Evaluation of TRMM satellite-based rainfall data in southern Haihe River Basin and suitability for SWAT model

被引:0
|
作者
Tan L. [1 ]
Huang F. [1 ]
Qiao X. [1 ]
Liu H. [1 ]
Li C. [2 ]
Li B. [1 ]
机构
[1] College of Land Science and Technology, China Agricultural University, Key Laboratory of Arable Land Conservation in North China, Beijing
[2] Meteorological Research Institute of Hebei Province, Shijiazhuang
来源
Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering | 2020年 / 36卷 / 06期
关键词
Hydrological simulation; Precipitation; Remote sensing; Southern Haihe River Basin; SWAT; TRMM; 3B42V7; evaluation;
D O I
10.11975/j.issn.1002-6819.2020.06.016
中图分类号
学科分类号
摘要
Accurate estimation of regional precipitation plays an important role in hydrologic process evaluation and water resources management. Southern Haihe River Basin is the region with the highest degree of water resources exploitation and utilization, however, excessive exploitation of surface water and groundwater causes a series of ecological and environmental problems, which leads to a serious threat to water security. In this study, the accuracy of 3B42V7 estimation of precipitation in Southern Haihe River Basin was evaluated on different spatial and temporal scales, and its applicability to hydrological model SWAT was verified. The daily rainfall data from 28 meteorological stations (2007-2016) and 101 rain gauges (2010-2016) were used to evaluate the accuracy on TRMM 3B42V7. Correlation coefficient, relative bias ratio, mean error and root mean square error were used to quantitatively evaluate the rainfall accuracy of 3B42V7. Moreover, determinate coefficient and Nash-Sutcliffe coefficient of efficiency were used to quantitatively evaluate SWAT simulation results. Two scenarios were set up to drive the SWAT model. In scenario I, the daily rainfall data (2010-2014) from rain gauges and 3B42V7 grid rainfall (2015-2016) were utilized to drive the model. In scenario II, the daily rainfall data (2010-2016) from meteorological stations were used to drive the model. The results showed 3B42V7 had strong estimation ability in monthly estimation of precipitation with the root mean square error less than 15 mm and average monthly precipitation error less than 8.5 mm. However, it was poor in daily precipitation estimation with the correlation coefficient less than 0.6. The number of rainfall stations with the relative bias ratio between -20% and 20% accounted for 81% and 79% during the summer season and growing period of maize, which indicated that 3B42V7 performed better during the wet seasons. In addition, 3B42V7 could well capture the rainfall intensity at all levels, however, zero/light rain were underestimated in four zones. In the yearly estimation, the relative bias ratio values in mountainous area of the Daqinghe watershed, plain of the Daqinghe watershed, mountainous area of the Ziyahe watershed and plain of the Ziyahe watershed were 2.64%, 9.59%, 7.72%, 20.32%, respectively. It means the overestimation in plain and mountainous areas, especially in plain areas. Moreover, 3B42V7 well captured the temporal and spatial distribution of extreme precipitation in this study area. The simulated discharges of SWAT driven by data from rain gauge and TRMM 3B42V7 were in good agreement with the observed ones. During the validation period, the determinate coefficient was between 0.56 and 0.96 and the Nash-Sutcliffe coefficient of efficiency was between -11.09 and 0.94. The TRMM 3B42V7 provides the possibility to expand the time and space scale of hydrological simulation and can provide data support for water resource management and ecological security research. © 2020, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
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页码:132 / 141
页数:9
相关论文
共 38 条
  • [1] Du L., Tian Q., Huang Y., Et al., Drought monitoring based on TRMM data and its reliability validation in Shandong province, Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 28, 2, pp. 121-126, (2012)
  • [2] Huffman G.J., Bolvin D.T., Nelkin E.J., Et al., The TRMM multisatellite precipitation analysis (TMPA): Quasi-global, multiyear, combined-sensor precipitation estimates at fine scales, Satellite Rainfall Applications for Surface Hydrology, (2010)
  • [3] Joyce R.J., Janowiak J.E., Arkin P.A., Et al., 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, pp. 487-503, (2004)
  • [4] Okamoto K.I., Ushio T., Iguchi T., Et al., The Global Satellite Mapping of Precipitation (GSMaP) project, Seoul, South Korea: Proc 25th Int Symp on Geoscience and Remote Sensing, pp. 3414-3416, (2005)
  • [5] Ushio T., Kubota T., Shige S., Et al., Global Satellite Mapping of Precipitation (GSMaP) with high resolution from microwave and infrared radiometer using Kalman filter, IEEE International Geoscience & Remote Sensing Symposium, (2005)
  • [6] Hou A.Y., Kakar R.K., Neeck S., Et al., The global precipitation measurement mission, Bulletin of the American Meteorological Society, 95, 5, pp. 701-722, (2014)
  • [7] Zubieta R., Getirana A., Espinoza J.C., Et al., Impacts of satellite-based precipitation datasets on rainfall-runoff modeling of the Western Amazon basin of Peru and Ecuador, Journal of Hydrology, 528, pp. 599-612, (2015)
  • [8] Sun M., Zhang H., Gong N., Et al., Study on maximum precipitation height zone in Qilian Mountains area based on TRRM precipitation data, Journal of Natural Resources, 34, 3, pp. 646-657, (2019)
  • [9] Chen S., Hong Y., Gourley J.J., Et al., Evaluation of the successive V6 and V7 TRMM multisatellite precipitation analysis over the Continental United States, Water Resources Research, 49, 12, pp. 8174-8186, (2013)
  • [10] Michot V., Vila D., Arvort D., Et al., Performance of TRMM TMPA 3B42V7 in replicating daily rainfall and regional rainfall regimes in the amazon basin (1998-2013), Remote Sensing, 10, 12, (2018)