Input data set: Lakes static information

Data set calculated from the GLOBathy (Khazaei et al., 2022) and HydroLAKES v1.0 (Messager et al., 2016) datasets (Golub et al., 2022).

Lake hypsographic volume: The gridded data set describes the volume at different depths of one hypothetical lake representing the typical characteristics of all real lakes in the grid cell according to the GLOBathy (Khazaei et al., 2022) and HydroLAKES v1.0 (Messager et al., 2016) datasets (Golub et al., 2022). Each hypsographic curve consists of 11 data pairs. Level refers to the depth of the lake taking the lake bottom as the reference. Volume is the volume at the corresponding level.

Lake hypsographic area: The gridded data set describes the lake area at different depths of one hypothetical lake representing the typical characteristics of all real lakes in the grid cell according to the GLOBathy (Khazaei et al., 2022) and HydroLAKES (Messager et al., 2016) datasets (Golub et al., 2022). Each hypsographic curve consists of 11 data pairs. Level refers to the depth of the lake taking the lake bottom as the reference.

Lake height: The gridded data set provides the elevation above sea level for the representative lakes described above. The information is derived from HydroLAKES v1.0 (Messager et al., 2016).

Maximum lake depth: Gridded data set that provides the maximum depth for the representative lakes described above and derived from GLOBathy (Khazaei et al., 2022). We recommend using the area or volume hypsographic curves described above as inputs for your lake model. Use this file only if your lake model does not accept a full hypsographic curve as an input.

Mean lake depth: Gridded data set that provides the mean depth for the representative lakes as calculated from GLOBathy and HydroLAKES v1.0 (Khazaei et al., 2022; Messager et al., 2016). We recommend using the area or volume hypsographic curves described above as inputs for your lake model. Use this file only if your lake model does not accept a full hypsographic curve as an input.

Lake volume: Gridded data set of volume (km3) for representative lakes described above as calculated from GLOBathy and HydroLAKES v1.0 (Khazaei et al., 2022; Messager et al., 2016). We recommend using the area or volume hypsographic curves described above as inputs for your lake model. Use this file only if your lake model does not accept a full hypsographic curve as an input.

Lake surface area: Gridded data set of surface area for the representative lakes described above as calculated from GLOBathy and HydroLAKES v1.0 (Khazaei et al., 2022; Messager et al., 2016). As opposed to the “Lake and reservoir surface area” listed above under “Direct human forcing”, this data set refers to one specific lake associated with each grid cell, and the corresponding surface area does not change over time. We recommend using the area or volume hypsographic curves described above as inputs for your lake model. Use this file only if your lake model does not accept a full hypsographic curve as an input.

HydroLAKES ID: HydroLAKES reference to relate HydroLAKES and GLOBathy database fields to the representative lakes described above. This dataset contains IDs of the 41449 representative lakes used in ISIMIP, which are a subset of the about 1.4 million lakes contained in the HydroLAKES and GLOBathy database.

HydroLAKES IDs for big lakes: This dataset is analogous to the one above, but only contains IDs of 93 large lakes. It can be used to produce global plots with conspicuous large lakes. To be used together with the file storing the big lakes mask.

Big lakes mask: This dataset indicates the 0.5° grid cells actually occupied by each of the 93 large lakes, which can be larger than a single grid cell. It can be used to produce global plots with conspicuous large lakes. To be used together with the big lakes IDs in the dataset above.

Khazaei, B., Read, L. K., Casali, M., Sampson, K. M., and Yates, D. N.: GLOBathy, the global lakes bathymetry dataset, Sci. Data, 9, 36, https://doi.org/10.1038/s41597-022-01132-9, 2022.

Messager, M. L., Lehner, B., Grill, G., Nedeva, I., and Schmitt, O.: Estimating the volume and age of water stored in global lakes using a geo-statistical approach, Nat. Commun., 7, 13603, https://doi.org/10.1038/ncomms13603, 2016.

Golub, M., Thiery, W., Marcé, R., Pierson, D., Vanderkelen, I., Mercado-Bettin, D., Woolway, R. I., Grant, L., Jennings, E., Kraemer, B. M., Schewe, J., Zhao, F., Frieler, K., Mengel, M., Bogomolov, V. Y., Bouffard, D., Côté, M., Couture, R.-M., Debolskiy, A. V., Droppers, B., Gal, G., Guo, M., Janssen, A. B. G., Kirillin, G., Ladwig, R., Magee, M., Moore, T., Perroud, M., Piccolroaz, S., Raaman Vinnaa, L., Schmid, M., Shatwell, T., Stepanenko, V. M., Tan, Z., Woodward, B., Yao, H., Adrian, R., Allan, M., Anneville, O., Arvola, L., Atkins, K., Boegman, L., Carey, C., Christianson, K., de Eyto, E., DeGasperi, C., Grechushnikova, M., Hejzlar, J., Joehnk, K., Jones, I. D., Laas, A., Mackay, E. B., Mammarella, I., Markensten, H., McBride, C., Özkundakci, D., Potes, M., Rinke, K., Robertson, D., Rusak, J. A., Salgado, R., van der Linden, L., Verburg, P., Wain, D., Ward, N. K., Wollrab, S., and Zdorovennova, G.: A framework for ensemble modelling of climate change impacts on lakes worldwide: the ISIMIP Lake Sector, Geosci. Model Dev., 15, 4597–4623, https://doi.org/10.5194/gmd-15-4597-2022, 2022.