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

SSF ANGLE - 比较山坡间地下风暴流的动态和路径

• 批准号: 493884602
• 负责人: Professor Dr. Peter Chifflard
• 金额: --
• 依托单位: Institut für Geographie und Regionalforschung
• 依托单位国家: 德国
• 项目类别: Research Units
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/493884602?language=en
• 关键词: SSF; ANGLE; Comparing; dynamics; pathways

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.

坡面水文动力学，特别是地下暴雨流（SSF），是高度复杂的，在空间和时间上是可变的。通常，现有的研究往往限于单一的斜坡或少数风暴事件，因此，这些研究结果转移到其他斜坡或集水区是与很大的不确定性。因此，为扩大规模和模型验证的定量水文动态的山坡和影响因素的空间和时间模式的地下暴雨流是迫切需要的。与山坡水文动力学密切相关的是有机碳从土壤向邻近河流的输出。但是，由于SSF在坡面内的确切流动路径尚不清楚，因此碳的空间来源在很大程度上仍不清楚。因此，我们提出了一个相结合的水文地球化学方法，措施的山坡地下水动态，包括稳定同位素和水溶性有机物（WSOM;浓度，吸光度和荧光）沿着480个地点在100个山坡在四个对比鲜明的集水区从低到高的山脉（绍尔兰，矿石山，黑森林，阿尔卑斯山）的特性。这使我们能够得出不同地貌之间的经验关系（即，收敛、发散和平面斜坡形状、流路长度和山谷形状）、基岩和土壤特性以及地下水动态，以量化地下水文过程模式的空间变异性和稳定性（例如，流向、通过时间、水化学和生物化学成分）。一个基于GIS的景观分析和预测建模与RoGeR模型之前，将进行仪器，包括地形，土壤和土地利用信息以及空间分布的地区SSF预计将占主导地位，分别，以采样代表性的山坡。将在所有配备设备的山坡上的480口威尔斯井中记录SSF引起的浅层地下水动态。沿着土壤剖面对水稳定同位素和WSOM进行分布式采样（每个剖面12个同位素和6个WSOM样本;在潮湿和干燥条件下进行两次）将有助于评估非饱和区和饱和区中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，荧光光谱仪，同位素分析仪）来分析稳定同位素和WSOM。多变量统计技术和机器学习工具的使用将有助于确定地下水文过程模式的时间和空间模式。我们将调查山坡流的连接，包括与RoGeR预测SSF通量，方向和动态，可用于预测运输的土壤有机质从山坡流的贡献面积的程度。