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• State of the art on precipitation frequency analysis based on remotely sensed datasets is depicted. • Investigation of climatic gradients and explicit inclusion of the areal scale is highly promising. • Current main issues: estimation accuracy, statistical methods, reference/validation methods. • Recommended research: uncertainty partition, dedicated statistical and validation methods. Information on extreme precipitation is essential to managing weather-related risks and designing hydraulic structures. Research attention to frequency analyses based on remotely sensed precipitation datasets, such as those obtained from weather radars and satellites, has been rapidly increasing owing to their potential to provide information for ungauged regions worldwide. Together with the ability to measure the areal scale directly, these analyses promise to overcome the sampling limitations of traditional methods based on rain gauges. This focused review of the literature depicts the state of the art after a decade of efforts, and identifies the crucial gaps in knowledge and methodology that currently hinder the quantitative use of remotely sensed datasets in water resources system design and operation. It concludes by highlighting a set of research directions promising immediate impact with regard to the separation of the sources of uncertainty currently affecting applications based on remotely sensed datasets, the development of statistical methods that can cope with the peculiar characteristics of these datasets, and the improvement of validation methods. Important gains in knowledge are expected from the explicit inclusion of the areal dimension in the analyses and from the fine-scale investigation of extreme precipitation climatology. [ABSTRACT FROM AUTHOR]

• Precipitation annual maxima estimated from NEXRAD are evaluated over the CONUS. • Accuracy of radar-based estimates varies with magnitude and duration. • Uncertainty in radar-based IDF curves exhibits climate dependence. The lack of knowledge on precipitation frequency over ungauged areas introduces a significant source of uncertainty in relevant engineering designs and risk estimation procedures. Radar-based observations offer precipitation information over ungauged areas and thus have gained increasing attention as a potential solution to this problem. However, due to their relative short data records and inherent uncertainty sources, their ability to provide accurate estimates on the frequency of precipitation extremes requires evaluation. This study involves the evaluation of at-site precipitation frequency estimates from NEXRAD Stage IV radar precipitation dataset. We derive precipitation annual maxima series from the 16yrs record (2002–2017) of NEXRAD and we compare against 539 long-term (50yrs) hourly gauge records. In addition, Intensity-Duration-Frequency (IDF) curves are estimated from both radar and gauge dataset and compared. IDF estimation is based on fitting the Generalize Extreme Value distribution to annual precipitation maxima. Evaluation is carried out over the contiguous United States and results are grouped and presented for five dominant climate classes and for a range of return period and precipitation durations. NEXRAD was shown to overestimate intensities at shorter durations (1- and 3-h) and low quantiles, while it tends to underestimate higher quantiles at longer durations (24 h). In addition, evaluation of the IDF curves estimated from NEXRAD revealed a distinct geographic dependence with certain regions exhibiting a tendency to overestimation (e.g. east of the Rocky Mountains) or underestimation (Midwest). Overall, this analysis suggests that, while significant discrepancies may exist, there are several cases where NEXRAD provide estimates within the uncertainty bounds of the reference rain gauge dataset. The climate/geographic region and the temporal duration are important aspects to consider. Findings provided in this work on these aspects will hopefully serve as a general guideline for those interested in using NEXRAD estimates for further research or applications on precipitation extremes. [ABSTRACT FROM AUTHOR]

This study expands the Metastatistical Extreme Value (MEV) framework to sub-daily rainfall frequency analysis and compares it to extreme value theory methods in presence of short records and measurement errors. Ordinary events are identified based on the temporal autocorrelation of hourly data and modeled with a Weibull distribution. MEV is compared to extreme value theory methods in the estimation of long return period quantiles from actual data (160 rain gauges with at least 60-year record in the contiguous United States) and on synthetic data perturbed with measurement errors typical of remote sensing rainfall estimation. MEV tends to underestimate the 100-year return period quantiles of hourly rainfall when 5–20 years of actual data are used, but presents diminished uncertainty. When a good model of the ordinary events and adequate number of events per year are available, MEV is able to provide information on the 100-year return period quantiles from 10–20, or even 5 years of data with significantly reduced uncertainty (<30% uncertainty for 5-year records). MEV estimates of 100-year return period quantiles from short records are much less sensitive than extreme value theory methods to additive/multiplicative errors, presence of cap values in the estimates, and missing of extreme values. Results from this study strongly support the use of MEV for rainfall frequency analyses based on remotely sensed datasets. [ABSTRACT FROM AUTHOR]

Summary This work aims to evaluate the potential benefits and limitations of radar rainfall estimates for the identification of debris flow occurrence rainfall thresholds. Observations from a C-band weather radar and raingauge data are analyzed for seven convective rainfall events that triggered 117 debris flows in the Upper Adige river basin (Eastern Italian Alps). Four radar rainfall scenarios characterized by incrementally increasing accuracy are used for the estimation of rainfall intensity–duration thresholds. Error sources considered in the radar correction chain include beam blockage, attenuation and vertical profile of reflectivity. The impact of rainfall accuracy on the estimation of the intensity–duration thresholds is analyzed by comparing the thresholds derived from the various radar rainfall scenarios, using the rainfall estimates obtained from the application of the complete correction algorithm as a reference scenario. Results show that the application of the complete correction algorithm improves significantly the accuracy of radar rainfall estimates: Fractional Standard Error is decreased by 20%, Correlation Coefficient is increased by 24% relative to uncorrected data. Correction for atmospheric attenuation is the most important step in the correction chain. The use of uncorrected radar estimates leads to substantially underestimated thresholds with respect to the reference scenario; adjusting radar data for bias is not sufficient to overcome this problem. Radar rainfall estimates which are corrected but not adjusted with raingauge data are able to provide intensity–duration thresholds which are almost indistinguishable from the reference scenario. The derivation of the radar-based threshold is shown to be very sensitive to spatial location errors of rainfall and debris flows. Raingauge-based thresholds are severely underestimated with respect to the reference scenario. This clearly demonstrates the severity of the raingauge-based estimation problem for the derivation of debris flow triggering rainfall and highlights the benefits of using weather radar observations, at least for the case of short duration convective storms. [ABSTRACT FROM AUTHOR]

Estimation of rainfall intensity-duration thresholds, used for the identification of debris flow/landslide triggering rainfall events, has been traditionally based on raingauge observations. The main drawback of using information from gauges is that the measurement stations are usually located far away from the debris flow initiation areas. In complex terrain where debris flows take place, the spatial variability of rainfall can be very high and this translates in large uncertainty of raingauge-based estimates of debris flow triggering rainfall. This work focuses on the assessment of the impact of rainfall estimation uncertainty on identification and use of rainfall thresholds for debris flow occurrence. The Upper Adige River basin, Northern Italy, is the area of study. A detailed database of more than 400 identified debris flow initiation points during the period 2000-2010 and a raingauge network of 100 stations comprise the database used for this work. The methodology examines the intensity-duration thresholds derived from a set of raingauges that are assumed to be located at debris flow initiation points (DFRs) and an equivalent set of raingauges assumed to have the role of the closest (to debris flow) available measurement (MRs). A set of reference rainfall thresholds is used to identify the rainfall events at DFRs that "triggered" debris flows (i.e. exceed the threshold). For these same events, the corresponding rainfall thresholds are derived from MR observations. Comparison between the rainfall thresholds derived from DFRs and MRs revealed that uncertainty in rainfall estimation has a major impact on estimated intensity-duration thresholds. Specifically, the results showed that thresholds estimated from MR observations are consistently underestimated. Evaluation of the estimated thresholds for warning procedures showed that while detection is high, the main issue is the high false alarm ratio, which limits the overall accuracy of the procedure. Overall performance on debris flow prediction was shown to be good for low rainfall thresholds and poor for high rainfall thresholds examined. Lastly, it was found that filtering out rainfall events with duration <12h may improve bias in estimated thresholds and performance for high rainfall thresholds. [ABSTRACT FROM AUTHOR]

Summary: The sensitivities of runoff generation to rainfall variability and initial wetness conditions were examined for a major flash flood event that occurred during August 29, 2003 on the upper Tagliamento river basin in the eastern Italian Alps. The Triangulated Irregular Network (TIN)-based Real-time Integrated Basin Simulator (tRIBS) distributed hydrologic model was used to simulate the hydrologic response over a range of sub-basins. The model was calibrated for the single flash flood event based on the observed hydrograph at the outlet of the basin examined (Fella basin) and was validated based on the available observed hydrographs at interior points of the basin. A series of hydrologic simulations were performed for different initial soil moisture conditions and rainfall forcing resolutions in order to evaluate the sensitivity of runoff generation to those variables. Evaluation of the results suggests that both initial wetness and rainfall resolution affect significantly the simulated peak flow and runoff volume during the flash flood event. Sensitivity to initial wetness exhibits a scale dependence with the sensitivity increasing with basin scale. The bias introduced to the basin-averaged rainfall due to aggregation had a significant effect on runoff generation for all basin scales, while the effect of variability smoothing was important only for the larger scale basins. Finally, the sensitivity of the flood hydrograph to rainfall aggregation was shown to be more important for drier initial wetness states. [Copyright &y& Elsevier]

Considering the reduction of steep power ramps caused by renewable energy penetration, the present study evaluates the potential of utilizing existing water supply infrastructure as small-scale pumped-hydroelectric storage (PHS) units. A novel methodology is developed that estimates the total storage capacity via the available space in five water supply systems in Connecticut (United States), by solely employing water level measurements and considering a water release schedule that favors power-demand peak reduction. The study culminates in the estimation of optimal areal requirements for hypothesized solar farms neighboring the studied systems, based on deficit minimization between storage capacity and energy generation over the study period. Analysis reveals that, in most cases, the seasonal pattern of the storage capacity is in accordance with the behavior of the corresponding hydraulic head timeseries, showing higher values in winter/autumn. Given certain assumptions regarding the infrastructure, a yearly average of approximately 95 MWh/day can be stored as an intersystem aggregate, illustrating that the amalgamation of existing water supply reservoirs could provide significant energy storage capacity to complement substation variability. Finally, the area covered by PV arrays increases proportionally with the storage capacity, while the calculated solar energy output demonstrates significant variability relative to the PHS storage potential. • Water supply infrastructure is evaluated as potential small-scale pumped-hydroelectric storage (PHS) units. • The storage capacity is estimated considering a water release pattern that favors power demand peak reduction. • Estimation of PHS potential is conducted via a novel methodology that depends solely on water level measurements. • The relation between storage potential and solar energy reveals the importance of complementary renewable energy sources. [ABSTRACT FROM AUTHOR]

• The Metastatistical Extreme Value Distribution (MEVD) framework applied to four short-record high-resolution quasi-global sub-daily precipitation products.. • MEVD parametric uncertainty and its propagation effect in the quantile is investigated. • Impact of spatial scale mismatch on the frequency analysis of extremes is quantified. • We provide an assessment of satellite-based precipitation products in different hydroclimatic regions. Precipitation extremes and associated hydrological hazards pose a significant global risk to society and economy. To be effective, mitigation strategies require the best possible estimation of the intensity and frequency of precipitation extremes. Traditional approaches to precipitation frequency analysis rely on long-term records from in-situ observations, which are limited in terms of global coverage. Satellite-based precipitation products provide global coverage, but errors in these estimates may lead to large biases in the quantification of extremes. Previous studies have demonstrated the ability of the novel Metastatistical Extreme Value Distribution (MEVD) framework to provide robust estimates of high quantiles in the presence of short-term data records and the uncertainties typical of remote sensing precipitation products. Here, we evaluate MEVD-based precipitation frequency analyses for four widely used quasi-global precipitation products (IMERG-v6, GSMaP-v6, CMORPH-v1.0, and MSWEP-v2) over high-density gauge networks in five hydroclimatic regions (Austria, Italy, Florida, Texas, and Arizona). We show dependence of MEVD-based estimation error on the characteristics of each dataset and the hydroclimatic region. Additionally, we evaluate the sub-grid variability of extreme precipitation and demonstrate the impact of spatial scale mismatch (that is, single in-situ gauge versus satellite pixel) on the frequency analysis of extremes. This work provides an assessment of the use of MEVD for estimating precipitation extremes from globally available datasets and an understanding of the variability of sub-daily precipitation extremes in different hydroclimatic regions of the world. [ABSTRACT FROM AUTHOR]

The concurrence of flash floods and debris flows is of particular concern, because it may amplify the hazard corresponding to the individual generative processes. This paper presents a coupled modelling framework for the predictions of flash flood response and of the occurrence of debris flows initiated by channel bed mobilization. The framework combines a spatially distributed flash flood response model and a debris flow initiation model to define a threshold value for the peak flow which permits identification of channelized debris flow initiation. The threshold is defined over the channel network as a function of the upslope area and of the local channel bed slope, and it is based on assumptions concerning the properties of the channel bed material and of the morphology of the channel network. The model is validated using data from an extreme rainstorm that impacted the 140 km 2 Vizze basin in the Eastern Italian Alps on August 4–5, 2012. The results show that the proposed methodology has improved skill in identifying the catchments where debris-flows are triggered, compared to the use of simpler thresholds based on rainfall properties. [ABSTRACT FROM AUTHOR]

Forecasting the occurrence of debris flows is fundamental for issuing hazard warnings, and often focuses on rainfall as a triggering agent and on the use of empirical rainfall thresholds based on rain gauge observations. A recognized component of the uncertainty associated with the use of rainfall thresholds is related to the sampling of strongly varying rainfall variability with sparse rain gauge networks. In this work we examine the spatial distribution of rainfall depth in areas up to 10 km from the debris flow initiation points as a function of return period, and we exploit this information to analyze the errors expected in the estimation of debris flow triggering rainfall when rain gauge data are used. In particular, we investigate the impact of rain gauge density and of the use of different interpolation methods. High-resolution, adjusted radar rainfall estimates, representing the best available spatially-distributed rainfall estimates at the debris flows initiation point and in the surrounding area, are sampled by stochastically generated rain gauge networks characterized by varying densities. Debris flow triggering rainfall is estimated by means of three rainfall interpolation methods: nearest neighbor, inverse distance weighting and ordinary kriging. On average, triggering rainfall shows a local peak corresponding to the debris flow initiation point, with a decay of rainfall with distance which increases with the return period of the triggering rainfall. Interpolation of the stochastically generated rain gauge measurements leads to an underestimation of the triggering rainfall that, irrespective of the interpolation methods, increases with the return period and decreases with the rain gauge density. For small return period events and high rain gauge density, the differences among the methods are minor. With increasing the return period and decreasing the rain gauge density, the nearest neighbor method is less biased, because it makes use only of the closest rain gauge to the debris flow initiation point. On the contrary, inverse distance weighting and ordinary kriging, which are using rain gauges located farther from the debris flows in addition to the closest one, exhibit negative biases that increase with return period. The standard deviation of the interpolated values is larger when the nearest neighbor is used with respect to inverse distance weighting and ordinary kriging. For large return period and low rain gauge density, the differences among the methods are minor. [ABSTRACT FROM AUTHOR]

In response to the vital role of geomorphological analysis in natural hazards study, geomorphology, distributed hydrology and other related disciplines, we present the first global basin morphometric product of 30 characteristics, 9 archived elementary including stream order, stream number, stream length, basin relief, basin length, basin perimeter, maximal flow length, down valley length and overland flow length, and 21 derivable from these elementary morphometric characteristics. Characteristics of basins discharging to every grid-cell of the global earth surface are generated at 30 arcsec resolution from the remotely sensed HydroSHEDS dataset. This product does not exist in previous literature because tree-structural grid-cells bring large computational redundancy to basin delineation and thus computing basin characteristics. To generate this product within a reasonable timeframe, we introduce in this paper an efficient framework to reduce the algorithm complexity to linear, O( N ). We have presented a few geomorphologic findings using the generated product. [ABSTRACT FROM AUTHOR]

• ML models trained in data rich regions can predict floods in data scarce regions. • Global precipitation data and ML can help with flood prediction in ungauged basins. • There is a strong dependence of predictive performance on climate and drainage area. Increasing flood risk due to urbanization and climate change poses a significant challenge to societies at global scale. Hydrologic information that is required for understanding flood processes and for developing effective warning procedures is currently lacking in most parts of the world. Procedures that can combine global climate dataset from satellite and reanalysis with fast and low computational cost prediction systems, are attractive solutions for addressing flood predictions in ungauged areas. This work develops and tests a prediction framework that relies on two fundamental components. First, meteorological data from global datasets (IMERG and ERA5-Land) provide key input variables and second, ML models trained in the data-rich contiguous US, are applied in climatically similar regions in other parts of the world. Catchments in Australia, Brazil, Chile, Switzerland, and Great Britain were used as pseudo-ungauged regions for testing. Results indicate acceptable performance for both IMERG and ERA5-Land forced models with relative difference in flood peak prediction within 30 % and similar overall performance to locally trained ML models. Specific climate regions for which ML models have revealed good performance include Mediterranean climates like the US West Coast, subtropical areas like the Southern Atlantic Gulf, and mild temperate regions like the Mid-Atlantic Basin. This work highlights the potential of combining global precipitation dataset with pre-trained ML models in data-rich areas, for flood prediction in ungauged areas with similar climate. [ABSTRACT FROM AUTHOR]

• We used the Simplified MEV to model extreme rainfall quantiles in tropical regions. • Weibull distribution well approximates the tail of daily rainfall distribution. • Statistical parameters and rainfall quantiles significantly depend on latitude. • Homogeneous patterns in regional temporal trends are related to altitude. • Extreme daily rainfall amounts in the Volta basin increased during 1978–2018. Sub-Saharan Africa's economy and livelihood are primarily dependent on agriculture, which makes it highly vulnerable to the impacts of extreme weather events and climate change. Modelling and quantifying extreme rainfall and its temporal changes in such environments could thus provide crucial information for design, insurance, management, ecology and climate adaptation. Rain gauge networks in the area are relatively sparse, and often characterized by missing data, which hamper the use of extreme-value methods for estimating extreme precipitation quantiles. In this study, we adopted the Simplified Metastatistical Extreme Value approach for the estimation of extreme return levels based on ordinary events (i.e., all the independent realizations of the variable of interest), which was shown to be more accurate than traditional extreme-value methods in the presence of short data records. We examined data from 66 rain gauges covering diverse hydro-climatic regions across Ghana with the aim of (i) validating the robustness of the statistical approach, (ii) characterising the climatic and altitudinal controls on the occurrence, frequency and intensity of rainfall extremes, and (iii) quantifying recent changes in the characteristics of extremes. We found that a two-parameter Weibull distribution well approximates the tail of the daily rainfall distribution throughout the area. Our statistical approach can quantify extremes with largely reduced uncertainties (7–17% uncertainty in the 100-year return levels computed using 10 years of data versus 11–62% of extreme-value based methods). Extreme precipitation statistics (daily intensity distribution, number of wet days, extreme rainfall quantiles) are found to significantly depend on latitude, so that the four latitudinally layered hydro-climatic regions typically adopted in the area well represent spatial variations. Elevation significantly affects the tail heaviness of the daily intensity distribution and thus extreme rainfall quantiles. Temporal changes during the period 1978–2018 are found to be non-homogeneous in the area as well as within the four hydro-climatic regions, but are homogeneous in three altitude-based regions. We report contrasting trends in extreme return levels in low-elevation (<200 m a.s.l.) and hilly regions, related to contrasting changes in the daily intensity distribution. Statistically significant positive trends in extreme daily rainfall amounts are observed in the inland low-elevation region of the Volta river basin, which call for further investigation of changes in future precipitation extremes in this extremely important hydrological region in Sub-Saharan Africa. [ABSTRACT FROM AUTHOR]

• The NCAR ensemble is able to represent the spatial and temporal characteristics of soil state across the CONUS. • Soil state estimates from the NCAR ensemble exhibited better performance compared to GFS over west and southeast CONUS. • The ensemble spread characteristics suggest that future limited-area continuously cycling EnKF systems could successfully assimilate soil state observations. Accurate characterization of soil state is imperative for weather and climate research, hydrological modeling, agriculture, water resources management, and drought monitoring. Limited-area data assimilation (DA) systems can provide high-resolution real-time soil state analyses. However, soil state representations from regional continuously cycling DA systems have not been examined. This study evaluates soil moisture and temperature estimates and spread characteristics from a real-time, limited-area, continuously-cycling ensemble Kalman filter (EnKF) data assimilation (DA) system run at the National Center for Atmospheric Research (NCAR) for 2.7 years over the conterminous United States (CONUS). Soil moisture and temperature data from both the EnKF and operational Global Forecast System (GFS) analysis are compared against the North American Land Data Assimilation System (NLDAS-2) soil state estimates. Results demonstrate the continuously-cycling EnKF was generally able to represent the spatial and temporal characteristics of soil moisture and temperature across the CONUS, demonstrating the feasibility of using limited-area continuously cycling EnKFs as a method to improve simulations of soil moisture and temperature. Soil state estimates from the NCAR ensemble exhibited lower Bias and RMSE compared to GFS analyses over west and southeast CONUS. Additionally, the ensemble spread characteristics suggest that future limited-area continuously cycling EnKF systems could successfully assimilate both in situ and remotely-sensed soil moisture and temperature observations to further improve soil state analyses. [ABSTRACT FROM AUTHOR]

• An analytical multifractal (MF) approximation for IDF curve estimation is tested. • The MF model is compared to annual maxima and peaks-over-threshold approaches. • The MF IDF estimates are more accurate, especially for short rainfall records. • The MF parameters resemble the local topography and rainfall climatology. • The MF parameter setting is particularly appealing for ungauged locations. The present study uses hourly rainfall measurements from 36 raingauge stations in the northeastern United States with more than 55 years of recordings, to assess the accuracy and robustness of three conceptually different parametric approaches to produce intensity-duration-frequency (IDF) curves from rainfall records: (a) the well celebrated parametric IDF model based on annual maxima (AM) series, initially introduced by Rossi and Villani (1994) and later extended by Koutsoyiannis et al. (1998), (b) a peaks-over-threshold (PoT) model based on proper fitting of a reparametrized generalized Pareto (GP) distribution to the scaled excesses of the average rainfall intensity over various durations d , and (c) the analytical approximation introduced by Langousis et al. (2009) for estimation of multifractal (MF) rainfall maxima. Since its development, the third approach has never been tested in real world applications, and/or compared to routinely used approaches for IDF curve estimation. The obtained results show that the MF approach may constitute an important tool for hydrological applications, as it produces more accurate and robust IDF estimates than the other two approaches, irrespective of the duration d and return period T of interest, especially in cases where rainfall records exhibit significant length limitations. In addition, the parameters of the MF analytical approximation are found to be descriptive of the local topography and rainfall climatology of the region, providing a physically meaningful modeling setting that is particularly suited for regional frequency analysis and IDF estimation at ungauged locations. [ABSTRACT FROM AUTHOR]

• Weibull distribution models well sub-daily precipitation extremes over CONUS. • Significant relation between storm characteristics and statistics of extremes is found. • Tail heaviness depends on climate type and storm characteristics. In this paper we use a statistical framework to analyze the relation between storm properties and the statistics of extreme precipitation. We identify storm events using a 24-hour dry hiatus separation. We investigate the statistics of the hourly maximum intensity for the heaviest storm events at durations of 1 and 24 h. A two-parameter Weibull distribution is used to represent precipitation frequencies at several stations from a quality-controlled hourly precipitation dataset over the contiguous United States, encompassing seven climate zones. The Spearman correlation between the distribution parameters and a selection of storm properties (duration, intensity, decorrelation time, convective-like ratio) and station properties (elevation and latitude) is used to measure the relation of these properties with the statistics of extreme precipitation. Our results indicate that observed annual maximum hourly precipitation over the entire study area are likely samples from the used distribution, implying that a two-parameter Weibull distribution is suitable for modeling hourly and 24-hour precipitation extremes over the contiguous United States. The spatial variability of shape parameters obtained for hourly events showed lighter tails in the west coast when compared to the rest of CONUS, while the central-north displays heavier tails. We identify statistically significant links (at the 95% confidence level) between storm characteristics connected with the underlying processes (e.g., typical storm duration, typical temporal autocorrelation, proportion of convective-like storms) and the parameters of the distribution. Notably, characteristics typical of convective precipitation, e.g., sharp decorrelation time and high hourly intensity, tend to be related to distributions with heavier tails. These results provide a first step towards linking the characteristics of storms with the local statistics of extremes. [ABSTRACT FROM AUTHOR]

This study uses an analytical hydrological framework to investigate the error propagation from satellite precipitation products to hydrological simulations. Specifically, the analytical formulation of the framework allows linking the error in hydrograph properties (i.e., cumulative volume, centroid, and dispersion) to the space-time characteristics of error in satellite-precipitation, runoff generation, and routing. Main finding from this study are that (i) the error in spatial and temporal covariance between rainfall and runoff generation is not contributing significantly to the error in cumulative volume of flood events; (ii) errors in runoff generation and routing time are of equal importance in terms of the overall error in the arrival of flood event centroid; and (iii) errors in the variability of runoff generation time is the main contributor to the error in dispersion of flood event hydrograph. Furthermore, sensitivity tests show that errors in hydrograph properties are strongly correlated with errors in the space-time characteristics of precipitation, runoff generation and routing parameters estimated by the analytical framework. [ABSTRACT FROM AUTHOR]

• Regional, machine learning models solely for flood peak prediction. • LSTM based storm classifier for flood warning. • Importance of flood predictors depends on flood severity. • Flood warning framework for early detection and prediction in ungauged basins. Flood prediction across scales and more specifically in ungauged areas remains a great challenge that limits the efficiency of flood risk mitigation strategies and disaster preparedness. Building upon the recent success of Machine Learning (ML) models on streamflow prediction, this work presents a prototype ML-based framework for flood warning and flood peak prediction. The fundamental elements of the proposed system consist of a) a Long-Short Term Memory (LSTM) model for classifying storm events to Flood/no-Flood given a threshold based on the 90th flow percentile and b) the flood peak prediction models. The selected ML-models for flood peak prediction are the Histogram based Gradient Boosting Regressor and the Random Forest. One of the strengths, and reason for selection, of these decision-tree models is their degree of interpretability. This is exploited in the study to help us spatially disentangle the role of both the static and dynamic drivers of flood peak response. Our analysis is presented for 18 distinct hydroclimatic regions across the contiguous US. Results reveal a significant regional dependence on both predictive performance and dominant flood predictors, which emphasize the variability in the complexity of a catchment's hydrologic behavior as well as its impact on forecasting flood response. Evaluation of the drivers of flood peaks noted distinct dependencies among the dynamic and static predictors considered in our models for flood peaks of different severity. Specifically, low-moderate flood events showed a clear preponderance for the static catchment attributes over dynamic predictors like precipitation whereas precipitation was the dominant driver for the highest flood peaks. The proposed flood peak prediction models were compared against a state-of-the-art LSTM model and were shown to outperform in ungauged basins for the majority of basins. Overall, the proposed system classified storms correctly for >80% in all cases and exhibited a percent relative difference in flood peak estimates of <30% in most cases. [ABSTRACT FROM AUTHOR]

Highlights: [•] Spatio-temporal rainfall variability effects on flood modeling. [•] Rainfall moments and storm velocity conceptual frameworks. [•] Experimental framework to assess the conceptual frameworks’ predictability. [ABSTRACT FROM AUTHOR]

• A comprehensive review and analysis of IMERG validation studies from 2016 to 2019. • There is robust representation of spatio-temporal patterns of precipitation. • Discrepancies can be found in extreme and light precipitation, and the winter season. • The 30-min scale has not yet been sufficiently evaluated. • Using IMERG in hydrological simulation results to high variance in their performance. Accurate, reliable, and high spatio-temporal resolution precipitation data are vital for many applications, including the study of extreme events, hydrological modeling, water resource management, and hydroclimatic research in general. In this study, we performed a systematic review of the available literature to assess the performance of the Integrated Multi-Satellite Retrievals for GPM (IMERG) products across different geographical locations and climatic conditions around the globe. Asia, and in particular China, are the subject of the largest number of IMERG evaluation studies on the continental and country level. When compared to ground observational records, IMERG is found to vary with seasons, as well as precipitation type, structure, and intensity. It is shown to appropriately estimate and detect regional precipitation patterns, and their spatial mean, while its performance can be improved over mountainous regions characterized by orographic precipitation, complex terrains, and for winter precipitation. Furthermore, despite IMERG's better performance compared to other satellite products in reproducing spatio-temporal patterns and variability of extreme precipitation, some limitations were found regarding the precipitation intensity. At the temporal scales, IMERG performs better at monthly and annual time steps than the daily and sub-daily ones. Finally, in terms of hydrological application, the use of IMERG has resulted in significant discrepancies in streamflow simulation. However, and most importantly, we find that each new version that replaces the previous one, shows substantial improvement in almost every spatiotemporal scale and climatic condition. Thus, despite its limitations, IMERG evolution reveals a promising path for current and future applications. [ABSTRACT FROM AUTHOR]

Abstract: Increasing our understanding of the small scale variability of drop size distributions (DSD), and therefore of several bulk characteristics of rainfall processes, has major implications for our interpretation of the remote sensing based estimates of precipitation and its uncertainty. During the spring and summer of 2002 the authors conducted the DEVEX experiment (disdrometer evaluation experiment) to compare measurements of natural rain made with three different types of disdrometers collocated at the Iowa City Municipal Airport in Iowa City, Iowa in the Midwestern United States. This paper focuses on the evaluation of the instruments rather than analysis of the hydrometeorological aspects of the observed events. The comparison demonstrates discrepancies between instruments. The authors discuss the systematic and random effects in terms of rainfall quantities, drop size distribution properties, and the observed drop size vs. velocity relationships. Since the instruments were collocated, the effects of the natural variability of rain are reduced some with time integration, isolating the instrumental differences. The authors discuss the status of DSD measurement technologies and the implications for a range of hydrologic applications from remote sensing of rainfall to atmospheric deposition to soil erosion and sediment transport in the environment. The data set collected during the DEVEX experiment is made available to the research community. [Copyright &y& Elsevier]