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);

本数据集包含英格兰、苏格兰及威尔士境内多流域的敏感性分析成果。我们采用wflow_sbm（Imhoff等，2020；van Verseveld等，2024）开展wflow_sbm模型的模拟计算；针对每个CAMELS-GB流域，均通过HydroMT（Eilander等，2022）构建wflow_sbm模型。模型采用CEH-GEAR降雨数据、ERA5衍生的潜在蒸散发（Potential Evapotranspiration, PET，以及经环境递减率降尺度的ERA5气温数据作为驱动。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模型结果对参数ksathorfrac的敏感性，我们将该参数在宽区间内开展多组试验，参数取值为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。径流观测数据采用CAMELS-GB数据集。在计算评价指标时，我们剔除了前两年（1970年9月1日至1972年9月30日）的模拟时段。我们采用非参数KGE（通过Python包hydromt实现）对各模拟方案的性能进行评估，并选取性能最优的模拟所对应的ksathorfrac参数值。除非参数KGE外，我们还通过Python包spotpy计算了标准KGE。将ksathorfrac与平均垂直水力传导率相乘，得到平均水平侧向水力传导率，该参数值用于绘图分析，并与McMillan等（2022）得到的基流退水K估计值开展斯皮尔曼秩相关分析。