基于水文模型的蒸散发数据同化实验研究

doi:10.11867/j.issn.1001-8166.2014.09.1075

流域蒸散发定量估算一直是水科学领域的研究前沿,水文模型和遥感反演是当前估算区域蒸散发的常用手段。研究通过数据同化,集成水文模型和遥感模型的优势,耦合遥感蒸散发到水文模型中以实现多源数据下的蒸散发数据同化。选择北京市沙河流域为研究区,分布式时变增益水文模型作为模型算子,基于集合卡尔曼滤波同化算法,利用双层遥感模型模拟的蒸散发同化水文模型,并基于地面通量站观测的日蒸散发进行验证。结果表明,同化结果与观测数据相比平均绝对百分比误差较同化前减少,精度进一步提升,且当遥感观测输入频繁时精度改善明显。研究证明基于水文模型的蒸散发数据同化系统,是一种可实现输出精度更高和时序连续的区域蒸散发的新型模式。该成果将进一步丰富创新蒸散发估算的学科内容,为准确理解区域水循环规律提供科学依据。

关键词: 蒸散发, 遥感反演, 水文模型, 集合卡尔曼滤波, 数据同化

The quantitative estimation of watershed Evapotranspiration (ET) has been an international frontier in water sciences for a long time. Hydrological models and remote sensing ET models are usually used to estimate regional ET at different spacetime scales, but these two methods are obviously insufficient to obtain precise and continuous regional ET. The hydrological models have the capability to simulate time-continuous daily or monthly ET processes, but the accuracy is not high compared with remote sensing ET models. The applicability of remote sensing ET models based on surface energy balance is restricted by the lack of high frequency and high resolution thermal data. A compromise between these two methodologies is represented by improving the optimization of hydrological models on the basis of a new ET series, which are produced by Data Assimilation (DA) scheme combining sparse remote estimates into the hydrological model. This study aimed to integrate the advantages of the two models to simulate the daily ET processes in Shahe River basin, Beijing. For this progect, the distributed hydrological model was fist constructed and the daily hydrological processes of 19992007 simulated. Then, the Ensemble Kalman Filter (EnKF) was used to assimilate the ET series calculated by remote sensing retrieval into the hydrological model to adjust the simulation. The results show that the ET estimation accuracy is improved after the data assimilation, and the MAPE between the DSMbased ETs and LASbased ETs in the study area is reduced. The integrated method is proved better, and improves the hydrology modeling accuracy. Therefore, the project successfully develops a new land surface ET mode with the advantages of hydrological model and remote sensing ET model, and the study founds the new method could simulate regional ET with high accuracy and continuous time series. The new land surface ET model not only follows the surface energy balance, but also meets the regional water balance, and has more perfect water thermal coupling mechanism. The study will further enrich the content of ET estimation disciplines, and provide a scientific basis for better understanding of the laws of regional water cycle.

Key words: Evapotranspiration, Remote sensing retrieval, Hydrological model, Ensemble Kalman filter, Data assimilation.

中图分类号:

P332. 2

图1 研究区示意图需要彩色印刷

Fig.1 Map of the study area

表1 研究收集的数据信息

Table 1 The data list for the study

数据类型	数据名称	年份	备注
遥感影像	Landsat TM	1999—2007	美国地质调查局(USGS: http://glovis.usgs.gov),30 m分辨率
DEM	沙河流域DEM	2005	中科院资源环境科学数据中心,30 m分辨率
土地利用	沙河流域土地利用图	2005	中科院资源环境科学数据中心,1:10万
土壤类型	沙河流域土壤类型图	2000	中科院资源环境科学数据中心,1:100万
雨量数据	流域站点日降雨量	1990—2007	北京水文总站,十三陵等6站日降雨量
流量数据	流域站点日流量	1990—2007	北京水文总站,沙河闸站日流量
水位数据	流域站点日水位	1990—2007	北京水文总站,沙河闸等主要监测断面
水库管理资料	流域内水库调度方案	1990—2007	北京水科所,王家园、响潭、十三陵、沙河水库等
气象数据	北京站气象数据	1990—2007	常规观测,昌平、海淀气象站等
实测ET	水热通量观测数据	2002—2007	沙河流域下游小汤山地面通量观测站

表1 研究收集的数据信息

Table 1 The data list for the study

数据类型	数据名称	年份	备注
遥感影像	Landsat TM	1999—2007	美国地质调查局(USGS: http://glovis.usgs.gov),30 m分辨率
DEM	沙河流域DEM	2005	中科院资源环境科学数据中心,30 m分辨率
土地利用	沙河流域土地利用图	2005	中科院资源环境科学数据中心,1:10万
土壤类型	沙河流域土壤类型图	2000	中科院资源环境科学数据中心,1:100万
雨量数据	流域站点日降雨量	1990—2007	北京水文总站,十三陵等6站日降雨量
流量数据	流域站点日流量	1990—2007	北京水文总站,沙河闸站日流量
水位数据	流域站点日水位	1990—2007	北京水文总站,沙河闸等主要监测断面
水库管理资料	流域内水库调度方案	1990—2007	北京水科所,王家园、响潭、十三陵、沙河水库等
气象数据	北京站气象数据	1990—2007	常规观测,昌平、海淀气象站等
实测ET	水热通量观测数据	2002—2007	沙河流域下游小汤山地面通量观测站

图2 流域蒸散同化技术路线图

Fig.2 The schematic framework of watershed evapotranspiration data assimilation

图3 DTVGM模拟流域月蒸散量(1999—2007年)

Fig.3 The monthly ET simulated by DTVGM with out data assimilation (1999-2007)

图4 不同方式获得的区域日蒸散发比较

Fig.4 The ET of typical day by different method

图5 1999—2007年沙河流域逐日流域平均蒸散发(5日移动均线)

Fig.5 Average daily ET processes in the Shahe river basin in 1999-2007 (5 days moving average curve)

图6 日蒸散发的DTVGM模拟和同化结果比较(2002年、2007年)

Fig. 6 The comparation of ET in typical days through DTVGM and data assimilation (2002, 2007)

表2 DTVGM模拟、数据同化输出与ET-Watch模型估算的年蒸散发比较

Table 2 The Comparation of yearly ET simulated by DTVGM, DA and ET-Watch

年份	同化前年ET(mm)	模拟相对误差(%)	同化后年ET(mm)	同化相对误差(%)	ET-Watch结果(mm)
2002	452.18	12.74	564.78	1.90	575.50
2003	454.78	-4.52	450.56	-3.42	435.12
2004	543.82	-12.09	506.31	-4.18	485.15
2005	501.09	10.85	520.25	8.04	562.05

表2 DTVGM模拟、数据同化输出与ET-Watch模型估算的年蒸散发比较

Table 2 The Comparation of yearly ET simulated by DTVGM, DA and ET-Watch

年份	同化前年ET(mm)	模拟相对误差(%)	同化后年ET(mm)	同化相对误差(%)	ET-Watch结果(mm)
2002	452.18	12.74	564.78	1.90	575.50
2003	454.78	-4.52	450.56	-3.42	435.12
2004	543.82	-12.09	506.31	-4.18	485.15
2005	501.09	10.85	520.25	8.04	562.05

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摘要

A Case Study of Evapotranspiration Data Assimilation Based on Hydrological Model