Extreme Flood Research Articles

Resistance of Australian fish communities to drought and flood: implications for climate change and adaptations

Climate change‐induced extreme weather and related drought and flood conditions are heterogeneous across space and time. The variability in location, timing, and magnitude of rainfall can alter how species respond to the drought and flood disturbances. To further complicate this matter, when droughts end they are often followed by extreme flooding, which are rarely considered as a disturbance (Humphries et al. 2024), let alone assessed with its own heterogeneity. Consequently, it is difficult to quantify impacts on ecological communities across large spatiotemporal scales without considering flood‐drought disturbance characteristics in sequence (Burton et al. 2020). We hypothesized that native organisms have evolved resistance to withstand repeated cycles of drought‐flood disturbances, and that established non‐native species have adapted to persist in novel conditions. To test this, we fit spatiotemporal models of species occurrence with local rainfall patterns as covariates in the drought and flood impacted Murray‐Darling basin in Australia during the decade long Millenium Drought, and its recovery period. During these drought conditions, river‐floodplain organisms in the Murray‐Darling became localized in refugia that limited longitudinal and lateral connectivity (Bond et al. 2008), and following flooding the same organisms were exposed to dispersal and recruitment opportunities (Humphries et al. 2020), as well as to hypoxic blackwater events that lead to the mortality of aquatic organisms (Small et al. 2014). At the basin‐scale we found that the range size of most native and non‐native fishes were highly resistant to the extreme drought and post‐flood conditions. At local scales, species richness, or detection, actually increased under drought conditions. Both findings highlight the resistance of species to climate change driven extreme weather, which opens new questions on community adaptations.

Evaluation navigation controlled gate of aging spillway on cavitation damage.

High-speed flow of clean water or water with sediment released from aging spillways may cause abrasion and cavitation on the concrete surface gradually. The occurrence of irregularities on the concrete surface can exacerbate the erosion problem. Which might jeopardize the safety of dams constantly, hence the rehabilitation efforts become urgent tasks in dam safety projects. This study employs a 3D Computational Fluid Dynamics (CFD) model to quantitatively analyze the cavitation risk on the aging concrete surface of the Chay 5 spillway in Ha Giang, Vietnam, under various operation scenarios. There are two standards used to measure cavitation: the cavitation index (σ) which indicates the danger due to the drop of pressure in rapid flow, and the new gasification index (β) which takes into consideration the formation and collapse of bubbles behind asperities. Three extreme flood cases may not result in potential cavitation because both σ and β exceed critical thresholds. Regarding the six controlled gate scenarios with normal water level, the σ profiles are approximated 1,0 showing a low likelihood of cavitation damage while the β values are smaller than 0.8, indicating a considerable risk of cavitation. Besides, the opening height of 100 cm poses the greatest risk of creating severe cavitation erosion in the concave area and slope portion. The flip bucket experienced the most vulnerable cavitation when the opening height is 400 cm. In addition, an approach to spillway surface rehabilitation involving specialized mortars has been presented. For aging conveyance structure, gasification index (β) takes into account irregularities surface, providing a more comprehensive assessment of the likelihood of cavitation damage than cavitation index (σ). After rehabilitation with anti-shrinkage high abrasion resistance mortar, the entire spillway surface is smooth. This allows for reducing the cavitation risk and improvement of life service thereof.

Flood risk assessment for Mulde River catchment transferring data from an observed meteorological flood event

Extreme floods can cause significant damage to infrastructure, property, and human life and have long-lasting social, economic, and environmental impacts. Flood risk assessment is critical in areas where the frequency of flood events is altering due to climate change, anthropogenic warming, land use changes. Since the primary goal of flood risk management is to reduce the risk to a tolerable level, the risk assessment based on hydrological, hydrodynamic modeling, and damage analysis is essential to identify areas that are at risk of flooding, assessing the vulnerability of assets in the area, and evaluating the potential impacts on the human life, economy, and environment. The purpose of this study is developing a transferred approach between similar catchments to observe how the catchment response in terms of discharge and damage based on a particular extreme flood event in the past. To conduct the research, the meteorological variables from the Ahr River valley extreme flood event are acquired, and an approach is developed to transfer the same dataset into the Mulde River catchment to observe consequences of the event.

Flood Risks Analysis in Upton Upon Severn: Assessing the Baseline Conditions and Future Scenarios for 100 and 1000-year Flood Events with a 2050 Climate Change Perspective.

This paper investigates the flood risks in Upton upon Severn, a town in Worcestershire, England, which is prone to fluvial flooding from the River Severn. Historical events, such as the significant floods of 1947 and 2007, highlight the town’s vulnerability and underscore the importance of analysing current and future flood risks. This research uses a one-dimensional hydraulic model, using Flood Modeller and QGIS software, to assess baseline flood conditions and predict the potential impacts of severe and extreme flood events, including 1 in 100-year and 1 in 1000-year flood scenarios. Additionally, it examines the projected effects of climate change by incorporating a 20% increase in flood risk estimates for the year 2050. The model results reveal that the baseline scenario poses limited flood risks, with only 17 properties affected, and key infrastructure like the A4104 road remains passable. However, under the 1 in 100-year event, approximately 336 properties are at risk, with significant road blockages and a flooded area of 5.09 Km2. The 1 in 1000-year flood scenario predicts a peak flow of 1807 m3/s, threatening more properties and covering a larger area of land. When climate change is factored into the 1 in 100-year scenario, the risks become similar to those of the 1 in 1000-year event, with 500 properties and key infrastructure, including the fire station and primary school, at risk and the A4104 road becoming impassable. These findings provide vital insights for flood risk management, especially given the possible effects of climate change, and stress the need for long-term solutions to flooding.

Context matters when evacuating large cities: Shifting the focus from individual characteristics to location and social vulnerability

Climate change is driving extreme weather, heat and flooding, which increasingly require evacuations. Recent studies have found inconclusive results on the determinants of evacuation-related decisions and have reported widely varying evacuation rates, especially in high-density areas. We use a large dataset of geotagged evacuation choices in Paris, France (n = 2976) during a flood, to show that while they are rarely addressed, location, social vulnerability, length of residence, and hazard exposure are critical predictors. They can be used to infer the impact of previous experience on evacuation intentions, which is usually difficult to collect at scales relevant to decision-making. We address multiple evacuation choices over time that have previously been overlooked, including gradually self-fueling spontaneous evacuations after observations of peers evacuating or flooding proximity and post-impact evacuation due to infrastructure disruption. Our findings reveal that many people wait until their home is flooded to evacuate. The gap between the initial share of people leaving immediately and the minority refusing to leave in any case even after their housing is flooded gradually fills when considering different evacuation behaviors over time. Such chronology might explain the wide range of evacuation rates reported in the literature. However, people in vulnerable situations and with increased hazard exposure are more likely to declare that they would disregard evacuation instructions. One key implication is that policies aimed at reducing social vulnerability might be more effective than communication campaigns to increase preparedness and support evacuation.

Flood prevention benefits provided by Canadian natural ecosystems

The escalating impacts of climate change have heightened concerns about the frequency and severity of natural disasters, particularly extreme flooding events. Future projections underscore the necessity for innovative flood prevention strategies, including broad-scale nature-based solutions. Here, we present the first comprehensive assessment of the flood prevention benefits provided by Canadian natural ecosystems and identify key areas crucial for human well-being. Using spatially explicit modeling, we (1) evaluated the potential runoff retention by natural ecosystems and (2) identified downstream urban and agricultural areas critically dependent on these natural benefits, particularly those in floodplains and close proximity to upstream natural ecosystems. The natural ecosystems within the top 5 % of sub-basins, representing regions with a high priority for conservation practices aimed at flood prevention, play a crucial role in safeguarding approximately 54 % (∼6,000 km2) of the total built-up area and 74 % (∼16,900 km2) of the total cropland situated within floodplains. Additionally, they are positioned upstream of floodplain-based urban zones belonging to 358 population centers, directly benefiting 3.7 million people (∼10 % of the Canadian population) and indirectly benefiting almost 20.1 million people (∼56 % of the Canadian population). Moreover, among Canada’s 5.2 million km2 of flood-preventing natural ecosystems, we identified a small fraction (10 %) whose loss or degradation would result in a significant (>50 %) increase in runoff. Several of these crucial ecosystems are situated in less populated northern regions, where local governments might want to incentivize conservation initiatives to support flood prevention. Our research underscores the imperative to integrate nature-based solutions into national strategies that consider the results of spatial planning analyses. Establishing other effective area-based conservation measures in the priority regions highlighted in this study can contribute towards reaching current ambitious environmental goals and provide critical flood prevention benefits. Additionally, our methods are transferable to other regions worldwide, leveraging globally available datasets and ensuring computational feasibility.

Geography, altitude, agriculture and hypoxia.

Reduced oxygen availability (hypoxia) represents a key plant abiotic stress in natural and agricultural systems, but conversely it is also an important component of normal growth and development. We review recent advances that demonstrate how genetic adaptations associated with hypoxia impact the known plant oxygen sensing mechanism through the PLANT CYSTEINE OXIDASE (PCO) N-degron pathway. Only three protein substrates of this pathway have been identified, and all adaptations identified to date are associated with the most important of these, the group VII ETHYLENE RESPONSE FACTOR transcription factors (ERFVIIs). We discuss how geography, altitude, and agriculture have all shaped molecular responses to hypoxia, and how these responses have emerged at different taxonomic levels through the evolution of land plants. Understanding how ecological and agricultural genetic variation acts positively to enhance hypoxia tolerance will provide novel tools and concepts to improve the performance of crops in the face of increasing extreme flooding events.

Detection of flood trends and drivers in the Taihu Basin, China

Study regionTaihu Basin, China Study focusFloods threaten humans, the environment, economic activity, and infrastructure. In this study, a new trend test and flood-frequency methods were adopted to detect extreme floods and their distributions based on flood-event identification. To fully understand the phased process of the influence of human activities on extreme hydrological processes, 12 copula functions were employed creatively in combined static and dynamic time-varying correlation aspects between extreme precipitation and floods. New hydrological insights for the regionAlthough both significant and insignificant increasing trends of the annual maximum water level in all three hydrological districts were examined, the periodic oscillations of all the stations were similar. Thus, it was significant to fully detect the periodical variation of floods. Extreme floods occurred mainly in the 1990s, as measured by frequency estimates. Generally, the nonstationary response relationship between heavy rain and an extreme water level was gradually strengthened; that is, a certain magnitude precipitation seemed to induce a greater-intensity flood event as time passed. Through the identification of historical flood events and the analysis of the rise and fall processes of floods, we found that the main reason for variation in the response relationship was the increase in the water level before the rising stage, rather than the water level rising in the Taihu Basin. Our study findings further existing knowledge on the regional flood-control design standard and can ensure the coexistence of humans and water systems in the future.

Empowering nurses to support Greener NHS policy

Climate change is a significant global health threat, projected to cause over 250000 deaths annually by 2050, as a result of factors such as extreme heat, flooding and poor air quality caused by pollution. The NHS, as a major healthcare provider, contributes around 4% of the UK's total emissions and in October 2020, became the world's first health service to commit to reaching net zero carbon, in response to the profound and growing threat to health posed by climate change. Nurses, as frontline healthcare providers, are uniquely positioned to drive sustainability initiatives within healthcare settings, reducing environmental impact while improving patient outcomes. Sue Thomas explores how nurses can make a difference in line with the Greener NHS policy.

The longer arc of channel recovery post-dam removal

Dam removals are opportunistic experiments to address fundamental questions about river recovery to disturbance. Previous studies have shown that gravel-bedded rivers are resilient with covariate adjustments to channel dimensions occurring rapidly in the wake of disturbance. Yet, beyond the cross section, at the reach or watershed-scale, adjustment appears to take much longer. Understanding of the longer arc of reach and watershed recovery is limited by the relatively few dam removals studies that include long-term monitoring. Here, we present results from a five-year dam removal study punctuated by an extreme flood and show that the initial, rapid response of a channel is driven by the prevailing hydrology whereas longer-term adjustments to morphology at the reach scale are driven by external forces imposed on the channel. We summarize these results by classifying various channel features as either ‘intrinsic channel properties’ that are rapidly adjustable by the prevailing hydrology or ‘extrinsic channel properties’ that respond over various time scales to external boundary conditions imposed on the channel by climate, vegetation, geology, and valley dimensions (extrinsic controls). We show that this framework applies to channel recovery beyond the former reservoir and thus may prove applicable to channel disturbances beyond dam removal, such as extreme floods.

Accurate simulation of extreme rainfall–flood events via an improved distributed hydrological model

Recently, the high incidence of extreme rainfall and flood events worldwide has severely harmed regional economies and societies. Therefore, flood simulations and forecasts, which can provide key technical support for regional flood control and disaster reduction, are urgently needed. The Liuxihe model, as a fully distributed, physically based hydrological model, was improved in this study to simulate extreme rainfall flood events in the Beijiang River Basin. This basin is a famous rainstorm centre in Guangxi Province, China. In this work, the Liuxihe model is improved in two aspects: first, its structure and runoff generation and confluence algorithm are improved, and second, the parameter calibration method is optimised. These two adjustments improve the flood simulation performance of the model and reduce the uncertainty of the simulation results. The results revealed that the flood simulated by the improved Liuxihe model was strongly consistent with the measured values, and the index values of the Nash coefficient, correlation coefficient, process relative error, and flood peak flow error performed very well in the scheme evaluation; in particular, the mean process relative error, flood peak error, and peak time difference decreased by 62%, 63%, and 80%, respectively, after model improvement. The error indicators of the simulation were within the allowable error range from the Standard for Hydrological Information and Hydrological Forecasting (GB/T-22482–2008), which meets the accuracy requirements of flood forecasting in the local hydrological department and can be used as a practical operational plan for flood forecasting. These satisfactory flood simulation results showed that the model and algorithm were improved; thus, the improved Liuxihe model can provide important theoretical guidance for regional flood forecasting and flood disaster mitigation.

The assessment of low flow at El Abid river basin for a good management of water resource (High Atlas/Morocco)

Abstract In a scenario where pressures on aquatic ecosystems and the water demands of communities are on the rise, coupled with the impacts of global changes, understanding low water flows becomes a critical concern. Low flow is characterized as a natural and seasonal phenomenon, typically stemming from varying degrees of prolonged and severe rainfall deficits. This phenomenon is prone to temporal and spatial fluctuations, ultimately causing a reduction in streamflow. The El Abid River Basin is located within the big Oum Er Rbia watershed, covering an area of 30.600 km2. El Abid river stands out as the principal tributary of the Oum Er Rbia, boasting an average annual flow of 32 m3/s−1, with a maximum average flow reaching 77 m3/s−1 and a minimum average flow of 10 m3/s−1. The hydrological regime of this river exhibits a pattern of intermittent and extreme floods, as well as periods of low flow sustained by water from the karst hydrosystems of the central High Atlas. The aim of this study is to characterize the low flow rates within the upstream part of El Abid river basin, intending to enhance the management of water resources in this basin. The methodology employed involves extracting the minimum flows, calculating the severity thresholds for low flow, determining the lowest average monthly flow of the year, computing low flow rates based on monthly flow averages, and calculating low flow values for specific return periods. Monthly discharge data from two hydrometric stations within our study area, namely Ait Ouchéne and Tizi Nisly, were utilized. The dataset spans the period from 1976 to 2018.

Spatial Variation of Changes in Extreme Discharge Seasonality Across the Northeastern United States

ABSTRACTThe Northeast United States exhibits significant spatial heterogeneity in flood seasonality, with spring snowmelt‐driven floods historically dominating northern areas, while other regions show more varied flood seasonality. While it is well documented that since 1996 there has been a marked increase in extreme precipitation across this region, the response of flood seasonality to these changes in extreme precipitation and the spatial distribution of these effects remain uncertain. Here we show that, historically, snowmelt‐dominated northern regions were relatively insensitive to changes in extreme precipitation. However, with climate warming, the dominance of snowmelt floods is decreasing and thus the extreme flood regimes in northern regions are increasingly susceptible to changes in extreme precipitation. While extreme precipitation increased everywhere in the Northeastern United States in 1996, it has since returned to near pre‐1996 levels in the coastal north while remaining elevated in the inland north. Thus, the inland north region has and continues to experience the greatest changes in extreme flooding seasonality, including a substantial rise in floods outside the historical spring flood season, particularly in smaller watersheds. Further analysis reveals that while early winter floods are increasingly common, the magnitude of cold season floods (Nov‐May) have remained unchanged over time. In contrast, warm season floods (June‐Oct), historically less significant, are now increasing in both frequency and magnitude in the inland north. Our results highlight that treating the entire Northeast as a uniform hydroclimatic region conceals significant regional variations in extreme discharge trends and, more generally, climate warming will likely increase the sensitivity of historically snowmelt dominated watersheds to extreme precipitation. Understanding this spatial variability in increased extreme precipitation and increased sensitivity to extreme precipitation is crucial for enhancing disaster preparedness and refining water management strategies in affected regions.

Model sensitivity in predicting extreme precipitation events in urban areas: A case study over Beijing

Understanding and forecasting the spatial and temporal distributions of extreme precipitation over urban areas is crucial for effective planning and mitigation efforts. However, this task remains challenging as accurate forecasting depends on properly representing urban surfacees and their interactions with the planetary boundary layer (PBL). We examined the hindcast of an extreme precipitation event over Beijing on 21–22 July 2012. The primary focus was assessing its sensitivity to two widely used PBL parameterizations (MYJ and YSU), two urban parameterizations (SLUCM and BEP_BEM), and two different land-use and land-cover (LULC) datasets.Sensitivity experiments were initialized at different times to explore the model dependence on initial conditions. The analyses were conducted over three selected regions: the entire model domain covering the Beijing metropolitan area, an upwind region of Beijing, and the entire urban area of Beijing. The results show that the MYJ PBL scheme performs better than the YSU PBL scheme in capturing near-surface air temperature as well as the location and timing of the heaviest precipitation. The variability in simulated precipitation among the chosen PBL schemes is lower compared to that among different time of initializations. The LULC impacted the spatial distribution of precipitation but its effect on the amount of precipitation was minimal. Overall, using a combination of the MYJ PBL scheme, SLUCM urban parameterization, and locally-enhanced Beijing LULC, and initializing the model simulations at 0000 UTC July 20, 2012, demonstrated superior performance in capturing precipitation levels, despite some spatial discrepancies in the precipitation distribution. The performance of BEP_BEM urban parameterization is similar to SLUCM across various factors such as average rain rate, maximum rain rate, and rain volume. These findings offer valuable insights towards better simulations of extreme precipitation and flooding in rapidly urbanizing areas such as Beijing.

Sediment Source Partitioning and Budgeting Over Historical Timescales in a Glacierized, Mountain Catchment

AbstractManaging and living with geohazards is especially challenging in mountain landscapes and requires an understanding of catchment‐scale sediment dynamics and internal system functioning. While sediment budgeting is a valuable framework, challenges remain including partitioning sediment yield by source and grain size and addressing scale issues. This study advances our understanding of bed material dynamics in glacierized mountain catchments by applying a range of complementary techniques to measure sediment transfers in the Fitzsimmons Creek watershed. First, we measured the historical bed material yield using field surveys and historical air photo analysis, revealing an average specific sediment yield of 210 Mg km−2 yr−1, that varied by a factor of 17 over the 76‐year record. Hydro‐meteorological and historical analyses suggest that gravel extraction had the largest impact over the past three decades, while an extreme landslide and flood event produced the highest recorded sediment yield. Second, we constructed a detailed sediment budget along the river system using high‐resolution, multi‐temporal lidar and geomorphic mapping data. Sediment source partitioning indicates that landslide, active channel, and floodplain sources each contributed about one‐third of the total sediment supply. Net degradation occurred along the valley bottom upstream of the fan‐delta, resulting in steadily increasing downstream sediment yield. This trend is punctuated by chronic landsliding near the outlet, driven by postglacial incision through glaciogenic sediments at a hanging valley step. Contemporary glacial and proglacial sources were not measured directly but surprisingly contributed minimally. These findings provide insight into the sediment dynamics of glacierized mountain catchments and their potential controls.

Assessment of tangible coastal inundation damage related to critical infrastructure and buildings: The case of Mauritius Island

Storm tides, which combine sea level rise (SLR), astronomical tides, and storm surges generated by tropical cyclones, pose significant threats to coastal zones, leading to flooding and substantial damage to property and infrastructure.There is a clear upward trend in the frequency of storms reaching tropical cyclone strength. A notable example is Cyclone Belal, which struck Mauritius on January 2024, during high tide, causing extensive infrastructure damage. This underscores the importance of conducting risk assessments to identify vulnerable areas and develop risk reduction strategies. However, quantitative risk assessments of storm tides are often challenging due to the lack of long-term projections. To address this, we developed a GIS-based flood model for Mauritius to simulate inundation areas and quantify the assets exposed to flooding. Under current conditions, the estimated damage exposure from extreme coastal flood events with return periods of 50 to 500 years is significant, with 6.2% and 27.1% of the area inundated, respectively. By 2100, damage exposure associated with these events is projected to increase by a factor of 1.1, with minimal variation between sea-level rise scenarios (0.3m). However, by 2200 and 2300, damage exposure is expected to rise by factors of 3.1 and 6.6, respectively. In the worst-case scenario for 2500, Mauritius could experience maximum inundation of 66.3 km2, with buildings covering 5.02 km2 submerged. Additionally, this study presents a detemporalized inundation scenario to assess impacts from any coastal flood event. This approach enables the identification of critical thresholds ( 1.5 m and 4.5 m) and , beyond which significant increases in damage exposure are likely, and allows for evaluating adaptation strategies against user-defined levels of change, rather than relying solely on predefined scenarios. These findings highlight the urgent need for strategic sectoral interventions to address the widespread consequences of coastal inundation, especially in light of critical thresholds for remedial action.

Late Holocene extraordinary palaeoflood events and their climatological context in the Shahe River, Huaihe River Basin, China

Palaeoflood events represent immediate hydrological responses to extreme climate change. A loess-paleosol sedimentary profile containing three overbank flood deposits (OFD1, OFD2, and OFD3) layers that recorded palaeoflood events was discovered on the platform scarp of the Shahe River, a tributary of the Huaihe River through a field investigation. Sediment samples were collected, and their physicochemical properties, as well as optically stimulated luminescence (OSL) dating, were analyzed. The results indicated that OFD1, OFD2, and OFD3 were indeed overbank flood deposits influenced by hydrodynamic forces. However, OFD3 and OFD1 consisted primarily of sand (>60 %) in terms of particle size composition, whereas paleosol (S0), transitional loess (Lt), and Malan loess (L1) mainly comprised silt (>70 %). Moreover, the magnetic susceptibility values of OFD3 and OFD1 exceeded those of S0, Lt, and L1. The contents of Na2O, K2O, and SiO2 were higher, while their contents of Al2O3 and Fe2O3 were lower in OFD3 and OFD1. These suggested that OFD3 and OFD1 may originate from weathered bedrock materials transported from the upper reaches of the Shahe River under significant hydrodynamic forces. The particle size composition, magnetic susceptibility value, and geochemical element composition of OFD2 were similar to those of S0, Lt, and L1 but diverged significantly from those of OFD1 and OFD3, indicating that OFD2 originated from loess and soil sediments widely distributed on both sides of the valley. Through OSL dating and stratigraphic chronological framework of the sedimentary profile, these three extraordinary palaeoflood events in the Huaihe River Basin occurred during the late Holocene, ∼1470 a. The analysis of high-resolution climate proxy indicators, atmospheric circulation factors, and global mean temperature demonstrated a direct correlation between these extreme flood events and abrupt climate changes during ∼ 1470 a. This period corresponded to severe climate deterioration during the Northern and Southern Dynasties (589–420 CE) in China. These findings are crucial for enhancing our understanding of regional hydrological climate responses to global changes.

Analysis of Flood Resistance of Masonry Arch Bridges Combining Numerical Simulation and Probabilistic Analysis

ABSTRACT In the context of global climate change, extreme weather events such as heavy rainfall and floods are becoming more frequent. The safety of masonry arch bridges is facing unprecedented challenges. This study aims to explore the collapse mechanism of bridges in flood environments by combining numerical simulation and stochastic analysis. Taking the collapse of the Original Zhenhai Bridge caused by heavy rainfall in July 2020 as a specific case, a three-dimensional numerical model is established to analyze the mechanical response of the bridge under the combined action of flood scour and water flow loads. Additionally, predictions of scour depth are made using both theoretical formulas and stochastic methods, comparing the impact of stochastic analysis on the mechanical performance of arch bridges under the same flood conditions. The study reveals that compared to pure scour, the structural characteristics of the bridge undergo significant changes under the coupled effects of scour and water flow loads. In extreme flood events, stochastic analysis has a significant impact on the structural safety under high flow conditions. The research results provide case references and scientific methods for the safety analysis of masonry arch bridges in flood environments.

The Changing Hydrology of an Irrigated and Dammed Yangtze River: Streamflow, Extremes, and Lake Hydrodynamics

AbstractUnderstanding the role of anthropogenic activities in the hydrological cycle is critical to support sustainable water management for the Yangtze River Basin (YRB), which experiences extensive dam operation, irrigation and water withdrawal. However, this remains challenging due to insufficient accuracies of existing process‐based models for fully depicting anthropogenic activities as part of the hydrological cycle. To this end, this study enhances a national‐scale coupled land surface‐hydrologic‐hydrodynamic model (CLHMS) with a dynamic irrigation scheme for distinct crops, an extended reservoir operation scheme incorporating both water storage anomalies and water demand anomalies, and a cost‐function‐based approach to link water demands with reservoir operation. The enhanced model is extensively validated against historical streamflow, water storage of 90 reservoirs, and irrigation water withdrawal in the YRB, and the water level and storage of the Poyang Lake (PYL). By setting up controlled experiments in the YRB, we show that the streamflow decreases by 2%–6% due to irrigation and water withdrawal, and manifests an attenuated seasonality due to reservoir operation. At the basin scale, the increasing trend of extreme flood peaks exhibits a reversal under human activities, with the flood mitigation effect of irrigation and water withdrawal accounting for up to 50% of that of reservoir operation. The hydrodynamics of the PYL also exhibits considerable human‐induced alterations, with a 1.79 m‐decrease in the water level at the end of flood season. Our study sheds light on quantifying anthropogenic hydrologic impacts at basin scales, with important implications for understanding the co‐evolution between anthropogenic activities and the hydrological cycle.

Comparative Evaluation of Water Level Forecasting Using IoT Sensor Data: Hydrodynamic Model SWMM vs. Machine Learning Models Based on NARX Framework

This study evaluates the accuracy of water level forecasting using two approaches: the hydrodynamic model SWMM and machine learning (ML) models based on the Nonlinear Autoregressive with Exogenous Inputs (NARX) framework. SWMM offers a physically based modeling approach, while NARX is a data-driven method. Both models use real-time precipitation data, with their predictions compared against measurements from a network of IoT sensors in a stormwater management system. The results demonstrate that while both models provide effective forecasts, NARX models exhibit higher accuracy, with improved Nash–Sutcliffe Efficiency (NSE) coefficients and 33–37% lower mean absolute error (MAE) compared to SWMM. Despite these advantages, NARX models may struggle with limited data on extreme flooding events, where they could face accuracy challenges. Enhancements in SWMM modeling and calibration could reduce the performance gap, but the development of SWMM models requires substantial expertise and resources. In contrast, NARX models are generally more resource-efficient. Future research should focus on integrating both approaches by leveraging SWMM simulations to generate synthetic data, particularly for extreme weather events, to enhance the robustness of NARX and other ML models in real-world flood prediction scenarios.