Last modified on Nov 08, 2023 12:52

Contributors: Katie Smith (UK CENTRE FOR ECOLOGY AND HYDROLOGY), Lucy Barker (UK CENTRE FOR ECOLOGY AND HYDROLOGY), Steve Turner (UK CENTRE FOR ECOLOGY AND HYDROLOGY), Matt Fry (UK CENTRE FOR ECOLOGY AND HYDROLOGY), Robyn Horan (UK CENTRE FOR ECOLOGY AND HYDROLOGY), Helen Houghton-Carr (UK CENTRE FOR ECOLOGY AND HYDROLOGY), Luis Samaniego (HELMHOLTZ CENTRE FOR ENVIRONMENTAL RESEARCH), Stephan Thober (HELMHOLTZ CENTRE FOR ENVIRONMENTAL RESEARCH), Rohini Kumar (HELMHOLTZ CENTRE FOR ENVIRONMENTAL RESEARCH), Oldrich Rakovec (HELMHOLTZ CENTRE FOR ENVIRONMENTAL RESEARCH), Ming Pan (PRINCETON UNIVERSITY), Justin Sheffield (PRINCETON UNIVERSITY), Eric Wood (PRINCETON UNIVERSITY), Niko Wanders (UTRECHT UNIVERSITY)

Acronyms

Introduction

Executive Summary

This document aims to provide the user of the "Water sector indicators of projected hydrological change for Europe from 2011 until 2095" dataset with information on the derivation of data to inform in its appropriate use.
The dataset provides a number of Sectoral Climate Impact Indicators (SCII) of the potential change in hydrological conditions over the 21^st Century based on an ensemble of climate and hydrological models. The indicators cover hydrological variables (Terrestial Essential Climate Variables, or tECVs) of streamflow (river discharge), soil moisture, settled snow (snow water equivalent) and groundwater recharge, produced by hydrological models using input variables of precipitation, temperature and potential evapotranspiration (PET) produced from standard climate model projections. Indicators of change based on these input climate variables are also provided to enable comparison with and understanding of the hydrological indicators.

The indicators are principally expressed as relative changes for a given 30-year projection window with respect to the reference period of 1971-2000 for each climate projection (with the exception of temperature which is produced as an absolute change).

All data are provided on a 5km grid over a pan-European area by downscaling the climate input data prior to running hydrological models at that scale.
The dataset was produced within a Copernicus Climate Change Service prototype Sectoral Information System development project called EDgE ("End-to-End Demonstrator for improved decision-making in the water sector in Europe").

Scope of Documentation

This document provides an overview of the dataset, describing the variables contained within it, and the processes by which it was created, including input datasets, pre-processing steps and models. References to journal papers with further technical detail of the modelling processes and validation are also provided.

Product Description

Product Target Requirements

The dataset was produced to provide a suite of state-of-the-art indicators of change in hydrological status over the 21st century, transforming information within climate projections into usable information for the European water sector. A range of widely-used and representative Global Climate Models (GCM) and standard projection scenarios were used and a multi-hydrological model approach was followed to provide useful information on potential uncertainty in the future projections at points throughout the 21^st Century. Indicators were defined in discussion with stakeholder groups working in different areas of the water sector (hydropower, irrigation, water supply) to provide clear information on climate projections for water resources as annual, seasonal and monthly change factors for a range of variables.

Product Overview

Data Description

The “Water sector indicators of projected hydrological change for Europe from 2011 until 2095” dataset provides a suite of hydrological climate change indicators, essentially change factors for a number of statistical attributes of hydrological variables for future time periods in relation to a reference period representing the recent past. For example, the variable streamflow has a number of indicators such as change in annual mean streamflow, change in annual maximum streamflow, change in mean seasonal streamflow (for each season), etc. In total there are 111 such Sectoral Climate Impact indicators (SCIIs) across the 7 terrestrial Essential Climate Variables (tECVs) which represent either inputs to or outputs of the hydrological models used to create the dataset. The full list of indicators can be seen in figure 2.

Indicators are calculated over 30-year periods at 5-year intervals over the 21st Century, centered on the years 2025 [2010 – 2039 inclusive], 2030 [2015 – 2044 inclusive], etc. to 2080 [2065 – 2094 inclusive] in comparison to the reference period of 1971-2000 (for the equivalent GCM). At each timestep indicator values are available for each cell across a 5km grid. 

An ensemble of model runs was used to produce the dataset, and each indicator is available for a combination of the climate scenarios (RCPs), Global Climate Models, and hydrological models described in this document. Indicators based on the hydrological model input variables of precipitation, temperature and potential evapotranspiration have 5 ensemble members (one for each GCM) for each RCP. Indicators based on the hydrological model output variables (streamflow, soil moisture, groundwater recharge, snow water equivalent) have 20 ensemble members (one for each combination of 5 GCMs and 4 hydrological models) for each RCP (15 for indicators of groundwater recharge which is only modelled by 3 of the hydrological models).

Table 1: Overview of key characteristics of the “Water sector indicators of projected hydrological change for Europe from 2011 until 2095” dataset

Data Description

Indicators provided within the dataset are based on 3 hydrological model input variables and 4 hydrological output variables (tECVs).

Figure 1: Overview image of the "Water sector indicators of projected hydrological change for Europe from 2011 until 2095" dataset
TECVs produced by the models, from which the indicators are derived, are described in Table 2, including the origin (climate model or hydrological model) and the unit of the modelled tECV.

Table 2: Overview and description of tECVs.

Table 3 provides a full list of the available indicators (SCIIs) within the dataset, defined by the modelled tECVs, the statistic calculated, and the time aggregation over which this statistic is calculated. As stated above, all indicators are provided as values for each 5km grid cell, with the exception of the soil moisture drought extent SCII, which is provided for specified basin boundaries (WFD basins), i.e. each grid cell within a basin contains the same number, representative of the whole basin.

Table 3: List of available indicators

Each individual file within the dataset, containing the indicators (in Table 3 above) for each applicable combination of RCP, GCM and hydrological model, contains file-level variables of change compared to the reference period, and also the absolute values of the indicator statistic over the reference period. It is strongly recommended that the change metrics are used to understand future projections of the tECVs in Table 2 TECVs produced by the models, from which the indicators are derived, are described in Table 2, including the origin (climate model or hydrological model) and the unit of the modelled tECV.
Table 2 above, rather than computing absolute change using the reference period values and the relative change. Absolute values over the reference period are provided to help with understanding of the outputs of the individual combinations of GCM and hydrological model.

Table 4: List of file-level variables and descriptions

Input Data

Table 5: Overview of climate model data used as input for the "Water sector indicators of projected hydrological change for Europe from 2011 until 2095" dataset, summarizing the model properties and available scenario simulations.

Climate Models

The EDgE modelling chain begins with climate variables from Global Climate Models (GCMs). These climate variables (e.g. precipitation and temperature) were used to derive the variables needed as inputs to the hydrological models. Five GCMs have been used in EDgE: GFDL-ESM2M, HadGEM2- ES, IPSL-CM5A-LR, MIROC-ESM-CHEM, and NOR-ESM1M. These models were chosen as they are the models from CMIP5 that were chosen for implementation in the Inter-Sectoral Impacts Model Intercomparison Project (ISI-MIP).

GFDL-ESM2M

The Geophysical Fluid Dynamics Laboratory (GFDL) constructed NOAA's first Earth System Models (ESMs) (Dunne et al. 2012, 2013) to advance understanding of how the Earth's biogeochemical cycles, including human actions, interact with the climate system. ESM2M evolved from GFDL's CM2.1 climate model, and building on this GFDL produced two new models representing ocean physics with alternative numerical frameworks to explore the implications of some of the fundamental assumptions embedded in these models. In ESM2M, pressure-based vertical coordinates are used along the developmental path of GFDL's Modular Ocean Model version 4.1.

HadGEM2-ES

The HadGEM2 family of climate models represents the second generation of HadGEM configurations, with additional functionality including a well-resolved stratosphere and Earth System components.

HadGEM2 stands for the Hadley Centre Global Environment Model version 2. The HadGEM2 family of models comprises a range of specific model configurations incorporating different levels of complexity but with a common physical framework. The HadGEM2 family includes a coupled atmosphere-ocean configuration, with or without a vertical extension in the atmosphere to include a well-resolved stratosphere, and an Earth-System configuration which includes dynamic vegetation, ocean biology and atmospheric chemistry.

IPSL-CM5A LR

The ICMC (IPSL Climate Modelling Center) continuously develops and improves the climate model and its various components. The IPSL CM5 is the last version of the IPSL model and is a full earth system model. In addition to the physical atmosphere-land-ocean-sea ice model, it also includes a representation of the carbon cycle, the stratospheric chemistry and the tropospheric chemistry with aerosols.

MIROC ESM CHEM

MIROC-ESM is based on a global climate model MIROC (Model for Interdisciplinary Research on Climate). A comprehensive atmospheric general circulation model (MIROC-AGCM 2010) including an on-line aerosol component (SPRINTARS 5.00), an ocean GCM with sea-ice component (COCO 3.4), and a land surface model (MATSIRO) are interactively coupled in MIROC.

NorESM1M

The Norwegian Earth System (NorESM) family of models are based on the Community Climate System Model version 4 (CCSM4) of the University Corporation for Atmospheric Research, but differs from the latter by, in particular, an isopycnic coordinate ocean model and advanced chemistry-aerosolcloud-radiation interaction schemes. NorESM1-M has a horizontal resolution of approximately 2° for the atmosphere and land components and 1° for the ocean and ice components.

Historical E-OBS data

The freely available E-OBS data (v12; Haylock et al. 2008) were used to drive the hydrological models over a historical period for model validation. E-OBS is a European land-only daily high-resolution gridded data set for precipitation and minimum, maximum, and mean surface temperature for the period 1950–2015. The daily precipitation and temperature data from this dataset have been downscaled from their native 25km to 5km resolution using External Drift Kriging (EDK).

Physio-geographic data for hydrological models

Hydrological models were established using consistent high-resolution morphologic, land-cover, and soil databases. Differences between models originate only from variations in process representation. A summary of all open-source data used is listed in Table 6. Terrain characteristics (e.g., elevation, slope, aspect, flow direction, and flow accumulation) were derived from the joined Europe-wide (EU) and Global (GOTOP30) Digital Elevation Model (DEM). The Global dataset was used for delineating river basins at those locations that were not covered by the EU-DEM dataset. All datasets were reprojected to the European Terrestrial Reference System (ETRS) 1989 Lambert azimuthal equal area coordinate reference system with a spatial resolution of 500 m for consistency. The spatial domain covers the entire drainage area of all rivers within the pan-EU territory.

Table 6: Summary of physo-geographic data used for hydrological modelling

Method

Background

The EDgE modelling chain, producing Sectoral Climate Impact Indicators from climate model projections, is summarized in Figure 2.

Climate forcings for 5 Global Climate Models are downscaled to a 5km grid over the pan-European area for a baseline period (1951-2010), and a climate projection period (2011 – 2099) for 2 climate scenarios (RCP 2.6, RCP 8.0). In addition a baseline historic observation dataset (EOBS) for the period 1981-2010 is also downscaled to the same 5km resolution.

The meteorological variables are used as inputs to 4 hydrological models set up to run at the 5km grid scale, producing transient time series of variables (terrestrial Essential Climate Variables) of runoff, soil moisture, potential evapotranspiration, groundwater recharge, and snow water equivalent, in addition to the input variables of temperature and precipitation.

Runoff is passed through a routing model to obtain discharge.

Sectoral Climate Impact Indicators (SCII) are produced as relative changes for a given 30-year projection window with respect to the reference period estimates of 1971-2000 for each climate projection. For each SCII, the relative changes are given for each grid cell.

Figure 2: Overview of the modelling chain

Model / Algorithm

Climate Models

The EDgE modelling chain begins with climate variables from Global Climate Models (GCMs). These climate variables (e.g. precipitation and temperature) were used to derive the variables needed as inputs to the hydrological models. Five GCMs have been used in EDgE: GFDL-ESM2M, HadGEM2- ES, IPSL-CM5A-LR, MIROC-ESM-CHEM, and NOR-ESM1M. These models were chosen as they are the models from CMIP5 that were chosen for implementation in the Inter-Sectoral Impacts Model Intercomparison Project (ISI-MIP).

Downscaling Methods

Daily values of precipitation (P), daily average temperature (Tmean), daily maximum and minimum temperature (Tmax and Tmin, respectively) have been downscaled for the climate projections from their native resolution to 25 km E-OBS resolution. The obtained daily fields at a 25km resolution were then downscaled to the 5km resolution using external drift Kriging (EDK). In a second step, the daily values were further disaggregated to 3-hourly values using methods described in Bohn et al. (2013) to provide the representation of the diurnal cycle needed by the hydrological models.

Hydrological Models

In the second step of the modelling chain the meteorological tECVs: potential evapotranspiration, precipitation and temperature were used to force four hydrological models: mHM, NOAH-MP, and VIC and PCR-GLOBWB. These models were used to derive the hydrological tECVs: soil moisture, groundwater recharge and snow water equivalent. Finally, runoff was routed through the routing model mRM to obtain streamflow.

Mesoscale Hydrologic Model (mHM)

The mHM is a spatially distributed, grid-based mesoscale hydrologic model (mHM; Samaniego et al. 2010, Kumar et al 2013a) that accounts for the following main hydrologic processes: canopy interception, snow accumulation and melt, root zone soil moisture and evapotranspiration, infiltration, surface and subsurface runoff, percolation, baseflow and flood routing.

The conceptualization of hydrologic processes in mHM is similar to these of other existing large scale models such as the HBV, the WaterGAP, or the VIC models. mHM uses a novel multiscale parameter regionalization (MPR) scheme to account for the sub-grid variability of fine scale physiographical characteristics (e.g., terrain, soil, vegetation characteristics) that facilitates the model to run efficiently across a range of spatial resolutions and locations other than those used in calibration. The source code of mHM is available freely at www.ufz.de/mhm. The model has been successfully applied to several river basins in Germany, North America and Europe, and other parts of world (Samaniego et al. 2010, 2013; Kumar et al. 2013a,b; Thober et al. 2015, Rakovec et al., 2016).

The model is set up over the EDgE domain at a spatial resolution of 5km. It utilises high resolution land (sub-)surface properties on terrain, soil, vegetation, and geological characteristics to derive effective parameters using the MPR technology. These static land surface characteristics are based on multiple data sources including EU-DEM (EEA) and GTOPO30-DEM, ISRIC SoilGrids1km, CORINE and GLOBCOVER land cover dataset, and IHME1500 Hydrogeological Map of Europe. These datasets are processed, resampled and mapped on to a common resolution of 500m. During the historical period of 1950-2014, the model is forced with the daily gridded fields of precipitation, air temperature, and potential evapotranspiration - all derived based on the publicly available, free, EOBS dataset.

The source code of mHM is available freely at www.ufz.de/mhm

Noah-MP

The land surface model (LSM) Noah-MP calculates fluxes and state variables within the energy and water cycles on the terrestrial land surface. It is the successor of the Noah LSM with the inclusion of multiple process parametrization (hence Noah-MP) and can be used as the land surface scheme for the atmosphere Weather Research and Forecasting Model (WRF).
Noah-MP incorporates a large number of process descriptions with a plenitude of parameters. Processes included, among others, are a two-stream radiation transfer model considering canopy gaps, a Ball-Berry type stomatal resistance scheme, a physically based three-layer snow model and different runoff generation schemes. It distinguishes between surface energy fluxes and states for the canopy and for the ground. Within the EDgE project, the parameters of Noah-MP are calibrated for selected catchments to guarantee a reliable simulation of tECVs. Only the most sensitive parameters, which have been identified in a previous study (Cuntz et al. 2016), are considered in the calibration because the parameter space of Noah-MP is highly-dimensional with a total of 150 parameters.

The setup of the Noah-MP LSM over the EDgE modelling domain uses the same static data information employed for the mesoscale Hydrologic Model (mHM) when appropriate. These static data encompasses the ISRIC SoilGrids1km soil database and the CORINE land cover dataset. Both of these are given at a spatial resolution that is higher than 5km. The predominant soil and land cover class are then taken for each 5km grid cell, which is in line with the model requirements. The vegetation and soil parameters are obtained from standard parameter files for the STATSGO soil dataset and IGBP-MODIS vegetation classes. Additionally, monthly climatological greenness fractions are derived from the JRC fapar dataset.

Variable Infiltration Capacity (VIC)

The Variable Infiltration Capacity (VIC) model (Liang et al., 1994, 1996; Cherkauer et al., 2002) simulates the terrestrial water and energy balances. It distinguishes itself from other land surface schemes through the representation of sub-grid variability in soil storage capacity as a spatial probability distribution, to which surface runoff is derived, and base flow from parameterising a deeper soil moisture zone as a nonlinear recession.

Horizontally, VIC represents the land surface by a number of tiled land cover classes. Evapotranspiration is calculated using a Penman-Monteith formulation with adjustments to canopy conductance to account for environmental factors. The subsurface is discretized into multiple soil layers. Movement of moisture between the soil layers is modelled as gravity drainage, with the unsaturated hydraulic conductivity a function of the degree of saturation of the soil. Cold land processes in the form of canopy and ground snow pack storage, seasonally and permanently frozen soils and sub-grid distribution of snow based on elevation banding are represented. Soil temperatures are calculated from the heat diffusion equation and ice content is estimated based on temperatures; infiltration and baseflow are restricted based on the reduced liquid soil moisture capacity. The VIC model has been implemented in applications from catchment to global scales for understanding catchment behaviour, extreme hydrological events, hydrological predictability, and climate change impacts (e.g. Sheffield and Wood, 2008; Clark et al, 2015; Sheffield et al., 2014; Yuan et al., 2015).

The VIC model is setup for the EDgE modelling domain similar to the other models. Soil parameter values are derived from the ISRIC SoilGrids1km database and adjusted to be consistent with largescale calibrated values derived from global scale simulations. Land cover spatial variability and associated leaf area index values are taken from AVHRR satellite observations, which are regridded to 5km. It should be noted that the VIC model does not have an explicit and horizontally coupled groundwater parameterisation and instead it relies on a very thick bottom soil layer for baseflow generation and capturing the discharge/recharge behaviour of a deep reservoir. For this reason it does not contribute data to the groundwater recharge indicators, which therefore have a smaller ensemble size.

PCR-GLOBWB

PCR-GLOBWB is a large-scale hydrological model intended for global to regional studies. For each grid cell, PCR-GLOBWB uses process-based equations to compute moisture storage in three vertically stacked soil layers as well as the water exchange between the soil and the atmosphere and the underlying groundwater reservoir.

Exchange to the atmosphere comprises precipitation, evapotranspiration and snow accumulation and melt, which are all modified by the presence of the canopy and snow cover. Sub-grid variability within PCR-GLOBWB takes into account the vegetation, glacier coverage, snow elevation bands and soil type distribution. The sub-grid soil type distribution affects the soil hydrological properties and distribution of water-holding capacity of the soil resulting in variable saturation excess overland flow (Improved Arno Scheme, Hagemann and Gates, 2003) as a result of variations in soil depth, effective porosity and elevation distribution. Saturation-excess overland flow is one of the three specific runoff components, along with interflow along hillslopes and baseflow from the groundwater reservoir. The model is able to simulate dynamic water demand, groundwater abstraction and irrigation, allowing for human interaction with the water cycle. Groundwater recharge is not modelled in areas where impermeable rock formations reduce the aquifer depth to zero.

PCR-GLOBWB is implemented in the PCRaster-Python environment and has been applied in many studies with regard to simulations of discharge (van Beek et al., 2011), water demand (Wada et al., 2011), drought (Wada et al., 2013) and seasonal predictions (Wanders and Wada, 2015). For EDgE, the PCR-GLOBWB model has been adjusted to simulate the EDgE domain similarly to the other models.

Sectoral Climate Impact Indicators

SCIIs are calculated from the climate model or hydrological model tECV outputs as described in section 2.2 above.

Validation

Samaniego et al (2019) describes the validation of the hydrological modelling. The performance of the hydrological models against observations and the employed spatial resolution was evaluated. In total, 357 diverse basins with a complete streamflow record for a 30-year period (1966–95) were evaluated using the Kling–Gupta efficiency (KGE). It was found that the model performance based on historical forcing data strongly depends on model type and region, which highlights the added value of using multiple hydrological models.
All models have some difficulties in capturing streamflow dynamics in the northeastern part of the domain, where snowmelt processes are dominant. The median KGE varies between 0.1 and 0.6. The mHM and PCR-GLOBWB models provide unbiased streamflow estimates at the majority of the basins, while Noah-MP and VIC tend to overestimate and underestimate the mean flows, respectively. In the majority of the 357 basins, the variability of observed streamflow flow is well captured by all models except for PCR-GLOBWB. Overall, mHM exhibits the best model performance followed by Noah-MP, VIC, and PCR-GLOBWB.

Uncertainty

The uncertainty within the EDgE Climate Projections is estimated using an ensemble approach. EDgE has applied five Global Climate Models (GCMs): GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-LR, MIROCESM-CHEM and NorESM1-M. These five models were made available by the ISI-MIP project (Warszawski et al.; 2014). It is worth noting that these GCMs only cover a fraction of the total uncertainty of the CMIP5 ensemble for temperature around 0.75 and 0.55 for precipitation (McSweeney and Jones, 2016). Each of these climate model outputs has been forced through the four hydrological models. Note that this ensemble approach does not account for all possible aspects of uncertainty. A detailed discussion of uncertainty is available in Samaniego et al (2019).

Concluding Remarks

The dataset "Water sector indicators of projected hydrological change for Europe from 2011 until 2095" provides a 20-member ensemble of metrics to underpin understanding of potential change in hydrological variables of streamflow, groundwater recharge, soil moisture, and snow water equivalent over the 21^st Century, with associated metrics of change in precipitation, temperature, and potential evapotranspiration. Modelling was undertaken at high resolution (5km across a panEuropean area) using consistent underpinning datasets. Indicators were selected to meet the needs of stakeholders across different aspects of the European water sector, and were co-designed with representatives of the sector. Change metrics are provided as relative change in comparison to a reference period in the recent past to reduce dependence on specific model structures. The ensemble approach provides inherent information on aspects of uncertainty in hydrological projections. As such, they aim to provide users with the information required to make informed decisions and improve decision-making in relation to planning and investment within the European water sector.

References

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Samaniego, L., and Coauthors, 2019: Hydrological Forecasts and Projections for Improved Decision-Making in the Water Sector in Europe. Bull. Amer. Meteor. Soc., 100, 2451–2472, https://doi.org/10.1175/BAMS-D-17-0274.1.

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