| According to the Intergovernmental Panel on Climate Change(IPCC,2013)the global average surface temperature warmed by 0.85℃ from 1880 to the 2012 year,causing changes in precipitation and considerably impacting hydrological processes.Variations in temperature and precipitation were found influential affect water yield,Evapotranspiration(ET),surface runoff,the magnitude,and frequency of floods in the river basins.Therefore hydrological models are developed to capture current hydrological processes as well as the associated effects of climate change on the water resources is extremely important for prevention and mitigation actions to be taken.The Soil Water Assessment Tool(SWAT),a semi-distributed model,was developed to analyze the impacts of land use and climate changes on discharge,erosion,sedimentation,and water quality in gauged and ungauged watersheds(Arnold et al.,1998).SWAT has received international acceptance as a robust interdisciplinary catchment-scale modeling tool because user-friendly nature,broad application capability,and the fact that is well-evaluated,well-promoted,and well-supported.Recent studies by the United Nations Environment Programme(UNEP)indicate that Vietnam is one of the countries most affected by climate change with the air temperature will increase by approximately 1,3 to 4℃ by end of the 21st century.Under these circumstances,water sources in rivers including the Cau river basin(CRB),a large river in northern Vietnam may be adversely affected.Surprisingly,this area has only been recognized for studies in the direction of assessing the current state of surface water quality.Therefore,a thorough understanding of the current status and changing trends of hydrological processes under changing climate conditions in the CRB for developing sustainable water resources management in the state.Investigating the possibility of CFSR and CMADS data in hydrometeorological studies in the Cau river basin,Northern VietnamIn Chapter Three,the potential application of two GCPs,the China Meteorological Assimilation Driving Datasets for the SWAT model(CMADS)and Climate Forecast System Reanalysis(NECP-CFSR),are compared for the first time with data from ground-based meteorological stations over the CRB,northern Vietnam.These products are used because they have higher spatial resolutions than other products and are openly available for the study areas,covering both temperature and precipitation,and can be used immediately in flow simulations.This is a major advantage of CFSR and CMADS over satellite precipitation data that often lack associated temperature data and heterogeneous time scales.Major input data for SWAT include DEM,LULC,soil properties,and daily weather data(includes grid points and ground measurement stations located around or covering the catchment area).The period for collection and processing from 1 January 2008 to 31 December 2013 to ensure consistency in the evaluation and comparison of the performances of the input data.The 2012 ArcSWAT version,an interface in ArcGIS used to perform simulations controlled by CFSR_,CMADS_,and GMS_.The lack of gauge stations is a major issue in different parts of the world,including the CRB.Besides,some uncertainties may arise during interpolation of measuring stations with grid-based monitoring data,so the evaluation is limited between grids containing corresponding measured observed values.Hence,the climate aspect comparison was conducted using the point-to-grid approach,where the gauge stations were directly compared to their respective grids’values.The mean CC value of Tmax and Tmin obtained from CFSR is>0.92,while that of CMADS is>0.96.In addition,the MAE ranged from 0.95 to 2.47,and the RMSE varied from 1.27 to 2.85 indicating that the GCPs are in good agreement with the temperature variation at the observation stations.Although the negative PBIAS value at most stations reflects that both the CFSR and CMADS data tend to underestimate the Tmax and Tmin temperatures but CFSR and CMADS can be used as an alternative to GMS in the CRB hydrometeorological studies.A difference is found in that the CMADS values underestimated the actual precipitation,with a PBIAS value of-16.64%,while CFSR overestimated with a PBIAS of 99.2%.Therefore,the MAE value of CMADS was much lower than that of CFSR,5.7 and 8.01 mm/day,respectively.Furthermore,the analysis results of the seasonal statistical indicators obtained from the CFSR data show the largest mean errors,with MAE and RMSE values that are too large.As expected,at the pixel scale in the basin,the CFSR rainfall data was overestimated over most of the basin,with a prevalence value between 60%and 150%.In contrast,the rainfall data of CMADS tends to underestimate with an average PBIAS of-16%,but the data exhibit different states rainfall is underestimated in the western mountains the while the data have slightly higher ratings in the southern plains.In areas with tropical climates such as the Cau river,rainfall is the major source and greatly affects the runoff simulation results.The analysis showed that the rainfall data obtained from GMS and CMADS reached an agreement better than the agreement between CFSR and GMS.In general,the SWAT model based on the GMS data is best suited during the calibration and validation periods at both daily and monthly scales.The simulated flow reproduced by SWAT_GMS at Gia Bay station is "Good",with NSE>0.79 and R2>0.68.The simulations performed using the SWAT_CMADS tend to underestimate the observed flow,with PBIAS values varying from-16.19 to-19.35%,but with R2>0.76 and NSE>0.78;thus,flow simulations performed by CMADS data were within "satisfactory" on the monthly scale according to the given criteria.Finally,the SWAT_CFSR is not suitable for flow simulations over the CRB basin with,R2 and NSE values that are "Unsatisfactory" based on the given criteria.Some studies have also found that integrating temperature data from CFRS with the precipitation data of the other GCPs did not cause any difference compared to conventional simulations mainly because these data overestimate the actual precipitation values.Because the research focus of Chapter 3 is to evaluate the possibility of re-analytical data in hydrological studies,in this section the parameters corrected in the GMS control model are preserved,and use CMDAS weather dataset in the SWAT model on a monthly scale(hereinafter referred to as SWAT model Using CMADS’s meteorological data and Calibrated parameters of GMS,SUC-CG).The observed flow was used for validation with simulation results by GCM,CMADS,and SUC-CG at Gia Bay hydrological station during calibration(2009-2011)and validation(2012-2013)period.The evaluation indicators with R2 value>0.8,while NSE>0.7 records result as "good" by the recommended performance rating for the monthly time step.These indicators show slightly better results than using calibration by conventional CMADS with PBIAS values reaching-5.47 and-9.3%(compare to-16.19 and19.35%for CMADS,respectively).The flow tends to peak in August,which is consistent with the rainiest times of the year for GMS,CMADS,and even SUC-CG.However,simulation results from SUC-CG reproduce better at peak flows and degradation phases than CMADS.The obtained flow curves are closer to the hydrological station than the CMADS in the flood season(May to October)showing SUC-CG can get better results than conventional CMADS simulations.Despite the same tendency to underestimate the actual flow as SWAT_CMADS but SWAT_SUC-CG has a better PBIAS value.The analyzes have shown that this method has significantly improved the performance of the model compared with the conventional strategy.This approach provides an additional new solution to the potential of reanalyzed data for hydrological studies if the parameters are calibrated to improve the performance of the model.If the model input,especially the precipitation variable,is verified before application in hydrological studies(e.g.CFSR)it gives the modeler confidence in the model outputs.Projections of Future Climate Change over the Cau river basin Using the BCSD Downscaling MethodDownscaling from global-scale meteorological data to high-resolution local-scale data in climate change projects is the research focus of Chapter Four.Global climate models(GCMs)are robust tools for quantitatively assessing climate change impacts.However,GCMs outputs are insufficient to provide accurate information for local to regional scale needs due to their inadequately coarse horizontal resolutions(typically at 100-300 km).The Bias Correction Spatial Disaggregation method,BCSD(Wood et al.2004)is widely used in climate-related impact assessment studies throughout the world and is regarded as one of the most trustworthy and successful methodologies.In this study,we first present a detailed description of the BCSD method for the convenience of future users,which has not been well documented in previous research.Firstly,the observed station data were interpolated to a 0.1°×0.1° gridded dataset(hereinafter called OBS)by using the interpolation techniques for T2m(mean temperatures),daily Tmax,Tmin,and rainfall.The newly-created gridded OBS dataset will be used further in this study to bias-correct GCM data and to estimate future climate patterns in the CRB.Then,The BCSD downscaling process was divided into two major stages,namely Bias Correction(BC)and Spatial Disaggregation(SD),to spatially translate GCM data from the intermediate resolution of 1° × 1° to the targeted high-resolution of 0.1° × 0.1°.To our knowledge,this is the best spatial resolution found in a study of a basin in Vietnam.The correlation of 5 representative GCMs(including CNRM-ESM2-1,EC-Earth3,GFDL-ESM4,HadGEM-GC31-LL,MPI-ESM1-2-HR)is downscaled with meter-based data in the CRB for the period 1985-2014.Based on the locations of all points on the scatter plot,it can be seen that BCSD produces similar monthly mean temperature and mean monthly precipitation outputs at all GCMs at the observation station in the basin.The accuracy of the GCMs with CC is mostly greater than 0.99 for both T2m,Tmax and Tmin.The correlation results of cumulative monthly observed precipitation(mm/day)and GCMs data show values in the range of 0.994 to 0.998 during this period.Dimensional and dimensionless measures are also recommended in this study to evaluate the performance of the model these results suggest that the statistics are within the reasonable range between representative GCM models and observation stations.Besides,the calculation results from a refined index of agreement(dr),indicate that the value of the BCSD model error,represented by MAE,is lower than the mean,implying that BCSD values can be reasonably used in the input of future climate/hydrology scenarios.Future scenarios are downgraded for climate variables(precipitation,T2m,Tmax,and Tmin)to detect the general trend for the period 1985-2100 in the CRB.Accordingly,a profound warming trend is recorded with the annual average Tmax and Tmin both increasing at all future meteorological stations and consistent with the increasing trend in average temperature.At the end of this century,SSP5-8.5 makes the worst assumption with increases in Tmax and Tmin of 3.3℃ and 3.2℃ respectively,significantly higher than the scenario SSP2-4.5.With regard to precipitation,the results showed an increasing trend at all SSPs in the near-future(the 2030s)and mid-future(the 2060s);while SSP5-8.5 showed the opposite trend with the decline of average annual rainfall in the distant future period(the 2080s).In general,these outcomes imply that the CRB is likely to be hotter in future periods,which may cause potential issues relating to agricultural activities and water consumption.Impacts of Climate Change on Hydrology Processes in the Cau river basinThe projected changes in climate will have direct and indirect effects on the natural environment as well as on human society,especially on hydrology and water resources.In Chapter Five,we introduce a quantitative assessment of the changes in the flow regime of the CRB under climate change impacts.First,historical streamflow on the basin was simulated from topography,land cover,soil,and ground weather observations by the SWAT model.Second,project streamflow on the basin by inputting climate change data under SSP scenarios over the twenty-first century into a well-validated SWAT model.Finally,differences in flow regime between climate change scenarios and baseline period were analyzed.The calculation results of the water balance in climate change scenarios show that precipitation will increase(2-12%),while ET will decrease(2-7%),leading to an increase in runoff(9-34%)compared to the baseline period(1997-2013).In terms of precipitation,climate projections in both scenarios SSP2.45 and 5.85 show a significant upward trend in the middle of the dry season or the wet season while the decreasing trend of rainfall occurs mainly at the beginning of the dry season(December)or the end of the dry season(April)or the beginning of the wet season(May).The values of ET are identical with an upward trend dominating,except for a decrease at the end of the dry season and the first half of the wet season.For water yield,the flow in the future tends to increase in both wet and dry seasons.Flow may decrease at the end of the dry season(April)and the beginning of the wet season(May).The discharge at the basin outlet in all scenarios will increase from 6 to 53%compared to the baseline.For the SSP2-4.5 scenario,the increased rate of streamflow is higher in the rainy season than in the dry season in the short and medium-term.However,this value is stronger in the dry season than in the rainy season in the long term.For the SSP5-8.5 scenario,streamflow acceleration is higher in the dry season than in the rainy season in all three periods of this century.Annual flow tends to increase more strongly in two periods 2021-40 and 2041-60 than in the 2080-99 period.On the other hand,the impact of climate change will make streamflow higher in most of the dry season(from November to March of the following year),and the rainy season(July-October).However,the flow projections during the transition period between the dry and wet seasons(April-June)are heterogeneous.Prediction of runoff,as well as extreme flow,is important for water management in the CRB.Accordingly,The changes in the high flow(Q5)under SSP2-4.5 showed an increasing tendency in three time periods(2021-40,2041-60,and 2080-99)whereas,in the SSP5-8.5 scenario the high flow increases in the short term but decreases in the medium term and the long term.For the low flow(Q95),the predicted value will decrease in the middle period for SSP2-4.5 and the last period under SSP5-8.5.The final content of this chapter covers the annual mean of surface runoff,soil water content,and ET at the sub-basin scale under SSPs over three periods(2021-2040,2041-60,and 2080-99).Whereby,surface runoff increases in most of the sub-basins according to SSP2-4.5,especially in the upstream and midstream regions of the basin in the two periods 2021-2040 and 2041-2060.But it decreased in all sub-basins during this century according to SSP5-8.5.Soil water content and ET are projected to increase in most sub-basins under climate change scenarios.However,the rate of rising of the two water balance components will higher in SSP5-8.5 than in SSP2-4.5.ConclusionGenerally,the results obtained are very encouraging,showing that hydrological processes in the Cau river have benefited from the SWAT model.The findings of this study can assist water resource managers in making informed water use decisions,creating public policies that support appropriate use,in applying mitigation and prevention measures,to ensure water security in the basin and agronomically similar basins. |
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