient concentration, and the loadings from watershed. Considering the scarcity of hydrological and water quality data associated with lakes and watersheds in China, as well as the low requirement of the watershed nutrient loadings models for these data, the soil conservation service (SCS) distributed hydrological model and the universal soil loss equation (USLE) were applied. The SCS model simulates the runoff process of the watershed, thereby calculating dissolved nutrients annually. The USLE estimates the soil erosion and particulate nutrients annually in a watershed. Then, with the loadings from atmospheric deposition and point source, the previous annual average nutrient concentrations could be acquired given the current nutrient concentrations in a lake. Therefore, the nutrient reference conditions minimally impacted by human activities could be estimated. Based on the proposed model, the reference conditions for total nitrogen and total phosphorus of Chaohu Lake, Anhui Province, China are 0.031 mg/L and 0.640 mg/L, respectively. The proposed reference conditions estimation model is of clear physical concept, and less data required. Thus, the proposed approach can be used in other lakes with similar circumstances.

Application of Interpolation Model Based on Genetic Algorithm to Comprehensive Evaluation of Flood Disaster Loss

Precise comprehensive evaluation of flood disaster loss can supply scientific decision-making basis for flood disaster loss management. Due to the evaluation result of each single index of the practical flood disaster sample is often incompatible, a new method, named project pursuit interpolation model (PPIM for short), was presented to evaluate the degree of flood disaster loss. In PPIM model multi-dimensional evaluation index data was condensed to one-dimensional data firstly, and then the one-dimensional index data and the corresponding evaluation grade were composed to form two dimensional coordinates sample points, finally proper control nodes were chosen to establish evaluation model with cubic natural spline and polynomial function. The evaluation results of flood disaster of Henan Province show that the average absolute grade error is below 0.1 grades, and that the method is simple, effective and general. So the method can be applied to many other grade evaluation systems.