Impacts of climate change and hydraulic structures on runoff and sediment discharge in the middle Yellow River

Due to climate change and its aggravation by human activities (e.g. hydraulic structures) over the past several decades, the hydrological conditions in the middle Yellow River have markedly changed, leading to a sharp decrease in runoff and sediment discharge. This paper focused on the impacts of climate change and hydraulic structures on runoff and sediment discharge, and the study area was located in the 3246 km² Huangfuchuan (HFC) River basin. Changes in annual runoff and sediment discharge were initially analysed by using the Mann–Kendall trend test and Pettitt change point test methods. Subsequently, periods of natural and disturbed states were defined. The results showed that both the annual runoff and sediment discharge presented statistically significant decreasing trends. However, compared with the less remarkable decline in annual rainfall, it was inferred that hydraulic structures might be another important cause for the sharp decrease in runoff and sediment discharge in this region. Consequently, sediment‐trapping dams (STDs, a type of large‐sized check dam used to prevent sediment from entering the Yellow River main stem) were considered in this study. Through evaluating the impacts of the variation in rainfall patterns (i.e. amount and intensity) and the STD construction, a positive correlation between rainfall intensity and current STD construction was found. This paper revealed that future soil and water conservation measures should focus on areas with higher average annual rainfall and more rainstorm hours. Copyright © 2015 John Wiley & Sons, Ltd.     

Soil moisture estimation using ERS 2 SAR data: a case study in the Solani River catchment/Estimation de l’humidité du sol grâce à des données ERS-2 SAR: étude de cas dans le bassin de la rivière Solani

Abstract

Abstract The information regarding spatial and temporal variation of soil moisture in a catchment is of utmost importance in hydrological, as well as many other studies. Point measurements from gravimetric and other methods for soil moisture determination are insufficient to understand the spatial behaviour of soil moisture in a region. Microwave remote sensing data from active sensors on board various satellites are increasingly being used to map spatial distribution of soil moisture within the 0–10 cm top surface. The northern part of India has a network of large rivers and canals and, therefore, spatial and temporal distribution of soil moisture in this region has a significant bearing on the hydrology of the region. In this paper, results on estimation of soil moisture from an ERS-2 SAR image in the catchment of the Solani River (a tributary to the River Ganga) in and around the town of Roorkee, India, have been presented. The radar backscatter coefficient for each pixel of the image has been modelled from the digital numbers of the SAR image. Gravimetric measurements have been made simultaneously during the satellite pass to determine the concurrent value of volumetric soil moisture at a large number of sample points within the satellite sweep area. The backscatter coefficient is found to vary from –30 dB to –42 dB for a variation in soil moisture from 30 to 75%. Regression analyses between volumetric soil moisture and both the digital numbers and backscatter coefficients were performed. Strong correlations between volumetric soil moisture and digital number were observed with R ² values of 0.84, 0.75 and 0.83 for bare soil, vegetative and combined surfaces, respectively. A similar trend was observed with the relationship between backscatter and volumetric soil moisture with R ² values of 0.60, 0.89 and 0.67 for bare soil, vegetative and combined surfaces, respectively. These results demonstrate the utilization of SAR data for estimation of spatial distribution of soil moisture in the region of the present study.     

Testing alternative erosivity indices to predict event soil loss from bare plots in Southern Italy

Methods for predicting unit plot soil loss for the ‘Sparacia’ Sicilian (Southern Italy) site were developed using 316 simultaneous measurements of runoff and soil loss from individual bare plots varying in length from 11 to 44 m. The event unit plot soil loss was directly proportional to an erosivity index equal to (Q_REI₃₀)^1·47, being Q_REI₃₀ the runoff ratio (Q_R) times the single storm erosion index (EI₃₀). The developed relationship represents a modified version of the USLE‐M, and therefore it was named USLE‐MM. By the USLE‐MM, a constant erodibility coefficient was deduced for plots of different lengths, suggesting that in this case the calculated erodibility factor is representative of an intrinsic soil property. Testing the USLE‐M and USLE‐MM schemes for other soils and developing simple procedures for estimating the plot runoff ratio has practical importance to develop a simple method to predict soil loss from bare plots at the erosive event temporal scale. Copyright © 2009 John Wiley & Sons, Ltd.     

A Modified Neural Network for Improving River Flow Prediction/Un Réseau de Neurones Modifié pour Améliorer la Prévision de L'Écoulement Fluvial

Abstract

Artificial neural network (ANN) models provide huge potential for simulating nonlinear behaviour of hydrological systems. However, the potential of ANN is yet to be fully exploited due to the problems associated with improving the model generalization performance. Generalization refers to the ability of a neural network to correctly process input data that have not been used for calibrating the neural network model. In the hydrological context, better generalization performance implies higher precision of forecasting. The primary objectives of this study are to explore new measures for improving the generalization performance of an ANN-based rainfall–runoff model, and to evaluate the applicability of the new measures. A modified neural network model (entitled goal programming (GP) neural network) for modelling the rainfall–runoff process has been developed, in which three enhancements are made as compared to the widely-used backpropagation (BP) network. The three enhancements are (a) explicit integration of hydrological prior knowledge into the neural network learning; (b) incorporation of a modified training objective function; and (c) reduction of network sensitivity to input errors. Seven watersheds across a range of climatic conditions and watershed areas in China were selected for examining the alternative networks. The results demonstrate that the GP consistently outperformed the BP both in the calibration and verification periods and three proposed measures yielded improvement of performance.     

The hydrogeomorphic influences on alluvial gully erosion along the Mitchell River fluvial megafan

Hydrogeomorphic processes influencing alluvial gully erosion were evaluated at multiple spatial and temporal scales across the Mitchell River fluvial megafan in tropical Queensland, Australia. Longitudinal changes in floodplain inundation were quantified using river gauge data, local stage recorders and HEC‐RAS modelling based on LiDAR topographic data. Intra‐ and interannual gully scarp retreat rates were measured using daily time‐lapse photographs and annual GPS surveys. Erosion was analysed in response to different water sources and associated erosion processes across the floodplain perirheic zone, including direct rainfall, infiltration‐excess runoff, soil‐water seepage, river backwater and overbank flood inundation. The frequency of river flood inundation of alluvial gullies changed longitudinally according to river incision and confinement. Near the top of the megafan, flood water was contained within the macrochannel up to the 100‐year recurrence interval, but river backwater still partially inundated adjacent gullies eroding into Pleistocene alluvium. In downstream Holocene floodplains, inundation of alluvial gullies occurred beyond the 2‐ to 5‐year recurrence interval and contributed significantly to total annual erosion. However, most gully scarp retreat at all sites was driven by direct rainfall and infiltration‐excess runoff, with the 24‐h rainfall total being the most predictive variable. The remaining variability can be explained by seasonal vegetative conditions, complex cycles of soil wetting and drying, tension crack development, near‐surface pore‐water pressure, soil block undermining from spalling and overland flow, and soil property heterogeneity. Implications for grazing management impacts on soil surface and perennial grass conditions include effects on direct rainfall erosion, water infiltration, runoff volume, water concentration along tracks, and the resistance of highly dispersible soils to gully initiation or propagation under intense tropical rainfall. Copyright © 2012 John Wiley & Sons, Ltd.     

改进的斜坡降雨入渗与坡面径流耦合算法研究

大气降雨时坡面径流与非饱和入渗的耦合计算,是降雨诱发滑坡及水土流失等研究中的重大问题之一。从坡面径流和降雨入渗控制方程着手,建立了基于有限元方法的耦合方程求解模型,实现了耦合问题的直接求解。该耦合计算模型与求解方法避免了坡面径流和降雨入渗间的迭代计算,也不需计算两者间的流量交换,较大程度地提高了计算效率和计算精度。算例表明,该方法正确可行,计算效率高,稳定性好。     

气候变化对径流影响的模拟

建立了气候-陆面单向连接模型,通过降尺度模型处理GCMs的输出结果,连接到分布式水文模型计算径流,以山西省为例进行了不同情景下未来径流变化趋势的分析.不仅使用了分布式的产汇流,而且提出了在网格上使用回归分析的植被系数综合反映该单元与蒸发有关的下垫面状况.简要验证了5个GCMs输出结果在山西省的适应性:气候模型对气温的模拟较好,对于降水更多的具有定性的揭示长期演变趋势的性质.比较分析了不同气候模型在不同排放情景下径流的状况,未来50a山西省径流的最可能的变化趋势是径流随气温的升高将逐步减少.     

降雨条件下坡面径流和入渗耦合的数值模拟

降雨条件下坡面径流和降雨入渗的模拟互为条件,必须采用耦合分析的方法才能较好地解决这一问题。从坡面径流和降雨入渗控制方程着手,建立了用有限元方法求解该问题的耦合方程,并编制了相应的计算程序。为了加快迭代收敛的速度,还对地表饱和的判断提出了一种近似处理的方法。算例表明,所提出的方法可行,能较好地反映实际现象。该方法可为边（滑）坡稳定、水土流失、山地洪水分析等提供计算依据。     

黑河过正义峡河川径流量减少的原因及对策分析

甘(甘肃)蒙(内蒙古)就黑河水量的分配问题由来已久。多年来,由于中游(甘肃)采用的是以开发河水为主,辅以开采地下水的水资源利用模式,导致了黑河过正义峡泄入下游(内蒙古)的河川径流量不断减少,近10a来尤为明显。文章在定量分析其减少原因的基础上,从兼顾中、下游用水角度出发,以《甘蒙分水原则》的最低值为约束条件,提出了中游地区应采取以开采地下水为主,辅以开发河水的水资源利用方案,从而确保了黑河过正义峡的径流量。