Analysis of Hydrological Simulation Models Using the Parameter Combinatorial Diagram

The aim of this paper is to present graphically the behaviour of a simulation model to the varying parameters and to establish the suitability of this representation as a valid tool for the analysis of the same parameters. In this paper, we define parameter combinatorial diagram as the joint graphical representation of all box plots related to the adjustment between real and simulated data, by setting and/or changing the parameters of the simulation model. To do this, we start with a box plot representing the values of an objective adjustment function, achieving these results when varying all the parameters of the simulation model, Then we draw the box plot when setting all the parameters of the model, for example, using the median or average. Later, we get all the box plots when carrying out simulations combining fixed or variable values of the model parameters. Finally, all box plots obtained are represented neatly in a single graph. It is intended that the new parameter combinatorial diagram is used to examine and analyze simulation models useful in practice. This paper presents combinatorial diagrams of different examples of application as in the case of hydrologic models of one, two, three, and five parameters.

Effect of calibration data series length on performance and optimal parameters of hydrological model

In order to assess the effects of calibration data series length on the performance and optimal parameter values of a hydrological model in ungauged or data-limited catchments （data are non-continuous and fragmental in some catchments）, we used non-continuous calibration periods for more independent streamflow data for SIMHYD （simple hydrology） model calibration. Nash-Sutcliffe efficiency and percentage water balance error were used as performance measures. The particle swarm optimization （PSO） method was used to calibrate the rainfall-runoff models. Different lengths of data series ranging from one year to ten years, randomly sampled, were used to study the impact of calibration data series length. Fifty-five relatively unimpaired catchments located all over Australia with daily precipitation, potential evapotranspiration, and streamflow data were tested to obtain more general conclusions. The results show that longer calibration data series do not necessarily result in better model performance. In general, eight years of data are sufficient to obtain steady estimates of model performance and parameters for the SIMHYD model. It is also shown that most humid catchments require fewer calibration data to obtain a good performance and stable parameter values. The model performs better in humid and semi-humid catchments than in arid catchments. Our results may have useful and interesting implications for the efficiency of using limited observation data for hydrological model calibration in different climates.

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.

Adaptability of machine learning methods and hydrological models to discharge simulations in datasparse glaciated watersheds

The accurate simulation and prediction of runoff in alpine glaciated watersheds is of increasing importance for the comprehensive management and utilization of water resources.In this study,long shortterm memory(LSTM),a state-of-the-art artificial neural network algorithm,is applied to simulate the daily discharge of two data-sparse glaciated watersheds in the Tianshan Mountains in Central Asia.Two other classic machine learning methods,namely extreme gradient boosting(XGBoost)and support vector regression(SVR),along with a distributed hydrological model(Soil and Water Assessment Tool(SWAT)and an extended SWAT model(SWAT_Glacier)are also employed for comparison.This paper aims to provide an efficient and reliable method for simulating discharge in glaciated alpine regions that have insufficient observed meteorological data.The two typical basins in this study are the main tributaries(the Kumaric and Toxkan rivers)of the Aksu River in the south Tianshan Mountains,which are dominated by snow and glacier meltwater and precipitation.Our comparative analysis indicates that simulations from the LSTM shows the best agreement with the observations.The performance metrics Nash-Sutcliffe efficiency coefficient(NS)and correlation coefficient(R^(2))of LSTM are higher than 0.90 in both the training and testing periods in the Kumaric River Basin,and NS and R^(2) are also higher than 0.70 in the Toxkan River Basin.Compared to classic machine learning algorithms,LSTM shows significant advantages over most evaluating indices.XGBoost also has high NS value in the training period,but is prone to overfitting the discharge.Compared with the widely used hydrological models,LSTM has advantages in predicting accuracy,despite having fewer data inputs.Moreover,LSTM only requires meteorological data rather than physical characteristics of underlying data.As an extension of SWAT,the SWAT_Glacier model shows good adaptability in discharge simulation,outperforming the original SWAT model,but at the cost of increasing the complexity of the model.Compared with the oftentimes complex semi-distributed physical hydrological models,the LSTM method not only eliminates the tedious calibration process of hydrological parameters,but also significantly reduces the calculation time and costs.Overall,LSTM shows immense promise in dealing with scarce meteorological data in glaciated catchments.

Hydrological Model to Simulate Daily Flow in a Basin with the Help of a GIS

Hydrological modeling is an essential tool to evaluate water resources in hydrological basins. The time invested in it depends on the structure of the hydrological model chosen, the amount and quality of information required and the efforts invested in calibration. CEQUEAU is a distributed hydrological model developed at the INRS-ETE, Quebec, Canada. The basin is divided into cells and the rainfall-runoff process is simulated cell by cell until the outlet. Recent advances in geomatics make it possible to develop modules integrated in geographic information systems (GIS) to facilitate the processing of information required by hydrological models. The objective of the present investigation is to implement the CEQUEAU model in Idrisi GIS for the hydrological modeling of basins, thereby reducing information processing time and improving limitations in the original version, such as the number of discretization cells and methods to calculate evapotranspiration. This document presents the results from the implementation of the CEQUEAU model, including evapotranspiration, water levels (in reservoirs, soil and aquifers) and hydrographs. These results show that these new changes provide more hydrology options to the user and with better results.

Application of hydrological models in a snowmelt region of Aksu River Basin

This study simulated and predicted the runoff of the Aksu River Basin, a typical river basin supplied by snowmelt in an arid mountain region, with a limited data set and few hydrological and meteorological stations. Two hydrological models, the snowmelt-runoff model （SRM） and the Danish NedbФr-AfstrФmnings rainfall-runoff model （NAM）, were used to simulate daily discharge processes in the Aksu River Basin. This study used the snow-covered area from MODIS remote sensing data as the SRM input. With the help of ArcGIS software, this study successfully derived the digital drainage network and elevation zones of the basin from digital elevation data. The simulation results showed that the SRM based on MODIS data was more accurate than NAM. This demonstrates that the application of remote sensing data to hydrological snowmelt models is a feasible and effective approach to runoff simulation and prediction in arid unguaged basins where snowmelt is a major runoff factor.

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.

Mixed D-vine copula-based conditional quantile model for stochastic monthly streamflow simulation

Copula functions have been widely used in stochastic simulation and prediction of streamflow.However,existing models are usually limited to single two-dimensional or three-dimensional copulas with the same bivariate block for all months.To address this limitation,this study developed a mixed D-vine copula-based conditional quantile model that can capture temporal correlations.This model can generate streamflow by selecting different historical streamflow variables as the conditions for different months and by exploiting the conditional quantile functions of streamflows in different months with mixed D-vine copulas.The up-to-down sequential method,which couples the maximum weight approach with the Akaike information criteria and the maximum likelihood approach,was used to determine the structures of multivariate Dvine copulas.The developed model was used in a case study to synthesize the monthly streamflow at the Tangnaihai hydrological station,the inflow control station of the Longyangxia Reservoir in the Yellow River Basin.The results showed that the developed model outperformed the commonly used bivariate copula model in terms of the performance in simulating the seasonality and interannual variability of streamflow.This model provides useful information for water-related natural hazard risk assessment and integrated water resources management and utilization.

Assessment of possible impacts of climate change on the hydrological regimes of different regions in China

The aim of this work is to investigate the soil water budget across China by means of hydrological modeling under current and future climate conditions and to evaluate the sensitivity to soil parameters. For this purpose, observed precipitation and temperature data(1981-2010) and climate simulations(2021-2050, 2071-2100) at high resolution(about 14 km) on a large part of China are used as weather forcing. The simulated weather forcing has been bias corrected by means of the distribution derived quantile mapping method to eliminate the effects of systematic biases in current climate modeling on water cycle components. As hydrological models, two 1D models are tested: TERRA-ML and HELP. Concerning soil properties, two datasets, provided respectively by Food and Agriculture Organization and U.S. Department of Agriculture, are separately tested. The combination of two hydrological models, two soil parameter datasets and three weather forcing inputs(observations, raw and bias corrected climate simulations) results in ?ve different simulation chains.The study highlights how the choice of some approaches or soil parameterizations can affect the results both in absolute and in relative terms and how these differences could be highly related to weather forcing in inputs or investigated soil. The analyses point out a decrease in average water content in the shallower part of the soil with different extents according to climate zone, concentration scenario and soil/cover features.Moreover, the projected increase in temperature and then in evapotranspirative demand do not ever result in higher actual evapotranspiration values, due to the concurrent variations in precipitation patterns.

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.

Application of isotopic information for estimating parameters in Philip infiltration model

Minimizing parameter uncertainty is crucial in the application of hydrologic models.Isotopic information in various hydrologic components of the water cycle can expand our knowledge of the dynamics of water flow in the system,provide additional information for parameter estimation,and improve parameter identifiability.This study combined the Philip infiltration model with an isotopic mixing model using an isotopic mass balance approach for estimating parameters in the Philip infiltration model.Two approaches to parameter estimation were compared:(a) using isotopic information to determine the soil water transmission and then hydrologic information to estimate the soil sorptivity,and(b) using hydrologic information to determine the soil water transmission and the soil sorptivity.Results of parameter estimation were verified through a rainfall infiltration experiment in a laboratory under rainfall with constant isotopic compositions and uniform initial soil water content conditions.Experimental results showed that approach(a),using isotopic and hydrologic information,estimated the soil water transmission in the Philip infiltration model in a manner that matched measured values well.The results of parameter estimation of approach(a) were better than those of approach(b).It was also found that the analytical precision of hydrogen and oxygen stable isotopes had a significant effect on parameter estimation using isotopic information.

Comparison of Parallel Genetic Algorithm and Particle Swarm Optimization for Parameter Calibration in Hydrological Simulation

Parameter calibration is an important part of hydrological simulation and affects the final simulation results.In this paper,we introduce heuristic optimization algorithms,genetic algorithm(GA)to cope with the complexity of the parameter calibration problem,and use particle swarm optimization algorithm(PsO)as a comparison.For large-scale hydrological simulations,we use a multilevel parallel parameter calibration framework to make full use of processor resources,and accelerate the process of solving high-dimensional parameter calibration.Further,we test and apply the experiments on domestic supercomputers.The results of parameter calibration with GA and PSO can basically reach the ideal value of 0.65 and above,with PSO achieving a speedup of 58.52 on TianHe-2 supercomputer.The experimental results indicate that using a parallel implementation on multicore CPUs makes high-dimensional parameter calibration in large-scale hydrological simulation possible.Moreover,our comparison of the two algorithms shows that the GA obtains better calibration results,and the PSO has a more pronounced acceleration effect.

Evaluating the suitability of TRMM satellite rainfall data for hydrological simulation using a distributed hydrological model in the Weihe River catchment in China

The objective of this study is to quantitatively evaluate Tropical Rainfall Measuring Mission （TRMM） data with rain gauge data and further to use this TRMM data to drive a Dis- tributed Time-Variant Gain Model （DTVGM） to perform hydrological simulations in the semi-humid Weihe River catchment in China. Before the simulations, a comparison with a 10-year （2001-2010） daily rain gauge data set reveals that, at daily time step, TRMM rainfall data are better at capturing rain occurrence and mean values than rainfall extremes. On a monthly time scale, good linear relationships between TRMM and rain gauge rainfall data are found, with determination coefficients R2 varying between 0.78 and 0.89 for the individual stations. Subsequent simulation results of seven years （2001-2007） of data on daily hydro- logical processes confirm that the DTVGM when calibrated by rain gauge data performs better than when calibrated by TRMM data, but the performance of the simulation driven by TRMM data is better than that driven by gauge data on a monthly time scale. The results thus suggest that TRMM rainfall data are more suitable for monthly streamfiow simulation in the study area, and that, when the effects of recalibration and the results for water balance components are also taken into account, the TRMM 3B42-V7 product has the potential to perform well in similar basins.

Uncertainty analysis of hydrological model parameters based on the bootstrap method:A case study of the SWAT model applied to the Dongliao River Watershed,Jilin Province,Northeastern China

As an important tool for the description and analysis of hydrological processes,the watershed hydrological model has been increasingly applied to watershed hydrological simulations and water resource management.However,in most cases,model parameters are only determined in a calibration scheme which fits the modeled data to observations,thus significant uncertainties exist in the model parameters.How to quantitatively evaluate the uncertainties in model parameters and the resulting uncertainty impacts on model simulations has always been a question which has attracted much attention.In this study,two methods based on the bootstrap method(specifically,the model-based bootstrap and block bootstrap)are used to analyze the parameter uncertainties in the case of the SWAT(Soil and Water Assessment Tool)model applied to a hydrological simulation of the Dongliao River Watershed.Then,the uncertainty ranges of five sensitivity parameters are obtained.The calculated variation coefficients and the variable parameter contributions show that,among the five parameters,ESCO and CN2 have relatively high uncertainties:the variation coefficients and contribution rates are 23.98 and 70%,14.43 and 18%,respectively.The three remaining parameters have relatively low uncertainties.We compare the two uncertainty ranges of parameters acquired by the two bootstrap methods,and find that the uncertainty ranges of parameters acquired by the block bootstrap are narrower than those acquired by the model-based bootstrap.Further analysis of the effects of parameter uncertainties on the model simulation reveals that the parameter uncertainties have great impacts on results of the model simulation,and in the model calibration stage 60%70%of runoff observations were within the corresponding 95%confidence interval.The uncertainty in the model simulation during the flood season(i.e.the wet period)is relatively higher than that during the dry season.

Integration of a statistical emulator approach with the SCE-UA method for parameter optimization of a hydrological model

Parameter optimization of a hydrological model is an indispensable process within model development and application.The lack of knowledge regarding the efficient optimization of model parameters often results in a bottle-neck within the modeling process,resulting in the effective calibration and validation of distributed hydrological models being more difficult to achieve.The classical approaches to global parameter optimization are usually characterized by being time consuming,and having a high computation cost.For this reason,an integrated approach coupling a meta-modeling approach with the SCE-UA method was proposed,and applied within this study to optimize hydrological model parameter estimation.Meta-modeling was used to determine the optimization range for all parameters,following which the SCE-UA method was applied to achieve global parameter optimization.The multivariate regression adaptive splines method was used to construct the response surface as a surrogate model to a complex hydrological model.In this study,the daily distributed time-variant gain model(DTVGM) applied to the Huaihe River Basin,China,was chosen as a case study.The integrated objective function based on the water balance coefficient and the Nash-Sutcliffe coefficient was used to evaluate the model performance.The case study shows that the integrated method can efficiently complete the multi-parameter optimization process,and also demonstrates that the method is a powerful tool for efficient parameter optimization.

GIS based generation of dynamic hydrological and land patch simulation models for rural watershed areas

This paper introduces a GIS based methodology to generate dynamic process model for the simulation based analysis of a sensitive rural watershed.The Direct Computer Mapping(DCM)based solution starts from GIS layers and,via the graph interpretation and graphical edition of the process network,the expert interface is able to integrate the field experts’knowledge in the computer aided generation of the simulation model.The methodology was applied and tested for the Southern catchment basin of Lake Balaton,Hungary.In the simplified hydrological model the GIS description of nine watercourses,121 water sections,57 small lakes and 20 Lake Balaton compartments were mapped through the expert interface to the dynamic databases of the DCM model.The hydrological model involved precipitation,evaporation,transpiration,runoff,infiltration.The COoRdination of INformation on the Environment(CORINE)land cover based simplified‘‘land patch”model considered the effect of meteorological and hydrological scenarios on freshwater resources in the land patches,rivers and lakes.The first results show that the applied model generation methodology helps to build complex models,which,after validation can support the analysis of various land use,with the consideration of environmental aspects.

Parameter regionalization of the FLEX-Global hydrological model

Global hydrological models(GHMs) are important tools for addressing worldwide change-related water resource problems from a global perspective. However, the development of these models has long been hindered by their low accuracy. In order to improve the streamflow simulation accuracy of GHMs, we developed a GHM—the FLEX-Global—based on the regionalization of hydrological model parameters. The FLEX-Global model is primarily based on the framework of the FLEX hydrological model coupled with the HAND-based Storage Capacity curve(HSC) runoff generation module to calculate net rainfall, and uses the global river-routing Ca Ma-Flood model to calculate river network routing. This new model allows for streamflow simulation at a spatial resolution of 0.5°×0.5° and a temporal resolution of 1 day in global catchments. To validate FLEX-Global accuracy, the FLEX-Global-simulated streamflow of 26 major rivers distributed in five different climate zones was compared with the observed data from the Global Runoff Data Center(GRDC). Next, the model performance of FLEXGlobal was further verified by comparing it with that of seven existing GHMs with varying accuracy in the five climate zones.Multi-metric evaluation indicated that the streamflow simulation accuracy was improved by the FLEX-Global model with regionalized parameters, especially in the tropical and dry climate zones. This newly-developed GHM with regionalized parameters can provide scientific support for the assessment of climate change impact, optimization of global water resource mangement, simulation of Earth's multi-sphere coupling, and implementation of the Inter-Sectoral Impact Model Intercomparison Project(ISIMIP).

IMPROVEMENTS OF A DISTRIBUTED HYDROLOGY MODEL DHSVM AND ITS CLIMATOLOGICAL-HYDROLOGICAL OFF-LINE SIMULATIONAL EXPERIMENTS

The advanced distributed hydrology-soil-vegetation model DHSVM,developed by Wigmosta et al.(1994)is introduced from US Pacific Northwest National Laboratory.To apply DHSVM in China for the first time some improvements have been made in terms of the basin characteristics: 1)to change evapotranspiration model,using the improved Penman-Monteith approach in place of the original one;2)to change the model structure,inserting datasets from 4 stations to grid cells for each river basin,instead of datasets from one or two stations;3)to develop new hydrology, vegetation and soil parameterization schemes for improving the simulated results,with focus on calculation and adjustment of 11 parameters,such as soil porosity (?),field capacity θ_(fc),leaf area index LAI,stochastic resistance γ_s,among the total 33 parameters.Then the improved DHSVM is driven by observed datasets for Luanhe River Basin and Sanggan River Basin,respectively.The simulated evapotranspiration(ET),runoff,snow water equivalent,water table,soil moisture and percolation are then gained as DHSVM outputs.The simulated ET shows that the highest peak appears in May or June instead of July or August.This is consistent with the real situations, owing to the improvement of ET model.The simulated runoff process and flood peak are quite consistent with the observed ones.The model efficiency values for Luanhe River and Sanggan River Basins are 0.89 and 0.82,respectively,which shows high simulating ability of the model system for both relatively humid and dry basins.

Evaluation of the applicability of climate forecast system reanalysis weather data for hydrologic simulation： A case study in the Bahe River Basin of the Qinling Mountains, China

In recent years, global reanalysis weather data has been widely used in hydrological modeling around the world, but the results of simulations vary greatly. To consider the applicability of Climate Forecast System Reanalysis（CFSR） data in the hydrologic simulation of watersheds, the Bahe River Basin was used as a case study. Two types of weather data（conventional weather data and CFSR weather data） were considered to establish a Soil and Water Assessment Tool（SWAT） model, which was used to simulate runoff from 2001 to 2012 in the basin at annual and monthly scales. The effect of both datasets on the simulation was assessed using regression analysis, Nash-Sutcliffe Efficiency（NSE）, and Percent Bias（PBIAS）. A CFSR weather data correction method was proposed. The main results were as follows.（1） The CFSR climate data was applicable for hydrologic simulation in the Bahe River Basin（R^2 of the simulated results above 0.50, NSE above 0.33, and ｜PBIAS｜ below 14.8. Although the quality of the CFSR weather data is not perfect, it achieved a satisfactory hydrological simulation after rainfall data correction.（2） The simulated streamflow using the CFSR data was higher than the observed streamflow, which was likely because the estimation of daily rainfall data by CFSR weather data resulted in more rainy days and stronger rainfall intensity than was actually observed. Therefore, the data simulated a higher base flow and flood peak discharge in terms of the water balance, except for some individual years.（3） The relation between the CFSR rainfall data（x） and the observed rainfall data（y） could berepresented by a power exponent equation： y=1.4789x0.8875（R2=0.98,P〈0.001）. There was a slight variation between the fitted equations for each station. The equation provides a theoretical basis for the correction of CFSR rainfall data.

Discharge Simulation in a Data-Scarce Basin Using Reanalysis and Global Precipitation Data: A Case Study of the White Volta Basin

Basins in many parts of the world are ungauged or poorly gauged, and in some cases existing measurement networks are declining. The purpose of this study was to examine the utility of reanalysis and global precipitation datasets in the river discharge simulation for a data-scarce basin. The White Volta basin of Ghana which is one of international rivers was selected as a study basin. NCEP1, NCEP2, ERA-Interim, and GPCP datasets were compared with corresponding observed precipitation data. Annual variations were not reproduced in NCEP1, NCEP2, and ERA-Interim. However, GPCP data, which is based on satellite and observed data, had good seasonal accuracy and reproduced annual variations well. Moreover, five datasets were used as input data to a hydrologic model with HYMOD, which is a water balance model, and with WTM, which is a river model;thereafter, the hydrologic model was calibrated for each datum set by a global optimization method, and river discharge were simulated. The results were evaluated by the root mean square error, relative error, and water balance error. As a result, the combination of GPCP precipitation and ERA-Interim evaporation data was the best in terms of most evaluations. The relative errors in the calibration and validation periods were 43.1% and 46.6%, respectively. Moreover, the results for the GPCP precipitation and ERA-Interim evaporation were better than those for the combination of observed precipitation and ERA-Interim evaporation. In conclusion, GPCP precipitation data and ERA-Interim evaporation data are very useful in a data-scarce basin water balance analysis.