Model Chain of the Water Sector

The variables of the water sector (water balance, evapotranspiration, runoff, groundwater recharge, discharge) are results of the hydrological model SWIM. SWIM was driven directly with the climate data of global climate models; no regional climate model lies in between. The chain has four steps:

Global climate models (CMIP6). Ten global climate models (CanESM5, CNRM-CM6-1, CNRM-ESM2-1, EC-Earth3, GFDL-ESM4, IPSL-CM6A-LR, MIROC6, MPI-ESM1-2-HR, MRI-ESM2-0, UKESM1-0-LL) provide daily data for the historical period and the scenarios SSP1-2.6, SSP3-7.0 and SSP5-8.5.
Bias adjustment and statistical downscaling (ISIMIP3b). Temperature (mean, minimum, maximum), precipitation, global radiation and relative humidity of these models were bias-adjusted against the observation-based dataset W5E5 v2.0 and statistically downscaled to a grid of 0.5° (about 55 km) with the method ISIMIP3BASD v2.5.0.
Hydrological model (SWIM). With these daily data, SWIM computed the water balance for 28 large European river basins, divided into subbasins and hydrotopes. Each subbasin receives the climate data of the 0.5° grid. This gives one run per climate model and scenario.
Preparation for the portal. The results of the subbasins were transferred to a regular grid and summarised per time period across the climate models (median and range), as in the other sectors.

Notes on interpretation

The map shows the subbasins of SWIM; within a subbasin the values are uniform. The spatial detail of the climate forcing is 0.5°.
The precipitation of the water sector is the ISIMIP3b precipitation used by SWIM. Its level follows the reference W5E5 and can therefore differ from the precipitation of the climate sector, which is based on other data in some regions (in Germany on regional climate models and DWD observations).
The historical period of the water sector is a model simulation with the same climate models; there are no observation-based water data in the portal.

Sources

Lange, S., 2019. Trend-preserving bias adjustment and statistical downscaling with ISIMIP3BASD (v1.0). Geoscientific Model Development, 12, 3055-3070. https://doi.org/10.5194/gmd-12-3055-2019
Lange, S., 2021. ISIMIP3BASD (v2.5.0). Zenodo. https://doi.org/10.5281/zenodo.4686991
Lange, S. et al., 2021. WFDE5 over land merged with ERA5 over the ocean (W5E5 v2.0). ISIMIP Repository. https://doi.org/10.48364/ISIMIP.342217
ISIMIP3b protocol: https://www.isimip.org/protocol/3/

SWIM Model

A separate model was used for generating the water sector variables data in this region: the Soil and Water Integrated Model (SWIM).

What does the model do?

SWIM was specifically developed to investigate climate and land use change impacts at the regional scale, where the impacts are manifested and adaptation measures take place. The model simulates interlinked processes at the mesoscale such as runoff generation, plant and crop growth, nutrient and carbon cycling, and erosion. It provides numerous model outputs such as river discharge, crop yield, and nutrient concentrations and loads. The approach allows simulation of all interrelated processes within a single model framework at a daily time step using regionally available data (climate, land use and soil) and considering feedbacks. The model setup and postprocessing are supported by a GIS interface (based on GRASS). The model uses 3-level disaggregation scheme: basin – subbasins – hydrotopes. The results are presented as time series and maps for a number of variables. SWIM is a model of intermediate complexity for the river basin and regional scale, because it is a) more comprehensive than purely hydrological and precipitation-runoff models due to a more reliable representation of interlinked hydrological, vegetation and nutrient processes; b) driven by commonly available regional data and can be easier parametrized than more complex hydrological models.

Who maintains it?

SWIM is maintained and further developed by the regional hydrological modelling group of Research Department 2 on PIK's own GitLab source code repository. The development is coordinated by Michel Wortmann, Stefan Liersch and Fred Hattermann.

In what way is the model different from other models in the community?

Simulation of hydrological cycle (water discharge, groundwater level): the same or better than in other comparable ecohydrological and hydrological models;
Simulation of hydrological cycle at the river basin scale: better and more detailed process description than in the continental-scale terrestrial models, and in large-scale water resources models;
Simulation of vegetation processes: better than in many hydrological models, which normally do not consider dynamical vegetation processes;
Simulation of water quality: better than in conceptual hydrological models which have no (or only minor) representation of soil processes;
Simulation of diffuse pollution: better than in other commonly used models because of a better temporal and spatial resolution and consideration of dynamical landscape processes;
Simulation of land use, agriculture management, crop yields and feedbacks of climate and land use changes: better than in other models with reduced representation of vegetation growth processes.

Sources:

Krysanova, V., F. Hattermann, Sh. Huang, C. Hesse, T. Vetter, S. Liersch, H. Koch and Z. W. Kundzewicz, 2014. Modelling climate and land use change impacts with SWIM: lessons learnt from multiple applications. Hydrological Sciences Journal, accepted.
Krysanova, F. Wechsung, J. Arnold, R. Srinivasan, J. Williams, 2000. PIK Report Nr. 69 "SWIM (Soil and Water Integrated Model), User Manual", 239p. SWIM_Manual
Krysanova, V., Mueller-Wohlfeil, D.I., Becker, A., 1998. Development and test of a spatially distributed hydrological / water quality model for mesoscale water-sheds. Ecological Modelling, 106, 261-289.

Climate Scenarios

The so-called Representative Concentration Paths (RCPs) were developed for the fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC). They describe different levels of greenhouse gases-induced radiation drives that might occur in the period 2011 till 2100. However, they did not include any socioeconomic “narratives” to go alongside them. Over the past few years, an international team of climate scientists, economists and energy system modelers developed a range of new “pathways” that examine how global society, demographics and economics might change over the next century. They are known as the “Shared Socioeconomic Pathways” (SSPs). The SSPs were published in 2017 (Riahi et al., 2017) and they will play a major role in the IPCC Sixth Assessment Report in 2021/2022.

SSPs are baseline scenarios that describe future developments in the absence of new climate policies, beyond those already in place today. They offer five pathways that the world could take. The five SSPs differ mainly in population growth, urbanization, economic growth, investments in education and health, energy system and land use, rate of technological development as well as drivers of demand, such as lifestyle changes (Riahi et al., 2017).

As the RCPs the SSPs are also not complete in their design, as these are only social futures with do not include climate change impacts. Moreover, no mitigation or adaption measures are implemented in these (O'Neill et al., 2020). Therefore, ClimateImpactsOnline uses three SSP–RCP combination to visualize climate data corresponding to different socioeconomic and radiation scenarios.

“Sustainability” SSP1 - 2.6

The low climate change scenario (SSP1) presents a relatively sustainable path, often referred to as “Taking the Green Road”. The scenario assumes high investments in education and health. These lead to an acceleration of the demographic transition and to a slower population growth, especially in the developing countries. In total there is a broader emphasis on human well-being. Driven by an increasing commitment to achieving development goals, inequality is reduced both across and within countries. Consumption is oriented toward low material growth and lower resource and energy intensity. The SSP1 baseline scenario has low challenges to climate mitigation and adaption, as climate targets can be reached globally (O’Neill et al., 2017). The SSP1 is combined with an forcing output of 2.6 Watt per square meter in 2100.

“Regional Rivalry” SSP3 - 7.0

The moderate climate change scenario (SSP3) also refered to as “Regional Rivalry” is characterized by resurgent nationalism and regional conflicts. This pushes countries to increasingly focus on domestic or, at most, regional issues. For example energy and food security goals are achieved only in their own region. Investments in education and technological development decline. Economic development is slow, consumption is material-intensive, and inequalities get worse over time. Furthermore, population growth is low in industrialized and high in developing countries. Overall SSP3 is characterized by a low international priority for addressing environmental concerns (Fujimori et al., 2017). The SSP3 scenario is combined with a forcing level of 7 Watt per square meter.

“Fossil-fueled Development” SSP5 - 8.5

The extreme scenario (SSP5) is characterized by rapid technological progress and development of human capital. Global markets are increasingly integrated. There are also strong investments in health, education, and institutions to enhance human and social capital. At the same time, this is coupled with the exploitation of abundant fossil fuel resources and the adoption of resource and energy intensive lifestyles all around the world. There is faith in the ability to manage social and ecological systems, including by geo-engineering if necessary (Kriegler et al., 2017). ClimateImpactsOnline uses the combination of the SSP5 scenario with an level of greenhhouse gases-induced radiation of 8.5 Watt per square meter.

See also:

SSP Database

The Shared Socio-Economic Pathways (SSPs): An Overview (Poster)
Sources:

O’Neill, B.C., Carter, T.R., Ebi, K. et al. Achievements and needs for the climate change scenario framework. Nat. Clim. Chang. 10, 1074–1084 (2020). https://doi.org/10.1038/s41558-020-00952-0

Riahi, K. et al., The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview, Global Environmental Change, Volume 42, 153-168 (2017). https://doi.org/10.1016/j.gloenvcha.2016.05.009.

Overview

ClimateImpactsOnline shows how the climate and its impacts on sectors such as agriculture, forests, water, energy, tourism and health may develop, for the region you selected. The climate sector shows the climatic changes themselves; the other sectors show possible climate impacts. Maps, diagrams and tables are based on observations and on climate model simulations for different scenarios.

The page at a glance

Region (top left): switch to another region or country.
Scenario (centre of the second bar): choose the future scenario; the arrows step through the scenarios.
Data info (top right): background on the data, the models and the methods.
Sidebar (left): choose the sector (e.g. Climate) and the variable group (e.g. Temperature), then the variable. The row of variables can hold more than fit on the screen: scroll it sideways (mouse wheel or touchpad, or swipe left on a phone) to reach the further variables of the group. Below, every region of the portal is compared for the selected variable; the current region is highlighted. Further down you find the description of the variable. „Continue to …“ leads to the next variable group.
Map (right) with the legend of the colour scale and, at the bottom, the time line: choose the period, start the animation with the play button, or switch to the Difference to the reference period.
Top bar: Settings, the language and „Old design“ (the previous version of the portal).

On smartphones and tablets

The portal adapts to the screen. On a smartphone the sidebar and the map are shown one after the other: „Show map“ and „Hide indicators“ switch between them, and the top bar is collapsed into the menu button (☰).

Settings

The button Settings in the top bar opens the settings. They apply to the map, the diagrams and the tables alike.

Time period length: means over 10 or 30 years.
Historical data: observations, or historical simulations of the climate models (where available).
Scenario selection: the future scenario (also selectable in the second bar).
Seasons: the whole year or a single season, for variables that offer seasons.
Map diagram layer: the regions the diagrams refer to, e.g. administrative regions or, where offered, protected areas, forests or mountain ranges.
Display type: Contours (smooth map), Region fill (one colour per region, the value of the regional mean) or the model grid.
Map info layer: show cities, rivers, mountains or forests on the map.
Difference map: in difference mode the change against the reference period 1991–2020, as an absolute or a relative (%) change.
Expert mode: additional diagrams, e.g. daily values for some climate variables.

Map, zoom and time line

Zoom with the mouse wheel, with two fingers or with the plus and minus buttons. The zoom level decides which regions you can select: zoomed out the whole region or country, zoomed in its sub-regions, e.g. federal states and, one level further, districts. The finer levels are only reached by zooming in – zoom in first to get the diagrams and tables of a single district.

In the time line at the bottom of the map you choose the period. The play button shows the periods one after the other, the arrows move one period back or forward. Difference switches between absolute values and the change against 1991–2020.

Diagrams and tables

Click on a region of the map to open its diagram. Which regions can be clicked depends on the zoom level: zoom in to reach the sub-regions, e.g. the districts, and their diagrams and tables. The name of the region is shown when you move over it. Regions without a value (e.g. a district for which a yield model failed its validation) are grey and cannot be selected.

Decadal

The tab Decadal shows the 10- or 30-year values of the selected variable from the observation period until 2100, for the selected scenario and season. The y-axis shows the variable in its unit, the dashed vertical line marks the end of the observation period. Three curves (or, for some variables, bars with error bars) summarise the model ensemble: the mean (in some regions the median) and the range from the 10th to the 90th percentile, i.e. 80 % of the model results lie between the lower and the upper value. Moving the mouse over the diagram shows the value of the nearest curve.

Table

The tab Table lists the values of the selected period for the whole year and the seasons.

Monthly

For many variables that offer seasons there is the tab Monthly with the monthly values of the selected period, January to December: the mean, the band from the 10th to the 90th percentile and, as a dashed line, the reference period 1991–2020. The period follows the selection on the time line and the 10/30-year setting; as for the table, it is given under Time frame. Moving the mouse over the diagram shows the month and the value of the nearest curve. The tab appears only where the monthly values have already been calculated for the region (so far Germany and Bavaria).

Climate diagram

For mean temperature and precipitation the tab is called Climate diagram. Temperature is given on the left axis and precipitation on the right axis, scaled so that 10 °C correspond to 20 mm; above 100 mm the precipitation axis is compressed to one tenth (horizontal line). Two views can be selected:

Walter-Lieth: temperature and precipitation as curves with the areas between them filled: arid (dotted yellow) where the temperature curve lies above the precipitation curve, humid (hatched blue) where precipitation lies above it, and perhumid (blue) for precipitation above 100 mm.
Bars: temperature as a line, precipitation as bars; where the temperature line lies above a bar, the month tends to be dry.

The reference period 1991–2020 is drawn as dashed lines. The selected view is kept for the session.

Daily

The tab Daily shows, for the region clicked, how the running year develops day by day, compared with the observed years since 1991 and with the future. It is offered for the climate variables and for many indicators derived from them, such as hot days, frost days, dry spells or growing degree days.

The running year (black) up to the last day with data; in the first days of January the year just completed.
The bandwidth of the observed years (10th to 90th percentile of each calendar day) and their mean.
The two warmest and coldest observed years, depending on the variable also the wettest and driest or those with the most and fewest days.
The future in the scenario of the time line: the mean of all model years of a period and a warm and a cool model year (90th and 10th percentile). The period is selected below the legend. These lines are hidden at first.

The buttons above the diagram switch between Absolute (daily values), Cumulative (sum since 1 January) and, for temperatures, Cumulative (−22 °C), the sum of the deviations from 22 °C, a measure of the cooling demand. Day counts such as hot days are summed up; each day counts with the share of the region where the threshold is passed. For spells such as dry spells, each day shows the longest spell up to that day. A click on a legend entry hides or shows the line; moving the mouse over the diagram shows the day and the value of the nearest line.

In expert mode the diagram adds all observed years as grey lines, the years with the most days above and below the bandwidth (10–90 %), how many days the running year has been outside the bandwidth so far, and its rank among all years.

The observations come from W5E5 v2.0 up to 2019 and from ERA5, the reanalysis of the Copernicus Climate Change Service, from 2020 on. ERA5 is updated daily with a delay of about six days and bias-corrected against W5E5, so that the recent years continue the observation series and match the future projections. The future comes from the same model runs as the maps.

Data download

Below the diagram you find the description of the data, the region, the period and the legend, and the download links: SVG / CSV gives the diagram shown as an SVG graphic (Decadal, Monthly and both views of the climate diagram), or, in the tab Table, the table as CSV. In the tab Monthly or Climate diagram a second link gives the twelve monthly values as CSV. The NetCDF files of the regions (nc Region) contain all variables of the sector for all periods, including the monthly values (<variable>-m01 to -m12); nc Grid are the gridded data of the map.