Flooding contributes to tremendous hazards every year; more accurate forecasting may significantly mitigate the damages and loss caused by flood disasters. Current hydrological models are either purely knowledge-based or data-driven. A combination of data-driven method (artificial neural networks in this paper) and knowledge-based method (traditional hydrological model) may booster simulation accuracy. In this study, we proposed a new back-propagation (BP) neural network algorithm and applied it in the semi-distributed Xinanjiang (XAJ) model. The improved hydrological model is capable of updating the flow forecasting error without losing the leading time. The proposed method was tested in a real case study for both single period corrections and real-time corrections. The results reveal that the proposed method could significantly increase the accuracy of flood forecasting and indicate that the global correction effect is superior to the second-order autoregressive correction method in real-time correction.

The coincidence of flood flows in a mainstream and its tributaries may lead to catastrophic floods. In this paper, we investigated the flood coincidence risk under nonstationary conditions arising from climate changes. The coincidence probabilities considering flood occurrence dates and flood magnitudes were calculated using nonstationary multivariate models and compared with those from stationary models. In addition, the “most likely” design based on copula theory was used to provide the most likely flood coincidence scenarios. The Huai River and Hong River were selected as case studies. The results show that the highest probabilities of flood coincidence occur in mid-July. The marginal distributions for the flood magnitudes of the two rivers are nonstationary, and time-varying copulas provide a better fit than stationary copulas for the dependence structure of the flood magnitudes. Considering the annual coincidence probabilities for given flood magnitudes and the “most likely” design, the stationary model may underestimate the risk of flood coincidence in wet years or overestimate this risk in dry years. Therefore, it is necessary to use nonstationary models in climate change scenarios.

Water scarcity in Huaihe River, the sixth largest river in China, is stressed by the changing climate and intense human activities, especially in the headwater area. Xixian, situated in the upper reaches of the Huai River, is an important agricultural county with a population of more than one million people, and nearly one billion kg of crop yield every year. The projected climate changes and increasing population are expected to further complicate the utilization of already stressed water resources, endangering the agricultural activities in this area. There is pressing need for a watershed model to better understand the interaction between land use activities and hydrologic processes and to support sustainable water use. This study evaluated the performance of the Soil and Water Assessment Tool (SWAT) for hydrologic modeling in the Xixian basin; three methods of calibration and uncertainty analysis (sequential uncertainty fitting, generalized likelihood uncertainty estimation, and parameter solu...

To date, floods have become one of the most severe natural disasters on Earth. Flood forecasting with hydrological models is an important non-engineering measure for flood control and disaster reduction. The Xin’anjiang (XAJ) model is the most widely used hydrological model in China for flood forecasting, while the Soil and Water Assessment Tool (SWAT) model is widely applied for daily and monthly simulation and has shown its potential for flood simulation. The objective of this paper is to evaluate the performance of the SWAT model in simulating floods at a sub-daily time-scale in a slightly larger basin and compare that with the XAJ model. Taking Qilijie Basin (southeast of China) as a study area, this paper developed the XAJ model and SWAT model at a sub-daily time-scale. The results showed that the XAJ model had a better performance than the sub-daily SWAT model regarding relative runoff error (RRE) but the SWAT model performed well according to relative peak discharge error (RPE) and error of occurrence time of peak flow (PTE). The SWAT model performed unsatisfactorily in simulating low flows due to the daily calculation of base flow but behaved quite well in simulating high flows. We also evaluated the effect of spatial scale on the SWAT model. The results showed that the SWAT model had a good applicability at different spatial scales. In conclusion, the sub-daily SWAT model is a promising tool for flood simulation though more improvements remain to be studied further.

Regional frequency analysis based on L moments has been used to analyze low flow in the karst area of southwest China. The 7-day low flow was regarded as the statistical quantity. Data from 12 stream flow gauging sites of the Wujiang water system in Guizhou Province are screened using the discordancy measure, and homogeneity of the region is then tested employing the L-moments-based heterogeneity measure. For computing heterogeneity measure H , 500 simulations are performed using the kappa distribution. Based on this test, it is observed that these 12 gauging sites constitute a homogeneous region. Based on the L-moments ratio diagram, | ZDIST | and t4 statistic criteria, generalized logistic (GLO) distribution is identified as the robust distribution for the study area. Regional low flow frequency relationships are developed for estimation of low flow of various return periods for 12 gauged catchments using the GLO distribution and index flood method. Examination of the observed and computed low flow show...