LUO Xian, WU Wenqi, HE Daming, LI Yungang, JI Xuan. Hydrological Simulation Using TRMM and CHIRPS Precipitation Estimates in the Lower Lancang-Mekong River Basin[J]. Chinese Geographical Science, 2019, 20(1): 13-25. doi: 10.1007/s11769-019-1014-6
Citation: LUO Xian, WU Wenqi, HE Daming, LI Yungang, JI Xuan. Hydrological Simulation Using TRMM and CHIRPS Precipitation Estimates in the Lower Lancang-Mekong River Basin[J]. Chinese Geographical Science, 2019, 20(1): 13-25. doi: 10.1007/s11769-019-1014-6

Hydrological Simulation Using TRMM and CHIRPS Precipitation Estimates in the Lower Lancang-Mekong River Basin

doi: 10.1007/s11769-019-1014-6
Funds:  Under the auspices of National Key R&D Program of China (No. 2016YFA0601601), National Natural Science Foundation of China (No. 41601026, 41661099), Science and Technology Planning Project of Yunnan Province, China (No. 2017FB073)
More Information
  • Corresponding author: HE Daming.E-mail:dmhe@ynu.edu.cn
  • Received Date: 2018-01-03
  • Rev Recd Date: 2018-04-30
  • Publish Date: 2019-02-27
  • Satellite-based products with high spatial and temporal resolution provide useful precipitation information for data-sparse or ungauged large-scale watersheds. In the Lower Lancang-Mekong River Basin, rainfall stations are sparse and unevenly distributed, and the transboundary characteristic makes the collection of precipitation data more difficult, which has restricted hydrological processes simulation. In this study, daily precipitation data from four datasets (gauge observations, inverse distance weighted (IDW) data, Tropical Rainfall Measuring Mission (TRMM) estimates, and Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) estimates), were applied to drive the Soil and Water Assessment Tool (SWAT) model, and then their capability for hydrological simulation in the Lower Lancang-Mekong River Basin were examined. TRMM and CHIRPS data showed good performances on precipitation estimation in the Lower Lancang-Mekong River Basin, with the better performance for TRMM product. The Nash-Sutcliffe efficiency (NSE) values of gauge, IDW, TRMM, and CHIRPS simulations during the calibration period were 0.87, 0.86, 0.95, and 0.93 for monthly flow, respectively, and those for daily flow were 0.75, 0.77, 0.86, and 0.84, respectively. TRMM and CHIRPS data were superior to rain gauge and IDW data for driving the hydrological model, and TRMM data produced the best simulation performance. Satellite-based precipitation estimates could be suitable data sources when simulating hydrological processes for large data-poor or ungauged watersheds, especially in international river basins for which precipitation observations are difficult to collect. CHIRPS data provide long precipitation time series from 1981 to near present and thus could be used as an alternative precipitation input for hydrological simulation, especially for the period without TRMM data. For satellite-based precipitation products, the differences in the occurrence frequencies and amounts of precipitation with different intensities would affect simulation results of water balance components, which should be comprehensively considered in water resources estimation and planning.
  • [1] Abbaspour K C, Rouholahnejad E, Vaghefi S et al., 2015. A continental-scale hydrology and water quality model for Europe:calibration and uncertainty of a high-resolution large-scale SWAT model. Journal of Hydrology, 524:733-752. doi: 10.1016/j.jhydrol.2015.03.027
    [2] AghaKouchak A, Nakhjiri N, 2012. A near real-time satellite-based global drought climate data record. Environmental Research Letters, 7:044037. doi: 10.1088/1748-9326/7/4/044037
    [3] Arnold J G, Srinivasan R, Muttiah R S et al., 1998. Large area hydrologic modeling and assessment part Ⅰ:model developm-ent. Journal of the American Water Resources Association, 34(1):73-89.
    [4] Arnold J G, Youssef M A, Yen H et al., 2015. Hydrological processes and model representation:impact of soft data on calibration. Transactions of the American Society of Agricultural and Biological Engineers, 58(6):1637-1660. doi:10. 13031/trans.58.10726
    [5] Cho J, Bosch D, Lowrance R et al., 2009. Effect of spatial distribution of rainfall on temporal and spatial uncertainty of SWAT output. Transactions of the American Society of Agricultural and Biological Engineers, 52(5):1545-1555. doi: 10.13031/2013.29143
    [6] Duan Z, Liu J, Tuo Y et al., 2016. Evaluation of eight high spatial resolution gridded precipitation products in Adige Basin (Italy) at multiple temporal and spatial scales. Science of the Total Environment, 573:1536-1553. doi:10.1016/j.scitotenv. 2016. 08.213
    [7] Fan H, He D, Wang H, 2015. Environmental consequences of damming the mainstream Lancang-Mekong River:a review. Earth-Science Reviews, 146:77-91. doi:10.1016/j.earscirev. 2015.03.007
    [8] Galván L, Olías M, Izquierdo T et al., 2014. Rainfall estimation in SWAT:an alternative method to simulate orographic precipi-tation. Journal of Hydrology, 509:257-265. doi:10.1016/j. jhydrol.2013.11.044
    [9] Gao J, Sheshukov A Y, Yen H et al., 2017. Impacts of alternative climate information on hydrologic processes with SWAT:a comparison of NCDC, PRISM and NEXRAD datasets. Catena, 156:353-364. doi: 10.1016/j.catena.2017.04.010
    [10] Haberlandt U, 2007. Geostatistical interpolation of hourly precipi-tation from rain gauges and radar for a large-scale extreme rainfall event. Journal of Hydrology, 332:144-157. doi:10. 1016/j.jhydrol.2006.06.028
    [11] Jiang Shanhu, Ren Liliang, Yong Bin et al., 2016. Evaluation of latest TMPA and CMORPH precipitation products with independent rain gauge observation networks over high-latitude and low-latitude basins in China. Chinese Geographical Science, 26(4):439-455. doi: 10.1007/s11769-016-0818-x
    [12] Jin Xin, He Chansheng, Zhang Lanhui et al., 2018. A modified groundwater module in SWAT for improved streamflow simulation in a large, arid endorheic river watershed in Northwest China. Chinese Geographical Science, 28(1):47-60. doi: 10.1007/s11769-018-0931-0
    [13] Johnston R, Kummu M, 2012. Water resource models in the Mekong Basin:a review. Water Resources Management, 26(2):429-455. doi: 10.1007/s11269-011-9925-8
    [14] Katiraie-Boroujerdy P S, Asanjan A A, Hsu K et al., 2017. Intercomparison of PERSIANN-CDR and TRMM-3B42V7 precipitation estimates at monthly and daily time scales. Atmospheric Research, 193:36-49. doi:10.1016/j.atmosres. 2017.04.005
    [15] Katsanos D, Retalis A, Michaelides S, 2016. Validation of a high-resolution precipitation database (CHIRPS) over Cyprus for a 30-year period. Atmospheric Research, 169:459-464. doi: 10.1016/j.atmosres.2015.05.015
    [16] Li D, Long D, Zhao J et al., 2017. Observed changes in flow regimes in the Mekong River basin. Journal of Hydrology, 551:217-232. doi: 10.1016/j.jhydrol.2017.05.061
    [17] Li X, Zhang Q, Xu C, 2012. Suitability of the TRMM satellite rainfalls in driving a distributed hydrological model for water balance computations in Xinjiang catchment, Poyang lake basin. Journal of Hydrology, 426-427:28-38. doi:10.1016/j. jhydrol.2012.01.013
    [18] Liu Miao, Li Chunlin, Hu Yuanman et al., 2014. Combining CLUE-S and SWAT models to forecast land use change and non-point source pollution impact at a watershed scale in Liaoning province, China. Chinese Geographical Science, 24(5):540-550. doi: 10.1007/s11769-014-0661-x
    [19] Ly S, Charles C, Degré A, 2011. Geostatistical interpolation of daily rainfall at catchment scale:the use of several variogram models in the Ourthe and Ambleve catchments, Belgium. Hydrology and Earth System Sciences, 15:2259-2274. doi: 10.5194/hess-15-2259-2011
    [20] Mei Y, Nikolopoulos E I, Anagnostou E N et al., 2016. Evaluating satellite precipitation error propagation in runoff simulations of mountainous basins. Journal of Hydrometeorology, 17:1407-1423. doi: 10.1175/JHM-D-15-0081.1
    [21] Mekong River Commission, 2005. Overview of the Hydrology of the Mekong Basin. Vientiane:Mekong River Commission.
    [22] Moriasi D N, Arnold J G, Van Liew M W et al., 2007. Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Transactions of the American Society of Agricultural and Biological Engineers, 50(3):885-900. doi: 10.13031/2013.23153
    [23] Mushore T D, Manatsa D, Pedzisai E et al., 2016. Investigating the implications of meteorological indicators of seasonal rainfall performance on maize yield in a rain-fed agricultural system:case study of Mt. Darwin District in Zimbabwe. Theoretical and Applied Climatology, 129(3):1-7. doi: 10.1007/s00704-016-1838-2
    [24] Neitsch S L, Arnold J G, Kiniry J R et al., 2011. Soil and Water Assessment Tool:Theoretical Documentation (Version 2009). Texas:Grassland, Soil and Water Research Laboratory, Agricul-tural Research Service, Blackland Research Center, Texas Agricultural Experiment Station.
    [25] Nikolopoulos E I, Anagnostou E N, Borga M, 2013. Using high-resolution satellite rainfall products to simulate a major flash flood event in northern Italy. Journal of Hydrometeorology, 14:171-185. doi: 10.1175/JHM-D-12-09.1
    [26] Räsänen T A, Someth P, Lauri H et al., 2017. Observed river discharge changes due to hydropower operations in the Upper Mekong Basin. Journal of Hydrology, 545:28-41. doi: 10.1016/j.jhydrol.2016.12.023
    [27] Rozante J R, Moreira D S, de Goncalves L G G et al., 2010. Combining TRMM and surface observations of precipitation:technique and validation over South America. Weather and Forecast, 25:885-894. doi: 10.1175/2010WAF2222325.1
    [28] Sabo J L, Ruhi A, Holtgrieve G W et al., 2017. Designing river flows to improve food security futures in the Lower Mekong Basin. Science, 358:eaao1053. doi: 10.1126/science.aao1053
    [29] Serrat-Capdevila A, Valdes J B, Stakhiv E Z, 2014. Water management applications for satellite precipitation products:Synthesis and recommendations. Journal of the American Water Resources Association, 50(2):509-525. doi: 10.1111/jawr.12140
    [30] Shrestha N K, Qamer F M, Pedreros D et al., 2017. Evaluating the accuracy of Climate Hazard Group (CHG) satellite rainfall estimates for precipitation based drought monitoring in Koshi basin, Nepal. Journal of Hydrology:Regional Studies, 13:138-151. doi: 10.1016/j.ejrh.2017.08.004
    [31] Son N T, Chen C F, Chen C R et al., 2012. Monitoring agricultural drought in the Lower Mekong Basin using MODIS NDVI and land surface temperature data. International Journal of Applied Earth Observation and Geoinformation, 18:417-427. doi: 10.1016/j.jag.2012.03.014
    [32] Tatsumi K, Yamashiki Y, 2015. Effect of irrigation water withdrawals on water and energy balance in the Mekong River Basin using an improved VIC land surface model with fewer calibration parameters. Agricultural Water Management, 159:92-106. doi: 10.1016/j.agwat.2015.05.011
    [33] Thilakarathne M, Sridhar V, 2017. Characterization of future drought conditions in the Lower Mekong River Basin. Weather and Climate Extremes, 17:47-58. doi:10.1016/j.wace.2017. 07.004
    [34] Thu H N, Wehn U, 2016. Data sharing in international transboun-dary contexts:the Vietnamese perspective on data sharing in the Lower Mekong Basin. Journal of Hydrology, 536:351-364. doi: 10.1016/j.jhydrol.2016.02.035
    [35] Tobin K J, Bennett M E, 2013. Temporal analysis of Soil and Water Assessment Tool (SWAT) performance based on remotely sensed precipitation products. Hydrological Processes, 27:505-514. doi: 10.1002/hyp.9252
    [36] Trejo F J P, Barbosa H A, Peñaloza-Murillo M A et al., 2016. Intercomparison of improved satellite rainfall estimation with CHIRPS gridded product and rain gauge data over Venezuela. Atmósfera, 29(4):323-342. doi: 10.20937/ATM.2016.29.04.04
    [37] Tuo Y, Duan Z, Disse M et al., 2016. Evaluation of precipitation input for SWAT modeling in Alpine catchment:a case study in the Adige river basin (Italy). Science of the Total Environment, 573:66-82. doi: 10.1016/j.scitotenv.2016.08.034
    [38] Wang W, Lu H, Yang D et al., 2016. Modeling hydrologic processes in the Mekong River Basin using a distributed model driven by satellite precipitation and rain gauge observations. PLoS ONE, 11(3):e0152229. doi:10.1371/journal.pone. 0152229
    [39] Wang W, Lu H, Zhao T et al., 2017. Evaluation and comparison of daily rainfall from latest GPM and TRMM products over the Mekong River Basin. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 99:1-10. doi: 10.1109/JSTARS.2017.2672786
    [40] Worqlul A W, Yen H, Collick A S et al., 2017. Evaluation of CFSR, TMPA 3B42 and ground-based rainfall data as input for hydrological models, in data-scarce regions:The upper Blue Nile Basin, Ethiopia. Catena, 152:242-251. doi:10.1016/j. catena.2017.01.019
    [41] Yong B, Liu D, Gourley J J et al., 2015. Global view of real-time TRMM multisatellite precipitation analysis:implications for its successor global precipitation measurement mission. Bulletin of the American Meteorological Society, 96:283-296. doi: 10.1175/BAMS-D-14-00017.1
    [42] Zhou M C, Ishidaira H, Takeuchi K, 2008. Comparative study of potential evapotranspiration and interception evaporation by land cover over Mekong basin. Hydrological Processes, 22:1290-1309. doi: 10.1002/hyp.6939
  • 加载中
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Article Metrics

Article views(245) PDF downloads(448) Cited by()

Proportional views
Related

Hydrological Simulation Using TRMM and CHIRPS Precipitation Estimates in the Lower Lancang-Mekong River Basin

doi: 10.1007/s11769-019-1014-6
Funds:  Under the auspices of National Key R&D Program of China (No. 2016YFA0601601), National Natural Science Foundation of China (No. 41601026, 41661099), Science and Technology Planning Project of Yunnan Province, China (No. 2017FB073)
    Corresponding author: HE Daming.E-mail:dmhe@ynu.edu.cn

Abstract: Satellite-based products with high spatial and temporal resolution provide useful precipitation information for data-sparse or ungauged large-scale watersheds. In the Lower Lancang-Mekong River Basin, rainfall stations are sparse and unevenly distributed, and the transboundary characteristic makes the collection of precipitation data more difficult, which has restricted hydrological processes simulation. In this study, daily precipitation data from four datasets (gauge observations, inverse distance weighted (IDW) data, Tropical Rainfall Measuring Mission (TRMM) estimates, and Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) estimates), were applied to drive the Soil and Water Assessment Tool (SWAT) model, and then their capability for hydrological simulation in the Lower Lancang-Mekong River Basin were examined. TRMM and CHIRPS data showed good performances on precipitation estimation in the Lower Lancang-Mekong River Basin, with the better performance for TRMM product. The Nash-Sutcliffe efficiency (NSE) values of gauge, IDW, TRMM, and CHIRPS simulations during the calibration period were 0.87, 0.86, 0.95, and 0.93 for monthly flow, respectively, and those for daily flow were 0.75, 0.77, 0.86, and 0.84, respectively. TRMM and CHIRPS data were superior to rain gauge and IDW data for driving the hydrological model, and TRMM data produced the best simulation performance. Satellite-based precipitation estimates could be suitable data sources when simulating hydrological processes for large data-poor or ungauged watersheds, especially in international river basins for which precipitation observations are difficult to collect. CHIRPS data provide long precipitation time series from 1981 to near present and thus could be used as an alternative precipitation input for hydrological simulation, especially for the period without TRMM data. For satellite-based precipitation products, the differences in the occurrence frequencies and amounts of precipitation with different intensities would affect simulation results of water balance components, which should be comprehensively considered in water resources estimation and planning.

LUO Xian, WU Wenqi, HE Daming, LI Yungang, JI Xuan. Hydrological Simulation Using TRMM and CHIRPS Precipitation Estimates in the Lower Lancang-Mekong River Basin[J]. Chinese Geographical Science, 2019, 20(1): 13-25. doi: 10.1007/s11769-019-1014-6
Citation: LUO Xian, WU Wenqi, HE Daming, LI Yungang, JI Xuan. Hydrological Simulation Using TRMM and CHIRPS Precipitation Estimates in the Lower Lancang-Mekong River Basin[J]. Chinese Geographical Science, 2019, 20(1): 13-25. doi: 10.1007/s11769-019-1014-6
Reference (42)

Catalog

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return