Temporal changes in nitrogen concentrations and stream discharge, as well as sediment and nitrogen losses from erosion plots with different land uses, were studied in an agricultural watershed in the Taihu Lake area in eastern China. The highest overland runoff loads and nitrogen losses were measured under the upland at a convergent footslope. Much higher runoff, sediment and nitrogen losses were observed under upland cropping and vegetable fields than that under chestnut orchard and bamboo forest. Sediment associated nitrogen losses accounted for 8-43.5% of total nitrogen export via overland runoff. N lost in dissolved inorganic nitrogen forms (NO(3-)-N + NH4+-N) accounted for less than 50% of total water associated nitrogen export. Agricultural practices and weather-driven fluctuation in discharge were main reasons for the temporal variations in nutrient losses via stream discharge. Significant correlation between the total nitrogen concentration and stream discharge load was observed. Simple regression models could give satisfactory results for prediction of the total nitrogen concentrations in stream water and can be used for better quantifying nitrogen losses from arable land. Nitrogen losses from the studied watershed via stream discharge during rice season in the year 2002 were estimated to be 10.5 kg N/ha using these simple models.

Accurate, fast forecasting of hydro-meteorological time series is presently a major challenge in drought and flood mitigation. This paper proposes a hybrid approach, wavelet de-noising (WD) and Rank-Set Pair Analysis (RSPA), that takes full advantage of a combination of the two approaches to improve forecasts of hydro-meteorological time series. WD allows decomposition and reconstruction of a time series by the wavelet transform, and hence separation of the noise from the original series. RSPA, a more reliable and efficient version of Set Pair Analysis, is integrated with WD to form the hybrid WD-RSPA approach. Two types of hydro-meteorological data sets with different characteristics and different levels of human influences at some representative stations are used to illustrate the WD-RSPA approach. The approach is also compared to three other generic methods: the conventional Auto Regressive Integrated Moving Average (ARIMA) method, Artificial Neural Networks (ANNs) (BP-error Back Propagation, MLP-Multilayer Perceptron and RBF-Radial Basis Function), and RSPA alone. Nine error metrics are used to evaluate the model performance. Compared to three other generic methods, the results generated by WD-REPA model presented invariably smaller error measures which means the forecasting capability of the WD-REPA model is better than other models. The results show that WD-RSPA is accurate, feasible, and effective. In particular, WD-RSPA is found to be the best among the various generic methods compared in this paper, even when the extreme events are included within a time series.

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