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Copyright of Iranian Journal of Geophysics (IJG) is the property of Iranian Journal of Geophysics (IJG) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study evaluates the use of remote sensing data to improve the understanding of groundwater resources in climate-sensitive regions with limited data availability and increasing agricultural water demands. The research focuses on estimating groundwater reserve dynamics in two major river basins in Morocco, characterized by significant local variability. The study employs data from Gravity Recovery and Climate Experiment satellite (GRACE) and ERA5-Land reanalysis. Two GRACE terrestrial water storage (TWS) products, CSR Mascon and JPL Mascon (RL06), were analyzed, along with auxiliary datasets generated from ERA5-Land, including precipitation, evapotranspiration, and surface runoff. The results show that both GRACE TWS products exhibit strong correlations with groundwater reserves, with correlation coefficients reaching up to 0.96 in the Oum Er-rbia River Basin and 0.95 in the Tensift River Basin (TRB). The root mean square errors (RMSE) were 0.99 cm and 0.88 cm, respectively. GRACE-derived groundwater storage (GWS) demonstrated a moderate correlation with observed groundwater levels in OERRB (R = 0.59, RMSE = 0.82), but a weaker correlation in TRB (R = 0.30, RMSE = 1.01). On the other hand, ERA5-Land-derived GWS showed a stronger correlation with groundwater levels in OERRB (R = 0.72, RMSE = 0.51) and a moderate correlation in TRB (R = 0.63, RMSE = 0.59). The findings suggest that ERA5-Land may provide more accurate assessments of groundwater storage anomalies, particularly in regions with significant local-scale variability in land and water use. High-resolution datasets like ERA5-land are, therefore, more recommended for addressing local-scale heterogeneity in regions with contrasted complexities in groundwater storage characteristics. [ABSTRACT FROM AUTHOR]

ERA5-Land is a valuable reanalysis data resource that provides near-real-time, high-resolution, multivariable data for various applications. Using daily precipitation data from 301 meteorological stations in the Yellow River Basin from 2001 to 2013 as benchmark data, this study aims to evaluate ERA5-Land's capability of monitoring extreme precipitation. The evaluation study is conducted from three perspectives: precipitation amount, extreme precipitation indices, and characteristics of extreme precipitation events. The results show that ERA5-Land can effectively capture the spatial distribution patterns and temporal trends in precipitation and extreme precipitation; however, it also exhibits significant overestimation and underestimation errors. ERA5-Land significantly overestimates total precipitation and indices for heavy precipitation and extreme precipitation (R95pTOT and R99pTOT), with errors reaching up to 89%, but underestimates the Simple Daily Intensity Index (SDII). ERA5-Land tends to overestimate the duration of extreme precipitation events but slightly underestimates the total and average precipitation of these events. These findings provide a scientific reference for optimizing the ERA5-Land algorithm and for users in selecting data. [ABSTRACT FROM AUTHOR]

The importance and vulnerability of municipalities in the eastern Amazon led to the evaluation of the distribution of seasonal precipitation and extreme events over protected and deforested sites along the Carajás Railroad (EFC) based on three datasets: Brazilian Daily Weather Gridded Data (BR-DWGD), European Reanalysis (ERA5-Land), and Climate Hazards Group InfraRed Precipitation with Station (CHIRPS). The main purpose is the understanding of the relationship between local land use change and the occurrence of extreme climate events. Results suggested that large-scale deforestation has a regional impact occasionally outweighing local effect. Significant trends reveal a general pattern of drought that has been more intense in the north of the study area, regardless of land cover, whether preserved or deforested. Moreover, the dry season is getting drier, but the wet season is not getting wetter over the entire area, as Carajás National Forest and Curionópolis (southern sector) deliver positive and accentuated trends in the rainy season. Extreme events are becoming more frequent. The number of consecutive dry days has increased in the north revealing an extension of dry periods, whereas, the maximum one-day precipitation has soared in the south indicating the intensification of rainfall. CHIRPS provides stronger correlations with the standard dataset (BR-DWGD), as both furnish primary data based on rain gauges observations. It also outperforms ERA5-Land in annual and seasonal analyses, which does not invalidate the latter as a great alternative to use in data-poor locations. Ultimately, further research and technology implementation were recommended to improve reforestation efforts given the reported results. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Reanalysis datasets provide a continuous picture of the past climate for every point on Earth. They are especially useful in areas with few direct observations, such as Siberia. However, to ensure these datasets are sufficiently accurate they need to be validated against readings from meteorological stations. Here, we analyse how values of six climate variables—the minimum, mean and maximum 2‐metre air temperature, snow depth (SD), total precipitation and wind speed (WSP)—from three reanalysis datasets—ERA‐Interim, ERA5 and ERA5‐Land—compare against observations from 29 meteorological stations across Siberia and the Russian Far East on a daily timescale from 1979 to 2019. All three reanalyses produce values of the mean and maximum daily 2‐metre air temperature that are close to those observed, with the average absolute bias not exceeding 1.54°C. However, care should be taken for the minimum 2‐metre air temperature during the summer months—there are nine stations where correlation values are <0.60 due to inadequate night‐time cooling. The reanalysis values of SD are generally close to those observed after 1992, especially ERA5, when data from some of the meteorological stations began to be assimilated, but the reanalysis SD should be used with caution (if at all) before 1992 as the lack of assimilation leads to large overestimations. For low daily precipitation values the reanalyses provide good approximations, however they struggle to attain the extreme high values. Similarly, for the 10‐metre WSP; the reanalyses perform well with speeds up to 2.5 ms−1 but struggle with those above 5.0 ms−1. For these variables, we recommend using ERA5 over ERA‐Interim and ERA5‐Land in future research. ERA5 shows minor improvements over ERA‐Interim, and, despite an increased spatial resolution, there is no advantage to using ERA5‐Land. [ABSTRACT FROM AUTHOR]

Understanding the spatial and temporal variability of heatwaves is crucial for climate change adaptation. This study examines heatwaves in Argentina from 1950 to 2022, analyzing temporal and spatial changes using four metrics: number of events (E), duration (D), mean intensity (MnI), and maximum intensity (MxI). It investigates seasonal variations (Warm and Cold Seasons—CS, WS) and the influence of different phases of the El Niño-Southern Oscillation (ENSO). Data from ERA5-LAND Reanalysis for 2 m daytime (Tx) and nighttime (Tn) temperatures are utilized. Our findings reveal regions with significantly higher heatwave intensities (Tx) in the North, east of Cuyo, west of Centro, and Southern Patagonia. Conversely, significant heatwave intensities (Tn) were observed, particularly in the north of the Litoral and Southern Patagonia. The Andes region (center and north) exhibited significant intensities for Tn. Both D and E exhibited similar significant trends for both Tn and Tx, except for the central zone. During the WS, the North-West and South Patagonia exhibit significant increasing trends for across most metrics. In contrast, during the CS, a higher number of significant increases in the studied metrics were observed in relation to Tx. El Niño amplifies heatwave intensities nationwide, except in Patagonia, where this occurs during the cold phase. In this phase, E and D of events increase in most Argentinian regions, resulting in a decoupling of intensity and duration, which increases in opposite periods. This study contributes to existing research by providing a detailed understanding of heatwave behavior with high spatial resolution. [ABSTRACT FROM AUTHOR]

Copyright of Mountain Research (10082786) is the property of Mountain Research Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Mountain Research (10082786) is the property of Mountain Research Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The fifth generation European Centre for Medium‐Range Weather Forecasts Reanalysis on global land surface (ERA5‐Land), the Multi‐Source Weighted‐Ensemble Precipitation (MSWEP), and the Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) are three representative precipitation estimates with quasi‐global coverage, high‐resolution and long‐term record. This study concentrates on investigating, for the first time, the long‐term spatiotemporal accuracy and regional applicability of these precipitation estimates at a daily scale in the Yellow River basin (YRB) using 39 complete years of data record (1981–2019), with a special focus on their capability on monitoring the extreme precipitation events with short duration and the continuous heavy precipitation events. Results indicate that MSWEP generally performs better than ERA5‐Land and CHIRPS in almost all seasons and subregions, with the highest Pearson correlation coefficient and critical success index, lowest root mean square error and false alarm ratio. ERA5‐Land presents a severe overestimation of precipitation amount, particularly in the plateau climate region (BIAS = 52.27%), but well reflects its spatial–temporal patterns in the YRB. As for the detecting capability, MSWEP shows the best accuracy in detecting extreme precipitation, particularly in maximum consecutive 5‐day precipitation (RX5day). The MSWEP better represents the spatial distribution of maximum 1‐day precipitation and maximum consecutive 5‐day precipitation in the YRB, but it shows a significant overestimation in zone Southern Qinghai. MSWEP and CHIRPS have better performance of temporal variation consistency in annual precipitation with ground reference than ERA5‐Land, while ERA5‐Land performs well in capturing extreme precipitation temporal variation, especially for continuous heavy precipitation events. This study can provide useful guidance when choosing long‐term precipitation products for hydrometeorological applications and climate‐related studies in the YRB. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This study focuses on the statistical downscaling of ERA5-Land reanalysis data using the Statistical DownScaling Model (SDSM) to generate climate change scenarios for the Spree catchment. Linear scaling was used to reduce the biases of the Global Climate Model for precipitation and temperature. The statistical analyses demonstrated that this method is a promising and straightforward way of correcting biases in climate data. SDSM was used to generate climate change scenarios, which considered three emission scenarios: RCP 2.6, RCP 4.5, and RCP 8.5. The results indicated that higher precipitation is expected under higher emission scenarios. Specifically, the summer and autumn seasons were projected to experience up to 50 mm more rainfall in the next 80 years, and the temperature was projected to increase by up to 1∘C by 2100. These projections of climate data for different scenarios are useful for assessing water management studies for agricultural and hydrologic applications considering changing climate conditions. This study highlights the importance of statistical downscaling and scenario generation in understanding the potential impacts of climate change on water resources. The results of this study can provide valuable insights into water resource management, especially on adapting to changing climate conditions. [ABSTRACT FROM AUTHOR]

Numerical simulations of dust emission processes are essential for dust cycle modeling and dust‐atmosphere interactions. Models have coarse spatial resolutions which, without tackling sub‐grid scale heterogeneity, bias finely resolved dust emission. Soil surface wind friction velocity (us*) drives dust emission non‐linearly with increasing model resolution, due mainly to thresholds of sediment entrainment. Albedo is area‐integrated, scales linearly with resolution, is related to us* and hence represents its sub‐grid scale heterogeneity. Calibrated albedo‐based global dust emission estimates decreased by only 2 Tg y−1 (10.5%) upscaled from 0.5 to 111 km, largely independent of resolution. Without adjusting wind fields, this scaling uncertainty is within recent estimates of global dust emission model uncertainty (±14.9 Tg y−1). This intrinsic scaling capability of the albedo‐based approach offers considerable potential to reduce resolution dependency of dust cycle modeling and improve the representation of local dust emission in Earth system models and operational air quality forecasting. Plain Language Summary: Global computer models for atmospheric dust were developed to understand the global dust cycle. However, these coarse resolution global dust models cannot accurately represent small‐scale dust emission processes and partly cause different dust emission estimations between the models. Using our new approach based on satellite products, we show that global dust emission estimation is largely independent of model resolution. Key Points: Wind friction velocity calibrated to linearly upscaled albedo was largely independent of model resolution between 0.5 and 111 kmWithout modifying wind fields, global dust emissions decreased by only 10.5% (within model uncertainty) from 0.5 to 111 km model resolutionSub‐grid scale heterogeneity is necessary to accurately represent grain‐scale sediment supply and entrainment in large scale modeling [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

High-resolution air temperature distribution data are of crucial significance for studying climate change and agriculture in the Yellow River Basin. Obtaining accurate and high-resolution air temperature data has been a persistent challenge in research. This study selected the Yellow River Basin as its research area and assessed multiple variables, including the land surface temperature (LST), Normalized Difference Vegetation Index (NDVI), Digital Elevation Model (DEM), slope, aspect, longitude, and latitude. We constructed three downscaling models, namely, ET, XGBoost, and LightGBM, and applied a stacking ensemble learning algorithm to integrate these three models. Through this approach, ERA5-Land reanalysis air temperature data were successfully downscaled from a spatial resolution of 0.1° to 1 km, and the downscaled results were validated using observed data from meteorological stations. The results indicate that the stacking ensemble model significantly outperforms the three independent machine learning models. The integrated model, combined with the selected set of multiple variables, provides a feasible approach for downsizing ERA5 air temperature data. The stacking ensemble model not only effectively enhances the spatial resolution of ERA5 reanalysis air temperature data but also improves downscaled results to a certain extent. The downscaled air temperature data exhibit richer spatial texture information, better revealing spatial variations in air temperature within the same land class. This research outcome provides robust technical support for obtaining high-resolution air temperature data in meteorologically sparse or topographically complex regions, contributing significantly to climate, ecosystem, and sustainable development research. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Flash droughts feature rapid onsets of soil moisture drought events and result in severe impacts and damages, especially on agricultural and ecological systems. How the flash drought regime across China varies on multitemporal scales with climate change is not fully clear yet. In this study, we extended the flash drought definition to apply to arid regions by adding an absolute soil moisture variation criterion. Then, we detected flash drought events across China during 1981–2021 and characterized their frequency, duration, and affected area changes on seasonal, annual, and decadal scales, using soil moisture data from the European Center for Medium-Range Weather Forecasts climate reanalysis-Land. Results show that flash drought hotspots appeared in North China and the Yangtze River Basin. During 1981–2021, the hotspots, even nationwide, underwent significant increases in frequencies, durations, and affected areas of flash droughts. The increases held in the extremely high values of the frequencies and durations in the decadal comparisons. Especially, North China saw the most extensive and rapid increases. Seasonally, flash drought frequencies and durations intensified more during spring and autumn, and seasonal hotspots in eastern China shifted in phase with spatial patterns of soil moisture loss balanced by precipitation and evapotranspiration. Thus, flash droughts tended to amplify atmospheric aridity. These findings on the hotspot regions and the spatiotemporal evolutions of flash droughts across China would pinpoint soil moisture responses to climate change and prepare for climate change impacts on local ecosystems. [ABSTRACT FROM AUTHOR]

In West Africa, the validation of distributed models is limited by the quality and availability of point station data measured in-situ. ERA5 is a climate reanalysis produced by European Centre for Medium-range Weather Forecasts (ECMWF) and suggested to overcome this constraint. This study assessed and compared over the Benin basins at spatial and monthly time scale, the quality of ERA5 and its variant ERA5-Land (namely LAND). ERA5 relies on the single-levels version with 0.25° x 0.25° resolution while LAND is the land surface version with 0.1° x 0.1° resolution. Four variables were collected including runoff, evapotranspiration (ETR), water table depth (WTD), and soil water content (SWC). Point station data were analyzed using the correlation performance evaluators, Mean Absolute Error (m) and Relative Mean Absolute Error (r). The results showed that LAND simulates well the peaks of mean runoff. It showed the best runoff performance in terms of correlation (~0.61) compared with ERA5 (correlation ~0.49). Both reanalysis showed high correlations (generally > 0.80) for SWC, but the correlations obtained from ETR are slightly lower (ERA5~0.58 vs. ERA5-Land~0.54). Correlations were below 0.5 on both reanalyses for WTD with slight overestimation (m=4.73 m for ERA5 vs. m=3.13 m for LAND). This study does not identify any reanalysis that is better than another, both spatially and monthly scale. Nevertheless, this study indicated that the choice of reanalyses must rely on their performance and the given water cycle element. Correcting the variables of these reanalysis could also improve their performance. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Low snow levels over the past few decades and predictions of a low‐to‐no snow future have spurred research into snow droughts, which pose a threat to water security and management. Systematic data‐model comparisons of snow drought have been lacking, hindering our understanding of the drivers of snow drought in the past. To address this gap, we analyzed snow drought events using standardized snow water equivalent index derived from monthly results of four numerical experiments using the E3SM Land Model (ELM) and ERA5‐Land data during the period of 1980–2014. Additionally, we compared snow drought duration calculated from models with those from the ERA5‐Land data during selected El Niño‐Southern Oscillation (ENSO) years. The numerical experiments were conducted with ELM driven by two prescribed atmospheric forcings, and with the coupled land‐atmosphere configuration of E3SM with and without plant hydraulics scheme feedback. Analysis reveals that 20%–30% of snow droughts occur due to factors other than above‐normal temperature and low snowfall, such as low soil moisture, warm soil temperature, and low relative humidity, etc., especially in high latitudes (50° North). Furthermore, our study highlights the exacerbating effect of ENSO events on snow drought conditions in various regions, despite some discrepancies between model and ERA5‐Land results. We also identified limitations of the coupled land‐atmosphere models in our current configuration in capturing the spatial patterns of snow droughts. This study underscores the challenge of predicting and mitigating snow drought and the need for a comprehensive understanding of the factors contributing to snow drought. Plain Language Summary: The decrease in snow levels over the past few decades and predictions for future snow droughts threaten water security and management. While there are various observational data products and model simulations available, a comparison between them has been lacking. This study conducted a comparison between different numerical experiments using the Energy Exascale Earth System Model and reference data from ERA5‐Land to understand the drivers of snow droughts in 1980–2014. The findings revealed that factors other than above‐normal temperature and low snowfall, such as low soil moisture, warm soil temperature, and low relative humidity, etc., can also contribute to snow droughts. The study also highlights the exacerbating effect of ENSO events on snow drought conditions in various regions. However, there are some discrepancies between the models and ERA5‐Land data, which may be attributed to factors such as model resolution, parameterization, and uncertainties in forcing data. The study underscores the complexity of predicting and mitigating snow drought and the need for a comprehensive understanding of the factors contributing to snow drought. Key Points: Snow droughts in Northern Hemisphere land are predominantly driven by low snowfall, warm temperature, or bothHowever, 20%–30% of snow droughts are driven by factors such as anomalous soil moisture and temperatureEl Niño‐Southern Oscillation events can exacerbate snow drought conditions in various regions, despite some discrepancies between model results [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Copyright of Plateau Meteorology is the property of Plateau Meteorology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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. [ABSTRACT FROM AUTHOR]

The Photovoltaic Geographical Information System (PVGIS) is a web application that provides free access to solar radiation and temperature data, typical meteorological year (TMY) data, and to photovoltaic performance assessment tools for any place in most parts of the world. The PVGIS was originally developed over 20 years ago, and since then, it has been under continuous development. At present, there are two versions of the PVGIS online—the older version 5.1 and the newest version 5.2. PVGIS 5.2 includes substantial improvements compared to the previous version, e.g., the update of the underlying data sets both in terms of quality, resolution, and geographical coverage and the extension of the time period used. This paper focuses on comparing the TMYs (and more specifically the TMY time series of air temperature), coming from both PVGIS 5.1 and 5.2, with the TMY produced on the basis of ground station meteorological data and with the ground station data itself. The results show that whereas overall the errors and biases for most locations are within the expected range (mean stationRMSE 4.27), these differences increase in places with complicated topography, e.g., in the Alps (maximum stationRMSE 9.50). [ABSTRACT FROM AUTHOR]

This study evaluates the reliability of ERA5 and ERA5‐Land reanalysis datasets in describing the mean daily air temperature of four climate domains in mainland Portugal. The reanalysis datasets were compared with ground observations from 94 meteorological stations (1980–2021). Overall, the results demonstrated a good degree of correlation between the observed and reanalysis data on both a daily and seasonal scale. Both the latitudinal distribution of the air temperature and the moderating effect of the Atlantic Ocean are well described. However, in the case of Portugal, the ERA5‐Land was shown to be considerably more effective at describing the mean daily air temperature than ERA5. The results also indicated that, in general, the reanalysis methodologies perform better when applied to air temperature simulation in flatter regions as opposed to regions with high‐altitude and complex terrain. The study further suggests that ERA5 and ERA5‐Land reanalysis should be used with caution in the case of short‐term environmental studies. In fact, relevant differences were shown to exist between the reanalyses and the observed daily mean air temperature datasets for certain specific years. Overall, considering the RMSE between the ERA5‐Land reanalysis datasets for mean daily air temperature and the observed datasets there is a 28% probability of locally having a mean RMSE <1.5°C, 52% probability of having a mean RMSE >1.5°C and <2.0°C, and 16% probability of having a RMSE >2.0°C and <3.0°C. These conclusions will hopefully contribute to improving our understanding of the uncertainty sources in relation to ERA5 and ERA5‐Land reanalysis data for different climate domains. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

As asphalt binder is a temperature-dependent viscoelastic material, the asphalt pavement performance is affected by the temperature during its service life. Traditionally, selecting the asphalt binder performance grade (PG) is based on long-term meteorological data analysis, but this study used MODIS and ASTER remotely-sensed (RS), reanalysis ERA5-Land, and in-situ meteorological datasets to select the PG. The multiple linear regression (MLR), genetic algorithm (GA), and deep-learning (DL) techniques were used to create models to estimate the maximum air and road pavement surface temperatures, considering the Superpave specifications. Model parameters involved the land surface temperature, vegetation index, elevation, ERA5-Land air temperature, soil moisture, wind speed, thermal radiation, evapotranspiration, latitude, and climate type. Comparative analyses revealed that the DL model yielded the most reliable PG results and the proposed RS-based method was more accurate than conventional approaches and could determine the PG accurately with 1 km resolution independent of distance to meteorological stations. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Near-surface air temperature is important for climate change, agriculture, animal husbandry, and ecosystems undergoing climate warming in Inner Mongolia. Land surface reanalysis products feature finer spatial and temporal resolutions, that can provide important data support for the determination of crop growth limits, grassland biomass growth, and desertification research in Inner Mongolia. In this study, 119 in situ observed sites were collected to compare and evaluate the performance of near-surface air temperature in three reanalysis products from 2018 to 2020 in Inner Mongolia. The three reanalysis products included three widely used products derived from the European Centre for Medium-Range Weather Forecasts (ECMWF) Fifth Generation Land Surface Reanalysis (ERA5-Land), and U.S. Global Land Data Assimilation System (GLDAS), as well as the latest reanalysis product from the High-Resolution Land Data Assimilation System reanalysis product by the China Meteorological Administration (HRCLDAS). Results are as follows: (1) The three reanalysis temperature products all reasonably reflect the characteristics of spatial and temporal changes in surface temperature in Inner Mongolia. Compared with ERA5L and GLDAS, HRCLDAS is more consistent with the observed results. (2) For the evaluation period, HRCLDAS has a certain underestimation of temperature, while ERA5-Land and GLDAS have a significant overestimation of temperature. (3) During high-temperature processes, HRCLDAS is more accurate in simulating higher temperatures than ERA5-LNAD and can demonstrate the changes in high-temperature drop zones. The major conclusion of this study is that the HRCLDAS product demonstrates a relatively high reliability, which is of great significance for the study of climate, ecosystem, and sustainable development. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

ERA5-Land service has been released recently as an integral and operational component of Copernicus Climate Change Service. Within its set of climatological and atmospheric parameters, it provides soil moisture estimates at different soil depths, represeting an important tool for retrieving saturation degree for predicting natural hazards as shallow landslides. This paper represents an innovative attempt aiming to exploit the use of saturation degree derived from ERA5-Land soil moisture products in a data-driven model to predict the daily probability of occurence of shallow landslides. The study was carried out by investigating a multi-temporal inventory of shallow landslides occurred in Oltrepò Pavese (northern Italy). The achieved results follow: (i) ERA5-Land-derived saturation degree reconstructs well field trends measured in the study area until 1 m from ground; (ii) in agreement with the typical sliding surfaces depth, saturation degree values obtained since ERA5-Land 28–100 cm layer represent a significant predictor for the estimation of temporal probability of occurrence of shallow landslides, able especially to reduce overestimation of triggering events; (iii) saturation degree estimated by ERA5-Land 28–100 cm layer allows to detect soil hydrological conditions leading to triggering in the study area, represented by saturation degree in this layer close to complete saturation. Even if other works of research are required in different geological and geomorphological settings, this study demonstrates that ERA5-Land-derived saturation degree could be implemented to identify triggering conditions and to develop prediction methods of shallow landslides, thanks also to its free availability and constantly updating with a delay of 5 days. [ABSTRACT FROM AUTHOR]

Background. Dead fuel moisture content (DFMC) is crucial for quantifying fire danger, fire behaviour, fuel consumption, and smoke production. Several previous studies estimating DFMC employed robust process-based models. However, these models can involve extensive computational time to process long time-series data with multiple iterations, and are not always practical at larger spatial scales. Aims. Our aim was to provide a more time-efficient method to run a previously established process-based model and apply it to Pinus yunnanensis forests in southwest China. Methods. We first determined the minimum processing time the process-based model required to estimate DFMC with a range of initial DFMC values. Then a long time series process was divided into parallel tasks. Finally, we estimated 1-h DFMC (verified with field-based observations) at regional scales using minimum required meteorological time-series data. Key results. The results show that the calibration time and validation time of the model-in-parallel are 1.3 and 0.3% of the original model, respectively. The model-in-parallel can be generalised on regional scales, and its estimated 1-h DFMC agreed well with field-based measurements. Conclusions. Our findings indicate that our model-in-parallel is time-efficient and its application in regional areas is promising. Implications. Our practical model-in-parallel may contribute to improving wildfire risk assessment. [ABSTRACT FROM AUTHOR]

The fragile ecological environment of the Mongolian Plateau (MP) is sensitive to climate change. It is necessary to fully understand the temperature and precipitation changes on the MP to ensure regional sustainable development. Most existing studies are conducted based on station observations which suffer from sparse distribution and limited spatial representativeness and cannot perfectly represent the climatic conditions of the whole region. By contrast, the long‐term, spatially continuous reanalysis products offer new opportunities for MP climate change research. However, the coarse spatial resolution and large uncertainties of reanalysis data limit their applicability to provide reliable climate information at finer scales. Within the delta downscaling framework, we downscale and correct the state‐of‐the‐art European Centre for Medium‐range Weather Forecasts ReAnalysis 5 land portion (ERA5‐Land) using a high‐resolution WorldClim reference climatology and generate a long‐term (1950–2020) 1‐km monthly air temperature and precipitation downscaled dataset. During the downscaling process, the results obtained by nearest neighbour, bilinear and bicubic interpolation methods are evaluated and compared. Air temperature result of bilinear method and precipitation result of nearest neighbour method, which have the best accuracy, are taken as the final downscaled data. The evaluation shows that the downscaled data has acceptable accuracy (overall MBE = −0.41°C, Corr = 0.997, RMSE = 1.51°C for air temperature, and MBE = 1.58 mm·month−1, Corr = 0.747, RMSE = 24.24 mm·month−1 for precipitation), and outperforms the original ERA5‐Land and widely used Climatic Research Unit (CRU) data. In addition, the downscaled data can also provide accurate and detailed temperature and precipitation change trends over the MP. With fine spatial resolution, long‐time span and good spatial continuity, the downscaled air temperature and precipitation will be a useful data source to explore climate change over the MP. [ABSTRACT FROM AUTHOR]

Soil moisture (SM) exists at the land-atmosphere interface and serves as a key driving variable that affects global water balance and vegetation growth. Its importance in climate and earth system studies necessitates a comprehensive evaluation and comparison of mainstream global remote sensing/reanalysis SM products. In this study, we conducted a thorough verification of ten global remote sensing/reanalysis SM products: SMAP DCA, SMAP SCA-H, SMAP SCA-V, SMAP-IB, SMOS IC, SMOS L3, LPRM_C1, LPRM_C2, LPRM_X, and ERA5-Land. The verification was based on ground observation data from the International SM Network (ISMN), considering both static factors (such as climate zone, land cover type, and soil type) and dynamic factors (including SM, leaf area index, and land surface temperature). Our goal was to assess the accuracy and applicability of these products. We analyzed the spatial and temporal distribution characteristics of global SM and discussed the vegetation effect on SM products. Additionally, we examined the global high-frequency fluctuations in the SMAP L-VOD product, along with their correlation with the normalized difference vegetation index, leaf area index, and vegetation water content. Our findings revealed that product quality was higher in regions located in tropical and arid zones, closed shrubs, loose rocky soil, and gray soil with low soil moisture, low leaf area index, and high average land surface temperature. Among the evaluated products, SMAP-IB, SMAP DCA, SMAP SCA-H, SMAP SCA-V, and ERA5-Land consistently performed better, demonstrating a good ability to capture the spatial and temporal variations in SM and showing a correlation of approximately 0.60 with ISMN. SMOS IC and SMOS L3 followed in performance, while LPRM_C1, LPRM_C2, and LPRM_X exhibited relatively poor results in SM inversion. These findings serve as a valuable reference for improving satellite/reanalysis SM products and conducting global-scale SM studies. [ABSTRACT FROM AUTHOR]

Long-term observation series of hydrological and climatic parameters should be obtained to solve the problems of assessing and forecasting the hydrological regime of river basins. In the absence of surface measurements of these parameters, reanalysis data can be used. The paper presents a comparison of the runoff magnitude and the consistency of runoff dynamics based on the observational data of gauging stations and the ERA5-Land and TerraClimate reanalysis data for seven rivers of Western Siberia (the left-bank tributaries of the Yenisei located in the zones of taiga and forest-tundra). Spearman's rank correlation coefficients for seasonal and annual runoff between the data of reanalysis and the actual data are equal to 0.35–0.98. The normalized root-mean-square error of annual runoff according to the reanalysis ranges from 13 to 21%. The values of errors and correlation coefficients depend on geographic location and area of the basins and vary during a season. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In this study, the latest release of all available Coupled Model Intercomparison Project Phase 6 (CMIP6) climate models with two future scenarios of Shared Socio-Economic Pathways, SSP2-4.5 and SSP5-8.5, over the period 2015–2100 are utilized in diagnosing climate extremes in Türkiye. Coarse-resolution climate models were downscaled to a 0.1° × 0.1° (~9 km) spatial resolution using the European Centre for Medium-Range Weather Forecasts Reanalysis 5-Land (ERA5-Land) dataset based on three types of quantile mapping: quantile mapping, detrended quantile mapping, and quantile delta mapping. The temporal variations of the 12 extreme precipitation indices (EPIs) and 12 extreme temperature indices (ETIs) from 2015 to 2100 consistently suggest drier conditions, in addition to more frequent and severe precipitation extremes and warming temperature extremes in Türkiye, under the two future scenarios. The SSP5-8.5 scenario indicates more severe water stress than the SSP2-4.5 scenario; the total precipitation decreases up to 20% for Aegean and Mediterranean regions of Türkiye. Precipitation extremes indicate a decrease in the frequency of heavy rains but an increase in very heavy rains and also an increasing amount of the total precipitation from very heavy rain days. Temperature extremes such as the coldest, warmest, and mean daily maximum temperature are expected to increase across all regions of Türkiye, indicating warming conditions by up to 7.5 °C by the end of the century. Additionally, the coldest daily maximums also exhibit higher variability to climate change in the subregions Aegean, Southeastern Anatolia, Marmara, and Mediterranean regions of Türkiye while the mean daily maximum temperature showed greater sensitivity in the Black Sea, Central Anatolia, and Eastern Anatolia regions. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Reference evapotranspiration (ETo) is a variable used to characterize the evaporative demand of the atmosphere and its impact on the water balance. During the last decades, significant ETo variabilities have been observed, especially at mid‐latitudes. These variabilities respond mainly to local variations in their physical drivers, such as solar radiation, vapour pressure deficit or wind speed. In this study, the annual and seasonal ETo estimates are generated using the Penman–Monteith method (FAO). Surface weather stations for the Argentine territory and reanalysis data for southern South America of the last four decades (1981–2020) are used. Contributions of both aerodynamic (ETaero) and radiative (ETrad) effects are evaluated to analyse their driving role. Significant positive ETo trends are observed from reanalysis data throughout Argentina, especially on the central east side of the Andes Mountain range with values up to 10 mm·year−1. Most of these ETo changes respond to positive trends in air temperature in the study area, while those in the central Andes also respond to negative trends in dew point temperatures. On the other hand, the increase in energy availability through positive trends in net surface radiation produced a slightly higher ETo in the northern regions of the country. Regional ETo values have shown to be more sensitive to variations in air temperature in the northeastern areas, although changes in humidity and solar radiation could also play a role. In a context of climate change, given that temperature and rainfall are expected to increase in the central and northeastern region of the country and decrease along the eastern side of the Andes Mountains in the coming decades, the characteristics observed over the 1981–2020 period are expected to intensify in the near future. [ABSTRACT FROM AUTHOR]