Water resource indicator dataset in Climate Guide

Climate change affect water resources through shorter snow seasons, changed rainfall patterns and increased evaporation. This dataset includes water resources indicators related to the duration of the snow cover, water equivalent of snow, soil moisture deficit, evaporation, runoff and time of occurrence of the greatest runoff. Data are available on a regular 10 km x 10 km over Finland under current and future climate.

Data processing

30-year average are shown on a regular 10 km x 10 km over Finland for the baseline period 1981-2010 and three future periods, 2010–2039, 2040–2069 and 2070–2099, under 20 climate scenarios selected from the CMIP5 ensemble: low emissions (RCP2.6), moderate emissions (RCP4.5), moderate-high emissions (RCP6.0) and high emissions (RCP8.5) simulated with 14 Global Climate Models (CCSM4, NorESM1-M, GISS-E2-H, MIROC5, HadGEM2-CC, HadGEM2-ES, GFDL-CM3, MIROC-ESM-CHEM, MIROC-ESM, MPI-ESM-MR, CESM1-BGC, GISS-E2-R, CESM1-CAM5, MRI-CGCM3).

Scenarios of variables related water resources have been produced with the Finnish Environment Institute's (Syke) Watershed Simulation and Forecasting System (WSFS) (Vehviläinen & Huttunen 2002). The WSFS is used to draft hydrological predictions and warn of floods in Finland (https://www.vesi.fi/). The most important part of the system is a runoff model that describes the hydrological cycle from precipitation to runoff using normal meteorological material as the starting information (Bergström 1976). The model covers the whole land area of Finland, a total of 390,000 km², including cross-boundary watersheds overrunning the border. The model operates on a watershed sub-division level and the area is divided to approximately 6,400 watershed sub-divisions. The model's input data include precipitation and temperature, while the modelled variables of hydrological cycle include snow accumulation and melting, soil moisture, evaporation, ground water, runoff and discharge and water levels of the main rivers and lakes. The following indicators are included:
Evaporation sum (unit: mm)
Maximum runoff (mm)
Seasonal and annual total runoff (mm)
Snow cover duration (days)
Maximum Snow Water Equivalent (kg/m2)
Soil moisture deficit (mm)
Seasonal flood timing (percent of years)

Additional information: https://www.climateguide.fi/articles/finnish-environment-institutes-watershed-simulation-and-forecasting-system

Details of the analysis to construct this dataset are described in Veijalainen et al. (2019).

Data access

https://paikkatiedot.ymparisto.fi/geoserver/ilmo-climateguide/wms?request=getcapabilities

The data can be accessed via a Web Feature Service (WFS) supporting versions 1.0.0 and 1.1.0. WFS services can be opened in various GIS applications, for example ArcGIS for Desktop and open source desktop application QGIS. Typically, the GIS applications loads data from a WFS service in a GML (Geography Markup Language) format. Additionally, the data can be loaded as zip compressed ESRI Shape files, a CSV file or a JSON file. The service includes 14 feature types representing WSFS simulations:
hydro_landevaporation -- Evaporation sum (mm)
hydro_runoffmaximumaverage – Maximum runoff (mm)
hydro_runoffsumaverage_1 – winter (Dec-Feb) total runoff (mm)
hydro_runoffsumaverage_2 – spring (Mar-May) total runoff (mm)
hydro_runoffsumaverage_3 – summer (Jun-Aug) total runoff (mm)
hydro_runoffsumaverage_4 – autumn (Sep-Nov) total runoff (mm)
hydro_runoffsumaverage – annual total runoff (mm)
hydro_snowcoverdays – Snow cover duration (days)
hydro_snowwaterequivalent – Maximum Snow Water Equivalent (kg/m2)
hydro_soilmoisturedeficit – Soil moisture deficit (mm)
hydro_timingmaxrunoff_1 – Percentage of years with the maximum flood in winter (Dec-Feb) (percent of years)
hydro_timingmaxrunoff_2 – Percentage of years with the maximum flood in spring (Mar-May) (percent of years)
hydro_timingmaxrunoff_3 – Percentage of years with the maximum flood in summer (Jun-Aug) (percent of years)
hydro_timingmaxrunoff_4 – Percentage of years with the maximum flood in autumn (Sep-Nov) (percent of years)

The attributes of each feature type are labelled as [climateModel]_[emissionScenario]_[timePeriod]_0_[valueType]. There are 14 climate models and four forcing (or emission) scenarios rcp26, rcp45, rcp60 and rcp85. The timePeriod includes the start year and the end year of the period. There are three options for valueType, absolute value “av”, absolute change “ac” and relative change “rc”. For example, the attribute labeled as mpiesmmr_rcp85_20702099_0_rc describes the climate model MPI-ES-MMR, the forcing scenario RCP8.5, the time period from 2070 to 2099 and relative change (expressed as %).