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Storage dynamics, hydrological connectivity and flux ages in a karst catchment: conceptual modelling using stable isotopes

机译:岩溶集水区中的储存动力学,水文连接和通量变量:使用稳定同位素的概念建模

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We developed a new tracer-aided hydrological model that disaggregates cockpit karst terrain into the two dominant landscape units of hillslopes and depressions (with fast and slow flow systems). The new model was calibrated by using high temporal resolution hydrometric and isotope data in the outflow of Chenqi catchment in Guizhou Province of south-western China. The model could track hourly water and isotope fluxes through each landscape unit and estimate the associated storage and water age dynamics. From the model results we inferred that the fast flow reservoir in the depression had the smallest water storage and the slow flow reservoir the largest, with the hillslope intermediate. The estimated mean ages of water draining the hillslope unit, and the fast and slow flow reservoirs during the study period, were 137, 326 and 493?days, respectively. Distinct seasonal variability in hydroclimatic conditions and associated water storage dynamics (captured by the model) were the main drivers of non-stationary hydrological connectivity between the hillslope and depression. During the dry season, slow flow in the depression contributes the largest proportion (78.4%) of flow to the underground stream draining the catchment, resulting in weak hydrological connectivity between the hillslope and depression. During the wet period, with the resulting rapid increase in storage, the hillslope unit contributes the largest proportion (57.5%) of flow to the underground stream due to the strong hydrological connectivity between the hillslope and depression. Meanwhile, the tracer-aided model can be used to identify the sources of uncertainty in the model results. Our analysis showed that the model uncertainty of the hydrological variables in the different units relies on their connectivity with the outlet when the calibration target uses only the outlet information. The model uncertainty was much lower for the “newer” water from the fast flow system in the depression and flow from the hillslope unit during the wet season and higher for “older” water from the slow flow system in the depression. This suggests that to constrain model parameters further, increased high-resolution hydrometric and tracer data on the internal dynamics of systems (e.g. groundwater responses during low flow periods) could be used in calibration.
机译:我们开发了一种新的跟踪水文模型,将驾驶舱喀斯特地形分解为山坡和凹陷的两个主导景观单位(快速和慢速系统)。通过在中国西南部贵州省澄清集水流出中使用高时分辨率的液压和同位素数据来校准新模型。该模型可以通过每个景观单元跟踪每小时水和同位素通量,并估计相关的储存和水时代动态。从模型结果中,我们推断抑郁症中的快速流量储存器具有最小的储水和慢流储存器,具有山坡中间体。估计的均衡山坡单元的均等为山坡单元,以及在研究期间的快速和慢速流量储存器分别为137,326和493?天。循环条件和相关储水动力学(模型捕获的储水动力学(模型捕获)的不同季节性变异性是山坡和抑郁症之间非静止水文连接的主要驱动因素。在干燥季节期间,抑郁症的缓冲流量促使流量排放集水区的地下流的最大比例(78.4%),从而导致山坡和抑郁症之间的水文连接弱。在湿时期,由于山坡和抑郁症之间的强水学连接,山坡单元由于储存的快速增加而导致到地下流的最大比例(57.5%)。同时,示踪剂辅助模型可用于识别模型结果中的不确定性源。我们的分析表明,当校准目标仅使用出口信息时,不同单元中水文变量的模型不确定性依赖于与插座的连接。 “较新”水的模型不确定性对于来自抑郁术中的快速流动系统的“更新”水远低得多,并从雨季单元流动,从抑郁症中的慢速流动系统的“较旧”水更高。这表明,为了进一步限制模型参数,可以在校准中使用对系统内部动态的高分辨率液度和示踪数据(例如,低流量时段期间的地下水响应)。

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