Enter Zoom Meeting


From sub-seasonal forecasting to climate projections: predicting water availability and servicing water sectors

Many water sectors are already having to cope with extreme weather events, climate variability and change. In this context, predictions on sub-seasonal and seasonal to decadal timescales (i.e. horizons ranging from months to a decade) are an essential part of hydrological forecasting. By providing science-based and user-specific information on potential impacts of variations in water availability, operational hydro-meteorological and climate services are invaluable to a range of water sectors such as water resources management, drinking water supply, transport, energy production, agriculture, disaster risk reduction, forestry, health, insurance, tourism and infrastructure.

This session aims to cover the research and operational advances in climate and hydro-meteorological forecasting, and their implications on predicting water availability for servicing water sectors. It welcomes, without being restricted to, presentations on:
- Technical challenges in making use of climate data for hydrological modelling (e.g. downscaling, bias correction, temporal disaggregation, spatial interpolation),
- Lessons learnt from forecasting and managing present day extreme conditions,
- Improved representations of hydrological extremes in a future climate,
- Seamless forecasting, including downscaling and statistical post- and pre-processing,
- Propagation of uncertainty through the forecasting chain for impact assessment and decision-making,
- Operational hydro-meteorological forecasting systems, hydro-climate services, and tools,
- Effective methods to link stakeholder interests and scientific expertise (e.g. service co-generation).

The session will bring together research scientists and operational managers in the fields of hydrology, meteorology and climate, with the aim of sharing experiences and initiating discussions on this momentous topic. We encourage presentations that utilise the WWRP/WCRP subseasonal-to-seasonal (S2S) prediction project database, and all hydrological relevant applications.

Convener: Louise ArnalECSECS | Co-conveners: Tim aus der Beek, Louise CrochemoreECSECS, Andrew SchepenECSECS, Christopher White
| Tue, 24 May, 15:55–18:29 (CEST)
Room 2.17

Tue, 24 May, 15:10–16:40

Chairpersons: Louise Crochemore, Louise Arnal, Tim aus der Beek

Sub-seasonal to seasonal forecasting studies

Gabriela Guimarães Nobre and Rogério Bonifácio

To support Mozambique and Zimbabwe in the mitigation and management of droughts, the World Food Programme (WFP) is seeking to implement innovative approaches to protect people’s livelihood who face drought risk. The approach that has potential of closing the humanitarian funding gap is Forecast-based Financing (FbF). FbF enables anticipatory actions against droughts using seasonal forecasts, which are implemented to reduce impacts in the critical window between a forecast and an event. An important step for leveraging seasonal forecasting information to implement FbF is the development of an operational trigger system for drought anticipatory action.

During the past year, WFP has been developing, and is currently testing, a FbF system for droughts in eight pilot districts across Mozambique and Zimbabwe using the ECMWF 7-month rainfall ensemble forecast. This system aims to reduce the impact of droughts by releasing anticipatory action based on the forecast of a drought of mild, moderate, and severe categories. In its current set up, droughts are defined in the system through the Standardized Precipitation Index (SPI), and therefore focuses on detecting rainfall anomalies within key months of the growing season in the pilot areas. Based on an extensive skill assessment, we find that there are several opportunities for implementing FbF against droughts in the four pilot districts using the ECMWF long-range forecasting information, which opens opportunities for scaling up.

In addition to reliable seasonal forecast information, a drought FbF system requires substantial articulation between national actors on the selection of which forecast trigger to be used for the identified anticipatory action. For this, WFP has been working with several governmental agencies and stakeholders for a joint development of a nationally agreed drought contingency plan and system. However, given the inherent complexity of this climate phenomenon, reaching a common agreement on the definition of drought and a trigger menu is a difficult, and yet, a prime task. In addition, forecasting information should be coupled with a systematic decision of when to act to effectively enable the reduction of impacts as well as with a clear and standardized procedures of how to mobilize resources, to target beneficiaries and to act.

With this abstract we seek to share lessons learnt and technical challenges experienced with the process of developing an operational drought forecast-based financing system embedded into national systems. Besides its complex and interlinked configuration, we believe that implementing FbF against droughts based on forecast information can help humanitarian organizations to prepare more articulated response plans that can better leverage and preserve the gain of development programming, reduce losses to livelihoods and cost of humanitarian operations while supporting communities in a more dignified manner.


How to cite: Guimarães Nobre, G. and Bonifácio, R.: Drought Forecast-based Financing: lessons learned in building a trigger menu for anticipatory action in Mozambique and Zimbabwe, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-621, https://doi.org/10.5194/egusphere-egu22-621, 2022.

Yiheng Du et al.

The attention given to hydro-climate services is continuously increasing due to the scientific improvements of hydrological models and numerical weather forecasts. However, there is still an urgent need to highlight the predictability of hydrological droughts and floods in order to meet the growing demand on hydrological forecast information from socio-economic sectors, such as energy production and agricultural irrigation. In this study, we evaluated the seasonal hydrological reforecasts generated by the E-HYPE hydrological model forced with predictions from the fifth-generation seasonal forecasting system of the European Centre for Medium-Range Weather Forecasts (ECMWF SEAS5), covering the period 1993-2015. The forecast skill was benchmarked to the simulated streamflow climatology by calculating the Brier Skill Scores for both high and low streamflow for each initialization month and lead time. Results show that both hydrological droughts and flooding over Europe are generally well predicted, with spatial and temporal variability depending on the initialization month and lead time. The results are of high importance since geographical areas and times are identified where the seasonal hydrological forecasts provide an added-value for flooding and droughts, and consequently contribute to decision-making in water resources management.

How to cite: Du, Y., Pechlivanidis, I., and Clemenzi, I.: Assessing the seasonal forecast performance of hydrological extremes over Europe, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-754, https://doi.org/10.5194/egusphere-egu22-754, 2022.

Ingrid Petry et al.

In the model-based streamflow forecasting context, initial conditions (ICs) and meteorological forcings (boundary conditions) are two important drivers of predictability. While the meteorological forcings increasingly influence forecasts at long time horizons, ICs’ influence tends to decrease, being nonetheless the predominant source of predictability at short time horizons. Quantifying the period of time when the ICs contribute to streamflow predictability can help researchers and water managers to choose the best forecasting method for each study area and horizon, balancing computational effort and streamflow forecast accuracy. In this work we quantify IC's prevalence time over boundary conditions in natural river flow forecasts in large basins (>1000km²) in South America (SA) from the MGB-SA model, a continental and hydrodynamic version of the MGB conceptual semi-distributed hydrological model. The methodology consisted of forcing MGB-SA with null precipitation as meteorological forcing, so that all the predictability obtained from the experiments was due to ICs. Streamflow experiments had a 215-day horizon, monthly initialization, daily timestep and comprehended the period of 1990 to 2010. The results were compared to the MGB-SA streamflow simulations with MSWEP as observed rainfall. Errors from the hydrological model were thus not considered in this analysis. The prevalence (T50) was estimated by the horizon (from 1 to 215 days) when streamflow predictability was degraded by 50%, i.e. when meteorological forcings start prevailing over ICs. Predictability was estimated by the performance indicator KGE, and the T50 for each of the river reaches of MGB-SA was presented in a map. The T50 map shows that the shortest IC’s prevalence on streamflow predictability is observed on riverheads, ranging from 1 to 3 days. IC's influence increases near the main reaches of the great rivers of SA, reaching up to 10 days on the Iguaçu River and up to 20 days on the Oricono River and Araguaia River. T50 is up to 40 and 90 days on reaches of the Amazon River, Atlantic coast of North Argentina and Pantanal plains. In general, IC’s influence is higher in the main river reaches of basins with flat relief, due to their greater drainage area and the slow response time of the basin.

How to cite: Petry, I., Fan, F., and Crochemore, L.: How long do initial conditions prevail over boundary conditions in streamflow forecasting in South America?, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6155, https://doi.org/10.5194/egusphere-egu22-6155, 2022.

Wei Yang et al.

About 70% of the annual streamflow volume in Northern Sweden is generated during the spring flood (i.e., May-July), and consequently this relatively short period is of great significance to the hydropower industry. Moreover, the mismatch in the timing between the energy demand and the natural streamflow generation, as well as the condition to regulate the reservoirs for flooding control, make the storage management challenging.

Over the past years, different methodologies have been developed to enhance the skilfulness of seasonal hydrological forecasts. Ensemble streamflow prediction (ESP) is a well-established approach in which a hydrological model is forced with historical meteorological observations, hence representing a climatological evolution constrained by the initial hydrological conditions. In addition, dynamic forecasting employs bias-adjusted (at least in most cases) seasonal forecasts of daily precipitation and temperature from Global Circulation Models (GCM) to force a hydrological model to estimate the hydrological evolution. Moreover, statistical forecasting is based on deriving links between predictors and predictands, for instance large-scale atmospheric variables and observed spring flooding volume can be used to make forecasts of the seasonal inflow volumes. Another approach is based on analogue conditioned ESP (A-ESP) and uses hydrological weather regimes (HWRs) as a condition to select analogues from the historical ensemble of meteorological observations and combining these together with the ESP to create a weighted ESP. The HWRs are based on large-scale circulation patterns and optimized using local precipitation observations.

Here, we compare the A-ESP approach against statistical and dynamic forecasting approaches in predicting the spring flood in 84 sub-catchments in Northern Sweden. The forecast skills are assessed by using the traditional ESP approach as a benchmark. The results show that: (1) the A-ESP can improve forecast skill at all lead-times, (2) statistical forecasting is of most benefit for forecasts with long lead-times, and (3) dynamic forecasting has limited skill at short lead-times. 

How to cite: Yang, W., Foster, K., and Pechlivanidis, I. G.: Evaluation of different forecasting approaches in predicting the spring flood in Northern Sweden, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8741, https://doi.org/10.5194/egusphere-egu22-8741, 2022.

Hector Macian-Sorribes et al.

Drought is one of the most severe weather-induced natural hazards, causing significant economic and environmental impacts to water and water-related systems. Drought indices can be used to monitor, manage and anticipate drought events and their undesired consequences. In this regard, large-scale hydrological models can provide drought indices to assess drought risks under a harmonized and integrated view, including meteorological, soil and hydrological processes. Moreover, seasonal forecasting of droughts can provide longer anticipation times than the application of drought indices to current and near past records.

In this study we take advantage of seasonal forecasts from large-scale hydrological models and generate drought indices for the anticipation of meteorological, agricultural (soil moisture) and hydrological droughts. Seasonal forecasts from the pan-European E-HYPE hydrological model, forced by bias‐adjusted ECMWF SEAS5 forecasts, are employed. The analysis period is 1993-2015. A sample of 617 sub-basins from E-HYPE was chosen taking into account the different hydroclimatic regimes found in Europe. The variables considered are: precipitation and precipitation less than potential evapotranspiration (meteorological drought); soil moisture (agricultural drought); and streamflow (hydrological drought). For each variable, different probability distributions are tested and the most suitable one is selected using a two-step automatic procedure programmed in Python. Firstly, the theoretical function for each variable with the best fit to the empirical distribution is selected using the sum of squares method, the Kolmogorov-Smirnov test and the QQ-plot. Afterwards, the fitting of the tails of the distribution is evaluated by the D’Agostino’s K-squared, Shapiro and Wilcoxon tests. In case of a failure in fitting the tails, the fitting of the distribution is re-calculated. The selected probability distribution is further used to compute the standardized drought indices (SPI and SPEI for meteorological, SSMI for agricultural, and SSI for hydrological droughts) at the monthly scale, with temporal aggregations of 1, 3, 6 and 12 months for the historical period. Afterwards, seasonal drought index forecasts are calculated for each initialization month, lead month, and temporal aggregation.

The skill of these forecasts is evaluated with respect to the modelled variables using the Absolute Mean Error (MAE) and the Continuous Ranked Probability Score (CRPS). The results show how the predictability of droughts changes across drought type, hydroclimatic regime, temporal aggregation and lead month.

Acknowledgements: This study has been supported by the ADAPTAMED project (RTI2018-101483-B-I00), funded by the Ministerio de Economia y Competitividad (MINECO) of Spain including EU FEDER funds; and the subvencions del Programa per a la promoció de la investigación científica, el desenvolupament tecnològic i la innovació a la Comunitat Valenciana (PROMETEO) under the WATER4CAST project.

How to cite: Macian-Sorribes, H., Vargas-Mora, T., Pechlivanidis, I., and Pulido-Velazquez, M.: Evaluation of the accuracy of drought-related seasonal forecasts using large-scale hydrological modelling and drought indices, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9612, https://doi.org/10.5194/egusphere-egu22-9612, 2022.

Tue, 24 May, 17:00–18:30

Chairpersons: Louise Crochemore, Louise Arnal, Tim aus der Beek

Pallav Kumar Shrestha et al.

It is a well-known fact that multi-model forecast systems provide greater reliability over single-model systems, as hydrological models have a solid contribution to forecast uncertainty [1]. Yet many prevalent skill scores for verification of forecasting systems are calculated relative to a benchmark skill. Benchmark skills across hydrological models could vary largely because the benchmark simulation is biased. This bias is not accounted for in the benchmark skill. For example, hydrological models with a high auto-correlation in their state variables tend to have increased skill but do not necessarily have the lowest error when compared against observations [2]. Thus, the correct interpretation of skill can only be conducted if the models are compared against the ground truth, i.e. observations.

With this outlook, we assess the performance of ULYSSES [3] - the first seamless global multi-model hydrological seasonal prediction system. Using four state-of-the-art hydrological models (Jules, HTESSEL, mHM, and PCR-GLOBWB), the production chain utilizes identical land surface datasets (e.g. DEM, soil properties) and forecast inputs for all HMs, and the same river routing scheme (i.e., the multi-scale Routing Model; mRM). The system initializes based on the ERA5-Land dataset, and the seasonal forecasts are driven by a 51-member ensemble generated by the ECMWF seasonal forecasting system 5.

The skill assessment includes the verification of seasonal streamflow forecast at 2400+ GRDC gauges distributed globally during the period from 1993 to 2019, at a monthly time scale. The set of skill scores considered includes metrics concerning monthly observations (Kling-Gupta efficiency skill score, i.e., KGESS, KGE components, Equitable Threat Score for droughts, relative bias), skills with reference to benchmark run (CRPSS) and skills on forecast characteristic (forecast extremity, spread). On average, the system was found to have the skill (monthly KGESS) at most for two months. At the lead of 1 month, mHM exhibits KGESS of 0.56, HTESSEL has KGESS of 0.5, Jules 0.48, and PCR-GLOBWB 0.46. A KGESS value of one corresponds to a perfect forecast. Evaluating the median KGE r component (or Pearson's correlation), mHM (0.68), PCR-GLOBWB (0.59), HTESSEL (0.59) and Jules (0.57). The percentage of gauges with positive KGESS is distributed evenly with PCR-GLOBWB (84.9 %), HTESSEL (84.2 %), Jules (82.4 %) and mHM (80.5 %). Model performances over median skill and gauges with positive skill indicates models to have contrasting performance at high and low skill gauges. Besides, the spatial distribution of KGESS shows marked seasonal changes in the skill of the hydrological models. All of this provides insights on the strengths and weaknesses of the models for further improvement of the system.

In the future, the skill assessment would be expanded to additionally compare forecasted fluxes and state variables (e.g., terrestrial water storage anomalies, soil moisture) against other observations such as GRACE, SMOS, etc. All ULYSSES outputs will be made available in the Copernicus Climate Data Store [4] and will be open access. We aim to engage institutions and researchers around the world that are willing to evaluate the forecasts model performance to improve the system in the future.

[1] https://doi.org/10.1175/BAMS-D-17-0274.1

[2] https://doi.org/10.1175/JHM-D-18-0040.1

[3] https://www.ufz.de/ulysses

[4] https://cds.climate.copernicus.eu

How to cite: Shrestha, P. K., Samaniego, L., Thober, S., Martínez-de La Torre, A., Sutanudjaja, E., Rakovec, O., Kelbling, M., Blyth, E., and Wanders, N.: Assessing skills of the ULYSSES global multi-model hydrological seasonal prediction system, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6456, https://doi.org/10.5194/egusphere-egu22-6456, 2022.

Silvia Terzago et al.

Mountain glacier shrinking, seasonal snow cover reduction and changes in the amount and seasonality of meltwater runoff are already affecting water availability for both local and downstream uses. Water is needed by different competing sectors including drinking water supply, energy production, agriculture, tourism, and extremely dry seasons can lead to economic losses. Reducing potential impacts of changes in water availability involves multiple time scales, from the decadal time scale for the realization of water management infrastructures to the seasonal scale, to plan the use of water resources and allocate them with some lead time.

In the framework of the MEDSCOPE ERA4CS project we focused on the seasonal time scale and we developed a climate service prototype to estimate the temporal evolution of snow depth and snow water equivalent with up to 7 months lead time. Forecasts are initialized on November 1st and run up to May 31st of the following year. The prototype has been co-designed with and tailored to the needs of water and hydropower plant managers and of mountain ski resorts managers. 

We present the modelling chain, based on the seasonal forecasts of ECMWF and Météo-France seasonal prediction systems, made available through the Copernicus Climate Change Service (C3S). Seasonal forecasts of precipitation, near-surface air temperature, radiative fluxes, wind and humidity are bias-corrected and downscaled to three high elevation sites in the North-Western Italian Alps, and finally used as input for a physically-based multi-layer snow model (SNOWPACK). The RainFARM stochastic downscaling procedure adapted for mountain regions is used for downscaling precipitation data, and stochastic realizations are employed to estimate the uncertainty due to the downscaling method.

The skill of the prototype in predicting the monthly snow depth evolution from November to May in each season of the hindcast period 1995-2015 is demonstrated using station observations as a reference. We show the correlation between forecasted and observed snow depth and we quantify the forecast quality in terms of reliability, resolution, discrimination and sharpness using a set of probabilistic measures (Brier Skill Score, Area Under the ROC Curve Skill Score and Continuous Ranked Probability Skill Score). We finally discuss implications of the forecast quality at different lead times as well as the added value of bias-correction and downscaling of precipitation data on snow depth forecasts. Real-time snow forecasts for the current season (2021-2022) and for earlier ones are available at this link: http://wilma.to.isac.cnr.it/diss/snowpack/snowseas-eng.html

How to cite: Terzago, S., Bongiovanni, G., and von Hardenberg, J.: Seasonal forecasting of Alpine snow depth: evaluation of a climate service prototype, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7149, https://doi.org/10.5194/egusphere-egu22-7149, 2022.

David MacLeod et al.

Early warning of drought conditions can help protect lives and livelihoods, especially in dry regions of subsistence agriculture and pastoralism. Regions such as the Horn of Africa Drylands (HAD) may benefit from advance warning of changes to available water supplies, as rural communities make critical decisions about planting and moving livestock at particular points in time. However whilst the regionally-mandated seasonal forecast for HAD provides information on rainfall totals, it does not quantify expected impacts on water balance components such as soil moisture and groundwater storage. This latter information may be more useful to rural communities who rely on groundwater for water resources for humans and livestock, and soil moisture for crop growth. These hydrological quantities can typically be estimated with hydrological models, but in drylands the processes governing water partitioning are complex and largely unrepresented in most existing regional and global hydrological models. 


Here we leverage the capability of a dryland-specific hydrological model (DRYP) to produce rainfall-driven water security forecasts for HAD. DRYP incorporates spatially varying rainfall and evaporative demand, dynamic surface-groundwater interactions, ephemeral flow through channels and focused groundwater recharge. We employ DRYP in a pilot application to produce seasonal forecasts of soil moisture and groundwater recharge for a large catchment within the HAD. We use the objective seasonal forecasts provided by the IGAD Climate Prediction and Application Centre (ICPAC) and disseminated within the Greater Horn of Africa Climate Outlook Forum (GHACOF). Methodological approaches to integrate DRYP with the regional climate outlook disseminated by ICPAC are described, along with evaluation of potential skill of these new water security forecasts for the regional pilot catchment. Finally, we describe and update on an active forecast pilot activity, where water security forecasts for the current rainfall season (March-May 2022) have been co-produced with ICPAC and disseminated to stakeholders in February 2022 as part of the GHACOF event, now publicly available via the ICPAC East Africa Hazards Watch platform, under the EU H2020-funded DOWN2EARTH project. Co-design activity arising from recent stakeholder workshops will be described.

How to cite: MacLeod, D., Asfaw, D., Michaelides, K., Otenyo, E., Tadege, A., Segele, Z., Otieno, G., Hassaballah, K., Quichimbo, A., Cuthbert, M., and Singer, M.: Translating seasonal climate forecasts into decision-relevant water security forecasts, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2804, https://doi.org/10.5194/egusphere-egu22-2804, 2022.

André Chanzy et al.

Many decisions in the field of agriculture, forestry and/or hydrology can get profit from seasonal forecast. However, the skill of such forecast is a critical issue to promote their use in operational context and get profitable decisions. If many methods to assess meteorological forecast performances are available, they are mostly implemented on raw climate variables, while their implementation in sectorial application remains limited to some case studies. In this study a wide range of indicators covering most of the decision-making needs in agriculture, forestry and in some extent to hydrology were considered. These indicators are either direct climate variables, a combination of climate variables, or variables calculated by dynamic models (e.g. a crop model). The study was implemented in southern France using the Méteo-France system 6 1993-2016 hindcast, downscaled using the UERRA reanalysis and the ADAMONT methods available in CS-Tools R package developed in the frame of the MEDSCOPE project. These computed indicators need various climate variables as wind speed, radiation and air humidity while most of the downscaling methods were designed for air temperature and precipitation. The main results are the following.

  • We showed that all variables led to comparable level of accuracy. Seasonal forecasts provide added value compared to climatological forecasts with Brier Skill Scores between 0.05 and 0.20.
  • The predictability of the number of rainy days or the number of days with temperature above a threshold is comparable to those of the corresponding scalar quantities such as cumulative precipitation or mean air temperature. However seasonal forecast of extreme events such as heat waves or drought episodes was not possible.
  • Indicators combining several climatic information such as potential evapotranspiration or fire weather index have comparable predictability than the individual climate variables used in the calculation.
  • With indicators based on dynamic models, the memory effect, i.e. the effect of the system state at the beginning of the forecast period, has a strong impact on the skill scores. We propose a methodology based on an ANOVA to qualify this memory effect by using the F-value. It is shown that when the memory effect is strong (F-value >10) the seasonal forecast does not bring any added value compared to the climatological forecast.
  • An evaluation of the interest of a seasonal forecast in a decision-making framework was carried out by an economic approach. We have based our analysis on the decision making based on the forecast of an event. We show that there is a generic relationship between the AUC score and the gain from the forecast. We show that this relationship depends on the frequency of the decision event, the rarer the event the higher the AUC value must be to have a profitable decision. In our case, a decision based on the detection of a tercile leads to a profitable decision in more than half of the indicators while no indicator leads to a profitable decision when it is based on the detection of a quintile.

How to cite: Chanzy, A., bertuzzi, P., Kamir, E., Davi, H., Dupuy, J.-L., Martin, N., Pouget, G., Lagier, M., Maury, O., Garcia de Cortazar, I., Viel, C., and Soubeyroux, J.-M.: Seasonal forecast of land indicators for decision-making in the field of agriculture, forestry and hydrology , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10164, https://doi.org/10.5194/egusphere-egu22-10164, 2022.

Celia Ramos Sánchez et al.

The development of climate services to address water-related challenges is rapidly progressing. Users’ engagement and adequate interaction between climate service providers and climate service consumers are recognized factors favouring climate services uptake among and the tracking and evaluation of their socio-economic benefits. Under the framework of the H2020 CLINT project, this research presents a preliminary analysis of user requirements in the Douro River Basin (Spain) to develop a climate service prototype, enhanced with artificial intelligence (AI), and intended to assist in the water allocation process; thereby improving drought adaptation. Participatory modelling is applied to explore the full decision-making process among competing needs, namely the allocation of water resources to environmental flows and irrigation. The research focuses on how different actors use historical and forecast hydrometeorological information to inform drought early warning and water use planning (e.g. user-inspired drought indicators), understanding how uncertainty influences decisions, the role of other types of information in the decision-making process, and the differing perceptions of droughts and drought impacts. These assessments of local user knowledge and the need for enhancements are key inputs to further develop and evaluate AI-enhanced climate service in the following stages of the CLINT project.

How to cite: Ramos Sánchez, C., De Stefano, L., Werner, M., and van Andel, S. J.: Unravelling user requirements for climate services to support water allocation decisions in a drought prone region in Spain, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11173, https://doi.org/10.5194/egusphere-egu22-11173, 2022.

Climate studies

Vogel Elisabeth et al.

Climate change is predicted to affect the availability of water resources, including changes in the frequency and severity of hydrological extremes, such as droughts or extreme precipitation. Hydrological impact studies are critical, for example, for ensuring sustainable water resource management, food production, and economic prosperity into the future. Such impact studies are commonly based on hydrological models forced with outputs of global climate models (GCMs) that simulate future climate conditions under a range of greenhouse gas emission scenarios. Generally, global climate models are run at relatively coarse resolution – coarser than what would be required to force hydrological models. In addition, small-scale processes that are below the climate model resolution are approximated using parameterisations, leading to potential biases in some variables or processes. A range of bias-correction and downscaling methods have been developed to remove systemic biases in GCM outputs and to increase the resolution of the model output to match the spatial resolution required by the impact models.

The Bureau of Meteorology (BoM) has recently released a National Hydrological Projections service as part of the new Australian Water Outlook (https://awo.bom.gov.au). This new projections service provides estimates of future climate change impacts on Australian water resources based on an ensemble of two greenhouse gas concentration pathways, four global climate models and a total of four statistical and dynamical bias-correction and downscaling methods (one dynamical downscaling and three bias-correction methods). This presentation provides an overview of the four bias correction and downscaling methods employed as part of the service and the evaluation of these methods for hydrological impact assessments in Australia.

The following methods have been applied to raw GCM outputs: 1) a trend-preserving quantile matching approach developed for the Intersectoral Impacts Model Intercomparison Project (ISIMIP2b) (Hempel et al., 2013), 2) a multi-variate recursive nested bias-correction method (MRNBC) (Johnson & Sharma, 2012; Mehrotra & Sharma, 2016; Nahar et al., 2017), and 3) a quantile matching method optimised for preserving extreme events (Dowdy, 2019). Additionally, dynamically downscaled projections based on the CCAM regional climate model (Watterson & Rafter, 2017) were bias corrected using the ISIMIP2b method. The Australian Water Availability Project data (AWAP; Jones et al., 2009), a gridded dataset that contains climate observations (including precipitation, temperature) at 0.5 km grid resolution, was used as target dataset for the bias-correction methods. Subsequently, we forced the gridded land surface water balance model AWRA-L (Frost et al., 2018) with the bias-corrected and downscaled outputs to produce hydrological simulations for the historical period (1950-2005). We evaluated the outputs against a historical reference run using AWAP data as climate inputs. Here, we present the evaluation of bias-corrected and downscaled climate inputs (particularly precipitation and temperature) as well as impact-model simulated soil moisture, evapotranspiration and runoff over a 30-year period (1976-2005). The evaluation includes assessments of mean biases, cross-correlations, and temporal autocorrelations, as well as biases in variability and extremes at multiple time scales (monthly to multi-annual). We discuss implications of our findings for impact assessments for water resource management and outline potential uses of these methods.

How to cite: Elisabeth, V., Peter, J., Bende-Michl, U., Wasko, C., Sharples, W., Wilson, L., Hope, P., Dowdy, A., Roussis, J., Duong, V. C., Donnelly, C., Khan, Z., and Srikanthan, S.: Evaluating dynamical downscaling and bias correction methods for hydrological impact assessments, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11124, https://doi.org/10.5194/egusphere-egu22-11124, 2022.

Nibedita Samal and Sanjeev Jha

Due to unavailability of observation data in the north-western Himalayas, reanalysis data is used as an alternative. The reanalysis dataset generally have bias compared to observation data. Hence, different bias correction approaches are used to post-process the data before using it for any hydro-climatic study. Although distribution parameters in the bias correction approaches are adjusted according to observation data, it can modify or misrepresent dependence structure between variables and sites. Ignoring the observed inter-site dependencies in the correction procedure can result in obtaining corrected outputs with mismatched spatial dependence. Hence a multi-site bias correction approach is used with Schaake shuffle approach to reconstruct the inter-site correlation with rank reordering.

 In this study, we apply multivariate bias correction with schaake shuffle approach on 12 observatory stations of north-western Himalayas. The approach is applied on the variables mean temperature, precipitation, downward longwave radiation, downward shortwave radiation, and wind speed obtained from High Asia Reanalysis dataset at 0.25° horizontal resolution. The bias correction is applied using the observation data availed from the Princeton University global meteorological forcings for a time period of 2001 to 2011. The Leave-One-Out-Cross-Validation approach is used to apply the bias correction by leaving one year data for validation on each loop.

The multi-site bias correction applied to all the variables in different seasons shows that it is reducing the inter-station bias considerably for monsoon, winter, and summer seasons. For Post-monsoon season the improvement is not significant. For monsoon season the RMSE, MAE, and Bias percentage is decreasing for all the variables except precipitation. The inter-site correlation is improved after application of multi-site bias correction.

How to cite: Samal, N. and Jha, S.: Applying multi-site bias correction approach preserving inter-site correlation in the north-western Himalayan region, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12101, https://doi.org/10.5194/egusphere-egu22-12101, 2022.

Teresa Pérez Ciria et al.

Human-induced climate change is already impacting hydrometeorological extremes in every region across the globe (IPCC, 2021). In fact, changes in the climate system are projected to become larger with increasing global warming. This includes regional increase of frequency and intensity of heavy precipitation, hydrological extremes, agricultural and ecological droughts. Recent studies indicate that this problematic seems to be particularly relevant also in Central Europe, a region usually perceived as an area of comparatively low vulnerability to climate change due to its high adaptive capacity. The presented study focuses on the Main river basin, a tributary to the Rhine river in Germany: the watershed, covering an area of 21.519 km² (at Kleinheubach gauging station) with over four million inhabitants, is characterized by intense gradients of topography and climate, and diversified land use. The region already suffers from water scarcity and consequently water use conflicts are becoming more relevant recently, especially during summer months. This study presents results from a single hydrological model initial condition large ensemble (i.e. the spatially explicit process-based hydrological model WaSiM (Willkofer et al., 2020)) being driven by 50 members of the Canadian Regional Climate Model Vers.5 (CRCM5) over Europe (Leduc et al., 2019) for the time interval 1950-2099. A remarkable decline of mean annual runoff in the Main river basin is projected, while both frequency and intensity of extreme floods show strong increasing trends. This work is meant to tackle this challenge as a first step to achieve co-designing systemic solutions and science-driven technical and cross-sectoral innovations to build new climate-resilient development pathways for efficient water resources management.

The presented study is supported by results from the project ClimEx (www.climex-project.org), funded by the Bavarian State Ministry for the Environment and Consumer Protection, and the project ARSINOE (GA: 101037424), funded under EU’s Horizon 2020 research and innovation programme. 

How to cite: Pérez Ciria, T., Wood, R., Gunnar, B., and Ludwig, R.: Assessment of hydrological extremes and water resources availability under climate change in the Main river basin, Germany, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11449, https://doi.org/10.5194/egusphere-egu22-11449, 2022.

Swapan Kumar Masanta and V. Vemavarapu Srinivas

Detection and quantification of climate change impact on hydrological processes (e.g., precipitation, evapotranspiration and streamflow) in different climatic regions is necessity for water resources planning and management in order to ensure water security for various purposes. Globally evapotranspiration is the major cause of water loss, which is about 62% of global land-surface precipitation. Reference or potential evapotranspiration (ET0) represents the atmospheric water demand, which would be the upper limit of actual evapotranspiration in humid climate. Climate change induced variation in meteorological variables will affect ET0 or crop water requirements. Increase of ET0 can intensify dry conditions in the arid and semi-arid regions of the world. In our previous study on historical records, we noted regional increase in ET0 in south and central India due to increase in net solar radiation and temperature; and decrease in regional ET0 in north-east and north-west India) due to either global stilling (i.e., decrease in wind speed) or global dimming (i.e., decrease in net solar radiation) during 1958-2013. The decreasing trend in ET0 despite significant increase evident in air temperature is widely referred to as “evaporation paradox”. The objective of the present study is to determine the regional-scale spatialtemporal variations of ET0 in future climate conditions using recently released GCMs of CMIP6 (Coupled Model Intercomparison Project Phase 6), as this can provide valuable information for future ET0 regional trend and fresh water availability in India. For this purpose, the homogeneous ET0 regions formed over India in our recent study are considered.   We considered three GCMs namely CanESM5, INM-CM4-8 and INM-CM5-0 because of the availability of all required climate variables for all four CMIP6  shared socioeconomic pathways (SSPs; SSP126, SSP245, SSP370, and SSP585). The projected changes were estimated for each GCM for the late 21st century (2015–2100). The results were discerned based on ensemble mean of the projections of climate variables obtained from the three GCMs. The trend analysis of ET0 as well as climate variables reveal that ET0 will increase significantly in all the homogeneous ET0 regions in India. Similarly, maximum and minimum temperatures, and net solar radiation are also projected to increase significantly. The evaporation paradox was not found in any parts of India in the future simulations. Among other climate variables, significantly increasing trend for relative humidity and decreasing trend for wind speed was found in majority of regions for higher SSPs. The ET0 and precipitation data are used in budyko relationship to obtain the futute fresh water availability in the regions. It is found that, despite increase in ET0 there is significant increase in futute fresh water availability across all the regions for higher SSPs due to increase in precipitation. 

How to cite: Masanta, S. K. and Srinivas, V. V.: Future Projections of Potential Evapotranspiration and Fresh Water Availability in India using CMIP6 GCMs, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12608, https://doi.org/10.5194/egusphere-egu22-12608, 2022.

Stephan Dietrich et al.

While decision makers in climate-dependent sectors are increasingly considering climate change (CC) in their risk portfolios, there is a structural lack of information on how to assess specific CC-related risks and what to do in practice. The ERA4CS (European Research Area for Climate Services) supported the CO-MICC research project (2017-2021) that aimed to co-develop in a participatory manner with potential end-users how the output of global hydrological models can be optimally used to support climate change risk assessment of freshwater-related hazards at different scales.

In particular, it was investigated how the output of multiple global hydrological models (e.g., groundwater recharge or streamflow), each driven by the output of multiple global climate models, can be best provided in an interactive map-based web service to show the range of plausible future impacts of climate change on freshwater. Data sources are state-of-the-art global future projections following the ISIMIP (Inter-Sectoral Impact Model Intercomparison Project) protocol simulated by the modelling groups of the CO-MICC consortium from PIK, IIASA and Goethe University Frankfurt. In addition, methods for using the relatively coarse information (0.5° by 0.5° grid cells) in regional and local climate change risk assessments were investigated. Through an iterative dialogue process in three rounds of workshops, scientists and end-users learned from each other which particular hydrologic information is valuable for end-user risk assessments - and how to best communicate that information so that it can be practically used by end-users around the world in local, transboundary, and global climate change adaptation and mitigation planning.

The climate service was developed by the CO-MICC consortium and is freely available as a pilot application to all users worldwide at www.co-micc.eu. The web portal of the climate service consists of two components, the knowledge portal and the data portal, respectively. The interactive data portal provides free and easy access to multi-model-based data on future freshwater availability on a global scale. It is a web-based information system that provides access to freshwater-related indicators of climate change hazards for all land areas of the globe except Greenland and Antarctica. The data are visualized and provided for individual 0.5° grid cells or aggregated at the basin or country level. The data viewer contains map display, showing spatio-temporal developments, and a data analysis tool can be used to create statistical and graphical representations of the data. In the knowledge portal, in addition to the introduction to the methodology, online trainings as well as the PUNI (Providing and Utilizing eNsemble Information) handbook are included.

In December 2021, the CO-MICC knowledge and data portal was launched supported by WMO and UNESCO. The pilot climate service is hosted by the UNESCO Center ICWRGC in conjunction with the German Federal Institute of Hydrology. We will demonstrate the capabilities of the interactive web platform and will provide details on the development process.

How to cite: Dietrich, S., Cáceres, D., Kneier, F., Doell, P., Koethe, H., and Schwanenberg, D. and the CO-MICC consortium: Introduction to the CO-MICC pilot climate service: Supporting risk assessment and adaptation by providing multi-model based information on freshwater-related hazards of climate change for all land areas of the globe, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7663, https://doi.org/10.5194/egusphere-egu22-7663, 2022.

Micha Werner et al.

Climate Services (CS) are crucial in empowering citizens, stakeholders and decision-makers in defining resilient pathways to adapt to climate change and extreme events. Whilst recent decades have seen significant advances in the science that underpins CS; from sub-seasonal, seasonal through to climate scale predictions; there are several barriers to the uptake of CS and realising of the full opportunity of their value-proposition. Challenges include incorporating the social and behavioural factors, and the local knowledge and customs of climate services users; the poorly developed understanding of the multi-temporal and multi-scalar dimension of climate-related impacts and actions; the translation of CS-provided data into actionable information; and, the consideration of reinforcing or balancing feedback loops associated to users’ decisions.

The ambition of the recently initiated EU-H2020 I-CISK research & innovation project in addressing these challenges, is to instigate a step-change to co-producing CS through a social and behaviourally informed approach. The trans-disciplinary framework the research sets out to develop recognises that climate relevant decisions consider multiple knowledges; innovating CS through integrating local knowledge, perceptions and preferences of users with scientific climate data and predictions.

In this contribution we reflect on initial steps in setting up seven living labs in climate hotspots in Europe and Africa. Instrumental to the research, we will work from these living labs with multi-actor platforms that span multiple sectors to co-design, co-create, co-implement, and co-evaluate pre-operational CS to address climate change and extremes (droughts, floods and heatwaves). We present the vision and plans of the I-CISK project, and explore links, contributions and collaborations with existing projects and networks within the community of CS research and practice. 

How to cite: Werner, M., Masih, I., Emerton, R., Pechlivanidis, I., Schaafsma, M., Pesquer, L., di Baldassare, G., van den Homberg, M., Bagli, S., Gamtkitsulashvili, M., De Stefano, L., Gräler, B., Bela, G., and Tzimas, A.: I-CISK: Towards a social and behaviourally informed approach to co-producing climate services, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10661, https://doi.org/10.5194/egusphere-egu22-10661, 2022.

Michael Peter Schwab et al.

Water is key to sustainable development and improving resilience to climate change yet, sixty percent of countries worldwide report declining water monitoring capabilities. This decline, combined with a growing information gap, hinders optimal use and planning of water resources.  

Water information is needed for effective and efficient water management and climate change adaptation. Currently, this information is fragmented, has large gaps and is partially inaccessible. We want to empower national water management by catalyzing international cooperation through trustful bilateral and multilateral water assessments and outlooks. We want to create a global system that is consistent, interconnected, and helps current and future generations to better understand how global hydrological cycles respond to a changing climate and anthropogenic factors. 

A fundamental arm of the Paris Agreement is the Global Stocktake: a component employed to monitor implementation and evaluate the collective progress made in achieving the agreed carbon goals. In conjunction with the climate stocktake, there is a need for a water resources assessment system that can feed local, regional and global hydrological data into modelling systems. This data can then support evaluations and inform decision processes – in other words, a water stocktake. 

An example of a water resources assessment system under the framework of the Water and Climate Coalition (water-climate-coalition.org) and its partners is the World Meteorological Organization (WMO) Hydrological Status and Outlook System (HydroSOS). HydroSOS aims to cement itself as a chief constituent of this water stocktake – capable of providing actionable information of current and future water availability. HydroSOS intends to strengthen the capacity of National Meteorological and Hydrological Services (NMHSs) to develop a system capable of assessing the status of surface and subsurface hydrological systems and predicting how they will change in the future. HydroSOS is the first global operational mechanism for integrating reliable and timely hydrological status assessments and outlooks that is consistent and comparable on a global scale in collaboration with producers and users of hydrological information.  

How to cite: Schwab, M. P., Davies-Jones, G., Mishra, S., and Cullmann, J.: The Water and Climate Coalition: Establishing a Globally Connected Water Resources Assessment System   , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5720, https://doi.org/10.5194/egusphere-egu22-5720, 2022.