Hydrogeological map of Lower Saxony 1: 50 000 — Change of average annual groundwater regeneration for the 30-year period 2021-2050 to 1971, climate change scenario (RCP2.6) (WMS Service)

The map shows the modeled change in the mean annual groundwater formation for the 30-year period 2021-2050 to 1971-2000 in mm/a calculated using the “Climate Protection” scenario (RCP2.6). Groundwater is a raw material that can regenerate and renew itself. The main supplier for the groundwater supply is precipitation water leaking in Lower Saxony. It ensures that the groundwater deposits of the storage rocks are replenished in the underground. The groundwater formation is particularly high in winter, as at this time a large part of the rainfall in the soil is leaking. In the warmer seasons, on the other hand, much of the precipitation already evaporates on the surface or is absorbed by plants. The new groundwater formation is widely distributed in different areas. It depends on the distribution of precipitation and evaporation, the characteristics of the soil, the land use (growth, degree of sealing), the relief of the land surface, the artificial drainage by drainage, the groundwater fluid level and the properties of the near-surface rocks. Since these parameters differ significantly in the smallest space in Lower Saxony, groundwater formation is also subject to large lateral fluctuations. In order to determine the new groundwater formation, there are different methods. The available maps show the area-differentiated designation of the mean groundwater formation, which was calculated using the mGROWA method (short for “monthly large-scale water balance”). The model mGROWA was developed for the large-scale simulation of the water balance at Forschungszentrum Jülich in cooperation with the LBEG (Herrmann et al. 2013) and updated methodically for Lower Saxony since 2016. In addition, a series of new input data has been used to provide an up-to-date data base for water management planning and water approval procedures. Daily and monthly climate projection data were used as climatic input data. The climate projection data represent the results of an ensemble of different climate models (the Lower Saxony climate ensemble AR5-NI v2.1 see Hajati et al. (2022)). The data was provided by the German Weather Service. The basis for this is the EURO-CORDEX Ensemble (Jacob et al., 2014). As part of the BMVI expert network, the DWD saw a downscale from a 12.5 km grid to a 5 km grid. The climate models are driven by the “climate protection” scenario (RCP2.6). This is a scenario of the IPCC, which means significant efforts in climate protection and low emissions. The results of all climate models are equally likely. Therefore, in addition to the mean, which shows a tendency, the upper (maximum) and lower (minimum) edge of the result bandwidth can be retrieved via the maptip. For better regionalisation, the climatic input parameters precipitation and potential evaporation with bilinear interpolation were scaled down to a 500 x 500 m grid for mGROWA22.该地图展示了基于“气候保护”情景（RCP2.6）计算得到的2021-2050年与1971-2000年两个30年时段的年平均地下水补给量（groundwater formation）的模拟变化，单位为毫米每年（mm/a）。 地下水是一种可循环更新的自然资源。地下水补给的主要来源是下萨克森州境内入渗的降水，这些降水会补给地下储水岩层中的地下水储量。冬季时地下水补给量尤为充沛，因为此时土壤中的大部分降雨会入渗至地下；而在暖季，多数降水会直接在地表蒸发或被植物吸收利用。 区域地下水补给量的空间分布广泛，其影响因素包括降水与蒸发的分布、土壤特性、土地利用状况（植被生长状况、地表封闭程度）、地表地形起伏、人工排水措施、地下水位以及近地表岩层的属性。由于下萨克森州内上述参数在极小空间尺度内即存在显著差异，因此地下水补给量也呈现出较大的横向空间波动。 当前可获取的地图采用mGROWA方法（“月尺度大尺度水平衡”的英文缩写，monthly large-scale water balance）计算得到分区平均地下水补给量。mGROWA模型由德国于利希研究中心（Forschungszentrum Jülich）与LBEG合作开发，用于大尺度水平衡模拟（Herrmann等，2013年），并自2016年起针对下萨克森州进行了系统性更新。此外，研究团队采用了一系列新的输入数据，为水资源管理规划与取水许可审批流程提供了最新的数据库支撑。 本研究采用逐日和逐月气候预估数据作为气候输入参数。该气候预估数据来自多气候模式集合模拟结果（下萨克森州气候集合AR5-NI v2.1，详见Hajati等，2022年），由德国气象局（DWD，Deutscher Wetterdienst）提供，其数据基础为EURO-CORDEX集合模拟数据集（Jacob等，2014年）。作为德国联邦交通和数字基础设施部（BMVI）专家网络的组成部分，德国气象局将原始数据从12.5公里分辨率的网格降尺度至5公里分辨率的网格。 本研究采用的气候模式由“气候保护”情景（RCP2.6）驱动。该情景是政府间气候变化专门委员会（IPCC）设定的减排情景，代表了大力开展气候保护、实现低排放的发展路径。 所有气候模式的模拟结果具有同等可信度。因此，除了体现变化趋势的平均值之外，用户还可通过地图提示（maptip）获取结果区间的上限（最大值）与下限（最小值）。 为实现更精准的区域化表达，研究采用双线性插值法将降水与潜在蒸发量这两项气候输入参数降尺度至500米×500米的网格分辨率，以适配mGROWA22模型。