Dataset underlying the publication: Revealing spatial patterns of lateral hydraulic conductivity through sensitivity analysis

This dataset contains the results from a sensitivity analysis conducted for many catchments in England, Scotland, and Wales; We used wflow_sbm (Imhoff et al., 2020; van Verseveld et al., 2024) to run the wflow_sbm models. Wflow_sbm models were built using HydroMT (Eilander et al., 2022) for each CAMELS-GB catchment. The models were forced with CEH-GEAR rainfall, ERA5 derived PET and downscaled ERA5 temperature (using environmental lapse rate). The models were run for the period 01/01/1970-31/12/2015 . To investigate the sensitivity of the wflow_sbm model results to ksathorfrac values, this parameter was varied over a wide range (1,2,3,4,5,6,7,8,9,10,15,20,25,30,40,50,60,80,100,200,400,800,1000,2000,4000,6000,8000,10000). CAMELS-GB data was used as source for discharge observations. The first two years (01/9/1970-30/09/1972) were disregarded when calculating the metrics. We used the non parametric KGE (using python package hydromt) to assess the performance of each simulation and we selected the ksathorfrac from run with the highest performance . Along side the non parametric KGE , we also calculated KGE (using python package spotpy). The Ksathorfrac was multiplied with the mean vertical hydraulic conductivity to derived the mean horizontal lateral hydraulic conductivity which was used for plotting and calculating the Spearman rank correlation with base flow recession K estimates from McMillan et al (2022);本数据集收录了针对英格兰、苏格兰及威尔士境内诸多流域开展的敏感性分析成果。我们采用wflow_sbm（Imhoff等，2020；van Verseveld等，2024）运行相关模型，该wflow_sbm模型针对每个CAMELS-GB流域，通过HydroMT（Eilander等，2022）构建完成。模型的驱动数据采用CEH-GEAR降雨数据、ERA5衍生的潜在蒸散发（PET），以及借助环境递减率进行降尺度处理的ERA5气温数据。模型模拟时段为1970年1月1日至2015年12月31日。为探究wflow_sbm模型结果对ksathorfrac参数的敏感性，我们将该参数在宽区间内进行了取值扰动，取值范围为：1、2、3、4、5、6、7、8、9、10、15、20、25、30、40、50、60、80、100、200、400、800、1000、2000、4000、6000、8000、10000。本数据集以CAMELS-GB数据作为径流观测数据来源。在计算评估指标时，我们剔除了1970年9月1日至1972年9月30日这前两年的模拟数据。我们采用非参数化KGE（通过Python包hydromt实现）评估每场模拟的性能，并选取表现最优的模拟所对应的ksathorfrac参数值。除非参数化KGE外，我们还通过Python包spotpy计算了标准KGE。将ksathorfrac与平均垂向水力导率相乘，得到平均水平侧向水力导率，以此用于绘图以及与McMillan等（2022）提出的基流退水K估计值开展斯皮尔曼秩相关分析。