Früher|Nächster

Evaluation of typical remote-sensing precipitation products in hydrological simulation

  • role： First author第一作者
  • Affiliation：

    School of Hydrology and Water Resources, Nanjing University of Information Science and Technology, Nanjing 210044, China

    Key Laboratory of Hydrometeorological Disaster Mechanism and Warning of Ministry of Water Resources, Nanjing University of Information Science and Technology, Nanjing 210044, China

  • Email：lyz@nuist.edu.cn
  • Introduction：李艳忠，研究方向为水文气象与3S技术应用研究。Email：lyz@nuist.edu.cn
LI Yanzhong12,  
  • Affiliation：

    School of Hydrology and Water Resources, Nanjing University of Information Science and Technology, Nanjing 210044, China

    Key Laboratory of Hydrometeorological Disaster Mechanism and Warning of Ministry of Water Resources, Nanjing University of Information Science and Technology, Nanjing 210044, China

XING Yincong12,  
  • Affiliation：

    School of Hydrology and Water Resources, Nanjing University of Information Science and Technology, Nanjing 210044, China

    Key Laboratory of Hydrometeorological Disaster Mechanism and Warning of Ministry of Water Resources, Nanjing University of Information Science and Technology, Nanjing 210044, China

ZHUANG Jiacheng12,  
  • Affiliation：

    School of Hydrology and Water Resources, Nanjing University of Information Science and Technology, Nanjing 210044, China

    Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

YANG Zelong13,  
  • Affiliation：

    School of Hydrology and Water Resources, Nanjing University of Information Science and Technology, Nanjing 210044, China

    Key Laboratory of Hydrometeorological Disaster Mechanism and Warning of Ministry of Water Resources, Nanjing University of Information Science and Technology, Nanjing 210044, China

ZHAO Zichun12,  
  • role： Corresponding author通信作者
  • Affiliation：

    School of Geography Science, Nanjing University of Information Science and Technology, Nanjing 210044, China

  • Email：lcf@nuist.edu.cn
  • Introduction：李超凡，研究方向为自然地理与3S技术应用研究。E-mail：lcf@nuist.edu.cn
LI Chaofan4*,  
  • Affiliation：

    School of Hydrology and Water Resources, Nanjing University of Information Science and Technology, Nanjing 210044, China

    Key Laboratory of Hydrometeorological Disaster Mechanism and Warning of Ministry of Water Resources, Nanjing University of Information Science and Technology, Nanjing 210044, China

WANG Qisu12,  
  • Affiliation：

    School of Hydrology and Water Resources, Nanjing University of Information Science and Technology, Nanjing 210044, China

    Key Laboratory of Hydrometeorological Disaster Mechanism and Warning of Ministry of Water Resources, Nanjing University of Information Science and Technology, Nanjing 210044, China

XIE Yuchu12,  
  • Affiliation：

    School of Hydrology and Water Resources, Nanjing University of Information Science and Technology, Nanjing 210044, China

    Key Laboratory of Hydrometeorological Disaster Mechanism and Warning of Ministry of Water Resources, Nanjing University of Information Science and Technology, Nanjing 210044, China

WANG Jie12,  
  • Affiliation：

    Water Supplies Bureau, Yanchi County, Wuzhong City, the Ningxia Hui Autonomous Region, Wuzhong 751500, China

DONG Jianping5,  
  • Affiliation：

    The Seventh Institute of Geology and Mineral Exploration of Shandong Province, Linyi 276000, China

Lin Bin6,  
  • Affiliation：

    Natural Resources and Planning Bureau of Linyi City, Shandong Province, Linyi 276037, China

XU Xingzhu7

Resümee

We comprehensively evaluated various Remote Sensing Precipitation Estimates (RSPEs) to identify the ones that can better capture the precipitation pattern in the Weihe River Basin. Our findings can serve as an important scientific reference for the evaluation and management of water resources in the basin and provide favorable support for the implementation of environmental protection and high-quality development planning in the Yellow River basin.Based on the gridded precipitation data of CMA and the five popular RSPEs (including CHIRPS v2.0, CMORPH v1.0, PERSIANN-CDR, TRMM 3B42, and MSWEP v2.0), we comprehensively evaluated the basic skill of precipitation products by using the four statistical metrics, namely, Pearson correlation coefficient (CORR), bias, Root-Mean-Square Error (RMSE), and Kling-Gupta Efficient (KGE). We further used three categorical metrics, namely, Probability Of Detection (POD), False Alarm Ratio (FAR), and Critical Success Index (CSI). Then, the hydrological simulation skill of RSPEs was also assessed by the traditional lumped hydrological model of ABCD and nash efficiency coefficient (NSE).All five RMPEs can capture the spatial distribution of precipitation. Among them, MSWEP, based on multi-source weighted-ensemble precipitation, can better capture the spatial heterogeneous of precipitation with superior performance. The spatial distribution of PERSIANN smoothly performed and was underestimated in most areas, resulting in lower performance. At the interannual level, all RMPEs generally performed well in the upper Weihe River, and TRMM was excellent, followed by MSWEP, whereas PERSIANN performed poorly. MSWEP products performed well for basic statistical skills, with lower RMSE, higher CORR, and KGE. However, the CHIRPS and PERSIANN had poor basic statistical skills. For the categorical skill of precipitation, the PERSIANN exhibited good skill for light rain, followed by MSWEP. Additionally, all RMPEs performed better than the other three basins in the upstream of Weihe River. To detect moderate rain and heavy rain, the performance of the PERSIANN was degraded. The MSWEP product had a better POD for these two types of rainfall, but its FAR was also higher. In terms of hydrological simulation performance, the hydrological simulation performance of the TRMM was the best, indicating that the retrieval algorithm of RMPEs based on active microwave had a high hydrological application prospect, followed by the MSWEP and COMRPH. The poor performance was the infrared/near-infrared-based CHIRPS and PERSIANN products, whose retrieval algorithms required further improvement in the climate-sensitive transition region.TRMM products performed better in capturing the temporal and spatial patterns of precipitation. The multi-source integrated product of MSWEP was significantly better than the other four products. The predictability of each precipitation estimate to moderate and heavy rain was unsatisfactory and was especially poor for the latter one. Using TRMM precipitation as the input of ABCD model, the performance of simulating runoff was significantly better than other remote-sensing products in the four sub-basins of the Weihe River, followed by MSWEP and COMRPH.

Schlüsselwort

remotely sensed precipitation;Wei river basin;ABCD model;performance evaluation;MSWEP

References

  1. 1.
    Allen R G, Pereira L S, Raes D and Smith M. 1998. Crop evapotranspiration-Guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56. Roma: Food and Agriculture Organization
  2. 2.
    Ashouri H, Hsu K L, Sorooshian S, Braithwaite D K, Knapp K R, Cecil L D, Nelson B R and Prat O P. 2015. PERSIANN-CDR: daily precipitation climate data record from multisatellite observations for hydrological and climate studies. Bulletin of the American Meteorological Society, 96(1): 69-83
  3. 3.
    Bai P and Liu X M. 2018. Evaluation of five satellite-based precipitation products in two gauge-scarce basins on the Tibetan Plateau. Remote Sensing, 10(8): 1316
  4. 4.
    Bai P, Liu X M, Liang K and Liu C M. 2015. Comparison of performance of twelve monthly water balance models in different climatic catchments of China. Journal of Hydrology, 529: 1030-1040
  5. 5.
    Bai P, Liu X M, Zhang Y Q and Liu C M. 2020. Assessing the impacts of vegetation greenness change on evapotranspiration and water yield in China. Water Resources Research, 56(10): e2019WR027019
  6. 6.
    Beck H E, Pan M, Roy T, Weedon G P, Pappenberger F, van Dijk A I J M, Huffman G J, Adler R F and Wood E F. 2019a. Daily evaluation of 26 precipitation datasets using Stage-IV gauge-radar data for the CONUS. Hydrology and Earth System Sciences, 23(1): 207-224
  7. 7.
    Beck H E, Vergopolan N, Pan M, Levizzani V, van Dijk A I J M, Weedon G P, Brocca L, Pappenberger F, Huffman G J and Wood E F. 2017. Global-scale evaluation of 22 precipitation datasets using gauge observations and hydrological modeling. Hydrology and Earth System Sciences, 21(12): 6201-6217
  8. 8.
    Beck H E, Wood E F, Pan M, Fisher C K, Miralles D G, Van Dijk A I J M, McVicar T R and Adler R F. 2019b. MSWEP V2 global 3-hourly 0.1° precipitation: methodology and quantitative assessment. Bulletin of the American Meteorological Society, 100(3): 473-500
  9. 9.
    Chen L and Dirmeyer P A. 2017. Impacts of land-use/land-cover change on afternoon precipitation over north America. Journal of Climate, 30(6): 2121-2140
  10. 10.
    CPC Central Committee, State Council. 2021. Outline of the Yellow River basin ecological protection and high-quality development plan. Beijing
  11. 11.
    Ding M Z, Yong B and Yang Z K. 2021. Extreme precipitation monitoring capability of IMERG and GSMaP pure satellite precipitation products of Global Precipitation Mission in Chinese mainland. National Remote Sensing Bulletin
  12. 12.
    Ding M Z, Yong B and Yang Z K. 2022. Extreme precipitation monitoring capability of the multi-satellite jointly retrieval precipitation products of Global Precipitation Measurement (GPM) mission. National Remote Sensing Bulletin, 26(4): 657-671
  13. 13.
    Donat M G, Lowry A L, Alexander L V, O’Gorman P A and Maher N. 2016. More extreme precipitation in the world’s dry and wet regions. Nature Climate Change, 6(5): 508-513
  14. 14.
    Funk C, Peterson P, Landsfeld M, Pedreros D, Verdin J, Shukla S, Husak G, Rowland J, Harrison L, Hoell A and Michaelsen J. 2015. The climate hazards infrared precipitation with stations-a new environmental record for monitoring extremes. Scientific Data, 2: 150066
  15. 15.
    Han P F and Wang X S. 2016. Forecasting the response of a catchment on extreme climate change with ABCD model. Yellow River, 38(11): 16-22
  16. 16.
    Huang S Z, Huang Q, Leng G Y, Zhao M L and Meng E H. 2017. Variations in annual water-energy balance and their correlations with vegetation and soil moisture dynamics: a case study in the Wei River Basin, China. Journal of Hydrology, 546: 515-525
  17. 17.
    Huffman G J, Adler R F, Morrissey M M, Bolvin D T, Curtis S, Joyce R, McGavock B and Susskind J. 2001. Global precipitation at one-degree daily resolution from multisatellite observations. Journal of Hydrometeorology, 2(1): 36-50
  18. 18.
    Joyce R J, Janowiak J E, Arkin P A and Xie P P. 2004. CMORPH: a method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution. Journal of Hydrometeorology, 5(3): 487-503
  19. 19.
    Li Y Z, Liu C M, Liu X M, Liang K, Bai P and Feng Y X. 2016. Impact of the grain for green project on the land use/cover change in the middle Yellow River. Journal of Natural Resources, 31(12): 2005-2020
  20. 20.
    Li Y Z, Tian D and Medina H. 2021. Multimodel subseasonal precipitation forecasts over the contiguous united states: skill assessment and statistical postprocessing. Journal of Hydrometeorology, 22(10): 2581-2600
  21. 21.
    Liu C M, Li Y Z, Liu X M, Bai P and Liang K. 2016. Impact of vegetation change on water transformation in the middle Yellow River. Yellow River, 38(10): 7-12
  22. 22.
    Liu J, Xia J, She D X, Li L C, Wang Q and Zou L. 2019. Evaluation of six satellite-based precipitation products and their ability for capturing characteristics of extreme precipitation events over a climate transition area in China. Remote Sensing, 11(12): 1477
  23. 23.
    Liu J, Xia J, Zou L, Wang Q and Yu J Y. 2018. Applicability analysis of multi-satellite remote sensing precipitation data in Tarim River basin. South-to-North Water Transfers and Water Science and Technology, 16(5): 1-8
  24. 24.
    Ma Y Z, Yang Y, Han Z Y, Tang G Q, Maguire L, Chu Z G and Hong Y. 2018. Comprehensive evaluation of ensemble multi-satellite precipitation dataset using the dynamic bayesian model averaging scheme over the Tibetan plateau. Journal of Hydrology, 556: 634-644
  25. 25.
    Martinez G F and Gupta H V. 2010. Toward improved identification of hydrological models: a diagnostic evaluation of the “abcd” monthly water balance model for the conterminous United States. Water Resources Research, 46(8): W08507
  26. 26.
    Motovilov Y G, Gottschalk L, Engeland K and Rodhe A. 1999. Validation of a distributed hydrological model against spatial observations. Agricultural and Forest Meteorology, 98-99: 257-277
  27. 27.
    Peng S M, Wang Y and Jiang J Q. 2017. Study on the relationship between irrigation water requirement and drought in the main irrigation area of the Yellow River basin. Yellow River, 39(11): 5-10
  28. 28.
    Peng Z H, Li Y Z, Yu W J, Xing Y C, Feng A Q and Du S W. 2021. Research on the applicability of remote sensing precipitation products in different climatic regions of China. Journal of Geo-information Science, 23(7): 1296-1311
  29. 29.
    Satgé F, Espinoza R, Zolá R P, Roig H, Timouk F, Molina J, Garnier J, Calmant S, Seyler F and Bonnet M P. 2017. Role of climate variability and human activity on poopó lake droughts between 1990 and 2015 assessed using remote sensing data. Remote Sensing, 9(3): 218
  30. 30.
    Siqueira V A, Fan F M, de Paiva R C D, Ramos M H and Collischonn W. 2020. Potential skill of continental-scale, medium-range ensemble streamflow forecasts for flood prediction in South America. Journal of Hydrology, 590: 125430
  31. 31.
    Sun Q H, Miao C Y, Duan Q Y, Ashouri H, Sorooshian S and Hsu K L. 2018. A review of global precipitation data sets: data sources, estimation, and intercomparisons. Reviews of Geophysics, 56(1): 79-107
  32. 32.
    Tang G Q, Wang W, Zeng Z Y, Guo X L, Li N, Long D and Hong Y. 2015. An overview of the global precipitation measurement (GPM) mission and it’s latest development. Remote Sensing Technology and Application, 30(4): 607-615
  33. 33.
    Thomas H A. 1981. Improved Methods for National Water Assessment, Water Resources Contract: WR15249270. Harvard Water Resources Group
  34. 34.
    Tian D, Wood E F and Yuan X. 2017. CFSv2-based sub-seasonal precipitation and temperature forecast skill over the contiguous United States. Hydrology and Earth System Sciences, 21(3): 1477-1490
  35. 35.
    Wang R, Shen Q, Peng H C, Yao Y, Li J S, Wang M X, Shi J R and Xu W T. 2022. Study on the applicability of multi-source high-resolution satellite images for monitoring black and odorous water body. National Remote Sensing Bulletin, 26(1): 179-192
  36. 36.
    Wei L Y, Jiang S H, Ren L L, Zhang L Q and Zhou M Y. 2019. Evaluation and comparison of multi-source satellite precipitation products in different climate regions over China’s mainland. China Rural Water and Hydropower, (11): 38-44
  37. 37.
    Wilson J W and Brandes E A. 1979. Radar measurement of rainfall-A summary. Bulletin of the American Meteorological Society, 60(9): 1048-1060
  38. 38.
    Wu G D, Xu J J, Gupta H and Zhang X. 2019. The “abcd” water balance model: application to Xin’an River Basin and sensitivity analysis. Journal of Yangtze River Scientific Research Institute, 36(7): 23-27, 40
  39. 39.
    Xie P P, Chen M Y, Yang S, Yatagai A, Hayasaka T, Fukushima Y and Liu C M. 2007. A gauge-based analysis of daily precipitation over East Asia. Journal of Hydrometeorology, 8(3): 607-626
  40. 40.
    Xing Y C, Lin Y X, Li Y Z, Liang K, Liu C M, Du S W, Li X Q, Zhang M and Yun Y E. 2021. Performance of merged multi-satellite precipitation data products based on the ensemble model output statistic (EMOS): a case study in the middle Yellow River. South-to-North Water Transfers and Water Science and Technology, 19(2): 226-236
  41. 41.
    Xu F L, Guo B, Ye B, Ye Q, Chen H N, Ju X H, Guo J Y and Wang Z L. 2019. Systematical evaluation of GPM IMERG and TRMM 3B42V7 precipitation products in the Huang-Huai-Hai Plain, China. Remote Sensing, 11(6): 697
  42. 42.
    Xu R, Tian F Q, Yang L, Hu H C, Lu H and Hou A Z. 2017. Ground validation of GPM IMERG and TRMM 3B42V7 rainfall products over southern Tibetan Plateau based on a high-density rain gauge network. Journal of Geophysical Research: Atmospheres, 122(2): 910-924
  43. 43.
    Zhang G Q, Wang M M, Zhou T and Chen W F. 2022. Progress in remote sensing monitoring of lake area, water level, and volume changes on the Tibetan Plateau. National Remote Sensing Bulletin, 26(1): 115-125

Lesen Sie die ganze Passage

The above content is generated by Large Model Translation. The translated content is for reference only. We do not assume any commercial or legal responsibilty for any consequences arising from the use of our website