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

在所有景观中，水在能量、养分、污染物和土壤的分配和再分配中起着主导作用。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，农民根据土壤湿度做出管理决策，以减少土壤压实，或根据降水做出管理决策，以避免农药损失——水是这些决策的间接驱动因素。调节直接和间接驱动因素的水文状态和过程在空间和时间上是高度可变的。如果我们想更好地理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生态系统成分在景观中的横向相互作用。这里我们的目标是建立这样一个建模工具。拟议的水文流域模型框架（CMF）基于开源、模块化建模结构，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，但也允许购买已经可用的模型。特别是，我们将实施模拟水文、生物地球化学、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成，例如在初步的DFG研究项目（BR 2238/7-1）和欧盟综合项目NitroEurope中。这些模型提供了这个建议的核心。目前还没有一个农药命运模型，将根据申请人在PRZM和MACRO方面的经验来开发。这个项目的最终目标是将不同的模型完全耦合起来，并用真实世界的数据测试得到的多模型。我们将在中欧25平方公里的集水区施温巴赫应用CMF。下游集中用于农业，而上游则被森林和草原覆盖。自2010年建立“Schwingbachtal”实验研究区以来，对许多水文和生物地球化学措施进行了监测。将测量农药浓度，以获得农药命运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。我们将以评估管理方案并调查其对流域尺度水、养分和农药通量的影响来结束我们的研究计划。

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.