Improving the spatio-temporal accuracy of mesoscale hydrological modelling through multi-sensor remote sensing data fusion and assimilation

通过多传感器遥感数据融合同化提高中尺度水文建模时空精度

• 批准号: 426111700
• 负责人: Professor Dr.-Ing. Markus Casper
• 金额: --
• 依托单位: Fach Physische Geographie
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/426111700?language=en
• 关键词: Improving; spatio; temporal; accuracy; mesoscale

The anticipated future impacts of climate and land use change on the terrestrial water cycle demand modelling approaches and frameworks that are capable to simulate hydrologic processes at the appropriate scales for local and regional water resource management. At the mesoscale, physically-based hydrological models should reflect all relevant local conditions that control the spatially differentiated rainfall-runoff transformation. To establish a model that realistically mimics the behaviour of the hydrologic system, we will develop an ‘integrated’ multi-objective calibration framework that exploits, in addition to streamflow data, spatially detailed information on dominant runoff generation processes and daily rates of actual evapotranspiration (ETa), the latter through the integration of time series of high-resolution remote sensing data. For three selected catchments in the Nahe basin (SW-Germany), we will develop our approach within the highly customizable framework of the WaSiM-ETH model to construct an exemplary behavioural model that adequately represents the spatio-temporal dynamics of the rainfall-runoff transformation at hourly time steps. To this end, remotely-sensed land surface parameters and ETa maps derived from thermal imagery, both with a pixel size of ~30 – 100 m, will be incorporated. To obtain these maps with up to daily temporal resolution, we will implement multi-sensor data fusion and land surface temperature (LST) downscaling approaches that combine the fine spatial resolution of narrow-swath sensors like Landsat-8 and Sentinel-2 with high temporal resolution Earth observation systems such as MODIS and VIIRS. ETa estimation will then be carried out with a suite of well-established Surface Energy Balance models, including SEBAL, METRIC and SSEB. This processing chain will allow the integration of LST and ETa products at field scale, which is essential for a coupling with a spatially distributed modelling approach at the mesoscale. The derived remote sensing products can be used directly in the calibration process of WaSiM-ETH. We will establish manual and automatic calibration schemes, both based on a set of spatial metrics that capture the spatio-temporal dynamics of ETa patterns within the studied catchments. The combination of clear-sky ETa retrievals from remote sensing with continuous hydrological model simulations enables us to generate gap-free high-resolution ETa composite products for the studied catchments, which then will be upscaled to the entire Nahe basin. In addition, the properly calibrated behavioural model is considered to reflect changes in boundary conditions adequately and will thus allow us to gain insight into the response of the hydrological cycle under anticipated future global environmental change scenarios (e.g. for the RCP6.0 and RCP8.5 scenarios, which represent a strong change in temperature and precipitation conditions).

气候和土地利用变化对陆地水循环的预期未来影响需要能够为地方和区域水资源管理模拟适当尺度水文过程的建模方法和框架。在中尺度上，基于物理的水文模型应反映控制空间差异的径流转化的所有相关当地条件。要建立一个模型，逼真地模仿水文系统的行为，我们将开发一个“综合”多目标校准框架，利用，除了径流数据，空间上的详细信息占主导地位的径流产生过程和每日实际蒸散率（ETA），后者通过整合高分辨率遥感数据的时间序列。对于三个选定的集水区在Nahe流域（SW-德国），我们将开发我们的方法在高度可定制的框架内的WaSiM-ETH模型构建一个示范性的行为模型，充分代表时空动态的径流转化在每小时的时间步长。为此，将纳入遥感地表参数和从热成像中得出的预计到达时间图，两者的像素大小都在30 - 100米之间。为了获得这些地图与日常的时间分辨率，我们将实施多传感器数据融合和土地表面温度（LST）降尺度的方法，联合收割机相结合的精细空间分辨率的窄测绘带传感器，如Landsat-8和哨兵2与高时间分辨率的地球观测系统，如中分辨率成像光谱仪和VIIRS。然后将使用一套完善的表面能量平衡模型（包括SEBAL，METRIC和SSEB）进行ETA估计。这一处理链将使LST和ETa产品在现场规模的整合，这是必不可少的耦合与空间分布的建模方法在中尺度。所得到的遥感产品可直接用于WaSiM-ETH的定标过程。我们将建立手动和自动校准方案，都基于一组空间指标，捕捉时空动态的ETa模式内的研究集水区。从遥感与连续的水文模型模拟晴空埃塔检索相结合，使我们能够生成无间隙的高分辨率埃塔复合产品的研究集水区，然后将扩大到整个纳河流域。此外，经过适当校准的行为模型被认为能够充分反映边界条件的变化，从而使我们能够深入了解在预期的未来全球环境变化情景下（例如，RCP6.0和RCP8.5情景，这代表了温度和降水条件的强烈变化）水文循环的响应。