Search Results (56)

This study analyzes precipitation patterns and future trends in the Kuye River Basin in the context of climate change, providing a scientific foundation for water resource management and ecological protection. Using methods such as the Mann–Kendall test, Pettitt test, and complex Morlet wavelet analysis, this study examines both interannual and intra-annual variability in historical precipitation data, identifying abrupt changes and periodic patterns. Future projections are based on CMIP5 models under RCP4.5 and RCP8.5 scenarios, forecasting changes over the next 30 years (2023–2052). The results reveal significant spatiotemporal variability in precipitation, with 88.16% concentrated in the summer and flood seasons, while only 1.07% falls in winter. The basin’s multi-year average precipitation is 445 mm, exhibiting stable interannual variability, but with a significant increase starting in 2006. Projections indicate that the average annual precipitation will rise to 524.69 mm from 2023 to 2052, with a notable change point in 2043. Precipitation is expected to increase spatially from northwest to southeast. This research underscores the importance of understanding precipitation dynamics in managing drought and flood risks. It highlights the role of soil and water conservation and vegetation restoration in improving water resource efficiency, supporting sustainable development, and guiding climate adaptation strategies. Full article

The Haihe River system, located in the East Asian monsoon climate zone, experiences uneven precipitation and significant variability, leading to frequent droughts and floods that disrupted economic and social development. While many studies have assessed the risks of droughts and floods in the Haihe River Basin, most focus on the basin as a whole, leaving a notable gap in research on the dynamics of the northern region. This study analyzed historical drought and flood data, incorporating instrument precipitation records from 1960 to 2009 to reconstruct conditions in the northern Haihe River Basin from 1470 to 2009. Using methods like the Mann–Kendall test, sliding averages, continuous wavelet technology, and spatial analysis, this study examined the trends, change points, periodicity, and spatial patterns of drought and flood variability. The findings showed that from 1470 to 2009, drought and flood variabilities occurred 73.15% of the time in the northern Haihe system, with peak disaster periods in the 17th, 19th, and 20th centuries. The region has alternated between wet and dry cycles, with a notable dry trend emerging in the 21st century. A prominent 35~50-year cycle in drought and flood occurrences was identified, along with high-frequency oscillations. Flood periods were most frequent in the eastern plains, while drought periods were more prevalent in the western areas, gradually shifting eastward since 1950. The research also revealed correlations between drought and flood variability and solar activity, with peak years coinciding with higher frequencies of these events. El Niño events were associated with drought periods, while La Niña events tended to cause flood periods. Factors such as solar activity, El Niño–Southern Oscillation, monsoon climate patterns, topography, and human influences shaped the dynamics of drought and flood variability in the northern Haihe River Basin. A comparison with other regions showed consistent wet and dry cycles over the past 500 years, particularly between the northern and southern parts of the basin. However, since the 21st century, the southern region has remained humid, while the northern region has become increasingly drier. Despite similar temperature trends, humidity changes have diverged in the modern warming period. Although the underlying factors driving drought and flood variability were not fully understood and required a further exploration of the global climate system’s interactions, these findings emphasized the need for targeted strategies to address the ongoing challenges of drought and flood management in the northern Haihe River Basin. Full article

The assumption of stationarity in historical hydroclimatic data, fundamental to traditional water resource planning models, is increasingly challenged by the impacts of climate change. This discrepancy can lead to inaccurate model outputs and misinformed management decisions. This study addresses this challenge by developing a novel monthly data adjustment approach, the Runoff Curve Year–Type–Monthly (RC-YTM) method. The application of this method is exemplified at five key California watersheds. The RC-YTM method accounts for the increasing variability and shifts in seasonal runoff timing observed in the historical data (1922–2021), aligning it with the contemporary climate conditions represented by the period from 1992 to 2021 at the study watersheds. This method adjusts both annual and monthly streamflow values using a combination of precipitation–runoff relationships, quantile mapping, and water year classification. The adjusted data, reflecting current climatic conditions more accurately than the raw historical data, serve as valuable inputs for operational water resource planning models like CalSim3, commonly used in California for water management. This approach, demonstrably effective in capturing the observed climate change impacts on streamflow at monthly timesteps, enhances the reliability of model simulations representing contemporary conditions, which can lead to better-informed decision-making in water management, infrastructure investment, drought and flood risk assessment, and adaptation strategies. While focused on specific California watersheds, this study’s findings and the adaptable RC-YTM method hold significant implications for water resource management in other regions facing similar hydroclimatic challenges in a changing climate. Full article

During winter 2023–2024, the Charente River experienced four successive flood events in six months, including one major flood and three moderate ones. These grouped floods affected a huge territory in the Charente valley, in particular the Territoire à Risque d’Inondation Important (TRI, i.e., Major Flood Risk Area) between Angoulême and Saintes (46 municipalities). Although they produced little immediate damage due to their slow kinematics and low flow speeds, they had a major impact on the functioning of the territory through prolonged house flooding and infrastructure disruption. This repeated flood sequence is all the more remarkable in that it occurs after the February 2021 extreme flood and a backdrop of severe and prolonged drought initiating in 2019. This article proposes to analyze grouped floods, a complex and little-studied hydrological phenomenon, from a geohistorical perspective in order to demonstrate that they are not emergent events and to look for historical precedents that show that these particular events have already occurred in the past but have been neglected or underestimated until now. Among past grouped flood sequences extending back to 1700, a significant similarity arises with the 1859–1860 flood sequence. In both cases, the first annual flood occurred early in the year in response to an early storm season and followed an uncommon hot and dry summer. Although the floods of 2023–2024 are well documented through both meteorological and hydrological data, as well as the surrounding context, the floods of 1859–1860 remain poorly constrained. By gathering a wide range of documentary sources and instrumental data, we try to better understand the context and the course of this past sequence of grouped floods, with particular emphasis on the first annual flood, the November 1859 flood. The analysis of similarities and divergences between sequences of past and recent grouped floods makes it possible to improve knowledge of their formation and course in order to better anticipate these particular events in the context of climate change. Full article

Assessing diverse parameters like water quality, quantity, and occurrence of hydrological extremes and their management is crucial to perform efficient water resource management (WRM). A successful WRM strategy requires a three-pronged approach: monitoring historical data, predicting future trends, and taking controlling measures to manage risks and ensure sustainability. Artificial intelligence (AI) techniques leverage these diverse knowledge fields to a single theme. This review article focuses on the potential of AI in two specific management areas: water supply-side and demand-side measures. It includes the investigation of diverse AI applications in leak detection and infrastructure maintenance, demand forecasting and water supply optimization, water treatment and water desalination, water quality monitoring and pollution control, parameter calibration and optimization applications, flood and drought predictions, and decision support systems. Finally, an overview of the selection of the appropriate AI techniques is suggested. The nature of AI adoption in WRM investigated using the Gartner hype cycle curve indicated that the learning application has advanced to different stages of maturity, and big data future application has to reach the plateau of productivity. This review also delineates future potential pathways to expedite the integration of AI-driven solutions and harness their transformative capabilities for the protection of global water resources. Full article

This study focuses on the impacts of climate change on hydrological processes in watersheds and proposes an integrated approach combining a weather generator with a multi-site conditional generative adversarial network (McGAN) model. The weather generator incorporates ensemble GCM predictions to generate regional average synthetic weather series, while McGAN transforms these regional averages into spatially consistent multi-site data. By addressing the spatial consistency problem in generating multi-site synthetic weather series, this approach tackles a key challenge in site-scale climate change impact assessment. Applied to the Jinghe River Basin in west-central China, the approach generated synthetic daily temperature and precipitation data for four stations under different shared socioeconomic pathways (SSP1-26, SSP2-45, SSP3-70, SSP5-85) up to 2100. These data were then used with a long short-term memory (LSTM) network, trained on historical data, to simulate daily river flow from 2021 to 2100. The results show that (1) the approach effectively addresses the spatial correlation problem in multi-site weather data generation; (2) future climate change is likely to increase river flow, particularly under high-emission scenarios; and (3) while the frequency of extreme events may increase, proactive climate policies can mitigate flood and drought risks. This approach offers a new tool for hydrologic–climatic impact assessment in climate change studies. Full article

The paper demonstrates the effectiveness of the SNOWED dataset, specifically designed for identifying water bodies in Sentinel-2 images, in developing a remote sensing system based on deep neural networks. For this purpose, a system is implemented for monitoring the Po River, Italy’s most important watercourse. By leveraging the SNOWED dataset, a simple U-Net neural model is trained to segment satellite images and distinguish, in general, water and land regions. After verifying its performance in segmenting the SNOWED validation set, the trained neural network is employed to measure the area of water regions along the Po River, a task that involves segmenting a large number of images that are quite different from those in SNOWED. It is clearly shown that SNOWED-based water area measurements describe the river status, in terms of flood or drought periods, with a surprisingly good accordance with water level measurements provided by 23 in situ gauge stations (official measurements managed by the Interregional Agency for the Po). Consequently, the sensing system is used to take measurements at 100 “virtual” gauge stations along the Po River, over the 10-year period (2015–2024) covered by the Sentinel-2 satellites of the Copernicus Programme. In this way, an overall space-time monitoring of the Po River is obtained, with a spatial resolution unattainable, in a cost-effective way, by local physical sensors. Altogether, the obtained results demonstrate not only the usefulness of the SNOWED dataset for deep learning-based satellite sensing, but also the ability of such sensing systems to effectively complement traditional in situ sensing stations, providing precious tools for environmental monitoring, especially of locations difficult to reach, and permitting the reconstruction of historical data related to floods and draughts. Although physical monitoring stations are designed for rapid monitoring and prevention of flood or other disasters, the developed tool for remote sensing of water bodies could help decision makers to define long-term policies to reduce specific risks in areas not covered by physical monitoring or to define medium- to long-term strategies such as dam construction or infrastructure design. Full article

Dams significantly impact the environment, industries, residential areas, and agriculture. Efficient dam management can mitigate negative impacts and enhance benefits such as flood and drought reduction, energy efficiency, water access, and improved irrigation. This study tackles the critical issue of predicting dam occupancy levels precisely to contribute to sustainable water management by enabling efficient water allocation among sectors, proactive drought management, controlled flood risk mitigation, and preservation of downstream ecological integrity. Our research suggests that combining physical models of water inflow and outflow “such as evapotranspiration using the Penman–Monteith equation, along with parameters like water consumption, solar radiation, and rainfall” with data-driven models based on historical reservoir data is crucial for accurately predicting occupancy levels. We implemented various prediction models, including Random Forest, Extra Trees, Long Short-Term Memory, Orthogonal Matching Pursuit CV, and Lasso Lars CV. To strengthen our proposed model with robust evidence, we conducted statistical tests on the mean absolute percentage errors of the models. Consequently, we demonstrated the impact of physical model parameters on prediction performance and identified the best method for predicting dam occupancy levels by comparing it with findings from the scientific literature. Full article

Riverine floodplains are recognized as centers of biodiversity, but due to intense anthropogenic pressures, many active floodplains have disappeared during the last century. This research focuses on the long-term changes in phytoplankton diversity in the floodplain lake situated in the Kopački Rit (Croatia), one of the largest conserved floodplains in the Middle Danube. The recent dataset from 2003 to 2016 and historical data from the 1970s and 1980s indicate high phytoplankton diversity, summarising 680 taxa for nearly half a century. The variability of species richness is driven by specific in-lake variables, particularly water temperature, water depth, total nitrogen, pH, and transparency, determined by a redundancy analysis of the current data. The high phytoplankton diversity levels are sustained regardless of intense pressures on the lake environment, including exposure to strong anthropogenic pollution in the past and extreme hydrological events, both droughts and floods, which have increasingly affected this part of the Danube in the last decades. The conserved hydrological connection between various biotopes along the river–floodplain gradient seems crucial in maintaining high phytoplankton diversity. Accordingly, conserving natural flooding is mandatory to maintain high biodiversity in complex and dynamic river–floodplain systems. Full article

Much of eastern and southeastern Australia (SEAUS) suffered from historic flooding, heat waves, and drought during the quasi-decadal 2010–2022 period, similar to that experienced globally. During the double La Niña of the 2010–2012 period, SEAUS experienced record rainfall totals. Then, severe drought, heat waves, and associated bushfires from 2013 to 2019 affected most of SEAUS, briefly punctuated by record rainfall over parts of inland SEAUS in the late winter/spring of 2016, which was linked to a strong negative Indian Ocean Dipole. Finally, from 2020 to 2022 a rare triple La Niña generated widespread extreme rainfall and flooding in SEAUS, resulting in massive property and environmental damage. To identify the key drivers of the 2010–2022 period’s precipitation and temperature extremes due to accelerated global warming (GW), since the early 1990s, machine learning attribution has been applied to data at eight sites that are representative of SEAUS. Machine learning attribution detection was applied to the 52-year period of 1971–2022 and to the successive 26-year sub-periods of 1971–1996 and 1997–2022. The attributes for the 1997–2022 period, which includes the quasi-decadal period of 2010–2022, revealed key contributors to the extremes of the 2010–2022 period. Finally, some drivers of extreme precipitation and temperature events are linked to significant changes in both global and local tropospheric circulation. Full article

To ensure water use and water resource security along “the Belt and Road”, the runoff and hydrological droughts and floods under future climate change conditions in the upper Heihe River Basin were projected in this study, based on the observed meteorological and runoff data from 1987 to 2014, and data from 10 GCMs from 1987 to 2014 and from 2026 to 2100, using the SWAT model, the Standardized Runoff Index, run length theory, and the entropy-weighted TOPSIS method. Both the multi-GCM ensemble (MME) and the optimal model were used to assess future hydrological drought and flood responses to climate change. The results showed that (1) the future multi-year average runoff from the MME was projected to be close to the historical period under the SSP245 scenario and to increase by 2.3% under the SSP585 scenario, and those from the optimal model CMCC-ESM2 were projected to decrease under both scenarios; (2) both the MME and the optimal model showed that drought duration and flood intensity in the future were projected to decrease, while drought intensity, drought peak, flood duration, and flood peak were projected to increase under both scenarios in their multi-year average levels; (3) drought duration was projected to decrease most after 2080, and drought intensity, flood duration, and flood peak were projected to increase most after 2080, according to the MME. The MME and the optimal model reached a consensus on the sign of hydrological extreme characteristic responses to climate change, but showed differences in the magnitude of trends. Full article

The Amazon, the world’s largest rainforest, faces a severe historic drought. The Rio Negro River, one of the major Amazon River tributaries, reached its lowest level in a century in October 2023. Here, we used a U-net deep learning model to map water surfaces in the Rio Negro River basin every 12 days in 2022 and 2023 using 10 m spatial resolution Sentinel-1 satellite radar images. The accuracy of the water surface model was high, with an F1-score of 0.93. A 12-day mosaic time series of the water surface was generated from the Sentinel-1 prediction. The water surface mask demonstrated relatively consistent agreement with the global surface water (GSW) product from the Joint Research Centre (F1-score: 0.708) and with the Brazilian MapBiomas Water initiative (F1-score: 0.686). The main errors of the map were omission errors in flooded woodland, in flooded shrub, and because of clouds. Rio Negro water surfaces reached their lowest level around the 25th of November 2023 and were reduced to 68.1% (9559.9 km²) of the maximum water surfaces observed in the period 2022–2023 (14,036.3 km²). Synthetic aperture radar (SAR) data, in conjunction with deep learning techniques, can significantly improve near-real-time mapping of water surfaces in tropical regions. Full article

Hydrology and meteorological data from relatively undisturbed watersheds aid in identifying effects on ecosystem services, tracking hydroclimatic trends, and reducing model uncertainties. Sustainable forest, water, and infrastructure management depends on assessing the impacts of extreme events and land use change on flooding, droughts, and biogeochemical processes. For example, global climate models predict more frequent high-intensity storms and longer dry periods for the southeastern USA. We summarized 17 years (2005–2021) of hydrometeorological data recorded in the 52 km², third-order Turkey Creek watershed at the Santee Experimental Forest (SEF), Southeastern Coastal Plain, USA. This is a non-tidal headwater system of the Charleston Harbor estuary. The study period included a wide range of weather conditions; annual precipitation (P) and potential evapotranspiration (PET) ranged from 994 mm and 1212 mm in 2007 to 2243 mm and 1063 in 2015, respectively. The annual runoff coefficient (ROC) varied from 0.09 in 2007 (with water table (WT) as deep as 2.4 m below surface) to 0.52 in 2015 (with frequently ponded WT conditions), with an average of 0.22. Although the average P (1470 mm) was 11% higher than the historic 1964–1976 average (1320 mm), no significant (α= 0.05) trend was found in the annual P (p = 0.11), ROC (p = 0.17) or runoff (p = 0.27). Runoff occurred on 76.4% of all days in the study period, exceeding 20 mm/day for 1.25% of all days, mostly due to intense storms in the summer and lower ET demand in the winter. No-flow conditions were common during most of the summer growing season. WT recharge occurred during water-surplus conditions, and storm-event base flow contributed 23–47% of the total runoff as estimated using a hydrograph separation method. Storm-event peak discharge in the Turkey Creek was dominated by shallow subsurface runoff and was correlated with 48 h precipitation totals. Estimated precipitation intensity–duration–frequency and flood frequency relationships were found to be larger than those found by NOAA for the 1893–2002 period (for durations ≥ 3 h), and by USGS regional frequencies (for ≥10-year return intervals), respectively, for the same location. We recommend an integrated analysis of these data together with available water quality data to (1) assess the impacts of rising tides on the hydroperiod and biogeochemical processes in riparian forests of the estuary headwaters, (2) validate rainfall–runoff models including watershed scale models to assess land use and climate change on hydrology and water quality, and (3) inform watershed restoration goals, strategies, and infrastructure design in coastal watersheds. Full article

Locust plagues and tidal disasters are primary natural hazards in China’s eastern coastal regions, yet their interrelationship remains unclear. This study, drawing on historical documents from the Ming and Qing dynasties (1368–1911 AD), focuses on Zhejiang Province and its Hangzhou Bay coastline, areas typically affected by tidal disasters. Employing advanced quantitative analysis and spatiotemporal models, the research aims to reveal the mechanisms behind tidal disasters and their impact on locust population dynamics. The findings indicate a limited spatiotemporal correlation between locust plagues and tidal or drought disasters but a significant association with flooding. The relationship between locust infestations and floods is notably strong in the unique geographical context of Hangzhou Bay’s northern shore. The ‘hydromarginal’ nature of the north coast creates an ideal habitat for locusts. This study pioneers in identifying flooding as a crucial mediator between tidal disasters and locust plagues, shedding light on the ‘typhoon-tidal-flood-locust’ disaster sequence and offering new insights into understanding and mitigating natural disasters in the region. In this study, we primarily employ a quantitative methodology, utilizing advanced data analysis and sophisticated spatiotemporal modeling to investigate the interplay between locust plagues and tidal disasters. Although some progress has been made in the study of historical natural disasters, systematic studies of the relationship between tidal floods and locust breeding sites along the east coast of China during the Ming and Qing dynasties are still scarce. This study fills this gap by employing advanced GIS and time series analysis techniques, combining traditional historical documentary studies with modern scientific methods and providing a new methodological approach to the analysis of historical disaster patterns. Full article

In the context of anomalous global climate change and the frequent occurrence of droughts and floods, studying trends in the conversion rate between precipitable water vapor (PWV) and actual precipitation in a certain region can help in analyzing the causes of these natural disasters. This paper examines the variation trend in the conversion rate between PWV and actual precipitation on a monthly scale in Hubei from 1960 to 2020. To estimate historical PWV data, we propose a new method for estimating PWV using water vapor pressure based on the RF algorithm. The new method was evaluated by radiosonde data and improved the accuracy by 1 mm over the traditional method in Hubei. Based on this method, we extrapolate the monthly average PWV in Hubei from 1960 to 2020 and analyze the conversion rate between PWV and precipitation during this period. Our results showed that there was no obvious cyclical pattern in the conversion rate in either the longitude or latitude directions. In Hubei, where the topography varies significantly in the longitude direction, the conversion rate is influenced by topography, with the smallest conversion rate being in the transition zone between the mountainous region of western Hubei and the Jianghan Plain. In the latitudinal direction, the conversion rate decreases with increasing latitude. Full article