Annual Maximum Research Articles

Increasing Optimum Temperature of Vegetation Activity Over the Past Four Decades

AbstractOver the past four decades, global temperatures have increased more rapidly than before, potentially reducing vegetation activity if temperatures exceed the optimum temperature (Topt). However, plants have the capacity to acclimate to rising temperatures by adjusting Topt, thereby maintaining or even enhancing photosynthesis and carbon uptake. Despite this, it remains unclear how Topt of vegetation activity changes over time and to what extent global vegetation can acclimate to current temperature changes. In this study, we evaluated the temporal trends of Topt of vegetation activity and the thermal acclimation magnitudes globally using three remote‐sensed vegetation indices and eddy‐covariance observations of gross primary productivity from 1982 to 2020. We found that the global Topt of vegetation activity has increased at an average rate of 0.63°C per decade over the past four decades. The increase in Topt closely tracked the rise in annual maximum daily mean temperature (Tmax), indicating that thermal acclimation has occurred widely across the globe. Globally, we found an average thermal acclimation magnitude of 0.38°C per 1°C increase in Tmax. Notably, polar and continental regions exhibited the highest thermal acclimation magnitudes, while arid areas showed the lowest. Additionally, the thermal acclimation magnitude was positively affected by interannual temperature variability and negatively affected by soil moisture and vapor pressure deficits. Our findings indicate that terrestrial ecosystems have acclimated to current climate warming trends with varying degrees, suggesting a greater potential for land carbon uptake. Moreover, these results highlight the necessity for earth system models to integrate the thermal acclimation of Topt to better forecast the global carbon cycle.

Enhancing long-term vegetation monitoring in Australia: a new approach for harmonising the Advanced Very High Resolution Radiometer normalised-difference vegetation (NVDI) with MODIS NDVI

Abstract. Long-term, reliable datasets of satellite-based vegetation condition are essential for understanding terrestrial ecosystem responses to global environmental change, particularly in Australia, which is characterised by diverse ecosystems and strong interannual climate variability. We comprehensively evaluate several existing global Advanced Very High Resolution Radiometer (AVHRR) normalised-difference vegetation index (NDVI) products for their suitability for long-term vegetation monitoring in Australia. Comparisons with the MODIS NDVI highlight significant deficiencies, particularly over densely vegetated regions. Moreover, all the assessed products failed to adequately reproduce the interannual variability in the pre-MODIS era as indicated by Landsat NDVI anomalies. To address these limitations, we propose a new approach to calibrating and harmonising NOAA's Climate Data Record of AVHRR NDVI to the MODIS MCD43A4 NDVI for Australia using a gradient-boosting decision tree ensemble method. Two versions of the datasets are developed, one incorporating climate data in the predictors (“AusENDVI-clim”: Australian Empirical NDVI-climate) and another that is independent of climate data (“AusENDVI-noclim”). These datasets, spanning 1982–2013 at a spatial resolution of 0.05° and with a monthly time step, exhibit strong correlations (r2=0.89–0.94) and low mean errors compared with MODIS MCD43A4 NDVI (mean absolute error (MAE) = 0.014–0.028, RMSE = 0.021–0.046), accurately reproducing seasonal cycles over densely vegetated regions. Furthermore, they closely replicate the interannual variability in vegetation condition in the pre-MODIS era. A reliable method for gap-filling the AusENDVI record is also developed that leverages climate, atmospheric CO2 concentration, and woody-cover fraction predictors. The resulting synthetic NDVI dataset shows excellent agreement with the MODIS MCD43A4 NDVI and the recalibrated AVHRR NDVI time series (r2=0.82–0.95, MAE = 0.016–0.029, RMSE = 0.039–0.041). Finally, we provide a complete 41-year dataset where the gap-filled AusENDVI-clim from January 1982 to February 2000 is joined with the MODIS MCD43A4 NDVI from March 2000 to December 2022. Analysing 40-year per-pixel trends in Australia's annual maximum NDVI revealed increasing values, and shifts in the timing, of the annual peak NDVI across most of the continent, underscoring the dataset's potential to address crucial questions regarding the changing vegetation phenology and its drivers. The AusENDVI dataset can be used for studying Australia's changing vegetation dynamics and downstream impacts on the terrestrial carbon and water cycles, and it provides a reliable foundation for further research into the drivers of vegetation change. AusENDVI is open access and available at https://doi.org/10.5281/zenodo.10802703 (Burton et al., 2024).

Parameter Estimation of the Inverted Kumaraswamy Distribution by Using L-Moments Method: An Application on Precipitation Data

Modeling precipitation data plays a critical role in water resource and flood management. Statistical distributions are frequently used in describing hydrological variables. Different distributions and estimation methods have been presented in previous studies for modeling precipitation data. In this study, the inverted Kumaraswamy distribution is considered for its advantageous properties, and the L-moments and maximum likelihood methods are employed in estimating the parameters of the inverted Kumaraswamy distribution. In the application part, the annual maximum monthly precipitations recorded in the Rize, Türkiye are modeled with the inverted Kumaraswamy distribution. To the best of the author’s knowledge, the L-moment method is considered for the first time to estimate the parameters of the inverted Kumaraswamy distribution. In addition, the efficiencies of the estimation methods are compared with a Monte-Carlo simulation study. For evaluating the performances of the estimation methods, the goodness of fit criteria including root mean square error, Kolmogorov Smirnov test, and coefficient of determination (R^2) are used in the application part of the study. The results show that for the data considered, the L-moments method yields more accurate results than the maximum likelihood method in estimating the parameters when the sample size is small. Accordingly, the corresponding distribution with L-moments estimations provides a better fit to precipitation data obtained from the Rize station.

Trend detection in the temperature of Quebec (Canada) rivers

ABSTRACT Trend detection in environmental data subjected to natural fluctuations can prove difficult. With river temperature, the difficulty also lies in the typically limited lengths of the time series. In this work, three different river temperature data sources (continuous and spot instream measurements, and satellite thermal images) were combined to build 30- to 44-year-long time series at 116 locations, distributed throughout most of the Quebec Province territory. Mann-Kendall tests and a non-linear model were used to detect and quantify trends. Uncertainty and biases due to the different data sources and uneven samplings were evaluated and included in the assessment of the trends’ quantification and reliability. Overall, most trends detected in the temperature of Quebec rivers are positive, with higher warming occurring in summer. Annual maxima are found to have increased by 0.023°C/year (median value) between 1979 and 2022, and this maximum appears to occur 2.6 days (median value) later.

Enhancing Coffee Agroforestry Systems Suitability Using Geospatial Analysis and Sentinel Satellite Data in Gedeo Zone, Ethiopia.

The Gedeo zone agroforestry systems are the main source of Ethiopia's coffee beans. However, land-use and suitability analyses are not well documented due to complex topography, heterogeneous agroforestry, and lack of information. This research aimed to map the coffee coverage and identify land suitability for coffee plantations using remote sensing, Geographic Information Systems (GIS), and the Analytical Hierarchy Process (AHP) in the Gedeo zone, Southern Ethiopia. Remote sensing classifiers often confuse agroforestry and plantations like coffee cover with forest cover because of their similar spectral signatures. Mapping shaded coffee in Gedeo agroforestry using optical or multispectral remote sensing is challenging. To address this, the study identified and mapped coffee coverage from Sentinel-1 data with a decibel (dB) value matched to actual coffee coverage. The actual field data were overlaid on Sentinel-1, which was used to extract the raster value. Pre-processing, classification, standardization, and reclassification of thematic layers were performed to find potential areas for coffee plantation. Hierarchy levels of the main criteria were formed based on climatological, edaphological, physiographic, and socioeconomic factors. These criteria were divided into 14 sub-criteria, reclassified based on their impact on coffee growing, with their relative weights derived using AHP. From the total study area of 1356.2 km2, the mapped coffee coverage is 583 km2. The outcome of the final computed factor weight indicated that average annual temperature and mean annual rainfall are the primary factors, followed by annual mean maximum temperature, elevation, annual mean minimum temperature, soil pH, Land Use/Land Cover (LULC), soil texture, Cation Exchange Capacity (CEC), slope, Soil Organic Matter (SOM), aspect, distance to roads, and distance to water, respectively. The identified coffee plantation potential land suitability reveals unsuitable (413 km2), sub-suitable (596.1 km2), and suitable (347.1 km2) areas. This study provides comprehensive spatial details for Ethiopian cultivators, government officials, and agricultural extension specialists to select optimal coffee farming locations, enhancing food security and economic prosperity.

Multimodel regional frequency analysis of CMIP extreme precipitation

Abstract A recurrent question in climate risk analysis is determining how climate change will affect
heavy precipitation patterns.
Dividing the globe into homogeneous sub-regions should improve the modeling of heavy precipitation by inferring common regional distributional parameters.
In addition, biases due to model errors in global climate models (GCMs) should be considered to understand the climate response to diffrent forcing effects.
Within this context, we propose an efficient clustering algorithm that, compared to classical regional frequency analysis (RFA) techniques, is covariate-free and accounts for dependence.
It is based on a new non-parametric dissimilarity that combines both the RFA constraint and the pairwise dependence.
We derive asymptotic properties of our dissimilarity estimator, and we interpret it for generalized extreme value distributed pairs.

As an application, we cluster annual daily precipitation maxima of 16 GCMs from the coupled model intercomparison project.
We combine the climatologically consistent subregions identified for all GCMs.
This improves the spatial clusters coherence and outperforms methods either based on margins or on dependence.
Finally, by comparing the natural forcings partition with the one with all forcings, we assess the impact of anthropogenic forcing on precipitation extreme patterns.

Analyzing trend and forecasting of temperature and rainfall in Shimla district of Himachal Pradesh, India using non-parametric and bagging REPTree machine learning approaches

The changing pattern of climate variables has caused extreme weather events and severe disasters, especially in mountainous regions. Such events have a detrimental impact on resources, environment and society. Thus, it has become imperative to examine the trends and forecasts of meteorological variables using a scientific modelling approach. This study investigates temperature and rainfall trends using the modified Mann-Kendall test and Sen's slope estimator between 1980 and 2021. A Bagging-REPTree machine learning model was utilized for forecasting temperature and rainfall trends for the next 30 years (2022–2051) to understand the temporal dynamics in Shimla district of the Indian Himalayan state. The mean absolute percentage error, mean absolute error, root mean squared error and correlation coefficient were determined to assess the effectiveness and precision of the model. The findings revealed that the frequency of intense rainfall in the district has increased during the monsoon season (June–September) from 1980 to 2021. Significant trends were found in annual rainfall, maximum, minimum and mean temperatures while rainfall during the winter, summer and post-monsoon seasons has shown a declining trend. The forecast analysis revealed a significant trend for rainfall during the monsoon season and an increasing trend in the maximum temperature has been observed during the winter and summer seasons. The analysis has provided sufficient evidence of variability and uncertainty in the behavior of meteorological variables. The outcome of the study may help in devising suitable adaptation and mitigation strategies to combat climate change in hilly regions. The methodology adopted in the study may help in the future progression of the research in different geographical regions for trend and climate forecasting.

Understanding the drivers of the catchment hydrological cycle of the Jonkershoek Valley catchment, South Africa

Understanding trends in hydro-climate variables and the impacts of land use on the streamflow is crucial for the development of appropriate catchment water management strategies. Jonkershoek (146 km2) is an important headwater catchment in the Table Mountain Group (TMG) geological region, contributing flow to the Cape Winelands District Municipality in the Western Cape. This study analyses hydro-climate variables at annual, monthly, and seasonal scales using an integrated approach composed of statistical homogeneity testing for abrupt changes, Mann-Kendall tests for trend analysis, and the Indicators of Hydrological Alteration (IHA) tool for streamflow alterations. Analyses were conducted for three headwater sub-catchments within the Jonkershoek value, each with different land use history.Homogeneity test of rainfall and streamflow data spanning from 1946 to 2019 identified gradual downwards change points for annual rainfall and streamflow across the sub-catchments. Moreover, the change points for streamflow were inconsistent with those of rainfall. The identified change points in streamflow were consistent with the timing of afforestation activities in Tierkloof, whereas in Bosboukloof and Langrivier they could be attributed to earlier climatic variability. Furthermore, Mann-Kendall test detected significant (p < 0.05) decreasing trends for both annual and seasonal rainfall which coincided with most of the streamflow trends. Trends were strongest for the winter season suggesting a possible shift in climate patterns which influence winter rainfall. Winter streamflows declined by 15.5%–39.5%. Analysis of hydrological flow indices indicated significant decrease in 1-,7- and 30-day annual maximum extremes during afforestation which were attributed to high evapotranspiration rates of pines. The opposite was observed during clearfelling period in Bosboukloof. The median monthly flow also showed a decrease for winter months. This shows that climate variability and land-use change by afforestation have major impacts on streamflow. The findings of this study are important to inform policymakers on the impacts of climate change and land use, allowing pro-active mitigation and adaptation.

Greenness moderates the relationship between self-rated social standing and depression among older adults in the Canadian longitudinal study on aging

IntroductionGreenness is considered to be a health-promoting feature of both natural and built environments and has the potential to influence mental health outcomes. However, most studies to date have neglected to address whether greenness differentially affects mental health outcomes for individuals across the socioeconomic spectrum. Our study explored if greenness is a moderating factor in the relationship between socioeconomic status (SES) and mental health using data from the Canadian Longitudinal Study on Aging (CLSA).MethodsA cross-sectional design was used to compare mental health outcomes between individuals with different levels of SES and residential greenness. We used self-rated social standing as a measure of SES and depression score measured using the Centre for Epidemiologic Studies 10-Item Depression Scale (CESD-10) as a measure of mental health. Greenness was measured using the annual maximum Normalized Difference Vegetation Index (NDVI) within a 1,000 m buffer area of participants' residential postal code locations.ResultsThere was a statistically significant moderating effect of greenness for the relationship between self-rated social standing and depression score. As greenness increased, individuals with lower self-rated social standing had the greatest decreases in depression score.DiscussionThe results of our study suggests that targeting greening interventions at individuals and communities with low SES may reduce depressive symptoms overall, as well as decrease socioeconomic inequalities in depression.

Probability Distribution Analysis of Annual Maximum Precipitation in the Niger Delta: A Critical Step towards Effective Flood Risk Management

Aims: The study aimed to analyze historical maximum precipitation data from seven cities in the Niger Delta to assess vulnerability to extreme rainfall events, identify optimal probability distribution models for future predictions, and provide insights for flood risk management strategies. Study Design: This is a retrospective analytical study based on historical precipitation data. Place and Duration of Study: The study was conducted across seven major cities in the Niger Delta region of Nigeria, utilizing data collected over several decades. Methodology: Historical maximum precipitation data were statistically analyzed to determine the best-fit probability distribution models. The Kolmogorov-Smirnov (KS) test and Mean Squared Prediction Error (MSPE) were used to validate the suitability of distributions, including Log-Normal, Normal, Log-Pearson III, and Generalized Extreme Value (GEV) for each city. Return periods of 10, 25, 50, and 100 years were calculated to predict future precipitation trends and assess flood risks. Results: Significant variations in annual maximum precipitation were observed across the cities, with Calabar recording the highest peak at 4062.7mm. The Log-Normal distribution was the best fit for Akure and Calabar, while the Normal distribution best described rainfall in Benin City. Log-Pearson III was optimal for Owerri, Umuahia, and Uyo, and GEV best fitted Port Harcourt’s data. High P-values (&gt;0.87) indicated good model fits across the cities. Return period analysis suggested greater risks of extreme precipitation events in coastal cities like Calabar and Port Harcourt. Conclusion: The study underscores the importance of tailored, city-specific flood management strategies in the Niger Delta to mitigate the impact of extreme rainfall events, particularly in the face of climate change.

Change and coincidence risk analysis of floods in the Mahanadi River Basin, India

ABSTRACT Coincidence flood risk due to the simultaneous flood occurrences on both mainstream and its tributary results in downstream inundation of a confluence. Therefore, this study was taken up for coincidence flood risk analysis of Mahanadi River basin considering the annual maximum (AM) and the peak over threshold (POT) series. In this study, the Mann–Kendall trend test was performed to analyze the trend in flood magnitudes, while circular statistics was used to analyze the persistence in flood timing. The joint distributions between the streams were established using bivariate copula functions considering flood magnitudes and occurrence dates as variables. The results of MK test revealed a mixture of significant and insignificant trends for AM series for the selected stations, while the trends were insignificant for POT series. Additionally, the flood occurrence dates showed a high level of persistence. It is evident from the results that the coincidence risk is high for Seorinarayan–Bamnidhi confluence point with a risk value of 7.63 × 10−3. The coincidence risk increases mostly from late July to mid-September, and the coincidence risk is high for more frequently occurring flood events. The obtained results will help in prioritizing flood hazard zones for effective flood mitigation strategies in Mahanadi River basin.

The Timing, Magnitude, and Relative Composition of Extreme Total Water Levels Vary Seasonally Along the U.S. Atlantic Coast

AbstractThe annual maximum (AM) method, which subsamples time series to retain the maximum event per year, and the peak‐over‐threshold (POT) method, which extracts values exceeding a threshold to define extremes, have long histories in determining flood event frequency. In practice, extreme value distributions applied to AM and POT events often assume that the data comes from the same statistical population. Locations across the world, like the United States (U.S.) Atlantic coastline, however, experience high coastal water levels driven by various individual processes and storms with different driving mechanisms during different seasons. This research investigates when extreme total water levels (TWLs) occur during the year along the U.S. Atlantic coast and whether individual components, like waves, tides, and storm surge, contributing to TWLs vary across regions and during the year. From 1980 to 2020, extreme TWLs occurred during the extratropical and tropical seasons, with the relative proportion of extreme TWLs occurring during the extratropical season increasing northward. Still water levels drive spatial variability in extreme TWL magnitude, while wave climate drives differences in extreme TWL magnitude between extratropical and tropical seasons. Month‐to‐month variability in the composition of extreme TWLs varies more than spatial variability, highlighting the importance of understanding the components driving extremes at different times of the year. Variations across storm seasons in the processes contributing to extreme TWLs may have implications for how large‐scale changes to the climate impact hazards along open sandy coastlines and influence the robustness of extrapolating rare events from models fit to a single population.

Frequency Analysis of Annual One Day Maximum Rainfall at Faisalabad, Lahore and Multan (Punjab)

Effective management of water systems and seepage projects depends on accurate precipitation data, particularly annual daily maximum precipitation, which is essential for designing hydraulic structures. This study conducts a frequency analysis of annual daily maximum precipitation for Faisalabad, Lahore, and Multan in Punjab, Pakistan, using over 50 years of historical data from these three stations. The analysis, performed with RAINBOW software, evaluates five probability distributions—normal, log-normal, weibull, gamma, and exponential. The fit of these distributions was assessed using Chi-square, Kolmogorov-Smirnov, and Anderson-Darling tests at significance levels of 5%, 10%, and 20%. Results indicate that the Weibull and Log-normal distributions most accurately represent the precipitation data. Based on this analysis, the study estimates the magnitude of maximum annual precipitation for return periods of 2, 5, 10, 25, 50, and 100 years. The findings aim to improve water management strategies, enhance flood and drought prevention measures, and optimize the hydraulic design of seepage structures, contributing to more effective planning and resilience in the face of extreme weather events.

Extreme sea levels and floods: the case study of Klaipėda City, Lithuania

Klaipėda City is located in the Southeast Baltic Sea region, where sea level rise has been observed for decades. The Klaipėda Strait, which separates the Baltic Sea and the Curonian Lagoon, where the port is located, is also more prone to sudden extreme changes in water levels, usually caused by windstorms. Extreme sea level changes pose a threat to port operations, technical structures, city residents, buildings, and infrastructure. Fluctuations in sea levels also affect the water level of the Danė River, which enters the Klaipėda Strait and divides the city into two parts. Therefore, this study aims to determine the past extreme sea level events and their influence on floods in the Danė River within Klaipėda City from 1961 to 2022. For this, the impact of meteorological parameters caused dangerous sea level rises in the Klaipėda Strait, and the following rise of the Danė River was studied. The results show that the annual mean, annual mean maximum, and annual mean minimum water levels in the Klaipėda Strait increased from 1961 to 2022. Also, the number of events where the water level in the Klaipėda Strait was ≥ 100 cm in the Baltic Elevation System was increasing. The increasing frequency of extreme water level events in the Klaipėda Strait puts urban areas at greater risk from Danė River compound floods.

Trends in extreme rainfall and their relationship to flooding episodes in Vhembe district, South Africa

Extreme weather events, such as heavy rainfall, are being increased by climate change in various regions, and such events often cause floods. This study examined the trends and variability of extreme rainfall indices using daily rainfall data (1981–2023) from three study sites at different socio-economic development spectra in Vhembe District, Limpopo Province, South Africa. The analyses focus on indices such as the annual total rainfall from wet days (PRCPTOT), the maximum number of consecutive dry days (CDD) and wet days (CWD), annual maximum 1-day and 5-day rainfall (RX1 day and RX5 day), the Simple Daily Intensity Index (SDII), the number of days exceeding varying amounts of precipitation (R10, R20, R40) and the annual number of wet days with rainfall greater than the 95th and 99th percentile (R95p and R99p) of the 1981–2023 daily rainfall. We discuss the observed trends in extreme rainfall indices in light of the actual flood occurrences to establish linkages. Several statistically significant and marginal changes in extreme rainfall trends were identified and provided key insights into reported flooding events in the district—flooding episodes were mainly attributed to the significant increases in total precipitation (PRCPTOT) and rainfall exceeding the 99th percentile of daily rainfall (R99p). Other significant contributors were declining CDD and increasing RX1day at Duthuni, increasing R40 at Musina as well as increasing R1 and declining CDD at Sane. However, the low altitude, urbanization, poor waste management and inadequate drainage systems were among the key non-climatic drivers of flood risk across the study sites, but these warrant further investigation. The complex interplay between climatic and non-climatic drivers of flood risk underscores the importance of localized climate studies and the need for adaptive strategies to minimize loss and damage. Overall, this research provides valuable insights into localized extreme rainfall trends, which are essential for developing site-specific flood mitigation and adaptation strategies. However, such initiatives require placing vulnerable communities at the centre in order to develop solutions that are locally led and relevant.

Assessing the impact of climate change on agricultural production in central Afghanistan

Afghanistan has faced extreme climatic crises such as drought, rising temperature, and scarce precipitation, and these crises will likely worsen in the future. Reduction in crop yield can affect food security in Afghanistan, where the majority of population and economy are completely dependent on agriculture. This study assessed the interaction between climate change and crop yield in Kabul of Afghanistan during the reference (1990–2020) and future (2025–2100) periods. Climate data (1990–2020) were collected from four meteorological stations and three local organizations, and wheat yield data (1990–2020) were acquired from the United States Agriculture Department. Data during the reference period (1990–2020) were used for the validation and calibration of the statistical downscaling models such as the Statistical Downscaling Model (SDSM) and Long Ashton Research Station Weather Generator (LARS-WG). Furthermore, the auto-regression model was used for trend analysis. The results showed that an increase in the average annual temperature of 2.15°C, 2.89°C, and 4.13°C will lead to a reduction in the wheat yield of 9.14%, 10.20%, and 12.00% under Representative Concentration Pathway (RCP)2.6, RCP4.5, and RCP8.5 during the future period (2025–2100), respectively. Moreover, an increase in the annual maximum temperature of 1.79°C, 2.48°C, and 3.74°C also causes a significant reduction in the wheat yield of 2.60%, 3.60%, and 10.50% under RCP2.6, RCP4.5, and RCP8.5, respectively. Furthermore, an increase in the annual minimum temperature of 2.98°C, 2.23°C, and 4.30°C can result in an increase in the wheat yield of 6.50%, 4.80%, and 9.30% under RCP2.6, RCP4.5, and RCP8.5, respectively. According to the SDSM, the decrease of the average monthly precipitation of 4.34%, 4.10%, and 5.13% results in a decrease in the wheat yield of 2.60%, 2.36%, and 3.18% under RCP2.6, RCP4.5, and RCP8.5, respectively. This study suggests that adaptation strategies can be applied to minimize the consequences of climate change on agricultural production.

Hourly Downscaling of Daily Climate Projection Data for the Nakdong River Basin using the Nearest Neighbor Search Method

Hydrological and water quality models have been continuously improved for assessing the impacts of climate change. Accordingly, meteorological data with high spatial and temporal resolutions are required. Current climate change scenario-based projections provide meteorological data on monthly and daily scales, which are too coarse for regional climate change assessments. In this study, we propose a temporal downscaling method based on the nearest neighbor search methodology for temporal downscaling from daily time-step meteorological data to hourly time-step data. To verify the temporal downscaling method, historical meteorological data from weather stations located in the Nakdong River basin were used, and the normalized root mean square error (NRMSE) between observational and simulated data was calculated. Consequently, the simulation errors were approximately 2–4% for temperature and precipitation, and approximately 7–16% for wind speed, relative humidity, and solar radiation. Next, using the temporal downscaling method, hourly future climate projection data were derived from the daily SSP5-8.5 climate scenario projections retrieved from weather stations in the target area. According to the downscaled hourly climate projection results, under the SSP5-8.5 climate change scenario, the future annual maximum temperature is projected to increase by up to 7.0 ℃ and 5.6 ℃ at Daegu and Busan weather stations, compared to the reference period maximum temperatures of 36.2 ℃ and 33 ℃, respectively. The annual precipitation duration is projected to decrease by up to 103.3 h (21.25%) and 157.2 h (27.57%), compared to the reference period durations of 486.2 h and 570.2 h, respectively. Annual precipitation intensity is projected to increase by up to 0.71 mm/h (33.5%) and 0.83 mm/h (31.2%), compared to the reference period precipitation intensities of 2.12 mm/h and 2.66 mm/h, respectively. The temporal downscaling method presented in this study is expected to contribute to the preparation of meteorological input data to effectively simulate detailed hourly rainfall runoff and the behavior of water quality factors, thereby improving the assessment of climate change impacts.

Streamflow trends and flood frequency analysis: a regional study of the UK.

In recent years, the escalating effects of climate change on surface water bodies have underscored the critical importance of analyzing streamflow trends for effective water resource planning and management. This study conducts a comprehensive regional investigation into the streamflow rate trends of 18 rivers across the United Kingdom (UK). An enhanced Mann-Kendall (MK) test was employed to meticulously analyze both rainfall and streamflow trends on monthly and annual scales. Additionally, the Innovative Trend Analysis (ITA) method was applied to elucidate the variability of streamflow rates, providing a more nuanced understanding of hydrological changes in response to climatic shifts. MK test reveals statistically significant positive trends in streamflow rates, particularly for rivers in south-central Scotland and northern England. Specifically, in January, rivers such as the Tay at Ballathie, Tweed at Peebles, and Teviot at Ormiston showed Z-scores above 2. Annually, similar positive trends were observed, with the Tay at Ballathie (Z = 3.42) and Nith at Friars Carse (Z = 3.35) exhibiting the highest increases in streamflow rates. The ITA method showed no relevant trends for the lowest values of streamflow, except for the Thames at Kingston, while considerable variability was observed for the highest streamflow rates, with several rivers showing positive trends and, however, some England rivers, like Bure at Ingworth, Test at Broadlands, and Trent at Colwick, showing negative trends. From this perspective, a more in-depth analysis of the extreme streamflow trends was carried out. In particular, the flood frequency of the maximum annual streamflow was assessed, based on the fitting of the Generalized Extreme Value (GEV) distribution on the annual maxima. Increasing location parameter (μ) and return period trends were observed for several rivers across the UK. In particular, the Tay at Ballathie (Scotland) showed the most marked increase, with μ that ranged from about 730 m3/s to more than 900 m3/s. At the same time, slight decreasing trends were observed for the Trent River (μ from 378 m3/s to 341 m3/s). The critical comparison of the MK test, ITA, and GEV distribution fitting revealed both agreements and discrepancies among the methods. While the analyses generally aligned in detecting significant trends in streamflow rates, notable discrepancies were observed, particularly in rivers with negligible trends. These inconsistencies highlight the complexity of hydrological responses and the limitations of individual methods. Overall, the study provides a comprehensive view of how streamflow dynamics are evolving in UK rivers, highlighting regional variations in the impact of climate change. This understanding can improve water resource management strategies by integrating diverse analytical approaches.

Possibilities of Using Regional Index-Flood Method and Annual Maximum and Partial Duration Series: A Case Study of Susurluk River Basin, Turkey

Floods are defined as disasters that occur as a result of a natural formation and cause negative effects on society life and economy. Floods that cause the greatest loss of life and property after earthquakes among the natural disasters experienced in our country, unplanned urbanization caused by the increasing population, uncontrolled settlements in river beds, and changing climate have increased its impact and frequency of occurrence over time. Therefore, it is important to estimate the magnitude of floods and the frequency of their occurrence in the most accurate way. This research aims to perform at-site and regional frequency analysis with L-moment methods in the Susurluk Basin. For regional frequency analysis, annual maximum and partial duration flood series were obtained using the data from 22 flow observation stations selected from the basin. In the first stage, the stations in the region were considered as a whole and the stations were found discordant with each other. Hierarchical cluster analysis was performed with the help of various physiological-hydrological characteristics of the stations located in the basin with geographic information systems, and the basin was divided into 2 regions, but homogeneity was still not achieved. Therefore, the basin is divided into 3 regions according to the dendrogram. There was no discordancy in all three regions and homogeneity was achieved. After achieving homogeneity, the most suitable frequency distribution was determined for each region. In addition, L-moments and L-moment ratios, probability distributions, and quantile discharges of the annual maximum and partial duration flood series were estimated for at-site frequency analysis. As a result, in regional flood frequency analysis, generalized logistic distribution provided the best fit in the annual maximum series, while Generalized Pareto and Pearson type 3 provided the best-fit distributions for the first and second regions in the partial duration series, respectively, in addition, generalized normal provided the best fit in the third region. It was concluded that it would be useful to use partial duration flood series as well as annual maximum series in the flood frequency analysis of the Susurluk basin.

Spatiotemporal changes in LULC and associated impact on urban Heat Islands over Pakistan using geospatial techniques

Urban Heat Islands arise when the natural land cover changes with dense concentrations of pavement, buildings, roads, and other surfaces that absorb and preserve heat. The current study aimed to assess Land Use Land Cover(LULC) changes and associated impact on Urban Heat Island (UHI) from 2000 to 2020 using Geospatial technologies for four metropolitan cities (Islamabad, Lahore, Multan and Muzaffarabad) of Pakistan. A Supervised Classification algorithm was performed on satellite imageries using ArcGIS software for analyzing LULC changes. The study areas were classified into 4 classes such as built-up, barren land, vegetation, and water bodies. Thermal bands of Landsat 4–5 and 8 were used for retrieved Land Surface Temperature (LST) and UHI. The findings of this study indicate that the urban area in all four districts i.e. Islamabad, Lahore, Multan and Muzaffarabad has increased by 21.3 %, 20.9 %, 6.1 % and 15 % respectively during 2000–2020. The built-up and barren land revealed the highest recorded LST, while vegetation and water bodies showed the lowest measured LST values. From 2000 to 2020, the annual maximum LST in Islamabad rose by 1.1 °C, Lahore by 2.1 °C, and Multan and Muzaffarabad by 1.9 °C and 1.5 °C. The expansion of built-up areas and the reduction in vegetation contribute to a favorable contribution to UHI. The study's findings showed that appropriate action plans are required to manage urban heat and promote sustainable city development.