Trend In Annual Precipitation Research Articles

Detection of changes in local-scale statistical distribution of daily temperature and precipitation in dryland region: a case study of Ningxia, China

Identifying alterations in the local-scale statistical distributions of daily temperature indices and precipitation is crucial for adapting to climate change and combating desertification in arid regions. This study focuses on Ningxia, a representative dryland area in China, where we investigated changes in the statistical distributions of daily temperature and precipitation across ten weather stations over two 30-year periods: 1991–2020 compared to 1961–1990. From 1961 to 2020, annual air temperature indices (mean, maximum, and minimum) exhibited a significant upward trend at all ten weather stations (P < 0.05). Specifically, when comparing the decades of 1961–1970 with those of 2001–2020, annual air temperature indices increased by 1.2–2.2 °C for annual air temperatures, by 0.91–1.97 °C for maximum air temperature, and by 1.26–3.28 °C for minimum air temperature; these corresponded to rates of increase ranging from 0.20–0.40 °C per decade for mean air temperatures, from 0.15–0.33 °C per decade for maximum air temperatures, and from 0.21–0.55 °C per decade for minimum air temperatures, respectively during this period. The observed differences in the statistical distributions of daily temperature indices between the two periods were statistically significant (P < 0.05), indicating an increase rate of approximately 1 °C during the period from 1991 to 2020 relative to that from 1961 to 1990 across various weather stations. In contrast, there were no significant increasing or decreasing trends in annual precipitation (P > 0.05). Similarly, the changes in statistical distributions of daily precipitation also did not reach a significance level (P > 0.05) when comparing data from both periods within these stations. Furthermore, the correlation coefficients between daily temperature indices and daily precipitation have decreased across these stations during the later period compared to earlier years. These findings suggest that while there has been a notable shift toward higher values in the statistical distribution of daily temperature indices—indicating rising temperatures—the corresponding distribution patterns for daily precipitation have remained relatively stable between these time frames. Consequently, this research confirmed an escalation in localized drought conditions within certain dryland areas despite broader regional trends toward humidification observed over recent decades; thus implying an increased risk of desertification as aridity intensifies.

Oberved trend of precipitation extreme in Kalimantan

&lt;p style="line-height:200%;margin-bottom:.0001pt;text-align:justify;text-justify:inter-ideograph;"&gt;&lt;span style="font-family:&amp;quot;Times New Roman&amp;quot;;" lang="IN"&gt;It has been suggested that the global warming due to an increase in green house gas concentrations could lead to extreme climate events, such as &lt;/span&gt;&lt;span style="color:#007BB8;font-family:&amp;quot;Times New Roman&amp;quot;;" lang="IN"&gt;precipitation extreme and droughts&lt;/span&gt;&lt;span style="font-family:&amp;quot;Times New Roman&amp;quot;;" lang="IN"&gt;. This study is designed to analyze trend in the long-term time series of preciptation extreme in Kalimantan, focusing on observed data obtained from eight meteorological stations spanning from January 1985 to December 2022.&amp;nbsp;&lt;/span&gt;&lt;span style="background-color:white;color:#0D0D0D;font-family:&amp;quot;Times New Roman&amp;quot;;" lang="EN-ID"&gt;Statistical analyses were employed to discern patterns and trends in extreme precipitation events. The findings reveal a discernible long-term trend characterized by fluctuations in precipitation extremes over the study period. The trend of total annual precipitation (PRCPTOT) has increased at all observation stations. Especially at SM. Nangapinoh and Supadio, the PRCPTOT trend increased significantly at the 99% and 90% levels. Meanwhile, the increase in consecutive wet days (CWD) trends only occurred at SM. Nangapinoh and Rahadi Oesman (West Kalimantan), and SM. Iskandar (South Kalimantan). A significant trend was found in SM. Nangapinoh (90%) and Iskandar (95%). In addition to experiencing an increase in the R20mm trend, there was a decrease in the trend. Significant trend increases at the 99% and 90% levels occurred in SM. Nangapinoh and Supadio.&amp;nbsp; In addition, an increase in CDD trend occurred only in SM. Rahadi Oesman and Tjilik Riwut, although not significant. Meanwhile, the majority of extreme precipitation indices have a greater correlation with ENSO than the IOD.&amp;nbsp;&lt;/span&gt;&lt;/p&gt;

Trends of Extreme Temperature and Rainfall Indices for Addis Ababa City, Ethiopia

The extreme temperature and precipitation index trend analysis for Addis Ababa city is presented in this study. The Bole and Tikur Anbesa observatories, two stations in the capital city of Addis Ababa, provide the temperature and rainfall variation used in this analysis. The RClim-Dex software has been used to find climate extreme indices for Addis Ababa city stations based on climate data for the period 1954 to 2019. The annual total precipitation trend (PRCPTOT) at the 5% significant level exhibits a positive slope at the Addis Ababa observatory and a negative slope at the Addis Ababa Bole station. SDII trends indicate an increase at the 5% significant threshold for both stations. At the 5% significance level, the trends for both stations’ maximum and lowest temperatures show a positive slope. Conversely, both stations exhibit a negative slope in the diurnal temperature ranges. The RClim-Dex results indicate that, while rainfall intensity is decreasing, there is a tendency for annual rainfall indices to increase at the Addis Ababa observatory station, but to decrease at the Bole station. Additionally, there is an upward slope for consecutive wet days for all stations. Nevertheless, steady warming patterns are evident for both the maximum and minimum temperature indices. In contrast, declining trends are shown in the diurnal temperature ranges.

Quantifying Sahel Runoff Sensitivity to Climate Variability, Soil Moisture and Vegetation Changes Using Analytical Methods

AbstractWhilst substantial efforts have been deployed to understand the “Sahel hydrological paradox”, most of the studies focused on small experimental watersheds around the central and western Sahel. To our knowledge, there is no study on this issue covering all the watersheds located within the Sahelian belt. The absence of relevant studies may be attributed to a sparsity of in situ data leading to a dearth of knowledge on the Sahel hydrology. To fill this knowledge gap, the present study leverages analytical methods and freely available geospatial datasets to understand the effects of climatic factors, soil moisture and vegetation cover changes on surface runoff in 45 watersheds located within the Sahelian belt over two decades (2000–2021). Analyses show increasing trends in annual precipitation and potential evapotranspiration (PET) in more than 80% of the watersheds. Surface runoff, soil moisture (SM), and vegetation cover measured using the normalised difference vegetation index (NDVI) also show increasing trends in all the watersheds. Multivariable linear regression (MLR) analyses reveal that precipitation, PET, SM, and NDVI contribute about 62% of surface runoff variance. Further analyses using MLR, and the partial least squares regression (PLSR) show that precipitation and NDVI are the main factors influencing surface runoff in the Sahel. Elasticity coefficients reveal that a 10% increase in precipitation, SM and NDVI may lead to about 22%, 26% and 45% increase in surface runoff respectively. In contrast, a 10% increase in PET may lead to a 61% decline in surface runoff in the Sahel. This is the first hydrological study covering all the watersheds located within the Sahelian belt with results showing that surface runoff is influenced by climate, SM and NDVI to varying degrees. Given the unique hydrological characteristics of the Sahel, a better understanding of the different factors influencing surface runoff may be crucial for enhancing climate adaptation and ecological restoration efforts in the region such as the Great Green Wall Initiative.

Precipitation in Kathmandu Valley: A Spatial-temporal Study

Over the past decade, advancements in atmospheric research have significantly improved the analysis of precipitation monitoring, understanding climate trends and variability, and identifying climatic anomalies associated with dry and wet periods. A key objective of this research is to enhance the understanding of precipitation patterns in the Kathmandu Valley, particularly in relation to the placement of gauges for long-range predictions and subsequent forecasting challenges. This study focuses on the evolving patterns of precipitation across the three districts within the Kathmandu Valley, the most developed and densely populated area in Nepal, encircled by four mountain ranges. We employ interpolation methods in ArcGIS Pro to identify, estimate, and forecast precipitation trends, and use the Mann-Kendall test to reveal statistically significant trends and provide insights into broader climatic processes. The research analyzes trends in both seasonal and annual precipitation and temperature across four time periods—pre-monsoon, monsoon, post-monsoon, and winter—using the Mann-Kendall test. The analysis reveals a decreasing trend in monthly rainfall from 2003 to 2017 at the majority of stations (14 stations out of 19), with areas along the valley slopes receiving significantly more precipitation compared to the central valley. The results of this study contribute to a deeper understanding of climate change in the Kathmandu Valley.

Climatic characteristics of centennial and extreme precipitation in Hangzhou, China

The precipitation characteristics in Hangzhou of Zhejiang Province, China under the background of global climate change are analyzed using the meteorological observation data obtained from the Hangzhou base station in this study. We investigate the climate characteristics of precipitation in Hangzhou from several aspects, such as centennial trend, seasonal change, periodicity and the variation of extreme precipitation. Our results show a linear decreasing trend and obvious interdecadal characteristics in the precipitation of Hangzhou on a centennial timescale. Significantly increased amplitude of precipitation fluctuation was observed since the beginning of the 21st century. For the interdecadal variation of seasonal precipitation on a centennial timescale, precipitation in autumn showed a decreasing trend of 8.1 mm/10a, whereas the trends for the other three seasons were statistically insignificant. The precipitation in Hangzhou showed a decreasing trend in spring and an increasing trend in winter over the past 30 years. Our analyses reveal distinct precipitation cycles, including a quasi-30-year cycle since the 1960s and a quasi-10-year cycle since the 1980s. However, the periodicity has weakened in the past 10 years. In addition, the occurrence of torrential rain has increased rapidly in the past 10 years. Furthermore, influenced by global climate change and regional processes, the variation of extreme precipitation in Hangzhou has changed, which shows strong correlations with the overall trend of annual precipitation. The annual maximum daily precipitation in Hangzhou was mainly in the range of 0–40 mm from 1951 to 1980 and in the range of 40–80 mm from 1981 to 2010 with the maximum daily precipitation occurrence rate of 4.7 times/10a and 6.3 times/10a, respectively. This study emphasizes the risk of urban waterlogging caused by short-term heavy rainfall and provides useful reference to the assessment of extreme meteorological and hydrological disaster risk in Hangzhou.

Characteristics of runoff changes and their climatic factors in two different glacier-fed basins

ABSTRACT The hydrological regulation function of glaciers in different watersheds is different. This study took the Yanggong River Basin (YRB) and Urumqi River Basin (URB) as two typical cases, to explore the runoff change differences and their responses to climate factors from 1979 to 2017. In the past 39 years, the YRB’s annual runoff showed an insignificant trend of increasing first and then decreasing, while the URB’s increasing trend was significant. From the 1980s to the 2010s, the YRB’s monthly runoff extremum occurred earlier than before, and the peak value decreased. The time of monthly runoff extremum in the URB has not changed, but the peak value is increasing. Both basins experienced an increase in annual temperatures from 1979 to 2017, with the rate of warming being more pronounced in the URB. The precipitation in the YRB had no significant trend from 1979 to 2017, and the significant increasing trend in annual precipitation extended in the URB from 1998 to 2010. The YRB’s runoff change was mainly influenced by the flood season precipitation, while the URB’s runoff increase was due to the temperature rise causing faster glacier and snow melt, and the summer precipitation change. In continental glacier basins with many glaciers, the regulation function of glaciers on total runoff is more significant.

Nonstationary frequency analysis of extreme precipitation: Embracing trends in observations

Knowledge of the recurrence intervals of precipitation extremes is vital for infrastructure design, risk assessment, and insurance planning. However, trends and shifts in rainfall patterns globally pose challenges to the application of extreme value analysis (EVA) which relies critically on the assumption of stationarity. In this paper, we explore: (1) the suitability of nonstationary (NS) models in the presence of statistically significant trends, and (2) their potential in modeling out-of-sample data to improve frequency analysis of extreme precipitation. We analyze the benefits of using a nonstationary Generalized Extreme Value (GEV) model for annual extreme precipitation records from Southern Brazil. The location of the GEV distribution is allowed to change with time. The unknown GEV model parameters are estimated using Bayesian techniques coupled with Markov chain Monte Carlo simulation. Next, we use GAME sampling to compute the evidence (and their ratios, the so-called Bayes factors) for stationary and nonstationary models of annual maximum precipitation. Our results show that the presence of a statistically significant trend in annual maximum precipitation alone does not justify the use of a NS model. The location parameter of the GEV distribution must also be well defined, otherwise, stationary models of annual maximum precipitation receive more support by the data. These findings reiterate the importance of accounting for GEV model parameters and predictive uncertainty in frequency analysis and hypothesis testing of annual maximum precipitation data records. Furthermore, a meaningful EVA demands detailed knowledge about the origin and persistence of observed changes.

Evaluating long‐term trends in annual precipitation: A temporal consistency analysis of ERA5 data in the Alps and Italy

AbstractReanalyses are utilized for calculating climatological trends due to their focus on temporal consistency. ERA5 reanalysis family has proven to be a valuable and widely used product for trend extraction. This study specifically examines long‐term trends in total annual precipitation across two climatic hotspots: the Alps and Italy. It is acknowledged by reanalysis producers that variations in the observational systems used for data assimilation impact water cycle components like precipitation. This understanding highlights the need of assessing to what extent temporal variations in ERA5 precipitation amounts are solely a result of climate variations and the influence of changes in the observational system impacting simulation accuracy. Our research examines the differences between ERA5 and similar reanalyses against homogenized, trend‐focused observational datasets. We find that discerning the climatological signal within ERA5 adjustments for observational system variations is challenging. The trend in ERA5 from 1940 to 1970 shows distinct patterns over the Alps and, to a lesser extent, Italy, diverging from later ERA5 trends and those in other reanalyses. Notably, ERA5 shows an increasing, although nonlinear, trend in the deviation between ERA5 and the observational datasets. Improving future reanalysis interpretability could involve adopting a model‐only integration for the same period, akin to the ERA‐20C and ERA‐20CM approach.

Analysis of the spatiotemporal velocity of annual precipitation based on random field

. Changes in precipitation directly impact river runoff volume, subsequently influencing food production, and the security of downstream urban areas. In this study, we introduce a random velocity field (RVF) capable of performing multi-step predictions while providing interpretable insights into precipitation variations. The RVF leverages the gradient of a Gaussian random field to learn spatiotemporal velocity patterns and employs a predictive process to reduce dimensionality and enable multi-step forecasting. Bayesian parameter estimation is obtained using the Markov Chain Monte Carlo (MCMC) method. Our analysis reveals a noticeable shifting trend in annual precipitation based on diverse real datasets. This trend serves as a valuable foundation for further exploration of urban flood control and agricultural development strategies.

Forecasting some climate parameters of Türkiye using the SSP3-7.0 scenario for the years 2040–2059

This study employs the Coupled Model Inter-comparison Projects (CMIPs) and the Sixth phase of CMIPs (CMIP6) to unravel the multifaceted impacts of global climate change on climate of Türkiye. The CMIP6 data, fundamental to the Intergovernmental Panel on Climate Change (IPCC) Assessment Reports, forms the basis for projecting future climate scenarios, specifically under the medium-high reference scenario SSP3-7. Utilizing a suite of global climate models, including the innovative Multi-Model Ensemble (MME) approach, this study combines predictions to enhance the precision climate projections of Türkiye. Historical data spanning from 1951 to 2020 were subjected to rigorous statistical analysis, including descriptive statistics and regression analysis. The findings reveal an unequivocal upward trajectory in Türkiye’s annual mean temperature, with an accelerated pace in recent decades. Despite a lack of a significant long-term trend in annual precipitation from 1951 to 2020, the rate of change in precipitation is accelerating, indicating potential future challenges. Projections for 2040-2059 under the SSP3-7.0 scenario indicate a non-uniform increase in mean temperature across Türkiye, with the southern and western regions facing the most significant impact. This warming trend poses imminent threats to agriculture, altering crop yields and increasing the risk of heat stress for livestock. Additionally, the projected decrease in precipitation, alongside a surge in hot days and tropical nights, underscores the urgency for adaptive measures. As Türkiye navigates the complex terrain of climate change, this study provides valuable insights, emphasizing the significance of robust climate modeling for informed decision-making. The results underscore the imminent challenges Türkiye faces and emphasize the critical importance of proactive climate action on both national and global fronts.

The Relationship between Climate, Agriculture and Land Cover in Matopiba, Brazil (1985–2020)

Climate change has been at the forefront of discussions in the scientific, economic, political, and public spheres. This study aims to analyze the impacts of climate change in the Matopiba region, assessing its relationship with land cover and land use, soybean crop production and yield, and ocean–atmosphere anomalies from 1985 to 2020. The analysis was conducted in four parts: (1) trends in annual and intra-annual climate changes, (2) the spatiotemporal dynamics of land cover and use, (3) the spatiotemporal dynamics of soybean production and yield, and (4) the relationship between climate change, agricultural practices, land cover and use, and ocean–atmosphere anomalies. Statistical analyses, including Mann–Kendall trend tests and Pearson correlation, were applied to understand these relationships comprehensively. The results indicate significant land cover and use changes over 35 years in Matopiba, with municipalities showing increasing soybean production and yield trends. There is a rising trend in annual and intra-annual maximum temperatures, alongside a decreasing trend in annual precipitation in the region. Intra-annual climate trends provide more specific insights for agricultural calendar planning. No correlation was found between the climate change trends and soybean production and yield in the evaluated data attributed to genetic and technological improvements in the region. The North Atlantic Ocean shows a positive correlation with soybean agricultural variables. Evidence suggests soybean production and yield growth under climate change scenarios. This study highlights soybeans’ adaptation and climate resilience in the Matopiba region, providing valuable insights for regional agricultural development and contributing to further research in environmental, water-related, social, and economic areas of global interest.

Investigation of Temperature, Precipitation, Evapotranspiration, and New Thornthwaite Climate Classification in Thailand

This study aims (1) to study the trend and characteristics of average annual air temperature (Tann), annual precipitation (Prann), and annual evapotranspiration (PETann) in Thailand over the present period (1987–2021) and (2) to extract the climate pattern in form of a map using the New Thornthwaite Climate Classification method in Thailand considering the present period. The data were prepared by the Thai Meteorological Department. Data variability, the mean of the data calculation in time series, the homogeneity test of data, and abrupt changes were examined. The trends of each variable were calculated using the Mann–Kendal and Sen’s slope test. The results indicated that the high Tann found in Bangkok gradually decreased in the next area. Tann data were heterogeneous with the abrupt change period, and increasing trends were found. Prann values were high in the west side of the southern area and the bottom area of the eastern area; in addition, low rainfall was found in the inner area of the land. Prann data were homogenous with no abrupt change period and slight changes in trends. PETann and %CV spatial distribution were determined for the same pattern of Tann. PETann data were heterogeneous with abrupt change periods and rising trends. The torrid thermal index determined based on the New Thornthwaite Climate Classification results indicated an overall torrid-type climate. A semi-arid climate pattern was found in the small area of the middle of Thailand, and then it shifted toward a moist-type pattern in the next area with an in precipitation. The most climate variability was found to be extreme with the power of temperature changes.

Projected annual precipitation trend in Ethiopia under CMIP6 models in the 21st century

Projected annual precipitation trend in Ethiopia under CMIP6 models in the 21st century

Small forested watershed weathers effects of climate change better than a nearby urban watershed in Northern Virginia, USA

AbstractSouth Fork Quantico Creek (SFQ; 19.8 square kilometre (km2), forested) and Fourmile Run (4MR; 32.4‐km2, urban) are small watersheds in northern Virginia, United States. Precipitation and streamflow data for both watersheds were examined from water year (WY) 1952 through 2022. Temporal changes in hydrologic metrics were identified by calculating trends in annual precipitation, annual peak flow, mean daily flow, minimum daily flow, stream flashiness, and the runoff ratio. The impact of climate and urbanization on watershed hydrology was assessed by computing trends on both raw and precipitation‐adjusted data. Despite increasing precipitation in both watersheds, increasing monotonic trends in most hydrologic metrics were observed only in 4MR. At 4MR, the long‐term trend in annual peak flow was non‐linear, thus trends were calculated on separate periods. Annual peak flow increased from WY 1952 through 1968, coinciding with a period of rapid urbanization. During WY 1969 through 1981, annual peak flows decreased, coinciding with construction of a flood channelization project. Trends for both periods were robust to precipitation adjustment. From WY 1982 through 2022, no change in the precipitation‐adjusted annual peak flows occurred, suggesting annual peak flows increased due to climate factors during this period. Comparison of area‐normalized hydrologic metrics between the two watersheds revealed higher flows in 4MR than SFQ across all flows, not just high flows. Runoff ratio and stream flashiness also were higher in 4MR. Differences in hydrologic metrics between the two watersheds were driven primarily by differences in land use, land cover, and modifications to the water balance related to urbanization. Climate change has altered watershed hydrology at both sites, but extensive urbanization in 4MR has altered the hydrology more than that of SFQ. We conclude that urban watersheds are likely at greater risk of increased flooding than less developed areas as the climate intensifies.

Trend analysis and forecast of annual precipitation and temperature series in the Eastern Mediterranean region

The present study aims to examine the current trend of annual precipitation and temperature series referred to the Eastern Mediterranean basin on a national basis, including the annual variability of sea surface temperature anomalies and the connection effect with the North Atlantic Oscillation and the Mediterranean Oscillation indices (NAOI and MOI, respectively). The period under consideration is mainly the last 32 years, from 1990 to 2021. Additionally, the prediction of monthly and yearly temperature and precipitation series, based on the autoregressive integrated moving average model on a national base, for the next four or eight years, is another interesting feature of the present study. Results indicate rising trends in the annual precipitation during 1990-2021, mostly non-significant and significant warming trends in the annual temperature, including sea surface temperature. The NAOI is highly correlated mostly with annual temperature, whereas the MOI does not affect the variation of annual precipitation and temperature. The results of the present study are in general agreement with the results of available studies in the literature, and they could be of high interest to national authorities and environmental unions/organizations, to help decision-makers face climate change.

Analysis of Meteorological Element Variation Characteristics in the Heilongjiang (Amur) River Basin

Located in the Heilongjiang (Amur) River in north-east Asia, spanning four countries, plays a crucial role as an international border river, and its meteorological changes significantly impact the variation in water resources in the basin. This study utilizes daily average temperature and precipitation data from 282 meteorological stations in the Heilongjiang (Amur) River Basin and its surrounding areas for the period 1980–2022. The analysis employs spatial interpolation, change point testing, and model construction prediction methods. The results indicate a significant increasing trend in both overall temperature and precipitation changes within the Heilongjiang (Amur) River Basin. At the spatial scale, the annual warming rate increases gradually from the southeastern coastal region to the northwestern plateau region, while the rate of precipitation increase decreases from the southern area towards its surroundings. Temporally, the warming amplitude during the growing season decreases gradually from east to west, and the trend in precipitation changes during the growing season aligns with the overall annual precipitation trend. During the non-growing season, the warming trend shows a decrease in the plains and an increase in the plateau, while precipitation increase concentrates in the central and southern plains, and precipitation decrease predominantly occurs in the northwestern plateau region. Temperature and precipitation change points occurred in the years 2001 and 2012, respectively. In precipitation prediction, the Long Short-Term Memory (LSTM) model exhibits higher accuracy, with R (Pearson correlation coefficient) and NSE (Nash-Sutcliffe efficiency coefficient) values approaching 1 and lower NRSME values. This study provides a research foundation for the rational development and utilization of water resources in the Heilongjiang (Amur) River Basin and offers valuable insights for research on climate change characteristics in large transboundary river systems.

Agroecological-based modeling of meteorological drought at 12-month time scale in the Western Cape Province of South Africa

To mitigate the effects of drought in water-stressed agro-ecological zones (AEZs), it is crucial to implement efficient monitoring of drought characteristics and establish proactive water-use strategies. The Western Cape (WC) province is being confronted with an escalating threat of diminished water availability due to unsustainable human activities. The objective of this study was to assess the spatio-temporal characteristics of meteorological drought within a 12-month timescale in the WC province of South Africa spanning from 1980 to 2020. The University of East Anglia's Climate Research Unit provided precipitation datasets from fifteen stations across the six AEZs of the WC province. These datasets were processed using the Standardized Precipitation Index (SPI). The Mann–Kendall (M–K) test was used to analyze the precipitation trend and SPI values at a significance level of p < 0.05. The results indicated substantial negative trends in annual precipitation across all AEZs. An examination of seasonal precipitation patterns revealed a rise in summer precipitation in subtropical cool-arid regions and subtropical warm semi-arid regions, with increases of 0.4 and 0.2 mm/year, respectively. Conversely, the overall annual precipitation trend fell by -1.0 mm/year in both AEZs. The SPI values consistently fell within the range of -2 to 0 in the subtropical warm-arid, subtropical cool-arid, and subtropical cool-arid zones. Conversely, the SPI values were consistently positive in the subtropical warm-arid, subtropical cool-humid, and subtropical warm-humid zones. This finding suggests that there will be persistent drought conditions that will affect agricultural production, surface flows in rivers, and groundwater levels across the WC province.

Factors Affecting the Spatiotemporal Variation of Precipitation in the Songhua River Basin of China

The study aimed to investigate the spatiotemporal variation of annual precipitation and extreme precipitation within the Songhua River Basin (SRB). It utilized precipitation data collected from 60 meteorological stations within the SRB during the period 1968–2019. Employing Empirical Orthogonal Function (EOF) analysis, it decomposed spatiotemporal characteristics of annual precipitation in the SRB. Through Pearson correlation analysis, application of the cross-wavelet transform, and wavelet coherence analysis, the current study explored the correlation between geographical factors, local air temperature, circulation factors, and annual and extreme precipitation. The results indicated an increasing trend for annual precipitation and for most indices of extreme precipitation within the SRB, apart from the consecutive dry days (CDD). Spatially, a general pattern of “more in the east and less in the west” was observed. Annual precipitation types in the basin were resolved into two modes with the first mode showing a general tendency of more (or less) precipitation over the entire basin, while the second mode exhibited less (or more) precipitation in the western areas and more (or less) in the eastern areas. Longitude, latitude, and altitude significantly impacted annual precipitation and extreme precipitation. Local air temperature notably affected the consecutive wet days (CWD). The West Pacific Subtropical High (WPSH) exerts a strong influence on the annual precipitation and extreme precipitation within the basin.

Drought Assessment in Greece Using SPI and ERA5 Climate Reanalysis Data

The present work aims to assess the spatial variability and the trends of the annual rainfall and meteorological drought in the entire territory of Greece utilising the ERA5 reanalysis precipitation dataset of the European Centre for Medium-Range Weather Forecasts (ECMWF), which spans from January 1940 to December 2022 (an 83-year period). Drought assessment took place based on the Standardized Precipitation Index (SPI) for timescales ranging from 1 month to 12 months. Evaluation was carried out by calculating SPI using observed rainfall data from five meteorological stations. The annual rainfall and drought severity trends for timescales of 1 (SPI-1), 3 (SPI-3), 6 (SPI-6) and 12 (SPI-12) months were analysed using the Theil–Sen slope method and the Mann–Kendall trend test. The results indicate significant, both increasing and decreasing, annual precipitation trends at the 95% significance level for the Aegean Islands, western Crete and western mainland of Greece. The results also indicate significant drought trends for SPI-12 for the Aegean Islands and western Peloponnese. Trend analysis for SPI-1, SPI-3 and SPI-6 indicate a mixture of non-significantly increasing wetting trends and increasing drought trends at the national scale. In conclusion, the ERA5 dataset seems to be a valuable tool for drought monitoring at the spatial scale.