Quantifying temporal variability and spatial heterogeneity in rainfall recharge thresholds in a montane karst environment

Quantifying temporal variability and spatial heterogeneity in rainfall recharge thresholds in a montane karst environment
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量化山地喀斯特环境中降雨补给阈值的时间变化和空间异质性

DOI:
10.1016/j.jhydrol.2021.125965
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发表时间:
2021
影响因子:
6.4
通讯作者:
Baker A
Baker A
中科院分区:
地球科学1区
文献类型:
--
作者:
Baker A

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量化降雨补给阈值及其时空异质性,对于更好地理解补给过程和改进补给率的估算具有重要意义。洞穴提供了一个独特的观测站,可以观察到在个别降雨补给事件的时间尺度上,水从地表渗透到地下水位。在这里,我们在澳大利亚东南部的一个山地洞穴遗址监测了6年多的9个渗透点。在监测期间,6个滴水水文时间序列对降雨有多达100个的水文响应,3个站点对降雨事件没有响应。我们使用两种方法来量化降雨补给阈值。在每年的时间尺度上,对于所有9个滴落点,每年的总渗透水量被确定为每年的数据。日降雨补给阈值是通过最大化年渗水量和总降水量在一个可变的日阈值之上的相关性来确定的。年补给量方法得出了7个站点的降雨补给阈值,其中日降水阈值在6毫米至38毫米/天之间存在高且显著的相关性(等级相关性> 0.75)。从一个年滴水量低且恒定的地点和一个表现出“下流”行为的地点无法获得降雨补给阈值。在事件时间尺度上,对有水文响应的6个站点,确定了7天前的降雨量。特定滴注点在水文曲线响应之前的最小7天降水量在13-28毫米之间,75%的补给事件的7天前降水量在20.7 - 38.1毫米之间。结合所有滴漏水监测点并按月分析数据,确定了产生潜在补给所需的最少7天降水的季节性变化,从冬季的15 - 25毫米到2月和3月的50毫米。我们应用一个简单的水收支模型,由P和ET驱动,并优化到观测到的潜在补给事件,来推断“全洞”土壤和表层喀斯特的储存能力。这种储存能力介于~ 50mm(使用潜在蒸散发,92%的事件模拟成功)到~ 60mm(使用实际蒸散发,79%的事件模拟成功)之间。对各个滴漏点的建模确定了土壤和表层蓄水能力的空间异质性。我们的方法使用多种方法,首次可以比较每日和每周降雨补给阈值以及模拟的土壤和表层喀斯特储水量。
Quantifying rainfall recharge thresholds, including their spatial and temporal heterogeneity, is of fundamental importance to better understand recharge processes and improving estimation of recharge rates. Caves provide a unique observatory into the percolation of water from the surface to the water table at the timescale of individual rainfall recharge events. Here, we monitor nine infiltration sites over six years at a montane cave site in south eastern Australia. Six of the drip hydrology time series have up to ~100 hydrograph responses to rainfall over the monitoring period, three sites do not respond to rainfall events. We use two approaches to quantify rainfall recharge thresholds. At an annual timescale, for all nine drip sites, the total annual percolation water volume was determined for each year of data. Daily rainfall recharge thresholds were then determined by maximising the correlation of annual percolation water volume and total precipitation above a variable daily threshold value. The annual recharge amount methodology produced rainfall recharge thresholds for seven sites, where high and significant correlations (rank correlations > 0.75) occur for daily precipitation thresholds between 6 mm and 38 mm/day. No rainfall recharge thresholds could be obtained from one site which had a low and constant annual drip amount, and from one site which exhibited ‘underflow’ behaviour. At an event timescale, for the six sites which had a hydrograph response to rainfall, the 7-day antecedent rainfall amounts were determined. Minimum 7-day precipitation amounts prior to a hydrograph response for specific drip sites were in the range 13–28 mm and 75% of all recharge events had a 7-day antecedent precipitation between 20.7 and 38.1 mm. Combining all drip water monitoring sites and analysing the data by month identifies a seasonal variability in the minimum 7-day antecedent precipitation necessary to generate potential recharge, from 15 to 25 mm in winter to >50 mm in February and March. We apply a simple water budget model, driven by P and ET and optimised to the observed potential recharge events, to infer a ‘whole cave’ soil and epikarst storage capacity. This storage capacity is between ~50 mm (using potential evapotranspiration, 92% of events simulated successfully) to ~60 mm (using actual evapotranspiration, 79% of events simulated successfully). Modelling of individual drip sites identifies spatial heterogeneity in soil and epikarst storage capacities. Our approach using multiple methodologies allows the comparison between both daily and weekly rainfall recharge thresholds and modelled soil and epikarst storage for the first time.
使用区域洞穴滴水监测网络确定亚热带气候下的降雨补给阈值
DOI: 10.1016/j.jhydrol.2020.125001
发表时间: 2020
影响因子: 6.4
作者:
Baker A
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发表时间: 2019
期刊: The Science of the total environment
影响因子: --
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DOI: 10.4225/08/58542e0f24569
发表时间: 2015
影响因子: 6.4
作者:
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通讯作者: N. Viney
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DOI: 10.1109/jstars.2015.2451088
发表时间: 2015
影响因子: 5.5
作者:
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发表时间: 2017-12-30
影响因子: 3.2
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