Search Results (36)

River spring and flash floods are highly dependent on variations in meteorological conditions. In the Baltic States, substantial changes in air temperature and precipitation have been observed between the two most recent climate normal periods (1961–1990 and 1991–2020). Therefore, changes in the magnitude of spring and flash floods across different hydrological regions between these periods were analyzed to better understand shifting hydrological patterns. Daily flow data from 1961 to 2020 were obtained from 68 water gauging stations on 55 rivers. The Pettitt and Mann–Kendall tests, as well as Sen’s slope estimator, were applied to analyze the time series of flood maximum discharges. The most pronounced negative trends in spring and flash floods were observed in Lithuanian rivers, with the magnitude of these trends gradually weakening toward Latvia and Estonia. The maximum flood heights (h_MAX) generally declined during 1961–2020, particularly in Lithuania and western Latvia. Spring flood data showed the most significant decrease, particularly during 1991–2020, when h_MAX declined on average by 0.14 mm/year in Lithuania and 0.05 mm/year in Latvia. Flash floods exhibited smaller declines, also concentrated in 1991–2020. In the major rivers (Nemunas, Neris, and Daugava), peak discharges of both floods declined consistently throughout the study period. Full article

Anthropogenic activities and climate change have significantly altered runoff generation in the upper Han River basin, posing a challenge to the water supply sustainability for the Middle Route of the South-to-North Water Diversion Project. Land use/cover changes (LUCCs) affect hydrological processes by modifying evapotranspiration, infiltration and soil moisture content. Based on hydro-meteorological data from 1961 to 2023 and LUCC data series from 1985 to 2023, this study aimed to identify the temporal trend in hydro-meteorological variables, to quantify the impacts of underlying land surface and climate factors at different time scales and to clarify the effects of LUCCs and basin greening on the runoff generation process. The results showed that (1) inflow runoff declined at a rate of −1.71 mm/year from 1961 to 2023, with a marked shift around 1985, while potential evapotranspiration increased at a rate of 2.06 mm/year within the same time frame. (2) Annual climate factors accounted for 61.01% of the runoff reduction, while underlying land surface contributed 38.99%. Effective precipitation was the dominant climatic factor during the flood season, whereas potential evapotranspiration had a greater influence during the dry season. (3) From 1985 to 2023, the LUCC changed significantly, mainly manifested by the increasing forest area and decreasing crop land area. The NDVI also showed an upward trend over the years; the actual evapotranspiration increased by 1.163 billion m³ due to the LUCC. This study addresses the climate-driven and human-induced hydrological changes in the Danjiangkou Reservoir and provides an important reference for water resource management. Full article

Climate change is becoming more pronounced and affecting all environmental components, leading to river flow changes. This study aimed to investigate the annual and seasonal discharge trends for six rivers in Bosnia and Herzegovina in Europe in the period from 1961 to 2020. The trends were analysed using a linear regression (LR) analysis, the Mann–Kendal test (MK), and an innovative trend analysis (ITA). The fewest significant trends were obtained by the LR analysis, followed by the MK test, and the most were obtained by the ITA method. The ITA method identified 76.67% significant negative trends and 13.33% significant positive trends in all data groups. It can be concluded that the discharges in the second part of the observed period (1991–2020) were significantly lower compared to the first part (1961–1990). A more detailed ITA of the flow by data group (low, medium, and high) was also carried out. The results showed the occurrence of increasingly large extremes. Therefore, in the second subperiod, the minimum values were smaller and the maximum values were larger than in the first subperiod. The occurrence of high water levels increases the possibility of floods, and a long dry period can cause problems with the generation of electricity from hydropower plants. For this reason, analysing discharge trends in the future is certainly a justified recommendation. Full article

Natural disaster events caused by extreme precipitation have far-reaching and widespread impacts on society, the economy, and ecosystems. However, understanding the long-term trends of extreme precipitation indices and their spatiotemporal correlations with disaster events remains limited. This is especially true given the diverse factors influencing their relationship in China, which makes their spatial linkage highly complex. This study aims to detect recent spatial trends in extreme precipitation indices in China and link them with related natural disaster events, as well as with the spatial evolution of land use and land cover and Gross Domestic Product (GDP). Daily precipitation data from 1274 rain gauge stations spanning the period from 1961 to 2021 were used to analyze the spatial distribution characteristics of extreme precipitation index climate trends in China. The results revealed a significant increasing trend of the intensity of extreme precipitation in eastern China, but a decreasing trend of amount, frequency, and duration of extreme precipitation in southwest China, accompanied by a significant increase in consecutive dry days. Natural disaster records related to extreme precipitation trends indicated a significant increase at an annual rate of 1.3 times in the frequency of flood, storm, drought, and landslide occurrences nationwide, with substantial regional dependence in disaster types. Furthermore, the spatial evolution of land use and GDP levels showed a close association with the spatial distribution of natural disaster events induced by extreme precipitation. Although the number of deaths caused by extreme precipitation-related disasters in China is decreasing (by 51 people per year), the economic losses are increasing annually at an annual rate of USD 530,991, particularly due to floods and storms. This study holds the potential to inform decision-making processes, facilitate the implementation of mitigation and adaptation measures, and contribute to reducing the impacts of natural disasters across diverse regions worldwide. Full article

Accurate forecasting of river flows is essential for effective water resource management, flood risk reduction and environmental protection. The ongoing effects of climate change, in particular the shift in precipitation patterns and the increasing frequency of extreme weather events, necessitate the development of advanced forecasting models. This study investigates the application of long short-term memory (LSTM) neural networks in predicting river runoff in the Velika Morava catchment in Serbia, representing a pioneering application of LSTM in this region. The study uses daily runoff, precipitation and temperature data from 1961 to 2020, interpolated using the inverse distance weighting method. The LSTM model, which was optimized using a trial-and-error approach, showed a high prediction accuracy. For the Velika Morava station, the model showed a mean square error (MSE) of 2936.55 and an R² of 0.85 in the test phase. The findings highlight the effectiveness of LSTM networks in capturing nonlinear hydrological dynamics, temporal dependencies and regional variations. This study underlines the potential of LSTM models to improve river forecasting and water management strategies in the Western Balkans. Full article

The impacts of climate change on snowfall have received great interest in cold regions for water resource and flood risk management. This study investigated the effects of rises in air and sea surface temperatures and sea ice on snowfall in Hokkaido, northern Japan, over the period from 1961 to 2020 (60 years). Climate data observed at the 22 weather stations operated by the Japan Meteorological Agency (JMA) were analyzed. Statistics describing the effects of climate change on snowfall were computed. The trend in these quantities was obtained using Sen’s slope estimator, and their statistical significance was evaluated by the Mann–Kendall test. The warming trends obtained at these stations were all positive and statistically significant. Annual snowfall increased at seven stations but decreased at two stations. The snowfall period decreased mainly on the southern coast. This is attributed to the fact that these sites are on the leeward side of the Eurasian monsoon, and that air temperatures on the coast and the surface temperature of the sea off Kushiro have risen sufficiently. The results suggest that the flood risk may increase in response to the acceleration of the increase in the level of a river due to early melting snow in spring (March and April). Although the weather stations on the east coast are also on the leeward side, the snowfall period has not shortened. The warming trends in April are very weak on the east coast. The correlation between the air temperature in March and April and the period of sea ice accumulation suggests that melting sea ice in spring plays an important role in preventing the winter period from shortening. Decrease in sea ice due to a rise in both air and sea surface temperatures may increase flood risk in early spring, and thus, some measures may need to be taken in the future. Full article

The seasonality of floods is a key factor affecting riparian agriculture. Flood season staging is the main means of identifying the seasonality of floods. In the process of staging the flood season, set pair analysis is a widely used method. However, the set pair analysis method (SPAM) cannot take into account the differences in and volatility of the staging indicators, and at the same time, the SPAM cannot provide corresponding staging schemes according to different scenarios. To address these problems, the improved set pair analysis method (ISPAM) is proposed. Kernel density estimation (KDE) is used to calculate the interval of the staging indicators to express their volatility. Based on the interval theory, the deviation method is improved, and the weights of the staging indicators are calculated to reflect the differences in different staging indicators. The theoretical correlation coefficient can be calculated by combining the weights and interval indicators and fitting the empirical connection coefficient corresponding to each time period. Finally, the ISPAM is established under different confidence levels to derive staging schemes under different scenarios. Based on the daily average precipitation flow data from 1961 to 2022 in the Nandujiang middle basin and surrounding areas in tropical island regions, the staging effect of the ISPAM was verified and compared using the SPAM, Fisher optimal segmentation method, and improved set pair analysis method without considering differences in the indicator weights (ISPAM-WCDIIW), and the improved set pair analysis method without considering indicator fluctuations (ISPAM-WCIF). According to the evaluation results from the silhouette coefficient method, it can be concluded that compared with the SPAM and ISPAM-WCIF, the ISPAM provided the optimal staging scheme for 100% of the years in the test set (2011–2022). Compared with the Fisher optimal segmentation method, the optimal staging scheme for more than 83% of the years (2011, 2013–2015, and 2017–2022) in the test set was provided by the ISPAM. Although the ISPAM-WCDIIW, like the ISPAM, can provide optimal staging schemes, the ISPAM-WCDIIW could not provide an exact staging scheme for more than 55% of the scenarios (the ISPAM-WCDIIW could not provide an exact staging scheme in scenarios (0.7, 0.6), (0.8, 0.6), (0.8, 0.9), (0.95, 0.6), and (0.95, 0.8)). The results show that the ISPAM model is more reasonable and credible compared with the SPAM, Fisher optimal segmentation method, ISPAM-WCDIIW, and ISPAM-WCIF. The purpose of this study is to provide a reference for flood season staging research during flood seasons. Full article

The knowledge of natural hazards that occurred in the past is essential for implementing forecasting and prevention actions, for managing risk, and identifying proper land use. Floods do not escape this rule. This article sheds light on an unknown intense rainfall period, which affected the Campania region and the territory of the current Molise region (Southern Italy) on October–November 1961. The period culminated in floods, particularly involving the town of Benevento (Campania region), which had been affected by several inundations over centuries. The research made an extensive use of unpublished archival sources. The documents allowed us to both outline the pluviometric and hydrological picture of the period and catalogue seventeen physical and environmental effects suffered by over two hundred municipalities. Furthermore, we also disclosed the economic consequences in the wide territory involved. Special attention was paid to Benevento, for which we also drew up the scenario map related to the 19 October flood. For this town, historical data were effective for developing and testing the hydraulic model of the Sabato and Calore Rivers, which overflowed at the site. In this regard, we made some considerations on the current flood risk of the town. From a methodological point of view, we stress the importance of a historical approach in close relationship to other expertise for the knowledge of natural hazards, tracing also some future perspectives. The research complies with the 2030 Agenda for Sustainable Development and its Goal 11 concerned with making cities and human settlements inclusive, safe, resilient, and sustainable. The research findings will be useful for scholars and practitioners for both improving flood hazard models and arranging archival research path. Finally, local authorities in charge of risk mitigation can also benefit from the research results. Full article

The source area of the Yellow River (SAYR) in China is an important water yield and water-conservation area in the Yellow River. Understanding the variability in rainfall and flood over the SAYR region and the related mechanism of flood-causing rainfall is of great importance for the utilization of flood water resources through the optimal operation of cascade reservoirs over the upper Yellow River such as Longyangxia and Liujiaxia, and even for the prevention of flood and drought disasters for the entire Yellow River. Based on the flow data of Tangnaihai hydrological station, the rainfall data of the SAYR region and NCEP-NCAR reanalysis data from 1961 to 2020, three meteorological conceptual models of flood-causing rainfall—namely westerly trough type, low vortex shear type, and subtropical high southwest flow type—are established by using the weather-type method. The mechanism of flood-causing rainfall and the corresponding flood characteristics of each weather type were investigated. The results show that during the process of flood-causing rainfall, in the westerly trough type, the mid- and high-latitude circulation is flat and fluctuating. In the low vortex shear type, the high pressures over the Ural Mountains and the Okhotsk Sea are stronger compared to other types in the same period, and a low vortex shear line is formed in the west of the SAYR region at the low level. The rain is formed during the eastward movement of the shear line. In the subtropical high southwest flow type, the low trough of Lake Balkhash and the subtropical high are stronger compared to other types in the same period. Flood-causing rainfall generally occurs in areas with low-level convergence, high-level negative vorticity, low-level positive vorticity, convergence of water vapor flux, a certain amount of atmospheric precipitable water, and low-level cold advection. In terms of flood peak increment and the maximum accumulated flood volume, the westerly trough type has a long duration and small flood volume, and the low vortex shear type and the subtropical high southwest flow type have a short duration and large flood volume. Full article

Under climate change, the frequency of drought-flood abrupt alternation (DFAA) events is increasing in Southeast China. However, there is limited research on the evolution characteristics of DFAA in this region. This study evaluated the effectiveness of the drought and flood indexes including SPI (Standardized Precipitation Index), SPEI (Standardized Precipitation Evapotranspiration Index), and SWAP (Standardized Weighted Average Precipitation Index) in identifying DFAA events under varying days of antecedent precipitation. Additionally, the evolution characteristics of DFAA events in Fujian Province from 1961 to 2021 were explored. The results indicate that (1) SPI-12d had the advantages of high effectiveness, optimal generalization accuracy, and strong generalization ability of identification results, and it can be used as the optimal identification index of DFAA events in Southeast China. (2) There was an overall increase in DFAA events at a rate of 1.8 events/10a. The frequency of DFAA events showed a gradual increase from the northwest to the southeast. (3) DTF events were characterized by moderate drought to flood, particularly in February, July, and August, while FTD events were characterized by light/moderate flood to drought, with more events occurring from June to October. (4) DTF event intensity increased in the northern and western regions from 1961 to 2021. For FTD events, the intensity notably increased in the western region from 1961 to 2001, while a significant increase occurred in all regions except the central region from 2001 to 2021. These findings emphasize the need for precautionary measures to address the increasing frequency and severity of DFAA events in Southeast China. Full article

This study examined the association between precipitation indices and atmospheric processes in Southeast Europe using ERA5 land data from 1961 to 2020. The Rx1day intensity index showed predominantly positive trends in heavy precipitation events, resulting in more intense precipitation over fewer days in various parts of Southeast Europe, particularly during autumn. These findings highlight the potential increase in extreme precipitation frequency and intensity due to ongoing climate change, leading to an elevated risk of flood events. Such insights provide valuable information for policymakers and stakeholders to adapt to the impacts of extreme precipitation events. Full article

In recent years, the dry-wet transition (DWT), which often leads to regional floods and droughts, has become increasingly frequent in the Poyang Lake basin and the Dongting Lake basin (hereinafter referred to as the two-lake region). This study aims to investigate the early warning signals (EWSs) for DWT events. Firstly, based on the standardized precipitation index (SPI) at 161 meteorological stations in the two-lake region from 1961 to 2020, the two-lake region is divided into four sub-regions by the Rotational Empirical Orthogonal Function (REOF) analysis method. Then, the occurrence time of the DWT events in each sub-region is determined by the moving t-test (MTT) technique. Finally, by using two indicators (variance and the auto-correlation coefficient) to describe the critical slowing down (CSD) phenomenon, the EWSs denoting the DWT events in all sub-regions are investigated. The results reveal that there was a significant dry-to-wet (wet-to-dry) event around 1993 (2003) in the two-lake region during the last 60 years. The phenomenon of CSD, where the auto-correlation coefficient and variance increases are found in all sub-regions around 10 years before the DWT, suggests that it can be taken as an EWS for the DWT events. This study confirms the effectiveness of applying the slowing down theory in investigating the EWSs for abrupt changes in the two-lake region, aiming to provide a theoretical basis for effective prevention and mitigation against disasters in this region. Moreover, it is expected to be well-applied to the middle and lower reaches of the Yangtze River. Full article

With global warming, previous studies have found nonuniformity responses of precipitation because of regional differences. However, climate change affects the mean, extreme, and data structure of precipitation. Quantile regression, which can reflect every part of the trends of data, was used to detect responses of each part of precipitation in China. The V2.0 dataset of daily precipitation grid data (0.5° × 0.5°) from 1961 to 2020 in China was used as practical observation data. Daily precipitation in 2015–2100 from the China Model BCC-CSM2-MR of scenarios SSP2-4.5 and SSP5-8.5 were chosen as future climate changes with moderate and high radiative forcing, respectively. On the basis of the sign consistency of the slope coefficients with quantile regression, the results of quantiles q = 0.3, 0.5, 0.7 and 0.9 were selected to represent low, median, high and flood precipitation, respectively. Precipitation in four seasons was separately analyzed to observe seasonal characteristics in China. For the observation data, precipitation had obviously different responses in the low and high percentiles and was present in mainly spring and summer. In spring, in the middle and lower Yangtze Plains, the low and median precipitation increased, whereas the high and flood precipitation significantly decreased. In summer, Heilongjiang Province and northern Inner Mongolia showed decreasing trends in the low quantile and increasing trends in the high quantile, indicating a completely opposite trend adjustment. These regions deserve more attention. However, obviously different responses in low and high percentiles were not so evident in future climate changes. Self-consistency in model data may weaken the heteroscedastic characteristics of precipitation. Full article

Local climates are responding to global warming differently, and the changes in rainstorms in mountainous areas of Southwest China are of particular interest. This study, using monthly NCEP/NCAR reanalysis and daily precipitation observation of 90 meteorological stations from 1961 to 2021, analyzed the temporal and spatial variation characteristics of rainstorms and floods in Southwest China and their relationship with atmospheric circulations. The results led us to the following five conclusions: (1) Rainstorms and floods in southwest China mainly occur from June to August, during which time July has the most weather events, followed by August. (2) The southwest of Guizhou province, the southern edge of Yunnan province, and regions from the east of the Sichuan Basin to the north of Guizhou have experienced more rainstorms and floods, while the northwest regions of Southwest China have had fewer. (3) Over the last 61 years, rainstorms and floods have exhibited an overall rising trend, especially in the last 10 years. The year 2012 was an abrupt inflection point in rainstorms and floods in Southwest China, from low to high frequency, while the correlation coefficient between rainstorms and floods and the global surface temperature is above the 95% significance level. (4) Rainstorms and floods exhibit changes at periods of 8 years, 16 years, and 31 years. (5) Rainstorms and floods show a good correlation with multiple variables, such as South Asian high-pressure systems west of 90°E, the upper trough front, the northwest side of the western Pacific subtropical high, and the convergence of warm and wet air in the middle and lower layers with cold air on the ground. Full article

Regional flood frequency analysis (RFFA) is a powerful method for interrogating hydrological series since it combines observational time series from several sites within a region to estimate risk-relevant statistical parameters with higher accuracy than from single-site series. Since RFFA extreme value estimates depend on the shape of the selected distribution of the data-generating stochastic process, there is need for a suitable goodness-of-distributional-fit measure in order to optimally utilize given data. Here we present a novel, least-squares-based measure to select the optimal fit from a set of five distributions, namely Generalized Extreme Value (GEV), Generalized Logistic, Gumbel, Log-Normal Type III and Log-Pearson Type III. The fit metric is applied to annual maximum discharge series from six hydrological stations along the Sava River in South-eastern Europe, spanning the years 1961 to 2020. Results reveal that (1) the Sava River basin can be assessed as hydrologically homogeneous and (2) the GEV distribution provides typically the best fit. We offer hydrological-meteorological insights into the differences among the six stations. For the period studied, almost all stations exhibit statistically insignificant trends, which renders the conclusions about flood risk as relevant for hydrological sciences and the design of regional flood protection infrastructure. Full article