Impacts of Land-use and Land-cover Changes on River Runoff in Yellow River Basin for Period of 1956–2012

River runoff is affected by many factors, including long-term effects such as climate change that alter rainfall-runoff relationships, and short-term effects related to human intervention(e.g., dam construction, land-use and land-cover change(LUCC)). Discharge from the Yellow River system has been modified in numerous ways over the past century, not only as a result of increased demands for water from agriculture and industry, but also due to hydrological disturbance from LUCC, climate change and the construction of dams. The combined effect of these disturbances may have led to water shortages. Considering that there has been little change in long-term precipitation, dramatic decreases in water discharge may be attributed mainly to human activities, such as water usage, water transportation and dam construction. LUCC may also affect water availability, but the relative contribution of LUCC to changing discharge is unclear. In this study, the impact of LUCC on natural discharge(not including anthropogenic usage) is quantified using an attribution approach based on satellite land cover and discharge data. A retention parameter is used to relate LUCC to changes in discharge. We find that LUCC is the primary factor, and more dominant than climate change, in driving the reduction in discharge during 1956–2012, especially from the mid-1980 s to the end-1990 s. The ratio of each land class to total basin area changed significantly over the study period. Forestland and cropland increased by about 0.58% and 1.41%, respectively, and unused land decreased by 1.16%. Together, these variations resulted in changes in the retention parameter, and runoff generation showed a significant decrease after the mid-1980 s. Our findings highlight the importance of LUCC to runoff generation at the basin scale, and improve our understanding of the influence of LUCC on basin-scale hydrology.

Quantifying the parameter dependent basin of the unsafe regime of asymmetric Lévy-noise-induced critical transitions

In real systems,the unpredictable jump changes of the random environment can induce the critical transitions(CTs)between two non-adjacent states,which are more catastrophic.Taking an asymmetric Lévy-noise-induced tri-stable model with desirable,sub-desirable,and undesirable states as a prototype class of real systems,a prediction of the noise-induced CTs from the desirable state directly to the undesirable one is carried out.We first calculate the region that the current state of the given model is absorbed into the undesirable state based on the escape probability,which is named as the absorbed region.Then,a new concept of the parameter dependent basin of the unsafe regime(PDBUR)under the asymmetric Lévy noise is introduced.It is an efficient tool for approximately quantifying the ranges of the parameters,where the noise-induced CTs from the desirable state directly to the undesirable one may occur.More importantly,it may provide theoretical guidance for us to adopt some measures to avert a noise-induced catastrophic CT.

Application of SWAT Model to Non-point Source Pollution in Xincai River Basin

[Objective]The study aimed to simulate the production and transportation process of surface runoff,sediment and non-point source pollution in Xincai River basin based on SWAT model.[Method]On the basis of analyzing the principles of SWAT model,the correlative parameters of runoff,sediment and water quality were calibrated,then the spatial and temporal distribution of runoff,sediment and non-point source pollutants in Xincai River basin were studied by using SWAT model.[Result]The results of calibration and validation showed that SWAT model was reasonable and available,and it can be used to simulate the non-point source pollution of Xincai River basin.The simulation results revealed that the load of sediment and various pollutants was the highest in the rainy year,followed by the normal year,while it was the minimum in the dry year,indicating that the production of sediment and non-point source pollutants was closely related to annual runoff.[Conclusion]The research could provide scientific references for the prevention of non-point source pollution in a basin.

Fast prediction of the mechanical response for layered pavement under instantaneous large impact based on random forest regression

The layered pavements usually exhibit complicated mechanical properties with the effect of complex material properties under external environment.In some cases,such as launching missiles or rockets,layered pavements are required to bear large impulse load.However,traditional methods cannot non-destructively and quickly detect the internal structural of pavements.Thus,accurate and fast prediction of the mechanical properties of layered pavements is of great importance and necessity.In recent years,machine learning has shown great superiority in solving nonlinear problems.In this work,we present a method of predicting the maximum deflection and damage factor of layered pavements under instantaneous large impact based on random forest regression with the deflection basin parameters obtained from falling weight deflection testing.The regression coefficient R^(2)of testing datasets are above 0.94 in the process of predicting the elastic moduli of structural layers and mechanical responses,which indicates that the prediction results have great consistency with finite element simulation results.This paper provides a novel method for fast and accurate prediction of pavement mechanical responses under instantaneous large impact load using partial structural parameters of pavements,and has application potential in non-destructive evaluation of pavement structure.

Improvement of the Algorithm for Computing Adjusted Structure Number for Determination of Backlog Maintenance

The pavement strength is very important for the evaluation of backlog maintenance. The current trend in many developing countries used pavement conditions index-PCI in estimating maintenance costs. The PCI can only justify periodic and routine recurrent maintenance. The condition strength is rarely determined in a flexible pavement creating an opportunity for back long maintenance. This paper reports the study conducted to develop and improve the algorithm for estimating the adjusted structure number to estimate the remaining thickness of the flexible pavement. The analysis of eight ways of computing structure numbers from FWD data ware analyzed and found that the improvement of the HDM 3 - 4 models can influence the usefulness of data collected from road asset management in Tanzania. The algorithm for estimating structural numbers from CBR was improved to compute adjusted structural numbers finally used to estimate the remaining life of the flexible pavement. The analysis of the network of about 6900 km including ST and AM was found that 64.72% were very good, 12% were Good, 10% were fair and 7.84% were poor and 5.4% were very poor.