Hydrogeological map of Lower Saxony 1: 50 000 — ittlere annual groundwater regeneration for the 30-year period 2071-2100 in the hydrological winter half-year, climate protection scenario (RCP2.6) (WMS Service)

The map shows the modeled average annual groundwater regeneration for the 30-year period 2071-2100 in the hydrological winter half-year (Nov.-Apr.) in mm/a calculated with 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.本地图展示了在"气候保护"情景（RCP2.6）下计算得到的2071-2100年这30年时段内，水文冬季半年（11月至次年4月）的年均地下水补给量（单位：mm/a）。 地下水是一种可自行再生更新的自然资源。下萨克森州的地下水补给主要来源于入渗的降水，其保障了储水岩层中的地下储层得以补充。冬季是地下水形成量最高的时段，因为此时土壤中的大部分降雨会渗入地下；而在较为温暖的季节，多数降水会直接在地表蒸发或被植物吸收。 地下水的新生补给广泛分布于不同区域，其影响因素包括降水与蒸发的分布、土壤特性、土地利用方式（植被覆盖、地表封闭程度）、地表地形、人工排水措施、地下水位以及近地表岩层的属性。由于下萨克森州内的这些参数在极小空间范围内就存在显著差异，地下水补给也随之呈现出较大的横向空间波动。 为确定新生地下水补给量，学界已开发出多种方法。本次公开的地图展示了经分区差异化表征的平均地下水补给量结果，该结果通过mGROWA方法（"月度大尺度水平衡"（monthly large-scale water balance）的缩写）计算得出。mGROWA模型由于利希研究中心（Forschungszentrum Jülich）与LBEG合作开发（Herrmann等，2013），并自2016年起针对下萨克森州进行了系统性更新。此外，研究团队还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。 本研究采用逐日及逐月气候预估数据作为气候输入参数。这些气候预估数据源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)），由德国气象局（DWD）提供，其基础数据集来自EURO-CORDEX集合（Jacob等，2014）。作为BMVI专家网络的组成部分，德国气象局将原始数据的空间分辨率从12.5km网格降尺度至5km网格。 这些气候模式以"气候保护"情景（RCP2.6）作为驱动条件。RCP2.6是联合国政府间气候变化专门委员会（IPCC）提出的情景之一，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模拟，研究团队采用双线性插值法，将气候输入参数（降水与潜在蒸发量）降尺度至500×500m的网格分辨率，用于mGROWA22模型。