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.

A MACRO-SCALE SEMI-DISTRIBUTED HYDROLOGICAL MODEL AND APPLICATION TO THE DATONG RIVER VALLEY

A daily distributed hydrological model was developed using routine hydro meteorological data on the basis of the raster DEM and land cover data. Then the model was used to model daily runoff of the Datong River Valley located in the upper catchment of the Yellow River Basin. The runoff comprises surface flow, subsurface flow and ground water flow. Evapotranspiration comprises canopy evaporation, snow sublimation and soil evapotranspiration. The infiltration to the soil was estimated with improved Green-Ampt model, and the potential evapotranspiration is estimated with Morton CRAE method, which only needs the routine meteorological data. Simulation results and the comparison with semi distributed SLURP hydrological model show that the structure of the model presented herein is reasonable.

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.

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.

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.

Light-absorbing Particles in Snow and Ice: Measurement and Modeling of Climatic and Hydrological impact

Light absorbing particles（LAP, e.g., black carbon, brown carbon, and dust） influence water and energy budgets of the atmosphere and snowpack in multiple ways. In addition to their effects associated with atmospheric heating by absorption of solar radiation and interactions with clouds, LAP in snow on land and ice can reduce the surface reflectance（a.k.a., surface darkening）, which is likely to accelerate the snow aging process and further reduces snow albedo and increases the speed of snowpack melt. LAP in snow and ice（LAPSI） has been identified as one of major forcings affecting climate change, e.g.in the fourth and fifth assessment reports of IPCC. However, the uncertainty level in quantifying this effect remains very high. In this review paper, we document various technical methods of measuring LAPSI and review the progress made in measuring the LAPSI in Arctic, Tibetan Plateau and other mid-latitude regions. We also report the progress in modeling the mass concentrations, albedo reduction, radiative forcing, and climatic and hydrological impact of LAPSI at global and regional scales. Finally we identify some research needs for reducing the uncertainties in the impact of LAPSI on global and regional climate and the hydrological cycle.

Modelling Hydrological Consequences of Climate Change—Progress and Challenges

The simulation of hydrological consequences of climate change has received increasing attention from the hydrology and land-surface modelling communities. There have been many studies of climate-change effects on hydrology and water resources which usually consist of three steps： （1） use of general circulation models （GCMs） to provide future global climate scenarios under the effect of increasing greenhouse gases, （2） use of downscaling techniques （both nested regional climate models, RCMs, and statistical methods） for ＂downscaling＂ the GCM output to the scales compatible with hydrological models, and （3） use of hydrologic models to simulate the effects of climate change on hydrological regimes at various scales. Great progress has been achieved in all three steps during the past few years, however, large uncertainties still exist in every stage of such study. This paper first reviews the present achievements in this field and then discusses the challenges for future studies of the hydrological impacts of climate change.

Simulations of a Hydrological Model as Coupled to a Regional Climate Model

Considering a detailed hydrologic model in the land surface scheme helps to improve the simulation of regional hydro-climatology. A hydrologic model, which includes spatial heterogeneities in precipitation and infiltration, is constructed and incorporated into the land surface scheme BATS. Via the coupled-model (i.e., a regional climate model) simulations, the following major conclusions are obtained: the simulation of surface hydrology is sensitive to the inclusion of heterogeneities in precipitation and infiltration; the runoff ratio is increased after considering the infiltration heterogeneity, a result which is more consistent with the observations of surface moisture balance over humid areas; the introduction of the parameterization of infiltration heterogeneity can have a greater influence on the regional hydro-climatology than the precipitation heterogeneity; and the consideration of the impermeable fraction for the region reveals some features that are closer to the trend of aridification over northern China.

Application of Radar-Measured Rain Data in Hydrological Processes Modeling during the Intensified Observation Period of HUBEX

On the basis of Digital Elevation Model data, the raster flow vectors, watershed delineation, and spatial topological relationship are generated by the Martz and Garbrecht method for the upper area of Huangnizhuang station in the Shihe Catchment with 805 km2 of area, an intensified observation field for the HUBEX/GAME Project. Then, the Xin’anjiang Model is applied for runoff production in each grid element where rain data measured by radar at Fuyang station is utilized as the input of the hydrological model. The elements are connected by flow vectors to the outlet of the drainage catchment where runoff is routed by the Muskingum method from each grid element to the outlet according to the length between each grid and the outlet. The Nash-Sutcliffe model efficiency coefficient is 92.41% from 31 May to 3 August 1998, and 85.64%, 86.62%, 92.57%, and 83.91%, respectively for the 1st, 2nd, 3rd, and 4th flood events during the whole computational period. As compared with the case where rain-gauge data are used in simulating the hourly hydrograph at Huangnizhuang station in the Shihe Catchment, the index of model efficiency improvement is positive, ranging from 27.56% to 69.39%. This justifies the claim that radar-measured data are superior to rain-gauge data as inputs to hydrological modeling. As a result, the grid-based hydrological model provides a good platform for runoff computation when radar-measured rain data with highly spatiotemporal resolution are taken as the input of the hydrological model.

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.

Distributed hydrological models for addressing effects of spatial variability of roughness on overland flow

In this study, we investigated the origin of the overland flow roughness problem and divided the current overland flow roughness research into three types, as follows: the first type of research takes into account the effects of roughness on the volume and velocity of surface runoff, flood peaks, and the scouring capability of flows, but has not addressed the spatial variability of roughness in detail; the second type of research considers that surface roughness varies spatially with different land usage types, land-cover conditions, and different tillage forms, but lacks a quantitative study of the spatial variability; and the third type of research simply deals with the spatial variability of roughness in each grid cell or land type. We present three shortcomings of the current overland flow roughness research, including(1) the neglect of roughness in distributed hydrological models when simulating the overland flow direction and distribution,(2) the lack of consideration of spatial variability of roughness in hydrological models, and(3) the failure to distinguish the roughness formulas in different overland flow regimes. To solve these problems,distributed hydrological model research should focus on four aspects in regard to overland flow: velocity field observations, flow regime mechanisms, a basic roughness theory, and scale problems.

Sensitivity Analysis and Calibration of Hydrological Modeling of the Watershed Northeast Brazil

Mathematical models of the quantity and quality of water in hydrographic basins enable simulation of a wide variety of processes, including the production of water and sediments, and the dynamics of point and nonpoint sources of pollution. These models have become increasingly complex, requiring large amounts of input data, which can increase the uncertainty of the results of simulations. For this reason, it is essential to perform calibration and validation procedures. The objective of this work was to conduct sensitivity analysis and calibration of a distributed hydrological model (SWAT) applied to the flows of water in the watershed of the Poxim River. Satisfactory performance of the model was indicated by the values obtained for the Nash-Sutcliffe efficiency coefficient (0.77), the percent bias (5.05), the root mean square error (0.48), and the ratio of the RMSE to the standard deviation of the observations (RSR) (0.49). The set of parameters identified here could be used for the simulation and evaluation of other scenarios.

Hydrological characteristics and changes in the Nu-Salween River basin revealed with model-based reconstructed data

The Nu-Salween River(NSR),the longest free-flow river in Southeast Asia,plays an irreplaceable role in social development and ecological protection.The lower NSR region is particularly valuable as it is inhabited by approximately 6.7 million people.The basin has limited hydraulic conservancy infrastructure and insufficient ability to cope with climate change risks.Studying the hydrological characteristics and changes in the basin provides the scientific basis for rational protection and development of the basin.However,owing to the limitation of observation data,previous studies have focused on the local area and neglected the study of the lower reaches,which is not enough to reflect the spatial characteristics of the entire basin.In this study,the ECMWF 5th generation reanalysis data(ERA5)and Multi-Source Weighted-Ensemble Precipitation(MSWEP)were applied to develop a geomorphology-based hydrological model(GBHM)for reconstructing hydrological datasets(i.e.GBHM-ERA5 and GBHM-MSWEP).The reconstructed datasets covering the complete basin were verified against the gauge observation and compared with other commonly used streamflow products,including Global Flood Awareness System v2.1,GloFAS-Reanalysis dataset v3.0,and linear optimal runoff aggregate(LORA).The comparison results revealed that GBHM-ERA5 is significantly better than the other four datasets and provides a good reproduction of the hydrological characteristics and trends of the NSR.Detailed analysis of GBHM-ERA5 revealed that:(1)A multi-year mean surface runoff represented 39%of precipitation over the basin during 1980–2018,which had low surface runoff in the upstream,while areas around the Three Parallel Rivers Area and the estuary had abundant surface runoff.(2)The surface runoff and discharge coefficient of variations in spring were larger than those in other seasons,and the inter-annual variation in the downstream was smaller than that in the upstream and midstream regions.(3)More than 70%of the basin areas showed a decreasing trend in the surface runoff,except for parts of Nagqu,south of Shan State in Myanmar,and Thailand,where surface runoff has an increasing trend.(4)The downstream discharge has dropped significantly at a rate of approximately 680 million cubic metresper year,and the decline rate is greater than that of upstream and midstream,especially in summer.This study provides a data basis for subsequent studies in the NSR basin and further elucidates the impact of climate change on the basin,which is beneficial to river planning and promotes international cooperation on the water-and eco-security of the basin.

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.

Developing a Framework to Measure Watershed Sustainability by Using Hydrological/Water Quality Model

A framework is built, wherein hydrological/water quality model is used to measure watershed sustainability. For this framework, watershed sustainability has been defined and quantified by defining social, environmental and biodiversity indicators. By providing weightage to these indicators, a “River Basin Sustainability Index” is built. The watershed sustainability is then calculated based on the concepts of reliability, resilience and vulnerability. The framework is then applied to a case study, where, based on watershed management principles, four land use scenarios are created in GIS. The Soil and Water Assessment Tool (SWAT) is used as a hydrology/water quality model. Based on the results the land uses are ranked for sustainability and policy implications have been discussed. This results show that landuse (both type and location) impact watershed sustainability. The existing land use is weak in environmental sustainability. Also, riparian zones play a critical role in watershed sustainability, although beyond certain width their contribution is not significant.

Hydrological Response to Environment Change in Himalayan Watersheds: Assessment from Integrated Modeling Approach

Land use changes such as deforestation,increase in cropping or grazing areas and built-up land, likely modify the water balance and land surface behavior in the Himalayan watersheds.An integrated approach of hydrological and hydraulic modeling was adopted for comparative analysis of hydrological pattern in three Himalayan watersheds i.e.Khanpur,Rawal and Simly situated in the Northern territory of Pakistan.The rainfall-runoff model SWAT- Soil and water assessment tool and Hydro CAD were calibrated for the selected watersheds.The correlation analysis of the precipitation data of two climate stations i.e.Murree and Islamabad, with the discharge data of three rivers was utilized to select best suitable input precipitation data for Hydro CAD rainfall-runoff modeling.The peak flood hydrograph were generated using Hydro CAD runoff to optimize the basin parameters like CN, runoff volume, peak flows of the three watersheds.The hydrological response of the Rawal watershed was studied as a case study to different scenarios of land use change using SWAT model.The scenario of high deforestation indicated a decline of about 6.3% in the groundwater recharge tostream while increase of 7.1% in the surface runoff has been observed under the scenario of growth in urbanization in the recent decades.The integrated modeling approach proved helpful in investigating the hydrological behavior under changing environment at watershed level in the Himalayan region.

Projection of future streamflow of the Hunza River Basin,Karakoram Range(Pakistan)using HBV hydrological model

Hydrologiska Byrans Vattenbalansavdeling(HBV) Light model was used to evaluate the performance of the model in response to climate change in the snowy and glaciated catchment area of Hunza River Basin. The study aimed to understand the temporal variation of streamflow of Hunza River and its contribution to Indus River System(IRS). HBV model performed fairly well both during calibration(R2=0.87, Reff=0.85, PBIAS=-0.36) and validation(R2=0.86, Reff=0.83, PBIAS=-13.58) periods on daily time scale in the Hunza River Basin. Model performed better on monthly time scale with slightly underestimated low flows period during bothcalibration(R2=0.94, Reff=0.88, PBIAS=0.47) and validation(R2=0.92, Reff=0.85, PBIAS=15.83) periods. Simulated streamflow analysis from 1995-2010 unveiled that the average percentage contribution of snow, rain and glacier melt to the streamflow of Hunza River is about 16.5%, 19.4% and 64% respectively. In addition, the HBV-Light model performance was also evaluated for prediction of future streamflow in the Hunza River using future projected data of three General Circulation Model(GCMs) i.e. BCC-CSM1.1, CanESM2, and MIROCESM under RCP2.6, 4.5 and 8.5 and predictions were made over three time periods, 2010-2039, 2040-2069 and 2070-2099, using 1980-2010 as the control period. Overall projected climate results reveal that temperature and precipitation are the most sensitiveparameters to the streamflow of Hunza River. MIROC-ESM predicted the highest increase in the future streamflow of the Hunza River due to increase in temperature and precipitation under RCP4.5 and 8.5 scenarios from 2010-2099 while predicted slight increase in the streamflow under RCP2.6 during the start and end of the 21 th century. However, BCCCSM1.1 predicted decrease in the streamflow under RCP8.5 due to decrease in temperature and precipitation from 2010-2099. However, Can ESM2 predicted 22%-88% increase in the streamflow under RCP4.5 from 2010-2099. The results of this study could be useful for decision making and effective future strategic plans for water management and their sustainability in the region.

Complementary system-theoretic modelling approach for enhancing hydrological forecasting

Hydrologic models generally represent the most dominant processes since they are mere simplifications of physical reality and thus are subject to many significant uncertainties. As such, a coupling strategy is proposed. To this end, the coupling of the artificial neural network （ANN） with the Xin＇anjiang conceptual model with a view to enhance the quality of its flow forecast is presented. The approach uses the latest observations and residuals in runoff/discharge forecasts from the Xin＇anjiang model. The two complementary models （Xin＇anjiang ＆ ANN） are used in such a way that residuals of the Xin＇anjiang model are forecasted by a neural network model so that flow forecasts can be improved as new observations come in. For the complementary neural network, the input data were presented in a patterned format to conform to the calibration regime of the Xin＇anjiang conceptual model, using differing variants of the neural network scheme. The results show that there is a substantial improvement in the accuracy of the forecasts when the complementary model was operated on top of the Xin＇anjiang conceptual model as compared with the results of the Xin＇anjiang model alone.

The Contribution of the Geospatial Information to the Hydrological Modelling of a Watershed with Reservoirs: Case of Low Oum Er Rbiaa Basin (Morocco)

Water is undoubtedly the most vital natural resource. Water use management is one of the greatest challenges that face humanity. The demand for water is continuously growing because of the population growth, the intensive urbanization and the development of industrial and agricultural activities. To face the increasing pressure on this vital resource, it is so necessary to set up the adequate instruments to ensure a rational and efficient management of this resource. In this context, the hydrological modeling is largely used as an instrument to assess the functioning of these resources at watershed scale. In addition, the use of spatial models let to depict and simulate the watershed processes at small spatial and heterogeneous scales that reflect the field reality more accurate and more realistic as possible. However, the use of spatial models requires geospatial data that must be gathered at very fine scales. The aim of this study is to highlight the contribution of geospatial data to assess the hydrologic modeling of watershed by using a spatial hydro-agricultural model, notably the SWAT model (Soil and water Assessment Tool). The study area is the Basin of Low Oum Er Rbiaa River which extends from the Al Massira dam to its outlet in the Atlantic Ocean. This watershed includes a set of dams (Daourat, Imfout and Sidi Maachou) built in waterfall fashion along the river. The objective was to simulate the hydrological functioning of this area that had never been modeled in order to assess the management of these reservoirs used essentially to produce electricity and fresh water. The implementation of the SWAT model required a spatial database that was built from topography, soil, land use and climate data. The calibration and validation of the model was carried out on a daily basis over several years (2001-2010) using The ArcSWAT tool integrated in ArcGIS software and the Parasol optimization method. The calibration of SWAT model was successfully done with 0.6 as value of Nash coefficient used commonly in hydrology to evaluate the model performance. The calibrated model was then used to estimate the hydrological balance sheet of the Low Oum Er Rbiaa to model the intermediate contribution of the three reservoirs situated in the watershed.

Optimization of the Hydrological Model Using Multi-objective Particle Swarm Optimization Algorithm

An application of multi-objective particle swarm optimization （MOPSO） algorithm for optimization of the hydrological model （HYMOD） is presented in this paper. MOPSO algorithm is used to find non-dominated solutions with two objectives： high flow Nash-Sutcliffe efficiency and low flow Nash- Sutcliffe efficiency. The two sets＇ coverage rate and Pareto front spacing metric are two criterions to analyze the performance of the algorithms. MOPSO algorithm surpasses multi-objective shuffled complex evolution metcopolis （MOSCEM_UA） algorithr~, in terms of the two sets＇ coverage rate. But when we come to Pareto front spacing rate, the non-dominated solutions of MOSCEM_ UA algorithm are better-distributed than that of MOPSO algorithm when the iteration is set to 40 000. In addition, there are obvious conflicts between the two objectives. But a compromise solution can be acquired by adopting the MOPSO algorithm.