Trend In Annual Precipitation Research Articles

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.

Contemporary Climate Change in Montenegro

Contemporary Climate Change in Montenegro

Spatio‐temporal analysis of trends in annual maximum rainfall in the North‐West of Algeria: Comparative analysis of recent and old non‐parametric methods

AbstractThis article examines the spatial variability of extreme precipitation trends in northwestern Algeria (Macta) and compares the results obtained from the four recent and old non‐parametric methods. A dataset of annual maximum precipitation consisting of 41 observation years (1970–2010) and 41 rain gauges was used. The results of the four old and new methods used to detect trends, Mann–Kendall (MK), Bravais–Pearson (BP), Spearman (SR) and innovative trend analysis (ITA), show good agreement. They revealed that a decrease in the trend of annual maximum precipitation was detected during the first period (1970–1992) with −44% (MK), −61% (BP), −68% (SR) and −76% (ITA). On the other hand, in the second period (1993–2010), a total shift occurred in which a significant increase in annual maximum precipitation trends was observed with +63% (BP), +34% (MK and SR) and +93% (ITA). These results show the ability of ITA to detect partial trends that the other three tests do not allow. Our results allow decision‐makers to properly design adaptation strategies in the face of the intensification of these extreme events.

Trend assessment of changing climate patterns over the major agro-climatic zones of Sindh and Punjab

The agriculture sector, due to its significant dependence on climate patterns and water availability, is highly vulnerable to changing climate patterns. Pakistan is an agrarian economy with 30% of its land area under cultivation and 93% of its water resources being utilized for agricultural production. Therefore, the changing climate patterns may adversely affect the agriculture and water resources of the country. This study was conducted to assess the climate variations over the major agro-climatic zones of Sindh and Punjab, which serve as an important hub for the production of major food and cash crops in Pakistan. For this purpose, the climate data of 21 stations were analyzed using the Mann–Kendall test and Sen's slope estimator method for the period 1990–2022. The results obtained from the analysis revealed that, in Sindh, the mean annual temperature rose by ~0.1 to 1.4°C, with ~0.1 to 1.2°C in cotton-wheat Sindh and 0.8 to 1.4°C in rice-other Sindh during the study period. Similarly, in Punjab, the mean annual temperature increased by ~0.1 to 1.0°C, with 0.6 to 0.9°C in cotton-wheat Punjab and 0.2 to 0.6°C in rainfed Punjab. Seasonally, warming was found to be highest during the spring season. The precipitation analysis showed a rising annual precipitation trend in Sindh (+30 to +60 mm) and Punjab (+100 to 300 mm), while the monsoon precipitation increased by ~50 to 200 mm. For winter precipitation, an upward trend was found in mixed Punjab, while the remaining stations showed a declining pattern. Conclusively, the warming temperatures as found in the analysis may result in increased irrigation requirements, soil moisture desiccation, and wilting of crops, ultimately leading to low crop yield and threatening the livelihoods of local farmers. On the other hand, the increasing precipitation may favor national agriculture in terms of less freshwater withdrawals. However, it may also result in increased rainfall-induced floods inundating the crop fields and causing water logging and soil salinization. The study outcomes comprehensively highlighted the prevailing climate trends over the important agro-climatic zones of Pakistan, which may aid in devising an effective climate change adaptation and mitigation strategy to ensure the state of water and food security in the country.

Evaluation and Projection of Climate Change in the Second Songhua River Basin Using CMIP6 Model Simulations

The aim of this study is to evaluate the performance of the Global Climate Model (GCM) of the Coupled Model Intercomparison Project Phase 6 (CMIP6) in historical simulations of temperature and precipitation. The goal is to select the best performing GCMs for future projection of temperature and precipitation in the Second Songhua River Basin under multiple shared socioeconomic pathways (SSPs). Interannual variability skill (IVS) and Taylor diagrams are used to evaluate the spatiotemporal performance of GCMs against temperature and precipitation data published by the China Meteorological Science Commons during 1956–2016. In addition, five relatively independent models are selected to simulate the temperature and precipitation for 2021–2050 using Hierarchical Clustering. The selected models are CMCC-ESM2, EC-Earth3-Veg-LR, IPSL-CM6A-LR, MIROC-ES2L, and MPI-ESM1-2-HR. The projected results find that SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios show an increasing trend of future annual mean temperature and precipitation. However, for annual precipitation, there is a mixed state of increase and decrease among different models on the seasonal scale. In general, future temperature and precipitation changes still show a trend of growth and uneven distribution in the Second Songhua River Basin, which may be further accelerated by human activities.

Reliability of simulating internal precipitation variability over multi‐timescales using multiple global climate model large ensembles in China

AbstractSingle model initial‐condition large ensembles (SMILEs) are valuable means to study the role of internal climate variability (ICV) in climate change. However, the ability of SMILEs to represent the multi‐timescale ICV is not fully understood, especially in the region of China. The main objective of this work is to assess the ability of six advanced SMILEs with ensemble members ranging from 30 to 100 in simulating ICV of precipitation at various timescales in China, using a set of observational datasets as the benchmark. To have a first insight into the selected models, the mean states and trends of annual precipitation were evaluated at the very beginning. The ICV of precipitation was then calculated at multiple timescales by two widely used methods of representing temporal variability and intermember variability. The minimum required ensemble size for each climate model to robustly capture the true value of ICV is investigated at last. The results show that the multiple SMILEs can capture the basic spatial distribution pattern of ICV across timescales. However, the ICV represented by temporal variability underestimates observed ICV in most regions across timescales. The ICV represented by intermember variability is similar to that represented by temporal variability at the interannual and interdecadal timescales, but significantly larger at the multidecadal timescales. Within ±10% error limit, 20–30 members are sufficient for all climate models at the interannual timescale while ensemble sizes of 74% or more of the full sizes for each climate model are needed at multidecadal timescales.

Analysis of annual and monthly precipitation trends based on long-term data (Case study: Zohreh–Jirahi Basin in Iran)

Analysis of annual and monthly precipitation trends based on long-term data (Case study: Zohreh–Jirahi Basin in Iran)

Projections of Mean and Extreme Precipitation Using the CMIP6 Model: A Study of the Yangtze River Basin in China

In this study, we conducted an analysis of the CN05.1 daily precipitation observation dataset spanning from 1985 to 2014. Subsequently, we ranked the 30 global climate model datasets within the NEX-GDDP-CMIP6 dataset using the RS rank score method. Multi-model weighted-ensemble averaging was then performed based on these RS scores, followed by a revision of the multi-model weighted-ensemble averaging (rs-MME) using the quantile mapping method. The revised rs-MME model data were utilized for simulating precipitation variations within the Yangtze River Basin. We specifically selected 11 extreme-precipitation indices to comprehensively evaluate the capability of the revised rs-MME model data in simulating extreme-precipitation occurrences in the region. Our investigation culminated in predicting the characteristics of precipitation and the potential shifts in extreme-precipitation patterns across the region under three distinct shared socioeconomic pathways (SSP1-2.6, SSP2-4.5, and SSP5-8.5) for three temporal segments: the Near 21C (2021–2040), Mid 21C (2041–2070), and Late 21C (2071–2100). Our findings reveal that the revised rs-MME model data effectively resolve the issues of the overestimation and underestimation of precipitation data present in the previous model. This leads to an enhanced simulation of mean annual precipitation, the 95th percentile of precipitation, and the extreme-precipitation index for the historical period. However, there are shortcomings in the simulation of linear trends in mean annual precipitation, alongside a significant overestimation of the CWD and CDD indices. Furthermore, our analysis forecasts a noteworthy increase in future mean annual precipitation within the Yangtze River Basin region, with a proportional rise in forced radiation across varying scenarios. Notably, an ascending trend of precipitation is detected at the headwaters of the Yangtze River Basin, specifically under the Late 21C SSP5-8.5 scenario, while a descending trend is observed in other scenarios. Conversely, there is an escalating pattern of precipitation within the middle and lower reaches of the Yangtze River Basin, with most higher-rate changes situated in the middle reaches. Regarding extreme-precipitation indices, similar to the annual average precipitation, a remarkable upsurge is evident in the middle and lower reaches of the Yangtze River Basin, whereas a relatively modest increasing trend prevails at the headwaters of the Yangtze River Basin. Notably, the SSP5-8.5 scenario portrays a substantial increase in extreme-precipitation indices.