HS4.6

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.