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Mediterranean catchments in southern France are threatened by potentially devastating fast floods which are difficult to anticipate. In order to improve the skill of rainfall-runoff models in predicting such flash floods, hydrologists use data assimilation techniques to provide real-time updates of the model using observational data. This approach seeks to reduce the uncertainties present in different components of the hydrological model (forcing, parameters or state variables) in order to minimize the error in simulated discharges. This article presents a data assimilation procedure, the best linear unbiased estimator (BLUE), used with the goal of improving the peak discharge predictions generated by an event-based hydrological model Soil Conservation Service lag and route (SCS-LR). For a given prediction date, selected model inputs are corrected by assimilating discharge data observed at the basin outlet. This study is conducted on the Lez Mediterranean basin in southern France. The key objectives of this article are (i) to select the parameter(s) which allow for the most efficient and reliable correction of the simulated discharges, (ii) to demonstrate the impact of the correction of the initial condition upon simulated discharges, and (iii) to identify and understand conditions in which this technique fails to improve the forecast skill. The correction of the initial moisture deficit of the soil reservoir proves to be the most efficient control parameter for adjusting the peak discharge. Using data assimilation, this correction leads to an average of 12 % improvement in the flood peak magnitude forecast in 75 % of cases. The investigation of the other 25 % of cases points out a number of precautions for the appropriate use of this data assimilation procedure. [ABSTRACT FROM AUTHOR]

The present study explores the application of a data assimilation (DA) procedure to correct the radar rainfall inputs of an event-based, distributed, parsimonious hydrological model. An extended Kalman filter algorithm was built on top of a rainfall-runoff model in order to assimilate discharge observations at the catchment outlet. This work focuses primarily on the uncertainty in the rainfall data and considers this as the principal source of error in the simulated discharges, neglecting simplifications in the hydrological model structure and poor knowledge of catchment physics. The study site is the 114 km² Lez catchment near Montpellier, France. This catchment is subject to heavy orographic rainfall and characterised by a karstic geology, leading to flash flooding events. The hydrological model uses a derived version of the SCS method, combined with a Lag and Route transfer function. Because the radar rainfall input to the model depends on geographical features and cloud structures, it is particularly uncertain and results in significant errors in the simulated discharges. This study seeks to demonstrate that a simple DA algorithm is capable of rendering radar rainfall suitable for hydrological forecasting. To test this hypothesis, the DA analysis was applied to estimate a constant hyetograph correction to each of 19 flood events. The analysis was carried in two different modes: by assimilating observations at all available time steps, referred to here as reanalysis mode, and by using only observations up to 3 h before the flood peak to mimic an operational environment, referred to as pseudo-forecast mode. In reanalysis mode, the resulting correction of the radar rainfall data was then compared to the mean field bias (MFB), a corrective coefficient determined using rain gauge measurements. It was shown that the radar rainfall corrected using DA leads to improved discharge simulations and Nash-Sutcliffe efficiency criteria compared to the MFB correction. In pseudo-forecast mode, the reduction of the uncertainty in the rainfall data leads to a reduction of the error in the simulated discharge, but uncertainty from the model parameterisation diminishes data assimilation efficiency. While the DA algorithm used is this study is effective in correcting uncertain radar rainfall, model uncertainty remains an important challenge for flood forecasting within the Lez catchment. [ABSTRACT FROM AUTHOR]

A good knowledge of rainfall is essential for hydrological operational purposes such as flood forecasting. The objective of this paper was to analyze, on a relatively large sample of flood events, how rainfall-runoff modeling using an event-based model can be sensitive to the use of spatial rainfall compared to mean areal rainfall over the watershed. This comparison was based not only on the model's efficiency in reproducing the flood events but also through the estimation of the initial conditions by the model, using different rainfall inputs. The initial conditions of soil moisture are indeed a key factor for flood modeling in the Mediterranean region. In order to provide a soil moisture index that could be related to the initial condition of the model, the soil moisture output of the Safran-Isba-Modcou (SIM) model developed by Météo-France was used. This study was done in the Gardon catchment (545 km²) in South France, using uniform or spatial rainfall data derived from rain gauge and radar for 16 flood events. The event-based model considered combines the SCS runoff production model and the Lag and Route routing model. Results show that spatial rainfall increases the efficiency of the model. The advantage of using spatial rainfall is marked for some of the largest flood events. In addition, the relationship between the model's initial condition and the external predictor of soil moisture provided by the SIM model is better when using spatial rainfall, in particular when using spatial radar data with R² values increasing from 0.61 to 0.72. [ABSTRACT FROM AUTHOR]

Characterizes different rainfall/runoff processes in a small peri-urban catchment in Quito, Ecuador. Physiographic units of the watersheds; Measurement of infiltration point-processes; Variations in the runoff coefficients.

This paper presents a coupled observation and modelling strategy aiming at improving the understanding of processes triggering flash floods. This strategy is illustrated for the Mediterranean area using two French catchments (Gard and Ardèche) larger than 2000 km². The approach is based on the monitoring of nested spatial scales: (1) the hillslope scale, where processes influencing the runoff generation and its concentration can be tackled; (2) the small to medium catchment scale (1-100 km²), where the impact of the network structure and of the spatial variability of rainfall, landscape and initial soil moisture can be quantified; (3) the larger scale (100-1000 km²), where the river routing and flooding processes become important. These observations are part of the HyMeX (HYdrological cycle in the Mediterranean EXperiment) enhanced observation period (EOP), which will last 4 years (2012-2015). In terms of hydrological modelling, the objective is to set up regional-scale models, while addressing small and generally ungauged catchments, which represent the scale of interest for flood risk assessment. Top-down and bottom-up approaches are combined and the models are used as "hypothesis testing" tools by coupling model development with data analyses in order to incrementally evaluate the validity of model hypotheses. The paper first presents the rationale behind the experimental set-up and the instrumentation itself. Second, we discuss the associated modelling strategy. Results illustrate the potential of the approach in advancing our understanding of flash flood processes on various scales. [ABSTRACT FROM AUTHOR]

On 22 October 2019, the Francolí River basin in Catalonia, north-eastern Spain, experienced a heavy precipitation event that resulted in a catastrophic flash flood, causing six fatalities. Few studies comprehensively address both the physical and human dimensions and their interrelations during extreme flash flooding. This research takes a step forward towards filling this gap in knowledge by examining the alignment among all these factors. The hydrometeorological factors are investigated using the new Triangle-based Regional Atmospheric Model, radar-derived precipitation estimates, post-flood field and gauge observations, and the Kinematic Local Excess Model. The social dimension is assessed by examining the relationship between catchment dynamics and warning response times and by quantifying human behaviour during the course of the flash flood through a post-event citizen science campaign. Results reveal that a persistent south-easterly airflow brought low-level moisture and established convective instability in the region, while local orography was instrumental to triggering deep moist convection. A convective train promoted intense, copious, and prolonged precipitation over the north-western catchment headwaters. Basin response was significantly modulated by the very dry initial soil moisture conditions. After the long-lasting rainfall, an acute burst of precipitation resulted in extreme flash flooding. Fast and abrupt increases in streamflow affect small spatial scales and leave limited time for the effective implementation of protective measures. The institutional organization–protection–prevention cycle unfolded at the spatial and temporal scales typically dominated by the meteorological rather than hydrological scales. Although the citizen science campaign reveals the effectiveness of the warnings in reaching the population living in the most affected areas, a significant proportion of the respondents expressed a lack of adequate information or were unfamiliar with the intended meaning. In addition, a majority of the interviewees did not perceive any significant threat to life or property. In view of these results, this study identifies potential areas for improving social preparedness for similar natural hazards in the future. [ABSTRACT FROM AUTHOR]

Floods are the primary natural hazard in the French Mediterranean area, causing damages and fatalities every year. These floods are triggered by heavy precipitation events (HPEs) characterized by limited temporal and spatial extents. A new generation of regional climate models at the kilometer scale have been developed, allowing an explicit representation of deep convection and improved simulations of local-scale phenomena such as HPEs. Convection-permitting regional climate models (CPMs) have been scarcely used in hydrological impact studies, and future projections of Mediterranean floods remain uncertain with regional climate models (RCMs). In this paper, we use the CNRM-AROME CPM (2.5 km) and its driving CNRM-ALADIN RCM (12 km) at the hourly timescale to simulate floods over the Gardon d'Anduze catchment located in the French Mediterranean region. Climate simulations are bias-corrected with the CDF-t method. Two hydrological models, a lumped and conceptual model (GR5H) and a process-based distributed model (CREST), forced with historical and future climate simulations from the CPM and from the RCM, have been used. The CPM model confirms its ability to better reproduce extreme hourly rainfall compared to the RCM. This added value is propagated on flood simulation with a better reproduction of flood peaks. Future projections are consistent between the hydrological models but differ between the two climate models. Using the CNRM-ALADIN RCM, the magnitude of all floods is projected to increase. With the CNRM-AROME CPM, a threshold effect is found: the magnitude of the largest floods is expected to intensify, while the magnitude of the less severe floods is expected to decrease. In addition, different flood event characteristics indicate that floods are expected to become flashier in a warmer climate, with shorter lag time between rainfall and runoff peak and a smaller contribution of base flow, regardless of the model. This study is a first step for impact studies driven by CPMs over the Mediterranean. [ABSTRACT FROM AUTHOR]

Extreme rainfall events frequently cause hazardous floods in many parts of the world. With growing human exposure to floods, studying conditions that trigger floods is imperative. Flash floods, in particular, require well-defined models for the timely warning of the population at risk. Intensity–duration–frequency (IDF) curves are a common way to characterize rainfall and flood events. Here, the copula method is employed to model the dependence between the intensity and duration of rainfall events flexibly and separately from their respective marginal distribution. Information about the localization of 93 flash floods in Jamaica was gathered and linked to remote-sensing rainfall data, and additional data on location-specific yearly maximum rainfall events were constructed. The estimated normal copula has Weibull and generalized extreme value (GEV) marginals for duration and intensity, respectively. Due to the two samples, it is possible to pin down above which line in the intensity duration space a rainfall event likely triggers a flash flood. The parametric IDF curve with an associated return period of 216 years is determined as the optimal threshold for flash flood event classification. This methodology delivers a flexible approach to generating rainfall IDF curves that can directly be used to assess flash flood risk. [ABSTRACT FROM AUTHOR]

This study aims to investigate the impact of spatial rainfall distribution scenarios from ground observation stations on runoff simulation using hydrological modeling specific to the Rainfall-Runoff-Inundation (RRI) model. The RRI model was applied with six different spatial distribution scenarios of input rainfall, including Inverse Distance Weight (IDW), Thiessen polygon (TSP), Surface Polynomial (SPL), Simple kriging (SKG), and Ordinary kriging (OKG), to simulate the runoff of a 13,000 km2 watershed, namely the Nan River Basin in Thailand. This study utilized data from the 2014 storm event, incorporating temporal information from 28 rainfall stations to estimate rainfall in the spatial distribution scenarios. The six statistics, Volume Bias, Peak Bias, Root Mean Square Error, Correlation, and Mean Bias, were used to determine the accuracy of the estimated rainfall and runoff. Overall, the Simple kriging (SKG) method outperformed the other scenarios based on the statistical values to validate with measured rainfall data. Similarly, SKG demonstrated the closest match between simulated and observed runoff, achieving the highest correlation (0.803), the lowest Root Mean Square Error (164.48 cms), and high Nash-Sutcliffe Efficiency coefficient (0.499) values. This research underscores the practical significance of spatial interpolation methods, such as SKG, in combination with digital elevation models (DEMs) and landuse/soil type datasets, in delivering reliable runoff simulations considering the RRI model on the river basin scale. [ABSTRACT FROM AUTHOR]

In response to flood risk, design flood estimation is a cornerstone of planning, infrastructure design, setting of insurance premiums, and emergency response planning. Under stationary assumptions, flood guidance and the methods used in design flood estimation are firmly established in practice and mature in their theoretical foundations, but under climate change, guidance is still in its infancy. Human-caused climate change is influencing factors that contribute to flood risk such as rainfall extremes and soil moisture, and there is a need for updated flood guidance. However, a barrier to updating flood guidance is the translation of the science into practical application. For example, most science pertaining to historical changes to flood risk focuses on examining trends in annual maximum flood events or the application of non-stationary flood frequency analysis. Although this science is valuable, in practice, design flood estimation focuses on exceedance probabilities much rarer than annual maximum events, such as the 1 % annual exceedance probability event or even rarer, using rainfall-based procedures, at locations where there are few to no observations of streamflow. Here, we perform a systematic review to summarize the state-of-the-art understanding of the impact of climate change on design flood estimation in the Australian context, while also drawing on international literature. In addition, a meta-analysis, whereby results from multiple studies are combined, is conducted for extreme rainfall to provide quantitative estimates of possible future changes. This information is described in the context of contemporary design flood estimation practice to facilitate the inclusion of climate science into design flood estimation practice. [ABSTRACT FROM AUTHOR]

Studies reviewed in this paper show anomaly for temperature pertaining to streamflow and rainfall showing different trends, especially in Ethiopia to support the research findings and interpretation. There are many hydrological models, including 54 physically distributed, lumped, and conceptual hydrological models, of which 28 have been used in Ethiopian river basins. The models include the most adaptable and commonly used SWAT model applicable from small areas up to large basins. It is indeed a challenge to use a single hydrological model as the data rely on consistency, limitation-free, and exactly fitted output. The overall performance of individual physically-based, conceptual, and machine learning (ML) models varied at different watersheds. Reasonably, ML performs very well, up to 0.99 for R² and NSE and up to 0.001 for PBIAS. Inopportunely, using a single hydrological model has its limitations; ensemble multi-individual models, coupling or hybridization of physical or conceptual models with machine learning, combining evolutionary optimization algorithms with ML, and also comparisons of multi-single hydrological models, and selecting the best one are recommended options. No single hydrological model is indispensable and can be termed as better than the other for any watershed. Somewhat, ML outperforms SWAT but cannot be considered an absolute substitute. The size of the watershed, the number of data used, and the ratio between calibrations year to validation year do not have a clear correlation with the performance, particularly for the SWAT model accounted for in this review. Optimization algorithms explore multiple options and choosing the right one is a tedious task before a final decision is taken. [ABSTRACT FROM AUTHOR]

The objective of this study is to simulate the rainfall-runoff process of mountain torrents in the Shou River Basin located in the southern part of Wenchuan County, Sichuan Province. The basin encountered a significant flood disaster on August 20, 2019, resulting in substantial human and economic losses, as well as limited hydrological data availability. This research employs a unidirectional coupling approach by integrating the Weather Research and Forecasting Model (WRF) with the Storm Water Management Model (SWMM) to accurately simulate rainfall-runoff processes. To account for the non-uniform distribution of precipitation within mountainous watersheds, high-precision rainfall data generated by WRF are extracted and utilized as input for the SWMM model. The quantitative comparison between the evolution of spatial distribution of rainstorms and hourly precipitation processes demonstrates that the WRF model effectively captures dynamic changes in rainstorms. Analysis results indicate a trend of rainfall center movement from midstream to downstream, with sub-watersheds' superimposed effect on rainfall response leading to increased flood peak response, duration, and destructive power. This study provides an effective method and reference for predicting and assessing mountain flood disasters in data-scarce areas such as the Shou River Basin. [ABSTRACT FROM AUTHOR]

Floods are natural disasters that often impact communities living in low-lying areas in the northern and central parts of Morocco. In this study, our aim was to create a flood susceptibility map using three methods; the hierarchy process (AHP) frequency ratio model (FR) and the weights of evidence (WoE) model. We extensively examined the area identified by these approaches using a hydraulic analysis software called HEC-RAS (version 6.3.1). Our analysis focused on the Essaouira watersheds in Morocco, where we identified around 197 flood locations. Out of these, we randomly selected 70% for modeling purposes while the remaining 30% were used for validation. Ten factors that influence floods were considered, such as slope, elevation, proximity to rivers, drainage density, stream order, land use patterns, rainfall data, lithology (permeability level) index (TWI), and curvature. We obtained these factors from data sources. Finally, we generated a flood susceptibility map and evaluated its accuracy by calculating the area under the curve (AUC). The validation results confirmed that all three models were robust and effective with an AUC of 90. Moreover, the research uncovered a trend of vulnerability with the most susceptible area being in close proximity to the city of Essaouira along the Oued Ksob. A detailed analysis using HEC-RAS was conducted at this identified location, pinpointing the village of Diabat as highly exposed. These findings hold significance for flood management, empowering decision makers, scholars, and urban planners to make informed choices and implement strategies that can minimize the impact of floods in susceptible regions while minimizing potential damages. [ABSTRACT FROM AUTHOR]

Landslide hazard assessment is an important component of risk management and land-use planning. This study aims to investigate the application of a physically-based model named after the fast shallow landslide assessment model (FSLAM) to rainfall-triggered landslide hazard assessment. In August 2015, a total of 123 landslides induced by Typhoon Soudelor in Wenzhou City, southeastern China, was taken as an example. Five input raster files (elevation, soil types, vegetation, antecedent rainfall, event rainfall) and two parameter files regarding soil properties and vegetation were determined. Considering the randomness and uncertainty of soil and vegetation parameters on the regional scale, FSLAM model computes the probability of failure (PoF) by using random parameters inputs. Finally, the landslide hazard map was generated for the study area to reflect the landslide risk. The results showed that FSLAM could accurately capture the effect of rainfall on PoF of slopes, and more than 70% of the landslide were identified in very high/high hazard zones. The accuracy of the receiver operating characteristic (ROC) reached 0.720, which was higher than that of the Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability (TRIGRS) model (0.620). Regarding the computational time, FSLAM had better efficiency, and the consuming time was 1/25 compared with TRIGRS. [ABSTRACT FROM AUTHOR]

Devastating floods have plagued many West African cities in the past decades. In an attempt to reduce flood damage in Bamako (Mali), an early warning system (EWS) demonstrator (Raincell App) was developed for flash floods. On 16 May 2019, while the demonstrator was partially operational, an intense rainfall event led to devastating floods. We carried out an experience feedback on this flood event by comparing EWS simulations to the results of a field survey. Given the synoptic situation and the rapid development pattern of the storm, none of the global forecasting systems were able to foresee its occurrence and magnitude. The hydrological model developed as part of the demonstrator correctly identified most of the locations where overbank flow occurred. In the absence of data, the predicted discharge and volume values could not be validated. However, they are realistic based on the water levels reported in the Post‐Disaster Needs Assessment report. It would be advisable to couple it to a two‐dimensional hydraulic model and add discharge and water level monitoring to the already existing rainfall surveillance scheme to further improve the system's performance. Increasing the local population's awareness of the dangers of clogged waterways is also mandatory. [ABSTRACT FROM AUTHOR]

The exceptional flood of July 2021 in central Europe impacted Belgium severely. As rainfall was the triggering factor of this event, this study aims to characterize rainfall amounts in Belgium from 13 to 16 July 2021 based on two types of observational data. First, observations recorded by high-quality rain gauges operated by weather and hydrological services in Belgium have been compiled and quality checked. Second, a radar-based rainfall product has been improved to provide a reliable estimation of quantitative precipitation at high spatial and temporal resolutions over Belgium. Several analyses of these data are performed here to describe the spatial and temporal distribution of rainfall during the event. These analyses indicate that the rainfall accumulations during the event reached unprecedented levels over large areas. Accumulations over durations from 1 to 3 d significantly exceeded the 200-year return level in several places, with up to 90 % of exceedance over the 200-year return level for 2 and 3 d values locally in the Vesdre Basin. Such a record-breaking event needs to be documented as much as possible, and available observational data must be shared with the scientific community for further studies in hydrology, in urban planning and, more generally, in all multi-disciplinary studies aiming to identify and understand factors leading to such disaster. The corresponding rainfall data are therefore provided freely in a supplement. [ABSTRACT FROM AUTHOR]

Floods are a major natural hazard in the Mediterranean region, causing deaths and extensive damages. Recent studies have shown that intense rainfall events are becoming more extreme in this region but, paradoxically, without leading to an increase in the severity of floods. Consequently, it is important to understand how flood events are changing to explain this absence of trends in flood magnitude despite increased rainfall extremes. A database of 98 stations in southern France with an average record of 50 years of daily river discharge data between 1959 and 2021 was considered, together with a high-resolution reanalysis product providing precipitation and simulated soil moisture and a classification of weather patterns associated with rainfall events over France. Flood events, corresponding to an average occurrence of 1 event per year (5317 events in total), were extracted and classified into excess-rainfall, short-rainfall, and long-rainfall event types. Several flood event characteristics have been also analyzed: flood event durations, base flow contribution to floods, runoff coefficient, total and maximum event rainfall, and antecedent soil moisture. The evolution through time of these flood event characteristics and seasonality was analyzed. Results indicated that, in most basins, floods tend to occur earlier during the year, the mean flood date being, on average, advanced by 1 month between 1959–1990 and 1991–2021. This seasonal shift could be attributed to the increased frequency of southern-circulation weather types during spring and summer. An increase in total and extreme-event precipitation has been observed, associated with a decrease of antecedent soil moisture before rainfall events. The majority of flood events are associated with excess rainfall on saturated soils, but their relative proportion is decreasing over time, notably in spring, with a concurrent increased frequency of short rain floods. For most basins there is a positive correlation between antecedent soil moisture and flood event runoff coefficients that is remaining stable over time, with dryer soils producing less runoff and a lower contribution of base flow to floods. In a context of increasing aridity, this relationship is the likely cause of the absence of trends in flood magnitudes observed in this region and the change of event types. These changes in flood characteristics are quite homogeneous over the domain studied, suggesting that they are rather linked to the evolution of the regional climate than to catchment characteristics. Consequently, this study shows that even in the absence of trends, flood properties may change over time, and these changes need to be accounted for when analyzing the long-term evolution of flood hazards. [ABSTRACT FROM AUTHOR]

This study reveals the spatiotemporal variability of event characteristics and their controls from 290,743 rainfall‐runoff events across 431 Great Britain catchments. Metrics characterizing event runoff coefficient, event timescale and normalized peak discharge and their mean, variability and seasonality statistics are extracted to quantify event characteristics. Using random forest models, we find climate attributes are most influential in controlling the spatial pattern, with land cover, geology and soil properties also playing a critical role especially for explaining the variability of event runoff response. Rainfall characteristics (particularly rainfall depth) exhibit dominant controls on the temporal dynamic at event scale, while catchment initial state (antecedent soil moisture) determines its seasonal variations. Clear regional patterns of event characteristics are observed in Great Britain, while the influences of geology and anthropogenic activities on local differences are detected. Extremely low runoff coefficient values (<0.05) but with high variability are typical in Chalk catchments in Southeast region. Flashy events with short event timescale (<10 hr) and less seasonality of runoff response are observed in urban catchments. A large reservoir close to a gauge leads to the reduction of peak flows, yet the deviation is relatively small in summer. The impact of surface water abstractions is not obvious in our data set, whereas the impact of groundwater abstractions largely reflects the geology. This study provides the basis for regionalization studies by revealing the (dis)similarities of catchment runoff response and emphasizes the need for further research into the role of geology and human activities on rainfall‐runoff characteristics. Key Points: Climate, land cover, and geology are key controls on the spatial pattern of rainfall‐runoff event characteristics across Great BritainRainfall depth and intensity control runoff response at event scale, while the catchment initial state controls its seasonal variabilityRunoff response often follows a clear regional pattern, yet local geology, reservoirs, and urbanization can cause anomalous behavior [ABSTRACT FROM AUTHOR]

The September 1973 flood in the Mahi Basin was one of the most catastrophic and widespread in the 20th century. However, the hydro-meteorological characteristics of the 1973 flood were not studied. Therefore, an attempt has been made to analyze the meteorological and hydrological processes that led to the 1973 flood. Accordingly, daily rainfall data, river discharge, and cross-section data were obtained for the analysis. The 1973 flood was associated with very heavy rainfall resulting from two successive low-pressure systems (LPS) from 26 to 31 August 1973 and 2 to 5 September 1973. The rainfall variability in the Mahi Basin was 24% (annual) and 25% (monsoon) in 1973. The analysis showed that out of 69 rainfall stations, 13 stations received 100% rainfall in the monsoon season in 1973. Under the influence of the second LPS (7 and 9 September 1973), 21 rain gauge stations recorded very heavy rainfall (124.5–244.4 mm) on 8 September. As a result, the maximum discharge of the Mahi River (40,663 m3/s) was observed at Wanakbori on 9 September. The flood hydrograph denoted two flood peaks of 28,125 m3/s and 33,097 m3/s magnitudes resulting from LPS at Kadana. A newly constructed bridge (in 1972) on the Mahi River at the Kailashpuri village washed out due to a large discharge of 21,553 m3/s magnitude on 7 September 1973. The hydro-meteorological analysis of the 1973 flood specified the significance of the LPS in a flash flood disaster in the Mahi Basin. This study will benefit hydrologists and civil engineers creating design floods for the construction of the hydraulic structures in the Mahi Basin, and will help to avoid any future catastrophic floods. [ABSTRACT FROM AUTHOR]

Flood warnings provide information about the timing and magnitude of impending floods, which can help mitigate the adverse impacts of flooding. Flood forecasts are highly influenced by uncertainty associated with rainfall forecasts as well as initial catchment wetness. Event-based models are simple and parsimonious and are widely favored by practitioners for flood estimation. However, these models require loss parameters to be manually specified for each simulated event, and this represents an additional source of uncertainty that needs to be considered along with errors in observations and rainfall forecasts. Little attention has been given to the coupling of updating techniques with event-based models to reduce the uncertainty associated with catchment wetness. To this end, we devised a sequential recalibration scheme to characterize uncertainty in ensemble forecasts derived using an event-based flood model. This scheme uses information on both observation and model errors to filter and update catchment loss estimates to improve the accuracy of the forecasts. Analysis of flood forecasts for 22 events showed that although initially, there was low skill in forecasts derived solely from external estimates of catchment wetness, the reliability and accuracy of the forecasts improved rapidly once the flood event commenced and the flood model was coupled with an updating scheme. Compared with forecasts made without any updating scheme, the conditioning and recalibration steps progressively improved the accuracy of the forecasts as measured by Nash-Sutcliffe efficiency from −0.14 to 0.88, bias was reduced by 78%, and root-mean square error reduced by 67%. The use of such schemes thus reinforces the advantages of using parsimonious models that have long been favored by practitioners for design and other purposes. [ABSTRACT FROM AUTHOR]

This research paper presents a systematic literature review on the use of remotely sensed and/or global datasets in distributed hydrological modelling. The study aims to investigate the most commonly used datasets in hydrological models and their performance across different geographical scales of catchments, including the micro-scale (<10 km2), meso-scale (10 km2–1000 km2), and macro-scale (>1000 km2). The analysis included a search for the relation between the use of these datasets to different regions and the geographical scale at which they are most widely used. Additionally, co-authorship analysis was performed on the articles to identify the collaboration patterns among researchers. The study further categorized the analysis based on the type of datasets, including rainfall, digital elevation model, land use, soil distribution, leaf area index, snow-covered area, evapotranspiration, soil moisture and temperature. The research concluded by identifying knowledge gaps in the use of each data type at different scales and highlighted the varying performance of datasets across different locations. The findings underscore the importance of selecting the right datasets, which has a significant impact on the accuracy of hydrological models. This study provides valuable insights into the use of remote sensed and/or global datasets in hydrological modelling, and the identified knowledge gaps can inform future research directions. [ABSTRACT FROM AUTHOR]

On 1 September 2021, the remnants of Hurricane Ida transformed into a lethal variant of tropical cyclone in which unprecedented short‐duration rainfall from clusters of supercells produced catastrophic flooding in watersheds of the Northeastern US. Short‐duration rainfall extremes from Ida are examined through analyses of polarimetric radar fields and rain gauge observations. Rainfall estimates are constructed from a polarimetric rainfall algorithm that is grounded in specific differential phase shift (KDP) fields. Rainfall accumulations at multiple locations exceed 1000‐year values for 1–3 hr time scales. Radar observations show that supercells are the principal agents of rainfall extremes. Record flood peaks occurred throughout the eastern Pennsylvania—New Jersey region; the peak discharge of the Elizabeth River is one of the most extreme in the eastern US, based on the ratio of the peak discharge to the sample 10‐year flood at the gaging station. As with other tropical cyclones that have produced record flooding in the Northeastern US, Extratropical Transition was a key element of extreme rainfall and flooding from Ida. Tropical and extratropical elements of the storm system contributed to extremes of atmospheric water balance variables and Convective Available Potential Energy, providing the environment for extreme short‐duration rainfall from supercells. Key Points: Remnants of Hurricane Ida produced unprecedented short‐duration rainfall extremes at 1–3 hr time scaleSupercells were the principal agents of extreme rainfall and floodingExtratropical Transition of Ida contributed to the environment of record short‐duration rainfall [ABSTRACT FROM AUTHOR]

The paper presents the results of stationary, detailed studies on the variability of the mutual share of two fluvial loads, i.e., suspended solids and dissolved material during floods caused by rainstorm, continuous rainfalls and snowmelt in selected rivers (Silnica, Sufraganiec) draining small catchments in central Poland, including two characterized by a high level of urbanization. Irrespective of the origin of the flood, the share of suspended solids load did not exceed 80% in urbanized catchments, in suburban catchments—44%, and in forest catchments—32%. In the former, the gradient of the increase in the share of suspended solids and concentration time in the first phase of the flood was several times higher than in the other catchments. It was proved that statistically significant relationships exist between the share of sealed surfaces (roads, car parks, roofs, etc.) in the total catchment area and the average share of suspended solids, both in the rising and falling phase of the flood wave, regardless of their origin. Similar relationships were documented by analyzing: the density of the drainage network (storm sewers, roads, etc.)—the share of suspension. The obtained results have an interesting cognitive aspect and in practice are used for the development of hydrotechnical documentation related to water management in the city. [ABSTRACT FROM AUTHOR]

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Resistivity is used to evaluate soil water content (SWC), which has the advantages of not causing soil disturbance and in low price. It is an effective way to assess the SWC variability. This paper aims to evaluate the variability of loess slope SWC through the change of resistivity. It provides a simple way for long term SWC monitoring to solve the expensive cost of deploying moisture sensors. In this context, geoelectric and environmental factors such as soil temperature and SWC were monitored for three years. The prediction model of apparent resistivity and SWC was calibrated. The post processing of geoelectric data was introduced. In addition, the SWC collected by Time-Domain Reflectometry (TDR) was used to verify the feasibility of electrical resistivity tomography (ERT) data. The SWC variability in the process of rainfall, the evolution of four seasons, and the alternation of drying and wetting were evaluated. The research results show that: i) the SWC monitored by ERT and TDR can reflect the response and hysteretic effect of water content at 0.5–3.0 m depth. ii) The moisture content monitored by ERT reflects that the soil is relatively wet in summer and autumn and dry in winter and spring. iii) From 2017 to 2020, the SWC increased in August, and the soil became dry in January. iv) Two areas with high SWC and three areas with low SWC on loess slope are reflected by resistivity. The outcome can provide the change information of SWC to a great extent without excavating boreholes. [ABSTRACT FROM AUTHOR]

River flooding has large societal and economic impacts across Africa. Despite the importance of this topic, little is known about the main flood generating mechanisms in Africa. This study is based on 13,815 flood events that occurred between 1981 and 2018 in 529 catchments. These flood events are classified to identify the different flood drivers: excess rains, long rains and short rains. Out of them, excess rains on saturated soils in Western Africa, and long rains for catchments in Northern and Southern Africa, are the two dominant mechanisms, contributing to more than 75% of all flood events. The aridity index is strongly related to the spatial repartition of the different flood generating processes showing the climatic controls on floods. Few significant changes were detected in the relative importance of these drivers over time, but the rather short time series available prevent a robust assessment of flood driver changes in most catchments. The major implication of these results is to underline the importance of soil moisture dynamics, in addition to rainfall, to analyze the evolution of flood hazards in Africa. [ABSTRACT FROM AUTHOR]

Plain farmland areas without significant topographic slope exhibit microtopographic features of different scales. Quantitative assessment of the effects of microtopography at different scales on runoff generation in typical farmland areas is of great significance for regional water resources management and flood disaster forecasting. The main objective of the study was to develop an event-based rainfall–runoff model based on the layered Green–Ampt model (LGAM) with the consideration of plot-scale microtopographic features in plain farmland areas. Our experimental field, located in Taihu Lake Basin, was classified into three types of topographic subunits (i.e., main field, rill, and ditch) according to the average elevation. To simplify the concentration process for three topographic subunits, the average concentration time method was employed. Here, various experimental scenarios were simulated, including two classical unsteady rainfall events in homogeneous soil, one ponding infiltration experiment, and two typical rainfall–runoff events in the experimental field. We found that the multilayered setting showed higher accuracy than the homogeneous setting for simulating infiltration in the ponding infiltration experiment in the field. The RMSE of simulated ponding water depth reduced from 0.166 cm to 0.035 cm and NSE rose from 0.988 to 0.999. The simulated hydrograph considering microtopography effects proved higher accuracy than that under unified topography assumption. After classifying topography, the RMSE and NSE of simulated hydrographs decreased and increased, respectively. The lower the topographic subunit, the earlier the outflow occurred. At the early stage, the runoff mostly originated from the relatively low topographic subunits. Infiltration-excess regime under saturated condition may initially dominate in the low-lying ditch under intense rainfall, with extremely high runoff coefficient. Concentration process in the plain farmland area was affected by both rainfall intensity and microtopography. The greater the rainfall intensity, the shorter the average concentration time. The concentration velocity under heavy rainfall was four times faster than that under light rainfall. The lower topographic subunit was characterized by shorter concentration pathway and average concentration time. Ditches reduced the peak flow and advanced the time to peak. This quantitative study provides new insights into effects of microtopography on runoff generation in plain farmland area as well as an effective alternative for plot-scale rainfall–runoff modeling. [ABSTRACT FROM AUTHOR]

As the world is changing, mainly due to climate change, extreme events such as floods and droughts are becoming more frequent and severe. Considering this, the predictive modeling of flow in small mountain catchments that are particularly vulnerable to flooding is critical. Rainfall data sources such as rain gauges, meteorological radars, and satellites provide data to the hydrological model with a lag. Only numerical weather predictions can achieve this in advance, but their estimates are often subject to considerable uncertainty. This article aims to verify whether Global Environmental Multiscale numerical precipitation prediction can be successfully applied for event-based rainfall–runoff hydrological modeling. These data were verified for use in two aspects: the flow modeling and determination of antecedent moisture conditions. The results indicate that GEM data can be satisfactorily used for hydrological modeling, and particularly good simulation results are obtained when significant rainfall occurs. In addition, these data can be used to correctly estimate the AMC groups for each sub-catchment in advance, which is one of the key elements flowing into the amount of projected outflow in the catchment. It is worth noting that, according to the literature review conducted by the article's author, this is the first published attempt to use GEM data directly in applied hydrological applications. [ABSTRACT FROM AUTHOR]

The tropical Rainfall Measuring Mission TRMM 3B42 V7 product and its successor, the Global Precipitation Measurement Integrated Multi-satellitE Retrievals for GPM IMERG high-resolution product GPM IMERG V5, have been validated against rain gauges precipitation in an arid mountainous basin where ground-based observations of precipitation are sparse, or spatially undistributed. This paper aims to evaluate hydro-statically the performances of the TRMM 3B42 V7 and GPM IMERG V05 satellite precipitations products SPPs, at multiple temporal scales, from 2014 to 2017. SPPs are compared with the gauge station and show good results for both statistical and contingency metrics with notable values R > 0.94. Moreover, the rainfall-runoff events implemented on the hydrological model were performed at 3-hourly time steps and showed satisfactory results based on the obtained Nash–Sutcliffe criteria ranging from 94.50% to 57.50%, and from 89.3% to 51.2%, respectively. The TRMM product tends to underestimate and not capture extreme precipitation events. In contrast, the GPM product can identify the variability of precipitation at small time steps, although a slight underestimation in the detection of extreme events can be corrected during the validation steps. The proposed method is an interesting approach for solving the problem of insufficient observed data in the Mediterranean regions. [ABSTRACT FROM AUTHOR]

The hydroclimatology and hydrometeorology of flooding over the Third Pole region is poorly understood, despite its societal and environmental significance. Here we examine extreme rainfall and flooding over the eastern Tibetan Plateau (TP) based on empirical analyses of long‐term, in‐situ rainfall and stream gaging observations as well as the ECMWF Reanalysis fields. We develop a flood catalog that consists of 168 extreme rainfall‐runoff floods over the eastern TP, and characterize the synoptic conditions for flood‐producing storms based on the principal component analysis. We show the transitional pattern form Asian summer monsoons to midlatitude systems as the most frequent synoptic conditions for flooding over the eastern TP. The interplay between complex terrains and moisture transport determines the contrasting flood characteristics (in terms of intensity and spatial pattern) under different synoptic environments. We carry out high‐resolution Weather Research and Forecasting modeling analysis for four flood‐producing storms over the eastern TP. We highlight the interaction of complex terrains and nocturnal low level jets as the key mesoscale ingredients in dictating a pronounced diurnal cycle of extreme rainfall over the southeastern portion of TP. Our results establish the link between large‐scale synoptic environment, mesoscale process, and regional flood hydrology over the eastern TP, and therefore contribute to improved flood risk management practices over the Third Pole region under a changing climate. Key Points: We examine meteorological floods over the eastern Tibetan Plateau based on long‐term, in‐situ stream gaging and rainfall observationsThe transitional pattern from Asian summer monsoons to midlatitude systems is the most frequent synoptic condition for floodingThe interplay between complex terrains and moisture transport dictates contrasting flood characteristics under different synoptic patterns [ABSTRACT FROM AUTHOR]

Floods resulting from high‐intensity rainfall events are known to be difficult to simulate. The catchment response to such events is very heterogeneous due to complex combinations of hydrological processes at fine temporal and spatial scales. The goal of this study is to find a way to inform the structure of a hydrological model on the variability in catchment response to rainfall events. To that end, rainfall intensity was used as a proxy for the activation of fast heterogeneous runoff processes. We developed three hypotheses that increase the versatility of a hydrological model by modifying the volume and temporal distribution of effective rainfall when high‐intensity rainfall events occur. These modifications were implemented within the GR5H lumped rainfall–runoff model. The different model versions were run on 229 French catchments where 10 652 flood events were selected. Model performance was assessed considering five groups of catchments, and model performance was also evaluated based on three event characteristics. Results showed that introducing a dynamic dependency of fluxes to rainfall intensities at the hourly time step helps to improve the simulation of floods, especially on Mediterranean catchment areas. Generic values of the additional parameters are proposed to limit the increase in calibration complexity. [ABSTRACT FROM AUTHOR]

Rain gauges provide accurate rainfall amount data; however, the interpolation of their data is difficult, especially because of the high spatial and temporal variability. On the other hand, a high-resolution type of information is highly required in hydrological modeling for discharge calculations in small catchments. This problem is partially solved by meteorological radars, which provide precipitation data with high spatial and temporal distributions over large areas. The purpose of this study is to validate a conditional merging technique (CMT) for 15 rainfall events that occurred on the southern slope of the Tibleș and Rodnei Mountains (Northern Romania). A Geographic Information System (GIS) methodology, based on three interpolation techniques—simple kriging, ordinary kriging, and cokriging—were utilized to derive continuous precipitation fields based on discrete rain gauge precipitation data and to derive interpolated radar data at rain gauge locations, and spatial analysis tools were developed to extract and analyze the optimal information content from both radar data and measurements. The dataset contains rainfall events that occurred in the period of 2015–2018, having 24 h temporal resolution. The model performance accuracy was carried out by using three validation metrics: mean bias error (MBE), mean absolute error (MAE), and root mean square error (RMSE). The validation stage showed that our model, based on conditional merging technique, performed very well in 11 out of 15 rainfall events (approximate 78%), with an MAE under 0.4 mm and RMSE under 0.7 mm. [ABSTRACT FROM AUTHOR]

The high sensitivity of loess slopes to water has been emphasized in many studies. However, it is still limited in terms of the understanding of slope morphological differentiation on the overall and local failure patterns in slopes, as well as on the acquisition method of hydrological dynamics. In this study, rainfall characteristics and slope surface morphological differences were introduced. Geoelectric and environmental factors were monitored. On this basis, apparent resistivity corrected by seasonal temperature and its relationship with soil water content was calibrated. The water migration characteristics and potential failure patterns of three slope morphologies were evaluated. The results are: (i) the improved resistivity method can better reflect the water flow movement within the slope, and it performs well after being corrected by temperature; (ii) the characteristics of surface runoff and water infiltration are directly affected by the cumulative rainfall value, and especially when the cumulative rainfall is >70 mm threshold, the surface runoff quickly infiltrates into the deep of the slope along the preferential paths; (iii) the interception ability of loess slope morphology to the surface runoff is concave slope > convex slope > linear slope; (iv) with the continuous rainfall, the convex surface of a slope is prone to be damaged by saturated mud flow. When the cumulative rainfall threshold is 70 mm, the preferential flow is easily excited on the concave surface of the slope, resulting in local collapse at the slope toe and mid-deep landslides. [ABSTRACT FROM AUTHOR]

This study, for the first time, investigates the physical association between Predecessor Rain Events (PREs) and peak runoff generation in seven catchments over the Upper Mahanadi River basin (UMRB), India. A statistical–dynamical framework is developed to assess the compounding impact of PREs (as preconditioned events) versus riverine floods during both retrospective and projected climate. Based on models' fidelity to capture historical climatology, we select four out of nine Global Climate Models (GCMs) during historical (1980–2005), and the three projected time windows, that is, near future (2010–2039), mid‐century (2040–2069), and the far future (2070–2099) planning horizons with RCP8.5 emission scenario. We assess changes in compound flood hazards in historical versus projected periods using a newly proposed Bivariate Hazard Ratio (BHR) index, which represents the ratio between bivariate return periods during the projected versus the historical time windows. Assessing bivariate return periods (characterized by 'AND' operator) of rainfall‐driven compound floods shows decreased flood hazard in the mid‐century and far‐future planning horizons. Accounting ranges of uncertainty from climate model simulations and the propagation of uncertainty across the numerical model chain, overall we show considering PRE as the covariate, floods in larger catchments show an increase in compound flood hazard in the projected period. [ABSTRACT FROM AUTHOR]

Urban floods due to intense precipitation are a major problem in many tropical regions as in Africa. Rainfall measurement using microwave links from cellular communication networks has been proposed as a cost-effective solution to monitor rainfall in these areas where the gauge network is scarce. The method consists in retrieving rainfall from the attenuation estimated along the commercial microwave links (CMLs) thanks to the power levels provided by an operator. In urban areas where the network is dense, rainfall can be estimated and mapped for hydrological prediction. Rainfall estimation from CMLs is subject to uncertainties. This paper analyzes the advantages and limitations of this rainfall data for a distributed hydrological model applied to an urban area. The case study is in West Africa in Ouagadougou, Burkina Faso, where a hydrological model has been set up. The analysis is based on numerical simulations, using high-resolution rain maps from a weather radar to emulate synthetic microwave links. Two sources of uncertainty in the rain estimation and on the simulated discharge are analyzed by simulations: (i) the precision of the raw information provided by the operator and (ii) the density and geometry of the network. A coarse precision (1 dB) in the signal provided by the operator can lead to substantial underestimation of rainfall and discharge, especially for links operating at low frequency (below 10 GHz) or of short length (less than 1 km). The density of the current mobile networks in urban areas is appropriate to analyze hydrological impact of tropical convective rainfall. [ABSTRACT FROM AUTHOR]

As climate change alters flood risk, there is a need to project changes in flooding for water resource management, infrastructure design and planning. The use of observed temperature relationships for informing changes in hydrologic extremes takes many forms, from simple proportional change approaches to conditioning stochastic rainfall generation on observed temperatures. Although generally focused on understanding changes to precipitation, there is an implied transfer of information gained from precipitation-temperature sensitivities to flooding as extreme precipitation is often responsible for flooding. While reviews of precipitation-temperature sensitivities and the non-stationarity of flooding exist, little attention has been given to the intersection of these two topics. Models which use temperature as a covariate to assess the non-stationarity of extreme precipitation outperform both stationary models and those using a temporal trend as a covariate. But care must be taken when projecting changes in flooding on the basis on precipitation-temperature sensitivities, as antecedent conditions modify the runoff response. Although good agreement is found between peak flow-temperature sensitivities and historical trends across Australia, there remains little evaluation of flood projections using temperature sensitivities globally. Significant work needs to be done before the use of temperature as a covariate for flood projection can be adopted with confidence. This article is part of a discussion meeting issue 'Intensification of short-duration rainfall extremes and implications for flash flood risks'. [ABSTRACT FROM AUTHOR]

The heavy rain event of July 2018 and Typhoon Hagibis in October 2019 caused severe flash flood disasters in numerous parts of western and eastern Japan. Flash floods need to be predicted over a wide range with long forecasting lead time for effective evacuation. The predictability of flash floods caused by the two extreme events is investigated by using a high-resolution (~ 150 m) nationwide distributed rainfall-runoff model forced by ensemble precipitation forecasts with 39 h lead time. Results of the deterministic simulation at nowcasting mode with radar and gauge composite rainfall could reasonably simulate the storm runoff hydrographs at many dam reservoirs over western Japan for the case of heavy rainfall in 2018 (F18) with the default parameter setting. For the case of Typhoon Hagibis in 2019 (T19), a similar performance was obtained by incorporating unsaturated flow effect in the model applied to Kanto Region. The performance of the ensemble forecast was evaluated based on the bias ratios and the relative operating characteristic curves, which suggested the higher predictability in peak runoff for T19. For the F18, the uncertainty arises due to the difficulty in accurately forecasting the storm positions by the frontal zone; as a result, the actual distribution of the peak runoff could not be well forecasted. Overall, this study showed that the predictability of flash floods was different between the two extreme events. The ensemble spreads contain quantitative information of predictive uncertainty, which can be utilized for the decision making of emergency responses against flash floods. [ABSTRACT FROM AUTHOR]

Standardized heavy rainfall profiles may contribute to a best knowledge concerning flash floods, ground erosion and runoff. Taking advantage of the rain rate gauges network of Barcelona city and applying a short integration time of 5 min, the heavy rainfall records with amounts above 25 mm and longer than 60 min have been selected along 15 years, being detected 499 records corresponding to 67 episodes. The amount distribution of these records are analysed at time deciles. By grouping these standardized rainfall profiles according to their similarity by means of the average linkage clustering algorithm, 10 clusters are derived, each one of them characterized by different time evolution of rainfall. The representative standardized rainfall profiles for every one of the clusters, together with maximum 5-min rain amounts and rainfall amounts characterizing them, permit to distinguish rainfall patterns. In addition, the extreme heavy rainfalls which may lead to flash floods are identified, as also their respective synoptic situations. In short, this analysis offers a description of heavy rainfall patterns in Barcelona city, complementing previous papers on the normalized intensity curves and 5-min intensity return periods. These heavy rainfall analyses would be very useful when designing drainage and sewerage systems in urban areas as Barcelona, where flash floods may be expected due to episodes of notable rainfall amount and intensity. The imperviousness density of the soil and an appropriate sewerage structure should be adapted to mitigate the effects of these copious and intense episodes. [ABSTRACT FROM AUTHOR]

Accurate estimates of intense rainfall that induce flash floods in urban areas are necessary for flood hazard preparedness. The city of Kuala Lumpur is frequently hit by flash floods after prolonged heavy storms, causing substantial damage to property and infrastructure, and disruptions to the socioeconomic activities of urbanites. The flood mitigation measures that have been implemented include the 2007 construction of the Stormwater Management and Road Tunnel (SMART), which diverts the flood water coming out of the Upper Klang Ampang catchment from entering the city prime areas. This study developed an integrated hydrological model of the catchment with input from radar rainfall to estimate the flood volume threshold value for SMART operation. The hydrologic and hydraulic modeling were performed using HEC-HMS with the HEC-GeoHMS tool to extract the hydrologic parameter information using GIS as input data for the catchment model. A radar processing system named RAINRATE AUTO V2 was developed to efficiently process raw radar data from the Subang radar and produce calibrated and gridded rainfall to the HEC-HMS flood estimation model for the Upper Klang Ampang urban catchment area. The results show promising performance regarding the use of radar rainfall, and there was an acceptable percent error in the peak discharge simulations. The advanced integrated system can be applied in managing flood risks at the catchment scale by efficiently estimating incoming flood events in the future. [ABSTRACT FROM AUTHOR]

Since the dawn of civilization, the Western Mediterranean region has been a unique path for encounters and exchanges. The stability of the general conditions prevailing in its surrounding was crucial to ensure this vital link between north and south and east and west. However, any imbalance would be the precursor of a harmful domino effect to these environments, where the natural vulnerabilities are combined to a worsening socioeconomic congestion. In both banks, efforts are made not to give way to the fragile balance of water availability and risks. Here, we show that the amount and recurrence of extreme rainfall events contradict with current management scenarios. Our pixel-based assessment of the rain components extracted from the African Rainfall Climatology product reveals an annual increase in the total amount of rainfall and extreme rainfall events. A spatial and a trend analysis of the studied rainfall components were carried out to determine their spatial patterns and magnitude. The cumulative annual rainfall alone will not mean if a year is dry or wet. A wet year can experience great periods of drought, which will have serious repercussions on the socioeconomic and ecological levels. In addition, the increased occurrence of extreme rains can produce floods, intensifying land degradation processes, loss of biodiversity, water availability and economic growth. Decision-makers should be aware that current models of territorial management will not be able to cope with this change and that interdisciplinary cohesion and cross-border actions are the most appropriate solution. [ABSTRACT FROM AUTHOR]

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Weather radar has become an invaluable tool for monitoring rainfall and studying its link to hydrological response. However, when it comes to accurately measuring small-scale rainfall extremes responsible for urban flooding, many challenges remain. The most important of them is that radar tends to underestimate rainfall compared to gauges. The hope is that by measuring at higher resolutions and making use of dual-polarization radar, these mismatches can be reduced. Each country has developed its own strategy for addressing this issue. However, since there is no common benchmark, improvements are hard to quantify objectively. This study sheds new light on current performances by conducting a multinational assessment of radar's ability to capture heavy rain events at scales of 5 min up to 2 h. The work is performed within the context of the joint experiment framework of project MUFFIN (Multiscale Urban Flood Forecasting), which aims at better understanding the link between rainfall and urban pluvial flooding across scales. In total, six different radar products in Denmark, the Netherlands, Finland and Sweden were considered. The top 50 events in a 10-year database of radar data were used to quantify the overall agreement between radar and gauges as well as the bias affecting the peaks. Results show that the overall agreement in heavy rain is fair (correlation coefficient 0.7–0.9), with apparent multiplicative biases on the order of 1.2–1.8 (17 %–44 % underestimation). However, after taking into account the different sampling volumes of radar and gauges, actual biases could be as low as 10 %. Differences in sampling volumes between radar and gauges play an important role in explaining the bias but are hard to quantify precisely due to the many post-processing steps applied to radar. Despite being adjusted for bias by gauges, five out of six radar products still exhibited a clear conditional bias, with intensities of about 1 %–2 % per mmh -1. As a result, peak rainfall intensities were severely underestimated (factor 1.8–3.0 or 44 %–67 %). The most likely reason for this is the use of a fixed Z – R relationship when estimating rainfall rates (R) from reflectivity (Z), which fails to account for natural variations in raindrop size distribution with intensity. Based on our findings, the easiest way to mitigate the bias in times of heavy rain is to perform frequent (e.g., hourly) bias adjustments with the help of rain gauges, as demonstrated by the Dutch C-band product. An even more promising strategy that does not require any gauge adjustments is to estimate rainfall rates using a combination of reflectivity (Z) and differential phase shift (Kdp), as done in the Finnish OSAPOL product. Both approaches lead to approximately similar performances, with an average bias (at 10 min resolution) of about 30 % and a peak intensity bias of about 45 %. [ABSTRACT FROM AUTHOR]

Spatial–temporal trends of extreme precipitation have great impacts on disaster risks, and relevant studies usually receive much attention. In China, spatial–temporal trends have been analysed in numerous studies based on daily rainfall data; however, the densities of rain gauges in many studies were relatively low, and the effects of complex topography on extreme precipitation were usually not considered, which have lowered the reliability and accuracy of the reported results. In this study, several widely used extreme precipitation indices were chosen, and the spatial–temporal trends of a typical watershed in the Hengduan Mountain Region in Southwest China, the Longchuanjiang watershed, were analysed using the Mann–Kendall trend analysis based on daily rainfall data. The effect of elevation on the extreme precipitation indices based on the high‐intensity rain gauges and the effect of elevation on the temporal trends of extreme precipitation indices were analysed, which have rarely been reported. The results indicated that: (a) significant positive correlations with elevation were reported for annual precipitation and most of the extreme precipitation indices, including PRCPTOT, Rx5day, R95P, R99P, RD, SDII, and CWD, while significant negative correlations were reported for CDD; the relationships between SDII and Rx1day were not significant with elevation; (b) the density of rain gauges in the regions with complex topography can have great impacts on the results; and (c) no significant correlation between the elevation and the temporal trends of extreme precipitation indices were reported. This study highlights the effects of the density of rain gauges and elevation, or topographical factors on the spatial distribution and temporal trends of extreme precipitation indices, which has rarely been reported in previous studies, implying that such effects should be fully taken into consideration in the relevant studies in the future. [ABSTRACT FROM AUTHOR]

Over the Cévennes–Vivarais region in southern France 5 h intensive rainfall covering an area of 1000 km 2 with more than 50 mm of rain accumulation was observed during IOP7a of HyMeX. This study evaluates the performance of a bin-resolved cloud model for simulating this heavy-precipitation event. The simulation results were compared with observations of rain accumulation, radar reflectivity, temporal and spatial evolution of precipitation, 5 min rain rates, and raindrop size distributions (RSDs). The different scenarios for aerosol number concentrations range from 1000 to 2900 cm -3 and represent realistic conditions for this region. Model results reproduce the heavy-precipitation event with respect to maximum rain intensity, surface area covered by intense rain and the duration, as well as the RSD. Differences occur in the short-term rainfall rates, as well as in the drop number concentration. The cloud condensation number concentration has a notable influence on the simulated rainfall, on both the surface amount and intensity but also on the RSD properties, and should be taken into account in microphysics parameterizations. [ABSTRACT FROM AUTHOR]

The western Mediterranean region is prone to devastating flash floods induced by heavy-precipitation events (HPEs), which are responsible for considerable human and material losses. Quantitative precipitation forecasts have improved dramatically in recent years to produce realistic accumulated rainfall estimations. Nevertheless, there are still challenging issues which must be resolved to reduce uncertainties in the initial condition assimilation and the modelling of physical processes. In this study, we analyse the HPE forecasting ability of the multi-physics-based ensemble model Prévision d'Ensemble ARPEGE (PEARP) operational at Météo-France. The analysis is based on 30-year (1981–2010) ensemble hindcasts which implement the same 10 physical parameterizations, one per member, run every 4 d. Over the same period a 24 h precipitation dataset is used as the reference for the verification procedure. Furthermore, regional classification is performed in order to investigate the local variation in spatial properties and intensities of rainfall fields, with a particular focus on HPEs. As grid-point verification tends to be perturbed by the double penalty issue, we focus on rainfall spatial pattern verification thanks to the feature-based quality measure of structure, amplitude, and location (SAL) that is performed on the model forecast and reference rainfall fields. The length of the dataset allows us to subsample scores for very intense rainfall at a regional scale and still obtain a significant analysis, demonstrating that such a procedure is consistent to study model behaviour in HPE forecasting. In the case of PEARP, we show that the amplitude and structure of the rainfall patterns are basically driven by the deep-convection parametrization. Between the two main deep-convection schemes used in PEARP, we qualify that the Prognostic Condensates Microphysics and Transport (PCMT) parametrization scheme performs better than the B85 scheme. A further analysis of spatial features of the rainfall objects to which the SAL metric pertains shows the predominance of large objects in the verification measure. It is for the most extreme events that the model has the best representation of the distribution of object-integrated rain. [ABSTRACT FROM AUTHOR]

Heavy precipitation events (HPEs) can lead to natural hazards (e.g. floods and debris flows) and contribute to water resources. Spatiotemporal rainfall patterns govern the hydrological, geomorphological, and societal effects of HPEs. Thus, a correct characterisation and prediction of rainfall patterns is crucial for coping with these events. Information from rain gauges is generally limited due to the sparseness of the networks, especially in the presence of sharp climatic gradients. Forecasting HPEs depends on the ability of weather models to generate credible rainfall patterns. This paper characterises rainfall patterns during HPEs based on high-resolution weather radar data and evaluates the performance of a high-resolution, convection-permitting Weather Research and Forecasting (WRF) model in simulating these patterns. We identified 41 HPEs in the eastern Mediterranean from a 24-year radar record using local thresholds based on quantiles for different durations, classified these events into two synoptic systems, and ran model simulations for them. For most durations, HPEs near the coastline were characterised by the highest rain intensities; however, for short durations, the highest rain intensities were found for the inland desert. During the rainy season, the rain field's centre of mass progresses from the sea inland. Rainfall during HPEs is highly localised in both space (less than a 10 km decorrelation distance) and time (less than 5 min). WRF model simulations were accurate in generating the structure and location of the rain fields in 39 out of 41 HPEs. However, they showed a positive bias relative to the radar estimates and exhibited errors in the spatial location of the heaviest precipitation. Our results indicate that convection-permitting model outputs can provide reliable climatological analyses of heavy precipitation patterns; conversely, flood forecasting requires the use of ensemble simulations to overcome the spatial location errors. [ABSTRACT FROM AUTHOR]

The purpose of this paper was to demonstrate the added value of the spatial distribution of rainfall and potential evapotranspiration (PE) in the prediction of the discharge for a small Mediterranean catchment located in the Medjerda basin in Tunisia, i.e. the Raghay. We compare therefore the performance of a conceptual hydrological model available in the ATHYS platform, using global and spatial distributed input data. The model was implemented in two different ways. The first implementation was in a spatially distributed mode, and the second one was in a non-distributed lumped mode by using spatially averaged data weighed with a Thiessen-interpolated factor. The performance of the model was analysed for the distributed mode and for the lumped mode with a cross-validation test and through several modelling evaluation criteria. Simultaneously, the impact of the spatial distribution of meteorological data was assessed for the two cases when estimating the model parameters, the flow and water amounts, and the flow duration curves. The cross-validation of the split-sample test shows a preference for the spatially distributed model based on accuracy criteria and graphical comparison. The distributed mode required, however, more simulation time. Finally, the results reported for the Raghay indicated that the added value of the spatial distribution of rainfall and PE is not constant for the whole series of data, depending on the spatial and temporal variability of climate data over the catchment that should be assessed prior to the modelling implementations. [ABSTRACT FROM AUTHOR]

The ability of precipitation assimilation is assessed in a convective scale model in order to improve the precipitation forecast for a Mediterranean heavy rain event that took place on November 4, 2017. The proposed assimilation method is based on a two‐step approach. First, one‐dimensional variational (1D‐Var) assimilation is applied on hourly accumulated precipitation to retrieve temperature and specific humidity profiles. These retrieved profiles are then combined in relative humidity profiles before being assimilated into the AROME (Application of Research to Operational at MEsoscale) 3D‐Var system. Three experiments are run for this case study. The results show that precipitation assimilation has a positive impact on both moisture analysis and the forecast of dynamic fields. A comparison of 24 hr‐accumulated precipitation forecasts with precipitation analysis from radar and gauge data (ANTILOPE) demonstrates the ability of rain assimilation to improve convective precipitation forecasts. A statistical evaluation against rain gauges indicates better scores due to the additional moisture information given by the precipitation assimilation. [ABSTRACT FROM AUTHOR]