• Energy Research

Monthly discharge (m3 s-1) and water temperature (K) simulated by a global hydrological model coupled to a surface water quality model (PCR-GLOBWB2-DynQual) for the time period 2005 - 2100, for an ensemble of 15 projections based on three combined climate and socio-economic scenarios (SSP1-RCP2.6; SSP3-RCP7.0 and SSP5-RCP8.5) and five general circulation models (GFDL-ESM4; UKESM1-0-LL; MPI-ESM1-2-hr; IPSL-CM6A-LR and MRI-ESM2-0) Output data are provided at 10km resolution and are averaged at monthly temporal resolution. These datasets were generated as part of the work presented in: Jones, E.R., Bierkens, M.F.P., van Puijenbroek, P.J.T.M. et al. Sub-Saharan Africa will increasingly become the dominant hotspot of surface water pollution. Nat Water 1, 602–613 (2023). https://doi.org/10.1038/s44221-023-00105-5 Relevant model description papers can be found at the following links: PCR-GLOBWB2: Sutanudjaja, E. H., van Beek, R., Wanders, N., Wada, Y., Bosmans, J. H. C., Drost, N., van der Ent, R. J., de Graaf, I. E. M., Hoch, J. M., de Jong, K., Karssenberg, D., López López, P., Peßenteiner, S., Schmitz, O., Straatsma, M. W., Vannametee, E., Wisser, D., and Bierkens, M. F. P.: PCR-GLOBWB 2: a 5 arcmin global hydrological and water resources model, Geoscientific Model Development, 11, 2429–2453, https://doi.org/10.5194/gmd-11-2429-2018, 2018. DynQual: Jones, E. R., Bierkens, M. F. P., Wanders, N., Sutanudjaja, E. H., van Beek, L. P. H., and van Vliet, M. T. H.: DynQual v1.0: a high-resolution global surface water quality model, Geoscientific Model Development, 16, 4481–4500, https://doi.org/10.5194/gmd-16-4481-2023, 2023. Additional information on the water temperature modelling can also be found at: Wanders, N., van Vliet, M. T. H., Wada, Y., Bierkens, M. F. P., & van Beek, L. P. H. (Rens): High-resolution global water temperature modeling. Water Resources Research, 55, 2760–2778, https://doi.org/10.1029/2018WR023250, 2019 van Beek, L. P. H., Eikelboom, T., van Vliet, M. T. H., and Bierkens, M. F. P.: A physically based model of global freshwater surface temperature, Water Resources. Research, 48, W09530, https://doi.org/10.1029/2012WR011819 , 2012.

Projections of Sea Level Rise (SLR) under RCP 4.5 and RCP 8.5 (AR5) along the Mekong Coast, Published1 by the Ministry of Natural Resources and Environment (MONRE), Hanoi, Vietnam. Projections of Mekong River discharge during the dry season under RCP 4.5 and RCP 8.5 at Kratie, Cambodia. The data contains the cumulative, minimum and maximum dry season (January-1st to April-30th) discharge from 5 different climate models. PCR-GLOBWB2 was run at 5 arc-min spatial resolution and forced with the data based on output from five ISIMIP CMIP5 global climate models (HadGEM2-ES, GFDL-ESM2, IPSL-CM5A-LR, MIROC-ESM-CHEM, NorESM1-M). 1. Ministry of Natural Resources and Environment (MONRE), V. Climate change and sea level rise scenarios for Vietnam, Ministry of Natural Resources and Environment. (2016). 2. Sutanudjaja, E. H. et al. PCR-GLOBWB 2: a 5 arcmin global hydrological and water resources model. Geosci. Model Dev. 11, 2429–2453 (2018). {"references": ["Sutanudjaja et al. (2018)", "Ministry of Natural Resources and Environment (2016)"]}

The dataset provided here belongs to Eslami et al. (2021) article. We suggest to refer to that article before using the data. The excel sheet contains the description of simulations as it relates to their drivers and forcings. Two types of forcing are considered in this study. The climatic and anthropogenic drivers. The Climatic or climate change driven forces are upstream discharge anomalies and downstream sea level rise. The anthropogenic forces are spatially-varying extraction-induced land subsidence and average riverbed level erosions driven by sediment starvation due to upstream dams and downstream sand mining. This excel file defines the simulation ID and the description (driving forces) of every simulation (including all the sensitivity analysis simulations). The actual data is in form of three different Python 2.7 dictionaries, saved in NumPy binary format (*.npy). The filenames are as SWI_Projections_pxx.npy (pxx can be p50, p90 or p100). P50/P90 refer to Spatial values of 50th/90th percentile of salinity in the dry season of the simulation year, and P100 (100th percentile) is basically the maximum salinity in during the dry season of the simulation year. The files can simply be read in a Python 2.7 platform with NumPy module installed. The line to read the data is: data = np.load(filename, allow_pickle=True).item() Each dictionary contains several keys, each representing the results of a simulation. Under every simulation, the results contain: x_grid : x-coordinates of a 2km x 2km grid projected and interpolated over the model [UTM 48N, m] y_grid : y-coordinates of a 2km x 2km grid projected and interpolated over the model [UTM 48N, m] s_grid : Salinity over a 2km x 2km grid projected and interpolated over the model [PSU] xy_utm : Easting & Northing [UTM 48N, m], at exact model grid points latlon : Latitude & Longitude at exact model grid points salinity : modelled salinity [PSU] at exact model grid points info : explaining the above information For further information and detailed background, you may refer to the following paper: Eslami, S.; Hoekstra, P.; Minderhoud, P. S. J.; Trung, N. N.; Hoch, J. M.; Sutanudjaja, E. H.; Dung, D. D.; Tho, T. Q.; Voepel, H. E.; Woillez, M.-N.; and van der Vegt, M.: Projections of salt intrusion in a mega-delta under climatic and anthropogenic stressors, Nat. Commun. Earth Environ.