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Arid Land Geography >

2020 , Vol. 43 >Issue 5: 1179 - 1191

DOI: https://doi.org/10.12118/j.issn.1000-6060.2020.05.03

Evaluating runoff simulation of multi-source precipitation data in small watersheds

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  • 1 School of Civil and Hydraulic Engineering, Ningxia University, Yinchuan 750021, Ningxia, China; 2 School of Civil Engineering and Environmental Science, University of Oklahoma, Norman, OK 73072, USA; 3 Ningxia Research Center of Technology on Water-saving Irrigation and Water Resources Regulation, Yinchuan 750021, Ningxia, China; 4 State Key Laboratory of Hydroscience and Engineering, Tsinghua University, Beijing 100084 China; 5 Engineering Research Center for Efficient Utilization of Water Resources in Modern Agriculture in Arid Regions, Yinchuan 750021, Ningxia, China

Received date: 2019-12-28

  Revised date: 2020-04-10

  Online published: 2020-09-25

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Abstract

Small watersheds are ideal objects for studying the evolution of small and microscale hydrology and water resources systems. A small watershed is the smallest unit for calculating river water and sediment production and is the best regional scale for hydrology and soil erosion research and management. Through RS technology, a climate model obtains precipitation estimation data and drives a distributed hydrological model to simulate and predict hydrological processes, which clarifies the inevitable trends of basin hydrology and water resources research. Using NOAA- CPC- US precipitation data as a reference, an analysis of the PERSIANN, PERSIANN- CDR, TRMM- 3B42V7, GPM- IMERG, StageIV, and ERA5 precipitation data products were compared for nine small watersheds in different regions of the United States. The accuracy of theseseven precipitation products allowed them to drive the CREST distributed hydrological model, which evaluated the hydrological simulation effects of the precipitation products. The study shows that the NOAA- CPC- US precipitation data product is the highest, followed in decreasing order by StageIV, PERSIANN-CDR,GPM-IMERG, PERSIANN,ERA5, and TRMM- 3B42V7. The precipitation estimation accuracy of each precipitation product in the small watersheds in the high latitudes and western mountains of the northern United States is lower; however, there is better precipitation accuracy in small watersheds in the central, southern, and eastern parts of the United States. In the hydrological simulation utility evaluation, the CREST model parameters were determined using seven kinds of precipitation products, respectively. After obtaining the set of parameters, the daily runoff process of the basin was simulated for the same verification period. The comparison results show that NOAA-CPC-US and StageIV have better effects on the hydrological simulation of small watersheds. However, caution should be exercised in hydrological simulations in the northern and western parts of the United States using PERSIANN, PERSIANN- CDR, GPM- IMERG, and ERA5 precipitation data, and the TRMM-3B42V7 simulation effect is not ideal.

Key words： Small Watersheds; CREST; Multi-source precipitation; Hydrological Simulation

Cite this article

FENG Ke-peng, HONG Yang , TIAN Jun-cang, TANG Guo-qiang, KAN Guang-yuan, LUO Xiang-yu . Evaluating runoff simulation of multi-source precipitation data in small watersheds[J]. Arid Land Geography, 2020 , 43(5) : 1179 -1191 . DOI: 10.12118/j.issn.1000-6060.2020.05.03

References

[1] 陈书军, 刘毅, 姜惠明, 等. 湖北省降雨和洪涝特征时空变化规 律 分 析 [J]. 排 灌 机 械 工 程 学 报, 2019, 37(3): 248- 250. [CHEN Shujun,LIU Yi,JIANG Huiming,et al. Analysis of precipitation and drought-flood spatio-temporal variation characteristics in Hu⁃ bei Province[J]. Journal of Drainage and Irrigation Machinery En⁃ gineering, 2019, 37(3): 248-250.] [2] TAPIADOR F J, TURK F J, PETERSEN W, et al. Global precipi⁃ tation measurement: Methods, datasets and applications[J]. Atmo⁃ spheric Research, 2012, 104: 70-97. [3] 江善虎, 任立良, 雍斌, 等. TRMM 卫星降水数据在洣水流域径 流模拟中的应用[J]. 水科学进展, 2014, 25(5): 641-649. [JIANG Shanhu，REN Liliang，YONG Bin，et al．Hydrological evaluation of the TRMM multi ⁃ satellite precipitation estimates over the Mi⁃ shui Basin[J]. Advances in Water Science, 2014, 25(5): 641⁃649.] [4] 唐国强, 李哲, 薛显武, 等. 赣江流域 TRMM 遥感降水对地面站 点 观 测 的 可 替 代 性 [J]. 水 科 学 进 展, 2015, 26(3): 340- 346. [TANG Guoqiang，LI zhe，XUE Xianwu，et al. A study of substitut⁃ ability of TRMM remote sensing precipitation for gauge-based ob⁃ servation in Ganjiang River basin[J]. Advances in Water Science, 2015, 26(3): 340⁃346. ] [5] IPCC. Climate change 2013: the physical science basis: Working Group I contribution to the Fifth assessment report of the Intergov⁃ ernmental Panel on Climate Change[M]. London: Cambridge Uni⁃ versity Press, 2014. [6] 陈晓晨, 徐影, 许崇海, 等. CMIP5 全球气候模式对中国地区降 水模拟能力的评估[J]. 气候变化研究进展, 2014, 10(3): 217- 225. [CHEN Xiaochen, XU Ying, XU Chonghai, et al. Assessment of precipitation simulations in China by CMIP5 multi- models [J]. Climate Change Research, 2014, 10(3): 217-225.] [7] 孙侦, 贾绍凤, 吕爱锋, 等. IPCC AR5 全球气候模式模拟的中国 地区日平均降水精度评价[J]. 地球信息科学学报, 2016, 18(2): 227- 237. [SUN Zhen, JIA Shaofeng, LV Aifeng, et al. Precision estimation of the average daily precipitation simulated by IPCC AR5 GCMs in China[J]. Journal of Geo-information Science, 2016, 18(2): 227-237. ] [8] 颜楚睿,刘浏,黄冠华. 多模式多情景下西北内陆干旱区未来气 候变化预估[J]. 排灌机械工程学报, 2018, 36(11): 1193-1199. [YAN Churui, LIU Liu, HUANG Guanhua. Prediction of future cli⁃ mate change in northwest inland arid areas of China under multi- mode and multiple scenarios[J]. Journal of Drainage and Irrigation Machinery Engineering, 2018, 36(11): 1193-1199.] [9] HOU A Y, KAKAR R K, NEECK S, et al. The global precipitation measurement mission[J]. Bulletin of the American Meteorological Society, 2014, 95(5): 701-722. [10] 唐国强,万玮,曾子悦,等.全球降水测量(GPM)计划及其最新进 展综述[J].遥感技术与应用,2015,30(4):607-615. [TANG Guoq⁃ iang, WAN Wei, ZENG Ziyue, et al. An overview of the Ggobal precipitation measurement (GPM)mission and it s latest develop⁃ ment[J]. Remote Sensing Technology and Application,2015,30(4): 607-615. ] [11] 唐国强, 龙笛, 万玮, 等. 全球水遥感技术及其应用研究的综述 与 展 望 [J]. 中 国 科 学 ：技 术 科 学, 2015, 45(10): 1013- 1023. [TANG Guoqiang, LONG Di, WAN Wei, et al. An overview and outlook of global water remote sensing technology and applications [J]. Scienentia Sinica Technologica, 2015, 45(10): 1013-1023. ] [12] MALDONADO M E S. Remote sensing based hydrologic modeling in the Babahoyo River Sub-basin for water balance assessment[M]. Enschede: University of Twente Faculty of Geo- Information and Earth Observation (ITC), 2011. [13] ALBERGEL C, DUTRA E, MUNIER S, et al. ERA- 5 and ERA- Interim driven ISBA land surface model simulations: Which one performs better?[J]. Hydrology and Earth System Sciences, 2018, 22(6): 3515-3532. [14] KUMAR D, PANDEY A, SHARMA N, et al. Evaluation of TRMM- precipitation with rain-gauge observation using hydrological model J2000[J]. Journal of Hydrologic Engineering, 2015, 22(5): E5015007. [15] 陈晓宏, 钟睿达, 王兆礼, 等. 新一代 GPM IMERG 卫星遥感降 水数据在中国南方地区的精度及水文效用评估[J]. 水利学报, 2017, 48(10): 1147- 1156. [ CHEN Xiaohong，ZHONG Ruida1， WANG Zhaoli, et al. Evaluation on the accuracy and hydrological performance of the latest- generation GPM IMERG product over South China[J]. Journal of Hydraulic Engineering, 2017, 48(10): 1147-1156.] [16] ZHAO Y, XIE Q, LU Y, et al. Hydrologic evaluation of TRMM multisatellite precipitation analysis for Nanliu River Basin in hu⁃ mid southwestern China[J]. Scientific Reports, 2017, 7(1): 2470. [17] NELSON B R, PRAT O P, SEO D J, et al. Assessment and implica⁃ tions of NCEP stage IV quantitative precipitation estimates for product intercomparisons [J]. Weather and Forecasting, 2016, 31 (2): 371-394. [18] TIAN X, ZOU X. Capturing size and intensity changes of hurri⁃ canes Irma and Maria (2017) from polar- orbiting satellite micro⁃ wave radiometers[J]. Journal of the Atmospheric Sciences, 2018, 75(8): 2509-2522. [19] OMRANIAN E, SHARIF H, TAVAKOLY A. How well can global precipitation measurement (GPM) capture hurricanes? Case study: Hurricane Harvey[J]. Remote Sensing, 2018, 10(7): 1150. [20] PASKA J, LAU A M S, TAN M L, et al. Evaluation of TRMM 3B42V7 product on extreme precipitation measurements over pen⁃ insular Malaysia[C]//Remote Sensing for Agriculture, Ecosystems, and Hydrology XIX. International Society for Optics and Photon⁃ ics, 2017, 10421: 104210D. [21] YANG Y, DU J, CHENG L, et al. Applicability of TRMM satellite precipitation in driving hydrological model for identifying flood events: A case study in the Xiangjiang River Basin, China[J]. Natu⁃ ral Hazards, 2017, 87(3): 1489-1505. [22] JIANG S, REN L, HONG Y, et al. Comprehensive evaluation of multi-satellite precipitation products with a dense rain gauge net⁃ work and optimally merging their simulated hydrological flows us⁃ ing the Bayesian model averaging method[J]. Journal of Hydrology, 2012, 452: 213-225. [23] JIANG S, REN L, HONG Y, et al. Improvement of multi- satellite real- time precipitation products for ensemble streamflow simula⁃ tion in a middle latitude basin in South China[J]. Water resources management, 2014, 28(8): 2259-2278. [24] MEI Y, ANAGNOSTOU E N, NIKOLOPOULOS E I, et al. Error analysis of satellite precipitation products in mountainous basins [J]. Journal of Hydrometeorology, 2014, 15(5): 1778-1793. [25] SARACHI S, HSU K, SOROOSHIAN S. A statistical model for the uncertainty analysis of satellite precipitation products[J]. Journal of Hydrometeorology, 2015, 16(5): 2101-2117. [26] HONG Y, HSU K, MORADKHANI H, et al. Uncertainty quantifi⁃ cation of satellite precipitation estimation and Monte Carlo assess⁃ ment of the error propagation into hydrologic response[J]. Water re⁃ sources research, 2006, 42(8). [27] RICO-RAMIREZ M A, LIGUORI S, SCHELLART A N A. Quanti⁃ fying radar- rainfall uncertainty in urban drainage flow modelling [J]. Journal of Hydrology, 2015, 528: 17-28. [28] KRAJEWSKI W F, VILLARINI G, Smith J A. Radar- rainfall un⁃ certainties: Where are we after thirty years of effort? [J]. Bulletin of the American Meteorological Society, 2010, 91(1): 87-94. [29] TRIPATHI M P, PANDA R K, RAGHUWANSHI N S, et al. Hy⁃ drological modelling of a small watershed using generated rainfall in the soil and water assessment tool model[J]. Hydrological pro⁃ cesses, 2004, 18(10): 1811-1821. [30] DU J, XIE S, XU Y, et al. Development and testing of a simple physically-based distributed rainfall-runoff model for storm runoff simulation in humid forested basins[J]. Journal of Hydrology, 2007, 336(3-4): 334-346. [31] LIU X, LI J. Application of SCS model in estimation of runoff from small watershed in Loess Plateau of China[J]. Chinese Geographi⁃ cal Science, 2008, 18(3): 235. [32] LEVESQUE E, ANCTIL F, VAN GRIENSVEN A N N, et al. Eval⁃ uation of streamflow simulation by SWAT model for two small wa⁃ tersheds under snowmelt and rainfall[J]. Hydrological sciences journal, 2008, 53(5): 961-976. [33] QIU L, ZHENG F, YIN R. SWAT-based runoff and sediment simu⁃ lation in a small watershed, the loessial hilly-gullied region of Chi⁃ na: Capabilities and challenges[J]. International Journal of Sedi⁃ ment Research, 2012, 27(2): 226-234. [34] HU W, SHE D, SHAO M, et al. Effects of initial soil water content and saturated hydraulic conductivity variability on small water⁃ shed runoff simulation using LISEM[J]. Hydrological Sciences Journal, 2015, 60(6): 1137-1154. [35] MCMANAMAY R A, DEROLPH C R. A stream classification sys⁃ tem for the conterminous United States[R]. Scientific Data 2018. [36] LIN Y. GCIP/EOP Surface: precipitation NCEP/EMC 4 km grid⁃ ded data (GRIB) Stage IV Data, Version 1.0, UCAR/NCAR – Earth Observing Laboratory[J]. 2017. [37] HUFFMAN G J. The transition in multi-satellite products from TRMM to GPM (TMPA to IMERG) [R]. National Aeronautics and Space Administration. Mission update, 2015. [38] HUFFMAN G J, Bolvin D T, Nelkin E J. Integrated Multi-satellitE Retrievals for GPM (IMERG) technical documentation[J]. NASA/ GSFC Code, 2015, 612(2015): 47. [39] US Geological Survey and US Department of Agriculture, Natural Resources Conservation Service. Federal Standards and Proce⁃ dures for the National Watershed Boundary Dataset (WBD) [J]. Techniques and Methods, 2013, 11: 63. [40] WANG J, HONG Y, LI L, et al. The coupled routing and excess storage (CREST) distributed hydrological model[J]. Hydrological Sciences Journal, 2011, 56(1): 84-98. [41] XUE X, HONG Y, LIMAYE A S, et al. Statistical and hydrological evaluation of TRMM- based multi- satellite precipitation analysis over the Wangchu Basin of Bhutan: Are the latest satellite precipi⁃ tation products 3B42V7 ready for use in ungauged basins? [J]. Journal of Hydrology, 2013, 499: 91-99. [42] KHAN S I, HONG Y, WANG J, et al. Satellite remote sensing and hydrologic modeling for flood inundation mapping in Lake Victo⁃ ria basin: Implications for hydrologic prediction in ungauged ba⁃ sins[J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(1): 85-95. [43] MENG J, LI L, HAO Z, et al. Suitability of TRMM satellite rainfall in driving a distributed hydrological model in the source region of Yellow River[J]. Journal of Hydrology, 2014, 509: 320-332. [44] SHEN X, HONG Y, ZHANG K, et al. Refining a distributed linear reservoir routing method to improve performance of the CREST model[J]. Journal of Hydrologic Engineering, 2016, 22 (3): 04016061. [45] MORIASI D N, ARNOLD J G, VAN LIEW M W, et al. Model evalu⁃ ation guidelines for systematic quantification of accuracy in water⁃ shed simulations[J]. Transactions of the ASABE, 2007, 50(3): 885- 900. [46] TAN M, IBRAHIM A, DUAN Z, et al. Evaluation of six high-reso⁃ lution satellite and ground- based precipitation products over Ma⁃ laysia[J]. Remote Sensing, 2015, 7(2): 1504-1528.
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