Extreme Rainfall Research Articles

Harmonizing models and measurements: Assessing soil erosion through RUSLE model.

Soil erosion poses significant ecological and socioeconomic challenges, driven by factors such as inappropriate land use, extreme rainfall events, deforestation, farming methods, and climate change. This study focuses on the Kozhikode district in Kerala, South India, which has seen increased vulnerability to soil erosion due to its unique geographical characteristics, increase in extreme events, and recent land use trends. The research employs RUSLE (Revised Universal Soil Loss Equation), considering multiple contributing factors such as rainfall erosivity (R), slope length and steepness (LS), cover management (C), conservation practices (P), and soil erodibility (K). The study is unique and novel, since it integrates extensive field data collected from agricultural plots across Kozhikode with the RUSLE model predictions, providing a more accurate and context-specific understanding of soil erosion processes and also suggesting management strategies based on risk priority. The study found that Kozhikode experiences an average annual soil loss of 28.7 tons per hectare. A spatial analysis revealed varying erosion risk levels across the district. 52.0% of the area experiences very slight erosion, 10.31% has slight erosion, 6.18% undergoes moderate erosion, 3.88% is moderately severe, 7.34% is at severe erosion risk, 5.6% has very severe erosion, and 14.65% faces extremely severe erosion. Field data collected from agricultural plots across Kozhikode were compared with RUSLE-predicted values, revealing a low root mean square error, indicating a strong correlation between observed and simulated data. Based on these findings, the district was categorized into low, medium, and high-priority regions, with tailored recommendations proposed for each. Implementing these measures could mitigate erosion, preserve soil fertility, and support the long-term sustainability of natural and agricultural ecosystems in Kozhikode. Given the practical challenges in estimating RUSLE factors in Southern India, where data scarcity is a common issue, this preliminary study underscores the need for expanded, long-term field observations to enhance understanding of soil erosion processes at the watershed level.

Water Resources Potential Assessment during Dry Season in Indonesia: A Systematic Literature Review

The dry season in Indonesia has been studied in the context of its impact on rainfall extremes, with research indicating that the dry season rainfall extremes are strongly affected by IOD and ENSO (El Niño-Southern Oscillation). The impact of dry season in 2023 based on the data from the Meteorological, Climatological, and Geophysical Agency (BMKG ) mentioned that this dry season started from march to September 2023. Java Island, whose land area is only 138,379 km² or 7 (seven) percent of Indonesia's area, has a very important position because it is crowded by 65 percent of Indonesia's population or an average of 864 people / km² and contributes to GNP of 60 percent. This article aims to review the water resources in Java Island in Indonesia during the dry season in 2017 to 2023. That causes the lack of water resources has become the main problem in some area in Java Island in Indonesia. This research result assesses the water resources during the dry season, the technique, the method, and the findings. There are 287 articles found only from Scopus sources, 40 article reviewed, and only 5 articles have been matched to the topic by using Prisma P. process. Finally, the result shows that one aspect that remains unclear about Indonesia's dry season is how exactly climate change influences its duration and intensity. While it's known that Indonesia undergoes a dry season, the specific impacts of climate change on factors like rainfall patterns, temperature, and the occurrence of extreme weather events are not fully grasped. This lack of understanding presents challenges for planning agriculture, managing water resources, and conserving ecosystems in the region.

Investigating the mechanisms of an intense coastal rainfall event during TAHOPE/PRECIP-IOP3 using a multiscale radar ensemble data assimilation system

Abstract The joint Taiwan-Area Heavy Rain Observation and Prediction Experiment (TAHOPE)/Prediction of Rainfall Extremes Campaign In the Pacific (PRECIP) field campaign between Taiwan and the United States took place from late May to mid-August in 2022. The field campaign aimed to understand the dynamics, thermodynamics, and predictability of heavy rainfall events in the Taiwan area. This study investigated the mechanisms of a heavy rainfall event that occurred on 6–7 June during the intensive observation period-3 (IOP3) of the field campaign. Heavy rainfall occurs on Taiwan’s western coast when a Meiyu front hovers in northern Taiwan. A multiscale radar ensemble data assimilation system based on the successive covariance localization (SCL) method is used to derive a high-resolution analysis for forecasts. Two numerical experiments are conducted with the use of convective-scale (RDA) or multiscale (MRDA) corrections in the assimilation of the radial velocity from operational radars at Chigu and Wufen, and the additional S-Pol radar deployed at Hsinchu during the field campaign. Compared with RDA, MRDA results in large-area wind corrections, which help reshape and relocate a low-level mesoscale vortex, a key element of this heavy rainfall event, offshore of western central Taiwan and enhances the front intensity offshore of northwestern Taiwan. Consequently, MRDA improves the 6-h heavy rainfall prediction over the coast of western Taiwan and better represents the elongated rainband in northern Taiwan during the 3- to 6-h forecast. Sensitivity experiments demonstrate the importance of assimilating winds from Chigu and S-Pol radar in establishing low-level mesoscale vortex and convergence zones.

Statistical downscaling model for future projection of daily IDF relationship by Markov chain and kernel density estimator

ABSTRACT Changes in climate might have a significant impact on rainfall characteristics, including extreme rainfall. This study aims to project the future daily rainfall, preserving most of the rainfall characteristics, including extreme rainfall incorporating climate changes. This paper presents two hybrid semi-parametric statistical downscaling models for future projection of IDF curves. The precipitation flux from seven scenarios of ten GCMs and observed daily rainfall data are considered as predictors and predictand variables, respectively. At site, daily rainfall occurrence is modeled using a two-state first-order Markov chain. Rainfall amounts on each wet day are modelled using a univariate nonparametric kernel density estimator. Two types of amount generation models are presented in this study. The bounded model (KDE-SP) is developed, considering the support for the kernel distribution as positive. In the unbounded model (KDE-Ext), the wet days are reclassified as extreme and non-extreme rainy days. A significant increasing trend can be observed in the future projected intensity–duration–frequency relationships. The maximum increment using empirical distribution is observed as 93.21 and 80.93% on a 5-year return period in the far future for the SSP5-8.5 scenario, using KDE-Ext and KDE-SP models, respectively. Although both methods show similar results, the KDE-Ext model performs better in simulating extreme rainfall.

Sea-ice loss in Eurasian Arctic coast intensifies heavy Meiyu-Baiu rainfall associated with Indian Ocean warming

Heavy Meiyu-Baiu rainfall can pose threat to the dense population in East Asia by catastrophic flooding. Although previous studies have identified Indian Ocean (IO) warming as the major cause of heavy Meiyu-Baiu rainfall, it failed to predict the record-breaking rainfall in July 2020. Synthesizing observational analysis, large-ensemble climate simulations, and atmospheric simulations, we show that sea-ice loss in the Kara Sea in May can intensify the IO warming-induced heavy Meiyu-Baiu rainfall and well explains the record-breaking rainfall in July 2020. In the precondition of IO warming, sea-ice loss tends to prolong Meiyu-Baiu season and strengthen convective activity over the Meiyu-Baiu region, thereby enhancing the IO warming-induced heavy Meiyu-Baiu rainfall by ~50% and doubling the risk of extreme events comparable to or greater than the one in 2020. A statistical model is further constructed to demonstrate that taking Arctic sea ice into consideration can significantly improve the seasonal prediction of extreme Meiyu-Baiu rainfall.

A simple and robust approach for adapting design storms to assess climate-induced changes in flash flood hazard

Hydrologists and civil engineers often use design storms to assess flash flood hazards in urban, rural, and mountainous catchments. These synthetic storms are not representations of real extreme rainfall events, but rather simplified versions parameterized to mimic extreme precipitation statistics often obtained from intensity–duration–frequency (IDF) curves. To construct design storms for the future climate, it is thus necessary first to recalculate IDF curves to represent rainfall under warmer conditions. We propose a framework for adjusting IDF curves and design storms to future climate conditions using the TENAX model, a novel statistical approach that can provide future short-duration precipitation return levels based on projected temperature changes. For most applications, information from climate models at the daily scale can be used to construct design storms at the sub-hourly scale without any downscaling or bias adjustment. Our approach is illustrated through a re-parameterization of the Chicago Design Storm (CDS) in the context of climate change. As a case study demonstration, we apply the TENAX model to data from the city of Zurich to calculate changes in the historical IDF curve for durations ranging from 10 min to 3 h. We then construct synthetic 100-year return period design storms based on the CDS for present and future climates and use the CAFlood model to produce flood inundation maps to assess changes in flood hazard. The codes for adapting design storms to climate change are simple to implement, easily applicable by practitioners, and made freely available.

Characterization of transient movements within the Joshimath hillslope complex: Results from multi-sensor InSAR observations

This paper investigates the spatiotemporal characteristics and life-cycle of movements within the Joshimath landslide-prone slope over the period from 2015 to 2024, utilizing multi-sensor interferometric data from Sentinel‑1, ALOS‑2, and TerraSAR‑X satellites. Multi-temporal InSAR analysis before the 2023 slope destabilization crisis, when the region experienced significant ground deformation acceleration, revealed two distinct deformation clusters within the eastern and middle parts of the slope. These active deformation regions have been creeping up to −200 mm/yr. Slope deformation analysis indicates that the entire Joshimath landslide-prone slope can be categorized kinematically as either Extremely-Slow (ES) or Very-Slow (VS) moving slope, with the eastern cluster mainly exhibiting ES movements, while the middle cluster showing VS movements. Two episodes of significant acceleration occurred on August 21, 2019 and November 2, 2021, with the rate of slope deformation increasing by 20% (from −50 to −60 mm/yr) and around threefold (from −60 to −249 mm/yr), respectively. Following the 2023 destabilization crisis, the rate of ground deformation notably increased across all datasets for both clusters, except for the Sentinel‑1 ascending data in the eastern cluster. Pre-crisis, horizontal deformation was dominant both in the eastern and middle clusters. Horizontal deformation remained dominant and increased significantly in the eastern cluster post-crisis phase, whereas vertical deformation became predominant in the middle cluster. Wavelet analysis reveals a strong correlation between two acceleration episodes and extreme precipitation in 2019 and 2021, but no similar correlation was detected in other years. This indicates that while extreme rainfall significantly influenced the dynamics of slope movements during these episodes, less strong precipitation had a minimal impact on slope movements during other periods.

Heavy rainfalls in Poland and their hyetographs.

In the light of observed variability in precipitation patterns, there is a growing need for comprehensive data mining of regularly updated rainfall recording databases. Therefore, an analysis of heavy rainfall and hyetographs was conducted using a 30-year high-resolution dataset from 100 rain gauges across Poland, covering 31 646 rainfall events. Distributions of rainfall depths, durations, and intensities were explored, and maxima were compared to global records. Spatial analysis revealed significant variations in the frequency, depths, and durations of extreme rainfall across different regions. Cluster analysis determined model hyetographs for each station. The likelihood of regions belonging to clusters with three to five model hyetographs was assessed using Indicator Kriging. Findings underscore the importance of using local, characteristics rainfalls in hydrodynamic modelling of drainage systems and future rainfall scenarios. These results provide a foundational step towards understanding and monitoring the impacts of climate change on rainfall characteristics, especially extremes, in future decades.

Effectiveness of wetlands as reservoirs for integrated water resource management in the Ruzizi plain based on water evaluation and planning (WEAP) approach for a climate-resilient future in eastern D.R. Congo

It is widely predicted that climate change’s adverse effects will intensify in the future, and along with inadequate agricultural practices, settlement development, and other anthropic activities, could contribute to rapid wetland degradation and thus exert significant negative effects on local communities. This study sought to develop an approach based on the Integrated Water Resource Management (IWRM) in the Ruzizi Plain, eastern Democratic Republic of Congo (DRC), where adverse effects of the climate change are increasingly recurrent. Initially, we analyzed the trends of climate data for the last three decades (1990–2022). Subsequently, the Water Evaluation and Planning (WEAP) approach was employed on two contrasting watersheds to estimate current and future water demands in the region and how local wetlands could serve as reservoirs to meeting water demands. Results indicate that the Ruzizi Plain is facing escalating water challenges owing to climate change, rapid population growth, and evolving land-use patterns. These factors are expected to affect water quality and quantity, and thus, increase pressure on wetland ecosystems. The analysis of past data shows recurrence of dry years (SPI ≤ − 1.5), reduced daily low-intensity rainfall (Pmm < 10 mm), and a significant increase in extreme rainfall events (Pmm ≥ 25 mm). The WEAP outcomes revealed significant variations in future water availability, demand, and potential stressors across watersheds. Cropland and livestock are the main water consumers in rural wetlands, while households, cropland (at a lesser extent), and other urban uses exert significant water demands on wetlands located in urban environments. Of three test scenarios, the one presenting wetlands as water reservoirs seemed promising than those considered optimal (based on policies regulating water use) and rational (stationary inputs but with a decrease in daily allocation). These findings highlight the impact of climate change in the Ruzizi plain, emphasizing the urgency of implementing adaptive measures. This study advocates for the necessity of the IWRM approach to enhance water resilience, fostering sustainable development and wetland preservation under changing climate.

Uncovering mechanisms behind Chennai's deluges during north-east monsoon season 2015: An observational and modeling analysis

The present study delves into the underlying processes responsible for the Chennai deluge during 9th, 15th of November, and 1st December, 2015 by employing both observational data and modeling approaches. The Chennai rainfall, as observed from the GPM satellite data, was substantially higher than the accumulated normal of ∼79 cm for the October-December period. These extreme events coincided with the strongest El Niño event of the century, which persisted from 2014 to early 2016. Further, it is found that the anomalies in Sea Surface Temperature (SST) during this period were more than 1° K above the climatological value and prevailing strong low-level easterly waves over the Indian Oceanic region aided the intensification of previously developing synoptic systems. Soil moisture analysis indicated saturation values nearing 70 %, resulting in increased surface runoff during rainfall events in the backdrop of rapid urban expansion from 1995 to 2015 and aggravated water logging issues. Calculation of thermodynamic indices revealed favourable conditions for the development and intensification of severe convective systems, leading to the catastrophic rainfall events over the Chennai region. A high resolution regional model NCUM (resolution ∼1.5 Km) was utilized to simulate various synoptic features and dynamics of the event over Chennai. Moisture transport at 700 hPa and integrated precipitable water up to 300 hPa were examined, revealing a strong convergence of moisture along the Chennai coast for all cases, with high values of precipitable water observed. Simulations of 3-hourly accumulated rainfall from model generally align with corresponding GPM satellite estimates, despite the model tending to underestimate the intensity of rainfall in all cases. The model simulated location specific rainfall is reasonably well matched with the in-situ observations around Chennai region. However, the model is underestimated the peak rainfall while compare with the observations in all the cases. Further, it successfully depicts the dynamics and structure of extreme rainfall events, including key features such as wind patterns and moisture convergence, demonstrating its utility for forecasting extreme weather events in the Chennai region.

Rainfall in California: Special Reference to 2023 Rains That Caused Floods

After years of crippling drought, California experienced a barrage of intense atmospheric river storms starting from late 2022 and continuing into early 2023 after so many years of extensive drought. This resulted in extensive flooding across the northern and central parts of the state. Major urban centers like San Francisco and Sacramento saw their wettest January on record, with damage to infrastructure and thousands evacuated in flooded areas. Southern California also saw higher than average rainfall. The extensive rainfall provided much-needed relief from the recent drought, helping to replenish reservoirs and groundwater basins, but also causing lot of catastrophic events due to California’s insufficient flood control infrastructure. Decades of underfunding and lack of coordination have left dams, levees, and urban flood control channels unable to handle increased precipitation, putting communities at risk. Historically, California has experienced periodic droughts, where precipitation falls well below average for multiple seasons or years. Significant droughts were observed in the past century such as in the years 1976–1977, 1987–1992, and 2012–2016. The most recent drought from 2012–2022 ranked as one of the most severe and most prolonged droughts on record in California. Although the 2023 storms helped in recovery from drought conditions, California must bolster its flood control systems and water storage capabilities. The state must prepare for a future where climate change drives intense precipitation alongside more intense droughts. Sustainable infrastructure and coordinated management will be vital for withstanding increased hydrological extremes. This article examines the rainfall extremes of 2023 within the context of California’s drought history and makes recommendations for long-term water security to overcome increased climate variability.

Quantifying the internal and external drivers of Southeast Asian rainfall extremes on decadal timescales

Rainfall over mainland Southeast Asia experiences variability on seasonal to decadal timescales in response to a multitude of climate phenomena. Historical records and paleoclimate archives that span the last millennium reveal extreme multi-year rainfall variations that significantly affected the societies of mainland Southeast Asia. Here we utilize the Community Earth System Model Last Millennium Ensemble (CESM-LME) to quantify the contributions of internal and external drivers to decadal-scale rainfall extremes in the Southeast Asia region. We find that internal variability was dominant in driving both Southeast Asian drought and pluvial extremes on decadal timescales although external forcing impacts are also detectable. Specifically, rainfall extremes are more sensitive to Pacific Ocean internal variability than the state of the Indian Ocean. This discrepancy is greater for droughts than pluvials which we suggest is attributable to external forcing impacts that counteract the forced Indian Ocean teleconnections to Southeast Asia. Volcanic aerosols, the most effective radiative forcing during the last millennium, contributed to both the Ming Dynasty Drought (1637–1643) and the Strange Parallels Drought (1756–1768). From the Medieval Climate Anomaly to the Little Ice Age, we observe a shift in Indo-Pacific teleconnection strength to Southeast Asia consistent with enhanced volcanism during the latter interval. This work not only highlights asymmetries in the drivers of rainfall extremes but also presents a framework for quantifying multivariate drivers of decadal-scale variability and hydroclimatic extremes.

Clarifying urban flood response characteristics and improving interpretable flood prediction with sparse data considering the coupling effect of rainfall and drainage pipeline siltation

With climate warming and accelerated urbanisation, severe urban flooding has become a common problem worldwide. Frequent extreme rainfall events and the siltation of drainage pipes further increase the burden on urban drainage networks. However, existing studies have not fully considered the effects of rainfall and pipeline siltation on the response characteristics of flooding when constructing numerical models of urban flooding simulations. To solve this problem, a surface-subsurface coupling model was constructed by combining the Saint-Venant equation, Manning equation, a one-dimensional pipeline model (SWMM), and a two-dimensional surface overflow model (LISFLOOD-FP). Then, the SWMM model considering pipeline siltation and the two-dimensional surface overflow model (LISFLOOD-FP) were coupled with the flow exchange governing equation, and the urban flooding response characteristics considering the coupling effect of “rainfall and drainage pipeline siltation” were analysed. To enhance the solvability of waterlogging prediction, an intelligent prediction model of urban flooding based on Bayes-CNN-BLSTM was established by combining a convolutional neural network (CNN), bidirectional long short-term memory neural network (BLSTM), Bayesian optimisation (Bayes), and an interpretable loss function error correction method. The actual rainfall events and flooding processes recorded by the monitoring equipment at Huizhou University were used to calibrate and verify the model. The results show that in the Rainfall 1 and Rainfall 2 scenarios, the overload rates of the pipelines in the current siltation scenario were 60.06 % and 68.37 %, respectively, and the proportions of overflow nodes were 24.87 % and 25.89 %, respectively. When the drainage network was initially put into operation, the overload rates of the pipeline were 36.67 % and 41.16 %, and the overflow nodes accounted for 3.05 % and 4.06 %, respectively. The inundated area and volume of urban flooding increased when the combined siltation coefficient (CSC) was 0.2; therefore, two desilting schemes were determined. Under Rainfall 1, Rainfall 2, and the four rainfall recurrence periods, the Bayes-CNN-BLSTM model had clear advantages in terms of accuracy, reliability, and robustness.

Extreme drought and rainfall had a large impact on potato production in the Netherlands between 2015 and 2020

Extreme weather events cause damage to crops. Quantifying their impact on crop yields in farmers’ fields however remains difficult, as empirical data generally lacks detail, preventing to unravel causal effects from soil and management practices. Here we exploit an observational dataset on potato production on sandy soils in the Netherlands and use a causal method employing a matching strategy to quantify the effect of extreme weather. We provide statistical evidence that drought and high-intensity rainfall in this region have large impact today, explaining 98% of the between-year yield differences. High-intensity rainfall led to a yield reduction of 36% and drought to 13%. Yields highly varied among fields, just as the impact of these weather extremes on individual fields. Identifying on which fields the impact is small provides direction for adaption strategies, which are urgently needed, considering the expected increased frequency of weather extremes according to the latest climate change scenarios.

Public Perception of Drought and Extreme Rainfall Impacts in a Changing Climate: Aconcagua Valley and Chañaral, Chile

Public Perception of Drought and Extreme Rainfall Impacts in a Changing Climate: Aconcagua Valley and Chañaral, Chile

Future Scenarios of Design Rainfall Due to Upcoming Climate Changes in NSW, Australia

The occurrence of rainfall is significantly affected by climate change around the world. While in some places this is likely to result in increases in rainfall, both winter and summer rainfall in most parts of New South Wales (NSW), Australia are projected to decrease considerably due to climate change. This has the potential to impact on a range of hydraulic and hydrologic design considerations for water engineers, such as the design and construction of stormwater management systems. These systems are currently planned based on past extreme rain event data, and changes in extreme rainfall amounts due to climate change could lead to systems being seriously undersized (if extreme precipitation events become more common and/or higher in magnitude) or oversized (if extreme rainfall events become less frequent or decrease in magnitude). Both outcomes would have potentially serious consequences. Consequently, safe, efficient, and cost-effective urban drainage system design requires the consideration of impacts arising from climate change on the approximation of design rainfall. This study examines the impacts of climate change on the probability of occurrence of daily extreme rainfall in New South Wales (NSW), Australia. The analysis was performed for 29 selected meteorological stations located across NSW. Future design rainfall in this research was determined from the projected rainfall for different time periods (2020 to 2039, 2040 to 2059, 2060 to 2079, and 2080 to 2099). The results of this study show that design rainfall for the standard return periods was, in most cases, lower than that derived employing the design rainfall obtained from the Australian Bureau of Meteorology (BoM). While most of the analysed meteorological stations showed significantly different outcomes using the climate change scenario data, this varied considerably between stations and different time periods. This suggests that more work needs to be performed at the local scale to incorporate climate change predicted rainfall data into future stormwater system designs to ensure the best outcomes.

Identifying and detecting causes of changes in spatial patterns of extreme rainfall in southwestern Iran

Identifying and detecting causes of changes in spatial patterns of extreme rainfall in southwestern Iran

Exploring Stormwater Best Management Practices for Urban Flood Reduction in Khulna City of Bangladesh Using PCSWMM

Khulna City Corporation (KCC), located in a low-lying area in southwestern Bangladesh, has been suffering from frequent water logging in the rainy season almost every year. This situation is intensified due to rapid urbanization and the impact of climate change, which have consequently created numerous problems in KCC related to stormwater management issues that require immediate consideration. Therefore, the current study aims to explore stormwater management problems in KCC and find possible countermeasures through different best management practices. Stormwater management must be incorporated into urban design and development, particularly in densely populated urban areas like KCC. In Ward No. 30 of KCC, controlling stormwater is a vital concern due to heavy rainfall, a lack of proper drainage infrastructure, unplanned growth, extensive land cover areas, etc. In the current study, the most widely used Personnel Computer Storm Water Management Model (PCSWMM) software was adopted to evaluate the city's current stormwater management practices and develop a long-term stormwater management strategy. A reconnaissance investigation of the existing states of the soil, drainage, and outlet infrastructures was conducted. The study area (KCC Ward No. 30) was then divided into many small catchments in the ArcGIS platform, and areas of each sub-catchment were identified. To determine the percentage of impervious area and the elevation of the study area, the land use and land cover (LULC) map and the digital elevation model (DEM) were generated using the ArcGIS software. All the aforementioned parameters were then entered into the PCSWMM software, and simulations were performed for various rainfall durations using different best management practices. The PCSWMM simulation provided the results of runoff, infiltration, peak runoff, floods, or surcharges at every drainage node or discharge point in the existing and altered drainage system of the study area due to the occurrence of extreme rainfall. The results indicate that various best management practices, increasing the slope, reducing the roughness of the drainage systems, reducing imperviousness, permitting more infiltrations, and promoting green infrastructure, are highly effective in controlling urban flooding or surcharges in the study area caused by the occurrence of extreme rainfall in the future. The findings of this study are expected to be supportive to policymakers, urban planners, and water managers in implementing and promoting sustainable and climate-resilient urban drainage infrastructures to tackle stormwater management problems in the KCC area. Journal of Engineering Science 15(1), 2024, 107-118

Late Pleistocene charcoal-rich sediments in the Puerto Rico Trench, possible remnants of gigantic wildfires in North-Eastern South America

An 18,000 km2 area of the Guyana Shield of South America, known as the Gran Sabana, is characterized by savannah vegetation that contrasts strongly with surrounding rain forests. Its origin has been linked to multiple episodes of forest fires. In this paper, we report a deposit encountered in two piston cores sampled during the CASEIS marine cruise, at 6000 m-depth at the southern entrance of the Puerto Rico Trench. The existence of this deposit call into question our understanding of the evolution of the Gran Sabana. We sampled its upper ∼60 cm, which comprises leaves and wood fragments, seeds, and charcoal, intermixed with siliciclastic sediment of igneous-metamorphic continental provenance. Radiocarbon dates of the vegetal fragments and charcoal range between 30 and 23 kyr BP. We propose that these deep ocean charcoal-rich sediments, located 2500 km offshore from the Orinoco Delta, may be remnants of gigantic forest fires of the Guyana Shield. We infer that this material was eroded during an extreme regional rainfall event, transported down rivers during one or more episodes to the Orinoco delta, and then travelled offshore via a deep turbiditic submarine system flowing on the Atlantic seafloor. It finally reached the Puerto Rico Trench, forming what we term, the Baracuda Trench Debrite. While published paleoclimatic analyses of lacustrine sediments have suggested that the Gran Sabana originated during episodes of wildfire ∼12.5 kyr BP ago, radiocarbon dating of Baracuda Trench Debrite suggests the occurrence of earlier fires in this region, leading us to re-evaluate the age of the Gran Sabana. These fires occurred during the low glacial maximum (LGM) and were likely promoted by climate change.

Developing an open-source flood forecasting system adapted to data-scarce regions: A digital twin coupled with hydrologic-hydrodynamic simulations

Economic and human losses from flooding have had a significant global impact. Undeveloped nations often require extended periods to recover from flood-related damage, exacerbating the climate poverty trap, specifically in flood-prone regions. To address this issue, early warning systems (EWS) provide lead time for preparedness and measures to reduce vulnerability. However, EWS are mainly empirical at large scales and often do not incorporate hydrodynamic behaviors in flood forecasting, at least in developing regions with a lack of information. This study presents an open-source system integrating a hydrodynamic model with satellite rainfall data (PERSIANN PDIR-Now) and weather prediction data (GFS). It functions as a near real-time Digital Twin (DT) and Early Warning System for high-resolution flood forecasting. Simulated data can be compared with gauge stations in real-time through the model monitoring interface. A proof-of-concept was made by assessing the model capabilities in two case studies. First, the system simulated two consecutive extreme events (hurricanes ETA and IOTA) over the Sula Valley, Honduras, showing fidelity in streamflow responses. Second, the system worked as a DT and EWS to monitor the current and future hydrological states for two periods in 2022 and 2023. Results indicate that satellite data coupled with DT can provide up-to-date system conditions for flood forecasts for regions of lack of data for extreme rainfall events. This tool offered insights to enhance civil protection and societal engagement through warning dissemination against extreme events to build resilience to cope with the increasing magnitude and frequency of disasters in regions with data scarcity.