Time filter

Source Type

Cheng W.,Changjiang River Scientific Research Institute
Shuikexue Jinzhan/Advances in Water Science | Year: 2013

The rainfall threshold is an important indicator of flash flood conditions. In this study, the existing methods for computing rainfall thresholds are divided into two categories and reviewed on the basis of their technical principles. The two categories include the data-driven statistical and inductive methods and the physical process-based hydrologic hydraulic methods. As expansions of rainfall thresholds, the dynamic rainfall threshold and the storm critical curve are also introduced and discussed together with advances in uncertainty analysis of rainfall thresholds. In our review, the statistical and inductive methods have been more widely accepted in China. Moreover, antecedent rainfall (or antecedent soil saturation) and cumulative rainfall at particular time intervals are the two governing factors commonly considered in the calculation of rainfall thresholds. Cumulative rainfall may be the loneliness factor to be considered at times. Further, it is found that the rainfall threshold conveys poorly the magnitude of flash flooding. Understanding of the uncertainty in rainfall threshold calculations would be helpful for the improvement of flash flood warnings. However, how to incorporate the uncertainty into the decision-making process still remains a major challenge.

El Kateb H.,TU Munich | Zhang H.,TU Munich | Zhang P.,Changjiang River Scientific Research Institute | Mosandl R.,TU Munich
Catena | Year: 2013

The southern of the Shaanxi Province in central China is a region of great magnitude for water conservation. Long term anthropogenic interference in terms of deforestation and inappropriate land use has dramatically accelerated soil erosion in this region. A field experiment in the Shangnan County using 33 small erosion plots of 7m2 in size was carried out to determine and compare the soil loss and surface runoff from five vegetation covers and three levels of slope gradient (>10°-≤20°, >20°-≤30°, and >30°). The five vegetation covers embraced the most frequent rural land-use forms in the study area: farmlands including horticulture (tea plantation with peanut as an intercrop) and agriculture (maize in a winter-wheat-summer-maize rotation) activities, grasslands that have developed on abandoned farmlands, and forestlands including low and high forests (Chinese cork-oak coppices and pine plantations, respectively). The change in the runoff among the vegetation covers and slope gradients was high but not as significantly pronounced as for the change in the soil loss. Results showed that the slope gradient has an impact on the runoff and soil loss: the greater the slope gradient the higher the potential for runoff and soil loss. In addition, results exhibited that the rate of erosion is substantially affected by changes in vegetation cover. Farmlands generated the highest runoff and soil loss, whereas the tea plantations at slopes >30° were most susceptible to erosion. Grasslands had less runoff and soil loss than farmlands. Forestlands provided evidence for their suitability for soil and water conservation in the study area, as negligible soil-losses in comparison to the other vegetation covers were generated. © 2013 Elsevier B.V.

Wu C.L.,Hong Kong Polytechnic University | Wu C.L.,Changjiang River Scientific Research Institute | Chau K.W.,Hong Kong Polytechnic University
Journal of Hydrology | Year: 2011

Accurately modeling rainfall-runoff (R-R) transform remains a challenging task despite that a wide range of modeling techniques, either knowledge-driven or data-driven, have been developed in the past several decades. Amongst data-driven models, artificial neural network (ANN)-based R-R models have received great attentions in hydrology community owing to their capability to reproduce the highly nonlinear nature of the relationship between hydrological variables. However, a lagged prediction effect often appears in the ANN modeling process. This paper attempts to eliminate the lag effect from two aspects: modular artificial neural network (MANN) and data preprocessing by singular spectrum analysis (SSA). Two watersheds from China are explored with daily collected data. Results show that MANN does not exhibit significant advantages over ANN. However, it is demonstrated that SSA can considerably improve the performance of prediction model and eliminate the lag effect. Moreover, ANN or MANN with antecedent runoff only as model input is also developed and compared with the ANN (or MANN) R-R model. At all three prediction horizons, the latter outperforms the former regardless of being coupled with/without SSA. It is recommended from the present study that the ANN R-R model coupled with SSA is more promisings. © 2011 Elsevier B.V.

Zhou J.,Tsinghua University | Zhang M.,Tsinghua University | Lu P.,Changjiang River Scientific Research Institute
Water Resources Research | Year: 2013

We investigated the effect of the Three Gorges Project and other dams on the load of phosphorus (P) to the middle and lower Yangtze River (MLY) and discussed the alteration of P on the ecosystem of the MLY. We collected data for continuous flow and sediment over the past 60 years and observed the concentrations of total P (TP) and particulate P (PP) in the pool reaches of the Three Gorges Reservoir (TGR), both before and after the impoundment in 2003. As a result, we obtained highly positive correlations between P and sediment and revealed two changes that were caused by the impoundments: (1) the sediment load to the MLY decreases by 91% and the river becomes almost clear; and (2) the loads of TP and PP to the MLY are sequestered by 77% and 83.5% annually and 75% and 92% in dry seasons, respectively. Because P was the limiting nutrient for bioactivity in the MLY before 2003, such significant reductions, along with the many other consequences of the dams, will not only further reduce the bioavailability of P but also increase the existing high ratio of nitrogen (N) to P. Therefore, it is quite possible to alter the nutrient regime and reduce the aquatic primary productivity of the MLY. Given that many large dams with huge reservoirs are under construction or planned upstream and elsewhere, studies focused on the long-term effects of sediment and P reduction deserve a high priority for the protection of lowland rivers and aquatic ecosystems. ©2013. American Geophysical Union. All Rights Reserved.

Wu C.L.,Hong Kong Polytechnic University | Wu C.L.,Changjiang River Scientific Research Institute | Chau K.W.,Hong Kong Polytechnic University | Fan C.,Ryerson University
Journal of Hydrology | Year: 2010

This study is an attempt to seek a relatively optimal data-driven model for rainfall forecasting from three aspects: model inputs, modeling methods, and data-preprocessing techniques. Four rain data records from different regions, namely two monthly and two daily series, are examined. A comparison of seven input techniques, either linear or nonlinear, indicates that linear correlation analysis (LCA) is capable of identifying model inputs reasonably. A proposed model, modular artificial neural network (MANN), is compared with three benchmark models, viz. artificial neural network (ANN), K-nearest-neighbors (K-NN), and linear regression (LR). Prediction is performed in the context of two modes including normal mode (viz., without data preprocessing) and data preprocessing mode. Results from the normal mode indicate that MANN performs the best among all four models, but the advantage of MANN over ANN is not significant in monthly rainfall series forecasting. Under the data preprocessing mode, each of LR, K-NN and ANN is respectively coupled with three data-preprocessing techniques including moving average (MA), principal component analysis (PCA), and singular spectrum analysis (SSA). Results indicate that the improvement of model performance generated by SSA is considerable whereas those of MA or PCA are slight. Moreover, when MANN is coupled with SSA, results show that advantages of MANN over other models are quite noticeable, particularly for daily rainfall forecasting. Therefore, the proposed optimal rainfall forecasting model can be derived from MANN coupled with SSA. © 2010 Elsevier B.V.

Discover hidden collaborations