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Data-driven models have shown remarkable achievements in runoff prediction, but their simulation results can be overly homogenized due to the distillation of all simulation aspects into the loss function. This can make the models unreliable for predicting extreme events, leading to issues subsequent reservoir operations. This paper proposes a novel data-driven hybrid machine-learning model called the multi-objective optimized extreme learning machine (MOELM) to provide an accurate runoff forecasting for reservoirs. The objective is to minimize simulation error, with an additional focus on flood deviation. The results show that: (1) MOELM can improve flood events prediction, reducing the root mean square error (RMSE) for flood series by 5.27% without increasing the overall prediction error at Longtan reservoir. Compared to hydrological models, MOELM can reduce operational risk with lower reservoir maximum outflow and water level during typical flood events, and it can potentially increase hydropower generation at Longtan reservoir by 130 million kW·h. (2) MOELM can be transferred to other cross-sections with excellent performances, demonstrating hydrological transferability from fluctuation to flatness in regime. (3) Partial mutual information is introduced for input variable selection, with discharge at lag times t-4, t-1, t-8, and t-2 being vital to the prediction model. Our model is practical, requiring no additional input, fitting the hydrological runoff holistically, and capable of providing accurate flood forecasts. [ABSTRACT FROM AUTHOR]

This research investigates the correlation between Precipitable Water Vapor (PWV), derived from Global Navigation Satellite System (GNSS) measurements, and rainfall during flood events in Chanthaburi, Surat Thani, and Songkhla, Thailand. Utilizing GPS-PWV and meteorological data collected from 2007 to 2016, the study reveals a significant increase in average PWV during flood events at CHAN, SRTN, and SOKA stations, suggesting its potential as an anticipatory indicator for impending rainfall. The robust correlation between PWV and rainfall patterns underscores the pivotal role of meteorological parameters in shaping PWV distribution. Categorized by flood events, consistent correlations were observed, with Case 1 (2009) showing correlation coefficients of 0.78 for CHAN, Case 2 (2010) displaying correlation coefficients of 0.70 for SRTN and 0.27 for SOKA, and Case 3 (2011) exhibiting correlation coefficients of 0.64 for SRTN and 0.71 for SOKA. These findings contribute valuable insights for precipitation forecasting and flood management, emphasizing the utility of PWV as a reliable tool. Future studies incorporating an expanded network of GNSS CORS stations aim to refine PWV distribution understanding for enhanced monitoring and prediction of precipitation events in the region. [ABSTRACT FROM AUTHOR]

On 1–3 May 2023, severe hydro-meteorological events occurred in the Italian Emilia-Romagna region. Such events caused extensive flooding, landslides, isolation of many areas, evacuation of many families, and severe damage to infrastructure, agriculture, buildings, and essential services. Several municipalities were affected, thousands of civilians had to be evacuated, and losses of life occurred. The consequences beyond the recorded immediate impacts on infrastructure and life were impressive, and extended to the regional economy, specifically in the Fruit Valley, where, in addition to immediate yield losses, long-term damage to orchard production is expected due to persistent flooding. The civil and cultural building heritage has also been heavily affected, both in the countryside and in inhabited centers. Some of the damage, direct and indirect, caused by flooding on buildings will also see an evolution in the medium- to long-term that needs to be addressed. This paper analyzes the manifold aspects of such an atmospheric phenomenon and its impacts to understand the potential increasing occurrence of similar events in the climate change context. [ABSTRACT FROM AUTHOR]

Predicting flood events is complex due to uncertainties from limited gauge data, high data and computational demands of traditional physical models, and challenges in spatial and temporal scaling. This research innovatively uses only three remotely sensed and computed factors: rainfall, runoff and temperature. We also employ three deep learning models—Feedforward Neural Network (FNN), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM)—along with a deep neural network ensemble (DNNE) using synthetic data to predict future flood probabilities, utilizing the Savitzky–Golay filter for smoothing. Using a hydrometeorological dataset from 1993–2022 for the Nile River basin, six flood predictors were derived. The FNN and LSTM models exhibited high accuracy and stable loss, indicating minimal overfitting, while the CNN showed slight overfitting. Performance metrics revealed that FNN achieved 99.63% accuracy and 0.999886 ROC AUC, CNN had 95.42% accuracy and 0.893218 ROC AUC, and LSTM excelled with 99.82% accuracy and 0.999967 ROC AUC. The DNNE outperformed individual models in reliability and consistency. Runoff and rainfall were the most influential predictors, while temperature had minimal impact. [ABSTRACT FROM AUTHOR]

Sediment trapping significantly influences the comprehensive benefits of reservoirs and hinders the connection between sediment and nutrients in the upstream and downstream of rivers. Quantifying the role of sediment trapping by dams is important because this operation controls fluvial geomorphology, aquatic ecology, and water quality, particularly for flood processes with highly variable inflows. This study developed a new model to calculate the sediment trap efficiency (TE) of flood events during the flood season in the Three Gorges Reservoir (TGR) using a generalized theoretical approach. The TE of 55 flood events that occurred since the impoundment of the TGR were calculated and ranged from 16.0% to 99.8%, with a mean of 77.8%. Contrary to the previous understanding that large reservoirs have consistently large TE values based on long‐term data, our results show that large reservoirs can have a wide range of TE values during short‐term flood events. The proposed model estimates TE using three variables: inflow discharge, outflow discharge, and reservoir water level. Furthermore, the additional sedimentation risk to the TGR by optimized scheduling during the flood season is discussed. The results provide a reference for sedimentation management and optimal operation in the TGR. Key Points: A new framework to estimate reservoir sediment trapping efficiency (TE) was introducedThe Three Gorges Reservoir (TGR) has a wide TE range during short‐term flood eventsThe operation mode of "low level during large inflow, high level during small inflow " can be adopted during the flood season to improve comprehensive benefits of the TGR [ABSTRACT FROM AUTHOR]

The increase in flood events observed in West African countries, and often in specific river basins, can be influenced by several factors, including anthropogenic land use and land-cover changes. However, the potential contribution of land cover changes to flood events still needs to be explored, especially in West Africa. Here, the fully coupled atmosphere-hydrology WRF-Hydro system, which comprises an atmospheric model and additionally incorporates the surface, subsurface, overland flow, and channel routing, is used to investigate the potential impact of a land cover change scenario on flood events in the Senegal River basin. The simulation was performed from 2010 to 2020, with a calibration period spanning from 2011 to 2012 and a validation period from 2013 to 2020. Several skill scores, including Nash-Sutcliffe Efficiency (NSE), BIAS, and Kling-Gupta Efficiency (KGE), were utilized to assess the calibration and validation performances. Additionally, two planetary boundary layer schemes (PBL5 and PBL7) were used to determine their associated uncertainty. Our results show that the best calibration results (NSE = 0.70; KGE = 0.83; PBIAS = -7% and BE = 0.67) in the Senegal River basin are obtained with PBL5 when the calibration is performed with a SLOPE parameter 0.03. A similar good performance was also obtained for the validation with NSE = 0.74, KGE = 0.84, and PBIAS = -8%. Likewise, our findings indicate that converting savanna to woody savannas can elevate water resources, with a 2% rise in precipitation and a 4% increase in runoff. This transition also correlates with an increase in moderate flood events (3500-4000 m3/s), a decrease in severe floods (4000-5000 m3/s), and their associated occurrence of extreme floods (>5000 m3/s) in the Senegal River basin. [ABSTRACT FROM AUTHOR]

The current study seeks to investigate the temporal and spatial distribution of flood events and casualties in the Yamuna River basin from 1978 to 2014. The data were compiled from the published reports on disastrous weather events and analyzed with simple quantitative methods (sums, averages, and percentages). The analysis revealed the occurrence of a total of 1886 flood events, which resulted in 5481 casualties (148 per year). The years 1995, 2003, 1993, and 1996 were observed as the most devastating years, contributing about 30 percent of the total flood events, while 1978, 1980, 1981, 1995, 2006, and 2013 were the deadliest years in terms of casualties (47 percent). The largest number of flood events and casualties were recorded during June–September months. Considering the event/casualty density and rates, the states of Himachal Pradesh and Uttarakhand experienced the highest event density (0.036 and 0.091) and casualty rates (2.1 and 1.9 persons). These findings will help in identifying and demarcating the worst flood-affected areas over the Yamuna River basin, which can subsequently help in reducing the flood risk and human casualties in these regions after initiating suitable flood mitigation measures. [ABSTRACT FROM AUTHOR]

This study used sedimentary archives obtained from a high-standing terrace along the coastal zone of the Coquimbo region to reconstruct past extreme events (floods and tsunamis). Using sedimentological (grain size), geochemical (XRF), and geochronological (14C and 137Cs) analyses, we identified nine flood deposits (1957, 1965, 1984, 1987, 1991, 1997, 2002, 2015, and 2017) and one tsunami deposit (1922). The sedimentary flood record appears complete for the last 70 years and is useful for reconstructing paleoflood events, highlighting the relevance of coastal high-standing terraces to improve our understanding of the frequency of floods in Chilean coastal areas. [ABSTRACT FROM AUTHOR]

In late December of 2022 and the first half of January 2023, an unprecedented series of atmospheric rivers (ARs) produced near-record heavy rains and flooding over much of California. Here, we employ the NASA GEOS AGCM run in a "replay" mode, together with more idealized simulations with a stationary wave model, to identify the remote forcing regions, mechanisms, and underlying predictability of this flooding event. In particular, the study addresses the underlying causes of a persistent positive Pacific–North American (PNA)-like circulation pattern that facilitated the development of the ARs. We show that the pattern developed in late December as a result of vorticity forcing in the North Pacific jet exit region. We further provide evidence that this vorticity forcing was the result of a chain of events initiated in mid-December with the development of a Rossby wave (as a result of forcing linked to the MJO) that propagated from the northern Indian Ocean into the North Pacific. As such, both the initiation of the event and the eventual development of the PNA depended critically on internally generated Rossby wave forcings, with the North Pacific jet playing a key role. This, combined with contemporaneous SST (La Niña) forcing that produced a circulation response in the AGCM that was essentially opposite to the positive PNA, underscores the fundamental lack of predictability of the event at seasonal time scales. Forecasts produced with the GEOS-coupled model suggest that useful skill in predicting the PNA and extreme precipitation over California was in fact limited to lead times shorter than about 3 weeks. [ABSTRACT FROM AUTHOR]

While many studies have examined intense rainfall and flash flooding during the North American monsoon (NAM) in Arizona, Nevada, and New Mexico, less attention has focused on the NAM's extension into southwestern Utah. This study relates flash flood reports and Multi-Radar Multi-Sensor (MRMS) precipitation across southwestern Utah to atmospheric moisture content and instability analyses and forecasts from the High-Resolution Rapid Refresh (HRRR) model during the 2021–23 monsoon seasons. MRMS quantitative precipitation estimates over southwestern Utah during the summer depend largely on the areal coverage from the KICX WSR-88D radar near Cedar City, Utah. Those estimates are generally consistent with the limited number of precipitation gauge reports in the region except at extended distances from the radar. A strong relationship is evident between days with widespread precipitation and afternoons with above-average precipitable water (PWAT) and convective available potential energy (CAPE) estimated from HRRR analyses across the region. Time-lagged ensembles of HRRR forecasts (initialization times from 0300 to 0600 UTC) that are 13–18 h prior to the afternoon period when convection is initiating (1800–2100 UTC) are useful for situational awareness of widespread precipitation events after adjusting for underprediction of afternoon CAPE. Improved skill is possible using random forest classification relying only on PWAT and CAPE to predict days experiencing excessive (upper quartile) precipitation. Such HRRR predictions may be useful for forecasters at the Salt Lake City National Weather Service Forecast Office to assist in issuing flash flood potential statements for visitors to national parks and other recreational areas in the region. Significance Statement: Summer flash floods in southwestern Utah are a risk to area residents and millions of visitors annually to the region's national parks, monuments, and recreational areas. The likelihood of flash floods within the region's catchments depends on the intense afternoon and early evening convection initiated by lift and instability primarily due to terrain–flow interactions over elevated plateaus and mountains. Forecasts at lead times of 13–18 h of moisture and instability from the operational High-Resolution Rapid Refresh model have the potential to predict summer afternoons that are likely to have increased risks for higher rainfall amounts across southwestern Utah, although they are not expected to predict the likelihood of flash floods in any specific locale. [ABSTRACT FROM AUTHOR]

This paper develops the concept of flood problem framing to understand decision-makers’ priorities in flood risk management in the Los Angeles Metropolitan Region in California (LA Metro). Problem frames shape an individual’s preferences for particular management strategies and their future behaviors. While flooding is a complex, multifaceted problem, with multiple causes and multiple impacts, a decision-maker is most likely to manage only those dimensions of flooding about which they are aware or concerned. To evaluate flood decision-makers’ primary concerns related to flood exposure, vulnerability, and management in the LA Metro, we draw on focus groups with flood control districts, city planners, nonprofit organizations, and other flood-related decision-makers. We identify numerous concerns, including concerns about specific types of floods (e.g., fluvial vs pluvial) and impacts to diverse infrastructure and communities. Our analyses demonstrate that flood concerns aggregate into three problem frames: one concerned with large fluvial floods exacerbated by climate change and their housing, economic, and infrastructure impacts; one concerned with pluvial nuisance flooding, pollution, and historic underinvestment in communities; and one concerned with coastal and fluvial flooding’s ecosystem impacts. While each individual typically articulated concerns that overlapped with only one problem frame, each problem frame was discussed by numerous organization types, suggesting low barriers to cross-organizational coordination in flood planning and response. This paper also advances our understanding of flood risk perception in a region that does not face frequent large floods.

Tidal flats provide crucial ecosystem services and coastal protection. Comprehending the evolution of tidal flats, influenced by tidal bores and runoff, is key to protecting these environments in the Qiantang Estuary′s tidal bore zones. Based on 151 observations of the Jianshan mudflat terrain, taken approximately every two days, the research explored the mudflat′s evolution. Utilizing water level, suspended sediment concentration, river discharge, and remote sensing data, the study further investigated the driving mechanisms behind this evolution. The results are as follows: ① The Jianshan mudflat maintained a dynamic equilibrium. In the observation area, the dry- season deposition rate was 0. 01 m / d, with the slope stabilizing at 1‰. During the flood season, the mudflat bank retreat rate reached 3 m / d, causing a retreat width of 113 m. After the flood season, the mudflat recovered at a rate of 0. 05 m / d. ② During the flood season, the intensity of tidal bores slightly increased. Flood events lead to a bifurcated river regime, directing the main sediment transport of tidal bores through the southern channel, thereby reducing sediment delivery to the tidal flats in the northern channel. ③ Qiantang River runoff was the key factor controlling seasonal erosion and deposition changes. The evolution rate of the tidal flats showed a multivariate linear relationship with river runoff, initial elevation, and tidal range. ④ Floods altered the river regime, reducing sediment supply to the tidal flats. Runoff and ebbing tides combined to cause bank erosion and retreat. However, when water erosion decreased and sediment transport by tidal bores and rising tides became dominant, the tidal flat rapidly recovered. [ABSTRACT FROM AUTHOR]

In coastal regions, compound flooding, driven by multiple flood hazard sources, can cause greater damage than when the flood drivers occur in isolation. This study focuses on compound flooding from extreme precipitation and storm surge in China's Qiantang Estuary. We quantify the potential of compound flooding by measuring bivariate joint statistical dependence and joint return period (JRP). We find a significant positive dependence between the two flood drivers considered, as indicated by Kendall's rank correlation coefficients. Compound events occur frequently, with an average of 2.65 events per year from 1979 to 2018, highlighting the significant concern of compound flooding for this estuary. Using a copula model, we demonstrate that considering the dependence between the two flood drivers shortens the JRP of compound flooding compared to the JRP assuming total independence. For a 1-in-10-yr precipitation event and 1-in-10-yr storm surge event, the JRP is 1 in 100 years when assuming total independence. However, it decreases to 1 in 32.44 years when considering their dependence. Ignoring the dependence between flood drivers can lead to an increase in the JRP of compound events, resulting in an underestimation of the overall flood risk. Our analysis reveals a strong link between the weather patterns creating compound events and extreme storm surge only events with tropical cyclone activity. Additionally, the extreme precipitation only events were found to be connected with the frontal system of the East Asian summer monsoon. This study highlights the importance of considering the dependence between multiple flood drivers associated with certain types of the same weather systems when assessing the flood risk in coastal regions. [ABSTRACT FROM AUTHOR]

The increase in flood events observed in West African countries, and often in specific river basins, can be influenced by several factors, including anthropogenic land use and land-cover changes. However, the potential contribution of land cover changes to flood events still needs to be explored, especially in West Africa. Here, the fully coupled atmosphere-hydrology WRF-Hydro system, which comprises an atmospheric model and additionally incorporates the surface, subsurface, overland flow, and channel routing, is used to investigate the potential impact of a land cover change scenario on flood events in the Senegal River basin. The simulation was performed from 2010 to 2020, with a calibration period spanning from 2011 to 2012 and a validation period from 2013 to 2020. Several skill scores, including Nash-Sutcliffe Efficiency (NSE), BIAS, and Kling-Gupta Efficiency (KGE), were utilized to assess the calibration and validation performances. Additionally, two planetary boundary layer schemes (PBL5 and PBL7) were used to determine their associated uncertainty. Our results show that the best calibration results (NSE = 0.70; KGE = 0.83; PBIAS = −7% and BE = 0.67) in the Senegal River basin are obtained with PBL5 when the calibration is performed with a SLOPE parameter 0.03. A similar good performance was also obtained for the validation with NSE = 0.74, KGE = 0.84, and PBIAS = −8%. Likewise, our findings indicate that converting savanna to woody savannas can elevate water resources, with a 2% rise in precipitation and a 4% increase in runoff. This transition also correlates with an increase in moderate flood events (3500–4000 m3/s), a decrease in severe floods (4000–5000 m3/s), and their associated occurrence of extreme floods (>5000 m3/s) in the Senegal River basin.

The Canadian province of British Columbia (BC) is subjected to large-scale, destructive floods. The most dramatic was a mid-November 2021 event when atmospheric rivers (ARs) linked to high-intensity storms caused heavy rainfall in southwestern BC, triggering catastrophic flooding. This study examines 37 floods from 2000 to 2021 using information from over 250 climatological stations and compares events with the mid-November 2021 flood. The dates of the floods showed a bi-modal pattern: a primary season (spring to early summer, 16 floods) and a secondary season (fall to early winter, 21 floods). Five mechanisms controlled these floods: heavy rainfall, rapid snowmelt, severe ice jam, rain-on-snow, and a mixture of snowmelt and ice jam; the mid-November 2021 flood was mainly driven by heavy rainfall. Of the 37 floods, those affected by either heavy rainfall (18 floods) or rain-on-snow (10 floods) were used to derive a relationship between the average daily precipitation amount over the duration of an event and the associated integrated water vapour transport IVT¯. Flood events showed a strong linear relationship between these variables with R2 ≥0.85, p 90% of the others, although they were not the highest. The mid-November 2021 flood was also one of the four rainfall-related floods that occurred in the secondary season with IVT‾ > 400 kg m−1 s−1. The frequency of flood events over the last five years of the study period has slightly decreased when considering flood events with unknown insured cost. In contrast, insured costs of these events have increased, suggesting that present-day floods are becoming more impactful and may require changes to flood management strategies to reduce costs.

Given the changing climate, understanding the recent variability in large-scale rainfall patterns is a crucial task in order to better understand the underlying hydrological processes that occur within a watershed. This study aims to investigate how rainfall events in western Japan have changed due to climate change and how these changes have affected runoff–turbidity dynamics during the rainy season. To address the research objectives, we analyzed two decades of precipitation records in the Gōno River watershed and examined the associated runoff–turbidity dynamics during floods using turbidity–discharge (T-Q) loops, quantified using an enhanced hysteresis index. Our findings revealed a kind of intense rainfall event occurring every 3 to 4 years. Additionally, spatial pentad analysis showed varying intensities of accumulated precipitation, indicating that extreme rainfall is not confined to a specific spatial zone. Regarding turbidity–discharge behavior, we found that clockwise hysteresis patterns were caused by sediment sources from near-channel areas, while anticlockwise patterns were caused by soil erosion from nearby areas. Another notable finding was that turbidity peaks during floods may represent the earlier (or later) arrival of turbid water from distant upstream sources due to intense precipitation. One of the key challenges in quantifying hysteresis patterns is that there is no agreed-upon definition for how to determine the start and end of a flood event. This can lead to bias in the quantification of these patterns.

On 1–3 May 2023, severe hydro-meteorological events occurred in the Italian Emilia-Romagna region. Such events caused extensive flooding, landslides, isolation of many areas, evacuation of many families, and severe damage to infrastructure, agriculture, buildings, and essential services. Several municipalities were affected, thousands of civilians had to be evacuated, and losses of life occurred. The consequences beyond the recorded immediate impacts on infrastructure and life were impressive, and extended to the regional economy, specifically in the Fruit Valley, where, in addition to immediate yield losses, long-term damage to orchard production is expected due to persistent flooding. The civil and cultural building heritage has also been heavily affected, both in the countryside and in inhabited centers. Some of the damage, direct and indirect, caused by flooding on buildings will also see an evolution in the medium- to long-term that needs to be addressed. This paper analyzes the manifold aspects of such an atmospheric phenomenon and its impacts to understand the potential increasing occurrence of similar events in the climate change context.

The understanding and identification of peak streamflow events are important for flood risk and water resources management. Precipitation products based on the radar and satellite are an alternative or supplementary product to the areas where ground-based rain gauge station is unavailable, limited in number or sparsely located. The objectives of this study are to (1) assess the performance of TRMM_3B42 product for selected seven flood events by use of statistical and categorical matrices, and identification of elevation-related trends in them and (2) drive the HEC-HMS model by use of bias-corrected TRMM_3B42 data to reproduce the flood hydrograph in Banas River Basin, semi-arid region, Rajasthan, India. The bias-corrected TRMM_3B42 data have improved the overall accuracy of hydrologic simulation as compared to ground-based rain gauge station data. The HEC-HMS results revealed that bias-corrected TRMM_3B42 data can simulate the streamflow with less than 30% deviation in the prediction of peak discharge and flood volume and show an NSE and R2 value of 0.90 and 0.95, respectively. The bias-corrected TRMM_3B42 data are found to be excellent to reproduce the flood hydrograph at a daily temporal scale. [ABSTRACT FROM AUTHOR]

Given the changing climate, understanding the recent variability in large-scale rainfall patterns is a crucial task in order to better understand the underlying hydrological processes that occur within a watershed. This study aims to investigate how rainfall events in western Japan have changed due to climate change and how these changes have affected runoff–turbidity dynamics during the rainy season. To address the research objectives, we analyzed two decades of precipitation records in the Gōno River watershed and examined the associated runoff–turbidity dynamics during floods using turbidity–discharge (T-Q) loops, quantified using an enhanced hysteresis index. Our findings revealed a kind of intense rainfall event occurring every 3 to 4 years. Additionally, spatial pentad analysis showed varying intensities of accumulated precipitation, indicating that extreme rainfall is not confined to a specific spatial zone. Regarding turbidity–discharge behavior, we found that clockwise hysteresis patterns were caused by sediment sources from near-channel areas, while anticlockwise patterns were caused by soil erosion from nearby areas. Another notable finding was that turbidity peaks during floods may represent the earlier (or later) arrival of turbid water from distant upstream sources due to intense precipitation. One of the key challenges in quantifying hysteresis patterns is that there is no agreed-upon definition for how to determine the start and end of a flood event. This can lead to bias in the quantification of these patterns. [ABSTRACT FROM AUTHOR]

Extreme hydrological events (including droughts and floods) produce severe social and economic impacts. Monitoring hydrological processes from remote sensing is necessary to improve understanding and preparedness for these events, with current missions focusing on a range of hydrological variables (i.e., SWOT, SMAP, and GRACE). This study uses output from three state-of-the-art land surface assimilation models and an event clustering algorithm to identify the characteristic spatial and temporal scales of large-scale extreme dry and wet events in the contiguous United States for three major hydrological processes: precipitation, runoff, and soil moisture. We also examine the sensitivity of extreme event characteristics to model resolution and assess intermodel differences. We find that models generally agree in terms of the mean characteristics of events: precipitation dry events are of shorter duration in comparison with soil moisture and runoff events, and more intense events tend to be smaller in area. We also find that mean spatial and temporal characteristics are highly dependent on model resolution—important in the context of detecting and monitoring these events. Results from this study can be used to inform land surface model development, extreme hydrology event detection, and sampling requirements of upcoming remote sensing missions in hydrology. Significance Statement: Understanding the fundamental characteristics of dry and wet extreme events (droughts and floods) is of importance for improving our preparedness and response to events, as well as for designing satellite observing systems that can adequately monitor them. Here we use output from land surface models to determine the average size and duration of large-scale extreme events for the contiguous United States using fine temporal data. We find that events that are most extreme—the most severe floods and droughts—tend to be shorter in duration and smaller in size. We also present an assessment of how three commonly used land surface models detect extreme hydrological events, which is important for assessments based on models. These findings are important for understanding the proportion of events that may be not adequately resolved by current hydrology remote sensing missions. [ABSTRACT FROM AUTHOR]

Fluvial flooding is a major cause of death and damages from tropical cyclones (TCs), so it is important to understand the predictability of river flooding in TC cases, and the potential of global ensemble flood forecast systems to inform warning and preparedness activities. This paper demonstrates a methodology using ensemble forecasts to follow predictability and uncertainty through the forecast chain in the Global Flood Awareness System (GloFAS) to explore the connections between the skill of the TC track, intensity, precipitation, and river discharge forecasts. Using the case of Hurricane Iota, which brought severe flooding to Central America in November 2020, we assess the performance of each ensemble member at each stage of the forecast, along with the overall spread and change between forecast runs, and analyze the connections between each forecast component. Strong relationships are found between track, precipitation, and river discharge skill. Changes in TC intensity skill only result in significant improvements in discharge skill in river catchments close to the landfall location that are impacted by the heavy rains around the eyewall. The rainfall from the wider storm circulation is crucial to flood impacts in most of the affected river basins, with a stronger relationship with the post-landfall track error rather than the precise landfall location. We recommend the wider application of this technique in TC cases to investigate how this cascade of predictability varies with different forecast and geographical contexts in order to help inform flood early warning in TCs. Significance Statement: This study demonstrates a methodology to analyze the cascade of predictability and uncertainty through the various stages of the tropical cyclone (TC) flood forecasting chain, illustrating how it can provide useful information to modelers interested in optimizing flood forecast skill, and to those who prepare and communicate flood forecasts with stakeholders and end-users in TC cases. The results highlight the importance of improving verification of ensemble TC precipitation forecasts, and of focusing on more than just the category of the storm and landfall location when forecasting and communicating flood impacts in TC cases. [ABSTRACT FROM AUTHOR]

Reservoir sedimentation leads to a loss of storage capacity, thereby affecting the ability of a reservoir to fulfil its designated purpose. Accordingly, it is important to investigate the regularity of sediment delivery to constrain reservoir sedimentation. On the basis of data recorded at Zhutuo, Beibei, Wulong, Cuntan, and other hydrological stations between 2003 and 2021, individual flood events in the Three Gorges Reservoir (TGR) were identified. The regularity of sediment delivery during each flood event was then analyzed. The impacts of factors such as the water level in front of the dam, the rate of sediment transport into and out of the reservoir, and flood detention time on sediment delivery during flood events were investigated. The results indicate that it is more reasonable to classify the flood events in the TGR by considering the asynchronous propagation of flood and sediment peaks on the basis of a unified quantitative standard. Sediment delivery in the TGR occurred mainly during flood events, accounting for 67. 0% of the annual sediment delivery volume. The sediment delivery ratio ( SDR) during the flood events was inversely proportional to the water level in front of the dam and proportional to the sediment transport rate into and out of the reservoir. However, the correlations were weaker than that between the SDR and flood detention time, whereby the SDR was inversely proportional to the flood detention time. The correlations between the SDR and other factors were strongest during flood events when the average inflow was greater than 30 000 m³ / s or the average water level in front of the dam was less than 150 m. This study provides an important reference for optimizing the operation of the TGR and reducing sediment deposition. [ABSTRACT FROM AUTHOR]

The extent of flooding in China is more significant than in any other country. Our research reveals that approximately 66 % of China’s landmass is submerged by flooding, affecting about 50 % of the population. Furthermore, the financial toll of flooding now accounts for approximately 1.42 % of the annual gross domestic product (GDP), which is almost 40 times higher than the corresponding figure for the United States. We have observed that Zhengzhou city in Henan province, which faced a devastating flood in 2021, received a significant amount of rainfall, specifically a total of 552.5 mm within a 24-hour period. The floods in Henan province in 2021 caused considerable damage, including impacting nearly 15 million people, resulting in almost 400 deaths, damaging over 10,000 square kilometers of agricultural land, causing almost $19 billion in economic losses, and leading to the collapse of over 35,000 households and damage to various properties. In a similar manner, the significant flooding that occurred in southern China in 2020 impacted approximately 7.1 million individuals across eight provinces and resulted in 54 fatalities, the collapse of 6,700 houses, and incurred a direct economic loss of US$3.33 billion. We found that rainstorms have significantly increased to 10 % in the last 60 years in China. In this paper, we delved into exploring the existing published articles, reports, and government and authoritative legal texts to analyze the causes and impacts of flooding in China’s flood-prone regions and potential mitigation strategies to reduce the repercussions of distressing flood events. We believe this study will help policymakers by providing new insights while formulating policy to reduce the loss caused by high-level flooding.

Study region: The Xiying and Heihe River basins, cold and arid mountainous regions of northwestern China. Study focus: In data-limited areas of Northwestern China, satellite- and reanalysis-based precipitation products provide crucial assistance. However, a comprehensive evaluation of their advantages, limitations, and hydrological performance has yet to be ascertained. This knowledge gap impedes a deeper understanding of hydrological processes within the region. This study evaluates the accuracy of ERA5L, IMERG-F, GSMaP-G, and FY4A-QPE from three aspects: time, space, and extreme precipitation. Comprehensive validation was conducted on four products using discharge, flood events, and peak flood simulated by Geomorphology-Ecology-Hydrology Model. New hydrological insights for the region: ERA5L data can be used to build long-term studies on regional meteorological and hydrological trends. IMERG-F is valuable for analyzing extreme precipitation and hydrological events. GSMaP-G is well-suited for identifying precipitation events in the region. It is essential to be cautious of precipitation underestimation when using satellite precipitation products at high elevations and during the winter.This study highlights the differences in accuracy among various precipitation products and provides recommendations for policymakers in selecting data for high-altitude research. It also offers critical information on hydrometeorology and directions for producing high-accuracy precipitation products.

Severe floods and droughts, including their back-to-back occurrences (weather whiplash), have been increasing in frequency and severity around the world. Improved understanding of systematic changes in hydrological extremes is essential for preparation and adaptation. In this study, we identified and quantified extreme wet and dry events globally by applying a clustering algorithm to terrestrial water storage (TWS) data from the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (FO). The most intense events, ranked using an intensity metric, often reflect impacts of large-scale oceanic oscillations such as El Niño–Southern Oscillation and consequences of climate change. The severity of both wet and dry events, represented by standardized TWS anomalies, increased significantly in most cases, likely associated with intensification of wet and dry weather regimes in a warmer world, and consequently, exhibited strongest correlation with global temperature. In the Dry climate, the number of wet events decreased while the number of dry events increased significantly, suggesting a drying trend that may be attributed to climate variability and possible increases in irrigation and reliance on groundwater. In the Continental climate where temperature has risen faster than global average, dry events increased significantly. Characteristics of extreme events often showed strong correlations with global temperature, especially when averaged over all climates. These results suggest changes in hydrological extremes and underscore the importance of quantifying total water storage changes when studying hydrological extremes. Extending the GRACE/FO record, which spans 2002 to the present, is essential to continuously tracking changes in TWS and hydrological extremes. [ABSTRACT FROM AUTHOR]

This article explores the application of thermodynamic perturbations to a historical midlatitude, wintertime, rain-on-snow flood event to evaluate how similar events may evolve under different climate forcings. In particular, we generate a hindcast of the 1996 Mid-Atlantic flood using an ensemble of 14-km variable-resolution simulations completed with the U.S. Department of Energy's global Energy Exascale Earth System Model (E3SM). We show that the event is skillfully reproduced over the Susquehanna River Basin (SRB) by E3SM when benchmarked against in situ observational data and high-resolution reanalyses. In addition, we perform five counterfactual experiments to simulate the flood under preindustrial conditions and four different levels of warming as projected by the Community Earth System Model Large Ensemble. We find a nonlinear response in simulated surface runoff and streamflow as a function of atmospheric warming. This is attributed to changing contributions of liquid water input from a shallower initial snowpack (decreased snowmelt), increased surface temperatures and rainfall rates, and increased soil water storage. Flooding associated with this event peaks from around +1 to +2 K of global average surface warming and decreases with additional warming beyond this. There are noticeable timing shifts in peak runoff and streamflow associated with changes in the flashiness of the event. This work highlights the utility of using storyline approaches for communicating climate risk and demonstrates the potential nonlinearities associated with hydrologic extremes in areas that experience ephemeral snowpack, such as the SRB. [ABSTRACT FROM AUTHOR]

Recent severe droughts, extreme floods, and increasing differences between seasonal high and low flows on the Amazon River may represent a twenty-first-century increase in the amplitude of the hydrologic cycle over the Amazon Basin. These precipitation and streamflow changes may have arisen from natural ocean-atmospheric variability, deforestation within the drainage basin of the Amazon River, or anthropogenic climate change. Tree-ring reconstructions of wetseason precipitation extremes, substantiated with historical accounts of climate and river levels on the Amazon River and in northeast Brazil found in the Brazilian Digital Library, indicate that the recent river-level extremes on the Amazon may have been equaled or possibly exceeded during the preinstrumental nineteenth century. The "Forgotten Drought" of 1865 was the lowest wet-season rainfall total reconstructed with tree-rings in the eastern Amazon from 1790 to 2016 and appears to have been one of the lowest stream levels observed on the Amazon River during the historical era according to firsthand descriptions by Louis Agassiz, his Brazilian colleague Joao Martins da Silva Coutinho, and others. Heavy rains and flooding are described during most of the tree-ring-reconstructed wet extremes, including the complete inundation of "First Street" in Santarem, Brazil, in 1859 and the overtopping of the Bittencourt Bridge in Manaus, Brazil, in 1892. These extremes in the tree-ring estimates and historical observations indicate that recent high and low flow anomalies on the Amazon River may not have exceeded the natural variability of precipitation and streamflow during the nineteenth century. [ABSTRACT FROM AUTHOR]

It is crucial for effective flood events management in a river basin that the relationship between land use, land cover (LULC) changes and peak flow. The flood in Central Vietnam got highly affected in recent decades due to several factors like deforestation, urbanization, lack of hydrometeorological stations, developmental monitoring and planning. This study quantifies the influence of LULC on the peak flow dynamics using a hydrological model, remote sensing technology in the Laigiang river basin. The responses of LULC changes on the peak flow and runoff volume were investigated using daily rainfall in December 2016 at the study area. The usage of the hydrological model defined that the changes in LULCs 2010-2020 caused momentous changes in hydrological response towards water flow. The rainy season floods in the bare land, low-density vegetation area, urban/built-up, and bare land while the normal flow gets the contribution from interflow generated from the dense forest. [ABSTRACT FROM AUTHOR]

Predicting winter flooding is critical to protecting people and securing water resources in California's Sierra Nevada. Rain-on-snow (ROS) events are a common cause of widespread flooding and are expected to increase in both frequency and magnitude with anthropogenic climate change in this region. ROS flood severity depends on terrestrial water input (TWI), the sum of rain and snowmelt that reaches the land surface. However, an incomplete understanding of the processes that control the flow and refreezing of liquid water in the snowpack limits flood prediction by operational and research models. We examine how antecedent snowpack conditions alter TWI during 71 ROS events between water years 1981 and 2019. Observations across a 500-m elevation gradient from the Independence Creek catchment were input into SNOWPACK, a one-dimensional, physically based snow model, initiated with the Richards equation and calibrated with collocated snow pillow observations. We compare observed "historical" and "scenario" ROS events, where we hold meteorologic conditions constant but vary snowpack conditions. Snowpack variables include cold content, snow density, liquid water content, and snow water equivalent. Results indicate that historical events with TWI > rain are associated with the largest observed streamflows. A multiple linear regression analysis of scenario events suggests that TWI is sensitive to interactions between snow density and cold content, with denser (>0.30 g cm−3) and colder (<−0.3 MJ of cold content) snowpacks retaining >50 mm of TWI. These results highlight the importance of hydraulic limitations in dense snowpacks and energy limitations in warm snowpacks for retaining liquid water that would otherwise be available as TWI for flooding. Significance Statement: The purpose of this study is to understand how the snowpack modulates quantities of water that reach the land surface during rain-on-snow (ROS) events. While the amount of near-term storm rainfall is reasonably predicted by meteorologists, major floods associated with ROS are more difficult to predict and are expected to increase in frequency. Our key findings are that liquid water inputs to the land surface vary with snowpack characteristics, and although many hydrologic models incorporate snowpack cold content and density to some degree, the complexity of ROS events justifies the need for additional observations to improve operational forecasting model results. Our findings suggest additional comparisons between existing forecasting models and those that physically represent the snowpack, as well as field-based observations of cold content and density and liquid water content, would be useful follow-up investigations. [ABSTRACT FROM AUTHOR]

Climate change has forced the world into a state of emergency, but the urgency can also become an opportunity to strengthen the focus on sustainable development and reduce social vulnerability. For developing economies, the first and foremost challenge regarding climate change is to address the knowledge gap on sustainable development and vulnerability. Besides this, evidence-based inputs are needed for the policies and programs that intend to enhance the adaptive capacity and social capital from the gender perspective in comparatively more disaster-prone districts of the country. The environmental impact in terms of socioeconomic conditions specifically pertaining to rural areas of Pakistan cannot be ignored. Natural events such as floods and droughts have raised the question of the social and socioeconomic vulnerability of the rural communities. This paper is an attempt toward understanding that everyone who is affected will be impacted differently by climate change both within the same gender and between different genders, including gender minorities. In addition, an attempt is made to identify the drivers of gender-disaggregated social vulnerability in selected disaster-prone rural communities of the district of Dadu, Sindh Province, Pakistan. Both quantitative and qualitative techniques are employed to examine the differences in gender perception on climate change, experiences related to climate change, disasters, and impacts on their lives. Women and households headed by them are found to be relatively more vulnerable due to their socioeconomic and social status in the rural areas of Pakistan. The paper gives policy directives that not only address the measures for reduction in climate change impacts but also suggest the development of effective disaster management programs, policies, and strategies. [ABSTRACT FROM AUTHOR]

AbstractIn the recent decades, mass casualties and property losses are caused by flood events. A combination of satellite and hydrological methods has been promoted owing to the advantages of satellite precipitation products (SPPs), such as their wide coverage and high spatiotemporal resolution; these benefits may make up for the deficiencies of gauge data and make SPPs a good supplementary data source, especially for sparsely gauged areas. The present study employs the double-kernel smoothing technique (DS) to integrate TRMM precipitation data and gauge data and evaluates the performance of this method in identifying extreme flood events via the Hydrological Engineering Center-Hydrological Modeling System (HEC-HMS) in the humid Xiangjiang River Basin, China. Results show that the HEC-HMS driven by TRMM precipitation data can capture the 9 selected flood events accurately both before and after the merging process despite some deficiencies in the TRMM precipitation data. In addition, the merging method generally improved the consistency (CC increased from 0.04 to 0.24) and rainfall-detection capability of the TRMM precipitation data (FAR decreased by 9.12%, POD increased by 56.91% and HSS increased by 15.43%) and also promoted the overall hydrological simulation accuracy and reliability (the average CC increased from 0.86 to 0.96, the average NSE increased from 0.58 to 0.73). However, the blended TRMM precipitation data did not always outperform the nonblended data in terms of certain flood feature simulation details, such as the flood volume, flood peak and peak time.

Flooding events are determining a significant amount of damages, in terms of economic loss and also casualties in Asia and Pacific areas. Due to complexity and ferocity of severe flooding, predicting flood-prone areas is a difficult task. Thus, creating flood susceptibility maps at local level is though challenging but an inevitable task. In order to implement a flood management plan for the Balrampur district, an agricultural dominant landscape of India, and strengthen its resilience, flood susceptibility modeling and mapping are carried out. In the present study, three hybrid machine learning (ML) models, namely, fuzzy-ANN (artificial neural network), fuzzy-RBF (radial basis function), and fuzzy-SVM (support vector machine) with 12 topographic, hydrological, and other flood influencing factors were used to determine flood-susceptible zones. To ascertain the relationship between the occurrences and flood influencing factors, correlation attribute evaluation (CAE) and multicollinearity diagnostic tests were used. The predictive power of these models was validated and compared using a variety of statistical techniques, including Wilcoxon signed-rank, t-paired tests and receiver operating characteristic (ROC) curves. Results show that fuzzy-RBF model outperformed other hybrid ML models for modeling flood susceptibility, followed by fuzzy-ANN and fuzzy-SVM. Overall, these models have shown promise in identifying flood-prone areas in the basin and other basins around the world. The outcomes of the work would benefit policymakers and government bodies to capture the flood-affected areas for necessary planning, action, and implementation. [ABSTRACT FROM AUTHOR]

The 1931 Yangtze River flood in eastern China, which had an associated death toll of over 2 million, is regarded as one of the world's deadliest natural disasters on record. However, due to the lack of meteorological data before the 1950s, the causes of this event are rarely investigated. Here, we combine multiple lines of evidence from recently available historical observations, reanalysis datasets, and atmospheric general circulation model simulations driven by historical sea surface temperatures (SSTs) that cover the early twentieth century to reveal the physical mechanisms underlying this flood. We find that the flooding in 1931 along the Yangtze River valley was dominated by July rainfall. Although the rainfall totals in July 1931 were not the largest in history (ranking second over the past century), the totals exceeded those for many other pluvial years between 1951 and 2010 in terms of its persistence, which was associated with a steady western Pacific subtropical high (WPSH). The flooding resulted from the combined effects of tropical El Niño–related SST forcing and extratropical wave activities over the Eurasian continent. On the one hand, warm SST anomalies in the tropical Indian Ocean following an El Niño event led to the southwestward extension of the WPSH. On the other hand, the southward shift of the westerly jet due to extratropical wave activities prevented the normal northward movement of the WPSH typical in July. Additionally, the impact that the preceding springtime precipitation and soil moisture had on the summer flood of 1931 is also discussed. [ABSTRACT FROM AUTHOR]

In the summer of 2022, a long-lasting La Niña entered its third year. In Asia, southern China was in the grip of a historic drought while heavy rainfall ravaged Pakistan. Using a climate model forced by observed sea surface temperatures (SST) over the equatorial Pacific, we show that the back-to-back La Niña events from 2020 to 2022 are a key contributor to the global SST pattern in 2022, including the negative-phase Pacific decadal oscillation and exceptionally strong negative Indian Ocean dipole. The model reproduces the observed precipitation pattern over South and East Asia, including enhanced rainfall over Pakistan-northwest India and reduced rainfall over southern China. Additional model simulations indicate that the negative Indian Ocean dipole combined with La Niña reduces southern China rainfall by causing anomalous subsidence and anticyclonic flows. These results highlight the dominant role of long-lasting La Niña in modulating rainfall over heavily populated monsoon Asia. [ABSTRACT FROM AUTHOR]

Post-wildfire debris flows (PFDF) threaten life and property in western North America. They are triggered by short-duration, high-intensity rainfall. Following a wildfire, rainfall thresholds are developed that, if exceeded, indicate high likelihood of a PFDF. Existing weather forecast products allow forecasters to identify favorable atmospheric conditions for rainfall intensities that may exceed established thresholds at lead times needed for decision-making (e.g., =24 h). However, at these lead times, considerable uncertainty exists regarding rainfall intensity and whether the high-intensity rainfall will intersect the burn area. The approach of messaging on potential hazards given favorable conditions is generally effective in avoiding unanticipated PFDF impacts, but may lead to "messaging fatigue" if favorable triggering conditions are forecast numerous times, yet no PFDF occurs (i.e., false alarm). Forecasters and emergency managers need additional tools that increase their confidence regarding occurrence of short-duration, high-intensity rainfall as well as tools that tie rainfall forecasts to potential PFDF outcomes. We present a concept for probabilistic tools that evaluate PFDF hazards by coupling a high-resolution (1-km), large (100-member) ensemble 24-h precipitation forecast at 5-min resolution with PFDF likelihood and volume models. The observed 15-min maximum rainfall intensities are captured within the ensemble spread, though in highest ~10% of members. We visualize the model output in several ways to demonstrate most likely and most extreme outcomes and to characterize uncertainty. Our experiment highlights the benefits and limitations of this approach, and provides an initial step toward further developing situational awareness and impact-based decision-support tools for forecasting PFDF hazards. [ABSTRACT FROM AUTHOR]

Understanding the risks associated with the likelihood of extreme events and their respective consequences for the stability of hydraulic infrastructures is essential for flood forecasting and engineering design purposes. Accordingly, a hydrological methodology for providing reliable estimates of extreme discharge flows approaching hydraulic infrastructures was developed. It is composed of a preliminary assessment of missing data, quality and reliability for statistically assessing the frequency of flood flows, allied to parametric and non-parametric methods. Model and parameter uncertainties are accounted for by the introduced and proposed modified model averaging (modified MM) approach in the extreme hydrological event's prediction. An assessment of the parametric methods accuracy was performed by using the non-parametric Kernel Density Estimate (KDE) as a benchmark model. For demonstration and validity purposes, this methodology was applied to estimate the design floods approaching the case study 'new Hintze Ribeiro bridge', located in the Douro river, one of the three main rivers in Portugal, and having one of Europe's largest river flood flows. Given the obtained results, the modified MM is considered a better estimation method. [ABSTRACT FROM AUTHOR]

Tropical cyclones (TCs) are high-impact events responsible for devastating rainfall and freshwater flooding. Quantitative precipitation estimates (QPEs) are thus essential to better understand and assess TC impacts. QPEs based on different observing platforms (e.g., satellites, ground-based radars, and rain gauges), however, may vary substantially and must be systematically compared. The objectives of this study are to 1) compute the TC rainfall climatology, 2) investigate TC rainfall extremes and flooding potential, and 3) compare these fundamental quantities over the continental United States across a set of widely used QPE products. We examine five datasets over an 18-yr period (2002-19). The products include three satellite-based products, CPC morphing technique (CMORPH), Integrated Multi-satellitE Retrievals for GPM (IMERG), and Tropical Rainfall Measuring Mission--Multisatellite Precipitation Analysis (TRMM-TMPA); the ground-radar- and rain-gauge-based NCEP Stage IV; and a state-of-the-art, high-resolution reanalysis (ERA5). TC rainfall is highest along the coastal region, especially in North Carolina, northeast Florida, and in the New Orleans, Louisiana, and Houston, Texas, metropolitan areas. Along the East Coast, TCs can contribute up to 20% of the warm season rainfall and to more than 40% of all daily and 6-hourly extreme rain events. Our analysis shows that Stage IV detects far higher precipitation rates in landfalling TCs, relative to IMERG, CMORPH, TRMM, and ERA5. Satellite- and reanalysisbased QPEs underestimate both the TC rainfall climatology and extreme events, particularly in the coastal region. This uncertainty in QPEs is further reflected in the TC flooding potential measured by the extreme rainfall multiplier (ERM) values, whose single-cell maxima are substantially underestimated and misplaced by the satellite and reanalysis QPEs compared to that using NCEP Stage IV. [ABSTRACT FROM AUTHOR]

Extreme droughts and floods in the Amazon have great implications for ecosystems and societies. Over the last decade, the region has undergone major extreme events with no equivalent in the previous 100 years. Wetlands have been greatly impacted by these events. This study aims at presenting new indicators for wetlands based on water surface extent (WSE): duration of the flooded and nonflooded season, number of days of extreme events, delay of the start of the flooded season, and severity for each season. These indicators are more adapted for monitoring of wetlands than those based on precipitation, discharge, or groundwater information. They are computed for seven major Amazon subbasins for flooded and nonflooded seasons. These indicators improve our knowledge of the temporal behavior of the water surface during different extreme events, such as the 2015/16 drought and the 2014 flood that occurred in the Madeira basin. For the Negro basin and from the point of view of wetlands, the 2015 nonflooded season was 55% more severe than the average of the nonflooded season during the 2011-18 period. For the Paru, Trombetas, Negro, and Solimões basins, we found that a delay in the arrival of the flooded season led to a weak flood season in terms of severity. No correlation between the onset of the flooded season and its severity was found for the Madeira, Xingu, and Tapajós basins. Future hydrometeorological monitoring systems would benefit from including, in addition to variables such as river discharge and water elevation, precipitation, and vegetation dynamics, a severity index based on water surfaces as proposed in this study. [ABSTRACT FROM AUTHOR]

Floods have frequently and severely impacted Peninsular Malaysia's East Coast region in recent years, with Pahang being the worst hit during the 2021 flood event. Therefore, this study was carried out to determine the characteristics and patterns of previous flood events in two districts in Pahang, namely, Temerloh and Kuantan, and to investigate the effects of floods. Both quantitative and qualitative data from secondary sources are utilized in this study, where qualitative data from a literature review are used to clarify and interpret the findings of the quantitative data analysis of the DID annual flood report. According to the findings, floods occurred annually in both districts, excluding 2016, when Temerloh did not record any flood events. The flood depth reported ranged from 1 to 2 meters and lasted approximately a day. Furthermore, Pahang experienced both types of floods: monsoon and flash floods. Floods have forced locals to evacuate to flood relief to seek shelter, food, and aid supplies. Therefore, this study serves as a preliminary investigation of the flood events that occurred in Pahang. Understanding flood trends and their consequences is essential for a variety of purposes, including urban planning and flood management by stakeholders. [ABSTRACT FROM AUTHOR]

Colloquium overview and goals Evaluating and implementing FIRO to simultaneously increase water supply reliability and mitigate flood risk is of increasing interest at reservoirs throughout the western United States. FIRO is a reservoir-operations strategy that better informs decisions to retain or release water by integrating additional flexibility in operational policies and rules with enhanced monitoring and improved weather and water forecasts ([1]; [3]; [4]). Keywords: Atmospheric river; Precipitation; Numerical weather prediction/forecasting; Education; Flood events; Resilience EN Atmospheric river Precipitation Numerical weather prediction/forecasting Education Flood events Resilience E1372 E1381 10 09/27/23 20230801 NES 230801 Forecast-Informed Reservoir Operations (FIRO) Colloquium B I What i b : Training provided for students and early-career scientists at the first Forecast-Informed Reservoir Operations (FIRO) Colloquium B I When i b : 11-28 July 2022 B I Where i b : La Jolla, California In July 2022, the first Forecast-Informed Reservoir Operations (FIRO) Colloquium took place at Scripps Institution of Oceanography (SIO). The lectures on the first full day of the colloquium delivered background information on watersheds, managed river systems, reservoir operations, and the FIRO screening process. [Extracted from the article]

The flood season is the main period for runoff and sediment yield, and understanding the variations of runoff and sediment of flood events is of great significance for distinguishing the runoff-sediment processes in the Loess Plateau. In this study, we analyzed the variations of runoff and sediment at the flood event scale in the Qiaogou catchment and investigated the influencing factors. The results showed that runoff and sediment of flood events were mainly produced by rainfall with short rainfall duration and heavy rainfall intensity in the study area. Based on the 28 flood events and corresponding precipitation data from the reference period (P-I, 1986–1989) and the revegetation period (P-II, 2001–2009), we analyzed the variations of runoff-sediment processes at the flood event scale in the Qiaogou catchment, a typical catchment of the Loess Plateau. The results showed that the flood variables were lower in the revegetation period than those in the reference period, except for the flood peak discharge time and the flood duration. The sediment transport capacity per unit runoff depth in the revegetation period was weaker than that in the reference period. The hysteresis analysis indicated that the patterns of the hysteretic loop were dominated by the figure-of-eight pattern and the compound pattern, respectively, in the reference period and revegetation period. Compared to the reference period, runoff and sediment-related variables for flood events of counter-clockwise, figure-of-eight, and compound patterns were less in the revegetation period. With similar rainfall conditions, the main reason for the noticeable difference in runoff and sediment of flood events for the two periods was the variations in vegetation cover. The mentioned results indicated that revegetation performed a critical function in the variations of runoff and sediment at the flood event scale. This study revealed the variations of runoff-sediment processes of flood events and their responses to rainfall and revegetation in a typical catchment of the Loess Plateau, which can provide a basis for decision-making on soil erosion management and sustainable development of the ecological environment in the Loess Plateau. [ABSTRACT FROM AUTHOR]

Many factors shape public perceptions of extreme weather risk; understanding these factors is important to encourage preparedness. This article describes a novel workshop designed to encourage individual and community decision-making about predicted storm surge flooding. Over 160 U.S. college students participated in this 4-h experience. Distinctive features included 1) two kinds of visualizations, standard weather forecasting graphics versus 3D computer graphics visualization; 2) narrative about a fictitious storm, role-play, and guided discussion of participants' concerns; and 3) use of an "ethical matrix," a collective decision-making tool that elicits diverse perspectives based on the lived experiences of diverse stakeholders. Participants experienced a narrative about a hurricane with potential for devastating storm surge flooding on a fictitious coastal college campus. They answered survey questions before, at key points during, and after the narrative, interspersed with forecasts leading to predicted storm landfall. During facilitated breakout groups, participants role-played characters and filled out an ethical matrix. Discussing the matrix encouraged consideration of circumstances impacting evacuation decisions. Participants' comments suggest several components may have influenced perceptions of personal risk, risks to others, the importance of monitoring weather, and preparing for emergencies. Surprisingly, no differences between the standard forecast graphics versus the immersive, hyperlocal visualizations were detected. Overall, participants' comments indicate the workshop increased appreciation of others' evacuation and preparation challenges. [ABSTRACT FROM AUTHOR]

Flooding, including inland and coastal flooding, is one of the most devastating and costly natural hazards in Canada, Mexico, and the United States. Recent research conducted by an international team has focused on understanding the true and comprehensive economic costs of floods, with an eye toward addressing their impacts, allocating adequate resources for monitoring and preparedness, and building resilient communities. Flood-costing methods vary greatly among federal and subnational jurisdictions across the three North American countries. Because the rigor and consistency of existing datasets across the three countries vary significantly, it is also difficult to determine the economic impacts of cross-border events. This paper aims to critically analyze the research methods used to conduct this trinational project and develop recommendations for enhancing impacts of such work in the future. We discuss three major research barriers: gaps in knowledge and research capacity, differences in data collation and analysis methods across the three countries, and linguistic barriers in working across disciplines and economic sectors. We also explore how the COVID-19 pandemic significantly added to these three barriers. We propose creation of new institutional mechanisms that can play a major role in developing comprehensive, consistent, and cohesive data gathering approaches in Canada, Mexico, and the United States. [ABSTRACT FROM AUTHOR]