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Representative weather data are fundamental to characterizing a place and determining ideal design approaches. This is particularly important for large countries like Brazil, whose extension and geographical position contribute to defining diverse climatic conditions along the territory. In this context, this study intends to characterize the Brazilian territory based on a 15-year weather record (2008–2022), providing a climatic assessment based on a climatic and bioclimatic profile for the whole country. The climate analysis was focused on temperature, humidity, precipitation, and solar radiation, followed by a bioclimatic analysis guided by the Givoni chart and the natural ventilation potential assessment. In both situations, the results were analyzed using three resolutions: country-level, administrative division, and bioclimatic zones. This study also identified representative locations for the Brazilian bioclimatic zones for a building-centered analysis based on the thermal and energy performance of a single-family house with different envelope configurations. The results proved that most Brazilian territories increased above 0.4 °C in the dry bulb temperature and reduced relative humidity. The precipitation had the highest reduction, reaching more than 50% for some locations. The warmer and drier conditions impacted also the Köppen–Geiger classification, with an increase in the number of Semi-Arid and Arid locations. The bioclimatic study showed that ventilation is the primary strategy for the Brazilian territory, as confirmed by the natural ventilation potential results, followed by passive heating strategies during the year’s coldest months. Finally, building performance simulation underlined that, in colder climates, indoor thermal comfort conditions and air-conditioning demands are less affected by solar absorptance for constructions with low U-values, while in warmer climates, low solar absorptance with intermediary U-values is recommended.

Reference evapotranspiration (ETo) estimation is essential for water resources management. The present research compares four different ETo estimators based on reanalysis data (ERA5-Land) and in situ observations from three different cultivation sites in Greece. ETo based on FAO56-Penman–Monteith (FAO-PM) is compared to ETo calculated from the empirical methods of Copais, Valiantzas and Hargreaves-Samani using both reanalysis and in situ data. The daily and monthly biases of each method are calculated against the FAO56-PM method. ERA5-Land data are also compared to ground-truth observations. Additionally, a sensitivity analysis is conducted on each site for different cultivation periods. The present research finds that the use of ERA5-Land data underestimates ground-truth-based ETo by 35%, approximately, when using the FAO56-PM method. Additionally, the use of other methodologies also shows underestimation of ETo when calculated with ERA5-Land data. On the contrary, the use of the Valiantzas and Copais methodologies with in situ observations shows overestimation of ETo when compared to FAO56-PM, in the ranges of 32–62% and 24–56%, respectively. The sensitivity analysis concludes that solar radiation and relative humidity are the most sensitive variables of the Copais and Valiantzas methodologies. Overall, the Hargreaves-Samani methodology was found to be the most efficient tool for ETo estimation. Finally, the evaluation of the ERA5-Land data showed that only air temperature inputs can be utilized with high levels of confidence.

Hydrological modeling is essential for runoff simulations in line with climate studies, especially in remote areas with data scarcity. Advancements in climatic precipitation datasets have improved the accuracy of hydrological modeling. This research aims to evaluate the APHRODITE, PERSIANN-CDR, and ERA5-Land climatic precipitation datasets for the Hablehroud watershed in Iran. The datasets were compared with interpolated ground station precipitation data using the inverse distance weighted (IDW) method. The variable infiltration capacity (VIC) model was utilized to simulate runoff from 1992 to 1996. The results revealed that the APHRODITE and PERSIANN-CDR datasets demonstrated the highest and lowest accuracy, respectively. The sensitivity of the model was analyzed using each precipitation dataset, and model calibration was performed using the Kling–Gupta efficiency (KGE). The evaluation of daily runoff simulation based on observed precipitation indicated a KGE value of 0.78 and 0.76 during the calibration and validation periods, respectively. The KGE values at the daily time scale were 0.64 and 0.77 for PERSIANN-CDR data, 0.62 and 0.75 for APHRODITE precipitation data, 0.50 and 0.66 for ERA5-Land precipitation data during the calibration and validation periods, respectively. These results indicate that despite varying sensitivity, climatic precipitation datasets present satisfactory performance, particularly in poorly gauged basins with infrequent historical datasets.

It is necessary to further investigate the spatial considerations, temporal characteristics, and drivers of change affecting the beginning and end of soil freezing and thawing, including the maximum depth of the seasonal freezing (MDSF) and the active layer thickness (ALT) in Northeast China. Hourly soil temperature, among other data, from 1983–2022 were investigated, showing a delay of about 6 days in freezing. In contrast, thawing and complete thawing advanced by about 26 and 20 d, respectively. The freezing period and total freeze-thaw days decreased by about 29 and 23 days, respectively. The number of complete thawing period days increased by about 22 days, while the MDSF decreased by about 25 cm. The ALT increased by about 22 cm. Land Surface Temperature (LST) is the main factor influencing the beginning and end of soil freezing and thawing, MDSF and ALT changes in Northeast China; air temperature, surface net solar radiation, and volumetric soil water content followed. The influence of the interacting factors was greater than the single factors, and the interactive explanatory power of the LST and surface net solar radiation was highest when the soil started to freeze (0.858). The effect of the LST and the air temperature was highest when the soil was completely thawed (0.795). LST and the volumetric soil water content interacted to have the first explanatory power for MDSF (0.866) and ALT (0.85). The results of this study can provide scientific reference for fields such as permafrost degradation, cold zone ecological environments, and agricultural production in Northeast China.

This research examines the intricate changes in the number of occurrences and cumulative rainfall of exceptional events in Portugal spanning 42 hydrological years (from 1981/1982 to 2022/2023). The study has two primary objectives: assessing the hydrological spatial dynamics of a region susceptible to climate-induced variations in exceptional rainfall and evaluating the proficiency of a ERA5-Land reanalysis rainfall dataset in capturing exceptional rainfall. Confronting methodological and data-related challenges (e.g., incomplete record series), the investigation uses continuous daily ERA5-Land rainfall series. Validation against the Sistema Nacional de Informação de Recursos Hídricos (SNIRH) and the Portuguese Institute for Sea and Atmosphere (IPMA) ensures the reliability of ERA5-Land data. Empirical non-exceedance probability curves reveal a broad consensus between reanalysis data and observational records, establishing the dataset’s suitability for subsequent analysis. Spatial representations of occurrences, cumulative rainfall, and rainfall intensity of events above thresholds throughout the overall 42-year period and two subperiods (late: 1981/1982–2001/2002; and recent: 2002/2003–2022/2023) are presented, illustrating spatial and temporal variations. A noteworthy shift in the spatial distribution of intense events from south to north is observed, emphasising the dynamism of such hydrological processes. The study introduces a novel dimension with a severity heat map, combining some key findings from the occurrences and cumulative rainfall through subperiods. This study significantly contributes to the understanding of hydrological dynamics in Portugal, providing valuable insights for risk management and the development of sustainable strategies tailored to the evolving patterns of exceptional rainfall.

The paper evaluates the usability of remote satellite-based and proximal ground-based agrometeorological data sources for precision agriculture and crop production in Croatia. The compared agrometeorological datasets stem from the open-access data sources Copernicus CDS and the Agri4Cast portal, and commercial in situ agrometeorological stations (PinovaMeteo) which monitor environmental parameters relevant to the physiological state of crops. The study compares relevant parameters for 10 different locations in Croatia for three consecutive years (2019, 2020, and 2021) to investigate whether model-based data from ERA5-Land and Agri4Cast are well-correlated with ground measurements from independent in situ stations (PinovaMeteo) for specific agrometeorological parameters (air and soil temperature, and precipitation). Our results indicate the following: both the ERA5-Land and Agri4Cast datasets show mostly strong positive correlations with ground observations for air temperature, modest correlations for soil temperature, but modest or even low correlations for precipitation. Analysis of the residuals indicates higher overall residual values, especially in areas with complex topography and near large bodies of water or the sea, and deviations of residuals that may limit the usability of satellite- and model-based data for decision-making in agriculture.

Due to the scarcity of meteorological stations on the Tibetan Plateau (TP), owing to the high altitude and harsh climate, studies often resort to satellite, reanalysis, and merged multi-source precipitation data. This necessitates an evaluation of TP precipitation data applicability. Here, we assess the following three high-resolution gridded precipitation datasets: the China Meteorological Forcing Dataset (CMFD), the European Center for Medium-Range Weather Forecasts Reanalysis V5-Land (ERA5-Land), and Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) during TP summers. Using observations from the original 133 China Meteorological Administration stations on the TP as a reference, the evaluation yielded the following conclusions: (1) In summer, from 2000 to 2018, discrepancies among the datasets were largest in the western TP. The CMFD showed the smallest deviation from the observations, and the annual summer precipitation was only overestimated by 12.3 mm. ERA5-Land had the closest trend (0.41 mm/y) to the annual mean summer precipitation, whereas it overestimated the highest precipitation (>150 mm). (2) The reliability of the three datasets at annual and monthly scales was in the following order: CMFD, ERA5-Land, and IMERG. The daily scales exhibited a lower accuracy than the monthly scales (correlation coefficient CC of 0.51, 0.38, and 0.26, respectively). (3) The CMFD assessments, referencing the 114 new stations post-2016, had a notably lower accuracy and precipitation capture capability at the daily scale (CC and critical success index (CSI) decreased by 0.18 and 0.1, respectively). These results can aid in selecting appropriate datasets for refined climate predictions on the TP.

The variability in soil hydrothermal conditions generally contributes to the diverse distribution of vegetation cover types and growth characteristics. Previous research primarily focused on soil moisture alone or the average values of soil hydrothermal conditions in the crop root zone (0–100 cm). However, it is still unclear whether changes in gross primary productivity (GPP) depend on the hydrothermal conditions at different depths of soil layers within the root zone. In this study, the soil hydrothermal conditions from three different layers, surface layer 0–7 cm (Level 1, L1), shallow layer 7–28 cm (Level 2, L2), and deep layer 28–100 cm (Level 3, L3) in the Qilian Mountains area, northwestern China, are obtained based on ERA5-Land reanalysis data. The Sen-MK trend test, Pearson correlation analysis, and machine learning algorithm were used to explore the influence of these three soil hydrothermal layers on GPP. The results show that soil moisture values increase with soil depth, while the soil temperature values do not exhibit a stratified pattern. Furthermore, the strong correlation between GPP and deep soil hydrothermal conditions was proved, particularly in terms of soil moisture. The Random Forest feature importance extraction revealed that deep soil moisture (SM-L3) and surface soil temperature (ST-L1) are the most influential variables. It suggests that regulations of soil hydrothermal conditions on GPP may involve both linear and nonlinear effects. This study can obtain the temporal and spatial dynamics of soil hydrothermal conditions across different soil layers and explore their regulations on GPP, providing a basis for clarifying the relationship between soil and vegetation in arid mountain systems.

In regions with limited precipitation information, like Peru, many studies rely on precipitation data derived from satellite products (SPP) and model reanalysis. These products provide near-real-time information and offer global spatial coverage, making them attractive for various applications. However, it is essential to consider their uncertainties when conducting hydrological simulations, especially in a key region like the Pacific drainage (Pd), where 56% of the Peruvian population resides (including the capital, Lima). This study, for the first time, assessed the performance of two bottom-up Satellite-based Precipitation Products (SPP), GPM + SM2RAIN and SM2RAIN-ASCAT, and one top-down approach SPP, ERA5-Land, for runoff simulation in the Pacific drainage of Peru. Hydrological modeling was conducted on 30 basins distributed across the Pd, which were grouped into 5 regions (I–V, ordered from south to north). The results showed that SM2RAIN-ASCAT performed well in regions I-III-IV, ERA5-Land in region II, and GPM + SM2RAIN in region V. The hydrological model GR4J was tested, and better efficiency criteria were obtained with SM2RAIN-ASCAT and GPM + SM2RAIN when comparing the simulated versus observed streamflows. The hydrological modeling using SM2RAIN-ASCAT and GPM + SM2RAIN demonstrated satisfactory efficiency metrics (KGE > 0.75; NSE > 0.65). Additionally, ten hydrological signatures were quantified to assess the variability of the simulated streamflows in each basin, with metrics such as Mean Flow (Q mean), 5th Quantile Flow (Q5), and 95th Quantile Flow (Q95) showing an overall better performance. Finally, the results of this study demonstrate the reliability of using bottom-up satellite products in Pd basins.

High spatial–temporal resolution surface net radiation (RN) data are of great significance to the study of climate, ecology, hydrology and cryosphere changes on the Tibetan Plateau (TP), but the verification of the surface net radiation products on the plateau is not sufficient. In this study, the China Meteorological Administration Global Land Surface Reanalysis Products (CRA/Land) and ECMWF Land Surface Reanalysis version 5 (ERA5-Land) RN data were validated using ground measurements at daily and monthly time scales, and the spatiotemporal patterns were also analyzed. The results indicate the following: (1) CRA/Land overestimated while ERA5-Land underestimated RN, but CRA/Land RN outperformed ERA5-Land in observations at the daily and monthly scale. (2) The CRA/Land RN data had a larger error in the central part and a smaller error in the northeast of the TP, while ERA5-Land showed the opposite. (3) The spatial patterns of RN revealed by CRA/Land and ERA5-Land data showed differences in most regions. The CRA/Land data showed that the RN of the TP had a downward trend during 2000 and 2020 with a slope of −0.112 W·m−2/a, while the ERA5-Land data indicated an upward trend with a change rate of 0.016 W·m−2/a. (4) Downwelling shortwave radiation (DSR), upwelling shortwave radiation (USR), downwelling longwave radiation (DLR) and upwelling longwave radiation (ULR) are the four components of RN, and the evaluation results indicate that the DSR, DLR and ULR recorded via CRA/Land and ERA5-Land are consistent with the observed data, but the consistency between the USR recorded via CRA/Land and ERA5-Land and the observed data is poor. (5) The inconsistency of the USR data is the main reason for the large differences in the spatiotemporal distribution of CRA/Land and ERA5-Land RN data across the TP.

Assessing and monitoring the spatial extent of drought is of key importance to forecasting the future evolution of drought conditions and taking timely preventive and mitigation measures. A commonly used approach in regional drought analysis involves spatially interpolating meteorological variables (e.g., rainfall depth during specific time intervals, deviation from long-term average rainfall) or drought indices (e.g., Standardized Precipitation Index, Standardized Precipitation Evapotranspiration Index) computed at specific locations. While plotting a drought descriptor against the corresponding percentage of affected areas helps visualize the historical extent of a drought, this approach falls short of providing a probabilistic characterization of the severity of spatial drought conditions. That can be overcome by identifying drought Severity-Area-Frequency (SAF) curves over a region, which establishes a link between drought features with a chosen probability of recurrence (or return period) and the corresponding proportion of the area experiencing those drought conditions. While inferential analyses can be used to estimate these curves, analytical approaches offer a better understanding of the main statistical features that drive the spatial evolution of droughts. In this research, a technique is introduced to mathematically describe the Severity-Area-Frequency (SAF) curves, aiming to probabilistically understand the correlation between drought severity, measured through the SPEI index, and the proportion of the affected region. This approach enables the determination of the area’s extent where SPEI values fall below a specific threshold, thus calculating the likelihood of observing SAF curves that exceed the observed one. The methodology is tested using data from the ERA5-Land reanalysis project, specifically studying the drought occurrences on Sicily Island, Italy, from 1950 to the present. Overall, findings highlight the improvements of incorporating the spatial interdependence of the assessed drought severity variable, offering a significant enhancement compared to the traditional approach for SAF curve derivation. Moreover, they validate the suitability of reanalysis data for regional drought analysis.

Precipitation is a key component of the hydrological cycle and directly affects water availability and hydrometeorological hazards. The objective of this study is to evaluate the performance of two reanalysis precipitation datasets, ERA5 and ERA5-land, in reproducing precipitation accumulations over Greece. These data are compared against rainfall measurements provided by the dense network of surface-automated weather stations operated by the National Observatory of Athens. The comparisons are performed over a 10-year period (January 2010 to December 2020) at multiple temporal and spatial scales. Several statistical metrics are used to assess the performance of the reanalysis precipitation against rain gauge observations. The suitability of gridded products is tested by capturing the temporal and spatial variability in precipitation using accuracy metrics.

In recent years, a considerable body of research has demonstrated the suitability of global and regional reanalysis data for human-biometeorological applications. These applications include the assessment of the outdoor thermal environment and the investigation of its relation to human health, especially in areas where the spatial coverage of surface observational networks is sparse. Here, we present the first comprehensive evaluation of the most recent pan-European regional reanalysis, namely the Copernicus European Regional Reanalysis (CERRA) dataset at 5.5 km spatial resolution, in terms of simulating the observed human bioclimate, as expressed by the modified physiologically equivalent temperature (mPET) that is computed through the RayMan Pro model, and its meteorological drivers. The validation was performed over Greece using up to 11 years of records of 2 m air temperature and relative humidity, 10 m wind speed and global solar radiation derived from 35 sites of the nationwide network of surface weather stations operated by the METEO Unit at the National Observatory of Athens. The ERA5-Land dataset at ~9 km spatial resolution, which represents the current state-of-the-art reanalysis, was also compared against the same observations. Our findings show that the CERRA dataset performs significantly better compared to the ERA5-Land reanalysis with respect to the replication of the examined meteorological variables and mPET. The added value of the CERRA data is particularly evident during the warm period of the year and in regions that are characterized by complex topography and/or proximity to the coastline. Combining the CERRA dataset with population and mortality data, we further showcase its applicability for human-biometeorological and heat–health studies at a local scale, using the regional unit of Rethymno (Crete) as a pilot area for the analysis.

Currently, satellite-based soil moisture (SM) products and land surface model assimilation techniques are widely utilized. However, the presence of systematic errors in the observation process, algorithmic discrepancies between products, and variations in spatial and temporal scales result in diverse accuracy characteristics and applicability. This study evaluates three prominent SM products in China, namely, the Essential Climate Variable Soil Moisture (ECV), the European Centre for Medium-Range Weather Forecasts’ Fifth-Generation Land Surface Reanalysis Data (ERA5-Land), and the Global Land Surface Data Assimilation System (GLDAS). The evaluation was conducted using extended triple collocation (ETC) analysis and in situ validation methods at a monthly scale from 2000 to 2020. The ETC analysis results show that among the three products, GLDAS exhibits the highest correlation coefficient (CC) and the lowest standard deviation of error (ESD), indicating its superior performance in China. ECV and ERA5-Land follow, with slightly lower performance. In the in situ validation results, ERA5-Land displays the highest correlation, capturing the temporal trend of the ground SM well. Comparatively, in terms of overall accuracy, ECV performs the best, with a slightly smaller mean error (ME) and root mean square error (RMSE) than GLDAS, and ERA5-Land has the lowest accuracy. The discrepancy between the in situ validation results and ETC analysis can be attributed to the limited number of sites and their representativeness errors. Notably, ERA5-Land exhibits a highly consistent trend of interannual fluctuations between ESD and precipitation. Furthermore, a strong association is observed between the ME and RMSE of ECV and GLDAS and precipitation. These findings serve as valuable references for future SM studies in China.

This study presents a comprehensive analysis of heatwaves in mainland Portugal from 1 October 1980 to 30 September 2021 (41 hydrological years). It addresses a research gap by providing an updated assessment using high-resolution reanalysis daily minimum and maximum temperature data (Tmin and Tmax) from the gridded ERA5-Land dataset, overcoming the lack of publicly available daily temperature records. To assess the representation of the previous dataset, nine different grid-point locations across the country were considered. By comparing monthly ERA5-Land temperature data to ground-based records from the Portuguese Met Office, a monthly validation of the data was conducted for the longest common period, demonstrating good agreement between the two datasets. The heatwave magnitude index (HWMI) was employed to establish the temperature thresholds and thus identify heatwaves (defined as three or more consecutive days above the threshold). With over 640 Tmin heatwave days recorded at each of the nine ERA5-Land grid-points, data analysis revealed a discernible upward trend in Tmin heatwaves. The grid-point situated in the capital city’s urban area, i.e., Lisbon, exhibited the highest number of Tmin heatwave days. With an average of more than 800 Tmax heatwave days over the 41-year period, the northern and interior regions of Portugal had the greatest number of occurrences, reaching up to 916. A kernel rate estimation method was applied to further investigate the annual frequency of Tmin and Tmax heatwave occurrences. Results exhibited clear temperature changes, with a widespread increase in the number of heatwave days over the past two decades, particularly for Tmax. In summary, the occurrence of this phenomenon displayed significant spatial variations, with the southern interior and coastal grid-points experiencing a greater increase in annual Tmax heatwave days, rising from 10 to 30 between 2018 and 2019.

High-resolution air temperature data is indispensable for analysing heatwave-related non-accidental mortality. However, the limited number of weather stations in urban areas makes obtaining such data challenging. Multi-source data fusion has been proposed as a countermeasure to tackle such challenges. Satellite products often offered high spatial resolution but suffered from being temporally discontinuous due to weather conditions. The characteristics of the data from reanalysis models were the opposite. However, few studies have explored the fusion of these datasets. This study is the first attempt to integrate satellite and reanalysis datasets by developing a two-step downscaling model to generate hourly air temperature data during heatwaves in London at 1 km resolution. Specifically, MODIS land surface temperature (LST) and other satellite-based local variables, including normalised difference vegetation index (NDVI), normalized difference water index (NDWI), modified normalised difference water index (MNDWI), elevation, surface emissivity, and ERA5-Land hourly air temperature were used. The model employed genetic programming (GP) algorithm to fuse multi-source data and generate statistical models and evaluated using ground measurements from six weather stations. The results showed that our model achieved promising performance with the RMSE of 0.335 °C, R-squared of 0.949, MAE of 1.115 °C, and NSE of 0.924. Elevation was indicated to be the most effective explanatory variable. The developed model provided continuous, hourly 1 km estimations and accurately described the temporal and spatial patterns of air temperature in London. Furthermore, it effectively captured the temporal variation of air temperature in urban areas during heatwaves, providing valuable insights for assessing the impact on human health.

Reservoir ice can have an important impact on the watershed scale and influence hydraulic operations. On the other hand, hydropower generation can also impact the ice regime. In this study, multi-source satellite and ERA5-land data are used to evaluate ice conditions. Specifically, ice-controlling variables (temperature, water levels), ice regime (cover/deformation, thickness) and their interrelations are assessed for a 5-year period from 2017 to 2021. The methodology is applied to the Manicouagan reservoir, one of the largest hydropower reservoirs in Quebec, Canada. The satellite-based land surface temperatures (LSTs) suggest that winter 2021 was the hottest one. Overall, MODIS and Landsat LSTs agree with the ERA5-land temperatures. Ice backscatter from Sentinel-1 indicates that, in general, the reservoir is completely covered by ice from January to March. A correlation of 0.6 and 0.8 is observed between C- and Ku-band Synthetic Aperture Radar (SAR) signal and ice thickness, respectively. Important ice changes inferred from Differential Interferometric SAR (D-InSAR) occur approximately at the position where the largest ERA5-land ice thickness differences are observed. Winter water levels are also evaluated using satellite altimetric data to verify their influence on the ice dynamics. They show a decreasing tendency as the winter advances.

In mountainous regions, the scarcity of air temperature (Ta) measurements is a major limitation for hydrological and crop monitoring. An alternative to in situ measurements could be to downscale the reanalysis Ta data provided at high-temporal resolution. However, the relatively coarse spatial resolution of these products (i.e., 9 km for ERA5-Land) is unlikely to be directly representative of actual local Ta patterns. To address this issue, this study presents a new spatial downscaling strategy of hourly ERA5-Land Ta data with a three-step procedure. First, the 9 km resolution ERA5 Ta is corrected at its original resolution by using a reference Ta derived from the elevation of the 9 km resolution grid and an in situ estimate over the area of the hourly Environmental Lapse Rate (ELR). Such a correction of 9 km resolution ERA5 Ta is trained using several machine learning techniques, including Multiple Linear Regression (MLR), Support Vector Regression (SVR), and Extreme Gradient Boosting (Xgboost), as well as ancillary ERA5 data (daily mean, standard deviation, hourly ELR, and grid elevation). Next, the trained correction algorithms are run to correct 9 km resolution ERA5 Ta, and the corrected ERA5 Ta data are used to derive an updated ELR over the area (without using in situ Ta measurements). Third, the updated hourly ELR is used to disaggregate 9 km resolution corrected ERA5 Ta data at the 30-meter resolution of SRTM’s Digital Elevation Model (DEM). The effectiveness of this method is assessed across the northern part of the High Atlas Mountains in central Morocco through (1) k-fold cross-validation against five years (2016 to 2020) of in situ hourly temperature readings and (2) comparison with classical downscaling methods based on a constant ELR. Our results indicate a significant enhancement in the spatial distribution of hourly local Ta. By comparing our model, which included Xgboost, SVR, and MLR, with the constant ELR-based downscaling approach, we were able to decrease the regional root mean square error from approximately 3 ∘C to 1.61 ∘C, 1.75 ∘C, and 1.8 ∘C, reduce the mean bias error from −0.5 ∘C to null, and increase the coefficient of determination from 0.88 to 0.97, 0.96, and 0.96 for Xgboost, SVR, and MLR, respectively.

Many extreme meteorological events are closely related to the strength of land–atmosphere interactions. In this study, the heat exchange regime between the shallow soil layer and the atmosphere over the Qinghai–Tibetan Plateau (QTP) was investigated using a reanalysis dataset. The analysis was conducted using a simple metric ΔT, defined as the difference between the temperatures of the shallow soil and the air. First, the performance of 4 widely used reanalysis data products (GLDAS-Noah, NCEP-R2, ERA5 and ERA5-land) in estimating ΔT on the QTP at soil depths of 0~7 or 0~10 cm was evaluated during the baseline period (1981–2010); the ERA5-land product was selected for subsequent analysis, because it yielded a better performance in estimating the annual and seasonal ΔT and finer spatial resolution than the other datasets. Using the soil temperature at depths of 0~7 cm and the air temperature at 2 m above the ground, as provided by the ERA5-Land reanalysis dataset, the entire QTP was found to be dominated by a positive ΔT both annually and seasonally during the baseline period, with large differences in the spatial distribution of the seasonal values of ΔT. From 1950 to 2021, the QTP experienced a significant decreasing trend in the annual ΔT at a rate of −0.07 °C/decade, and obvious decreases have also been detected at the seasonal level (except in spring). In the southern and northeastern parts of the QTP, rapid rates of decrease in the annual ΔT were detected, and the areas with significantly decreasing trends in ΔT were found to increase in size gradually from summer, through autumn, to winter. This study provides a holistic view of the spatiotemporal variations in ΔT on the QTP, and the findings can improve our understanding of the land–atmosphere thermal interactions in this region and provide important information pertaining to regional ecological diversity, hydrology, agricultural activity and infrastructural stability.

Smart management of weather data is an essential step toward implementing sustainability and precision in agriculture. It represents an important input for numerous tasks, such as crop growth, development, yield, and irrigation scheduling, to name a few. Advances in technology allow collecting this weather data from heterogeneous sources with high temporal resolution and at low cost. Generating and using these data in their raw form makes no sense, and therefore implementing adequate infrastructure and tools is necessary. For that purpose, this paper presents a smart weather data management system evaluated using data from a meteorological station installed in our study area covering the period from 2013 to 2020 at a half-hourly scale. The proposed system makes use of state-of-the-art statistical methods, machine learning, and deep learning models to derive actionable insights from these raw data. The general architecture is made up of four layers: data acquisition, data storage, data processing, and application layers. The data sources include real-time sensors, IoT devices, reanalysis data, and raw files. The data are then checked for errors and missing values using a proposed method based on ERA5-Land reanalysis data and deep learning. The resulting coefficient of determination (R2) and Root Mean Squared Error (RMSE) for this method were 0.96 and 0.04, respectively, for the scaled air temperature estimate. The MongoDB NoSQL database is used for storage thanks to its ability to deal with real-world big data. The system offers various services such as (i) weather time series forecasts, (ii) visualization and analysis of meteorological data, and (iii) the use of machine learning to estimate the reference evapotranspiration (ET0) needed for efficient irrigation. To this, the platform uses the XGBoost model to achieve the precision of the Penman–Monteith method while using a limited number of meteorological variables (air temperature and global solar radiation). Results for this approach give R2 = 0.97 and RMSE = 0.07. This system represents the first incremental step toward implementing smart and sustainable agriculture in Morocco.

Long-term and high-resolution reanalysis precipitation datasets provide important support for research on climate change, hydrological forecasting, etc. The comprehensive evaluation of the error performances of the newly released ERA5-Land and CRA40-Land reanalysis precipitation datasets over the Yongding River Basin in North China was based on the two error decomposition schemes, namely, decomposition of the total mean square error into systematic and random errors and decomposition of the total precipitation bias into hit bias, missed precipitation, and false precipitation. Then, the error features of the two datasets and precipitation intensity and terrain effects against error features were analyzed in this study. The results indicated the following: (1) Based on the decomposition approach of systematic and random errors, the total error of ERA5-Land is generally greater than that of CRA40-Land. Additionally, the proportion of random errors was higher in summer and over mountainous areas, specifically, the ERA5-Land accounts for more than 75%, while the other was less than 70%; (2) Considering the decomposition method of hit, missed, and false bias, the total precipitation bias of ERA5-Land and CRA40-Land was consistent with the hit bias. The magnitude of missed precipitation and false precipitation was less than the hit bias. (3) When the precipitation intensity is less than 38 mm/d, the random errors of ERA5-Land and CRA40-Land are larger than the systematic error. The relationship between precipitation intensity and hit, missed, and false precipitation is complicated, for the hit bias of ERA5-L is always smaller than that of CRA40-L, and the missed precipitation and false precipitation are larger than those ofCRA40-L when the precipitation is small. The error of ERA5-Land and CRA40-Land was significantly correlated with elevation. A comprehensive understanding of the error features of the two reanalysis precipitation datasets is valuable for error correction and the construction of a multi-source fusion model with gauge-based and satellite-based precipitation datasets.

Condensation and desublimation are important processes of nocturnal land–atmosphere interactions, energy transfer, and the water cycle, and have important ecological and hydrological roles in mitigating physiological water deficits caused by low temperatures and reducing the risk of frost damage to plants, animals, and microorganisms near the surface in the Alpine Region. The aim of the present study is to evaluate the spatial and temporal variations of condensation and desublimation from 1950 to 2020 based on Penman model using hourly ERA5-Land and ERA5 reanalysis datasets on the Qinghai–Tibet Plateau (QTP), where condensation and desublimation occur frequently but lack quantitative evaluation. The results showed that: (1) Condensation showed a decreasing trend from southeast to northwest, with annual mean condensation ranging from 0 mm to 72.8 mm, while desublimation showed regional enrichment rather than zonal variation, with the annual mean desublimation ranging from 0 mm to 23.6 mm; (2) At 95% confidence level, condensation showed a significant increasing trend in the central and western QTP, while desublimation showed a significant decreasing trend in most regions of the QTP, and the decreasing trend of desublimation was more obvious than the increasing trend of condensation; (3) Both condensation and desublimation showed significant seasonal characteristics; the maximum monthly condensation was 2.37 mm and the monthly mean condensation was 0.70 mm, while the maximum monthly desublimation was 1.45 mm and the monthly mean desublimation was 0.95 mm; (4) The annual mean condensation was 8.45 mm, with an increasing trend of 0.24 mm/10a, the annual mean desublimation was 11.45 mm, with a decreasing trend of −0.26 mm/10a, and the total annual mean condensation and desublimation was 19.89 mm, with a weak decreasing trend on the QTP; (5) The increase in condensation is most associated with the increase in precipitation, while the decrease in desublimation is most associated with the increase in air temperature on the QTP.

Climate change, urbanization, and a growing population have led to a rapid increase in groundwater (GW) use. As a result, monitoring groundwater changes is essential for water managers and decision-makers. Due to the lack of reliable and insufficient in situ information, remote sensing and hydrological models may be counted as alternative sources to assess GW storage changes on regional and global scales. However, often, these hydrological models have a low spatial resolution for water-related applications on a small scale. Therefore, the main purpose of this study is to downscale the GW storage anomaly (GWSA) of the WaterGAP Global Hydrology Model (WGHM) from a coarse (0.5 degrees) to a finer spatial resolution (0.1 degrees) using fine spatial resolution auxiliary datasets (0.1 degrees), such as evaporation (E), surface (SRO), subsurface runoff (SSRO), snow depth (SD), and volumetric soil water (SWVL), from the ERA5-Land model, as well as the global precipitation (Pre) measurement (GPM-IMERG) product. The Qazvin Plain in central Iran was selected as the case study region, as it faces a severe decline in GW resources. Different statistical regression models were tested for the GWSA downscaling to find the most suitable method. Moreover, since different water budget components (such as precipitation or storage) are known to have temporal lead or lag relative to each other, the approach also incorporates a time shift factor. The most suitable regression model with the highest skill score during the training-validation was selected and applied to predict the final 0.1-degree GWSA. The downscaled results showed high agreement with the in situ groundwater levels over the Qazvin Plain on both interannual and monthly time scales, with a correlation coefficient of 0.989 and 0.62, respectively. Moreover, the downscaled product represents clear proof that the developed downscaling technique is able to learn from high-resolution auxiliary data to capture GWSA features at a higher spatial resolution. The major benefit of the proposed method lies in the utilization of only the auxiliary data that are available with global coverage and are free of charge, while not requiring in situ GW records for training or prediction. Therefore, the proposed downscaling technique can potentially be applied at a global scale and to aquifers in other geographical regions.

Analyzing historical droughts is essential to improve the assessment of future hydrological risks and to understand the effects of climate variability on streamflow. However, prolonged and consistent hydrological time series are scarce in the Brazilian savanna region. This study aimed to analyze the performance of climate reanalysis products in precipitation estimation, hydrological modeling, and historical drought analysis in a Brazilian savanna basin. For this purpose, precipitation data from the twentieth-century atmospheric model ensemble (ERA-20CM) and the land component of the fifth generation of European ReAnalysis (ERA5-Land) with bias correction were used. The weather variables were obtained from the Climatic Research Unit (CRU) and the hydrological modeling was performed using the Soil and Water Assessment Tool (SWAT). The Standardized Streamflow Index (SSI) was used to calculate hydrological drought in the basin. Overall, ERA5-Land performed satisfactorily in precipitation estimation, mainly on the monthly time scale, hydrological modeling, and drought prediction. Since ERA-20CM showed unsatisfactory values for the performance statistics in all analyses, the hydrologic drought (1950 to 2018) was performed with ERA5-Land. The results showed both an increase in the number of dry months and a decrease in wet months in recent decades.

Climate change has a strong impact on inland water bodies such as lakes. This means that the increase in lake temperature recorded in recent decades-in Europe as well-can change the evaporation regime of the lakes. This, together with the variation of the water cycle, in particular precipitation, implies that the water mass balance of lakes may vary due to climate change. Water mass balance modeling is therefore of paramount importance to monitor lakes in the context of global warming. Although many studies have focused on such a modeling, there is no shared approach that can be used for any lake across the globe, irrespective of the size. This becomes even more problematic for shallow and small lakes, for which few studies exist. For this reason, in this paper the use of reanalysis data, in particular ERA5-Land provided by the European Centre for Medium-Range Weather Forecasts (ECMWF), is proposed for the mass balance modeling. In fact, ERA5-Land has a global coverage and it is the only data source comprising a specific model for lakes, the Fresh-water Lake model (FLake). The chosen case study is the Trasimeno lake, a small and shallow lake located in Central Italy. The use of the reanalysis was preceded by data validation by considering both ground-based and satellite observations. The results show that there is a good agreement between the observed monthly variation of the lake level, ΔH, and the corresponding values of the water storage, δ, computed by means of the ERA5-Land data (Pearson coefficient larger than 70%). Discrepancies between observations and the ERA5-Land data happen in periods characterized in Europe by an extreme climate anomaly. This promising result encourages the use of ERA5-Land for other lakes.

Understanding the spatio-temporal variations in the frost-free period (FFP) and the number of frost days (FD) is beneficial to reduce the harmful effects of climate change on agricultural production and enhancing agricultural adaptation. However, the spatio-temporal variations in FFP and FD and their response to climate change remain unclear across China. To investigate the impact of climate change on FFP and FD, the trends and variations in FFP and FD across China from 1950 to 2020 were quantified using ERA5-Land, a reanalysis dataset with high spatial and temporal resolution. The results showed that ERA5-Land has good applicability in quantifying the trends and variations in FFP and FD across China under climate change. The spatial distribution of multi-year average FFP and FD across China showed significant latitudinal zonality and altitude dependence, i.e., FFP decreased with increasing latitude and altitude, while FD increased with increasing latitude and altitude. As a result of climate warming across China, the FFP showed an increasing trend with an increase rate of 1.25 d/10a and the maximum increasing rate of FFP in the individual region was 6.2 d/10a, while the FD showed a decreasing trend with a decrease rate of 1.41 d/10a and the maximum decreasing rate of FD in the individual region was −6.7 d/10a. Among the five major climate zones in China, the subtropical monsoon climate zone (SUMZ) with the greatest increasing rate of 1.73 d/10a in FFP, while the temperate monsoon climate zone (TEMZ) with the greatest decreasing rate of −1.72 d/10a in FD. In addition, the coefficient of variation (Cv) of FFP showed greater variability at higher altitudes, while the Cv of FD showed greater variability at lower latitudes in southern China. Without considering the adaptation to temperature of crops, a general increase in FFP and a general decrease in FD were both beneficial to agricultural production in terms of FFP and FD promoting a longer growing period and reducing frost damage on crops. This study provides a comprehensive understanding of the trends and variations in FFP and FD under climate change, which is of great scientific significance for the adjustment of the agricultural production layout to adapt to climate change in China.

Accurate assessment of agricultural drought is useful for ecosystem services. This is a successive work of our previous study that assessed agricultural drought using the soil water deficit index (SWDI) based on ERA5-Land in the four southern provinces of China from 2017 to 2019. Firstly, in addition to ERA5-Land, the suitability of CLDAS (China Land Data Assimilation System) soil moisture for drought assessment was investigated. Then, the study was extended with more comprehensive analysis and a much longer period (1981–2020). Based on three climate zones, in situ soil moisture was used for evaluation of both reanalysis datasets and agricultural drought. It was found that ERA5-Land_SWDI and CLDAS_SWDI have a good correlation with the in situ SWDI. ERA5-Land and CLDAS demonstrate some differences in representing agricultural drought but have a similar performance evaluated by in situ soil moisture. Droughts from 2001 to 2010 were more serious than in the other three decades, and droughts have become longer and severer in some areas in the last 40 years. There was a good correlation between agricultural drought and meteorological drought. Our work offers important insights for agricultural drought risk management in the four southern provinces of China.

Nowadays, a major issue is land-use change by urban development that alters the catchment response to meteorological events. Urban basins have less storage capacity and more rapid runoff, so urban rivers rise more quickly during storms and have higher peak discharge rates than rural catchments. An exemplary case of this situation is the city of Milan (northern Italy) and its whole territory that extends towards the north collecting meteoric precipitation through the Seveso, Olona and Lambro (SOL) rivers. To assess the impact of anthropogenic development on urban catchment scale hydrology, a reanalysis of 40 years of simulations was carried out with the Curve Number (CN) map based on current land use and compared to simulations using the CN maps based on past land use. A coupled hydro-meteorological system was built that combined a physically based rainfall-runoff hydrological model FEST-WB, developed by the Politecnico di Milano, with the ERA5-Land hourly dataset for the period 1981 to 2020, that was provided by the ECMWF under the framework of the Copernicus Climate Change Service Programme. The study (named SOL40) analyses 40 year trends in the main meteorological (air temperature, precipitation) and hydrological variables (runoff) over the SOL area and tried to quantify and separate the impact of land use change from that of climate change.

Nowadays, the Mediterranean region is generally recognized as a climate change hot spot given its strong response to global warming, with relevant impacts on rainfall amount and distribution. Within this context, in this work the temporal variability of rainfall at annual, seasonal and monthly scale was analyzed in Italy using rainfall data extracted from the reanalysis dataset ERA5-Land during the period 1950–2020. In particular, rainfall trend magnitude and significance have been estimated by means of non-parametric tests applied to 3215 grid points falling within the Italian territory. The main results of this analysis evidenced only a few relevant trends at the annual scale, mostly involving northern Italy (positive trend) and the Sardinia region (negative trend). At seasonal scale, the results showed a marked negative trend in winter, characterizing almost all the Italian territory, while in the other seasons a positive trend was identified in the majority of grid points, especially in the Alps. Finally, at the monthly scale, September was identified as the month of the year with the highest percentage of grid points with positive trends mainly located in central, southern, and north-eastern Italy.

Temperature is one of the most important meteorological variables for global climate change and human sustainable development. It plays an important role in agroclimatic regionalization and crop production. To date, temperature data have come from a wide range of sources. A detailed understanding of the reliability and applicability of these data will help us to better carry out research in crop modelling, agricultural ecology and irrigation. In this study, temperature reanalysis products produced by the China Meteorological Administration Land Data Assimilation System (CLDAS), the U.S. Global Land Data Assimilation System (GLDAS) and the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis version5 (ERA5)-Land are verified against hourly observations collected from 2265 national automatic weather stations (NAWS) in China for the period 2017–2019. The above three reanalysis systems are advanced and widely used multi-source data fusion and re-analysis systems at present. The station observations have gone through data Quality Control (QC) and are taken as “true values” in the present study. The three reanalysis temperature datasets were spatial interpolated using the bi-linear interpolation method to station locations at each time. By calculating the statistical metrics, the accuracy of the gridded datasets can be evaluated. The conclusions are as follows. (1) Based on the evaluation of temporal variability and spatial distribution as well as correlation and bias analysis, all the three reanalysis products are reasonable in China. (2) Statistically, the CLDAS product has the highest accuracy with the root mean square error (RMSE) of 0.83 °C. The RMSEs of the other two reanalysis datasets produced by ERA5-Land and GLDAS are 2.72 °C and 2.91 °C, respectively. This result indicates that the CLDAS performs better than ERA5-Land and GLDAS, while ERA5-Land performs better than GLDAS. (3) The accuracy of the data decreases with increasing elevation, which is common for all of the three products. This implies that more caution is needed when using the three reanalysis temperature data in mountainous regions with complex terrain. The major conclusion of this study is that the CLDAS product demonstrates a relatively high reliability, which is of great significance for the study of climate change and forcing crop models.

It is important to accurately assess agricultural drought because of its harmful impacts on the ecosystem and economy. Soil moisture reanalysis datasets provide an important way to assess agricultural drought. In this study, the ERA5-Land surface and subsurface soil moisture was used to estimate the soil water deficit index (SWDI) in four southern provinces of China. The ERA5-Land dataset was evaluated with in situ soil moisture observations from agrometeorological stations. Agricultural drought was assessed for three climate zones at a weekly scale from 2017 to 2019 and was compared with the atmospheric water deficit (AWD). It was found that both ERA5-Land soil moisture and the derived SWDI have relatively high accuracy, and the wet bias in the ERA5-Land dataset can be reduced by the calculation of the SWDI. The subsurface layer has better performance than the surface layer in drought monitoring, though they are highly correlated. Different climate zones demonstrate different drought periods and drought severity, and the temperate climate zone with no dry season has less droughts. The most severe droughts with the largest spatial extent occurred in the early winter, especially in 2019. Differences in the SWDI and AWD are mainly shown in southwestern Yunnan. The results of this study have important reference values for drought risk management.

This manuscript aims to create large-scale calculations of agro-climatic factors from global climatic data with high granularity-climatic ERA5-Land dataset from the Copernicus Climate Change Service in particular. First, we analyze existing approaches used for agro-climatic factor calculation and formulate a frame for our calculations. Then we describe the design of our methods for calculation and visualization of certain agro-climatic factors. We then run two case studies. Firstly, the case study of Kojčice validates the uncertainty of input data by in-situ sensors. Then, the case study of the Pilsen region presents certain agro-climatic factors calculated for a representative point of the area and visualizes their time-variability in graphs. Maps represent a spatial distribution of the chosen factors for the Pilsen region. The calculated agro-climatic factors are frost dates, frost-free periods, growing degree units, heat stress units, number of growing days, number of optimal growing days, dates of fall nitrogen application, precipitation, evapotranspiration, and runoff sums together as water balance and solar radiation. The algorithms are usable anywhere in the world, especially in temperate and subtropical zones.

Land surface temperature (LST) plays a critical role in the water cycle and energy balance at global and regional scales. Large-scale LST estimates can be obtained from satellite observations and reanalysis data. In this study, we first investigate the long-term changes of LST during 2003–2017 on a per-pixel basis using three different datasets derived from (i) the Atmospheric Infrared Sounder (AIRS) onboard Aqua satellite, (ii) the Moderate Resolution Imaging Spectroradiometer (MODIS) also aboard Aqua, and (iii) the recently released ERA5-Land reanalysis data. It was found that the spatio-temporal patterns of these data agree very well. All three products globally showed an uptrend in the annual average LST during 2003–2017 but with considerable spatial variations. The strongest increase was found over the region north of 45° N, particularly over Asian Russia, whereas a slight decrease was observed over Australia. The regression analysis indicated that precipitation (P), incoming surface solar radiation (SW↓), and incoming surface longwave radiation (LW↓) can together explain the inter-annual LST variations over most regions, except over tropical forests, where the inter-annual LST variation is low. Spatially, the LST changes during 2003–2017 over the region north of 45° N were mainly influenced by LW↓, while P and SW↓ played a more important role over other regions. A detailed look at Asian Russia and the Amazon rainforest at a monthly time scale showed that warming in Asian Russia is dominated by LST increases in February–April, which are closely related with the simultaneously increasing LW↓ and clouds. Over the southern Amazon, the most apparent LST increase is found in the dry season (August–September), primarily affected by decreasing P. In addition, increasing SW↓ associated with decreasing atmospheric aerosols was another factor found to cause LST increases. This study shows a high level of consistency among LST trends derived from satellite and reanalysis products, thus providing more robust characteristics of the spatio-temporal LST changes during 2003–2017. Furthermore, the major climatic drivers of LST changes during 2003–2017 were identified over different regions, which might help us predict the LST in response to changing climate in the future.

Reanalysis data are being increasingly used as gridded weather data sources for assessing crop-reference evapotranspiration (ET0) in irrigation water-budget analyses at regional scales. This study assesses the performances of ET0 estimates based on weather data, respectively produced by two high-resolution reanalysis datasets: UERRA MESCAN-SURFEX (UMS) and ERA5-Land (E5L). The study is conducted in Campania Region (Southern Italy), with reference to the irrigation seasons (April–September) of years 2008–2018. Temperature, wind speed, vapor pressure deficit, solar radiation and ET0 derived from reanalysis datasets, were compared with the corresponding estimates obtained by spatially interpolating data observed by a network of 18 automatic weather stations (AWSs). Statistical performances of the spatial interpolations were evaluated with a cross-validation procedure, by recursively applying universal kriging or ordinary kriging to the observed weather data. ERA5-Land outperformed UMS both in weather data and ET0 estimates. Averaging over all 18 AWSs sites in the region, the normalized BIAS (nBIAS) was found less than 5% for all the databases. The normalized RMSE (nRMSE) for ET0 computed with E5L data was 17%, while it was 22% with UMS data. Both performances were not far from those obtained by kriging interpolation, which presented an average nRMSE of 14%. Overall, this study confirms that reanalysis can successfully surrogate the unavailability of observed weather data for the regional assessment of ET0.