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Heatwaves have caused significant damage to human health, infrastructure, and economies in recent decades, and the occurrences of heatwaves are becoming more frequent and severe across the globe under climate change. The previous studies on heatwaves have primarily focused on air temperature, neglecting other variables like wind speed, relative humidity, and radiation, which could lead to a serious underestimation of the adverse effects of heatwaves. To address this issue, this study proposed to the use of more sophisticated thermal indices, such as universal thermal climate index (UTCI) and apparent temperature (AT), to define heatwaves and carry out a comprehensive heatwave assessment over mainland southeast Asia (MSEA) from 1961 to 2020. The traditional temperature-based method was also compared. The results of the study demonstrate that the annual maximum temperature in heatwave days (HWA) and the annual average temperature in heatwave days (HWM) are significantly underestimated if only air temperature is considered. However, UTCI and AT tend to predict a lower frequency of yearly heatwave occurrences and shorter durations. Trend analysis indicates a general increase in heatwave occurrences across MSEA under all thermal indices in the past six decades, particularly in the last 30 years. This study's approach and findings provide a holistic view of heatwave characteristics based on thermal indices and highlight the risk of intensified heat stress during heatwaves in MSEA., (© 2023. The Author(s) under exclusive licence to International Society of Biometeorology.)

Bioclimatic variables (BCVs) are the most widely used predictors within the field of species distribution modeling, but recent studies imply that BCVs alone are not sufficient to describe these limits. Unfortunately, the most popular database, WorldClim, offers only a limited selection of bioclimatological predictors; thus, other climatological datasets should be considered, and, for data consistency, the BCVs should also be derived from the respective datasets. Here, we investigate how well the BCVs are represented by different datasets for the extended Mediterranean area within the period 1970-2020, how different calculation schemes affect the representation of BCVs, and how deviations among the datasets differ regionally. We consider different calculation schemes for quarters/months, the annual mean temperature (BCV-1), and the maximum temperature of the warmest month (BCV-5). Additionally, we analyzed the effect of different temporal resolutions for BCV-1 and BCV-5. Differences resulting from different calculation schemes are presented for ERA5-Land. Selected BCVs are analyzed to show differences between WorldClim, ERA5-Land, E-OBS, and CRU. Our results show that (a) differences between the two calculation schemes for BCV-1 diminish as the temporal resolution decreases, while the differences for BCV-5 increase; (b) with respect to the definition of the respective month/quarter, intra-annual shifts induced by the calculation schemes can have substantially different effects on the BCVs; (c) all datasets represent the different BCVs similarly, but with partly large differences in some subregions; and (d) the largest differences occur when specific month/quarters are defined by precipitation. In summary, (a) since the definition of BCVs matches different calculation schemes, transparent communication of the BCVs calculation schemes is required; (b) the calculation, integration, or elimination of BCVs has to be examined carefully for each dataset, region, period, or species; and (c) the evaluated datasets provide, except in some areas, a consistent representation of BCVs within the extended Mediterranean region., (© 2023 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.)

This study used the geostatistical Kriging methodology to reduce the spatial scale of a host of daily meteorological variables in the Department of Cauca (Colombia), namely, total precipitation and maximum, minimum, and average temperature. The objective was to supply a high-resolution database from 01/01/2015 to 31/12/2021 in order to support the climate component in a project led by the National Institute of Health (INS) named "Spatial Stratification of dengue based on the identification of risk factors: a pilot study in the Department of Cauca". The scaling process was applied to available databases from satellite information and reanalysis sources, specifically, CHIRPS (Climate Hazards Group InfraRed Precipitation with Station Data), ERA5-Land (European Centre for Medium-Range Weather Forecasts), and MSWX (Multi-Source Weather). The 0.1° resolution offered by both the MSWX and ERA5-Land databases and the 0.05° resolution found in CHIRPS, was successfully reduced to a scale of 0.01° across all variables. Statistical metrics such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Person Correlation Coefficient (r), and Mean Bias Error (MBE) were used to select the database that best estimated each variable. As a result, it was determined that the scaled ERA5-Land database yielded the best performance for precipitation and minimum daily temperature. On the other hand, the scaled MSWX database showed the best behavior for the other two variables of maximum temperature and daily average temperature. Additionally, using the scaled meteorological databases improved the performance of the regression models implemented by the INS for constructing a dengue early warning system., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Author(s).)

In this study, the trend of widely used MODIS MxD11 and MxD21 Land Surface Temperature (LST) and ERA5-Land Skin Temperature (SKT) and 2 m air temperature products were validated using 2 m air temperature trends obtained by ground observations from 266 stations in 2000-2021 over Turkey, known to have complex topography. The results show that colder regions have substantially higher temporal temperature variability than warmer ones. MxD21 and MxD11 products are 4.4 °C and 2.9 °C warmer than ERA5-Land products, respectively, while ERA5-Land products (SKT and 2 m) have nearly similar averages (12.5 °C). The consistency between MODIS and ERA5-Land data is significantly lower over areas with more complex topography and irrigation activities, despite the fact that the products show a high linear relationship over the study area. While February trends are consistently much higher than other months (2.2 and 1.4 °C/decade for MODIS and ERA5-Land, respectively), overall MODIS skin temperature products (0.7 °C/decade) generally exhibit smaller trends than ERA5-Land skin and air temperature trends (0.94 °C/decade). The results suggested that MODIS and ERA5-Land trends, which are highly consistent with observations, might replace observations in the absence of long-term station-based records., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Surface soil moisture (SM) is essential for existence of biotic lifeform and geophysical processes. However, with increasing global warming due to climatic changes, its spatiotemporal evolution is uncertain and largely unknown. In this study we detected long-term (40 years; 1981-2020) SM patterns of global vegetated areas through spatial timeseries clustering using the state-of-the-art ERA5-Land dataset. In addition, we also analyzed long-term patterns of precipitation (P), evapotranspiration (bare soil evaporation (BSe) and vegetation transpiration (VT)), and normalized difference vegetation index (NDVI). Our results indicate that surface SM (0-7 cm depth) of about 48 % and 9 % of the global vegetated area is showing drying and wetting pattern over the past 40 years, respectively. The detected soil drying, and wetting patterns were largely consistent across different soil depth, with 90 % and 80 % pattern similarity of surface soil layer with 2 nd soil layer (7-28 cm) and 3 rd soil layer (28-100 cm), respectively. About 80 % of areas with drying soil pattern also showed increasing evapotranspiration and/or decreasing precipitation. Specifically, decreasing P, increasing BSe and VT pattern were detected for 11 % of the soil drying pattern area. Similarly, increasing BSe and VT pattern, only decreasing P and only increasing VT pattern were detected for 17 %, 25 % and 12 % of soil drying areas, respectively. Both decreasing precipitation and increasing evapotranspiration patterns showed about 40 % similarity with decreasing soil moisture patterns. Across different landcover types, broadleaved forests, and cropland areas showed largest drying pattern. Under the future global warming scenario, the global soil water is expected to decrease as evapotranspiration would increase with inconsistent trend of global precipitation change. Our findings are of utmost importance for global soil water resource conservation and management., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.)

Water turbidity is one of the more important water quality parameters that is strictly linked with the productivity of the lake and is commonly used as an indicator of the trophic state. However, limited field data availability across wide geographic gradients may hinder the conduction of large scale longitudinal studies. In this study, time series of lake turbidity and trophic state index (TSI) between 2002 and 2012 were obtained from the Copernicus Lake Water products to create a large longitudinal dataset of lake variables for 22 European lakes. The dataset was combined with estimates of nutrient concentrations and surface water temperature obtained from the Hydrological Predictions for the Environment (HYPE) and ERA5-Land data repositories, that were used as environmental predictors. Hence, the validity of the lake water quality parameters was tested by a) exploring their spatial and temporal variability and b) identifying associations with the environmental predictors. For this purpose, seasonal Mann-Kendall tests were applied to find significant inter-annual trends of turbidity and TSI for each lake, and generalized additive models (GAMs) were employed to identify the main parameters that shape their temporal dynamics. Although we did not find significant inter-annual changes, our findings highlighted the strong influence of seasonality and surface water temperature in defining the temporal variability patterns in most of the lakes. In addition, the importance of nutrients varied among lakes as several lakes exhibited narrow nutrient gradients reflecting relatively stable nutrient conditions during the examined period. Other lake intrinsic factors, such as local climate and biotic interactions, are important drivers of shaping turbidity and nutrient dynamics. This study highlighted the usefulness of combining lake data from large repositories in conducting large scale spatial studies as a valuable asset for future lake research and management purposes., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Soil moisture flash drought can cause extensive damage to agriculture, ecosystems, and economies due to its sudden onset. Previous research identified soil moisture flash drought using stationary methods, in which, stationary probability distributions were employed to derive cumulative percentages (CPs) of given soil moisture values, and then based on the CPs sequence, the run theory was used to identify soil moisture flash drought events. However, because changes in climate or land cover can induce significant variations in the underlying probability distributions of soil moisture, the method's usual assumption of stationarity should be questioned. In this study, a nonstationary framework based on the nonstationary frequency analysis method and the run theory was developed for identifying soil moisture flash drought events. This framework was applied to the study of the pentad average root-zone soil moisture (PRZSM) of the Pearl River basin (PRB) in South China based on the ERA5-Land and the GLEAM datasets from 1981 to 2020. The results of the ERA5-Land were general consistent with those of the GLEAM, and the major findings include: (1) without considering the nonstationarity of soil moisture, the onset rate of flash droughts may be underestimated. The average onset rate of flash droughts in nonstationary conditions is slightly greater by 1 %/pentad -2 %/pentad than that in stationary conditions; (2) without considering the nonstationarity of soil moisture, the severity of flash droughts may be overestimated. The average severity of flash droughts in nonstationary conditions is smaller by 10% ⋅ pentads-20% ⋅ pentads than that in stationary conditions; and (3) the trends of the characteristics of soil moisture flash drought are consistent between the stationary and the nonstationary conditions. In conclusion, the above findings contribute to a better understanding of the implications of soil moisture nonstationarity on flash drought identification., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Many environmental processes and ecological systems are being affected by the warming temperatures as a result of climate change. To correctly identify and attribute the uncertainty estimates in these systems, an investigation of temperature trend signal existing in the datasets that are used to study such systems is necessary. In this study, the trend of widely used ERA5 and ERA5-Land temperature estimates between 1951 and 2020 were validated using temperature trends from ground station-based observations in Turkey. The investigation included datasets obtained over 540 stations, and the seasonality and spatio-temporal variability of the trend accuracy was also investigated. On average, the trends of observations over all stations were 0.82 °C/decade and 0.30 °C/decade for the periods 2001-2020 and 1951-2020, respectively, indicating strong evidence of climate change. When the model datasets used spatially and temporally continuous datasets, the trends identified were 0.91 °C/decade and 0.21 °C/decade over the entire Turkey for the years 2001-2020 and 1951-2020, respectively. The difference in the 70-year trends of the two estimates was attributed to the missing datasets in observations. The differences between the trends of model estimates and observations were higher for the first decade than for the last two decades, stressing the impact of improved model estimates over time. All products showed heavy seasonality, suggesting that winter trends (1.3 °C/decade on average) are much higher than the summer (0.3 °C/decade) between 2001 and 2020. The results indicated a high degree of consistency between the trends of ERA5/ERA5-Land and observations, implying they may be used as a replacement to observations., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

This paper describes the extension of the previously CMIP5 based high-resolution climate projections with additional ones based on the more recent climate projections from the CMIP6 experiment. The downscaling method and data processing are the same but the reference dataset is now the ERA5-Land reanalysis (compared to ERA5 previously) allowing to increase the resolution of the new downscaled projections from 0.25° x 0.25° to 0.1°x 0.1°. The extension comprises 5 climate models and includes 2 surface variables at daily resolution: air temperature and precipitation. Three greenhouse gas emissions scenarios are available: Shared Socioeconomic Pathways with mitigation policy (SSP1-2.6), an intermediate one (SSP2-4.5), and one without mitigation (SSP5-8.5)., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that have or could be perceived to have influenced the work reported in this article., (© 2022 The Author(s).)

In many high altitude river basins, the hydro-climatic regimes and the spatial and temporal distribution of precipitation are little known, complicating efforts to quantify current and future water availability. Scarce, or non-existent, gauged observations at high altitudes coupled with complex weather systems and orographic effects further prevent a realistic and comprehensive assessment of precipitation. Quantifying the contribution from seasonal snow and glacier melt to the river runoff for a high altitude, melt dependent region is especially difficult. Global scale precipitation products, in combination with precipitation-runoff modelling may provide insights to the hydro-climatic regimes for such data scarce regions. In this study two global precipitation products; the high resolution (0.1° × 0.1°), newly developed ERA5-Land, and a coarser resolution (0.55° × 0.55°) JRA-55, are used to simulate snow/glacier melts and runoff for the Gilgit Basin, a sub-basin of the Indus. A hydrological precipitation-runoff model, the Distance Distribution Dynamics (DDD), requires minimum input data and was developed for snow dominated catchments. The mean of total annual precipitation from 1995 to 2010 data was estimated at 888 mm and 951 mm by ERA5-Land and JRA-55, respectively. The daily runoff simulation obtained a Kling Gupta efficiency (KGE) of 0.78 and 0.72 with ERA5-Land and JRA-55 based simulations, respectively. The simulated snow cover area (SCA) was validated using MODIS SCA and the results are quite promising on daily, monthly and annual scales. Our result showed an overall contribution to the river flow as about 26% from rainfall, 37-38% from snow melt, 31% from glacier melt and 5% from soil moisture. These melt simulations are in good agreement with the overall hydro-climatic regimes and seasonality of the area. The proxy energy balance approach in the DDD model, used to estimate snow melt and evapotranspiration, showed robust behaviour and potential for being employed in data poor basins., Competing Interests: Declaration of competing interest The authors declare that there is no conflict of interest., (Copyright © 2021 The Authors. Published by Elsevier B.V. All rights reserved.)