CRU TS Research Articles

Downscaling precipitation over High-mountain Asia using multi-fidelity Gaussian processes: improved estimates from ERA5

Abstract. The rivers of High-mountain Asia provide freshwater to around 1.9 billion people. However, precipitation, the main driver of river flow, is still poorly understood due to limited in situ measurements in this area. Existing tools to interpolate these measurements or downscale and bias-correct precipitation models have several limitations. To overcome these challenges, this paper uses a probabilistic machine learning approach called multi-fidelity Gaussian processes (MFGPs) to downscale the fifth ECMWF climate reanalysis (ERA5). The method is first validated by downscaling ERA5 precipitation data over data-rich Europe and then data-sparse upper Beas and Sutlej river basins in the Himalayas. We find that MFGPs are simpler to implement and more applicable to smaller datasets than other state-of-the-art machine learning methods. MFGPs are also able to quantify and narrow the uncertainty associated with the precipitation estimates, which is especially needed over ungauged areas and can be used to estimate the likelihood of extreme events that lead to floods or droughts. Over the upper Beas and Sutlej river basins, the precipitation estimates from the MFGP model are similar to or more accurate than available gridded precipitation products (APHRODITE, TRMM, CRU TS, and bias-corrected WRF). The MFGP model and APHRODITE annual mean precipitation estimates generally agree with each other for this region, with the MFGP model predicting slightly higher average precipitation and variance. However, more significant spatial deviations between the MFGP model and APHRODITE over this region appear during the summer monsoon. The MFGP model also presents a more effective resolution, generating more structure at finer spatial scales than ERA5 and APHRODITE. MFGP precipitation estimates for the upper Beas and Sutlej basins between 1980 and 2012 at a 0.0625° resolution (approx. 7 km) are jointly published with this paper.

Global-scale ERA5 product precipitation and temperature evaluation

ERA5 (European Centre for Medium-Range Weather Forecasts Reanalysis Fifth Generation) is a significant dataset in climate reanalysis. Conducting a global-scale accuracy assessment and spatiotemporal distribution characteristic study of its temperature and precipitation data is crucial for gaining in-depth understanding of extreme climate changes. This study, based on CRU TS v4.04 (Climatic Research Unit Time-Series version 4.04) gridded data, employs multiple statistical indicators and detection metrics to assess the accuracy of the ERA5 temperature and precipitation dataset at a monthly scale over the past 70 years. Additionally, it utilizes Empirical Orthogonal Functions (EOF) to explore its spatiotemporal distribution characteristics. The results indicate that: (1) ERA5 demonstrates good consistency in most regions globally; however, the influence of uneven station density distribution results in weaker correlation in areas with sparse station density. (2) The accuracy of ERA5 data exhibits certain differences between the two periods (1950–1979, 1979–2019); with the overall performance of ERA5 temperature data near the Earth’s surface being better than that of precipitation data. (3) ERA5 precipitation data in the mid and low latitudes of the Southern and Northern Hemispheres exhibit characteristics of positive–negative distribution, while temperature data show an opposite distribution in the Northern and Southern Hemispheres (with a predominant pattern of warmer temperatures in the north and colder temperatures in the south). Additionally, the typical patterns for both variables occur in January and July each year. The above study will provide reference and insights for future climate change prediction and adaptation efforts.

Spatial–Temporal Analysis of Impacts of Climate Variability on Maize Yield in Kenya

This study examined the spatial temporal impacts of climate variability on maize yield in Kenya. The maize yield data were obtained from the Kenya Maize Yield Database while climatic variable data were obtained from the Climatic Research Unit gridded Time Series (CRU TS) with a spatial resolution of 0.5° × 0.5°. The non-parametric Mann–Kendall and Sen’s slope tests showed no trend in the data for maximum temperature, minimum temperature and precipitation. The spatial maps patterns highlight the rampancy of wetter areas in the Lake Victoria basin and Highlands East of Rift Valley compared to other regions. Additionally, there is a decreasing trend in the spatial distribution of precipitation in wetter areas and an increasing trend in maximum temperature in dry areas, albeit not statistically significant. Spearman’s rank correlation test showed a strong positive correlation between maize yield and the climatic parameters for the Lake Victoria basin, Highlands East of Rift Valley, Coastal Strip and North Western Regions. The findings suggest that climate variability has a significant impact on maize yield for four out of six climatological zones. We recommend adoption of policies and frameworks that will augment adaptive capacity and build resilience to climatic changes.

Different habitats of ferns growing in the Amonkoton stream, Zerafshan Valley, Uzbekistan

Abstract. Muminov D, Khaydarov K, Zhalov K, Hasanov M, Abdumuminova R, Ochilov U. 2024. Different habitats of ferns growing in the Amonkoton Stream (Zerafshan Valley), Uzbekistan. Biodiversitas 25: 2696-2704. The Zerafshan Valley, Uzbekistan, characterized by a warm and arid climate, has declined vegetation and soil quality over recent decades, marking a stark departure from its once lush past. Understanding the nuanced relationship between species and their habitat is imperative to unraveling this ecological transformation. This research delves into ferns, which are highly responsive to environmental factors, seeking to elucidate their circumstances within the changing ecosystem. Through strategically planned intensive transects and plots as proxies for fern growth and habitat analysis, statistical methods like the Shannon-Wiener Index (H) and Bray-Curtis index were deployed. The climate-related information analysis was conducted using the CRU TS 4.07 tool. We made use of average temperatures and precipitation over 120 years. The results of analyses have been acquired from 490 fern individuals belonging to 13 species, 7 genera, and 6 families. There are four species: Equisetum arvense L., Equisetum ramosissimum Desf., Cystopteris fragilis (L.) Bernh. and Adiantum capillus-veneris L. were employed as indicators of habitat. The findings underscore a substantial decline in fern-populated areas, resulting in diminished diversity and abundance and a relatively high rarity level. Furthermore, a latitudinal correlation with ferns along the stream direction is exhibited by four indicator species, offering valuable insights into the intricate dynamics of the Zerafshan Valley's ecosystem.

Evaluation of Grid-Based Aridity Indices in Classifying Aridity Zones in Iraq

In this study, the aridity index (AI) based on gridded climate data was validated for defining aridity and classifying aridity zones in Iraq through comparison with the results obtained by the station-based aridity index. Gauge-based gridded climate data taken from Climatic Research Unit Timeseries (CRU TS) were used to determine the annual value of four aridity indices (Lang, De Martonne, Ernic and UNEP AI) over the period 1998-2011. The results showed that the aridity distribution maps derived using grid-based aridity indices were reasonably close to those found using station-based ones. The four aridity indices properly identified similar aridity (dryness) classifications in both the station-based and grid-based aridity maps. The area percentage of each aridity class predicted by grid-based AIs was also compared with that obtained by the station-based AIs. The results showed that the variances between the area percentages predicted by grid-based AIs and those estimated using station-based AIs are fairly slight. The Lang AI exhibited the least variance (0.4%) while the De Martonne AI had the biggest variance (-4.8%). Despite these minor variances, it is however possible to conclude that the grid-based aridity index classified the aridity zones of Iraq as properly as the station-based aridity index did.

Influence of changes in solar activity on Nigerian Rainfall

This paper focuses on the impact of solar activity changes on rainfall in Nigeria. Rainfall data were got from Gridded Time Series (CRU TS v4) of the University of East Anglia, United Kingdom. Various statistical tools like Pearson rank correlation coefficient, regression analysis, covariance, skewness, and kurtosis are employed in the study. The results show that there are generally weak correlations between rainfall and solar activity in most seasons. However, low correlation coefficients (0.0424 and 0.1040, respectively for sunspot numbers and F10.7 mean a very weak positive relationship between early rainfall, main rainfall and annual rainfall and negative for dry season rainfall and Harmattan rainfall. Also, there is a negative time-series relationship between the rainfall amount in dry and Harmattan seasons, with the highest rainfall amount during low solar activity and vice versa. Therefore, it can be concluded that solar activity has little effect on Nigeria's annual and main season rainfall. However, during the dry season and Harmattan, heavy rainfall appears to be associated with low solar activity, while high solar activity appears to be associated with low rainfall. This study will be beneficial to farmers in Nigeria, majority of whom are peasant farmers that practice rain-fed farming.

Longitudinal assessment of extreme climate events in Kinnaur district, Himachal Pradesh, north-western Himalaya, India.

It is vital to keep an eye on changes in climatic extremes because they set the stage for current and potential future climate, which usually have a reasonable adverse impact on ecosystems and society. The present study examines the variability and trends in precipitation and temperature across seasons in the Kinnaur district, offering valuable insights into the complex dynamics ofthe Himalayan climate. Using Climatic Research Unit gridded Time Series (CRU TS) datasets from 1951 to 2021, the study analyzes the data to produce 28 climate indices based on India Meteorological Department (IMD) convention indices and Expert Team on Climate Change Detection and Indices (ETCCDI). Although there may be considerable variation in climate indices in terms of absolute values within different products, there is consensus in both long-term trends and inter-annual variability. Analysis shows that even within a small area, there is variability in the magnitude and direction of historic temperature trends. Initially, the datawere subjected to rigorous quality control procedures, which involved identifying anomalies. Statistical analysis like trend analysis, employing Mann-Kendall test and Sen's slope estimator, reveal significant (p < 0.05) increase in consecutive dry days (CDD) at 0.03days/year and decrease in consecutive wet days (CWD) at 0.02days/year. Notably, the frequency of heavy precipitation occurrences showed an increasing trend. Changes in precipitation in the Western Himalaya are driven by a complex interplay of orographic effects, monsoonal dynamics, atmospheric circulation patterns, climate change, and localized factors such as topography, atmospheric circulation patterns, moisture sources, land-sea temperature contrasts, and anthropogenic influences. Moreover, in case of temperature indices, there is significant increasing trend observed. Temperature indices indicate a significant annual increase in warm nights (TN90p) at 0.06%/year and warm days (TX90p) at 0.11%/year. Extreme temperature events have been trending upward, with monthly daily maximum temperature (TXx) increasing by 1.5°C yearly. This study enhances our comprehension of the global warming phenomenon and underscores the importance of acknowledging alterations in the water cycle and their repercussions on hydrologic resources, agriculture, and livelihoods in the cold desert of the northwestern Indian Himalaya.

Compound dry and hot events over major river basins of the world from 1921 to 2020

Compound dry and hot events (CDHEs) are among the most destructive compound extremes. Under global warming, changes in precipitation, temperature and their dependence make profound contributions to CDHEs. In this paper, the contributions of these three factors are explicitly quantified based on a novel mathematical method. Specifically, time series of precipitation and temperature are employed to identify CDHEs and then changes of CDHEs are attributed by using the partial derivatives-based sensitivity analysis. Based on the Climatic Research Unit Time-Series (CRU TS), a case study of CDHEs is devised for the major river basins (MRBs) of the world. The results highlight that from the period 1921–1970 to the period 1971–2020, CDHEs did occur more frequently across most MRBs. The temperature tended to make the largest contribution, followed by precipitation and the dependence between precipitation and temperature. In Africa, South America and Western Europe, the rising temperature is generally the dominant factor for increases of heatwaves that contribute to CDHEs. In Asia, increases of droughts along with increases of heatwaves raise the risk of CDHEs. For MRBs with moderate increases in temperature, increasing precipitation is shown to mitigate or even offset the risks of CDHEs. In the meantime, the increasing dependence is observed to reduce the frequency of CDHEs in the Huai He and the Mississippi even though temperature is increasing. Overall, the attributing results of CDHEs from 1921 to 2020 can serve as a reference for the preparation and mitigation of CDHEs for MRBs across the world.

Response of beech and fir to different light intensities along the Carpathian and Dinaric Mountains.

Predicting global change mitigations based on environmental variables, like temperature and water availability, although yielding insightful hypothesis still lacks the integration of environmental responses. Physiological limits should be assessed to obtain a complete representation of a species' fundamental niche. Detailed ecophysiological studies on the response of trees along the latitudinal gradient are rare. They could shed light on the behaviour under different light intensities and other studied traits. The forests of the Dinaric Mountains and the Carpathians represent the largest contiguous forest complexes in south-eastern Europe. In uneven-aged Carpathian (8 plots) and Dinaric Mountain (11 plots) forests, net assimilation (Amax) and maximum quantum yield (Φ) were measured for beech and fir in three predefined light intensity categories according to the indirect site factor (ISF%) obtained by the analysis of hemispherical photographs in managed and old growth forests, all located above 800 m a.s.l. The measurements were carried out under fixed environmental conditions in each light category per plot for three consecutive years. Data from the last 50-year average period from the CRU TS 4.01 dataset were used for the comparison between Amax, Φ, and climate. The highest Φ for beech were observed in the central part of the Dinaric Mountains and in the south westernmost and northwesternmost part of the Carpathians for both beech and fir, while they were highest for fir in the Dinaric Mountains in the northwesternmost part of the study area. The Φ-value of beech decreased in both complexes with increasing mean annual temperature and was highest in the open landscape. For fir in the Carpathians, Φ decreased with increasing mean annual temperature, while in the Dinaric Mountains it increased with higher temperature and showed a more scattered response compared to the Carpathians. Short-term ecophysiological responses of beech and fir were consistent to long-term radial growth observations observed on same locations. The results may provide a basis and an indication of the future response of two tree species in their biogeographical range to climate change in terms of competitiveness, existence and consequently forest management decisions.

Spatial and Temporal Evolution of Precipitation in the Bahr el Ghazal River Basin, Africa

Accurate and punctual precipitation data are fundamental to understanding regional hydrology and are a critical reference point for regional flood control. The aims of this study are to evaluate the performance of three widely used precipitation datasets—CRU TS, ERA5, and NCEP—as potential alternatives for hydrological applications in the Bahr el Ghazal River Basin in South Sudan, Africa. This includes examining the spatial and temporal evolution of regional precipitation using relatively accurate precipitation datasets. The findings indicate that CRU TS is the best precipitation dataset in the Bahr el Ghazal Basin. The spatial and temporal distributions of precipitation from CRU TS reveal that precipitation in the Bahr el Ghazal Basin has a clear wet season, with June–August accounting for half of the annual precipitation and peaking in July and August. The long-term annual total precipitation exhibits a gradual increasing trend from the north to the south, with the southwestern part of the Basin having the largest percentage of wet season precipitation. Notably, the Bahr el Ghazal Basin witnessed a significant precipitation shift in 1967, followed by an increasing trend. Moreover, the spatial and temporal precipitation evolutions reveal an ongoing risk of flooding in the lower part of the Basin; therefore, increased engineering counter-measures might be needed for effective flood prevention.

Perceptions of environmental changes among a climate-vulnerable population from Bangladesh

Effective climate change adaptation requires a thorough understanding of whether and how affected populations perceive climatic and environmental changes. Existing research has been inconclusive regarding the consistency of these perceptions compared to objective meteorological indicators. Moreover, no systematic comparison has been done for the perception of discrete environmental events such as floods or erosion. This study relies on novel panel survey data of approximately 1700 households residing along the Jamuna River in Bangladesh as well as on unique individual-level, satellite-based erosion data. It compares respondents’ perceptions of environmental events, namely riverbank erosion, and three climate change indicators, specifically long-term temperature change and changes in precipitation during wet and dry seasons, to objective measurements using satellite imagery and climatic time-series data (CRU TS). I find that long-term temperature change is perceived more accurately than long-term changes in precipitation. Given that educational attainment and climate change literacy among the study population are low, this indicates that global temperature increases are felt even by remote populations who have never heard the term climate change. Erosion is strongly overestimated, especially by those respondents who had been personally affected by it. Since human behavior is guided by perceptions rather than objective data, this has important policy implications, underlining the importance of considering people’s perceptions if the goal is to assist them in adapting to environmental changes.

Water cycle changes in Czechia: a multi-source water budget perspective

Abstract. The water cycle in Czechia has been observed to be changing in recent years, with precipitation and evapotranspiration rates exhibiting a trend of acceleration. However, the spatial patterns of such changes remain poorly understood due to the heterogeneous network of ground observations. This study relied on multiple state-of-the-art reanalyses and hydrological modeling. Herein, we propose a novel method for benchmarking hydroclimatic data fusion based on water cycle budget closure. We ranked water cycle budget closure of 96 different combinations for precipitation, evapotranspiration, and runoff using CRU TS v4.06, E-OBS, ERA5-Land, mHM, NCEP/NCAR R1, PREC/L, and TerraClimate. Then, we used the best-ranked data to describe changes in the water cycle in Czechia over the last 60 years. We determined that Czechia is undergoing water cycle acceleration, evinced by increased atmospheric water fluxes. However, the increase in annual total precipitation is not as pronounced nor as consistent as evapotranspiration, resulting in an overall decrease in the runoff. Furthermore, non-parametric bootstrapping revealed that only evapotranspiration changes are statistically significant at the annual scale. At higher frequencies, we identified significant spatial heterogeneity when assessing the water cycle budget at a seasonal scale. Interestingly, the most significant temporal changes in Czechia occur during spring, while the spatial pattern of the change in median values stems from summer changes in the water cycle, which are the seasons within the months with statistically significant changes.

Standardized precipitation index analysis and drought frequency tendencies in lower eastern counties of Kenya

The standardized precipitation index (SPI) is a fundamental indicator of meteorological, hydrological, and agricultural droughts in the world. This study aims to evaluate different timescales, 3 months (SPI-3), 6 months (SPI-6), 9 months (SPI-9), and 12 months (SPI-12) indices from meteorological data in quantifying drought characterization in lower eastern counties of Kenya from 1990 to 2018 for observed data and from 1986 to 2018 for Climatic Research Unit Time Series (CRU) data. Precipitation in-situ data (annual) and high-resolution (0.5 × 0.5 degree grid) monthly-scale precipitation data were sought from Kenya Meteorological Department and CRU TS, respectively. Z-Score (SPI) was computed for each year (in-situ data) and month (CRU TS data) using the SPI algorithm, expressed as the departure from the mean in standard deviation units. Quality control of CRU TS data was done by checking outlier values and comparing the data with precipitation data obtained from the meteorological department as well as ERA5 reanalysis data. Results showed that extreme to mild drought was experienced across the Kenyan counties for both annual in-situ and monthly gridded data. Machakos county experienced a year of extreme drought, while Makueni and Taita-Taveta have had 2 and 4 years of severe droughts, respectively. The monthly SPI indices of 3, 6, 9, and 12 months showed a remarkably consistent behavioral pattern detecting extreme droughts across the counties. Considering the uncertainties, unpredictability, and shifting of the long and short rainy seasons in Kenya, results were obtained related to dry and wet episodes and to their relationship with agricultural production as well as water availability and environmental management.

The effects of urban thermal heterogeneity and feather coloration on oxidative stress and metabolism of pigeons (Columba livia)

Urbanization stands out as a significant anthropogenic factor, exerting selective pressures on ecosystems and biotic components. A notable outcome of urbanization is thermal heterogeneity where the emergence of Urban Heat Islands is characterized by elevated air and surface temperatures compared to adjacent rural areas. Investigating the influence of thermal heterogeneity on urban animals could offer insights into how temperature variations can lead to phenotypic shifts. Urban pigeons (Columba livia) serve as an excellent model for studying urban thermal effects, given the melanism variations, which are associated with the pleiotropy of the melanocortin system. To examine the development of physiological plasticity in response to urban thermal variations, we conducted a study on pigeons in Santiago, Chile, during the rainy season. We assessed the influence of habitat on physiological traits related to metabolism and antioxidant capacities, which are theoretically affected by feather coloration. Our findings reveal that variations in melanism significantly impact pigeon physiology, affecting both antioxidant capacities and the mitochondrial activity of red blood cells. It was found that higher urban temperatures, from both the current sampling month and the prior sampling month (from CRU TS dataset), were negatively and strongly associated with lower antioxidant and metabolic activities. This suggests that elevated urban temperatures likely benefit the energetic budgets of pigeon populations and mitigate the negative effects of oxidative metabolism, with differential effects depending on feather colorations.

Human or climate? Differentiating the anthropogenic and climatic drivers of lake storage changes on spatial perspective via remote sensing data

Lakes are an essential part of the terrestrial water system in which storage changes are controlled by water balance and human impact. Although there are some attempts to define storage changes on a global scale, examination of spatial relations is poorly quantified. In this study, therefore, lake storage changes have been investigated using remote-sensing-derived data around the globe. Hence, 372 artificial/natural lakes were obtained, covering between 1992 and 2019. Watersheds belong to river was extracted via HydroSHED data. Based on watershed, dominant climate types were determined via Köppen-Geiger classification. Similarly, the areal average CRU TS v.4.05 monthly gridded precipitation time series and human footprint data based on watersheds were obtained to understand the drivers of lake storage changes. The nonparametric Mann-Kendall and Sen's slope trend analyses were applied to the lake storage change and precipiation values in order to determine long-term increases and decreases. A bivariate map was constructed between storage changes trend vs precipitation trend and human footprint to reveal the drivers of lake storage changes in terms of spatial aspects. The trend analysis and bivariate map results show that North America, the East African Highlands, and the Tibet plateau are important increasing hotspots, where precipitation is a significant driver for storage oscillations, except for the Tibet plateau. Besides, the Brazilian Highlands, Pacific Mountain System, and Intermontane of conterminous USA are other decreasing hotspots in which human footprint and decreasing precipitation collectively affect these changes. Furthermore, results clearly show that anthropogenic influence is low in the northern and mountainous areas, and storage changes have a linear relationship with precipitation. In contrast, intense human climate interaction influences lake changes in plains areas and arid/temperate climates.

Permafrost on the Tibetan Plateau is degrading: Historical and projected trends

Permafrost degradation on the Tibetan Plateau (TP) will significantly affect local water cycle processes, downstream water ecology, and water security. In this study, we evaluate the long-term interannual dynamics of permafrost distribution and active layer thickness (ALT) on the TP based on historical data from Climatic Research Unit gridded Time Series (CRU TS) downscaling and projected data under four shared socio-economic pathways (SSPs) in Scenario Model Intercomparison Project (ScenarioMIP) of the Coupled Model Intercomparison Project Phase 6 (CMIP 6). To achieve this, we employ a data-driven scheme at 1 km resolution for both historical and future periods (1901–2100) that compares the performance of four machine learning algorithms to select the optimal algorithm for permafrost distribution and ALT simulations. Our results indicate that the permafrost on the TP has been undergoing degradation in both historical and future periods, with a decrease in permafrost area and an increase in ALT. The changing rates of permafrost area and regionally averaged ALT during the historical period (1901–2020) are −1.05 × 104 km2 decade–1 and 0.012 m decade–1, while an accelerated degradation is observed after the 1970 s (with changing rates of permafrost area and regionally average ALT of −3.62 × 104 km2 decade–1 and 0.055 m decade–1). Our results also suggested that permafrost degradation on the TP will continue in the future under the four SSP scenarios. The individual global climate models (GCMs) exhibit a consistent degradation trend but great uncertainty in degradation speed. The ensemble mean of simulations across 15 selected GCMs showed that the degradation percentage of permafrost area on the TP under scenarios SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 was 26.0 ± 6.8 %, 50.4 ± 5.6 %, 79.2 ± 4.5 %, and 89.0 ± 4.0 % by 2100, and the regionally average ALT increased by 0.301 ± 0.112 m, 0.628 ± 0.113 m, 1.204 ± 0.119 m, and 1.486 ± 0.125 m, respectively. We also analyze permafrost stability and elevation-dependent changes of ALT on the TP. The permafrost stability increases with elevation and latitude, and ALT changes more intensely with increasing elevation. This study will provide valuable data for hydrological and ecological studies related to permafrost on the TP.

Drought characteristics projections based on CMIP6 climate change scenarios in Syria

Study regionSyria Study focusThis study delves into predicting drought characteristics in Syria, focusing on their duration, frequency, and intensity. It utilizes a set of 13 models sourced from the latest CMIP6 dataset, encompassing two distinct SSP scenarios. To evaluate how well CMIP6 represents drought attributes in Syria, the research conducts a comparison with observed monthly climate data from CRU TS v4.06 and ERA 5, as well as the CMIP6 model ensemble outputs for the reference period (1970–2000). New hydrological insights for the regionThis study provides new hydrological insights for Syria. It reveals robust projections of increasing drought severity, frequency, and duration, particularly in the north and northeast arid and semi-arid regions, even under aggressive climate mitigation scenarios. Additionally, the study highlights that higher emissions scenarios are associated with more prolonged and intense drought events, potentially impacting even less vulnerable areas. These findings emphasize the urgent need for drought adaptation and mitigation measures, as well as improved water resource planning, in order to address the changing hydrological landscape of the region. Furthermore, it underscores the long-lasting effects of drought on ecosystem recovery, which may span several decades.

Projections of drought characteristics based on combined drought index under CMIP6 models

Abstract This study assesses climate change's impact on drought in Iran's Dez Basin. It introduces the Hydro-Meteorological Drought Index (HMDI), integrating the Standardized Precipitation Evapotranspiration Index (SPEI) and Standardized Runoff Index (SRI). Using Climatic Research Unit Time Series (CRU TS) data (1980-2012) and downscaling forecasted data from three CMIP6 models (2020-2052) for SSP1-2.6 and SSP5-8.5 scenarios, we employ the rainfall-runoff Hydrologiska Byråns Vattenbalansavdelning Hydrological Bureau's Water Balance Model (HBV)-Light model to predict future streamflow. Drought characteristics are analyzed. Under SSP5-8.5, CanEsm5 shows substantial temperature and runoff increases, notably in Bakhtiari and Borujerd sub-basins (63% and 56%). Future droughts are expected to intensify, particularly under SSP5-8.5. The most severe HMDI-derived drought (HMDI 12) in Borujerd station is projected to increase from -43.44 to -44.05. SSP5-8.5 is likelier to cause severe and prolonged HMDI-derived droughts than SSP1-2.6 or the historical period. The analysis suggests that normal drought levels will persist, while mild and severe drought levels will rise in the future.

Математико-картографическое моделирование динамики климата Ростовской области по глобальным метеорологическим данным

The scientific work presents mathematical and cartographic modeling of the climate dynamics of the Rostov region using the global meteorological product CRU TS version 4.06 in the period from 2020 to 2040. The work used CRU TS global climate data from 1960 to 2020 to model data for 2030 and 2040. A technique for mathematical-cartographic modeling of climate dynamics based on global climate products is presented. Using the geographic information system QGIS version 3.18, raster images were vectorized, which made it possible to export them to a MS Excel spreadsheet. Based on the data obtained, 2 tables were prepared. One includes actual data for 2020 and modeling data in terms of the average annual air temperature for 2030 and 2040 for the municipal districts of the Rostov region. The second table includes actual data for 2020 and simulation data in terms of the average annual air temperature for 2030 and 2040 for the urban districts of the Rostov region. The relationship between actual data for 2020 and data for 2030 and 2040 does not exceed 10%. This allows us to say that a stable climate has been established on the territory of the Rostov region, which is not subject to serious climatic changes in the future. According to the actual data and simulation data, maps were created and compiled using the interpolation method of inversely weighted distances in terms of the average annual air temperature for 2020, 2030, and 2040.

Comparison of gridded precipitation estimates for regional hydrological modeling in West and Central Africa

Study regionData-scarce basins located in West Africa and northern Central Africa. Study focusMultiple studies have shown that global gridded precipitation datasets could provide an alternative to the lack of observed data in Sub-Saharan Africa. This work evaluated 15 precipitation datasets based on satellite rainfall (ARC v.2, CHIRP v.2, CHIRPS v.2, PERSIANN-CDR, MSWEP v2.2 and TAMSAT v3), reanalysis (ERA5, JRA-55 Adj, MERRA-2 PRECTOT, MERRA-2 PRECTOTCORR, WFDEI-CRU and WFDEI-GPCC) and ground measurements (CPC v.1, CRU TS v.4.00 and GPCC v.7), as well as a regional estimation method, based on spatial proximity, for the parameters of a simple monthly water balance model, GR2M. The regional simulations of the GR2M model were evaluated based on a Kling-Gupta Efficiency score in a split-sample spatiotemporal validation scheme. New hydrological insights for the regionThe results show that among all the precipitation products, CHIRPS is the most effective for hydrological modeling in West and Central Africa at a monthly timestep. Also, among the top five products are WFDEI-CRU, CRU, WFDEI-GPCC and GPCC. Overall, regional hydrological modeling is more effective for basins smaller than 80,000 km2. The method of regionalization by spatial proximity causes an overall drop in the ability of the various precipitation products to reproduce discharge, most notably with WFDEI-GPCC and GPCC. CHIRPS remains the best product in terms of KGE2 values in regionalization.