## 3 Groundwater and Surface Water Data Sources and Availability
The \"Copernicus Land MonitoringSystem\" provides information about the spatial distribution and changes in land cover on a continental scale through its Corine product for Land Cover (CLC), covering the period from 1990 to 2018. In order to obtain accurate results, a multi-constraint measurement is frequently necessary. The use of appropriate data and their availability for model validation and measurement is still the crucial issue that determines the effectiveness of the model. To validate and calibrate surface models, various findings have assessed the effectiveness of the moderate resolution imaging spectroradiometer (MODIS) product with encouraging outcomes. Data related to soil water content, snow cover, Normalized Vegetation Index (NDVI), and evapotranspiration can be obtained using the AppEEARS interface [43]. In addition to these open datasets, a number of modeling products have become available over the past decade. Two of the global hydrological models that have been developed are PCR-GLOBWB v2.0 [44] and WaterGAP v2.2d [45], which aim to quantify human use of surface water and groundwater, along with storage, water flows, and resources on a global level. They also provide the capability to output the post-process, such as groundwater spatiotemporal recharge and volume of river flow. However, their main limitation is the low spatial resolution. It is important to note that while these globally available datasets can be useful, it is critical to be cautious when using them as they may contain errors and inconsistencies that can result in inaccuracies in simulations. Estimating the share of groundwater through a simulation of flood hydrographs using two different time-based rainfall distributions is presented in [46]. Table 1 shows the various datasets available for use in modeling parameters and their prediction possibilities. Table 2 shows the various datasets available for use in modeling parameters and their corresponding links. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Main Terminology** & \multicolumn{2}{c}{**Sub-Terminology**} & **Prediction Possibilities** \\ \hline \multirow{6}{*}{Weather data} & Precipitation & Clear, rain or snow \\ \cline{2-3}  & Temperature & Minimum or maximum, \\  & & positive or negative \\ \cline{2-3}  & & Solar radiation \\ \cline{2-3}  & & Relative humidity \\ \cline{2-3}  & & Wind speed \\ \cline{2-3}  & & Evaporation \\ \hline \multirow{6}{*}{Aquire layers} & Saturated and unsaturated zone & Hourly, daily, weekly, \\  & & Hydraulic conductivity \\ \cline{2-3}  & & Transmissivity \\ \cline{2-3}  & & Aquifer storage \\ \cline{2-3}  & & Number of layers \\ \cline{2-3}  & & Thickness \\ \hline Land cover & Crop, Urban, rural, or industrial \\ \hline Stream flow & Variation measurement \\ \hline Soil & Soil texture \\ \hline Morphology & Digital elevation model (DEM) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Various datasets available for use in modeling parameters and their prediction possibilities [15]. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Data Shape** & **Data Classification** & **Data Source** & **Access Date** & **Availability** \\ \hline \multirow{2}{*}{Shapefile} & \multirow{2}{*}{Classification of land use} & [[https://land.opernicus.eu/pan-european/](https://land.opernicus.eu/pan-european/)]([https://land.opernicus.eu/pan-european/](https://land.opernicus.eu/pan-european/)) & (10 February 2023) & Europe \\  & & corine-land-cov & \\ \hline \multirow{2}{*}{Shapefile} & \multirow{2}{*}{Property of soil classification} & [[https://www.isric.org/explore/woosis](https://www.isric.org/explore/woosis)]([https://www.isric.org/explore/woosis](https://www.isric.org/explore/woosis)) & (10 February 2023) & Worldwide \\ \cline{2-3}  & & & \\ \hline \end{tabular}
\end{table} 
Table 2: Various datasets available for use in modeling parameters.