Indicators used to assess irrigation water demand in Sweden

Data Description The dataset includes three indicators used to assess irrigation water demand (IWD) in Sweden: Growth Start Date – The starting date of crop growth.PLWD-days – The number of days per year when the water demand of crops exceeds the available soil water if no irrigation is applied.IWD – The total volume of irrigation water required. PLWD-days and IWD were calculated for each catchment over four 30-year periods, both for an average year and for the driest years. It is important to note that the driest year (i.e., the year with the most PLWD-days/IWD) may differ between subbasins. PLWD-days was analyzed separately for each of the nine crop groups. IWD was calculated as the total irrigation water applied to all crop groups in a catchment and was not analyzed per crop type. Changes in the growth start date (in days) were analyzed for each crop group, except for autumn grain and oil plants, which are sown in autumn. Note: The IWD files contain results from the scenario with an irrigation load of 22 mm irrigation water applied per irrigation event. The files IWD_15 and IWD_30 contain the results from the scenarios with 15 mm and 30 mm irrigation load. There are also IWD results for each of the crops listed below, these files are named IWD_X where X denots crop by a letter (see the crop list) Data Sources and Climate Projections The data was obtained from the S-HYPE model (Lindström et al., 2010; Strömqvist et al., 2012) driven by a bias-corrected ensemble of 17 Euro-CORDEX regional climate models (RCMs) (Strandberg et al., 2024). Simulations were performed under three Representative Concentration Pathways (RCPs): RCP 2.6, RCP 4.5, and RCP 8.5. The climate data was bias-adjusted using the multi-scale bias adjustment method (Berg et al., 2022). The simulations span 1981–2100 and are divided into four 30-year periods: Reference period: 1981–2010 (baseline for comparison) Future periods: 2011–2040, 2041–2070, 2071–2100 Crops: (the crops are refred to by a letter in the file names) Winter grain (a) Spring grain (b) Fruit and berries (c) Fodder crops (corn + protein crops) (d) Potatoes (e) Sugar beets (f) Ley (forage crops) (g) Winter oil plants (h) Vegetables (i) Data Structure and Files For each indicator, a .zip file contains multiple NetCDF files, corresponding to different RCPs and RCM ensemble members. Each file includes: Absolute values for each 30-year period. Changes relative to the reference period (both absolute and percentage change). Statistical summary for the entire ensemble for each RCP and time period, including: Maximum, minimum, mean, median, 25th and 75th percentiles Standard deviation Ensemble agreement There is also a .shp file (with corresponding .dbf, -prj and .shx files) with the subbasin areas in Sweden which the results are mapped to and a csv file (Agricultural_areas.csv) which contain the areas for each crop and the total agricultural area in square meters in every subbasin with agricultural land. File Naming Structure Example: irrayearmax-tmean_abs_EUR-11_CNRM-CERFACS-CNRM-CM5_rcp26_r1i1p1_CNRM-ALADIN63-v2_2011-2040_1981-2010.nc Explanation: Indicator(*)_Value(**)_Domain_GCM-Institute-GCM-Name_Scenario_Initialization_RCM_Institute_RCM_Name_Version_Period_Period-for-comparion.nc * IWD = irraXyearmax-tmean (driest year) irrayearmean-tmean (average years) PLWD-days = plwdXdaysmax-tmean (driest year) and plwdXdaysmean-tmean (average years) Growthstart = sswtXgrowthstart-tmean (X denots crop by a letter, see the crop list above) ** abssolute (abs) or percentage (rel) References Berg, P., Bosshard, T., Yang, W., Zimmermann, K., 2022. MIdASv0.2.1 – MultI-scale bias AdjuStment. Geosci. Model Dev. 15, 6165–6180. https://doi.org/10.5194/gmd-15-6165-2022 Lindström, G., Pers, C., Rosberg, J., Strömqvist, J., Arheimer, B., 2010. Development and testing of the HYPE (Hydrological Predictions for the Environment) water quality model for different spatial scales. Hydrol. Res. 41, 295–319. https://doi.org/10.2166/nh.2010.007 Strandberg, G., Andersson, B., Berlin, A., 2024. Plant pathogen infection risk and climate change in the Nordic and Baltic countries. Environ. Res. Commun. 6, 031008. https://doi.org/10.1088/2515-7620/ad352a Strömqvist, J., Arheimer, B., Dahné, J., Donnelly, C., Lindström, G., 2012. Water and nutrient predictions in ungauged basins: set-up and evaluation of a model at the national scale. Hydrol. Sci. J. 57, 229–247. https://doi.org/10.1080/02626667.2011.637497