Implementation of a coupled agricultural landscape model to predict water, carbon and nitrogen fluxes on the catchment scale

实施耦合农业景观模型来预测流域规模的水、碳和氮通量

• 批准号: 183697193
• 负责人: Professor Dr. Lutz Breuer
• 金额: --
• 依托单位: Professur für Landschafts-, Wasser- und Stoffhaushalt
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2010
• 资助国家: 德国
• 起止时间: 2009-12-31 至 2012-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/183697193?language=en
• 关键词: Implementation; coupled; agricultural; landscape; model

Water plays a dominant role in the distribution and re-distribution of energy, nutrients, pollutants and soils across all landscapes. It directly regulates plant growth, creates floods and drives trace gas emissions from soils. In agriculture, farmers base their management decisions on soil moisture to reduce soil compaction or on precipitation to avoid pesticide losses - water is an indirect driver of these decisions. Hydrological states and processes that regulate the direct and indirect drivers are highly variable in space and time. If we want to better understand the intrinsic interaction of water and associated matter fluxes in landscapes, or if we want to test hypotheses such as “what happens...if”, models are perfect tools to help us studying processes and solve these questions. While many models have been developed for particular ecosystems, we are lacking comprehensive tools to study the many possible feedback mechanisms and lateral interaction of ecosystem components across landscapes. Here we aim at setting up such a modeling tool. The proposed hydrological Catchment Model Framework (CMF) is based on an open source, modular modeling structure, following a fully distributed and deterministic approach. CMF allows coupling specifically developed models but also the buy-in of already available ones. In particular, we will implement models that simulate hydrology, biogeochemistry, pesticide transport and fate, agricultural management as well as plant and tree growth. Several of these models have already been built, e.g. within a preliminary DFG research project (BR 2238/7-1) and the EU Integrated Project NitroEurope. These models provide the core of this proposal. A pesticide fate model is not at hand yet and will be developed based on the applicant´s experience with PRZM and MACRO. A full coupling of the different models and testing of the resulting multi-model with real world data is the final goal of this project. We will apply CMF in the Schwingbach, a 25 km² sized catchment of central Europe. The catchment is intensively used for agriculture downstream while the headwaters are covered by forests and grasslands. Many hydrological and biogeochemical measures have been monitored since 2010 when the experimental study area “Studienlandschaft Schwingbachtal” has been established. Pesticide concentrations will be measured to obtain a sound data set for pesticide fate model validation. To proof whether the spatial and temporal dynamics of processes are captured by CMF, we will study hot spots and hot moments of pesticide concentrations and N stream concentrations. We will end our research program with the evaluation of management scenarios and investigate their impact on catchment scale water, nutrient and pesticide fluxes.

水在所有景观中的能量、养分、污染物和土壤的分布和再分布中起着主导作用。它直接调节植物生长，造成洪水，并推动土壤中微量气体的排放。在农业方面，农民根据土壤湿度作出管理决定，以减少土壤压实，或根据降水量作出管理决定，以避免农药损失-水是这些决定的间接驱动因素。调节直接和间接驱动力的水文状态和过程在空间和时间上变化很大。如果我们想更好地了解景观中水和相关物质通量的内在相互作用，或者如果我们想测试诸如“发生了什么..如果”，模型是帮助我们研究过程和解决这些问题的完美工具。虽然已经为特定的生态系统开发了许多模型，但我们缺乏全面的工具来研究许多可能的反馈机制和跨景观生态系统组成部分的横向相互作用。在这里，我们的目标是建立这样一个建模工具。建议的水文流域模型框架（CMF）是基于一个开源的，模块化的建模结构，遵循完全分布式和确定性的方法。CMF允许耦合专门开发的模型，但也可以购买已经可用的模型。特别是，我们将实施模拟水文，地球化学，农药运输和命运，农业管理以及植物和树木生长的模型。其中一些模型已经建立，例如在DFG初步研究项目（BR 2238/7-1）和欧盟综合项目NitroEurope中。这些模式是本建议的核心。目前还没有农药归宿模型，将根据申请人在PRZM和MACRO方面的经验开发。该项目的最终目标是实现不同模型的完全耦合，并使用真实的世界数据对所产生的多模型进行测试。我们将在中欧25平方公里的Schwingbach流域应用CMF。该集水区集中用于下游的农业，而源头则被森林和草原覆盖。自2010年建立“Schwingbachtal”实验研究区以来，对许多水文和地球化学措施进行了监测。将测量农药浓度，以获得用于农药归趋模型验证的可靠数据集。为了证明CMF是否捕获了过程的时空动态，我们将研究农药浓度和N流浓度的热点和热点时刻。我们将结束我们的研究计划与管理方案的评估，并调查其对集水区规模的水，养分和农药通量的影响。