Frequency Of Extreme Rainfall Research Articles

Urban development pattern’s influence on extreme rainfall occurrences

Growing urban population and the distinct strategies to accommodate them lead to diverse urban development patterns worldwide. While local evidence suggests the presence of urban signatures in rainfall anomalies, there is limited understanding of how rainfall responds to divergent urban development patterns worldwide. Here we unveil a divergence in the exposure to extreme rainfall for 1790 inland cities globally, attributable to their respective urban development patterns. Cities that experience compact development tend to witness larger increases in extreme rainfall frequency over downtown than their rural surroundings, while the anomalies in extreme rainfall frequency diminish for cities with dispersed development. Convection-permitting simulations further suggest compact urban footprints lead to more pronounced urban-rural thermal contrasts and aerodynamic disturbances. This is directly responsible for the divergent rainfall responses to urban development patterns. Our analyses offer significant insights pertaining to the priorities and potential of city-level efforts to mitigate the emerging climate-related hazards, particularly for countries experiencing rapid urbanization.

Frequency analysis of extreme rainfall over the Japanese archipelago by leveraging gauge-adjusted radar and satellite estimates

With the increasing frequency of extreme rainfall and difficulty in capturing their spatial variability by rain gauges, especially over regions with complex topographic and climatic features like Japan, there is a growing interest in using radar and satellite-derived precipitation estimates for building intensity–duration–frequency (IDF) curves. Such a statistical method provides references for studying extreme rainfall statistics at varying spatiotemporal scales. Yet, a comparative evaluation of the efficiency of these datasets to derive reliable IDFs over the Japanese archipelago is lacking. This study compares IDFs developed from four rain gauge-adjusted remotely sensed datasets (RSDs) derived from weather radars (R/A) and satellites (GsMAP_G, CMORPH_G, and IMERG_G) with those created from long-term rain gauge records (i.e., reference IDFs) for evaluating their reliabilities, particularly for 10- and 100-year return periods. A Bayesian Generalized Extreme Value model was adopted to estimate the IDFs. The results show that the four RSDs exhibited varying performances in deriving reliable IDFs within the 5–95% confidence bounds of the reference IDFs depending on rainfall duration and climate settings. While R/A performed better for one-hour rainfall return levels, particularly in the cold climate with warm summers, satellite-driven RSDs, especially IMERG_G, showed better performances for long-duration rainfall return levels. The performance discrepancies were primarily linked to the differences in rainfall estimation and bias adjustment schemes rather than the differences in record lengths and spatial resolutions among the tested RSDs. Thus, it is crucial to consider the rainfall duration and climate settings when using the derived IDFs for diverse practical applications.

Development and Evaluation of Flood Early Warning System for the Small Streams

As the frequency of local extreme rainfall increases due to climate change, frequent flood damage is occurring in small stream watersheds. Casualties in these small-streams can only be significantly reduced through non-structural measures, such as the development of flood early warning systems. Howeve owever, the development and evaluation of flood early warning systems have been limited due to a lack of measurement data in small streams. Recently, the National Disaster Management Institute operated five test beds for small streams across the country. A dditionally, the Ministry of Interior and Safety, along with local governments, established a smart measurement and management system for approximately 10% of all small streams, totaling 2,200, by 2027, making it possible to secure measurement data in small streams. Considering onsidering the flow characteristics of small streams, an optimization system is developed using the rainfall-discharge nomograph and rating curve rather than a numerical-based method that requires considerable calculation time. To evaluate the developed system, the discharge and depth data measured at the time of a flood accident on August 16, 2021, are compared on a one-to-one basis with the prediction results. The prediction results show that the measured discharge and depth distribution of the five small streams over time were accurately reproduced. Based on these results, the developed rainfall-discharge nomograph and rating curve are expected to serve as effective tools to support flood early warning systems.

How Might the May 2015 Flood in the U.S. Southern Great Plains Induced by Clustered MCSs Unfold in the Future?

AbstractThe historic 22–26 May 2015 flood event in Texas and Oklahoma was caused by anomalous clustered mesoscale convective systems (MCSs) that produced record‐breaking rainfall and $3 billion of damage in the region. A month‐long regional convection‐permitting simulation is conducted to reconstruct multiple clustered MCSs that lead to this flood event. We further use the pseudo global warming approach to examine how a similar event may unfold in a warmer climate and the driving physical factors for the changes. Tracking of MCSs in observations and simulations shows that the historical simulation reproduces the salient characteristics of the observed MCSs. In a warmer climate under a high‐emission (SSP5‐8.5) scenario, the Southern Great Plains is projected to experience a near surface warming of 4–6 K, accompanied by enhanced moisture transport by the strengthened Great Plains low‐level jet. A warmer and moister lower troposphere leads to 36%–59% larger convective available potential energy, supporting wider and more intense convective updrafts and rainfall production. Consistently, MCSs have wider convective areas and stronger rainfall intensities, producing 50% larger rain volumes during the mature stage. Extreme (99.5%) MCS rainfall frequency and amount will increase by threefold. However, MCS stratiform rain area decreases as a result of elevated stratiform cloud bases that lead to stronger sublimation and evaporation of precipitation in response to warming, resulting in reduced weak‐to‐moderate surface precipitation. Results suggest that global warming greatly increases precipitation intensity of clustered MCS events under strong synoptic influence, with much higher potential to produce serious floods without additional climate adaptation.

Response of road erosion to hydrological connectivity under a heavy rainstorm in an agricultural watershed on the Loess Plateau

Road network extension is closely associated with soil erosion in watersheds during frequent extreme rainfall events. Identifying potential risk areas that actively deliver surface runoff to road structures and channel networks is critical for comprehending hydrological processes and controlling road erosion. In this study, the road erosion (landslides, collapses, scour pits, and gullies on monitored roads) on the Loess Plateau during a heavy rainstorm was analyzed using field survey and unmanned aerial vehicle (UAV) data. We defined hydrological connections as the intersections of roads and channel networks, and the activated hydrological connections represent hydrological connectivity. The results of this study highlight the exacerbating effect of hydrological connectivity on road erosion during a rainstorm in an agricultural watershed in the Loess Plateau region. The results showed that the road erosion intensity ranged from 2.16 × 102 to 1.02 × 104 t/ha. Strong hydrological connectivity resulted in average soil erosion amounts of landslides, collapses, scour pits, and gullies in the downslope area that were 2.16, 1.78, 5.46, and 6.98 times greater than those without hydrological connectivity, respectively. The presence of hydrological connectivity generated a 1.88 times higher road surface erosion intensity than that without hydrological connectivity. The Upslope-Road connections created the most significant enhancement effect on road erosion, and up to 65.37 % landslide erosion and 41.97 % collapse erosion amounts were influenced by these connections.The enhancement effect of Upslope–Road connections on road erosion varied with different road types, this impact of Upslope–Road connections on road erosion were only observed on the landslide erosion of petroleum transport roads and the collapse erosion of agricultural roads. Conversely, Road–Downslope connections did not significantly impact road erosion (p > 0.05). To effectively control the negative effect of road erosion, hydrological connections should be identified and regulated to impede hydrological connectivity activation during rainstorms by modifying tillage activities, improving road maintenance, and strengthening drainage systems.

Interpreting and modelling the daily extreme sediment events in karst mountain watersheds

Increasingly frequent extreme rainfall as a result of climate change is strongly damaging the global soil and water environment. However, few studies have focused on daily extreme sediment events (DESE) in heterogeneous karst watersheds based on long-term in-situ observations. This study quantitatively assessed the time effect of DESE on rainfall response, decoupled the impact of environmental factors on DESE by using structural equation modelling, and finally explored the modelling scheme of DESE based on the hybrid model. The results showed that DESE had the highest frequency of occurrence in May–July, with dispersed distribution in the value domain. Rainfall with a time lag of 1 day and a time accumulation of 2 or 3 days was an important contribution to DESE (P < 0.01, R = 0.47–0.68). Combined effects of environmental factors explained 53.6 %–64.1 % of the variation in DESE. Runoff and vegetation exerted the strongest direct and indirect effects on DESE, respectively (β = 0.66/−0.727). Vegetation was the dominant driver of DESE in Dabanghe and Yejihe (β = −0.725/−0.758), while the dominant driver in Tongzhihe was climate (β = 0.743). In the future, the risk of extreme sediments should be prevented and resolved through the comprehensive regulation of multiple paths, such as runoff and vegetation. Hybrid models significantly improved the modelling performance of machine learning models. Generalized additive model-Extreme gradient boost had the best performance, while Partial least squares regression-Extreme gradient boost was the most valuable when considering performance and input data cost. Two methods can be used as recommended solutions for DESE modelling. This study provides new and in-depth insights into DESE in karst watersheds and helps the region develop forward-looking soil and water management models to cope with future extreme erosion hazards.

On The Interannual Variability of Indonesian Monsoon Rainfall (IMR): A Literature Review of The Role of its External Forcing

The IMR variability is notorious for its hydrometeorological disasters. This paper examines recent studies on IMR and the main factors controlling its variability. The focus of this study is to investigate the impact of the atmosphere-ocean interaction that acts as the external forcing of IMR in the tropical Indian and Pacific Oceans. Specifically, the study will examine the influence of two climate phenomena, namely the El Nino Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) and their interdecadal changes associated Pacific Decadal Oscillation (PDO), on the IMR. The review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach. Furthermore, data sets (such as rainfall, wind field, and SST) spanning 1990-2020 were used to verify the key findings. In general, this study concludes that the majority of the authors coincided with the following conclusion: ENSO and IOD events impact IMR by changing its amplitude, duration, intensity, and frequency of mean and extreme rainfall. Additionally, it has been shown that their impacts on IMR are most substantial during the dry seasons, specifically in June, July, and August (JJA), and not as strong as during the wet seasons, specifically in December, January, and February (DJF). Spatially, the effects of ENSO and IOD on IMR variability are clearly found more eastward and westward of the region, respectively. The expansions towards the east and west directions were facilitated by the displacement of the ascending and descending of Walker circulation patterns in the Indonesian region, respectively. Given the interannual fluctuations in IMR, caused mainly by ocean-atmosphere interactions, the knowledge gap of atmospheric factors like the Quasi-Biennial Oscillation (QBO) must be investigated in the future, as suggested by previous research and our preliminary study.

Observed Changes in Extreme Precipitation Associated with United States tropical cyclones

Abstract Numerous recent tropical cyclones have caused extreme rainfall and flooding events in the CONUS. Climate change is contributing to heavier extreme rainfall around the world. Modeling studies have suggested that tropical cyclones may be particularly efficient engines for transferring the additional water vapor in the atmosphere into extreme rainfall. This paper develops a new indicator for climate change using the enhanced rainfall metric to evaluate how the frequency and/or intensity of extreme rainfall around tropical cyclones has changed. The enhanced rainfall metric relates the amount of rain from a storm over a given location to the 5-year return period rainfall in that location to determine the severity of the event. The annual area exposed to tropical cyclone-related 5-year rainfall events is increasing, which makes a compelling climate change indicator. Quantile regression illustrates that the distribution of tropical cyclone rainfall is also changing. For tropical storms, all quantiles are increasing. However, major hurricanes show large increases in their most extreme rainfall. This study does not attempt to make any detection claims (vs. natural variability) or attribution of the observed trends to anthropogenic forcing. However, the sensitivity of the results to natural variability in tropical cyclone frequency was somewhat constrained by comparing two decades from the previous active era (1951–1970) with two from the current era (2001–2020). This comparison also shows that both the mean and maximum rainfall associated with tropical cyclones is increasing over most areas of the eastern CONUS with the most significant increases from northern Alabama to the southern Appalachians.

A preliminary assessment on spatial prioritization for integrated urban flood management based on source tracking method

With rapid urbanization and global climate change, the frequency of extreme rainfall and urban flooding has increased significantly, posing a serious threat to human life and property safety. Integrated urban flood management is an important component of urban planning that combines various infrastructure to effectively manage and utilize stormwater. This is essential for dealing with urban inundation disasters. Despite the implementation of numerous engineering and non-engineering measures, flood mitigation still faces a challenge in identifying spatial prioritization due to the lack of consideration for regional spatial connectivity. Taking Jinan City, China, as a case study, this research combined the flood source tracking method with the hydrodynamic model LISFLOOD-FP to identify the inundation area and its sources. Additionally, this study analyzed spatial hydrological connectivity was also analyzed in. The spatial prioritization was classified based on four intervention criteria: (Criterion 1) flooding extent; (Criterion 2) maximum inundation depth; (Criterion 3) flooding road network; (Criterion 4) flood exposure to buildings. Furthermore, the prioritization results of various combinations of criteria were also analyzed. Finally, the impact of rainfall intensity and grid cell size on the spatial prioritization was discussed. The results of this study emphasize the importance of spatial hydrological connectivity and flood sources. Various intervention criteria determine different spatial prioritization, highlighting the importance of developing multi-intervention criteria based on specific needs and objectives. The results indicated that sponge city pilot area in Jinan City has a high spatial prioritization. Rainfall intensity and grid cell size strongly influence the spatial prioritization, while these factors have little impact on their spatial distribution. This study demonstrates the novelty of combining intervention criteria and flood source tracking method and provides new perspectives and valuable references for urban flood mitigation.

Quantifying impacts of precipitation scenarios projected under climate change on annual probability of rainfall-induced landslides at a specific slope

Because of climate change, the intensity, duration, and frequency of future extreme rainfalls are projected to increase in many regions worldwide according to results from global climate models (GCMs). As rainfall is a major triggering factor for landslides, the frequency and risks of landslides are expected to increase. Consequently, it is crucial to quantify the impacts of climate change on landslide risks at a specific site. This study develops a statistical downscaling method based on the generalized extreme value (GEV) distribution and a probabilistic method for assessing the annual probability of rainfall-induced landslides at a specific site under projected precipitation scenarios from GCMs. The GEV-based statistical downscaling method bridges the gap between site-specific extreme rainfall (e.g., annual maximum 1-day rainfall) projection for a future scenario and GCM outputs with large spatial and temporal scales. A physics-based slope model is also used to properly depict slope failure mechanisms. The results indicate that the projected increase in extreme rainfall due to climate change leads to increases in annual frequency of heavy rainstorms and the annual slope failure probability, which are primarily controlled by the increase in the standard deviation, instead of the mean, of future extreme rainfalls.

Impact analysis of hydraulic loading rate and antibiotics on hybrid constructed wetland systems: Insight into the response to decontamination performance and environmental-associated microbiota

Hybrid constructed wetlands (HCWs) are a promising solution for water ecology and environmental treatment, not only for conventional types of water pollution but also for antibiotics. Among the critical parameters for wetlands, the hydraulic loading rate (HLR) is especially important given the challenges of antibiotics treatment and frequent extreme rainfall. To investigate the removal performance of different HLRs on nutrients and antibiotics, as well as the response of antibiotics to nutrient removal, and the impact of HLRs on microbial communities, new HCWs with vertical flow constructed wetlands (VFCWs) and floating constructed wetlands (FCWs) in series were built. The results of the study showed that: (1) HCWs are highly effective in removing chemical oxygen demand (COD), NH4+−N, NO2−−N, and total phosphorus (TP) at low HLR (L_HLR), with removal efficiencies as high as 97.8%, 99.6%, 100%, and 80.5%. However, high HLR (H_HLR) reduced their removal efficiencies; (2) The average removal efficiency of fluoroquinolones (FQs) under different HLRs was consistently high, at 99.9%, while the average removal efficiency of macrolides (MLs) was 96.3% (L_HLR) and 88.4% (H_HLR). The removal efficiency of sulfonamides (SAs) was susceptible to HLRs, and the removal of antibiotics occurred mainly in the rhizosphere zone of wetland; (3) High concentrations of antibiotics in HCWs were found to inhibit and poison plant growth and to reduce the removal efficiency of TP by 12%. However, they had a minor effect on the removal efficiency of carbon and nitrogen nutrients; (4) H_HLR altered the diversity and abundance of microbial communities in different compartments of the wetland and also reduced the relative abundance of Bacillus, Hydrogenophaga, Nakamurella, Denitratisoma and Acidovorax genera, which are involved in denitrification and phosphorus removal processes. This alteration in microbial communities was one of the main reasons for the reduced performance of nitrogen and phosphorus removal.

Evaluation of Precipitation Frequency and Intensity as Estimated by the GPM IMERG Precipitation Product at Daily and Hourly Scales over the Tibetan Plateau

The IMERG V06 hourly rainfall product at daily and hourly scales was evaluated during the warm season (May to September) from 2015 to 2020 using 651 high-quality rain-gauge stations over the Tibetan Plateau (TP). Based on hourly observed rain-gauge precipitation, four categories were classified: light rainfall (0–12 mm·d−1), moderate rainfall (12–20.1 mm·d−1), torrential rainfall (20.1–32.2 mm·d−1), and extreme torrential rainfall (&gt;32.2 mm·d−1). Precipitation frequency and intensity were calculated to further validate the accuracy and suitability of the IMERG estimated-precipitation product. At the daily scale, IMERG underestimated the number of days with less than moderate rainfall, but overestimated the frequency of torrential and extreme torrential rainfall. IMERG estimated the main characteristics of precipitation frequency at different daily precipitation amount levels better than the precipitation intensity, but its best estimate was at the moderate rainfall level, with the highest correlation coefficient (0.69) and the lowest root mean square error (0.17). At the hourly scale, IMERG underestimated the hourly precipitation amount mainly between the early morning and midday (the average deviation was 0.019 mm·h−1) and overestimated it between the afternoon and late night (the average deviation was 0.047 mm·h−1). IMERG overestimated precipitation frequency and underestimated precipitation intensity between the afternoon and the evening, which means that this analysis shows that IMERG estimated more precipitation hours than the observation and underestimated precipitation intensity. These results further our understanding of the suitability of the IMERG precipitation products over the TP and further improve the IMERG retrieval algorithm to better apply the corresponding precipitation product to light and extreme rainfall over regions with complicated topography.

Effect of Groundwater Level Rise on the Critical Velocity of High-Speed Railway

The increasing frequency of extreme rainfall is leading to a rise in groundwater levels in coastal areas, significantly affecting high-speed railway operations. To address this concern, this study developed a 2.5-dimensional finite element model of a coupled track-embankment-ground system based on Biot’s porous media theory to analyze the effect of groundwater level rise on the critical velocity of high-speed railways and vibration responses. The findings reveal a consistent decrease in the critical velocity of high-speed railways with rising groundwater levels. Particularly, the increase in groundwater levels within the embankment significantly influences the critical velocity compared to a similar rise in the foundation’s groundwater level. Furthermore, deformations induced by passing trains significantly increase as groundwater levels rise. Specifically, when the groundwater level rises from the foundation bottom to the subgrade surface, subgrade surface deformation increases by approximately 55%. As trains approach the critical velocity, significant vibration phenomena, known as the “Mach effect,” occur at the foundation surface. Importantly, as groundwater levels rise, the “Mach effect” intensifies. Analyzing the vibrating frequency spectrum of the displacement response demonstrates a substantial increase in vibration amplitude, particularly in the high-frequency region, as groundwater levels rise. This study highlights that the rise in groundwater level not only amplifies vibrations but also extends the propagation of high-frequency vibrations, underscoring the importance of effective embankment waterproofing in controlling track vibrations.

Climate‐Smart IPM: Key to U.S. Ag Resilience

AbstractFarmers, ranchers, and foresters are facing increasing challenges related to changing climate and weather, manifested in rising average temperatures, more frequent extreme rainfall events, extended drought, and heat waves. These changes are also impacting pest abundance and distribution, creating new pest problems and straining current pest management options and decision‐making. Key factors for U.S. agriculture and producer resilience in tackling new environmental and pest challenges lie in the ability of producers to apply effective, economical integrated pest management (IPM) approaches and in support for research and development of new and renewed IPM tactics and systems to meet 21st century needs. Earn 0.5 CEUs in Integrated Pest Management by reading this article and taking the quiz at https://web.sciencesocieties.org/Learning‐Center/Courses.

Investigation of landslide hazard areas in the municipality of Cunha (Brazil) and climate projections from 2024 to 2040

Climate change can modify the frequency and intensity of extreme rainfall across the globe, leading to changes in hazards posed by rainfall-induced landslides. In Brazil, an example of a location with a high occurrence of landslides and with few studies on the subject is the municipality of Cunha, located in the State of São Paulo. In this way, the main aims of this study are twofold: 1) to identify and analyze areas susceptible to landslides and rainfall-induced landslide hazard in the municipality of Cunha; and 2) by using climate projections from the regional model Eta-HadGEM2-ES under RCP 4.5, to predict new extreme events and possible new landslides in this municipality, from 2024 to 2040. The susceptibility map has been prepared using selected physical-biotic environment elements whereas the hazard map has been produced using precipitation data. Using Eta-HadGEM2-ES climate projections under RCP 4.5, for precipitation events, results project the occurrence of 49 days with landslides for the period between 2024 and 2040. The outcomes of this study are considered of extreme relevance for the local community, public authorities, and decision-makers, to raise awareness regarding future urban planning strategies to prevent and mitigate the effects of catastrophes arising from natural hazard-induced disasters.

Canopy gaps and associated losses of biomass – combining UAV imagery and field data in a central Amazon forest

Abstract. Understanding mechanisms of tree mortality and the dynamics of associated canopy gaps is relevant for robust estimates of carbon balance in forests. We combined monthly RGB images acquired from an unoccupied aerial vehicle with field surveys to identify gaps in an 18 ha plot installed in an old-growth central Amazon forest. We measured the size and shape of gaps and analyzed their temporal variation and correlation with rainfall over a period of 28 months. We further described associated modes of tree mortality (i.e., snapping, uprooting and standing dead) and branch fall and quantified associated losses of biomass. In total, we detected 32 gaps either in the images or field ranging in area from 9 to 835 m2. Relatively small gaps (&lt; 39 m2) opened by branch fall were the most frequent (11 gaps). Out of 18 gaps for which both field and image data were available, three could not be detected remotely. Gaps observed in the field but not captured on the imagery were relatively small and mainly formed by the fall of branches from live and standing dead trees. Our data show that ∼ 17 % of the tree-mortality and branch-fall events only affected the lower canopy and the understory of the forest and are likely neglected by top-of-the-canopy assessments. Regardless of the detection method, the size distribution was best described by a lognormal function for gaps starting from the smallest detected size (9 and 10 m2 for field and imagery data, respectively), and the Weibull and Power functions for gaps larger than 25 m2. Properly assessing associated confidence intervals requires larger sample sizes. Repeated field measurements reveal that gap area does not differ significantly among modes of tree mortality or branch fall in central Amazon forests, with the last contributing the least to biomass loss. Predicting mechanisms of gap formation based on associated area and biomass loss remains challenging, which highlights the need for larger datasets. The rate of gap area formation was positively correlated with the frequency of extreme rainfall events, which may be related to a higher frequency of storms propagating extreme rain and wind gusts. While remote sensing has proven to be an accurate and precise method for mapping gaps compared to field data (i.e., ground truth), it is important to note that our sample size was relatively small. Therefore, the extrapolation of these results beyond our study region and landscape shall be made cautiously. Apart from improving landscape assessments of carbon balance, regional information on gap dynamics and associated mechanisms of formation are fundamental to address forest responses to altered disturbance regimes resulting from climate change.

El Niño–Southern Oscillation (ENSO)-driven hypersedimentation in the Poechos Reservoir, northern Peru

Abstract. Although extreme El Niño events (EENEs) have always impacted hydrological anomalies and sediment transport in South America, their intensification by global warming and their association with changes in human activities and land cover after humid periods may lead to the acceleration of sediment transfers in river systems and dam reservoirs. This situation may threaten soil and water resources in arid and semiarid regions highly dependent on water originating from large dams. In this study, we investigated the sediment sequence accumulated in the Poechos Reservoir (northern Peru) and provided a retrospective reconstruction of the interactions of El Niño–Southern Oscillation (ENSO), agricultural practices and vegetation cover changes with sediment dynamics (1978–2019). To this end, a sediment core was dated and characterized by physical and chemical analyses (e.g., scanner tomography, X-ray fluorescence, particle size analysis) to estimate the evolution of sedimentation rates and changes in sediment sources during the last 5 decades. Sediment tracing results indicated the occurrence of changes in sediment sources associated with positive and negative phases of the Eastern Pacific index with a greater contribution of the lowland dry-forest area in comparison to that of the Andean area to sediment during the El Niño events (mean contribution of 76 %; up to 90 % during the coastal El Niño events (CENEs) of 2016–2017). This source contribution was mostly controlled by the stationary rainfall occurring during the EENEs in the lowland dry-forest area characterized by a low vegetation cover. Overall, after an extreme phase of ENSO, like after the EENE 1982–1983, the normal discharges and persistent sediment supplies from the middle- and upper-catchment parts led to river aggradation and the storage of substantial amounts of sediment in alluvial plains. In the absence of a significant EENE between 1983 and 1997, the large volume of sediment stored in the alluvial plains was exported by the EENE 1997–1998 resulting in an increase in sedimentation rate of 140 % after 1997 with a significant aggradation of the deltaic zone of the reservoir. In addition to the impact of extreme climate events on sediment dynamics, the development of agriculture along the riverine system after an extreme phase of ENSO increased the availability of sediments in the main channel of the rivers, easily transported by the next EENE. This study suggests that intensification of human activities associated with a higher frequency of extreme rainfall events amplified the quantity of sediment transported by the river system, which will significantly decrease the lifespan of the reservoir, which is essential to meeting the freshwater demands of the farmers and the populations living in this arid and semiarid region.

Intensification of Extreme Rainfall in Indian River Basin: Using Bias Corrected CMIP6 Climate Data

AbstractThe changing frequency of extreme rain events in the past few decades over the Indian river basins (IRBs) contributed to floods and drought and resulted in economic losses and gross domestic product. In this study, we evaluated the performance of 12 Global Circulation Models from the Coupled Model Intercomparison Project Phase 6‐ experiment with India Meteorological Department observed data sets to reproduce the extreme rainfall events as well as project the changes in frequency and intensity of the hydroclimate extremes in future. We found that under low emission scenarios (SSP1‐2.6), the frequency of extreme rainfall is going to increase over the western ghat and northeast IRBs, while an increase in heavy rainfall intensity (14.3%) noticed under SSP2‐4.5 in the upper Ganga and Indus basin. Also, approximately 4%–10% of the heavy rainfall is projected to increase over the western part of IRBs during the Near (2021–2040) and Mid (2041–2060) future. The study explored the new hotspot regions for future urban flooding due to increasing pattern of heavy rainfall in future. Moreover, the lower Ganga basin will experience agricultural drought in near future due to decreasing areal mean rainfall, which needs to be seen by policymakers for managing the excess (less) water. Also, India's northern, central, and western river basins may experience more extremes under high‐emission (SSP5‐8.5) scenarios that indicate challenges to mitigation. The findings of this study highlight the importance of developing long‐term adaptation and mitigation strategies aimed at reducing hydroclimate vulnerability. It emphasizes the need to implement measures that enhance resilience and minimize risks associated with hydroclimate extremes at the basin level.

Transcriptomic and Metabolomic Analyses of Seedlings of Two Grape Cultivars with Distinct Tolerance Responses to Flooding and Post-Flooding Stress Conditions

Grapes, an important and widespread fruit crop providing multiple products, face increasing flooding risks due to intense and frequent extreme rainfall. It is thus imperative to fully understand the flood-tolerance mechanisms of grapevines. Here, RNA-seq and LC-MS/MS technologies were used to analyze the transcriptome and metabolome changes in the roots of SO4 (tolerant to flooding) and Kyoho (sensitive to flooding) grapes under flooding and post-flooding conditions. The results showed that the abundance of many metabolites in the phenylpropanoids and polyketides, organic acids and their derivatives, and organic oxygen compounds superclasses changed in different patterns between the Kyoho and SO4 grapes under flooding and post-flooding conditions. Jasmonic acid and the ascorbic acid–glutathione cycle played a pivotal role in coping with both hypoxia stress and reoxygenation stress incurred during flooding and post-flooding treatments in the SO4 cultivar. Under flooding stress, the regulatory mechanistic shift from aerobic respiration to anaerobic fermentation under hypoxia is partly missing in the Kyoho cultivar. In the post-flooding stage, many genes related to ethylene, gibberellins, cytokinins, and brassinosteroids biosynthesis and brassinosteroids-responsive genes were significantly downregulated in the Kyoho cultivar, adversely affecting growth recovery; however, their expression was not reduced in the SO4 cultivar. These findings enhance our understanding of the flooding-tolerance mechanisms in grapes.

An Integrative Framework for Assessment of Urban Flood Response to Changing Climate

AbstractIncreasing frequency of extreme rainfall induced catastrophic urban flood events in recent decades demands proactive efforts to assess flood risk and vulnerability. Here, we develop an integrative framework for fine spatiotemporal scale assessment of urban flood response to historical and future projected changes in extreme precipitation. The framework includes three main components—nonstationary modeling of historical extreme precipitation, modeling of future precipitation, and urban flood simulations. It also provides robust estimates of uncertainty in design precipitation from statistical modeling, multiple climate models and stochastic uncertainty, estimated using machine learning techniques. We demonstrate the proposed framework for White Oak Bayou watershed in Houston, Texas, US. Two‐dimensional hydrologic‐hydraulic Interconnected Channel and Pond Routing model is used to simulate flood response from design precipitation for historical (1986–2020) and two future (2021–2050 and 2071–2100) periods in three (SSP1‐2.5, SSP2‐4.5, and SSP5‐8.5) future climate scenarios. Results show that nonstationary design estimates for historical precipitation are 14%–25% higher than the stationary estimates for 100‐year event of 1‐ to 24‐hr duration. Contrary to general global trends, we found a significant reduction in future design precipitation in all three emission scenarios. Additionally, stochastic uncertainty in future design precipitations is found to be larger than the modeling uncertainty in historical estimates and climate model uncertainty in future estimates. Flood response in terms of peak flood and total flood volume suggests that the difference in stationary versus nonstationary historical and future design precipitation are substantial to cause considerable change in simulated flood.