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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.

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.

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.

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.

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.

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.

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.