Analysis of Mean Monthly Rainfall Runoff Data of Indian Catchments Using Dimensionless Variables by Neural Network

This paper focuses on a concept of using dimensionless variables as input and output to Artificial Neural Network (ANN) and discusses the improvement in the results in terms of various performance criteria as well as simplification of ANN structure for modeling rainfall-runoff process in certain Indian catchments. In the present work, runoff is taken as the response (output) variable while rainfall, slope, area of catchment and forest cover are taken as input parameters. The data used in this study are taken from six drainage basins in the Indian provinces of Madhya Pradesh, Bihar, Rajasthan, West Bengal and Tamil Nadu, located in the different hydro-climatic zones. A standard statistical performance evaluation measures such as root mean square (RMSE), Nash–Sutcliffe efficiency and Correlation coefficient were employed to evaluate the performances of various models developed. The results obtained in this study indicate that ANN model using dimensionless variables were able to provide a better representation of rainfall–runoff process in comparison with the ANN models using process variables investigated in this study.

Analysis of rainfall runoff characteristics from a subtropical urban lawn catchment in South-east China

Characteristics of rainfall runoff from a 3.26 hm^2 urban catchment with predominant land-use as lawn in Xiamen City, South-east China were investigated and analyzed. Water quality and quantity measurements of rainfall runoff were conducted for ten rainfall events over the period March, 2008 to April, 2009. The results indicated that chemical oxygen demand （COD） and total phosphorus （TP） were the major pollutants with event mean concentrations of 56.09 and 0.44mg.L^-1. From hydrograph and pollutograph analysis of two typical rainfall events, it was clear that the peak rainfall preceded the peak flowrate by about 15-20 min. Meanwhile, concentrations of major pollutants showed multiple peaks and these peaks usually preceded peak flowrate. There were no distinctive first-flush effects except for the rainfall events with the longest rainfall duration and largest runoff volume, which was verified by the fact that the first 30% runoff volume （FF30） carried 39.36% of the total suspended solids （TSS） load, 35.17% of the COD load, 28.13% of the TP load and 39.03% of the nitrate nitrogen load. Multivariate regression analysis further demonstrated that the total runoffvolume had a positive correlation with the FF30 of TSS and COD.

ENHANCING RAINFALL-RUNOFF POLLUTION MODELING BY INCORPORATION OF NEGLECTED PHYSICAL PROCESSES

The growing need to mitigate rainfall-runoff pollution,especially first flush,calls for accurate quantification of pollution load and the refined understanding of its spatial-temporal variation.The wash-off model has advantages in modeling rainfall-runoff pollution due to the inclusion of two key physical processes,build-up and wash-off.However,this disregards pollution load from wet precipitation and the relationship between rainfall and runoff,leading to uncertainties in model outputs.This study integrated the Soil Conservation Service curve number(SCS-CN)into the wash-off model and added pollutant load from wet precipitation to enhance the rainfall-runoff pollution modeling.The enhanced wash-off model was validated in a typical rural-residential area.The results showed that the model performed better than the established wash-off model and the commonly-used event mean concentrations method,and identified two different modes of pollution characteristics dominated by land pollution and rainfall pollution,respectively.In addition,the model simulated more accurate pollutant concentrations at high-temporal-resolution.From this,it was found that 12%of the total runoff contained 80%to 95%of the total load for chemical oxygen demand,total N,and total P,whereas it contained only 15%of the total load for NH4+-N.The enhanced model can provide deeper insights into non-point pollution mitigation.

Response of runoff and its components to climate change in the Manas River of the Tian Shan Mountains

A warming-wetting climate trend has led to increased runoff in most watersheds in the Tian Shan Mountains over the past few decades.However,it remains unclear how runoff components,that is,rainfall runoff(Rrain),snowmelt runoff(Rsnow),and glacier meltwater(Rglacier),responded to historical climate change and how they will evolve under future climate change scenarios.Here,we used a modified Hydrologiska Byrans Vattenbalansavdelning(HBV)model and a detrending method to quantify the impact of precipitation and temperature changes on runoff components in the largest river(Manas River)on the northern slope of the Tian Shan Mountains from 1982 to 2015.A multivariate calibration strategy,including snow cover,glacier area,and runoff was implemented to constrain model parameters associated with runoff components.The downscaled outputs of 12 general circulation models(GCMs)from the Sixth Coupled Model Intercomparison Project(CMIP6)were also used to force the modified HBV model to project the response of runoff and its components to future(2016-2100)climate change under three common socio-economic pathways(SSP126,SSP245,and SSP585).The results indicate that Rrain dominates mean annual runoff with a proportion of 42%,followed by Rsnow(37%)and Rglacier(21%).In terms of inter-annual variation,Rrain and Rsnow show increasing trends(0.93(p<0.05)and 0.31(p>0.05)mm per year),while Rglacier exhibits an insignificant(p>0.05)decreasing trend(-0.12 mm per year),leading to an increasing trend in total runoff(1.12 mm per year,p>0.05).The attribution analysis indicates that changes in precipitation and temperature contribute 8.16 and 10.37 mm,respectively,to the increase in runoff at the mean annual scale.Climate wetting(increased precipitation)increases Rrain(5.03 mm)and Rsnow(3.19 mm)but has a limited effect on Rglacier(-0.06 mm),while warming increases Rrain(10.69 mm)and Rglacier(5.79 mm)but decreases Rsnow(-6.12 mm).The negative effect of glacier shrinkage on Rglacier has outweighed the positive effect of warming on Rglaciers resulting in the tipping point(peak water)for Rglacier having passed.Runoff projections indicate that future decreases in Rglacier and Rsnow could be offset by increases in Rrain due to increased precipitation projections,reducing the risk of shortages of available water resources.However,management authorities still need to develop adequate adaptation strategies to cope with the continuing decline in Rgacier in the future,considering the large inter-annual fluctuations and high uncertainty in precipitation projection.

Output Characteristics of Non-point Nitrogen and Phosphorus from a Typical Small Watershed in Yimeng Mountainous Area during a Rainstorm

[ Objective] The study aimed to reveal the output characteristics of non-point nitrogen and phosphorus from a typical small watershed in Yimeng mountainous area during a rainstorm. [Method] The dynamic changes of pollutant concentration, precipitation and flow during the rainstorm on August 12, 2010 were monitored at the outlet of Menglianggu watershed. [ Result] During the rainstorm, the generation of runoff was sudden and ephemeral, and the peak of the runoff lagged behind that of rainfall intensity; the concentration of AN and TN increased firstly and then tended to be stable, while NN concentration had no significance change at the beginning of the rainfall, then improved gradually and tended to be stable fi- nally; DOP concentration had no obvious change during the rainstorm, but the concentration of DIP, DP, PP and TP rose firstly and then tended to be stable, and the peak values appeared before the peak of the flow. In addition, the output concentration of TN and TP was far higher than the standard concentration of water eutrophication. [ Conclusion] The study can provide scientific references for the reasonable control of non-point source pollution pollution in Yimeng mountainous area.

Two-dimensional hydrodynamic robust numerical model of soil erosion based on slopes and river basins

Erosion is an important issue in soil science and is related to many environmental problems,such as soil erosion and sediment transport.Establishing a simulation model suitable for soil erosion prediction is of great significance not only to accurately predict the process of soil separation by runoff,but also improve the physical model of soil erosion.In this study,we develop a graphic processing unit(GPU)-based numerical model that combines two-dimensional(2D)hydrodynamic and Green-Ampt(G-A)infiltration modelling to simulate soil erosion.A Godunov-type scheme on a uniform and structured square grid is then generated to solve the relevant shallow water equations(SWEs).The highlight of this study is the use of GPU-based acceleration technology to enable numerical models to simulate slope and watershed erosion in an efficient and high-resolution manner.The results show that the hydrodynamic model performs well in simulating soil erosion process.Soil erosion is studied by conducting calculation verification at the slope and basin scales.The first case involves simulating soil erosion process of a slope surface under indoor artificial rainfall conditions from 0 to 1000 s,and there is a good agreement between the simulated values and the measured values for the runoff velocity.The second case is a river basin experiment(Coquet River Basin)that involves watershed erosion.Simulations of the erosion depth change and erosion cumulative amount of the basin during a period of 1-40 h show an elevation difference of erosion at 0.5-3.0 m,especially during the period of 20-30 h.Nine cross sections in the basin are selected for simulation and the results reveal that the depth of erosion change value ranges from-0.86 to-2.79 m and the depth of deposition change value varies from 0.38 to 1.02 m.The findings indicate that the developed GPU-based hydrogeomorphological model can reproduce soil erosion processes.These results are valuable for rainfall runoff and soil erosion predictions on rilled hillslopes and river basins.

Multi-Span and Multiple Relevant Time Series Prediction Based on Neighborhood Rough Set

Rough set theory has been widely researched for time series prediction problems such as rainfall runoff.Accurate forecasting of rainfall runoff is a long standing but still mostly signicant problem for water resource planning and management,reservoir and river regulation.Most research is focused on constructing the better model for improving prediction accuracy.In this paper,a rainfall runoff forecast model based on the variable-precision fuzzy neighborhood rough set(VPFNRS)is constructed to predict Watershed runoff value.Fuzzy neighborhood rough set dene the fuzzy decision of a sample by using the concept of fuzzy neighborhood.The fuzzy neighborhood rough set model with variable-precision can reduce the redundant attributes,and the essential equivalent data can improve the predictive capabilities of model.Meanwhile VFPFNRS can handle the numerical data,while it also deals well with the noise data.In the discussed approach,VPFNRS is used to reduce superuous attributes of the original data,the compact data are employed for predicting the rainfall runoff.The proposed method is examined utilizing data in the Luo River Basin located in Guangdong,China.The prediction accuracy is compared with that of support vector machines and long shortterm memory(LSTM).The experiments show that the method put forward achieves a higher predictive performance.