Evaluation and disaggregation of climate model outputs for european drought prediction

Landwirtschaftliche Dürren führen zu hohen sozialen und wirtschaftlichen Schäden. Die Auswirkungen dieser Extremereignisse können mit der Hilfe von einem saisonalen Vorhersagesystem, welches Dürren mehrere Monate im Voraus prognostiziert, abgeschwächt werden. Das Vorhersagesystem, welches in dieser Arbeit entwickelt wird, basiert auf meteorologischen Prognosen des nordamerikanischen Multi-Modell Ensembles (NMME), die verwendet werden um das mesoskalige Hydrologische Modell (mHM) anzutreiben. Ein neu entwickeltes stochastisches Verfahren wird benutzt um die monatlichen Niederschlagsvorhersagen des NMME Datensatzes in tägliche zu disaggregieren. Dieses Verfahren erhält die räumliche Kovarianz von Niederschlag durch das neu vorgestellte "Anchor"-Sampling auf beliebig großen Gittern. Die erhaltenen Prognosen werden mit denen des Ensemble Streamflow Prediction (ESP) Ansatzes verglichen. Der Bewertungszeitraum reicht von 1983 bis 2009. Das Simulationsgebiet umfaßt große Teile Kontinentaleuropas. Die auf dem NMME basierenden Prognoses zeigen bei einer sechsmonatigen Vorhersagezeit eine 69% höhere Vorhersagegüte auf als die des ESP Ansatzes. Dabei gibt es eine substantielle räumliche und zeitliche Variabilität von bis zu 40%. Ein Standardansatz in der Dürrevorhersage ist es entweder die Prognosen der einzelnen Modelle oder die des gesamten Ensemblemittels auszuwerten. Das gesamte Ensemblemittel zeigte eine höhere Güte als jedes Einzelmodell. Die Vorhersagegüte eines Subensembles (eine Subgruppe von Modellen), welches vom neu entwickeltem Rückwärtseliminiatiosalgorithmus effizient gefunden wurde, ist jedoch nur 1% geringer. Die Anzahl der Modellläufe dieses Subensembles beträgt jedoch nur 60% der des gesamten Ensembles. Die in dieser Arbeit vorgestellten Methoden zur Identifizierung von Subensembles und zur zeitlichen Disaggregierung von Niederschlag sind nicht auf die hier betrachtete Anwendung beschränkt und können auch auf andere Anwendungen übertragen werden

Shifts in flood generation processes exacerbate regional flood anomalies in Europe

Anomalies in the frequency of river floods, i.e., flood-rich or -poor periods, cause biases in flood risk estimates and thus make climate adaptation measures less efficient. While observations have recently confirmed the presence of flood anomalies in Europe, their exact causes are not clear. Here we analyse streamflow and climate observations during 1960-2010 to show that shifts in flood generation processes contribute more to the occurrence of regional flood anomalies than changes in extreme rainfall. A shift from rain on dry soil to rain on wet soil events by 5% increased the frequency of flood-rich periods in the Atlantic region, and an opposite shift in the Mediterranean region increased the frequency of flood-poor periods, but will likely make singular extreme floods occur more often. Flood anomalies driven by changing flood generation processes in Europe may further intensify in a warming climate and should be considered in flood estimation and management.publishedVersio

High-resolution drought simulations and comparison to soil moisture observations in Germany

Germany\u27s 2018–2020 consecutive drought events resulted in multiple sectors – including agriculture, forestry, water management, energy production, and transport – being impacted. High-resolution information systems are key to preparedness for such extreme drought events. This study evaluates the new setup of the one-kilometer German drought monitor (GDM), which is based on daily soil moisture (SM) simulations from the mesoscale hydrological model (mHM). The simulated SM is compared against a set of diverse observations from single profile measurements, spatially distributed sensor networks, cosmic-ray neutron stations, and lysimeters at 40 sites in Germany. Our results show that the agreement of simulated and observed SM dynamics in the upper soil (0–25 cm) are especially high in the vegetative active period (0.84 median correlation R) and lower in winter (0.59 median R). The lower agreement in winter results from methodological uncertainties in both simulations and observations. Moderate but significant improvements between the coarser 4 km resolution setup and the ≈ 1.2 km resolution GDM in the agreement to observed SM dynamics is observed in autumn (+0.07 median R) and winter (+0.12 median R). Both model setups display similar correlations to observations in the dry anomaly spectrum, with higher overall agreement of simulations to observations with a larger spatial footprint. The higher resolution of the second GDM version allows for a more detailed representation of the spatial variability of SM, which is particularly beneficial for local risk assessments. Furthermore, the results underline that nationwide drought information systems depend both on appropriate simulations of the water cycle and a broad, high-quality, observational soil moisture database

Improving global hydrological simulations through bias-correction and multi-model blending

There is an immediate need to develop accurate and reliable global hydrological forecasts in light of the future vulnerability to hydrological hazards and water scarcity under a changing climate. As a part of the World Meteorological Organization's (WMO) Global Hydrological Status and Outlook System (HydroSOS) initiative, we investigated different approaches to blending multi-model simulations for developing holistic operational global forecasts. The ULYSSES (mULti-model hYdrological SeaSonal prEdictionS system) dataset, to be published as “Global seasonal forecasts and reforecasts of river discharge and related hydrological variables ensemble from four state-of-the-art land surface and hydrological models” is used in this study. The first step for improving these forecasts is to investigate ways to improve the model simulations, as global models are not calibrated for local conditions. The analysis was performed over 119 different catchments worldwide for the baseline period of 1981–2019 for three variables: evapotranspiration, surface soil moisture and streamflow. This study evaluated blending approaches with a performance metric based (weighted) averaging of the multi-model simulations, using the catchment's Kling-Gupta Efficiency (KGE) for the variable to define the weight. Hydrological model simulations were also bias-corrected to improve the multi-model blending output. Weighted blending in conjunction with bias-correction provided the best improvement in performance for the catchments investigated. Applying modelled weights during blending original simulations improved performance over ungauged catchments. The results indicate that there is potential to successfully and easily implement the bias-corrected weighted blending approach to improve operational forecasts globally. This work can be used to improve water resources management and hydrological hazard mitigation, especially in data-sparse regions

Spatio-temporal analysis of compound hydro-hazard extremes across the UK

There exists an increasing need to understand the impact of climate change on the hydrological extremes of flood and drought, collectively referred to as ‘hydro-hazards’. At present, current methodology are limited in their scope, particularly with respect to inadequate representation of the uncertainty in the hydroclimatological modelling chain. This paper proposes spatially consistent comprehensive impact and uncertainty methodological framework for the identification of compound hydro-hazard hotspots – hotspots of change where concurrent increase in mean annual flood and drought events is projected. We apply a quasi-ergodic analysis of variance (QE-ANOVA) framework, to detail both the magnitude and the sources of uncertainty in the modelling chain for the mean projected mean change signal whilst accounting for non-stationarity. The framework is designed for application across a wide geographical range and is thus readily transferable. We illustrate the ability of the framework through application to 239 UK catchments based on hydroclimatological projections from the EDgE project (5 CMI5-GCMs and 3 HMs, forced under RCP8.5). The results indicate that half of the projected hotspots are temporally concurrent or temporally successive within the year, exacerbating potential impacts on society. The north-east of Scotland and south-west of the UK were identified as spatio-temporally compound hotspot regions and are of particular concern. This intensification of the hydrologic dynamic (timing and seasonality of hydro-hazards) over a limited time frame represents a major challenge for future water management. Hydrological models were identified as the largest source of variability, in some instances exceeding 80% of the total variance. Critically, clear spatial variability in the sources of modelling uncertainty was also observed; highlighting the need to apply a spatially consistent methodology, such as that presented. This application raises important questions regarding the spatial variability of hydroclimatological modelling uncertainty. In terms of water management planning, such findings allow for more focussed studies with a view to improving the projections which inform the adaptation process

The 2018–2020 Multi‐Year Drought Sets a New Benchmark in Europe

During the period 2018–2020, Europe experienced a series of hot and dry weather conditions with significant socioeconomic and environmental consequences. Yet, the extremity of these multi‐year dry conditions is not recognized. Here, we provide a comprehensive spatio‐temporal assessment of the drought hazard over Europe by benchmarking past exceptional events during the period from 1766 to 2020. We identified the 2018–2020 drought event as a new benchmark having an unprecedented intensity that persisted for more than 2 years, exhibiting a mean areal coverage of 35.6% and an average duration of 12.2 months. What makes this event truly exceptional compared with past events is its near‐surface air temperature anomaly reaching +2.8 K, which constitutes a further evidence that the ongoing global warming is exacerbating present drought events. Furthermore, future events based on climate model simulations Coupled Model Intercomparison Project v5 suggest that Europe should be prepared for events of comparable intensity as the 2018–2020 event but with durations longer than any of those experienced in the last 250 years. Our study thus emphasizes the urgent need for adaption and mitigation strategies to cope with such multi‐year drought events across Europe.Plain Language Summary: This manuscript demonstrates that the 2018–2020 multi‐year drought event constitutes a new benchmark in Europe, with an unprecedented level of intensity over the past 250 years. What makes this event truly exceptional compared with past events is its temperature anomaly reaching +2.8 K. This finding provides new evidence that the ongoing global warming exacerbates current drought events. The key message of this study is that the projected future events across the European continent will have a comparable intensity as the 2018–2020 drought but exhibit considerably longer durations than any of those observed during the last 250 years. Our analysis also shows that these exceptional temperature‐enhanced droughts significantly negatively impact commodity crops across Europe.Key Points: The 2018–2020 multi‐year drought shows unprecedented level of intensity during the past 250 years. The 2018–2020 event reached record‐breaking +2.8 K temperature anomaly and negatively impacted major crops. Future drought events reach comparable intensity of 2018–2020 but with considerably longer durations.Deutsche Forschungsgemeinschaft (DFG) http://dx.doi.org/10.13039/501100001659Grantová Agentura České Republiky (GAČR) http://dx.doi.org/10.13039/501100001824Helmholtz‐Fonds (Helmholtz‐Fonds e.V.) http://dx.doi.org/10.13039/50110001365

Seasonal soil moisture drought prediction over Europe using the North American Multi-Model Ensemble (NMME)

Droughts diminish crop yields and can lead to severe socioeconomic damages and humanitarian crises (e.g., famine). Hydrologic predictions of soil moisture droughts several months in advance are needed to mitigate the impact of these extreme events. In this study, the performance of a seasonal hydrologic prediction system for soil moisture drought forecasting over Europe is investigated. The prediction system is based on meteorological forecasts of the North American Multi-Model Ensemble (NMME) that are used to drive the mesoscale hydrologic model (mHM). The skill of the NMME-based forecasts is compared against those based on the ensemble streamflow prediction (ESP) approach for the hindcast period of 1983-2009. The NMME-based forecasts exhibit an equitable threat score that is, on average, 69% higher than the ESP-based ones at 6-month lead time. Among the NMME-based forecasts, the full ensemble outperforms the single best-performing model CFSv2, as well as all subensembles. Subensembles, however, could be useful for operational forecasting because they are showing only minor performance losses (less than 1%), but at substantially reduced computational costs (up to 60%). Regardless of the employed forecasting approach, there is considerable variability in the forecasting skill ranging up to 40% in space and time. High skill is observed when forecasts are mainly determined by initial hydrologic conditions. In general, the NMME-based seasonal forecasting system is well suited for a seamless drought prediction system as it outperforms ESP-based forecasts consistently over the entire study domain at all lead times.</p

The German drought monitor

The 2003 drought event in Europe had major implications on many societal sectors, including energy production, health, forestry and agriculture. The reduced availability of water accompanied by high temperatures led to substantial economic losses on the order of 1.5 Billion Euros, in agriculture alone. Furthermore, soil droughts have considerable impacts on ecosystems, forest fires and water management. Monitoring soil water availability in near real-time and at high-resolution, i.e., 4 × 4 km ^2 , enables water managers to mitigate the impact of these extreme events. The German drought monitor was established in 2014 as an online platform. It uses an operational modeling system that consists of four steps: (1) a daily update of observed meteorological data by the German Weather Service, with consistency checks and interpolation; (2) an estimation of current soil moisture using the mesoscale hydrological model; (3) calculation of a quantile-based soil moisture index (SMI) based on a 60 year data record; and (4) classification of the SMI into five drought classes ranging from abnormally dry to exceptional drought. Finally, an easy to understand map is produced and published on a daily basis on www.ufz.de/droughtmonitor . Analysis of the ongoing 2015 drought event, which garnered broad media attention, shows that 75% of the German territory underwent drought conditions in July 2015. Regions such as Northern Bavaria and Eastern Saxony, however, have been particularly prone to drought conditions since autumn 2014. Comparisons with historical droughts show that the 2015 event is amongst the ten most severe drought events observed in Germany since 1954 in terms of its spatial extent, magnitude and duration

The multiscale routing model mRM v1.0: simple river routing at resolutions from 1 to 50 km

Routing streamflow through a river network is a fundamental requirement to verify lateral water fluxes simulated by hydrologic and land surface models. River routing is performed at diverse resolutions ranging from few kilometres to 1∘. The presented multiscale routing model mRM calculates streamflow at diverse spatial and temporal resolutions. mRM solves the kinematic wave equation using a finite difference scheme. An adaptive time stepping scheme fulfilling a numerical stability criterion is introduced in this study and compared against the original parameterisation of mRM that has been developed within the mesoscale hydrologic model (mHM). mRM requires a high-resolution river network, which is upscaled internally to the desired spatial resolution. The user can change the spatial resolution by simply changing a single number in the configuration file without any further adjustments of the input data. The performance of mRM is investigated on two datasets: a high-resolution German dataset and a slightly lower resolved European dataset. The adaptive time stepping scheme within mRM shows a remarkable scalability compared to its predecessor. Median Kling–Gupta efficiencies change less than 3 % when the model parameterisation is transferred from 3 to 48 km resolution. mRM also exhibits seamless scalability in time, providing similar results when forced with hourly and daily runoff. The streamflow calculated over the Danube catchment by the regional climate model REMO coupled to mRM reveals that the 50 km simulation shows a smaller bias with respect to observations than the simulation at 12 km resolution. The mRM source code is freely available and highly modular, facilitating easy internal coupling in existing Earth system models

Tracking large-scale simulations through unified metadata handling

Simulation is an essential pillar of knowledge generation in science. The numerical models used to describe, predict, and understand real-world systems are typically complex. Consequently, applying these models by means of simulation often poses high demands on computational resources, and requires high-performance computing (HPC) or other dedicated hardware architectures. Metadata describing the details of a numerical experiment arise at all stages of the simulation process: the conceptual description of the model, the model implementation, and the tools and machines used to run the simulation. Capturing these metadata and provenance information along the processing chain is a vital requirement for several purposes, e.g. reproducibility, benchmarking and validation, assessment of the reliability of the simulations, and data exploration [1,2]. The ability to search, share, and evaluate metadata and provenance traces from heterogeneous simulations and environments is a major challenge in provenance-driven analysis. The availability of a common metadata framework, which can be adopted by scientists from different scientific domains, would foster the meta-analysis of HPC simulation workflows [3]. Here, we develop a metadata management framework for generic HPC-based simulation research comprising concepts and tools for efficiently generating, organizing, and exploring metadata along a given simulation workflow. The derived solutions cope with the modularity and flexibility demands of rapidly progressing science and are applicable to diverse research fields. As a proof of concept, we will apply these solutions to use cases from environmental research and computational neuroscience.[1] Guilyardi, E., et. al. (2013) doi: 10.1175/BAMS-D-11-00035.1[2] Manninen, T., et. al. (2018) doi: 10.3389/fninf.2018.00020[3] Ivie, P., & Thain, D. (2018). doi: 10.1145/318626