How uncertain are precipitation and peak flow estimates for the July 2021 flooding event?

The disastrous July 2021 flooding event made us question the ability of current hydrometeorological tools in providing timely and reliable flood forecasts for unprecedented events. This is an urgent concern since extreme events are increasing due to global warming, and existing methods are usually limited to more frequently observed events with the usual flood generation processes. For the July 2021 event, we simulated the hourly streamflows of seven catchments located in western Germany by combining seven partly polarimetric, radar-based quantitative precipitation estimates (QPEs) with two hydrological models: a conceptual lumped model (GR4H) and a physically based, 3D distributed model (ParFlowCLM). GR4H parameters were calibrated with an emphasis on high flows using historical discharge observations, whereas ParFlowCLM parameters were estimated based on landscape and soil properties. The key results are as follows. (1) With no correction of the vertical profiles of radar variables, radar-based QPE products underestimated the total precipitation depth relative to rain gauges due to intense collision–coalescence processes near the surface, i.e., below the height levels monitored by the radars. (2) Correcting the vertical profiles of radar variables led to substantial improvements. (3) The probability of exceeding the highest measured peak flow before July 2021 was highly impacted by the QPE product, and this impact depended on the catchment for both models. (4) The estimation of model parameters had a larger impact than the choice of QPE product, but simulated peak flows of ParFlowCLM agreed with those of GR4H for five of the seven catchments. This study highlights the need for the correction of vertical profiles of reflectivity and other polarimetric variables near the surface to improve radar-based QPEs for extreme flooding events. It also underlines the large uncertainty in peak flow estimates due to model parameter estimation.</p

Modeling approaches to detect land-use changes: Urbanization analyzed on a set of 43 US catchments

International audiencePaired catchment approach probably provides the most robust method to detect the effects of land-use change on catchments' flow characteristics. This approach is limited by the availability of two neighbor catchments with and without land-use change under similar climate conditions. This paper uses a hydrological model to detect the hydrological change caused by urbanization. This study describes 1) use a statistical method to evaluate change detection relative to variation of land use change, 2) simulation of non-urban condition for the urban catchment with an alternative approach, to this aim stream flow series of urban catchments have been reconstructed from the period that urbanization had not taken place yet, and 3) the model validation with observed data. This paper intends to compare the flow changes detected by two different approaches: a regional statistical approach (the paired-catchment approach) and a conceptual modelling approach (the residual approach) on the particular case of urbanized catchments. To investigate the sensitivity of the results to the settings of both approaches, the comparison is made on a relatively large number of 43 catchments located in the United States, with relatively large gradients in terms of geomorphology and hydroclimatic characteristics. Results show that the two approaches are generally in relative good agreement in terms of detection and quantification of changes for the three flow characteristics analyzed (mean annual flow, high and low flow characteristics). Besides, it is found that the impact of urbanization on the catchment's hydrologic response is difficult to generalize: the proportion of nonsignificant trends, significantly increasing decreasing trends are on the 2 same order of magnitude, even if an increase in urban areas generally has a greater impact on mean flows and high flows than on low flows

An Interannual Probabilistic Assessment of Subsurface Water Storage Over Europe Using a Fully Coupled Terrestrial Model

The years 2018 and 2019 were two of the hottest and driest in Mid‐Europe, highlighting the need for a comprehensive assessment of available water resources. In this study, we propose a probabilistic, terrestrial water assessment method, which utilizes a terrestrial forward model that closes the coupled water and energy cycles, from groundwater to the top of the atmosphere. In this methodology, the model is initialized with the current state of the water year and forced with a climatologic ensemble of atmospheric forcing to account for atmospheric uncertainty and natural variability. The simulations result in an ensemble of ensuing water years that are analyzed for subsurface water storage anomalies. The methodology was applied to the water years 2011/2012 and 2018/2019 and showed an increased probability of a significant water deficit in regions that had a water deficit in the previous year. This was also observed in an evaluation simulation. The results were compared to simulations with perfect forcing and uncertain initial conditions, and showed predictability at the interannual timescale and beyond, depending on the strength of the anomaly. The methodology was then applied to 2019/2020 to provide an outlook of the evolution of the current anomalies. The results emphasize the importance of accounting for groundwater dynamics in applied terrestrial models to account for long‐term memory effects in the terrestrial water cycle in forward simulations, over large spatial scales. This method of probabilistic subsurface water storage assessment may provide crucial information to public and industrial sectors for long‐term water resource planning

An Interannual Drought Feedback Loop Affects the Surface Energy Balance and Cloud Properties

Long-term groundwater droughts are known to persist over timescales from multiple years up to decades. The mechanisms leading to drought persistence are, however, only partly understood. Applying a unique terrestrial system modeling platform in a probabilistic simulation framework over Europe, we discovered an important positive feedback mechanism from groundwater into the atmosphere that may increase drought persistence at interannual time scales over large continental regions. In the feedback loop, groundwater drought systematically increases net solar radiation via a cloud feedback, which, in turn, increases the drying of the land. In commonly applied climate and Earth system models, this feedback cannot be simulated due to a lack of groundwater memory effects in the representation of terrestrial hydrology. Thus, drought persistence and compound events may be underestimated in current climate projections

Pan-European groundwater to atmosphere terrestrial systems climatology from a physically consistent simulation

Applying the Terrestrial Systems Modeling Platform, TSMP, this study provides the first simulated long-term (1996–2018), high-resolution (~12.5 km) terrestrial system climatology over Europe, which comprises variables from groundwater across the land surface to the top of the atmosphere (G2A). The data set offers an unprecedented opportunity to test hypotheses related to short- and long-range feedback processes in space and time between the different interacting compartments of the terrestrial system. The physical consistency of simulated states and fluxes in the terrestrial system constitutes the uniqueness of the data set: while most regional climate models (RCMs) have a tendency to simplify the soil moisture and groundwater representation, TSMP explicitly simulates a full 3D soil- and groundwater dynamics, closing the terrestrial water cycle from G2A. As anthopogenic impacts are excluded, the dataset may serve as a near-natural reference for global change simulations including human water use and climate change. The data set is available as netCDF files for the pan-European EURO-CORDEX domain

Comparison of Three Radar-Based Precipitation Nowcasts for the Extreme July 2021 Flooding Event in Germany

Quantitative precipitation nowcasts (QPN) can improve the accuracy of flood forecasts, especially for lead times up to 12 h, but their evaluation depends on a variety of factors, namely, the choice of the hydrological model and the benchmark. We tested three precipitation nowcasting techniques based on radar observations for the disastrous mid-July 2021 event in seven German catchments (140–1670 km2). Two deterministic [advection-based and spectral prognosis (S-PROG)] and one probabilistic [Short-Term Ensemble Prediction System (STEPS)] QPN with a maximum lead time of 3 h were used as input to two hydrological models: a physically based, 3D-distributed model (ParFlowCLM) and a conceptual, lumped model (GR4H). We quantified the hydrological added value of QPN compared with hydrological persistence and zero-precipitation nowcasts as benchmarks. For the 14 July 2021 event, we obtained the following key results. 1) According to the quality of the forecasted hydrographs, exploiting QPN improved the lead times by up to 4 h (8 h) compared with adopting zero-precipitation nowcasts (hydrological persistence) as a benchmark. Using a skill-based approach, obtained improvements were up to 7–12 h depending on the benchmark. 2) The three QPN techniques obtained similar performances regardless of the applied hydrological model. 3) Using zero-precipitation nowcasts instead of hydrological persistence as benchmark reduced the added value of QPN. These results highlight the need for combining a skill-based approach with an analysis of the quality of forecasted hydrographs to rigorously estimate the added value of QPN

Mapping the race between crop phenology and climate risks for wheat in France under climate change

International audienceAbstract Climate change threatens food security by affecting the productivity of major cereal crops. To date, agroclimatic risk projections through indicators have focused on expected hazards exposure during the crop’s current vulnerable seasons, without considering the non-stationarity of their phenology under evolving climatic conditions. We propose a new method for spatially classifying agroclimatic risks for wheat, combining high-resolution climatic data with a wheat’s phenological model. The method is implemented for French wheat involving three GCM-RCM model pairs and two emission scenarios. We found that the precocity of phenological stages allows wheat to avoid periods of water deficit in the near future. Nevertheless, in the coming decades the emergence of heat stress and increasing water deficit will deteriorate wheat cultivation over the French territory. Projections show the appearance of combined risks of heat and water deficit up to 4 years per decade under the RCP 8.5 scenario. The proposed method provides a deep level of information that enables regional adaptation strategies: the nature of the risk, its temporal and spatial occurrence, and its potential combination with other risks. It’s a first step towards identifying potential sites for breeding crop varieties to increase the resilience of agricultural systems