SSF ANGLE - Comparing dynamics and pathways of subsurface stormflow among hillslopes

山坡的水文动力学，特别是地下暴雨流，在空间和时间上都是高度复杂多变的。通常，现有的研究往往仅限于单个斜坡或少数风暴事件，因此将这些发现转移到其他斜坡或集水区具有很大的不确定性。因此，为了实现模型的升级和验证，迫切需要对山坡水文动态和影响地下暴雨流时空格局的因素进行量化。与山坡水文动态密切相关的是有机碳从土壤向邻近溪流的输出。但是碳的空间来源在很大程度上仍然是不清楚的，因为斜坡内SSF的确切流动路径尚不清楚。因此，我们提出了一种综合的水文-生物地球化学方法，测量山坡地下水动态，包括稳定同位素和水溶性有机质（WSOM；浓度，吸光度和荧光）的特征，沿着100个山坡的480个地点，从低山脉到高山脉（Sauerland, Ore Mountains, Black Forest, Alps）。这使我们能够推导出不同地形（即收敛、发散和平面斜坡形状、流道长度和山谷形状）、基岩和土壤性质以及地下水动力学之间的经验关系，以量化地下水文过程模式（如水流方向、过境时间、水化学和生化组成）的空间变异性和稳定性。基于GIS的景观分析和使用RoGeR模型的预测建模将在结合地形、土壤和土地利用信息以及预计SSF占主导地位的地区的空间分布的仪器之前进行，以便对具有代表性的山坡进行采样。由于SSF引起的浅层地下水动态将在所有装备的山坡上的480口井中记录。水稳定同位素和WSOM沿土壤剖面分布采样（每个剖面12个同位素和6个WSOM样本，干湿两种条件下各采样一次）将有助于评估非饱和带和饱和带水的垂直和横向地下流动路径以及SSF源区的空间离散化。我们将使用一系列最先进的实验室设备和方法（toc分析仪，荧光光谱分析仪，同位素分析仪）来分析稳定同位素和WSOM。多元统计技术和机器学习工具的使用将有助于识别地下水文过程模式的时空格局。我们将研究坡地-河流的连通性，包括贡献面积的程度，罗杰预测SSF通量，方向和动力学，这些可以用于预测土壤有机质从坡地到河流的运输。

Hydrological dynamics of hillslopes, particularly subsurface stormflow (SSF), are highly complex and variable in space and time. Frequently, available studies are often limited to single slopes or few storm events, so that the transfer of these findings to other slopes or catchments is associated with great uncertainties. Thus, for upscaling and model validation a quantification of the hydrological dynamics of hillslopes and the factors influencing the spatial and temporal patterns of subsurface stormflow is urgently needed. Closely related to the hydrological dynamics of hillslopes is the export of organic carbon from the soils to the adjacent stream. But the spatial sources of carbon are still largely unclear because the exact flow paths of SSF within the slope are not well known. We therefore propose a combined hydro-biogeochemical approach, that measures the hillslope groundwater dynamics including a characterisation of stable isotopes and the water-soluble organic matter (WSOM; concentration, absorbance and fluorescence) along 480 locations at 100 hillslopes in four contrasting catchments from the low to high mountain ranges (Sauerland, Ore Mountains, Black Forest, Alps). This enables us to derive empirical relations among different landforms (i.e., convergent, divergent and planar slope shapes, flow path lengths and valley shapes), bedrock and soil properties and groundwater dynamics to quantify the spatial variability and stability of subsurface hydrological process patterns (e.g., flow directions, transit times, hydrochemical and biochemical composition). A GIS based landscape analysis and a predictive modeling with the RoGeR model will be conducted prior to instrumentation that incorporates topographic, soil and land use information as well as spatial distribution of areas were SSF is expected be dominant, respectively, in order to sample representative hillslopes. Shallow groundwater dynamics due to SSF will be recorded in 480 wells across all equipped hillslopes. Distributed sampling of water stable isotopes and WSOM along the soil profile (12 isotope and 6 WSOM samples per profile; two times during wet and dry conditions) will help to assess the vertical and lateral subsurface flowpaths of water in the unsaturated and saturated zone and the spatial discretization of source areas for SSF. We will use an array of state-of-the-art laboratory equipment and methods (TOC-Analyzer, Fluorescence Spectrometry, Isotope Analyzer) to analyze stable isotopes and WSOM. The use of multivariate statistical techniques and machine learning tools will help to identify temporal and spatial patterns of subsurface hydrological process patterns. We will investigate hillslope-stream connectivity including the extent of contributing area with RoGeR predicting SSF fluxes, directions and dynamics which can later be used for predicting transport of soil organic matter from the hillslope to the stream.