Multi reanalysis data driven swat model building and its application in hydrology response to climate change in cau river basin of northern vietnam

分类号 密 级 U 编 号 10486 D C 博 士 学 位 论 文 基于多源再分析数据的 SWAT 模型构建及 其在越南北部 Cau 河流域水文对气候变化 的响应研究 研 究 生 姓 名 ：DAO DUY MINH 指导教师姓名、 职称 陈晓玲 教授 ： 陆建忠 副教授 学 科 、 专 业 名 称 ：地图与地理信息系统 研 究 方 向 ：遥感 GIS 应用；水文模型 二〇二二年五月 Tai ngay!!! Ban co the xoa dong chu nay!!! PhD Dissertation of Wuhan University Multi reanalysis data-driven SWAT model building and its application in hydrology response to climate change in Cau river basin of northern Vietnam By DAO DUY MINH Supervised by Prof Xiaoling Chen A Prof Jianzhong Lu Wuhan University May, 2022 Multi reanalysis data-driven SWAT model building and its application in hydrology response to climate change in Cau river basin of northern Vietnam By DAO DUY MINH Ph.D Dissertation Submitted to State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS) Of the WUHAN UNIVERSITY In partial Fulfillment of the Requirements for the Degree of DOCTOR OF PHILOSOPHY In CARTOGRAPHY and GEOGRAPHIC INFORMATION SYSTEM Prof Xiaoling Chen A Prof Jianzhong Lu May, 2022 学位论文创新点 (INNOVATION) 本论文具有如下三个创新点： (1) 探讨 CFSR 和 CMADS 数据用于越南北部 Cau 河流域水文气象研究的可能性 由于第 章的研究重点是评估水文研究中重新分析数据的可能性，因此本节保留了 GMS 控制模型中修正的参数，在 SWAT 模型中使用月尺度的 CMDAS 天气数据集(以下 简称 SWAT 模型，使用 CMADS 气象数据和校准的 GMS、SUC-CG 参数)。在校准(20092011)和验证(2012-2013)期间，利用 GCM、CMADS 和 SUC-CG 在 Gia Bay 水物站的模 拟结果对观测流量进行验证。根据每月时步的推荐性能评级记录结果为“良好”， 将评价指标采用 R2>0.8 和 NSE>0.7。这些指标的结果略优于使用常规的 CMADS 进行 校准, PBIAS 值达到-5.47 和-9.3% (相比之下，CMADS 分别为-16.19 和-19.35%)。 分析表明，与传统策略相比，该方法显著提高了模型的性能。如果对参数进行校准 以提高模型的性能，这种方法为水文研究重新分析数据的潜力提供了一种新的解决 方案。 (2) 气候预测的降尺度 第四章的研究重点是气候变化项目中全球尺度的气象数据降尺度到高分辨率局部尺 度数据。偏差校正空间分解方法 BCSD (Wood et al 2004)被认为是最可靠、最有 效的方法之一，在世界许多地区的各种气候相关影响评估研究中被广泛采用。在本 研究中，我们首先对 BCSD 方法进行了详细的描述，以方便未来的用户，这在以前 的研究中没有很好的文献记录。将 BCSD 降尺度过程分为偏置校正(BC)和空间分解 (SD)两个主要阶段，将 GCM 数据从 1°× 1°的中间分辨率转化为 0.1°× 0.1°的 目标分辨率。据我们所知，这是在越南盆地研究中发现的最佳空间分辨率。 (3) 气候变化对 Cau 河流域水文过程的影响 CMADS 数据在访问、数据使用和在中国的研究中令人鼓舞的表现方面已被广泛使用， 并具有许多便利性。 我们的研究是在越南水文研究中引入该数据集的首次尝试。 此外，首次使用 GCM-CMIP6 数据对 CRB 上的温度、降水和径流变化进行的预测是 最新的，目前尚未广泛使用 附页二 论文原创性声明 本人郑重声明： 所呈交的学位论文， 是本人在导师指导下，独 立进行研究工 作所取得的研究成果。除文中已经标明引用的内容外, 本论文不包含任何其他个 人或集体已发表或撰写的研究成果。对本 文的研究做出贡献的个人和集体，均 已在文中以明确方式标明。本声明的法律结果由本人承担。 学位论文作者（签名） ：DAO DUY MINH 2022 年 04 月 25 日 武汉大学博士学位论文 ABSTRACT According to the Intergovernmental Panel on Climate Change (IPCC, 2013) the global average surface temperature warmed by 0.85°C 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°C 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 Vietnam In 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 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 i Multi reanalysis data-driven SWAT model building and its application in hydrology response to climate change in Cau river basin of northern Vietnam 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 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 ii 武汉大学博士学位论文 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 and 19.35% 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 Method Downscaling 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° x 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 iii Multi reanalysis data-driven SWAT model building and its application in hydrology response to climate change in Cau river basin of northern Vietnam The correlation of 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 3.3oC and 3.2oC 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 basin The 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 iv Multi reanalysis data-driven SWAT model building and its application in hydrology response to climate change in Cau river basin of northern Vietnam models are expected to be necessary to interface with economic and social models The focus of future study will be on the link and interaction between hydrological models and future economic-social models This allows users to better comprehend the model's results and judgments, as well as the dependability and hazards connected with them 112 武汉大学博士学位论文 BIBLIOGRAPHY 10 11 12 13 14 15 16 17 18 19 20 Faramarzi, M.; Srinivasan, R.; Iravani, M.; Bladon, K.D.; Abbaspour, K.C.; Zehnder, A.J.B.; Goss, G.G Setting up a Hydrological Model of Alberta: Data Discrimination Analyses Prior to Calibration Environ Model Softw 2015, 74, 48–65, doi:10.1016/j.envsoft.2015.09.006 Chu, J., J Xia, C.-Y Xu, and V.S Hydrologic Models 2002 IPCC 2013 The Physical Science Basis Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change Cambridge Univ Press Cambridge, United Kingdom 2013 Huntington, T.G Evidence for Intensification of the Global Water Cycle: Review and Synthesisitle 2006 Johnson, T., J Butcher, D Deb, M Faizullabhoy, P Hummel, J Kittle, S.M.; L Mearns, D Nover, and A.P Modeling Streamflow and Water Quality Sensitivity to Climate Change and Urban Development in 20 US Watersheds 2015 Melillo, J M., T T Richmond, and G.Y Climate Change Impacts in the United States Third Natl Clim Assess 2014, doi: 10.7930/J0Z31WJ2 Pervez, M S., and G.M.H Assessing the Impacts of Climate and Land Use and Land Cover Change on the Freshwater Availability in the Brahmaputra River Basin 2015 Changnon, S A., and K.E.K Climate-Related Fluctuations in Midwestern Floods during 1921-1985 1995 Chien, H., P J.-F Yeh, and J.H.K Modeling the Potential Impacts of Climate Change on Streamflow in Agricultural Watersheds of the Midwestern United States 2013 Small, D., S Islam, and R.M.V Trends in Precipitation and Streamflow in the Eastern US: Paradox or Perception? Geophys Res Lett 2006, 33 (3), https://doi.org/10.1029/2005GL024995 Jha, M., J G Arnold, P W Gassman, F Giorgi, and R.R.G Climate Change Sensitivity Assessment on Upper Mississippi River Basin Streamflows Using SWAT 2006 Harry F Lins & James R Slack (2005) Seasonal and Regional Characteristics of U.S Streamflow Trends in the United States from 1940 to 1999, Physical Geography 2006, 26 (6), 489-501, doi: 10.2747/0272-3646.26.6.489 Novotny, E V., and H.G.S Stream Flow in Minnesota: Indicator of Climate Change 2007, 334, 319-333, doi:10.1016/j.jhydrol.2006.10.011 Changnon, S A., and K.E.K Climate-Related Fluctuations in Midwestern Floods during 1921-1985 J Water Resour Plan Manag 1995 Douglas, E., R Vogel, and C.K No TitleTrends in Floods and Low Flows in the United States: Impact of Spatial Correlation J Hydrol 2000 Groisman, P Y., R W Knight, and T.R.K Heavy Precipitation and High Streamflow in the Contiguous United States: Trends in the Twentieth Century 2001 Kunkel, K.E North American Trends in Extreme Precipitation Nat Hazards 2003 Ficklin, D L., Y Luo, E Luedeling, and M.Z Climate Change Sensitivity Assessment of a Highly Agricultural Watershed Using SWATo Title 2009 Lirong, S., and Z.J Hydrological Response to Climate Change in Beijiang River Basin Based on the SWAT Model 2012 Johnson, T., J Butcher, D Deb, M Faizullabhoy, P Hummel, J Kittle, S.M.; L Mearns, D Nover, and A.P Modeling Streamflow and Water Quality Sensitivity to Climate Change and Urban Development in 20 US Watersheds JAWRA J Am Water Resour 113 Multi reanalysis data-driven SWAT model building and its application in hydrology response to climate change in Cau river basin of northern Vietnam 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 Assoc 2015 Khoi, D.N.; Thom, V.T Parameter Uncertainty Analysis for Simulating Streamflow in a River Catchment of Vietnam Glob Ecol Conserv 2015, 4, 538–548, doi:10.1016/j.gecco.2015.10.007 Khoi, D.N.; Suetsugi, T Impact Des Changements Climatiques et de l’utilisation Des Terres Sur Les Processus Hydrologiques et La Production de Sédiments-Étude de Cas Du Bassin Versant de La Rivière Be, Vietnam Hydrol Sci J 2014, 59, 1095–1108, doi:10.1080/02626667.2013.819433 Le, T.B.; Sharif, H.O Modeling the Projected Changes of River Flow in Central Vietnam under Different Climate Change Scenarios Water (Switzerland) 2015, 7, 3579–3598, doi:10.3390/w7073579 Tran, V.B.; Ishidaira, H.; Nakamura, T.; Do, T.N.; Nishida, K Estimation of Nitrogen Load with Multi-Pollution Sources Using the SWAT Model: A Case Study in the Cau River Basin in Northern Vietnam J Water Environ Technol 2017, 15, 106–119, doi:10.2965/jwet.16-052 Bui, H.H.; Ha, N.H.; Nguyen, T.N.D.; Nguyen, A.T.; Pham, T.T.H.; Kandasamy, J.; Nguyen, T.V Integration of SWAT and QUAL2K for Water Quality Modeling in a Data Scarce Basin of Cau River Basin in Vietnam Ecohydrol Hydrobiol 2019, 19, 210–223, doi:10.1016/j.ecohyd.2019.03.005 Ha Ngoc Hien, Bui Huy Hoang, Tran Thi Huong, Tran Thanh Than, Pham Thi Thu Ha, Ta Dang Toan, N.M.; Son Study of the Climate Change Impacts on Water Quality in Upstream Portion of the Cau River Basin, Vietnam Environ Model Assess 2016 Chu, J., J Xia, C.-Y Xu, and V.S Statistical Downscaling of Daily Mean Temperature, Pan Evaporation and Precipitation for Climate Change Scenarios in Haihe River, China 2010 Theor Appl Climatol 99, 149–161, https://doi.org/10.1007/s00704-009-0129-6 Monteith, J Evaporation and Environment The State and Movement of Water in Living Organisms 1965 Roth, V.; Lemann, T.; Ebert, B.Y.E.E.; Janowiak, J.E.; Kidd, C.; De Almeida Bressiani, D.; Srinivasan, R.; Jones, C.A.; Mendiondo, E.M.; Liu, J.; et al Intercomparison of Rain Gauge, Radar, and Satellite-Based Precipitation Estimates with Emphasis on Hydrologic Forecasting Remote Sens 2015, 10, 497–517, doi:10.3965/j.ijabe.20150803.970 De Almeida Bressiani, D.; Srinivasan, R.; Jones, C.A.; Mendiondo, E.M Effects of Different Spatial and Temporal Weather Data Resolutions on the Stream Flow Modeling of a Semi-Arid Basin, Northeast Brazil Int J Agric Biol Eng 2015, 8, 1–16, doi:10.3965/j.ijabe.20150803.970 Neitsch, S ; Arnold, J ; Kiniry, J ; Williams, J Soil & Water Assessment Tool Theoretical Documentation Version 2009 Texas Water Resour Inst 2011, 1–647, doi:10.1016/j.scitotenv.2015.11.063 Arnold, J G., R Srinivasan, R S Muttiah, and J.R.W Large Area Hydrologic Modeling and Assessment Part I: Model Development1 Wiley Online Libr 1998 Thai, T.H.; Thao, N.P.; Dieu, B.T Assessment and Simulation of Impacts of Climate Change on Erosion and Water Flow by Using the Soil and Water Assessment Tool and GIS: Case Study in Upper Cau River Basin in Vietnam Vietnam J Earth Sci 2017, 39, doi:10.15625/0866-7187/39/4/10741 Tan, M.L.; Tan, K.C.; Chua, V.P.; Chan, N.W Evaluation of TRMM Product for Monitoring Drought in the Kelantan River Basin, Malaysia Water (Switzerland) 2017, 9, doi:10.3390/w9010057 Zhang, W X., Zhou, T J., Zou, L W., Zhang, L X., & Chen, X.L Reduced Exposure to Extreme Precipitation from 0.5 °C Less Warming in Global Land Monsoon Regions 114 武汉大学博士学位论文 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 2018 Hu, Q.; Yang, D.; Li, Z.; Mishra, A.K.; Wang, Y.; Yang, H Multi-Scale Evaluation of Six High-Resolution Satellite Monthly Rainfall Estimates over a Humid Region in China with Dense Rain Gauges Int J Remote Sens 2014, 35, 1272–1294, doi:10.1080/01431161.2013.876118 Fuka, D.R.; Walter, M.T.; Macalister, C.; Degaetano, A.T.; Steenhuis, T.S.; Easton, Z.M Using the Climate Forecast System Reanalysis as Weather Input Data for Watershed Models Hydrol Process 2014, 28, 5613–5623, doi:10.1002/hyp.10073 Lu, J.; Cui, X.; Chen, X.; Sauvage, S.; Perez, J.M.S Evaluation of Hydrological Response to Extreme Climate Variability Using SWAT Model: Application to the Fuhe Basin of Poyang Lake Watershed, China Hydrol Res 2017, 48, 1730–1744, doi:10.2166/nh.2016.115 Son, N.T.; Huong, H Le; Phuong, T.T.; Loc, N.D Application of SWAT Model to Assess Land Use and Climate Changes Impacts on Hydrology of Nam Rom River Basin in Vietnam Preprints 2020, 1–17, doi:10.20944/preprints202001.0362.v1 Tan, M.L.; Gassman, P.; Yang, X.; Haywood, J A Review of SWAT Applications, Performance and Future Needs for Simulation of Hydro-Climatic Extremes Advances In Water Resources 2020, 143: 103662 doi:10.1016/j.advwatres.2020.103662 Dinh, K.D.; Anh, T.N.; Nguyen, N.Y.; Bui, D.D.; Srinivasan, R Evaluation of GridBased Rainfall Products and Water Balances over the Mekong River Basin Remote Sens 2020, 12, doi:10.3390/rs12111858 Ebert, B.Y.E.E.; Janowiak, J.E.; Kidd, C NUMERICAL MODELS 2007, 47–64, doi:10.1175/BAMS-88-I-47 Milly, P C D., K A Dunne, and A.V.V Global Pattern of Trends in Streamflow and Water Availability in a Changing Climate 2005 Shiklomanov, I.A World Water Resources: Modern Assessment and Outlook for the 21stCentury Fed Serv Russ Hydrometeorol Environ Monit 1999 Busman, L., and G.S Agricultural Drainage Publication Series: Issues and Answers Univ Minnesota Ext Serv 2002 MONRE (Ministry of Natural Resources and Environment) Climate Change, Sea Level Rise Scenarios for Vietnam In, Ed by MONRE Vietnam NARENCA Publ CO., LTD 2016 MONRE (Ministry of Natural Resources and Environment) Water Quality Sampling Part 6: Guidance on Sampling of Rivers and Streams: TCVN 6663-6:2008 (ISO 56676:2005) 2015 Vu, M.T.; Raghavan, S V.; Liong, S.Y SWAT Use of Gridded Observations for Simulating Runoff - A Vietnam River Basin Study Hydrol Earth Syst Sci 2012, 16, 2801–2811, doi:10.5194/hess-16-2801-2012 Nam, D., Hoa, T., Duong, P., Thuan, D., and Mai, D Assessment of Flood Extremes Using Downscaled CMIP5 High-Resolution Ensemble Projections of Near-Term Climate for the Upper Thu Bon Catchment in Vietnam 2019 Alexander, L V., Tapper, N., Zhang, X., Fowler, H J., Tebaldi, C., & Lynch, A Climate Extremes: Progress and Future Directions 2009 Miguel, P.; Paramés, M Civil and Environmental Engineering Civ Environ Eng 2016, doi:10.4018/978-1-4666-9619-8 Zhou, F.; Xu, Y.; Chen, Y.; Xu, C.Y.; Gao, Y.; Du, J Hydrological Response to Urbanization at Different Spatio-Temporal Scales Simulated by Coupling of CLUE-S and the SWAT Model in the Yangtze River Delta Region J Hydrol 2013, 485, 113– 115 Multi reanalysis data-driven SWAT model building and its application in hydrology response to climate change in Cau river basin of northern Vietnam 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 125, doi:10.1016/j.jhydrol.2012.12.040 Singh, V.P Rainfall-Runoff Modeling 1988 Devia, G.K.; Ganasri, B.P.; Dwarakish, G.S A Review on Hydrological Models Aquat Procedia 2015, 4, 1001–1007, doi:10.1016/j.aqpro.2015.02.126 Burnash, R., R Ferral, and R.M A Generalised Streamflow Simulation System– Conceptual Modelling for Digital Computers Joint Federal and State River Forecast Center 1973 Beven, K., and M.J.K A Physically Based, Variable Contributing Area Model of Basin Hydrology/Un Modèle Base Physique de Zone d’appel Variable de l’hydrologie Du Bassin Versant Hydrol Sci J 1979 Arnold, J.G.; Moriasi, D.N.; Gassman, P.W.; Abbaspour, K.C.; White, M.J.; Srinivasan, R.; Santhi, C.; Harmel, R.D.; Van Griensven, A.; Van Liew, M.W.; et al SWAT: Model Use, Calibration, and Validation Trans ASABE 2012, 55, 1491–1508 Abbott, M B., J C Bathurst, J A Cunge, P E O’Connell, and J.R An Introduction to the European Hydrological System—Systeme Hydrologique Europeen,“SHE”, 1: History and Philosophy of a Physicallybased, Distributed Modelling System J Hydrol 1986 Beven, K., A Calver, and E.M The Institute of Hydrology Distributed Model 1987 Williams, J.R.; Arnold, J.G.; Kiniry, J.R.; Gassman, P.W.; Green, C.H History of Model Development at Temple , Texas 2010, 6667, doi:10.1623/hysj.53.5.948 FAO/UNESCO Soil Map of the World Http://Www.Fao.Org/Soils-Portal/SoilSurvey/Soil-Maps-and-Databases/Faounesco-Soil-Map-of-the-World/En/ Water Resources Research 2014, 8775–8790, doi:10.1002/2013WR014500.Received Jha, M., J G Arnold, P W Gassman, F Giorgi, and R.R.G Climate Change Sensitivity Assessment on Upper Mississippi River Basin Streamflows Using SWAT 2006 Chen, J.; Gao, C.; Zeng, X.; Xiong, M.; Wang, Y.; Jing, C.; Krysanova, V.; Huang, J.; Zhao, N.; Su, B Assessing Changes of River Discharge under Global Warming of 1.5 °C and °C in the Upper Reaches of the Yangtze River Basin: Approach by Using MultipleGCMs and Hydrological Models Quat Int 2017, 453, 63–73, doi:10.1016/j.quaint.2017.01.017 Lu, J.; Liu, Z.; Liu, W.; Chen, X.; Zhang, L Assessment of CFSR and CMADS Weather Data for Capturing Extreme Hydrologic Events in the Fuhe River Basin of the Poyang Lake J Am Water Resour Assoc 2020, doi:10.1111/1752-1688.12866 Meng, X.; Wang, H.; Shi, C.; Wu, Y.; Ji, X Establishment and Evaluation of the China Meteorological Assimilation Driving Datasets for the SWAT Model (CMADS) Water (Switzerland) 2018, 10, 1–18, doi:10.3390/w10111555 Ju, Q.; Yu, Z.; Hao, Z.; Ou, G.; Wu, Z.; Yang, C.; Gu, H Response of Hydrologic Processes to Future Climate Changes in the Yangtze River Basin J Hydrol Eng 2014, 19, 211–222, doi:10.1061/(ASCE)HE.1943-5584.0000770 Saha, S.; Moorthi, S.; Pan, H.L.; Wu, X.; Wang, J.; Nadiga, S.; Tripp, P.; Kistler, R.; Woollen, J.; Behringer, D.; et al The NCEP Climate Forecast System Reanalysis Bull Am Meteorol Soc 2010, 91, 1015–1057, doi:10.1175/2010BAMS3001.1 Neitsch, S., J Arnold, J Kiniry, J Williams, and K.K Soil and Water Assessment Tool Theoretical Documentation 2005 J.G Arnold, J.R Kiniry, R Srinivasan, J.R Williams, E.B Haney, S.L.N S Oil and W Ater Soil Water Assess Tool 2011, 643 Arnold, J., D Moriasi, P Gassman, K Abbaspour, M White, R Srinivasan, C.; Santhi, R Harmel, A Van Griensven, and M.V.L SWAT: Model Use, Calibration, and 116 武汉大学博士学位论文 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 Validation Trans ASABE 2012 Memarian, H., S K Balasundram, K C Abbaspour, J B Talib, C.T.B.S.; Sood., and A.M SWAT-Based Hydrological Modelling of Tropical Landuse Scenarios 2014 Abbaspour, K.C.; Rouholahnejad, E.; Vaghefi, S.; Srinivasan, R.; Yang, H.; Kløve, B A Continental-Scale Hydrology and Water Quality Model for Europe: Calibration and Uncertainty of a High-Resolution Large-Scale SWAT Model J Hydrol 2015, 524, 733– 752, doi:10.1016/j.jhydrol.2015.03.027 Tan, M.L.; Gassman, P.W.; Cracknell, A.P Assessment of Three Long-Term Gridded Climate Products for Hydro-Climatic Simulations in Tropical River Basins Water (Switzerland) 2017, 9, doi:10.3390/w9030229 Saleh, A.; Arnold, J.G.; Gassman, P.W.; Hauck, L.M.; Rosenthal, W.D.; Williams, J.R.; McFarland, A.M.S Application of SWAT for the Upper North Bosque River Watershed Trans Am Soc Agric Eng 2000, 43, 1077–1087, doi:10.13031/2013.3000 Moriasi, D.N.; Gitau, M.W.; Pai, N.; Daggupati, P Hydrologic and Water Quality Models: Performance Measures and Evaluation Criteria Trans ASABE 2015, doi:10.13031/trans.58.10715 Wang, Z.; Du, Q.; Zhao, D.; Cao, B Simulation on the Effects of Non-Point Source Pollution in Qingjiang River Basin Based on SWAT Model and GIS 2009 17th Int Conf Geoinformatics, Geoinformatics 2009 2009, 1–4, doi:10.1109/GEOINFORMATICS.2009.5293542 Yilmaz, K.K.; Hogue, T.S.; Hsu, K.L.; Sorooshian, S.; Gupta, H V.; Wagener, T Intercomparison of Rain Gauge, Radar, and Satellite-Based Precipitation Estimates with Emphasis on Hydrologic Forecasting J Hydrometeorol 2005, 6, 497–517, doi:10.1175/JHM431.1 Groisman, P.Y.; Knight, R.W.; Easterling, D.R.; Karl, T.R.; Hegerl, G.C.; Razuvaev, V.N Trends in Intense Precipitation in the Climate Record J Clim 2005, 18, 1326– 1350, doi:10.1175/JCLI3339.1 Wang, H.; Zhong, H.; Lu, J.; Yan, Q.; Li, S.; Zhou, Y Understanding the River-Lake Relationship after the Operation of TGR Based on SWAT Model J Coast Res 2020, 104, 593–600, doi:10.2112/JCR-SI104-100.1 Levizzani, V.; Cattani, E Satellite Remote Sensing of Precipitation and the Terrestrial Water Cycle in a Changing Climate Remote Sens 2019, 11, doi:10.3390/rs11192301 Villarini, G., J A Smith, M L Baeck, and W.F.K Examining Flood Frequency Distributions in the Midwest US 2011 Thom, V.T.; Khoi, D.N.; Linh, D.Q Using Gridded Rainfall Products in Simulating Streamflow in a Tropical Catchment - A Case Study of the Srepok River Catchment, Vietnam J Hydrol Hydromechanics 2017, 65, 18–25, doi:10.1515/johh-2016-0047 Tan, M.L.; Ibrahim, A.L.; Duan, Z.; Cracknell, A.P.; Chaplot, V Evaluation of Six HighResolution Satellite and Ground-Based Precipitation Products over Malaysia Remote Sens 2015, 7, 1504–1528, doi:10.3390/rs70201504 Wang, Y.; Yang, G.; Gu, X.; He, X.; Gao, Y.; Tian, L.; Liao, N Application of SWAT Model with CMADS Data for Hydrological Simulation in Western China 2020 Dao, D.M.; Lu, J.; Chen, X.; Kantoush, S.A.; Binh, D Van; Phan, P.; Tung, N.X Predicting Tropical Monsoon Hydrology Using CFSR and CMADS Data over the Cau River Basin in Vietnam Water 2021, 13, 1314, doi:10.3390/w13091314 Meng, X.; Zhang, X.; Yang, M.; Wang, H.; Chen, J.; Pan, Z.; Wu, Y Application and Evaluation of the China Meteorological Assimilation Driving Datasets For The Swat Model (CMADS) in Poorly Gauged Regions in Western China Water (Switzerland) 117 Multi reanalysis data-driven SWAT model building and its application in hydrology response to climate change in Cau river basin of northern Vietnam 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 2019, 11, 1–28, doi:10.3390/w11102171 Kim, H K., P B Parajuli, and S.D.F.T Assessing Impacts of Bioenergy Crops and Climate Change on Hydrometeorology in the Yazoo River Basin 2013 Zhang, L., Z Nan, W Yu, and Y.G Hydrological Responses to Land-Use Change Scenarios under Constant and Changed Climatic Conditions 2016 Neupane, R P., and S.K Estimating the Effects of Potential Climate and Land Use Changes on Hydrologic Processes of a Large Agriculture Dominated Watershed 2015 Zhang, G.-H., M Nearing, and B.-Y.L Potential Effects of Climate Change on Rainfall Erosivity in the Yellow River Basin of China 2005 Zierl, B., and H.B Global Change Impacts on Hydrological Processes in Alpine Catchments 2005 Hanson, R.L Evapotranspiration and Droughts Natl Water Summ 1988 Hu, Q., G D Willson, X Chen, and A.A Effects of Climate and Landcover Change on Stream Discharge in the Ozark Highlands, USA 2005 Uncertainty in Climate Change Impacts on Streamflow in Be River Catchment, Vietnam.Pdf Fontaine, T.A.; Klassen, J.F.; Cruickshank, T.S.; Hotchkiss, R.H Hydrological Response to Climate Change in the Black Hills of South Dakota, USA Hydrol Sci J 2001, 46, 27–40, doi:10.1080/02626660109492798 Guo, H., Q Hu, and T.J Annual and Seasonal Streamflow Responses to Climate and Land-Cover Changes in the Poyang Lake Basin, China 2008 Anjum, M.N.; Ding, Y.; Shangguan, D.; Ijaz, M.W.; Zhang, S Evaluation of HighResolution Satellite-Based Real-Time and Post-Real-Time Precipitation Estimates during 2010 Extreme Flood Event in Swat River Basin, Hindukush Region Adv Meteorol 2016, 2016, doi:10.1155/2016/2604980 Ficklin, D L., I T Stewart, and E.P.M Effects of Projected Climate Change on the Hydrology in the Mono Lake Basin, California 2013 Oki, T., and S.K Global Hydrological Cycles and World Water Resources 2006 Johnson, S L., and H.G.S Indicators of Climate Warming in Minnesota: Lake Ice Covers and Snowmelt Runoff 2006 Wu, Y., S Liu, and A.L.G Predicting Impacts of Increased CO2 and Climate Change on the Water Cycle and Water Quality in the Semiarid James36 River Basin of the Midwestern USA 2012 Novotny, E V., and H.G.S Stream Flow in Minnesota: Indicator of Climate Change J Hydrol 2007 Driessen, T., R Hurkmans, W Terink, P Hazenberg, P Torfs, and R.U The Hydrological Response of the Ourthe Catchment to Climate Change as Modelled by the HBV Model 2010 Jha, M K., and P.W.G Changes in Hydrology and Streamflow as Predicted by a Modelling Experiment Forced with Climate Models 2014 Gedney, N., P M Cox, R A Betts, O Boucher, C Huntingford, and P.A.S Detection of a Direct Carbon Dioxide Effect in Continental River Runoff Records 2006 Villarini, G., E Scoccimarro, K D White, J R Arnold, K E Schilling, and J.; Ghosh Projected Changes in Discharge in an Agricultural Watershed in Iowa 2015 Regonda, S K., B Rajagopalan, M Clark, and J.P Seasonal Cycle Shifts in Hydroclimatology over the Western United States 2005 Garbrecht, J., M Van Liew, and G.-O.B Trends in Precipitation, Streamflow, and Evapotranspiration in the Great Plains of the United States 2004 118 武汉大学博士学位论文 110 Maraun, D., F Wetterhall, A Ireson, R Chandler, E Kendon, M Widmann, S.; Brienen, H Rust, T Sauter, and M.T Precipitation Downscaling under Climate Change: Recent Developments to Bridge the Gap between Dynamical Models and the End User 2010 111 Phan TAD, Nguyen TTH, Tran XP, Phan TTH, L.; TT Initial Studies for Pollution Sources in Cau River Basin tenth Conf Inst Meteorol Hydrol Environ 2010 112 Zhang, L.; Meng, X.; Wang, H.; Yang, M.; Cai, S Investigate the Applicability of CMADS and CFSR Reanalysis in Northeast China Water (Switzerland) 2020, 12, doi:10.3390/W12040996 113 Aghakouchak, A.; Behrangi, A.; Sorooshian, S.; Hsu, K.; Amitai, E Evaluation of Satellite-Retrieved Extreme Precipitation Rates across the Central United States J Geophys Res Atmos 2011, doi:10.1029/2010JD014741 114 Zhang, L.; Lu, J.; Chen, X.; Sauvage, S.; Sanchez Perez, J.-M Stream Flow Simulation and Verification in Ungauged Zones by Coupling Hydrological and Hydrodynamic Models: A Case Study of the Poyang Lake Ungauged Zone Hydrol Earth Syst Sci Discuss 2017, 1–26, doi:10.5194/hess-2017-64 115 Steiner, M.; Bell, T.L.; Zhang, Y.; Wood, E.F Comparison of Two Methods for Estimating the Sampling-Related Uncertainty of Satellite Rainfall Averages Based on a Large Radar Dataset J Clim 2003, 16, 3759–3778, doi:10.1175/15200442(2003)0162.0.CO;2 116 Lin, R.; Zhou, T.; Qian, Y Evaluation of Global Monsoon Precipitation Changes Based on Five Reanalysis Datasets J Clim 2014, 27, 1271–1289, doi:10.1175/JCLI-D-1300215.1 117 Anagnostou, M.N.; Nikolopoulos, E.I.; Kalogiros, J.; Anagnostou, E.N.; Marra, F.; Mair, E.; Bertoldi, G.; Tappeiner, U.; Borga, M Advancing Precipitation Estimation and Streamflow Simulations in Complex Terrain with X-Band Dual-Polarization Radar Observations Remote Sens 2018, 10, doi:10.3390/rs10081258 118 Huffman, G.J.; Adler, R.F.; Morrissey, M.M.; Bolvin, D.T.; Curtis, S.; Joyce, R.; McGavock, B.; Susskind, J Global Precipitation at One-Degree Daily Resolution from Multisatellite Observations J Hydrometeorol 2001, 2, 36–50, doi:10.1175/15257541(2001)0022.0.CO;2 119 Lu, J.; Chen, X.; Zhang, L.; Sauvage, S.; Sćnchez-Pérez, J.M Water Balance Assessment of an Ungauged Area in Poyang Lake Watershed Using a Spatially Distributed Runoff Coefficient Model J Hydroinformatics 2018, 20, 1009–1024, doi:10.2166/hydro.2018.017 120 Dinku, T.; Chidzambwa, S.; Ceccato, P.; Connor, S.J.; Ropelewski, C.F Validation of High‐resolution Satellite Rainfall Products over Complex Terrain Int J Remote Sens 2008, 29, 4097–4110, doi:10.1080/01431160701772526 121 Dile, Y.T.; Srinivasan, R Evaluation of CFSR Climate Data for Hydrologic Prediction in Data-Scarce Watersheds: An Application in the Blue Nile River Basin J Am Water Resour Assoc 2014, 50, 1226–1241, doi:10.1111/jawr.12182 122 Worqlul, A.W.; Collick, A.S.; Tilahun, S.A.; Langan, S.; Rientjes, T.H.M.; Steenhuis, T.S Comparing TRMM 3B42, CFSR and Ground-Based Rainfall Estimates as Input for Hydrological Models, in Data Scarce Regions: The Upper Blue Nile Basin, Ethiopia Hydrol Earth Syst Sci Discuss 2015, 12, 2081–2112, doi:10.5194/hessd-12-2081-2015 123 Worqlul, A.W.; Maathuis, B.; Adem, A.A.; Demissie, S.S.; Langan, S.; Steenhuis, T.S Comparison of Rainfall Estimations by TRMM 3B42, MPEG and CFSR with GroundObserved Data for the Lake Tana Basin in Ethiopia Hydrol Earth Syst Sci 2014, 18, 4871–4881, doi:10.5194/hess-18-4871-2014 119 Multi reanalysis data-driven SWAT model building and its application in hydrology response to climate change in Cau river basin of northern Vietnam 124 Zhu, Q.; Xuan, W.; Liu, L.; Xu, Y.P Evaluation and Hydrological Application of Precipitation Estimates Derived from PERSIANN-CDR, TRMM 3B42V7, and NCEPCFSR over Humid Regions in China Hydrol Process 2016, 30, 3061–3083, doi:10.1002/hyp.10846 125 Cao, Y.; Zhang, J.; Yang, M.; Lei, X.; Guo, B.; Yang, L.; Zeng, Z.; Qu, J Application of SWAT Model with CMADS Data to Estimate Hydrological Elements and Parameter Uncertainty Based on SUFI-2 Algorithm in the Lijiang River Basin, China Water (Switzerland) 2018, 10, doi:10.3390/w10060742 126 Neitsch, S L., J G Arnold, J R Kiniry, and J.R.W Soil and Water Assessment Tool Theoretical Documentation Version 2009 2011 127 Karl, T R., Nicholls, N., & Ghazi, A Clivar/GCOS/WMO Workshop on Indices and Indicators for Climate Extremes Workshop Summary Clim Change 1999 128 Moriasi, D.N.; Gitau, M.W.; Pai, N.; Daggupati, P Hydrologic and Water Quality Models: Performance Measures and Evaluation Criteria Trans ASABE 2015, 58, 1763– 1785, doi:10.13031/trans.58.10715 129 Motovilov, Y.G.; Gottschalk, L.; Engeland, K.; Rodhe, A Validation of a Distributed Hydrological Model against Spatial Observations Agric For Meteorol 1999, 98–99, 257–277, doi:10.1016/S0168-1923(99)00102-1 130 Zhi-, C Swat 2 2010, 3–6 131 Srivastava, A.; Sahoo, B.; Raghuwanshi, N.S.; Singh, R Evaluation of VariableInfiltration Capacity Model and MODIS-Terra Satellite-Derived Grid-Scale Evapotranspiration Estimates in a River Basin with Tropical Monsoon-Type Climatology J Irrig Drain Eng 2017, 143, 04017028, doi:10.1061/(asce)ir.19434774.0001199 132 Roth, V.; Lemann, T Comparing CFSR and Conventional Weather Data for Discharge and Soil Loss Modelling with SWAT in Small Catchments in the Ethiopian Highlands Hydrol Earth Syst Sci 2016, 20, 921934, doi:10.5194/hess-20-921-2016 133 Blacutt, L.A.; Herdies, D.L.; de Gonỗalves, L.G.G.; Vila, D.A.; Andrade, M Precipitation Comparison for the CFSR, MERRA, TRMM3B42 and Combined Scheme Datasets in Bolivia Atmos Res 2015, 163, 117–131, doi:10.1016/j.atmosres.2015.02.002 134 Wang, H J., & He, S.P The North China/Northeastern Asia Severe Summer Drought in 2014 2015 135 Tian, L.; Liu, T.; Bao, A.; Huang, Y Application of CFSR Precipitation Dataset in Hydrological Model for Arid Mountainous Area: A Case Study in the Kaidu River Basin 2017 136 Li, Y.; Wang, Y.; Zheng, J.; Yang, M Investigating Spatial and Temporal Variation of Hydrological Processes in Western China Driven by CMADS Water (Switzerland) 2019 137 Hu, S.; Cao, M.; Qiu, H.; Song, J.; Wu, J.; Gao, Y.; Li, J.; Sun, K Applicability Evaluation of CFSR Climate Data for Hydrologic Simulation: A Case Study in the Bahe River Basin 2016 138 Gao, X.; Zhu, Q.; Yang, Z.; Wang, H Evaluation and Hydrological Application of CMADS against TRMM 3B42V7, PERSIANN-CDR, NCEP-CFSR, and Gauge-Based Datasets in Xiang River Basin of China 2018 139 Cao, Y.; Zhang, J.; Yang, M.; Lei, X.; Guo, B.; Yang, L.; Zeng, Z.; Qu, J Application of SWAT Model with CMADS Data to Estimate Hydrological Elements and Parameter Uncertainty Based on SUFI-2 Algorithm in the Lijiang River Basin, China Water 2018 140 Roth, V.; Lemann, T Comparing CFSR and Conventional Weather Data for Discharge 120 武汉大学博士学位论文 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 and Soil Loss Modelling with SWAT in Small Catchments in the Ethiopian Highlands Hydrol Earth Syst Sci 2016, 20, 921–934, doi:10.5194/hess-20-921-2016 Meng, X.; Wang, H.; Lei, X.; Cai, S.; Wu, H.; Ji, X.; Wang, J Hidrološko Modeliranje u Porječju Rijeke Manas Primjenom Alata Za Procjenu Tla i Vode Pomoću CMADS-A Teh Vjesn 2017, 24, 525–534, doi:10.17559/TV-20170108133334 Xu, Y., Gao, X., Shen, Y., Xu, C., Shi, Y., and Giorgi, F A Daily Temperature Dataset over China and Its Application in Validating a RCM Simulation 2009 Eyring, V.; Bony, S.; Meehl, G.A.; Senior, C.A.; Stevens, B.; Stouffer, R.J.; Taylor, K Overview of the Coupled Model Intercomparison Project Phase (CMIP6) Experimental Design and Organization 2016 Fowler, H J., Blenkinsop, S., & Tebaldi, C Linking Climate Change Modelling to Impacts Studies: Recent Advances in Downscaling Techniques for Hydrological Modelling 2007 Ines, A V M., & Hansen, J.W Bias Correction of Daily GCM Rainfall for Crop Simulation Studies 2006 Maurer, E P., & Pierce, D.W Bias Correction Can Modify Climate Model Simulated Precipitation Changes without Adverse Effect on the Ensemble Mean 2014 Tran Anh, Quan, and K.T “VARIATIONS OF PRECIPITATION AND WATER RESOURCES IN THE NORTHERN PART OF VIETNAM UNDER CLIMATE CHANGE.” 2014 Liu H, Chen J, Z.X et al A Markov Chain-Based Bias Correction Method for Simulating the Temporal Sequence of Daily Precipitation 2020 Ngai, S.T.; Juneng, L.; Tangang, F.; Chung, J.X.; Salimun, E.; Tan, M Future Projections of Malaysia Daily Precipitation Characteristics Using Bias Correction Technique 2020 Meehl, G A., T F Stocker, W D Collins, P Friedlingstein, A T Gaye, J.M.; Gregory, A Kitoh, R Knutti, J M Murphy, and A.N Global Climate Projections Clim Chang 2007 Wilby, R L., J Troni, Y Biot, L Tedd, B C Hewitson, D M Smith, and R.T.S “A Review of Climate Risk Information for Adaptation and Development Planning.” 2009 Wood, A W., Leung, L R., Sridhar, V., & Lettenmaier, D.P Hydrologic Implications of Dynamical and Statistical Approaches to Downscaling Climate Model Outputs 2004 Maurer, E P., & Hidalgo, H.G Utility of Daily vs Monthly Large-Scale Climate Data: An Intercomparison of Two Statistical Downscaling Methods 2008 Ngo-Duc, T.; Kieu, C.; Thatcher, M.; Nguyen-Le, D.; Phan-Van, T Climate Projections for Vietnam Based on Regional Climate Models 2014 Raghavan, S V., Hur, J., & Liong, S.Y Evaluations of NASA NEX-GDDP Data over Southeast Asia: Present and Future Climates 2018 Thanh, N.T.; Dutto, L.A.R Projected Changes of Precipitation Idf Curves for Short Duration under Climate Change in Central Vietnam 2018 Nam, D.H.; Hoa, T.D.; Duong, P.C.; Thuan, D.H.; Mai, D.T Assessment of Flood Extremes Using Downscaled CMIP5 High-Resolution Ensemble Projections of NearTerm Climate for the Upper Thu Bon Catchment in Vietnam 2019 Peel, M.C.; Finlayson, B.L.; McMahon, T.A Updated World Map of the Köppen-Geiger Climate Classification 2007 Fick, S.E.; Hijmans, R.J WorldClim 2: New 1-Km Spatial Resolution Climate Surfaces for Global Land Areas 2017 Ribbe, L.; Trinh, V.Q.; Firoz, A.; Nguyen, A.T.; Nguyen, U.; Nauditt, A Integrated 121 Multi reanalysis data-driven SWAT model building and its application in hydrology response to climate change in Cau river basin of northern Vietnam 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 River Basin Management in the Vu Gia Thu Bon Basin 2017 Laux, P.; Fink, M.; Waongo, M.; Pedroso, R.; Salvini, G.; Tran, D.H.; Thinh, D.Q.; Cullmann, J.; Flügel, W.-A.; Kunstmann, H Hydrological and Agricultural Impacts of Climate Change in the Vu Gia-Thu Bon River Basin in Central Vietnam 2017 Nguyen-Xuan, Thanh, Thanh Ngo-Duc, Hideyuki Kamimera, Long Trinh-Tuan, Jun Matsumoto, Tomoshige Inoue, and T.P.-V The Vietnam Gridded Precipitation (VnGP) Dataset: Construction and Validation Sola 2016 Maraun, D., Shepherd, T G., Widmann, M., Zappa, G., Walton, D., Gutiérrez, J M., Hagemann, S., Richter, I., Soares, P M., Hall, A., & Mearns, L.O Towards ProcessInformed Bias Correction of Climate Change Simulations 2017 Wood, A W., Maurer, E P., Kumar, A., & Lettenmaier, D.P.( Long-Range Experimental Hydrologic Forecasting for the Eastern United States 2002 Cannon, A J., Sobie, S R., & Murdock, T.Q (2015) Bias Correction of GCM Precipitation by Quantile Mapping: How Well Do Methods Preserve Changes in Quantiles and Extremes? 2015 Kilsby, C.G.; Jones, P.; Burton, A.; Ford, A.; Fowler, H.J.; Harpham, C.; James, P.; Smith, A.; Wilby, R A Daily Weather Generator for Use in Climate Change Studies Environ Model Softw 2007 Akhtar, M.; Ahmad, N.; Booij, M.J The Impact of Climate Change on the Water Resources of Hindukush–Karakorum–Himalaya Region under Different Glacier Coverage Scenarios J Hydrol 2008 Kilsby, C.G.; Jones, P.; Burton, A.; Ford, A.; Fowler, H.J.; Harpham, C.; James, P.; Smith, A.; Wilby, R A Daily Weather Generator for Use in Climate Change Studies 2007 Akhtar, M.; Ahmad, N.; Booij, M.J The Impact of Climate Change on the Water Resources of Hindukush–Karakorum–Himalaya Region under Different Glacier Coverage Scenarios 2008 Hay, L.E.; Wilby, R.L.; Leavesley, G.H A Comparison of Delta Change and Downscaled GCM Scenarios for Three Mountainous Basins in the United States 2000 Şen, Z Innovative Trend Analysis Methodology J Hydrol Eng 2012 Willmott, C.J.; Robeson, S.M.; Matsuura, K A Refined Index of Model Performance Int J Clim 2012 Legates, D.R.; McCabe, G.J A Refined Index of Model Performance: A Rejoinder Int J Clim 2013 Li, H B., Sheffield, J., & Wood, E.F Bias Correction of Monthly Precipitation and Temperature Fields from the IPCC AR4 Models Using Equidistant Quantile Matching 2010 Yang, X L., Wood, E F., Sheffield, J., Ren, L., Zhang, M., & Wang, Y Bias Correction of Historical and Future Simulations of Precipitation and Temperature for China from CMIP5 Models 2018 Arnell, N.W Relative Effects of Multi-Decadal Climatic Variability and Changes in the Mean and Variability of Climate Due to Global Warming: Future Streamflows in Britain J Hydrol 2003, 270, 195–213, doi:10.1016/S0022-1694(02)00288-3 Barnett, T.P.; Adam, J.C.; Lettenmaier, D.P Potential Impacts of a Warming Climate on Water Availability in Snow-Dominated Regions Nature 2005, 438, 303–309, doi:10.1038/nature04141 Van Zuidam, R.A Aerial Photo Interpretation in Terrain Analysis and Geomorphologic Mapping; Smits Publishers: The Hague, Netherlands, 1986; 122 武汉大学博士学位论文 179 FAO/UNESCO Digital Soil Map of the World 1995 180 Moriasi, D.N.; Arnold, J.G.; Liew, M.W Van; Bingner, R.L.; Harmel, R.D.; Veith, T.L Model Evaluation Guidelines for Systematic Quantification of Accuracy in Watershed Simulations Am Soc Agric Biol Eng 2007, 50, 885–900 181 USDA Soil Conservation Service National Engineering Handbook Section Hydrology; 1972; 123 Multi reanalysis data-driven SWAT model building and its application in hydrology response to climate change in Cau river basin of northern Vietnam PUBLICATIONS During the course of the research, the following SCI/ SCIE papers were written, submitted and/or published in peer reviewed international journals and also several posters/papers were submitted to workshops/conferences: DuyMinh Dao, Jianzhong Lu, Xiaoling Chen, Phamchimai Phan, Dinh Kha Dang; Prediction of tropical monsoon hydrology using gridded meteorological products over the Cau river basin in Vietnam 5th International Electronic Conference on Water Sciences (ECWS-5, MDPI), Online (11/2020); DuyMinh Dao, Jianzhong Lu, Xiaoling Chen, Sameh A.Kantoush, Doan Van Binh, Phamchimai Phan, Nguyen Xuan Tung; Predicting tropical monsoon hydrology using gridded meteorological products over the Cau River basin in Vietnam, Water, Volume 13, Issue 9, 1314, doi.org/10.3390/w13091314; Phamchimai Phan, Nengcheng Chen, Lei Xu, DuyMinh Dao, Dinh Kha Dang; NDVI Variation and Yield Prediction in Growing Season: A Case Study with Tea in Tanuyen Vietnam, Atmosphere, 12(8), 962, doi.org/10.3390/atmos12080962 124 武汉大学博士学位论文 ACKNOWLEDGEMENTS I would like to express my warmest gratitude to all my mentors, colleagues, friends and family who helped me in the completion of the work on my PhD’s thesis I would like to express my deepest gratitude to my two supervisors, Professor Xiaoling Chen and Associate Professor Jianzhong Lu This thesis would not have been possible without the expertise, incredible patience, and consistent and discerning guidance and direction of the teachers They have guided me throughout my graduate life, and have always been there with motivation, encouragement and support Their accurate and valuable criticism has helped me academically and professionally, helping me to overcome countless obstacles during my PhD pursuit Next, I would like to thank the Professors, PhDs and assistants at the State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS) They have helped me acquire rich professional knowledge and supported me a lot in the process of doing the thesis I would also like to thank my friends, and fellow graduate students who have supported me throughout this endeavor I would like to express my gratitude to my family, especially my parents Without their unconditional love and support and endless motivation this achievement would not be gained 125 Multi reanalysis data-driven SWAT model building and its application in hydrology response to climate change in Cau river basin of northern Vietnam 附页三 武汉大学学位论文使用授权协议书 （一式两份，一份论文作者保存，一份留学校存档） 本学位论文作者愿意遵守武汉大学关于保存、使用学位论文的管理办法及规定， 即：学校有权保存学位论文的印刷本和电子版，并提供文献检索与阅览服务；学校可 以采用影印、缩印、数字化或其它复制手段保存论文；在以教学与科研服务为目的前 提下，学校可以在校园网内公布部分或全部内容。 一、在本论文提交当年，同意在校园网内以及中国高等教育文献保障系统 （CALIS）、高校学位论文系统提供查询及前十六页浏览服务。 二、在本论文提交□当年/□一年/□两年/□三年以后，同意在校园网内允许读 者在线浏览并下载全文，学校可以为存在馆际合作关系的兄弟高校用户提供文献传递 服务和交换服务。（保密论文解密后遵守此规定） 论文作者（签名）： 学 号： 2016176190006 学 院： 测绘遥感信息工程国家重点实验室 日期： 2022 年 06 月 10 日 126