Modelling the terrestrial hydrological cycle at ‘hyper-resolution’, i.e., with a grid cell size of 1 km or below) was and still is a major quest in hydrological sciences. With an increase in computational power and the number of readily available and open datasets at useful spatial resolutions increasing as well, hyper-resolution modelling efforts have grown in number as well. We here present a first continental-scale application of the global hydrological model PCR-GLOBWB over Europe at 1 km spatial resolution, and offset it against runs with traditional resolutions of 10 km and 50 km, respectively. Model output was validated for more than 200 water provinces against observed discharge and the following remotely sensed data products: ESA-CCI soil moisture data and GRACE/GRACE-FO terrestrial water storage anomalies. Evaporation estimates were compared to GLEAM data. Evaluation metrics indicate good model performance over Europe and increased accuracy with finer spatial resolutions, particularly for discharge simulations. While the used validation products have the advantage of global coverage and long observational records, their spatial resolution is actually too coarse to fully assess the accuracy of models at hyper-resolution. At that scale, more recent satellite products can be of more use but at the cost of only short observation record. We thus additionally validated 1 km model output against Sentinel-1 surface soil moisture and compared it against results obtained for ESA-CCI soil moisture data. Besides challenges related to global-scale fine-resolution observational data, we also acknowledge that additional work needs to focus on model parameterization for hyper-resolution as well model improvements such as routing schemes better utilizing the available spatial detail. Another challenge we identified is required run time and computational power to analyze continental-scale 1 km data, even when using the state-of-the-art Dutch supercomputer. Here, efficient programming and use of latest parallelization techniques will become even more crucial. Despite these solvable challenges, our research shows that large-scale hyper-resolution modelling is now feasible and that further pursuing these efforts can eventually lead to more locally-relevant hydrological information and process understanding.

How to cite: Hoch, J., Sutanudjaja, E., Wanders, N., van Beek, R., and Bierkens, M.: Hyper-resolution hydrological modelling over Europe: results and emerging challenges, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1417, https://doi.org/10.5194/egusphere-egu22-1417, 2022.

Water used to irrigate accounts for 70% of total water withdrawal and 90% of water consumption worldwide. As a result, irrigation has a strong impact on water and energy budgets, on the associated biogeochemical cycles, and on local and regional climate. Furthermore, water volume for irrigation is projected to increase, due to population growth and climate change. This has encouraged the inclusion of irrigation in an increasing number of land surface models (LSM), which represent the continental branch of the water cycle in Earth System Models. To this end, three key aspects of irrigation must be described: when to irrigate (timing), how to irrigate (irrigation method), and how much to irrigate (water amount).

We present a new irrigation scheme for the ORCHIDEE land surface model, developed to account for flood and drip irrigation techniques. In grid cells with irrigated areas, the water demand is deduced from the soil moisture deficit in the crop and grass soil column, i.e. is partially controlled by soil parameters. The soil column contains both irrigated and rainfed crops, but the fraction equipped for irrigation limits the water demand. The deficit is the difference between the actual soil moisture in the root zone and a soil moisture target. Both the root zone depth and the soil moisture target are user-defined. The volume of water utilized for irrigation is constrained by water availability from rivers and the unconfined groundwater reservoirs, while guaranteeing an environmental flow (i.e. irrigation cannot deplete completely the reservoirs). Additionally, priority in abstraction source (surface vs groundwater) is imposed based on the maps of Siebert et al., (2010). This means that a grid cell without infrastructure for groundwater irrigation, for example, will take all the water from the river, and vice versa. This adds an additional constraint to water availability. The water volume is put in the surface of the crop and grass soil column for infiltration, regardless of water source.

Using offline simulations at global scale, we will evaluate the sensitivity of four key factors:  definition of the root zone, setting of the soil moisture target, water availability and the decay of soil hydraulic conductivity with depth. We will then tune the irrigation scheme to match the irrigation volumes reported at country level by the AQUASTAT dataset, and evaluate the effect of irrigation on soil surface hydrology and energy balance. The perspective of this work is to explore the effects of irrigation over present and future climates, using coupled land surface – atmosphere simulations with the IPSL-CM6 climate model. 

S. Siebert et al., “Groundwater use for irrigation - A global inventory,” Hydrol. Earth Syst. Sci., vol. 14, no. 10, pp. 1863–1880, 2010.

How to cite: Arboleda, P., Ducharne, A., Yin, Z., and Ciais, P.: Tuning an improved irrigation scheme inside ORCHIDEE land surface model and assessing its sensitivity over land surface hydrology and energy budget , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1984, https://doi.org/10.5194/egusphere-egu22-1984, 2022.

Global hydrological models simulate water storages and fluxes of the water cycle, motivated to assess water problems such as water scarcity, high flows and more generally the impact of anthropogenic change on the global water system. However, the models include many uncertainties due to the model inputs (e.g. climate forcing data), model parameters, and model structure which can lead to disagreements when simulation results are compared to observations. To reduce and quantify these uncertainties, some of the models are calibrated against in-situ streamflow observations or compared against total water storage anomalies (TWSA) derived from the Gravity Recovery And Climate Experiment (GRACE) satellite mission. In recent years, TWSA data are integrated into some models via data assimilation to directly improve the realism of the models.

In this study, we present our framework for jointly assimilating satellite and in-situ observations into the WaterGAP Global Hydrological Model (WGHM). In addition to GRACE TWSA maps, for the first time here we experimentally jointly assimilate in-situ streamflow observations from gauge stations. This is in preparation for the Surface Water and Ocean Topography (SWOT) satellite, which will be launched this year and is expected to allow the derivation of streamflow observations globally for rivers wider than 50-100m.

GRACE assimilation strongly improves the TWSA simulations in the Mississippi River Basin, e.g. the correlation increases to 91%, with which our results are consistent with previous studies. However, we find in this case that the streamflow simulation deteriorates, for example, correlation reduces from 92% to 61% at the most downstream gauge station. In contrast, jointly assimilating GRACE data and streamflow observations from GRDC gauge stations improves the streamflow observations by up to 33% in terms of e.g. RMSE and correlation while maintaining the good TWSA simulations. In view of the upcoming SWOT mission, our data suggest that the SWOT data will help to further improve the structure and simulations of global hydrological models.

How to cite: Schulze, K., Kusche, J., Gerdener, H., Engels, O., Döll, P., Müller Schmied, H., Ackermann, S., and Shadkam, S.: Joint assimilation of GRACE Total Water Storage Anomalies and In-Situ Streamflow Data into a Global Hydrological Model, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5321, https://doi.org/10.5194/egusphere-egu22-5321, 2022.

Future drought projection studies typically use multi-model ensemble climate and hydrological simulations. In particular, precipitation, soil moisture, and streamflow simulations are used to quantify the changes in meteorological, agricultural, and hydrological droughts under future climate. Many different drought indices have thus been developed and employed in these projections with different indices often leading to varying states of future droughts. Recently, terrestrial water storage (TWS) has also been used to examine future droughts considering integrated climatic and hydrologic impacts on water stores. This presentation will shed light on drought projections using precipitation, soil moisture, runoff, and TWS drought indices and highlight uncertainties in these projections, including those arising from differences in drought definition or the diversity in drought indices. The presentation will then discuss how the consideration of vulnerability alters drought risk projections, specifically by incorporating human development projections as a proxy of broad vulnerability. Results presented will be based on several dozen ensemble hydrological simulations that include multiple climate models, hydrological models, Representation Concentration Pathways (RCPs), and Shared Socioeconomic Pathways (SSPs). Emphasis will be placed on global scale analyses and regional projections over drought hotspots. The results have appeared in three recent publications.

How to cite: Pokhrel, Y., Satoh, Y., and Elkouk, A.: Uncertainties in multi-model ensemble drought projections and implications on future drought risk, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6268, https://doi.org/10.5194/egusphere-egu22-6268, 2022.

Water resources are considered to be a major challenge for the coming century, particularly in the context of climate change and increasing demographic pressure. Water resources are directly linked to the continental water cycle and its processes are mainly described by hydrological models. ISBA-CTRIP, developed at the CNRM, is an example of a coupled land surface - river routing model used for this purpose. However, anthropogenic impacts on water resources, and in particular the effects of dams-reservoirs on river flows, are still poorly known and generally neglected in global hydrological models, including ISBA-CTRIP. This study focuses on the improvement of the CTRIP river routing model, recently upgraded to 1/12° resolution, by integrating the effects of man-made reservoirs. This work is in preparation for the upcoming SWOT mission, which will provide the data necessary to make improved global scale river and reservoir storage and flow estimates.

A parameterized reservoir model was developed based on Hanasaki's scheme (Hanasaki et al., 2006). The model differentiates between irrigation and non-irrigation reservoirs, computes the mass balance in the reservoir and calculates monthly releases based on inflows and water demands. Using a first default parameterization, the model is run on the highly anthropized river basins in Spain. An operating rule is determined for each of the 215 largest reservoirs and simulated outflows and water storage variations are evaluated against in situ observations over the overall period 1979-2014. Results reveal the positive contribution of the model in representing the seasonal cycle of discharge and storage variation, specifically for irrigation large-storage capacity reservoirs as the model succeeds in reproducing the seasonal shift between inflows and outflows caused by irrigation management rules. The Nash-Sutcliff Efficiency (NSE) median index for discharge was 0.68, which corresponds to an outflow representation improvement of 28%, if compared to the naturalized representation of river flows. For irrigation reservoirs, the improvement rate reaches 67% in the median. 

An exhaustive sensitivity analysis regarding the 7 parameters of the model was conducted on the performance of an NSE bounded version on outflows using the Sobol method. Following Saltelli's approach, sampling is performed using the probability density functions defined for each parameter input, and first-, second- and total-order Sobol indices are estimated. The study is carried out separately on irrigation and non-irrigation reservoirs. It is shown that the most influencing parameter is the threshold coefficient describing demand-controlled release level : in the median, ~54% and ~80% of the total variance, respectively in the two reservoir categories, is assigned to this parameter alone. On the other hand, parameters specifying the ideal reservoir filling level and the minimum release have less influence on monthly long-term mean outflows variance. The second-order Sobol indices revealed several interactions between parameters and explained the observed bias between first- and total-order indices.

The results highlight the importance of incorporating reservoir operation in large scale hydrological models and represent a very useful step to further improve river flow modeling, through calibration schemes and SWOT data assimilation, by targeting the most influencing reservoir model parameters.

How to cite: Sadki, M., Munier, S., and Boone, A.: Implementation and sensitivity analysis of a dam-reservoir model over Spain, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4658, https://doi.org/10.5194/egusphere-egu22-4658, 2022.

More frequent, longer, and more intense droughts are expected in many regions of the world because of climate change. Although drought can propagate from precipitation to runoff and soil moisture, the anticipated climate change impact, however, varies with different drought types. We investigate the response of meteorological, hydrological, and agricultural droughts to climate change for the end of this century using a multimodal ensemble of the Coupled Model Intercomparison Project Phase 6 (CMIP6) under the four Tier 1 ScenarioMIP scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5). The projected changes in five characteristics (median duration, longest duration, median intensity, peak intensity, and frequency) of the different drought types at the global level are compared on seasonal and annual time scales. Our results show that the spatial extent and magnitude of the increasing signals in all the characteristics rise from meteorological to hydrological and agricultural droughts. This gradient is highest for the median and longest duration of droughts with the largest increases among the five characteristics considered.

How to cite: Hosseinzadehtalaei, P., Van Schaeybroeck, B., and Tabari, H.: How do different drought types respond to climate change?, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8161, https://doi.org/10.5194/egusphere-egu22-8161, 2022.

Vegetation modulates Earth’s water, energy and carbon cycles and provides a key link between water stores in the deep soil and the atmosphere. How its functions may change in the future largely depends on how it copes with droughts. There is evidence that in places-times of drought, vegetation shifts water uptake to deeper soil and rock moisture and groundwater. We differentiate and assess plant use of four types of water source: precipitation (P) in current month, past P stored in deeper unsaturated soils/rocks, past P stored in locally recharged groundwater, and groundwater from P fallen on uplands via river-groundwater convergence toward lowlands. We examine global and seasonal patterns and drivers in plant uptake of the four sources using inverse modeling and isotope-based estimates. We find that globally and annually, 70% (std 24%) of plant transpiration relies on current month P, 18% (std 15%) on deep soil moisture, only 1% (std 3%) on locally recharged groundwater, and 10% (std 22%) on groundwater or river water from upland more distant sources; (2) regionally and seasonally, recent P is only 19% in semi-arid, 32% in Mediterranean, and 17% in winter-dry tropics in the driest months; (3) at landscape scales, deep soil moisture, taken up by deep roots in the deep vadose zone, is critical in uplands in dry months, but groundwater and river water from uplands is up to 47% in valleys where riparian forests and desert oases are found. Because the four sources originate from different places-times, move at different spatial-temporal scales, and respond with different sensitivity to climate and anthropogenic forces, understanding space-time origin of plant water source can inform ecosystem management and Earth System Models on the critical hydrologic pathways linking precipitation to vegetation.

How to cite: Miguez Macho, G. and Fan, Y.: A Global Assessment of the Spatial-Temporal Origin of Soil Water Taken up by Vegetation, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10789, https://doi.org/10.5194/egusphere-egu22-10789, 2022.

The Budyko framework consists of a curvilinear relationship between the evaporative ratio (i.e., actual evaporation over precipitation) and the aridity index (potential evaporation over precipitation) and defines evaporation’s water and energy limits. A basin’s movement within the Budyko space illustrates its hydroclimatic change and can help identify the main drivers of change. Basins are expected to move along their Budyko curves when only long-term changes in the aridity index drive changes in the evaporative ratio. We hypothesize that the increasing effects of global warming on the hydrological cycle will cause basins to move along their Budyko curves. To test our hypothesis, we quantify the movement in Budyko space of 353 river basins from 1901 to 2100 based on the outputs of nine models from the Coupled Model Intercomparison Project - Phase 5 (CMIP5). We find that significant increases in potential evaporation due to global warming will lead to basins moving primarily horizontally in Budyko space accompanied by minor changes in the evaporative ratio. However, 37% of the basins will still deviate from their Budyko curve trajectories, with less evaporation than expected by the framework. We elaborate on how land-use change, vegetation changes, or shifts in precipitation or snow to rain ratios can explain these deviations.

How to cite: Jaramillo, F., Piemontese, L., Berghuijs, W., WAng-Erlandsson, L., and Greve, P.: Most River Basins will Follow their Budyko Curves under Global Warming, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11224, https://doi.org/10.5194/egusphere-egu22-11224, 2022.

Floods and droughts may become more severe in a warming world, potentially furthering the significant adverse impacts they cause to lives and livelihoods, infrastructure, and economies. To adapt to future changes in water quantity and regimes, we need to detect and attribute emerging trends in hydrological variables such as river flow, and we require updated projections of future flood and drought occurrence.

Numerical simulation models are used to provide such scenarios, but they are complex and highly uncertain. We can use long records of past hydrological observations to better understand and constrain these model-based projections; river flows are especially useful because they integrate climate processes over the areas covered by drainage basins.

There have been many studies of long-term changes in river flows around the world although, at a global scale (as represented by successive IPCC (Intergovernmental Panel on Climate Change) reports), confidence in observed trends remains very low. This is primarily due to the modification of river flows by human activities (e.g., presence of dams, land-cover change, channelisation and the abstraction of water for public water supplies, industry and agriculture). These human disturbances can obscure climate change signals and distort trends in river flows and in some cases lead to a complete reversal of the trends / changes caused by climate change. It is also challenging to integrate the results of various regional- and national-scale studies due to the many different methods used, hampering consistent continental- and global-scale assessments.

Therefore, to detect climate-driven trends we need to analyse river basins that are relatively undisturbed by human impacts. Recognising this, many countries have ‘Reference Hydrometric Networks’ (RHNs) consisting of catchments where river flows are measured, and where human impacts are absent or minimal. Globally however, these are sparse and there is a need for an integrated approach to advance international assessments of hydrological change on a consistent basis, such that they can provide a robust foundation for global and regional assessments such as those undertaken by the IPCC.

Here we introduce the 'Reference Observatory of Basins for INternational hydrological climate change detection' or ROBIN initiative, where we are advancing a worldwide effort to bring together a global RHN. With a growing network of partners from 20 countries spanning a broad range of climates and geographies, over the next two years ROBIN will develop a consistently defined network of near-natural catchments across the world, sharing knowledge from countries with established RHNs to enable other countries to define similar networks. ROBIN will use this network to undertake the first, truly global scale analysis of trends in river flows using minimally disturbed catchments.

With the support of international organisations, including WMO, UNESCO and IPCC, ROBIN will lay the foundations for an enduring network of catchments, to support global assessments of climate-driven trends and variability in the future.

How to cite: Turner, S., Barker, L., Dixon, H., Hannaford, J., Griffin, A., and Killeen, A. and the ROBIN Network: ROBIN - A Reference Observatory of Basins for INternational hydrological climate change detection  , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8551, https://doi.org/10.5194/egusphere-egu22-8551, 2022.

There is general agreement about the water cycle acceleration in the community, although its strength over land has been debated lately. While some common behavior is observed under similar climatic conditions across the globe, at the regional scale the water cycle's response to global warming is specific to its location's unique characteristics. Herein, we quantify the water cycle and characterize its climatology over the Czech Republic, which constitutes an essential headwaters area of the European continent, and in hydrological terms, it can be called the “roof of Europe”. The country's location involves three drainage catchments: the Elbe, Oder, and Danube rivers, which lead to the North Sea, the Baltic Sea, and the Black Sea respectively. Our analysis includes various data sets at  different spatiotemporal scales like: The Twentieth Century Reanalysis (20CR), CPC Merged Analysis of Precipitation (CMAP), CPC Global Unified Gauge-Based Analysis of Daily Precipitation (CPC), Climatic Research Unit gridded Time Series (CRU TS), Global Historical Climatology Network monthly (GHCN), Global Land Data Assimilation System (GLDAS), Global Land Evaporation Amsterdam Model (GLEAM), Global Precipitation Climatology Centre (GPCC), The Global Precipitation Measurement Integrated Multi-satellite Retrievals (GPM IMERG), Global Runoff Data Centre (GRDC), Global Runoff Reconstruction (GRUN), Moderate Resolution Imaging Spectroradiometer Terra Net Evapotranspiration (MOD16A2), National Centers for Environmental Prediction DOE Reanalysis 2 (NCEP DOE), National Centers for Environmental Prediction and the National Center for Atmospheric Research (NCEP NCAR), NOAA's Precipitation Reconstruction over Land (PRECL), Tropical Rainfall Measuring Mission Multi-Satellite Precipitation Analysis (TRMM 3B43), and University of Delaware Precipitation (UDEL). To exploit the availability of the various data sets for each component of the water cycle we merged them via simple weighted averages, a multi-source data integration method that has proven to be effective and with low computational requirements. Subsequently, we linked the computed components constraining them by the water budget equation. Thereafter, the time series were analyzed to quantify trends and their statistical significance, as well as their uncertainty derived by the multiple datasets. In addition to the time series analysis and the statistics involved so far, a spatial analysis explored the water cycle climatology and its variability over the whole Czech Republic and then its behavior in subdomains defined by the watersheds within the borders of the country.

How to cite: Vargas Godoy, M. R., Bešťáková, Z., Pradhan, R. K., Součková, M., Hanel, M., Juras, R., Kyselý, J., and Markonis, Y.: Assessment of the Water Cycle Acceleration in the Czech Republic, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-310, https://doi.org/10.5194/egusphere-egu22-310, 2022.

Soil moisture is an important component of the water balance despite accounting for a small volume relative to other hydrological cycle components. With the continuing evolution of land surface and global hydrological models, characterizing soil moisture dynamics at sub-kilometer scales is becoming ever important. To help with that, the cosmic-ray neutron sensing is an established technology that provides estimates of root-zone soil moisture at 200-300m radius. In simple terms, cosmic-ray neutron sensors can estimate root zone soil moisture through an indirect relationship between measured neutrons scattered from the soil and the amount of hydrogen atoms observed in the soil water.

Following its development in the late 2010s and the establishment of the first COsmic-ray Soil Moisture Observing System (COSMOS) network in the USA, a continuing adoption of this technology has been observed over the years, notably with the establishment of other national scale networks in Germany, Australia, and in the UK. As the cosmic-ray neutron sensing technology matures, so does our understanding on how to better isolate the soil moisture signal from other sources of hydrogen within the sensor footprint. However, despite recent improvements in our understanding, continental and global-scale datasets from cosmic-ray stations are still inexistent, partially due to a lack of proper data harmonization. This is simply because distinct networks operate under their own data processing protocols. This poses unwanted limitations to the use of these data by the wider scientific community.

Here, we introduce the initial steps towards the harmonization of cosmic-ray neutron sensors worldwide. The harmonization is performed using the state-of-the-art and recent developed Cosmic-Ray Sensor PYthon data processing tool, applied to publicly available data from more than 200 stations. We highlight examples of applications using this global harmonized dataset in hydrology, agriculture, and environmental sciences; and present an open discussion about challenges and opportunities in potentially establishing a Global COSMOS network.

How to cite: Rosolem, R., Power, D., Rico-Ramirez, M., Stowell, J. P., McJannet, D., Schrön, M., and Bogena, H.: Towards the establishment of a global COsmic-ray Soil Moisture Observing System, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7102, https://doi.org/10.5194/egusphere-egu22-7102, 2022.

Terrestrial water storage (TWS) is a socially (e.g., floods and drought) and scientifically (e.g., water and carbon cycles) important variable. Hydrological models have been extensively used to study variations in TWS but the models are showing significant uncertainties, especially for the interannual variability (IAV). It is therefore essential for TWS IAV studies to further improve model accuracy, calling for a better understanding of the TWS IAV simulation error. We conducted a covariance matrix analysis to spatially attribute the contributions to global TWS IAV and its simulation error by two hydrological models: 1) a parsimonious process-based one, implemented in the Strategies to INtegrate Data and BiogeochemicAl moDels (SINDBAD) framework, and 2) a hybrid one, the hybrid hydrological model (H2M), which combines a dynamic neural network and a water balance concept. Both models were calibrated against observation-based data streams for evapotranspiration, snow water equivalent, and runoff, as well as against the Gravity Recovery and Climate Experiment (GRACE) satellite observations of TWS. Both models indicate that the global TWS IAV is largely driven by some regions such as Amazon, Zambezi, Mekong basins, and India. The analysis also identified hotspots of the global TWS IAV error from river basins (e.g., Amazon, Paraná, Congo, and Mekong basins) and inland water bodies (e.g., the Laurentian Great Lakes). Excluding those hotspots in the global integration, the 12-month running mean of the global TWS IAV showed a large improvement: R2 of the global TWS IAV time series was improved from 0.62 to 0.82 for SINDBAD and from 0.62 to 0.88 for H2M. Therefore, the model simulation of the global TWS can efficiently be improved by focusing on correcting the hotspot regions. Comparing GRACE and modelled TWS IAV time series revealed various potential sources of errors, including anthropogenic factors (e.g., reservoir management) and model structure (e.g., insufficient storage capacity and missing storage processes), while the latter was prevalent over many regions. A further comparison to surface water data could characterize the hotspots as areas of 1) more dynamic surface water, and 2) wetlands (i.e., near-inland water bodies). These characteristics of hotspots imply that surface water processes (e.g., seasonal inundation) are relevant for understanding global TWS IAV and are underestimated in the two tested models, calling for further improvement in that respect. Our approach presents a general avenue to identify model simulation errors for global data streams and can guide efficient model development.

How to cite: Lee, H., Jung, M., Carvalhais, N., Trautmann, T., Kraft, B., Reichstein, M., Forkel, M., and Koirala, S.: Error hotpots of the modelled global terrestrial water storage interannual variation, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5118, https://doi.org/10.5194/egusphere-egu22-5118, 2022.

Skillful Drought Early Warning Systems (DEWSs) to predict drought a few months in advance are of utmost importance to reduce the impacts of the drought hazard. Previous studies on meteorological drought forecasts e.g. using the standardized precipitation index (SPI) show that drought can be sufficiently predicted up to 1-3 months ahead. The skill of hydrological drought forecasts e.g. using the standardized runoff index (SRI) and standardized ground index (SGI), on the other hand, is even 2-3 months higher than the meteorological ones. The high skill in hydrological drought forecasts is anticipated coming from the catchment storage/memory (e.g. lakes, soils, groundwater) that pools, attenuates, and lengthens the effect of the driving forces (i.e. precipitation). Yet, the importance of catchment memory in explaining hydrological drought forecast performance has not been studied. Here, we have conducted a pioneering study that investigates the importance of catchment memory on the forecast performance of streamflow drought across Europe. We identified streamflow drought using the Standardized Streamflow Index (SSI). The observed and forecasted streamflow droughts at major European rivers were derived from the streamflow data obtained from the European Flood Alert System (EFAS) driven by observed and forecasted weather data. Catchment memory was derived from the Baseflow Index (BFI) and the groundwater Recession Coefficient (gRC), which through the streamflow, give information on the catchment memory. Performance of streamflow drought forecasts was evaluated using the Brier Score (BS) for rivers across Europe. Results show that the use of higher accumulation periods in the SSI (e.g. SSI-3) forecasts improves forecast performance. The performance is even higher for catchment that has large memory. We found that BS is negatively correlated with BFI, meaning that rivers with high BFI (large memory) yield better drought prediction (low BS). A significant positive correlation between gRC and BS demonstrates that catchments slowly releasing groundwater to streams (low gRC), i.e. large memory, generates higher drought forecast performance. The higher performance of hydrological drought forecasts in catchments with relatively large memory (high BFI and low gRC) implies that Drought Early Warning Systems have more potential to be implemented there and will appear to be more useful.

How to cite: Sutanto, S. J. and Van Lanen, H. A. J.: Catchment memory explains hydrological drought forecast performance, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5657, https://doi.org/10.5194/egusphere-egu22-5657, 2022.

Understanding global river flood risk is fundamental for impact assessment of future climate and socio-economic change. There is a growing interest in understanding future flood risk using alternative methods that are independent of the uncertainties associated with the common approach based on a model cascade of global climate models coupled with hydrological and inundation models. Here, we propose a new sensitivity index that quantifies whether river reaches are more sensitive to flooding from low or high return periods using flood hazard data from a global flood model. We assess the sensitivity of flood extents and population exposure to increasing river flow magnitudes of 1.1 million river reaches globally. The dominant control on the sensitivity of reaches is the local topography and upstream drainage area. We find that steep bedrock and low slope alluvial streams are sensitive to high return periods, while intermediate and transitional streams are sensitive to low return periods. We find a clear spatial pattern in where the largest proportions of populations have settled on floodplains, which are found in North Africa, South America and South and East Asia. This analysis allows us to identify regions where river reaches and populations might be most affected by climate change and an increase in frequency and magnitude of flood events. 

How to cite: Devitt, L., Neal, J., Coxon, G., and Wagener, T.: Global sensitivity of inundation extent and population exposure to flood magnitude , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4203, https://doi.org/10.5194/egusphere-egu22-4203, 2022.

Southeast Asia’s electricity supply largely depends on the hydropower resources of the Mekong, Chao Phraya, Irrawaddy, and Salween River Basins. Uncertain precipitation patterns, rising temperature, and other climate-driven changes are exposing these resources to unprecedented risks, prompting decision makers to re-evaluate existing reservoir management strategies through climate change risk assessments. These assessments are important in shaping the operators’ response to hydro-climatic variability and are necessary to ensure energy security in the region. In this study, we developed high-resolution, semi-distributed hydrological models to examine the potential changes of hydropower availability under projected future climate scenarios in the four largest river basins in South East Asia. Specifically, we relied on a novel variant of the Variable Infiltration Capacity (VIC) model that integrates reservoir operations into the routing scheme, warranting a more accurate representation of cascade reservoir systems. Climate change impacts were derived from the outputs of five Global Circulation Models (GCMs) forced by two Shared Socioeconomic Pathways (SSPs 2.6 and 8.5) emission scenarios in the Coupled Model Intercomparison Project Phase 6 (CMIP6). We find that hydropower generation would be altered significantly in all scenarios in terms of temporal variability and magnitude due to the changes in duration and magnitude of the summer monsoon. Our findings further stress the importance of exploring how the impact of climate change on hydropower availability propagates through water-energy systems and call for adaptive reservoir operation strategies.

How to cite: Galelli, S. and Dang, T. D.: Assessing the impact of climate change on Southeast Asia’s hydropower availability, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4561, https://doi.org/10.5194/egusphere-egu22-4561, 2022.