Precipitation Index Research Articles

Climate impacts on high-intensity sapling mortality: Comparative study and future warnings for Larix kaempferi, Abies sachalinensis, and Picea glehnii

Climate change profoundly affects plant viability and forest dynamics, creating uncertainty in future forest management. Post-planting, high-intensity sapling mortality may require costly replanting, disrupting plans, and risking forest management goals. Therefore, this study focused on high-intensity sapling mortality (mortality rates exceeding 30 %) in Larix kaempferi, Abies sachalinensis, and Picea glehnii planted in northern Japan attributed to summer drought. Utilizing a dataset spanning approximately three decades, the correlation between high-intensity mortality occurrence and climatic stress was explored employing a Bayesian approach. Additionally, the probability of high-intensity mortality occurrence by the end of this century was predicted based on two representative climate scenarios (RCP2.6 and RCP8.5). The predictive accuracy of high-intensity mortality occurrence probabilities exceeded 70 % after comprehensive consideration of climatic stress factors, thereby accentuating the robustness of the proposed model. These encompass the antecedent precipitation index (API), which serves as a soil drought indicator; 30-day mean daily maximum temperature (MaxTMP̅); 30-day mean daily total solar radiation (TSR̅); and day of year (DOY). All sapling species manifested a pronounced negative correlation between API and the probability of high-intensity mortality occurrence, whereas TSR̅ exhibited a positive impact on this probability. Additionally, MaxTMP̅ positively influenced this probability for L. kaempferi and A. sachalinensis. Delving into interspecific differentials, API exhibited the most pronounced impact on P. glehnii in comparison to the other two tree species, whereas TSR̅ exerted a substantial influence on A. sachalinensis and P. glehnii. The susceptibility to MaxTMP̅ was hierarchically ordered as L. kaempferi >A. sachalinensis >P. glehnii. DOY, used to assess phenological effects like leaf morphology, negatively affected the probability of high-intensity mortality occurrence exclusively for L. kaempferi. The median probability of high-intensity mortality occurrence in Hokkaido, calculated from the estimated probabilities across all regions of Hokkaido (encompassing non-forested areas but excluding remote islands), at a 1 km resolution, was 0.37 for L. kaempferi, 0.57 for A. sachalinensis, and 0.41 for P. glehnii under RCP2.6 by the century’s end. These probabilities have decreased in comparison to those observed in 2018–2022. In stark contrast, under RCP8.5, the probabilities soared to 0.91, 0.87, and 0.56, representing an increase of 2.12 times for L. kaempferi, 1.36 times for A. sachalinensis, and 1.14 times for P. glehnii in comparison to 2018–2022. Even the least affected species, P. glehnii, witnessed a increase in mortality probability. This study underscores the importance of forest management and adaptation to climate change.

Analysis of trends and abrupt changes in groundwater and meteorological droughts in the United Kingdom

In recent years, the escalating impact of climate change, coupled with growing concerns regarding groundwater drought, has underscored the critical importance of analyzing hydrological trends. This is a foundational step in advancing effective water resource planning and management. This study presents a regional investigation of groundwater and meteorological droughts in the UK, utilizing the Standardized Groundwater Index (SGI) and the Standardized Precipitation Index (SPI). The analysis encompasses drought duration and an evaluation of the correlation between the SGI and SPI. Subsequently, the seasonal Mann-Kendall (MK) test and the Bayesian Changepoint Detection and Time Series Decomposition (BEAST) algorithm were employed to assess overall trends for both SGI and SPI. Additionally, these methods were utilized to pinpoint trend and seasonal abrupt changes within the time series of both indices.The seasonal MK test revealed statistically significant trends in many SGI stations across the UK, spanning from Northern Scotland down to the South of England, encompassing both increasing and decreasing trends. Additionally, the MK test showed slightly positive trends at the low SPI time scale, though these were not statistically significant. In contrast, more consistent and significant increasing trends were observed throughout the entire UK, except for Northern Scotland, which exhibited negative trends. Despite indications of a progressively more humid UK, the BEAST analysis enabled the detection of decreasing abrupt changes in trends. These changes result in sudden transitions from wet to dry conditions, which can be challenging to identify using the MK test alone. However, BEAST revealed both positive and negative abrupt changes across the entire UK, as well as positive or negative variations in seasonality. This suggests a high degree of variability in seasonal patterns. In summary, this study provides a detailed assessment of groundwater and meteorological drought trends and abrupt changes, considering the impact of climate change on various regions of the UK.

Climatic Variations and Challenges to Socio-economic Development amongst Mbororo Communities in Mbum Plateau, North West Region of Cameroon

This study sets out to investigate the challenges hindering Mbororo sources of livelihood in Mbum Plateau, North West Region of Cameroon. Explanatory research was used where it specify the nature and direction of the relationships between the studied variables. The study used mixed methods of qualitative and quantitative approaches. The study employed the primary and secondary data sources to bring out realistic results. Rainfall Seasonality Index (SI) and Standardized Precipitation Index (SPI) were employed to assess the vulnerability of the agro-hydrological system to climate variation. The major primary data sources used were field survey, interviews and questionnaires. A total of 200 questionnaires were distributed using stratified random and snow ball sampling techniques. The data obtained was complemented by secondary sources from published and unpublished materials. Data collected during the field survey were analyzed through two statistical techniques. The qualitative data obtained was analyzed through the content analysis whereas themes and codes were given to the different opinions and perceptions of the informants and their frequencies and percentages were being determined from there. Data were presented in tables, charts, maps, and graphs with some attempted interpretations. Data were analyzed using the chi-square and multiple regression models. The findings revealed that climate variability, dwindling pasture and low streams discharge are having negative effects on Mbororo communities. It was observed from satellite images that, land use over the years has been changing with reducing aerial extent and quality of pasture, reducing water points in Mbum Plateau. In order to adapt the decreasing cattle numbers on the side of the Mborroro, they have taken farming, diving, hawking arm rubbery, and schooling as new livelihoods sustenance. The study recommends a sustainable pastoral activity by encouraging the introduction of drought resistant cattle species, Bracharia and Guatemala grass to supplement the natural pasture. MINEPAI should regularly control herd size so as to ensure ecosystem balance and avoid overgrazing. Transhumance tracts and areas should be well demarcated to avoid frequent farmer-grazers’ conflicts and frequent cattle theft observed in the region.

Intensified response of extreme precipitation to rising temperature over the Tibetan Plateau from CMIP6 multi-model ensembles

This study conducted bias correction of the high-performing general circulation model (GCM) data in combination with observed data based on the latest GCM data released by the Coupled Model Intercomparison Project Phase 6 (CMIP6). The ability of GCMs to simulate the characteristics of extreme precipitation over the Tibetan Plateau (TP) was systematically assessed, and the CMIP6 multi-model ensemble was used to accurately project extreme precipitation. Furthermore, the response of extreme precipitation over the TP to global warming was explored. The results demonstrated that daily translation efficiently addressed the issue of apparent overestimation in the model’s raw output, and CMIP6 multi-model ensemble mean exhibited better simulation performance for all extreme precipitation indices over the TP. Additionally, the simulation performance of each index exhibited a divergence across various elevation bands as altitude increased, with the best and worst performance observed at the altitude bands of 1500–2000 m and above 5000 m, respectively. For future changes in extreme precipitation, the indices representing the amount and intensity of extreme precipitation increase significantly, whereas the indices representing the duration of precipitation decrease. Moreover, the plateau will show a spatial pattern of “wet regions becoming dry and dry regions becoming wet”. The relationship between extreme precipitation and temperature follows a positive and peak pattern over most areas of the plateau, while the response of extreme precipitation to temperature will increase by 0.6 %–52.6 % with rising temperature. The present results have important implications for investigating the impacts of climate change on the water cycle in alpine regions.

Space-Time Variability of Drought Characteristics in Pernambuco, Brazil

Drought is the most complex natural hazard that can occur over large spatial scales and during long time periods. It affects more people than any other natural hazard, particularly in areas with a dry climate, such as the semiarid region of the Brazilian Northeast (NEB), which is the world’s most populated dry area. In this work, we analyzed trends and the spatial distribution of drought characteristics (frequency, affected area, and intensity) based on the Standardized Precipitation Index (SPI) on annual (SPI-12) and seasonal (SPI-3) scales. The study used monthly precipitation data recorded between 1962 and 2012 at 133 meteorological stations in Pernambuco State, Brazil, which is located in the eastern part of the NEB and has more than 80% of its territory characterized by a semiarid climate. The regions of Sertão, Agreste, and Zona da Mata of Pernambuco were considered for comparison. The Mann–Kendall and Sen’s slope tests were used to detect the trend and determine its magnitude, respectively. The results indicated that annual droughts in the state of Pernambuco became more frequent from the 1990s onwards, with summer having the greatest spatial coverage, followed by winter, autumn, and spring. Sertão presented a greater number of stations with a significant positive trend in drought frequency. Regarding the drought-affected area, global events occurred in a greater number of years on an annual scale and during the summer. Trend analysis pointed to an increase in areas with drought events on both scales. As for the drought intensity, the entire state of Pernambuco experienced drought events with high intensity during the autumn. The relationship between drought characteristics indicated an increase in the affected area as the result of an increase in drought intensity.

Spatio-temporal characterization of drought variability in data-scarce regions using global precipitation data: a case study in Cauto river basin, Cuba.

Drought is considered the most severe water-related disaster in the Cauto river basin, which is the longest river and the main agricultural producer in Cuba. Better understanding of drought characteristics is crucial to drought management. Given the sparsity of ground-based precipitation observations in the Cauto, this study aims at using gridded global precipitation to analyze the spatio-temporal variations of drought in this river basin. Firstly, the monthly Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) was calibrated with the gauged precipitation using the Thiessen polygon-based method and linear least squares regression equations. Then, the gridded standardized precipitation index (SPI) with time scales of 3, 6, 9months and drought characteristics, namely, drought frequency, duration and intensity were calculated using the calibrated CHIRPS. Finally, the spatio-temporal analysis was performed to investigate the variations of drought in the Cauto river basin in time and space. The obtained results show that the calibrated CHIRPS is highly consistent with the gauged observations and is capable of determining the magnitude, time, and spatial extent of drought events in the Cauto river basin. The trend analysis by the Mann-Kendall test reveals that although the trend is not statistically significant, the SPI tends to decrease with time in the dry season, which indicates the more severe drought. The spatial analysis indicates that the lower altitude area of the Cauto river basin is suffered from longer drought duration and higher drought intensity than the upper one. This study expresses the importance of open global precipitation data sources in monitoring and quantifying drought characteristics in data-scarce regions.

Cyclical ‘wetting’ phenomenon in the source region of Yellow River under long-term trends from 1956 to 2022

Study regionthe source region of the Yellow River (SRYB) Study focusDue to environmental changes, the drying and wetting processes have become increasingly intricate, and it is uncertain whether the wetting phenomenon in the SRYB indicates a long-term trend or a temporary cyclic event. This study utilized monthly streamflow and precipitation records from 1956 to 2022 to calculate hydrological and meteorological-dryness/wetness, represented by the standardized streamflow and precipitation indices, respectively. This study used detrended, wavelet, and cross-wavelet methods to investigate dry-wet variability and identified the atmospheric circulation phenomena driving the dryness/wetness and cyclical changes in the region. New hydro-meteorological insights for the SRYB(i) The SRYB showed an overall fluctuating wetting trend, which intensified after 2000. (ii) There were notable oscillatory cycles at 3–5 years, 10–12 years, and approximately 20–25 years in both hydrological and meteorological dry-wet variability. (iii) Transitions from dry to wet conditions had no significant link to short-term cycles (e.g., 3–5 years), but were linked to decadal cycles (e.g., 20–25 years). (iv) Spring and winter precipitation and streamflow dominated the interannual dry-wet variability. (v) The ENSO, AMO, and PDO influenced short-term cyclic fluctuations, while the AO and PDO influenced decadal cyclic fluctuations. This indicates that the wetting trend follows a cyclic pattern, and the intensification since 2000 is consistent with a decadal cyclic signal.

Assessment of Hydrological and Meteorological Composite Drought Characteristics Based on Baseflow and Precipitation

Traditional univariate drought indices may not be sufficient to reflect comprehensive information on drought. Therefore, this paper proposes a new composite drought index that can comprehensively characterize meteorological and hydrological drought. In this study, the new drought index was established by combining the standardized precipitation index (SPI) and the standardized baseflow index (SBI) for the Jiaojiang River Basin (JRB) using the copula function. The prediction model was established by training random forests on past data, and the driving force behind the combined drought index was explored through the LIME algorithm. The results show that the established composite drought index combines the advantages of SPI and SBI in drought forecasting. The monthly and annual droughts in the JRB showed an increasing trend from 1991 to 2020, but the temporal characteristics of the changes in each subregion were different. The accuracies of the trained random forest model for heavy drought in Baizhiao (BZA) and Shaduan (SD) stations were 83% and 88%, respectively. Furthermore, the Local Interpretable Model-Agnostic Explanations (LIME) interpretation identified the essential precipitation, baseflow, and evapotranspiration features that affect drought. This study provides reliable and valid multivariate indicators for drought monitoring and can be applied to drought prediction in other regions.

Spatiotemporal Drought Assessment in Ningxia Autonomous Region: A Machine Learning and Remote Sensing Approach

Drought represents a significant disaster that directly impacts the economic and ecological welfare of any nation it afflicts. This study focused on the climatic anomalies of drought over the Ningxia Hui autonomous region in northwest China over the last two decades. The study employed an in-depth machine learning model, which incorporated drought indices, thus leading to a data-informed analysis of Ningxia drought patterns. The study accomplished this by using MODIS satellite data products available for vegetation and moisture monitoring. The MOD09GA, MOD11A2, and MCD43A4 data streams were loaded into Google Earth Engine as factors to develop a time-series dataset of vegetation indices. Indices are Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Land Surface Temperature (LST) measurements are taken into account. Data on temperature, precipitation, and evapotranspiration was compiled for the period from 2003 to 2023 and calculated standardized indices on pixel level for the whole Ningxia region to develop the Standardized Precipitation Index (SPI), Keetch-Byram Drought Index (KBDI), and Standardized Precipitation-Evapotranspiration Index ( The study results indicated that SPI fell significantly from the year 2003 to 2023, from 0.7 to -0.3. The SPEI plummeted from 0.5 to -0.2 during the observed time frame. KBDI also went up, through 581.33 in 2003 and 681.091 in 2023, showing deterioration of aridity and drying of the soil. The conclusion of this study focuses on the deterioration of drought conditions in the Ningxia region in the last 20 years.

The phenomenon of drought in Ethiopia: Historical evolution and climatic forcing

ABSTRACT This study examines drought patterns in Ethiopia's 12 major river basins from 1981 to 2018 using the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI). Both indices reveal historical drought episodes with slight variations, with significant differences in 1984, 2009, and 2015. Except for the Wabi-Shebelle catchment in southern Ethiopia, all river basins show an increasing trend in SPI12 and SPEI12 indices. The eastern and central regions experience more drought according to SPEI3. Seasonal correlations show that during the March–May rainy season, precipitation is negatively correlated with the Indian Ocean Dipole (IOD) index, while in the June–September season, it negatively correlates with Nino 3.4 and positively with IOD. The study also found that El Niño leads to less rainfall in the Ethiopian highlands, while La Niña results in more rainfall in the central and northern highlands but less in the south.

Investigating meteorological wet and dry transitions in the Dutch Meuse River basin

The Netherlands has traditionally focused on managing flood risk. However, the frequent occurrence of droughts in recent years has brought attention to managing both extremes. Transitions between these opposite extremes pose additional challenges to water management, requiring a trade-off between water storage during dry periods and flood control during wet periods. In this study, we develop a framework to define wet and dry meteorological events and study their transitions using timeseries of meteorological data namely, precipitation, temperature and potential evapotranspiration. The magnitudes of event characteristics are retained, which presents a different approach to the normalized climate indices (like the Standardized Precipitation Index) commonly used in literature. We apply this framework to the Dutch part of the Meuse River basin in northwestern Europe using climate observations between 1951 and 2022. Our analysis shows a statistically significant increase in the amount of water lost from potential evapotranspiration compared to water gained from precipitation between April and September of the water year and an increase in the length of this drying period over the past decades. Such trends in the drying period are related to variability in potential evapotranspiration caused by rising temperatures in the region, indicating the potential for increased water shortage in Spring and Summer due to future temperature increases. We also identify abrupt transitions between opposite extreme events where there is a lack of water at the end of the second event as meteorological situations that challenge water management due to overlapping impacts like flash flooding, less time for water storage, and reduced water availability. We see such conditions occur in 6% of the wet-dry transitions and 20% of the dry-wet transitions, highlighting meteorological scenarios to which the hydrological response of the catchment can be simulated to increase our understanding of the combined risk of floods and droughts.

Analysis of extreme precipitation variation characteristics in mountain grasslands of arid and semi-arid regions in China

Introduction: With global warming, the disaster losses caused by extreme precipitation events are increasing. The poor natural conditions and climate change make the arid and semi-arid mountainous grassland area a sensitive region of climate change. The study on the spatio-temporal variation characteristics of extreme precipitation events in this region is helpful to improve the ability of climate prediction and disaster prevention and reduction in grassland.Methods: Based on the daily precipitation data of four meteorological monitoring stations in the Yinshanbeilu from 1970 to 2020, the trend analysis, M-K test and wavelet analysis were used to select seven typical extreme precipitation indicators to analyze the temporal and spatial characteristics of extreme precipitation.Results and Discussion: The results showed that the precipitation in the Yinshanbeilu increased in the past 51a, and the number of heavy rain days increased significantly. The significance test of CDD and CWD showed that the number of continuous dry days and continuous wet days decreased abruptly. The spatial analysis showed that the high value areas of R95p, R95d and PRCRTOT were all located in Siziwang Banner, and it could be concluded that the extreme precipitation risk was the highest in Siziwang Banner, while the low value areas of SDII, Rx1day, R95p and PRCRTOT were all located in Sonid Right Banner, which could be inferred that the Sonid Right Banner was relatively dry. The first main cycle of the seven indexes of extreme precipitation almost runs through the whole time series, and the starting point of the minimum main cycle changes inconsistent. In addition to the number of consecutive dry days, the other indices have a good correlation with annual precipitation, flood season and monthly precipitation from June to September, and July is the peak period of extreme precipitation events.

Projected Changes in Extreme Precipitation Patterns across Algerian Sub-Regions

Extreme precipitation events play a crucial role in shaping the vulnerability of regions like Algeria to the impacts of climate change. To delve deeper into this critical aspect, this study investigates the changing patterns of extreme precipitation across five sub-regions of Algeria using data from 33 model simulations provided by the NASA Earth Exchange Global Daily Downscaled Climate Projections (NEX-GDDP-CMIP6). Our analysis reveals a projected decline in annual precipitation for four of these regions, contrasting with an expected increase in desert areas where annual precipitation levels remain low, typically not exceeding 120 mm. Furthermore, key precipitation indices such as maximum 1-day precipitation (Rx1day) and extremely wet-day precipitation (R99p) consistently show upward trends across all zones, under both SSP245 and SSP585 scenarios. However, the number of heavy precipitation days (R20mm) demonstrates varied trends among zones, exhibiting stable fluctuations. These findings provide valuable foresight into future precipitation patterns, offering essential insights for policymakers and stakeholders. By anticipating these changes, adaptive strategies can be devised to mitigate potential climate change impacts on crucial sectors such as agriculture, flooding, water resources, and drought.

Meteorological Drought Analysis Using Multiple Indices for Selected Stations in Iraq

This study aims to assess the performance of three drought indices for drought monitoring to study the characteristics of meteorological drought in Iraq, which are the Standard Precipitation Index (SPI), the China-Z Index (CZI), and the Modified China-Z Index (MCZI) on 1-, 3-, 6-, 9-, and 12-month timescales, using monthly precipitation data from 1980 to 2021. These indices were used to analyze the spatiotemporal dynamics of droughts using the rainfall data collected from five meteorological stations scattered across the four climatic zones as classified by Köppen in Iraq. According to the study, the Pearson correlation coefficient (r) values among the indices increase with increasing time scale and give similar drought characteristics at the 9-month and 12-month time scales. SPI generally indicated the drought event earlier and with more severe characteristics than the other indices. MCZI showed wetness earlier than the other two indices, and it also described the drought categories similarly to SPI and CZI, especially in the northern regions of Iraq, but it was less official in describing it as we headed down to the south. While CZI described the drought as having less severity than SPI, it frequently gives the same classifications of drought as SPI. CZI could be used as a good meteorological drought monitor, depending on the month, the length of the drought duration, and the climatic conditions of the region. It might be an alternative to the SPI, which needs long rainfall records and has a complicated structure. SPI and CZI can be considered good indices for describing drought in all regions of Iraq. The result shows that 1980, 1993, 1995, and 2019 were the most wet years; 2007–2010 was the most severe drought event; and Iraq's climate was normal to moderate drought during the studied period for all considered stations.

Impacts of extreme precipitation on water conservation in Beijiang River Basin, China

Water conservation, which has been regarded as one of the most crucial ecosystem service functions of river basins, is a key factor for sustainable development, that has garnered widespread attention in the context of climate change. However, little attention has been paid to daily water conservation and its response to extreme precipitation events. In this study, we selected the Beijiang River Basin (BJRB) as the study area, which is an important ecological barrier for the Guangdong-Hong Kong-Macau Greater Bay Area (GBA), based on the distributed hydrological model WEP-L (Water and Energy transfer Processes in Large River basin) and water balance equation, the spatiotemporal characteristics of extreme precipitation and four daily water conservation indicators (maximum 1 day, maximum 3 day, maximum 5 day, maximum 7 day water conservation) in the BJRB from 1965 to 2009 were analysed, and the responses of these indicators to extreme precipitation were investigated. The results reveal that (1) the amount (RX1day, RX3day, RX5day, RX7day, R95p, and R99p), intensity (SDII), and frequency (R50mm) of extreme precipitation increased in the BJRB during 1965–2009, with SDII undergoing a significant increase. (2) The four water conservation indicators were positive, indicating that the watershed reduced peak flow during the extreme precipitation period by accumulating precipitation. The rate of increase of the four water conservation indicators were lower than that of extreme precipitation indices, there will be more extreme precipitation converted into surface runoff, thereby increasing the risk of flooding. The four indicators increased in the southern and northern regions of the BJRB, while decreased in the central region during 1965–2009. (3) The four indicators were generally positively correlated with most of the extreme precipitation indices in the northern BJRB, especially RX1day, RX3day, RX5day, RX7day, R99p, and R50mm. The results provide a scientific reference for water conservation adaptation to extreme precipitation variations, protection of the ecological environment, and the prevention and reduction of disasters in the region.

Assessing decadal- to centennial-scale nonstationary variability in meteorological drought trends

Abstract. There are indications that the reference climatology underlying meteorological drought has shown nonstationarity at seasonal, decadal, and centennial timescales, impacting the calculation of drought indices and potentially having ecological and economic consequences. Analyzing these trends in meteorological drought climatology beyond 100 years, a time frame which exceeds the available period of observation data, contributes to a better understanding of the nonstationary changes, ultimately determining whether they are within the range of natural variability or outside this range. To accomplish this, our study introduces a novel approach to integrate unevenly scaled tree-ring proxy data from the North American Seasonal Precipitation Atlas (NASPA) with instrumental precipitation datasets by first temporally downscaling the proxy data to produce a regular time series and then modeling climate nonstationarity while simultaneously correcting model-induced bias. This new modeling approach was applied to 14 sites across the continental United States using the 3-month standardized precipitation index (SPI) as a basis. The findings showed that certain locations have experienced recent rapid shifts towards drier or wetter conditions during the instrumental period compared to the past 1000 years, with drying trends generally found in the west and wetting trends in the east. This study also found that seasonal shifts have occurred in some regions recently, with seasonality changes most notable for southern gauges. We expect that our new approach provides a foundation for incorporating various datasets to examine nonstationary variability in long-term precipitation climatology and to confirm the spatial patterns noted here in greater detail.

Spatial Differentiation and Influencing Factors Analysis of Drought Characteristics Based on the Standardized Precipitation Index: A Case Study of the Yellow River Basin

It is crucial to identify drought characteristics and determine drought severity in response to climate change. Aiming at the increasingly serious drought situation in the Yellow River Basin, this study firstly selected the standardized precipitation index (SPI) and streamflow drought index (SDI) to analyze the characteristics of drought seasons, then identified the frequency, duration, and intensity of drought based on the run theory, and finally recognized the abrupt changing and driving factors of major drought events in specific years by the Mann–Kendall trend test. The conclusions showed the following: (1) The drought in the downstream of the Yellow River Basin was more severe than that in the upstream. The drought characteristics showed significant regional differentiation and deterioration. (2) The drought intensity and duration had an obvious spatial correlation. Compared with the other seasons, the drought duration and severity in spring and autumn were the most serious, and in winter, they showed an aggravating trend. (3) According to a time series analysis of drought conditions in the Yellow River Basin, the worst drought occurred in 1997–2001 with the least rainfall on record and a sudden rise in temperatures. This study could provide a scientific reference for agricultural drought disaster prevention and mitigation.

Urbanizational impact on climatic variables and geographical analysis of physical land use land cover variation of a city using Remote Sensing.

<p>Climate change has serious implications foron the rise and variation of the average earth’s temperature, and intensified rainfall. In urban areas, temperatures and intensified rainfall variation are noticeably seenin the city which disrupts the normal life of a living things. Also, it affects the storage of rainfall runoff water in tanks and lakes. Green cover reduction due to the increase ofagriculture, industrialization, population explosion and urbanization have a direct impact on the climate pattern.The average temperature of a city is increasing in every decade due to a drastic reduction in the green cover.This is visible today from the abnormal melting of glaciers. The rainfall pattern is highly intensified, and the frequency of rainfall is on the a decreasing trend. This intensified rainfall creates high surface runoff during rainfall and disrupts the normal life in a city. The Standard Precipitation Index (SPI) for a city indicates a noticeable shift in its climatic pattern, particularly with consistent rainfall during the southwest monsoon. Over the span of four decades, LULC research has revealed an increase in residential areas and a decrease in green cover. This urbanization process is associated with changes in LULC, resulting in decreased vegetation and diminished storage water bodies.Variations in the temperature of a city are on an increasing trend. Urban forestry or the dense green coverof an area reduces the temperature and creates cool pockets in the city. The regression model for temperature and vegetation indicates the negative correlation of temperature with high vegetation growth. Keywords: Urbanization, Climate change, Rainfall-runoff, Temperature, Green Cover</p>

Precipitation patterns and their variability in the southern region of Brazil

Climate change is a long-term transformation of climate patterns, which can be natural or anthropogenic. Signs of climate change can manifest themselves in different ways: through the occurrence of extreme events, rising ocean levels, melting glaciers, among others. Negative impacts are produced by climate change and are one of humanity's biggest concerns, since its effects reach the entire planet with different consequences. Considering the need for actions aimed at contributing to the process of mitigating climate change, the importance of knowing changes in regional climate patterns is emphasized. This study aims to analyze changes in precipitation patterns over time in the southern region of Brazil, including variability, trends, anomalies, droughts and excess rainfall. Daily precipitation data from 1961 to 2020 were used, collected at 15 meteorological stations in the region. Various statistical analyzes were carried out, such as averages, maximum and minimum values, seasonal and annual anomalies, frequency distribution, Mann-Kendall trend tests and the Normalized Precipitation Index (SPI). The results indicate that climate change trends are positive in terms of increasing rainfall volumes, altering rainfall dispersion patterns (volume). In other words, rainfall is more irregular in terms of distribution throughout the seasons. The big highlight was Florianópolis -SC, which during the summer had the highest volume of rain among the meteorological stations studied.

Extreme precipitation and temperature indices under future climate change in central Asia based on CORDEX-CORE

The present study analyzes the projected changes of extreme climate indices over Central Asia using regional climate model (RCM) simulations from the Coordinated Regional Climate Downscaling Experiment (CORDEX) - Coordinated Output for Regional Evaluations (CORE). The extreme indices are based on precipitation and temperature and are inspected for present (1981–2005) and future periods - near- (2031–2055) and far-future (2071–2095) - to assess the long-term climate change under the representative concentration pathway RCP8.5. Projected changes are analyzed for three different model ensembles. These ensembles are based on CORDEX-Central Asia (ENS_CAS, four ensemble members) and CORDEX-East Asia (ENS_EAS, six ensemble members), and a combination of both (ENS, ten ensemble members) for our study area centered over high mountain Asia, called Central East Asia (CEAS). For precipitation indices, an increase of consecutive dry days (CDD) in ENS_EAS and a slight to moderate decrease in northern parts in ENS_CAS during near-future is observed. Consecutive wet days (CWD), very heavy precipitation events (R20mm), maximum one-day precipitation (RX1day), and very wet days (R95p) are projected to increase in most areas. All indices show a further intensification towards the end of the century over large parts of the domain, e.g., + 7.8% / +5.6 days for CDD, + 96.6% / +0.26 days for R20mm, and + 19.7% for RX1day as median of ENS over CEAS. For temperature indices, the ensembles project a strong increase over the high mountain regions and southern parts for consecutive summer days (CSU, + 108.5% / +38.3 days), heat wave duration index (HWDI, + 1379.1% / +91.37 days), and the percentage of very hot days (TX90p, + 391.1% / +34.54 days). Accordingly, the number of consecutive frost days (CFD, -43.7% / -25.2 days) and the percentage of very cold days (TX10p, -83.4% / -8.13 days) are projected to decrease. The first-time usage of CORDEX-CORE and the larger ensemble size by considering simulations from overlapping domains increase the robustness of the findings from earlier studies. However, some discrepancies in the projected changes prevail among the different RCMs being part of the two CORDEX-domains and in specific landscapes like complex mountainous or lake areas. These uncertainties may be tackled by further model development with improved land-surface processes and potentially higher spatial resolution.