地球信息科学学报 ›› 2021, Vol. 23 ›› Issue (3): 395-404.doi: 10.12082/dqxxkx.2021.200054

• 地球信息科学理论与方法 • 上一篇    下一篇

TRMM及GPM降水数据在高寒内陆河流域的准确性评估

金鑫(), 金彦香*   

  1. 1.青海师范大学地理科学学院, 西宁 810016
    2.青海省自然地理与环境过程重点实验室, 西宁 810016
    3.高原科学与可持续发展研究院, 西宁 810016
  • 收稿日期:2020-02-06 修回日期:2020-04-17 出版日期:2021-03-25 发布日期:2021-05-25
  • 通讯作者: 金彦香 E-mail:jinx13@lzu.edu.cn
  • 作者简介:金 鑫(1988- ),女,青海西宁人,博士,副教授,研究方向为高寒区水文模拟研究。E-mail: jinx13@lzu.edu.cn
  • 基金资助:
    国家自然科学基金项目(41801094);青海省科技厅自然科学基金项目(2019-ZJ-939Q)

Accuracy Assessment of TRMM and GPM Datasets in an Alpine Inland River Basin

JIN Xin(), JIN Yanxiang*   

  1. 1. School of the Geographical Science, Qinghai Normal University, Xining 810016, China
    2. Key Laboratory of Physical Geography and Environmental Processes, Xining 810016, China
    3. Academy of Plateau Science and Sustainability, Xining 810016, China
  • Received:2020-02-06 Revised:2020-04-17 Online:2021-03-25 Published:2021-05-25
  • Contact: JIN Yanxiang E-mail:jinx13@lzu.edu.cn
  • Supported by:
    National Natural Science Foundation of China(41801094);Natural Science Foundation of Qinghai Province(2019-ZJ-939Q)

摘要:

降水数据的准确性和时空分辨率成为水文过程模拟的关键。卫星遥感降水资料的日益丰富为资料缺乏区的水文模拟带来了新的突破。本研究拟在资料缺乏、下垫面复杂,观测难、建模难的柴达木盆地高寒内陆河流域—巴音河中上游,基于近5年的TMPA 3B42、GPM IMERG V5及GPM IMERG V6逐日降水数据和气象站点观测数据建立SWAT模型,采用流域出口径流数据及不同的参数化方案分别率定4个模型,比较和探究不同数据在巴音河流域的适用性。结果表明:① 在月、年尺度上,实测降水数据及TMPA 3B42 V07对应的SWAT模型径流模拟效果较好,前者模拟精度较后者仅高6%~12%,且二者对于流域水量平衡的刻画均较准确。说明TMPA 3B42数据对应的径流模拟结果误差相对较小,可直接用于高寒内陆河流域水文模拟;② GPMIMERG V5数据对应的那什系数NSE值为0.13(月)、-1.58(年),误差百分数PBIAS值为41.2%(月、年),均方根误差与标准误差比率RSR值为0.93(月)、1.61(年),其径流模拟误差较大,模拟效果不可信,说明GPMIMERG V5数据集并不适用于巴音河流域水文模拟;③ GPMIMERG V6-F对应的月径流模拟结果明显优于GPMIMERG V5-F,前者模拟精度较后者提高4倍,但其模拟的年径流对应的NSE值为-0.12,RSR值为1.09。该研究可为资料缺乏的高寒内陆河流域生态水文过程模拟提供参考。

关键词: TRMM, GPM, SWAT, 巴音河流域, 降水, 径流量, 水量平衡, 柴达木盆地

Abstract:

Precipitation is an essential part of the Earth's water cycle and a key variable linking atmospheric processes to surface processes, as well as an important parameter in hydrological process simulations. It is one of the atmospheric variables that are most difficult to observe due to its large variability over time and space, and its tendency to exhibit non-normal distributions. The spatial and temporal resolution of precipitation data has become a key concern for process simulations. Traditional station observations usually have poor regional representation of precipitation data, due to factors such as sparse and/or uneven station distribution. This, in turn, affects the accuracy of hydrological simulations. Remotely sensed precipitation data have brought breakthroughs in modelling the hydrology of data-deficient regions. To best simulate the hydrological processes in Bayinhe River Basin, a special area with limited data located in Northeast of the Qaidam Basin, three most popular remote sensing precipitation datasets (TMPA 3B42, GPM IMERG V5, and GPM IMERG V6) along with weather station observation data were used to build SWAT model. The accuracy of TMPA 3B42, GPM IMERG V5, and GPM IMERG V6 datasets were first evaluated and then the performance of different SWAT models based on different precipitation datasets and parameterization schemes were assessed. Results show that: 1) The observed precipitation data and TMPA 3B42 data both generated good stream flow simulation results with the former accuracy 6-12% higher than the latter. The both datasets can accurately express the water balance of the basin. Thus, TMPA 3B42 data can be directly used in alpine inland river hydrological process simulation; 2) GPM IMERG V5 performed poor in either yearly or monthly streamflow simulation with a NSE value (Nash-Sutcliffe efficiency) of -1.58 and 0.13, a PBIAS value (percent bias) of 41.2% and 41.2%, and a RSR value (ratio of the root mean square error to the standard deviation of measured data) of 1.61 and 0.93, respectively. This indicates that GPM IMERG V5 was unsuitable to model the hydrological process in Bayinhe River Basin; and 3) GPM IMERG V6 performed better than GPM IMERG V5 in monthly streamflow simulation, and the accuracy of GPM IMERG V6 was four times higher than GPM IMERG V5. However, GPM IMERG V6 dataset performed less well in yearly streamflow simulation with a NSE value of -0.21, a PBIAS value of 14.9%, and a RSR value of 1.09. This research could be a useful reference for future ecological and hydrological processes modeling in the alpine inland river basin region with sparse data.

Key words: TRMM, GPM, SWAT, The Alpine Inland River, precipitation, streamflow, water balance, Qaidam Basin