End-to-end Demonstrator for improved decision making in the water sector in Europe (EDgE)

End-to-end Demonstrator for improved decision making in the water sector in Europe (EDgE) Deliverable D2.2a Interim report on end to end modelling chain using observed climate Issued by: UFZ, CPL Date: 09/02/2017 REF.: 2015/C3S_441-LOT1 NERC/SC1 D2.2a

This document has been produced in the context of the Copernicus Climate Change Service (C3S). The activities leading to these results have been contracted by the European Centre for Medium-Range Weather Forecasts, operator of C3S on behalf of the European Union (Delegation Agreement signed on 11/11/2014). All information in this document is provided "as is" and no guarantee or warranty is given that the information is fit for any particular purpose. The user thereof uses the information at its sole risk and liability. For the avoidance of all doubts, the European Commission and the European Centre for Medium-Range Weather Forecasts has no liability in respect of this document, which is merely representing the authors view.

Interim report on end to end modelling chain using observed climate Deliverable D2.2a CPL (E. F. Wood, J. Sheffield, N. Wanders, M. Pan) UFZ (L. Samaniego, R. Kumar, S. Thober) Date: 09/02/2017 REF.: 2015/C3S_441-LOT1 NERC/SC1 D2.2a

Content: Interim report on end-to-end modelling chain using observed climate 1. Introduction... 1 2. Definition of Uncertainty Assessment Parameters... 2 3. Protocol for Uncertainty Estimation and Skill Assessment... 3 3.1 Introduction 3 3.2 Historical Skill and Uncertainty Assessment 4 3.2.1 Observed Data 5 3.2.2 Assessment of historic modelling skill and uncertainty 6 4. Modelling chain for assessing SCII uncertainty for seasonal forecast time horizon... 7 5. Modeling chain for assessing SCII uncertainty for climate projection time horizon... 9 6. References... 11

1. Introduction This deliverable provides details on the EDgE modelling chain used to assess the uncertainty in the Sectoral Climate Impact Indicators (SCIIs, or tier 1 variables) identified as important by the project through stakeholder engagement. The SCIIs are described and key equations for their derivations are provided in EDgE deliverable D2.1 (Stage 2 Sectoral Climate Impact Indicators (SCIIs)). This deliverable on the assessment of SCII modelling chain for their uncertainty includes: Definition of uncertainty assessment parameters Protocol for SCII uncertainty assessment Modelling chain for assessing SCII uncertainty for seasonal forecast time horizon Modelling chain for assessing SCII uncertainty for climate projection time horizon End-to-end modelling chain using observed climate 2015/C3S_441-LOT1 NERC/SC1 D2.2a 1

2. Definition of Uncertainty Assessment Parameters HM: Hydrological models, which are mhm, Noah-MP, PCR-GLOBWB and VIC, and a river routing model (mrm) CP: Climate projections CM: CMIP5 climate projection models, which are HadGEM2-ES, IPSL-CM5A-LR, MIROC-ESM-CHEM, GFDL-ESM2M, NorESM1-M Reference period for CP: 1971-2000 The reference data for CP is derived from CMs forcing over the CP-reference period Relative/absolute changes in climate projections are calculated over 30-year time slices with 5-year increments. The metrics are associated with the middle year of the 30-year window and the first period to be calculated is for 2011-2040. Every hydrological model run is related back to its own reference period. SF: Seasonal forecast Lead-time for SF is 1 to 6 months SPM: Seasonal Prediction Models providing seasonal re-forecasts 1, which are ECMWF-S4, LFPW, CanCM3, GFDL-FLOR Reference period for SF: 1993-2010 The reference data for SF is derived from E-OBS forcing over the SF-reference period Spatial grids (5kmx5km) with the baseline reference data, on which the relative change metric is based, are provided for each indicator Streamflow (Qr, m 3 s -1 ) is the runoff (Q, mm) routed through the catchment with mrm PET (reference evapotranspiration ET 0) is derived from the modified FAO-56 equations as described in D2.1 1 Note that re-forecast is also referred to as hindcast End-to-end modelling chain using observed climate 2015/C3S_441-LOT1 NERC/SC1 D2.2a 2

3. Protocol for Uncertainty Estimation and Skill Assessment 3.1 Introduction Users have many challenges to address when faced with decision-making, whether for short-term tactical and operational decision-making, or for long-term planning. When using climate information to improve operational decision-making or responding to future climate change, the challenges can be broadly categorised in terms of the transient changes and the uncertainty in the changes (e.g. Hallegatte, 2009) due to the climate model structure and downscaling method, the skill or plausibility of the model, and in the case of long-term changes, natural variability and emission scenario. Not taking into account these uncertainties can lead to underestimation of "worst-case" outcomes (Burke et al., 2011). Despite improvements in climate models and understanding of processes, there is inherent uncertainty in modelling such complex systems, and current models are likely to be underestimating the true range of uncertainties (e.g. rare but high impact events), especially for the long-term. Ideally, users should be able to take into account this uncertainty and acknowledge that the range of outcomes will remain highly uncertain in the future, despite expected improvements in science. To address decision-making, EDgE SCIIs were identified by the project through stakeholder engagement and are described in deliverable D2.1 (Stage 2 Sectoral Climate Impact Indicators (SCIIs)). To make robust decisions based on SCIIs, stakeholders require information on the uncertainty in the SCIIs. EDgE provides quantitative estimates of SCII uncertainties related to climate projection as a function of the different sources of uncertainty. Figure 1 shows the three time scales of the EDgE system and the sources of uncertainty and skill in each: the historic simulations, the seasonal forecasts and the climate projections. Each of these has different sources of uncertainty and skill, themselves having different characteristic spatial and temporal scales: The uncertainties in the historic simulations are derived from the uncertainty in the driving meteorological data (due to station density and measurement errors) that is not assessed in this protocol, and the uncertainty from the choice of hydrological model, its parameter values and process description, which is partially assessed through using four models (mhm, Noah-MP, PCR-GLOBWB and VIC). For the seasonal forecasts, the uncertainty mainly comes from the choice of seasonal prediction model, the initial conditions that generate different ensemble members, and the choice of hydrological model. The uncertainty in the earlier part of the forecast is mostly dominated by the uncertainty in the hydrological initial conditions that dominate the forecast in this period. For the climate projections, the uncertainty is derived not only from the climate model choice but also natural variability, the emissions scenario (RCP) and the hydrological model and its parameter choices. And as with the seasonal forecasts, the major source of uncertainty changes over time, moving from hydrological model uncertainty to climate projection uncertainty. End-to-end modelling chain using observed climate 2015/C3S_441-LOT1 NERC/SC1 D2.2a 3

Figure 1. Main sources of uncertainty and skill in the EDgE modelling chain 3.2 Historical Skill and Uncertainty Assessment The historical simulations are forced by downscaled E-OBS precipitation and temperature data. The E-OBS data are downscaled from 0.25-deg daily resolution to 5km using Kriging with external drift based on elevation. Daily windspeed is taken from the EFAS dataset available at 5km. For the NOAH-MP and VIC land surface models, 3-hourly forcing data for precipitation, temperature, humidity, surface pressure, surface downward short- and long-wave radiation, and windspeed are required. In EDgE, these are generated from the downscaled daily E-OBS/EFAS data using the MTCLIM downscaling method. The uncertainty in the downscaled E-OBS/EFAS meteorological data are not assessed due to the lack of an independent verification dataset. Parameter values for vegetation and soils are derived separately for each of the four models, based on the common EDgE physiographic maps: Digital elevation model EU-DEM v1 (EEA) Global 30 Arc-Second Elevation (USGS) CORINE Land use data v18 4 (EC-EEA) Global land cover data v2.2 (ESA) Global 3D Soil Information System (ISRIC) Hydrogeological Map of Europe v11 (BGR IHME) Pan-European River and Catchment Database v2.1 (EC-JRC) The historic simulations are run for 1950-2015 using model-specific initial conditions. The skill of these simulations is evaluated for streamflow and soil moisture against a set of observed records based on standard skill metrics using the protocol described in the End-to-end modelling chain using observed climate 2015/C3S_441-LOT1 NERC/SC1 D2.2a 4

rest of this section. Streamflow and soil moisture are used in a subset of the sectoral indicators related to their relative changes between a historical reference period and a future period as described in deliverable D2.1. 3.2.1 Observed Data The terrestrial Essential Climate Variables (tecvs, or tier 1 variables) used in computing the SCIIs are: streamflow (Qr), soil moisture (SM) and groundwater recharge (R), as described in deliverable 2.1. Historical observations are only available for streamflow and soil moisture. Observed daily and monthly streamflow data are taken from the GRDC/EWA database for about 4000 stations (Figure 2). Stations are considered useable for streamflow evaluations if they have at least 10 years of data within the 1950-2015 historic simulation. For the sectoral indicators based on streamflow distributions (e.g. streamflow-drought Q95), useable stations need at least 30 years of records. The stations are filtered for management based on identifying upstream reservoirs from the Global Reservoir and Dam (GRanD) database. Streamflow data from the case study regions are particularly useful since these are the regions where the SCIIs will tested for usefulness in decision making by EDgE. Figure 2. Location of 4105 stations with daily streamflow data in the European Water Archive; colour relates to the end year of the record. End-to-end modelling chain using observed climate 2015/C3S_441-LOT1 NERC/SC1 D2.2a 5

Soil moisture data are available through the International Soil Moisture Network (https://ismn.geo.tuwien.ac.at/ismn/) hosted by the Technical University in Vienna. To be useful for model validation, sites that are dominated by snow and frozen ground should be screened out, and stations should have a minimum of five years of data. 3.2.2 Assessment of historic modelling skill and uncertainty Skill is evaluated based on daily and monthly comparisons of historic simulations with observed streamflow for a set of standard skill metrics (KGE, RMSE, Correlation) for the whole record. Specific metrics will depend on the hydrological variable. The modelling skill in the historic period simulations of streamflow is estimated using the Kling-Gupta Efficiency (KGE) metric (Gupta et al., 2009) computed using the model simulated flows and historical observations. KGE is a skill metric that takes into consideration the correlation, the bias, and a relative variability measure between the simulated and observed datasets. The computation of KGE therefore incorporates measures of uncertainty (correlation and bias) in the simulated variables. KGE values below 0.0 indicate model simulations that are less skillful than climatology while values of 1.0 indicate perfect skill. The uncertainty in the historic period simulation of soil moisture is estimated using the correlation and root mean square error (RMSE). Correlations of zero indicate no relationship between the historic and modelled datasets while a value equal to 1 indicates the time series are perfectly correlated. Lower values of RMSE indicates higher skill, with zero indicating zero uncertainty between the data sets. Uncertainty and skill are computed for each hydrological model separately. Uncertainty due to hydrological model parameterisations is not considered. End-to-end modelling chain using observed climate 2015/C3S_441-LOT1 NERC/SC1 D2.2a 6

4. Modelling chain for assessing SCII uncertainty for seasonal forecast time horizon Two main types of uncertainty in the climate driving data are captured by the EDgE SF (seasonal forecast) ensemble: - SPM (seasonal prediction model) uncertainty and climate variability, captured by an ensemble of realisations for each SPM type - SPM formulation, captured by different SPM types Uncertainties in climate re-forecasts, at a range of lead-times, are represented by four SPMs used at seasonal lead time. SPM model outputs for precipitation P and air temperature Tair (daily Tmean, Tmax, and Tmin) are downscaled to daily, 5km resolution for the EDgE domain using the same algorithm as for historical observation (Kriging with external drift based on elevation), as described in M2.2 (Stage 2 modelling chain). Windspeed W is taken from the EFAS climatology assuming that variations in windspeed do not contribute significantly to hydrological forecast uncertainty or skill. The daily (and downscaled 3-hourly) data are used to force an ensemble of hydrological models to produce an ensemble of predictions of hydrological variables that are used to compute the SCIIs for the re-forecast period of 1993-2010. The four seasonal prediction models used in EDgE for seasonal re-forecasts (SPM) are: NMME-2: CanCM4 (10 realisations), GFDL-FLOR (12 realisations) ECMWF: ECMWF-S4 (15 realisations), LFPW (15 realisations) Uncertainty in the hydrological models is represented by four hydrological models (HM): mhm, Noah-MP, PCR-GLOBWB and VIC, each forced by the ensemble of climate reforecasts. The terrestrial Essential Climate Variables (tecvs, or tier 1 variables) used in computing the SF SCIIs are: streamflow (Qr), soil moisture SM and groundwater recharge (R). Each SCII is computed as a function of SPM and their realisations (52), HM (4), forecast month (12), lead time (6), reforecast year (18); with 7 SCIIs calculated. As described in D2.1, the seasonal forecast SCII definitions embeds their uncertainty, which is based on the variability in SCII values across the 52 SPM ensemble members computed by the four HM. For each forecast and lead time, the uncertainty in a SCII is computed from the 208 ensemble members (52 SPM ensembles times 4 HM). The number of re-forecast years (18) is used to estimate the average skill in a SCII over the re-forecast period. The computation of the SCIIs results in 1,886,976 seasonal re-forecasts SCII computations per EDgE grid (52 SPM realisations x 4 HM x 12 months x 6 lead time x 18 years x 7 SCII). Information on each individual source of uncertainty (e.g., numerical weather prediction model, lead time) will be accessible from the EDgE SIS Demonstrator. The SPM modelling chain for the assessment of the SCII uncertainty associated with seasonal re-forecast is shown in Figure 3. The SCII uncertainty will be computed using 4 SPM (52 ensembles) and 4 hydrology models (208 total ensembles), which we expect will provide the lowest uncertainty. Uncertainty from subsets of models (e.g. 2 SPM and 1 HM) will be computed and assessed against the full suite of SPM and HM models. This will provide guidance on how SCII uncertainty varies with the number of models. End-to-end modelling chain using observed climate 2015/C3S_441-LOT1 NERC/SC1 D2.2a 7

Figure 3. Modelling chain for assessing SCII uncertainty in seasonal forecast time horizon Figure 4. Modelling chain for assessing SCII uncertainty for climate projection time horizon End-to-end modelling chain using observed climate 2015/C3S_441-LOT1 NERC/SC1 D2.2a 8

5. Modeling chain for assessing SCII uncertainty for climate projection time horizon Uncertainties in climate projection (precipitation and temperature) are represented by an ensemble of climate models (subset from CMIP5 experiment and bias-corrected from the ISI-MIP project) and two RCP scenarios (S) (RCP2.6 and RCP8.5). The climate projections are downscaled to EDgE domain using the same algorithm as for historical observation (Kriging with external drift based on elevation), which is described in milestone M2.2 (Stage 2 modelling chain). The climate models (CM) are: GFDL-ESM2M; HadGEM2-ES; IPSL-CM5A-LR; MIROC-ESM-CHEM; NorESM1-M. The precipitation and temperature data are used to force four hydrological models (HM), with the reference period being 1971-2000 and the future projections from 2011-2040. The future climate simulations are divided into 30-year windows, in 5-year shifts, for which SCIIs are computed. The terrestrial Essential Climate Variables (tecvs, or tier 1 variables) used in computing the climate projection based SCIIs are: streamflow (Qr), soil moisture (SM), groundwater recharge (R), potential evapotranspiration (PET), precipitation (P), snow water equivalent (SWE) and air temperature (Tair). The CP SCIIs (deliverable D2.1 Stage 2 Sectoral Climate Impact Indicators (SCIIs)) are calculated using the simulated tecvs from five climate model runs, four hydrological models, and two emission scenarios. Note that the {CM, HM} combination is held constant when the tecvs are used in computing a SCII. Since the SCIIs are changes computed from hydro-climatic variables (e.g. streamflow-high Q10) for two 30-year simulation/analysis periods, the true uncertainty in the hydrologic variables for each period is probably underestimated. While order statistics could in theory be used to understand the uncertainty in the quantiles of the resulting SCIIs, the true distribution for the SCIIs needs to be known, which is not possible. Alternatively, we use the well-known bootstrap sampling algorithm to estimate the empirical distribution for the underlying hydro-climatic variable for each analysis period (reference and future). This allows us to estimate the uncertainty in the reference and future hydro-climatic variable values used for the computation of the change SCII (e.g. streamflow-high Q10 or streamflow-low Q90) and does not require assumptions on the distribution of the variable. The advantage of this technique is that even under changing climatic conditions, there is no assumed distribution allowing for flexibility in the uncertainty computation of the SCII. The bootstrapping is done by generating 1000 30- year time series for each hydrological variable from each {CM, HM} simulation combination for both the historic reference period and the future climate projection windows. The 1000 pairs of 30-year (10,950 days) time series are used for the computation of 1000 SCIIs realisations, that are a function of {CM, HM}, and future period window (k). From the 1000-member empirical distribution for the SCII, the quantile values related to p: {0.1, 0.25, 0.50, 0.75, 0.90} are used to represent the uncertainty in the projected SCII. Since the underlying SCII distribution may be highly non-normal and bounded, these quantiles are the best measure of their uncertainty and require no assumption on the SCII distribution. This analysis is carried out for each land cell in the EDgE domain. End-to-end modelling chain using observed climate 2015/C3S_441-LOT1 NERC/SC1 D2.2a 9

The modelling chain for the assessment of the climate projection SCII uncertainty assessment is given in Figure 4. An example illustration of using the bootstrap approach to estimate uncertainty is provided in Figure 5. Figure 5. Illustration using bootstrap algorithm to estimate an empirical distribution End-to-end modelling chain using observed climate 2015/C3S_441-LOT1 NERC/SC1 D2.2a 10

6. References Burke, M., Dykema, J., Lobell, D., Miguel, E. & Satyanath, S. 2011. Incorporating Climate Uncertainty into Estimates of Climate Change Impacts, with Applications to U.S. and African Agriculture. National Bureau of Economic Research Working Paper Series No. 17092, doi:10.3386/w17092. Gupta, Hoshin V., Harald Kling, Koray K. Yilmaz, Guillermo F. Martinez. 2009. Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling, J Hydrology, 377, 80 91, doi:10.1016/j.jhydrol.2009.08.003. Hallegatte, S. 2009. Strategies to adapt to an uncertain climate change. Glob. Environ. Chge 19, 240-247, doi:10.1016/j.gloenvcha.2008.12.003 (2009). End-to-end modelling chain using observed climate 2015/C3S_441-LOT1 NERC/SC1 D2.2a 11

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