Towards consistent predictions of water and energy cycles in intermediate scale catchments

对中等规模流域的水和能源循环进行一致的预测

• 批准号: 274054947
• 负责人: Dr.-Ing. Uwe Ehret
• 金额: --
• 依托单位: Max-Planck-Institut für Biogeochemie (MPI-BGC)
• 依托单位国家: 德国
• 项目类别: Research Units
• 财政年份: 2014
• 资助国家: 德国
• 起止时间: 2013-12-31 至 2018-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/274054947?language=en
• 关键词: Towards; consistent; predictions; water; energy

The proposed project continues the work of projects S and C within the first phase of the DFG Research Group FOR 1598 CAOS. Our goal is to improve distributed modeling of water-, energy- and mass cycles in intermediate scale catchments (few to few hundred km^) by investigating the optimum balance between model complexity and parsimony and the value of different observations for model identification and operation. In particular, we will further develop the functional core of the CAOS model as started in phase 1, investigate the potential of a novel concept for dynamical and hierarchical grouping of model elements to avoid redundant computations, and further develop and apply an information-based multi-parameter, multi-scale verification concept. Distributed predictions on the lower mesoscale require distributed input; we will therefore further develop quantitative precipitation estimation techniques based on multi-sensor observations (Polarimetrie radar, distrometers, and vertical radar) and a novel approach based on high-resolution weather modeling with WRF in combination with data assimilation. We will use the large set of observations generated in CAOS phase 1 to set up, run and compare a representative sample of state-of-the-art mesoscale hydrological models (CAOS, ROGeR, CATFLOW, NOAH-MP) to assess their relative strengths, develop a framework for structured model intercomparison and to assess the informative value of different observables.

拟议的项目继续开展DFG 1598 CAOS研究组第一阶段内项目S和C的工作。我们的目标是通过研究模型复杂性和简约性之间的最佳平衡，以及模型识别和操作的不同观测值，来改善中尺度集水区（几到几百km^）的水、能量和质量循环的分布式建模。特别是，我们将进一步开发CAOS模型的功能核心，如在第一阶段开始，研究模型元素的动态和层次分组的新概念的潜力，以避免冗余计算，并进一步开发和应用基于信息的多参数，多尺度验证概念。分布式预测低中尺度需要分布式输入，因此，我们将进一步发展定量降水估计技术的基础上，多传感器观测（偏振雷达，distrometers和垂直雷达）和一种新的方法，基于高分辨率的天气建模与WRF结合数据同化。我们将使用CAOS第1阶段产生的大量观测数据来建立、运行和比较最先进的中尺度水文模型（CAOS、ROGeR、CATFLOW、NOAH-MP）的代表性样本，以评估它们的相对优势，开发结构化模型相互比较的框架，并评估不同观测值的信息价值。