Hydrologic Response to Future Climate Change in the Dulong-Irra-waddy River Basin Based on Coupled Model Intercomparison Project 6

Within the context of the Belt and Road Initiative(BRI)and the China-Myanmar Economic Corridor(CMEC),the Dulong-Ir-rawaddy(Ayeyarwady)River,an international river among China,India and Myanmar,plays a significant role as both a valuable hydro-power resource and an essential ecological passageway.However,the water resources and security exhibit a high degree of vulnerabil-ity to climate change impacts.This research evaluates climate impacts on the hydrology of the Dulong-Irrawaddy River Basin(DIRB)by using a physical-based hydrologic model.We crafted future climate scenarios using the three latest global climate models(GCMs)from Coupled Model Intercomparison Project 6(CMIP6)under two shared socioeconomic pathways(SSP2-4.5 and SSP5-8.5)for the near(2025-2049),mid(2050-2074),and far future(2075-2099).The regional model using MIKE SHE based on historical hydrologic processes was developed to further project future streamflow,demonstrating reliable performance in streamflow simulations with a val-idation Nash-Sutcliffe Efficiency(NSE)of 0.72.Results showed that climate change projections showed increases in the annual precip-itation and potential evapotranspiration(PET),with precipitation increasing by 11.3%and 26.1%,and PET increasing by 3.2%and 4.9%,respectively,by the end of the century under SSP2-4.5 and SSP5-8.5.These changes are projected to result in increased annual streamflow at all stations,notably at the basin’s outlet(Pyay station)compared to the baseline period(with an increase of 16.1%and 37.0%at the end of the 21st century under SSP2-4.5 and SSP5-8.5,respectively).Seasonal analysis for Pyay station forecasts an in-crease in dry-season streamflow by 31.3%-48.9%and 22.5%-76.3%under SSP2-4.5 and SSP5-8.5,respectively,and an increase in wet-season streamflow by 5.8%-12.6%and 2.8%-33.3%,respectively.Moreover,the magnitude and frequency of flood events are pre-dicted to escalate,potentially impacting hydropower production and food security significantly.This research outlines the hydrological response to future climate change during the 21st century and offers a scientific basis for the water resource management strategies by decision-makers.

Analysing the Potential Impact of Climate Change on the Hydrological Regime of the Upper Benue River Basin (North Cameroon)

In this study, we analyse the climate variability in the Upper Benue basin and assess its potential impact on the hydrology regime under two different greenhouse gas emission scenarios. The hydrological regime of the basin is more vulnerable to climate variability, especially precipitation and temperature. Observed hydroclimatic data (1950-2015) was analysed using a statistical approach. The potential impact of future climate change on the hydrological regime is quantified using the GR2M model and two climate models: HadGEM2-ES and MIROC5 from CMIP5 under RCP 4.5 and RCP 8.5 greenhouse gas emission scenarios. The main result shows that precipitation varies significantly according to the geographical location and time in the Upper Benue basin. The trend analysis of climatic parameters shows a decrease in annual average precipitation across the study area at a rate of -0.568 mm/year which represents about 37 mm/year over the time 1950-2015 compared to the 1961-1990 reference period. An increase of 0.7°C in mean temperature and 14% of PET are also observed according to the same reference period. The two climate models predict a warming of the basin of about 2°C for both RCP 4.5 and 8.5 scenarios and an increase in precipitation between 1% and 10% between 2015 and 2100. Similarly, the average annual flow is projected to increase by about +2% to +10% in the future for both RCP 4.5 and 8.5 scenarios between 2015 and 2100. Therefore, it is primordial to develop adaptation and mitigation measures to manage efficiently the availability of water resources.

Integrated Hydrological Modeling of the Godavari River Basin in Maharashtra Using the SWAT Model: Streamflow Simulation and Analysis

Hydrological modeling plays a crucial role in efficiently managing water resources and understanding the hydrologic behavior of watersheds. This study aims to simulate daily streamflow in the Godavari River Basin in Maharashtra using the Soil and Water Assessment Tool (SWAT). SWAT is a process-based hydrological model used to predict water balance components, sediment levels, and nutrient contamination. In this research, we used integrated remote sensing and GIS data, including Digital Elevation Models (DEM), land use and land cover (LULC) maps, soil maps, and observed precipitation and temperature data, as input for developing the SWAT model to assess surface runoff in this large river basin. The Godavari River Basin under study was divided into 25 sub-basins, comprising 151 hydrological response units categorized by unique land cover, soil, and slope characteristics using the SWAT model. The model was calibrated and validated against observed runoff data for two time periods: 2003-2006 and 2007-2010 respectively. Model performance was assessed using the Nash-Sutcliffe efficiency (NSE) and the coefficient of determination (R2). The results show the effectiveness of the SWAT2012 model, with R2 value of 0.84 during calibration and 0.86 during validation. NSE values also ranged from 0.84 during calibration to 0.85 during validation. These findings enhance our understanding of surface runoff dynamics in the Godavari River Basin under study and highlight the suit-ability of the SWAT model for this region.

Effect of sand-fixing vegetation on the hydrological regulation function of sand dunes and its practical significance

Soil water content is a key controlling factor for vegetation restoration in sand dunes.The deep seepage and lateral migration of water in dunes affect the recharge process of deep soil water and groundwater in sand dune ecosystems.To determine the influence of vegetation on the hydrological regulation function of sand dunes,we examined the deep seepage and lateral migration of dune water with different vegetation coverages during the growing season in the Horqin Sandy Land,China.The results showed that the deep seepage and lateral migration of water decreased with the increase in vegetation coverage on the dunes.The accumulated deep seepage water of mobile dunes(vegetation coverage<5%)and dunes with vegetation coverage of 18.03%,27.12%,and 50.65%accounted for 56.53%,51.82%,18.98%,and 0.26%,respectively,of the rainfall in the same period.The accumulated lateral migration of water in these dunes accounted for 12.39%,6.33%,2.23%,and 7.61%of the rainfall in the same period.The direction and position of the dune slope affected the soil water deep seepage and lateral migration process.The amounts of deep seepage and lateral migration of water on the windward slope were lower than those on the leeward slope.The amounts of deep seepage and lateral migration of water showed a decreasing trend from the bottom to the middle and to the top of the dune slope.According to the above results,during the construction of sand-control projects in sandy regions,we suggest that a certain area of mobile dunes(>13.75%)should be retained as a water resource reservoir to maintain the water balance of artificial fixed dune ecosystems.These findings provide reliable evidence for the accurate assessment of water resources within the sand dune ecosystem and guide the construction of desertification control projects.

Initiatives to clarify mechanisms of hydrological evolution in human-influenced Yellow River Basin

Significant changes in water cycle elements/processes have created serious challenges to regional sustainability and high-quality development in the Yellow River Basin in China.It is necessary to investigate the impacts of climate change and human activities on hydrological evolution and disaster risk from a holistic perspective of the basin.This study developed initiatives to clarify the mechanisms of hydrological evolution in the human-influenced Yellow River Basin.The proposed research method includes:(1)a tool to simulate multiple factors and a multi-scale water cycle using a grid-based spatiotemporal coupling approach,and(2)a new algorithm to separate the responses of the water cycle to climate change and human impacts,and de-couple the eco-environmental effects using artificial intelligence techniques.With this research framework,key breakthroughs are expected to be made in the understanding of the impacts of land cover change on the water cycle and blue/green water redirection.The outcomes of this research project are expected to provide theoretical support for ecological protection and water governance in the basin.

Elucidating Dominant Factors Affecting Land Surface Hydrological Simulations of the Community Land Model over China

In order to compare the impacts of the choice of land surface model(LSM)parameterization schemes,meteorological forcing,and land surface parameters on land surface hydrological simulations,and explore to what extent the quality can be improved,a series of experiments with different LSMs,forcing datasets,and parameter datasets concerning soil texture and land cover were conducted.Six simulations are run for the Chinese mainland on 0.1°×0.1°grids from 1979 to 2008,and the simulated monthly soil moisture(SM),evapotranspiration(ET),and snow depth(SD)are then compared and assessed against observations.The results show that the meteorological forcing is the most important factor governing output.Beyond that,SM seems to be also very sensitive to soil texture information;SD is also very sensitive to snow parameterization scheme in the LSM.The Community Land Model version 4.5(CLM4.5),driven by newly developed observation-based regional meteorological forcing and land surface parameters(referred to as CMFD_CLM4.5_NEW),significantly improved the simulations in most cases over the Chinese mainland and its eight basins.It increased the correlation coefficient values from 0.46 to 0.54 for the SM modeling and from 0.54 to 0.67 for the SD simulations,and it decreased the root-mean-square error(RMSE)from 0.093 to 0.085 for the SM simulation and reduced the normalized RMSE from 1.277 to 0.201 for the SD simulations.This study indicates that the offline LSM simulation using a refined LSM driven by newly developed observation-based regional meteorological forcing and land surface parameters can better model reginal land surface hydrological processes.

Hydrological Processes in a Small Research Watershed under Forest Coverage in the Coast of Chiapas, Mexico

In the hydrological watershed, some natural processes take place in which the interaction of water, soil, climate and vegetation favors the capture of water. The present study aimed to evaluate preliminary information regarding the hydrological response and the water balance in a small research watershed with tropical forest cover (15°01'44''N and 92°13'55''W, 471 m, 2.3 has). Events of precipitation, direct runoff, infiltration rate and baseflow were performed. The amount, duration and intensity of rainfall events were recorded with the use of a pluviograph. Surface runoff was quantified with an established gauging station, an H-type gauging device and a horizontal mechanical gauging limnograph. Runoff base flow was measured at the gauging station using the volume-time method. Infiltration was measured using a triple ring infiltrometer, taking two measurements in the upper part and two in the lower part of the microbasin. Evapotranspiration was measured with the amount of rainfall entering and runoff leaving the watershed. In the study period, annual rainfall of 4417.6 mm distributed over 181 events were recorded;about 70% of the storms showed lower intensities at 20 mm·h-1. The total runoff was 345.8 mm caused by half of the rainfall events, which represents 7.8% of the total rain;77% of runoff events showed lower sheets of 5 mm and an average specific rate of 20.7 L·s-1·ha-1 with a maximum of 113.6 L·s-1·ha-1. Three runoff events were greater than 20.1 mm and caused the 22.5% of the total runoff depth in the study period showing the equilibrium conditions in the hydrological response of the forest. Water outputs like baseflow was 669.5 mm. In this way, 90% of the rainfall is infiltrated every year in the micro-watershed, which shows the importance of the plant cover in the hydrological regulation and the groundwater recharge.

Ecological and hydrologic evolution history in the sensitive zone of both East Asian summer monsoon and Westerly since the Last Glacial Maximum

The Qilian Mountains,located in the northeastern Qinghai-Tibet Plateau,is a sensitive zone of both East Asian summer monsoon(EASM)and westerly winds(WW).The evolution history and driving mechanism of the ecosystem and hydrologic cycle in this region on long-term timescales have not yet been clarified.In this study,we comprehensively study the hydrologic and ecological evolution history in the sensitive zone since the Last Glacial Maximum(LGM)by integrating surface sediments,paleoclimate records,TraCE-21ka transient simulations,and PMIP3-CMIP5 multi-model simulation.Results show that hydrologic and ecological proxies from surface sediments are significantly different from west to east and mainly divided into three sections:the monsoonaffected region in the eastern Qilian Mountains,the intersection region in the central Qilian Mountains,and the westerly-affected region in the western Qilian Mountains.Meanwhile,paleo-ecological and paleohydrologic reconstructions from the surroundings uncover a synchronous climate evolution that the EASM mainly controls the eastern Qilian Mountains and penetrates the central Qilian Mountains in monsoon intensity maximum,while the WW dominates the central and western Qilian Mountains on both glacial-interglacial and millennial timescales.The simulation results further bear out the glacial humid climate in the central and western Qilian Mountains caused by the enhanced WW,and the humidity maximum in the eastern Qilian Mountains controlled by the strong mid-Holocene monsoon.In general,east-west differences in climate pattern and response for the EASM and the WW are integrally stable on both short-term and long-term timescales.

Intensity Estimation of Extreme Meteorological and Hydrological Factors Induced by Tropical Cyclones Affecting Hong Kong

Hong Kong is often affected by tropical cyclones.The Hong Kong observatory issues warning signals based on the impact of tropical cyclones on the region.The joint frequency analysis of tropical cyclones in Hong Kong can provide a scientific basis for disaster reduction and prevention and post-disaster reconstruction of tropical cyclones.First,the maximum hourly mean wind speed(W),warning signal duration(D),maximum sea level(L),and total rainfall(R)of each tropical cyclone that affected Hong Kong from 1985 to 2019 are selected and fitted using the Gumbel,Weibull,Pearson type 3,and lognormal distributions.Then,bivariate copula functions,such as the Clayton,Frank,Gumbel-Hougaard,and Gaussian copulas,are applied to construct the joint probability models of W,D,L,and R,respectively.The joint return periods of W and D and those of L and R are defined as the meteorological and hydrological intensities of tropical cyclones,respectively.The results show that the joint return periods are good indicators of the comprehensive effect of the meteorological and hydrological intensities of tropical cyclones.No necessary correlation between meteorological and hydrological intensities of tropical cyclones exists.The meteorological and hydrological intensities of tropical cyclones show an upward trend in recent years.

Anthropogenic activity,hydrological regime,and light level jointly influence temporal patterns in biosonar activity of the Yangtze finless porpoise at the junction of the Yangtze River and Poyang Lake,China

Under increasing anthropogenic pressure,species with a previously contiguous distribution across their ranges have been reduced to small fragmented populations.The critically endangered Yangtze finless porpoise(Neophocaena asiaeorientalis asiaeorientalis),once commonly observed in the Yangtze River-Poyang Lake junction,is now rarely seen in the river-lake corridor.In this study,static passive acoustic monitoring techniques were used to detect the biosonar activities of the Yangtze finless porpoise in this unique corridor.Generalized linear models were used to examine the correlation between these activities and anthropogenic impacts from the COVID-19 pandemic lockdown and boat navigation,as well as environmental variables,including hydrological conditions and light levels.Over approximately three consecutive years of monitoring(2020–2022),porpoise biosonar was detected during 93%of logged days,indicating the key role of the corridor for finless porpoise conservation.In addition,porpoise clicks were recorded in 3.80%of minutes,while feeding correlated buzzes were detected in 1.23%of minutes,suggesting the potential existence of localized,small-scale migration.Furthermore,both anthropogenic and environmental variables were significantly correlated with the diel,lunar,monthly,seasonal,and annual variations in porpoise biosonar activities.During the pandemic lockdown period,porpoise sonar detection showed a significant increase.Furthermore,a significant negative correlation was identified between the detection of porpoise click trains and buzzes and boat traffic intensity.In addition to water level and flux,daylight and moonlight exhibited significant correlations with porpoise biosonar activities,with markedly higher detections at night and quarter moon periods.Ensuring the spatiotemporal reduction of anthropogenic activities,implementing vessel speed restrictions(e.g.,during porpoise migration and feeding),and maintaining local natural hydrological regimes are critical factors for sustaining porpoise population viability.

Dimensioning Urban Drainage Systems in Housing Subdivisions in the Amazon Using Different Hydrological Models

Hydrological studies for sizing urban drainage systems in the Amazon have often been neglected and little investigated for rainwater projects. This research evaluated alternative hydrological models used in sizing urban drainage network projects in subdivisions with subsidized houses in the Amazonian region in Brazil. Statistical tests of these models were performed for both original and alternative scenarios. The methodological steps we conducted as follows: 1) evaluate the dimensioning of infrastructure project networks, considering two case studies contemplated by the Calha Norte Program (CNP) in the state of Amapá;2) test the statistical significance of the dimensioning of network diameters (α < 0.05), considering a) benchmark project (MD or M1) approved by the Ministry of Defense;b) determination of concentration time (Ct) and rainfall intensity-duration-frequency (IDF) relationships, as well as estimating diameters using alternative models. The results indicated a significant influence on the diameters of the projected rainfall networks (p < 0.05), suggesting that alternative models predicted more unfavorable flow peaks than the original model. We conclude that the benchmarking model underestimated the diameter of the project compared to alternative models, which means the optimized Ct parameter significantly impacts dimensioning estimates in rainwater projects in these Amazonian municipalities. This suggests that underestimated parameters in MD may cause inefficiency in the stormwater system projects in future similar scenarios.

Artificial Intelligence Technique in Hydrological Forecasts Supporting for Water Resources Management of a Large River Basin in Vietnam

Hydrological forecasting plays an important role in water resource management, supporting socio-economic development and managing water-related risks in river basins. There are many flow forecasting techniques that have been developed several centuries ago, ranging from physical models, physics-based models, conceptual models, and data-driven models. Recently, Artificial Intelligence (AI) has become an advanced technique applied as an effective data-driven model in hydrological forecasting. The main advantage of these models is that they give results with compatible accuracy, and require short computation time, thus increasing forecasting time and reducing human and financial effort. This study evaluates the applicability of machine learning and deep learning in Hanoi water level forecasting where it is controlled for flood management and water supply in the Red River Delta, Vietnam. Accordingly, SANN (machine learning algorithm) and LSTM (deep learning algorithm) were tested and compared with a Physics-Based Model (PBM) for the Red River Delta. The results show that SANN and LSTM give high accuracy. The R-squared coefficient is greater than 0.8, the mean squared error (MSE) is less than 20 cm, the correlation coefficient of the forecast hydrology is greater than 0.9 and the level of assurance of the forecast plan ranges from 80% to 90% in both cases. In addition, the calculation time is much reduced compared to the requirement of PBM, which is its limitation in hydrological forecasting for large river basins such as the Red River in Vietnam. Therefore, SANN and LSTM are expected to help increase lead time, thereby supporting water resource management for sustainable development and management of water-related risks in the Red River Delta.

Hydrological Process Factors Analysis of Heihe River Mountain Basin Based on GIS

Hydrological process factors are a reflection of the physical mechanism of basin hydrology,which can provide important basis for the use and protection of water resources.Taking Heihe River Mountain Basin as the study area,the hydrological simulation was made based on SWAT-GIS integrated model platform.The calculation methods of hydrological process factors using SWAT model were described based on the simulation results of runoff from 1990 to 2000.Hydrological process factors in the study area were analyzed by using GIS technology.The spatial and temporal characteristics of precipitation,runoff,infiltration,evapotranspiration and snowmelt in the basin were calculated and analyzed.

TIME SERIES NEURAL NETWORK MODEL FOR HYDROLOGIC FORECASTING

Time series analysis plays an important role in hydrologic forecasting,while the key to this analysis is to establish a proper model.This paper presents a time series neural network model with back propagation procedure for hydrologic forecasting.Free from the disadvantages of previous models,the model can be parallel to operate information flexibly and rapidly.It excels in the ability of nonlinear mapping and can learn and adjust by itself,which gives the model a possibility to describe the complex nonlinear hydrologic process.By using directly a training process based on a set of previous data, the model can forecast the time series of stream flow.Moreover,two practical examples were used to test the performance of the time series neural network model.Results confirm that the model is efficient and feasible.

Influence of Rainfall Run-off in Hydrologic Process on Non-Point Pollution

[Objective] This study aimed to study on influence of rainfall runoff on non-point pollution and to reduce the pollution through control of the contamination produced from rainfall runoff. [Method] In order to explore effective methods to decrease non-point pollution, we conducted analysis on hydrological process of rainfall runoff, interaction mechanism between the process and non-point pollutants, the influence on non-point pollution and hydrological model application in the research. [Result] It was proved that rainfall runoff was the main factor of non-point pollution. Control from source strengthened clearing and controlling of non-point pollutants on the ground. Growing plants in slope effectively reduced the scour and erosion of rainfall runoff on soil. The study became simple thanks for the hydrological process. [Conclusion] The research indicated that non-point pollution would be effectively reduced through control of rainfall runoff.

Discretization Approach in Integrated Hydrologic Model for Surface and Groundwater Interaction

The commonly used discretization approaches for distributed hydrological models can be broadly categorized into four types,based on the nature of the discrete components:Regular Mesh,Triangular Irregular Networks(TINs),Representative Elementary Watershed(REWs) and Hydrologic Response Units(HRUs).In this paper,a new discretization approach for landforms that have similar hydrologic properties is developed and discussed here for the Integrated Hydrologic Model(IHM),a combining simulation of surface and groundwater processes,accounting for the interaction between the systems.The approach used in the IHM is to disaggregate basin parameters into discrete landforms that have similar hydrologic properties.These landforms may be impervious areas,related areas,areas with high or low clay or organic fractions,areas with significantly different depths-to-water-table,and areas with different types of land cover or different land uses.Incorporating discrete landforms within basins allows significant distributed parameter analysis,but requires an efficient computational structure.The IHM integration represents a new approach interpreting fluxes across the model interface and storages near the interface for transfer to the appropriate model component,accounting for the disparate discretization while rigidly maintaining mass conservation.The discretization approaches employed in IHM will provide some ideas and insights which are helpful to those researchers who have been working on the integrated models for surface-groundwater interaction.

Spatial distribution characteristics and mechanism of nonhydrological time-variable gravity in China's Mainland

The purpose of this study is to explore nonhydrological mass transfer in China's Mainland.For this purpose,gravity recovery and climate experiment(GRACE)data were obtained to study the spatial distribution of time variant gravity signals in China's Mainland.Then,from auxiliary hydrological data processed according to the current hydrological model,a new more comprehensive hydrological model of China's Mainland was constructed.Finally,the time variant signals of this new hydrological model were removed from the time variant gravity field computed from GRACE data,thus obtaining a description of the nonhydrological mass transfer of China's Mainland.The physical sources and mechanisms of the resulting mass transfer are then discussed.The improved,more realistic,hydrological model used here was created by selecting the hydrological components with the best correlations in existing hydrological models,by use of correlation calculation,analysis,and comparison.This improved model includes water in soils and deeper strata,in the vegetation canopy,in lakes,snow,and glaciers,and in other water components(mainly reservoir storage,swamps,and rivers).The spatial distribution of the transfer signals due to nonhydrological mass in China's Mainland was obtained by subtracting the combined hydrological model from the GRACE time-variable gravity field.The results show that the nonhydrological signals in China's Mainland collected in GRACE data were mainly positive signals,and were distributed in the Bohai Rim and the northern and eastern parts of the Tibetan Plateau.The above nonhydrological mass transfer signals have been studied further and are discussed.The results show that the nonhydrological mass migration signals in the Bohai Rim region originate primarily from sea level change and marine sediment accumulation.The mass accumulation from Indian plate collision in the Tibetan Plateau appears to be the main reason for the increase in the residual gravity field in that region.

Identification of Suitable Hydrologic Response Unit Thresholds for Soil and Water Assessment Tool Streamflow Modelling

Use of a non-zero hydrologic response unit(HRU) threshold is an effective way of reducing unmanageable HRU numbers and simplifying computational cost in the Soil and Water Assessment Tool(SWAT) hydrologic modelling. However, being less representative of watershed heterogeneity and increasing the level of model output uncertainty are inevitable when minor HRU combinations are disproportionately eliminated. This study examined 20 scenarios by running the model with various HRU threshold settings to understand the mechanism of HRU threshold effects on watershed representation as well as streamflow predictions and identify the appropriate HRU thresholds. Findings show that HRU numbers decrease sharply with increasing HRU thresholds. Among different HRU threshold scenarios, the composition of land-use, soil, and slope all contribute to notable variations which are directly related to the model input parameters and consequently affect the streamflow predictions. Results indicate that saturated hydraulic conductivity, average slope of the HRU, and curve number are the three key factors affecting stream discharge when changing the HRU thresholds. It is also found that HRU thresholds have little effect on monthly model performance, while evaluation statistics for daily discharges are more sensitive than monthly results. For daily streamflow predictions, thresholds of 5%/5%/5%(land-use/soil/slope) are the optimum HRU threshold level for the watershed to allow full consideration of model accuracy and efficiency in the present work. Besides, the results provide strategies for selecting appropriate HRU thresholds based on the modelling goal.

Hydrologic Modeling Impacts of Post-mining Land Use Changes on Streamflow of Peace River, Florida

Whether mining activity results in reduced flow of surface water in the Peace River Watershed of Florida has been the subject of much debate. With increased dependence of downstream users on surface water flow of the Peace River as a source of drinking water for four coastal counties in Southwest Florida and problems of water security, the debate has been intensified. It is possible to assess relationships of mining with streamflow in the upper reaches of the Peace River Basin using hydrologic modeling and identify mined sub-basins. In this work, land-use change impacts were simulated by the Hydrological Simulation Program--Fortran （HSPF） model based on geographical information system （GIS） tools, to compare pre- and post-mining streamflows at a study site of the Peace River in west-central Florida. The purpose of this study was to determine if land-use changes caused by mining have negatively impacted streamflow in the Peace River. Changes of land use were identified before and after mining activities. A coupled volume-water depth-discharge （V-h-Q） model based on stage/storage and stage/discharge was applied using HSPF for the pre-mining and post-mining models, respectively. Daily simulated post-mining hydrographs from HSPF were plotted with the calibrated pre-mining results and streamflow hydrographs from the 18 gauging stations, to compare timing of peaks, low fows and flow trends. Analyses of percent ex- ceedances of flow frequency curves of the streams indicated that most streams had similar distributions for mined （reclaimed） and pre- mining periods. In the streamflow change analysis, streamflows actually increased in mining-affected basins at nearly half the stations. Streamflows at other stations diminished. Overall from this comprehensive study, there were declines in streamflow at most gauging stations on the mainstem of the Peace River and its tributaries. The results of this study suggest that regional planning is urgently needed to propose reclamation schemes that enhance regional hydrology.

Hydrological assessment of TRMM rainfall data over Yangtze River Basin

High-quality rainfall information is critical for accurate simulation of runoff and water cycle processes on the land surface. In situ monitoring of rainfall has a very limited utility at the regional and global scale because of the high temporal and spatial variability of rainfall. As a step toward overcoming this problem, microwave remote sensing observations can be used to retrieve the temporal and spatial rainfall coverage because of their global availability and frequency of measurement. This paper addresses the question of whether remote sensing rainfall estimates over a catchment can be used for water balance computations in the distributed hydrological model. The TRMM 3B42V6 rainfall product was introduced into the hydrological cycle simulation of the Yangtze River Basin in South China. A tool was developed to interpolate the rain gauge observations at the same temporal and spatial resolution as the TRMM data and then evaluate the precision of TRMM 3B42V6 data from 1998 to 2006. It shows that the TRMM 3B42V6 rainfall product was reliable and had good precision in application to the Yangtze River Basin. The TRMM 3B42V6 data slightly overestimated rainfall during the wet season and underestimated rainfall during the dry season in the Yangtze River Basin. Results suggest that the TRMM 3B42V6 rainfall product can be used as an alternative data source for large-scale distributed hydrological models.