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28 pages, 10416 KB

Open AccessArticle

One Country, Several Droughts: Characterisation, Evolution, and Trends in Meteorological Droughts in Spain Within the Context of Climate Change

by David Espín Sánchez and Jorge Olcina Cantos

Climate 2025, 13(10), 202; https://doi.org/10.3390/cli13100202 - 26 Sep 2025

Abstract

In this paper, we analyse drought variability in Spain (1950–2024) using the Standardised Precipitation–Evapotranspiration Index (SPEI) at 6-, 12-, and 24-month scales. Using 43 long-record meteorological observatories (AEMET), we compute SPEI from quality-controlled (QC), homogenised series, and derive coherent drought regions via clustering and assess trends in the frequency, duration, and intensity of dry episodes (SPEI ≤ −1.5), including seasonality and statistical significance (p < 0.05). Short-term behaviour (SPEI-6) has become more complex in recent decades, with the emergence of a “Catalonia” type and stronger June–October deficits across the northern interior; Mediterranean coasts show smaller or non-significant changes. Long-term behaviour (SPEI-24) is more structural, with increasing persistence and duration over the north-eastern interior and Andalusia–La Mancha, consistent with multi-year drought. Overall, short and long scales converge on rising drought severity and persistence across interior Spain, supporting multi-scale monitoring and region-specific adaptation in agriculture, water resources, and forest management. Key figures are as follows: at 6 months—frequency 0.09/0.08 per decade (Centre–León/Catalonia), duration 0.59/0.50 months per decade, intensity −0.12 to −0.10 SPEI per decade; at 24 months—frequency 0.5 per decade (Cantabrian/NE interior), duration 0.8/0.7/0.4 months per decade (Andalusia–La Mancha/NE interior/Cabo de Gata–Almería), intensity −0.06 SPEI per decade; Mediterranean changes are smaller or non-significant. Full article

(This article belongs to the Section Weather, Events and Impacts)

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22 pages, 4411 KB

Open AccessArticle

Near-Surface Temperature Climate Change in the Caspian Region: A Study Using Meteorological Station Data, Reanalyses, and CMIP6 Models

by Ilya Serykh, Svetlana Krasheninnikova, Said Safarov, Elnur Safarov, Ebrahim Asadi Oskouei, Tatiana Gorbunova, Roman Gorbunov and Yashar Falamarzi

Climate 2025, 13(10), 201; https://doi.org/10.3390/cli13100201 - 25 Sep 2025

Abstract

The climatic variability of near-surface air temperature (NSAT) over the Caspian region (35–60° N; 40–65° E) was analyzed in this study. The analysis was based on a comparison of data from various sources: weather stations, NOAA OISSTv2 satellite-based data, atmospheric reanalyses ECMWF ERA5, NASA MERRA-2, and NCEP/NCAR, and the outputs from 33 Earth system models (ESMs) participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6). CMIP6 models results from the historical and Shared Socioeconomic Pathways (SSPs) experiments were utilized. Over the period 1940–2023, NSAT exhibited variable changes across the Caspian region. Weather stations in the northwestern part of the region indicated NSAT increases of 0.9 ± 0.2 °C for 1985–2023. In the central-western part of the Caspian region, the increase in average NSAT between 1940–1969 and 1994–2023 was 1.4 °C with a spatial standard deviation of 0.3 °C. In the southern part of the Caspian region, the increase in average NSAT between 1986–2004 and 2005–2023 was 0.8 ± 0.1 °C. Importantly, all 33 CMIP6 models, as well as the ERA5 reanalysis, captured an average NSAT increase of approximately 1.3 ± 0.5 °C for the whole Caspian region between 1940–1969 and 1994–2023. From the ERA5 data, the increase in NSAT was more pronounced in the north (~1.6 °C) than in the central Caspian region, with the most significant warming observed in the mountainous regions of Iran (up to 3.0 °C). Under various CMIP6 SSPs scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5), projections indicate an increase in average NSAT across the study region. Comparing the periods 1994–2023 and 2070–2099, the projected NSAT increases are 1.7 ± 0.7 °C, 2.8 ± 0.8 °C, 4.0 ± 0.9 °C, and 5.2 ± 1.2 °C, respectively. For the earlier period of 2024–2053 relative to 1994–2023, the projected NSAT increases are 1.2 ± 0.4 °C, 1.3 ± 0.4 °C, 1.4 ± 0.4 °C, and 1.7 ± 0.5 °C. Notably, the projected increase in NSAT is slower over the Caspian Sea compared to the surrounding land areas. Full article

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25 pages, 20264 KB

Open AccessArticle

Assessing Urban Resilience Through Physically Based Hydrodynamic Modeling Under Future Development and Climate Scenarios: A Case Study of Northern Rangsit Area, Thailand

by Detchphol Chitwatkulsiri, Kim Neil Irvine, Lloyd Hock Chye Chua, Lihoun Teang, Ratchaphon Charoenpanuchart, Fa Likitswat and Alisa Sahavacharin

Climate 2025, 13(10), 200; https://doi.org/10.3390/cli13100200 - 24 Sep 2025

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Abstract

Urban flooding represents a growing concern on a global scale, particularly in regions characterized by rapid urbanization and increased climate variability. This study concentrates on the Rangsit area in Pathum Thani Province, Thailand, an urbanizing peri-urban area north of Bangkok and within the Chao Phraya River Basin where transitions in land use and the intensification of rainfall induced by climate change are elevating flood risks. A physically based hydrodynamic model was developed utilizing PCSWMM to assess current and future flood scenarios that considered future build-out plans and climate change scenarios. The model underwent calibration and validation using a continuous modeling approach that conservatively focused on wet year conditions, based on available rainfall and water level data. In assessing future scenarios, we considered land use projections based on regional development plans and climate projections downscaled under RCP4.5 and RCP8.5 pathways. Results indicate that both urban expansion and intensifying rainfall are likely to increase flood magnitudes, durations, and impacted areas, although in this rapidly developing peri-urban area, land use change was the most important driver. The findings suggest that a physically based modeling approach could support a smart-control framework that could effectively inform evidence-based urban planning and infrastructure investments. These insights are of paramount importance for flood-prone regions in Thailand and Southeast Asia, where dynamic modeling tools must underpin governance, climate adaptation, and risk communication. Furthermore, given the greater impact of future build-out on flood risk, as compared to climate change, there is an opportunity to effectively and proactively improve flood resilience through the implementation of integrated Nature-based Solution and hard engineering approaches, in combination with effective flood management policy. Full article

(This article belongs to the Special Issue Modelling for the Influences of Climate and Landscape Processes on Hydrology)

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19 pages, 1442 KB

Open AccessArticle

Benova and Cenova Models in the Homogenization of Climatic Time Series

by Peter Domonkos

Climate 2025, 13(10), 199; https://doi.org/10.3390/cli13100199 - 23 Sep 2025

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Abstract

For the correct evaluation of climate trends and climate variability, it is important to remove non-climatic biases from the observed data. Such biases, referred to as inhomogeneities, occur for station relocations or changes in the instrumentation or instrument installation, among other reasons. Most inhomogeneities are related to a sudden change (break) in the technical conditions of the climate observations. In long time series (>30 years), usually multiple breaks occur, and their joint impact on the long-term trends and variability is more important than their individual evaluation. Benova is the optimal method for the joint calculation of correction terms for removing inhomogeneity biases. Cenova is a modified, imperfect version of Benova, which, however, can also be used in discontinuous time series. In the homogenization of section means, the use of Benova should be preferred, while in homogenizing probability distribution, only Cenova can be applied. This study presents the Benova and Cenova methods, discusses their main properties and compares their efficiencies using the benchmark dataset of the Spanish MULTITEST project (2015–2017), which is the largest existing dataset of this kind so far. The root mean square error (RMSE) of the annual means and the mean absolute trend bias were calculated for the Benova and Cenova results. When the signal-to-noise ratio (SNR) is high, the errors in the Cenova results are higher, from 14% to 24%, while when the SNR is low, or concerted inhomogeneities in several time series occur, the advantage of Benova over Cenova might disappear. Full article

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Climate, Volume 13, Issue 10 (October 2025) – 4 articles

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