Precipitation Series Research Articles

Homogeneity and Change Point Detection Analysis of Seasonal and Annual Precipitation and Temperature Series Van, Türkiye

The quality and consistency of historical temperature and precipitation records are extremely important to researchers who study water resources, hydrological processes, and climate change. In this regard, Homogeneity tests are helpful tools for managing the accuracy and consistency of the data. In this study, the homogeneity of long-term annual and seasonal precipitation and temperature records obtained from five meteorological stations located in Van, Turkey, is examined using Standard Normal Homogeneity (SNHT), Pettitt (PT), Buishand Range (BR), and Cumulative Deviation (CD) tests at a significance level of 0.05. Finally, change-points were determined for each station where the homogeneity was disturbed. As a result of the study, there is mostly homogeneity in the precipitation data, and the homogeneity in the temperature data is deteriorated. The results of this study constitute a source of information in terms of the reliability of the meteorological data series. As a result, the reliability of the data should be questioned in the hydrological studies to be carried out in the Van region and the data should be made reliable in the projects to be carried out.

Vegetation response to droughts: The case of northern Italy

AbstractDrought is a normal and recurrent climate feature, but one of the most important climate hazards that can produce negative effects on natural and socioeconomic systems. The northern part of Italy is historically rich in water resources and a very fertile and productive area. Recently, however, drought events increased their influence on the hydrological behaviour of the main rivers in this region. In this work, we analyse the spatial distribution of drought events and their effects on vegetation greenness in northern Italy during the 2000–2020 period, using MODIS images at 1 km spatial resolution. Correlation maps between fields of bi‐weekly vegetation indices (normalized difference vegetation index [NDVI] and enhanced vegetation index [EVI]) and drought indices (standardized precipitation index [SPI] and standardized precipitation evapotranspiration index [SPEI]) were estimated. To generate the SPEI index, 150 daily precipitation and temperature ground station series were collected, aggregated at bi‐weekly scale, reconstructed, homogenized and spatialized at 1 km resolution by mean of Universal Kriging with auxiliary variables. The NDVI and EVI indices (MOD13A2) and the land surface temperature (LST; MOD11A2) were collected from MODIS images and quality checked, removing pixels with clouds and low‐quality data. Drought events were detected by means of the SPEI and SPI at medium (12‐months) and long (24 and 36 months) timescales. The events were characterized by drought intensity, duration and percentage of affected area. For each pixel, the Pearson's correlation between NDVI/EVI and SPEI/SPI at different timescales was estimated. On the basis of the correlation maps and the CORINE Land Cover 2020 (MCD12Q1), we estimated the drought impact on different vegetation types as the percentage of vegetation undergoing drought stress. The investigation on drought indices has revealed nine main summer and spring events in the study period. The events of summer 2003 and 2017 were especially remarkable, with respectively 50% and 40% of the total area under drought according to the SPEI index at 12 months. The correlation between drought and vegetation indices revealed that in the Po Valley and Tuscan hills, the vegetation communities perturbed by agriculture are those that are most affected by droughts, with 13% of vegetation under drought on long timescales. This study also indicated that in the last two decades, the main trigger of droughts in northern Italy was the lack of precipitation rather than pure temperature increase.

Simple scaling of extreme precipitation regime in Senegal

Extreme precipitation exhibits high temporal and spatial variability, and understanding this variability is crucial for designing hydraulic infrastructures and assessing the impacts of natural risks such as floods and droughts. The Simple Scaling (SS) model, which describes the dependence of extreme rainfall statistics on timescales, is used to estimate Intensity Duration Frequency (IDF) curves. This study aims to evaluate the validity of the Simple Scaling (SS) hypothesis for 14 rain gauges in Senegal and explore the existence of breaks in temporal scaling regimes (transition regimes). For the analysis of extreme characteristics, we considered the Annual Maximum Series (AMS) of precipitation in sub-hourly and supra-daily durations ranging from 10 min to 7 days. The empirical validation of the SS models was confirmed for the majority of the scaling intervals. We identified two scaling breaks, indicating the presence of three scaling regimes: short durations (SD), intermediate durations (ID), and long durations (LD). The spatial variation of scaling exponents reveals the existence of different scaling regimes across Senegal. The results are valuable for modeling the spatial distribution of scaling exponents, which can aid in characterizing IDF curves at ungauged locations.

Non-stationary modeling of seasonal precipitation series in Turkey: estimating the plausible range of seasonal extremes

Non-stationary modeling of seasonal precipitation series in Turkey: estimating the plausible range of seasonal extremes

Decadal prediction of Northeast Asian winter precipitation with CMIP6 models

This study evaluates the decadal prediction skill of 13 forecast systems in predicting winter precipitation over Eurasia, contributing to the Decadal Climate Prediction Project of the Coupled Model Intercomparison Project Phase 6. Northeast Asia stands out as a region with improved decadal prediction skill for forecast years 2–5 due to the initialization. Observations show anticyclonic and cyclonic wind anomalies over the North Pacific and Northeast Asia, respectively, with southwesterly flow to the east of Northeast Asia. Ten forecast systems reproduce such circulation anomalies favoring abundant winter precipitation in Northeast Asia. The significant positive (negative) correlations between the detrended Northeast Asian precipitation (NEAP) and AMV (PDO-like) time series are reproduced by seven (nine) forecast systems. However, most forecast systems underestimate the correlation between the NEAP and the AMV, and have relatively low skill in predicting the PDO. Further improvements in these aspects will help to improve the decadal prediction skill of winter precipitation over Northeast Asia. The multi-model ensemble (MME) is able to reproduce both links of NEAP with AMV and PDO-like variability. The MME demonstrates significant skill and outperforms the individual forecast systems in predicting the NEAP for all 4-year averaged periods in the range of 1–8 years, demonstrating the benefits of using the ensemble mean of multiple models.

Attribution Analysis on Areal Change of Main Wetland and Its Influence on Runoff in the Naolihe River Basin

Wetlands have powerful runoff regulation functions, which can effectively store and retain surface runoff. The runoff regulation function of wetlands is affected by wetland areas, which affect the capacity of flood control. To explore the law of the area change of the main wetlands of the Naolihe River Basin (MWNRB), the visual interpretation method was used to extract wetlands. To identify the reasons for area changes in the MWNRB, the maximum likelihood method, minimum distance method, and neural network method were used to classify land use types from remote sensing images; the M-K variation point test and Theil-Sen trend analysis were used to test the variation point and calculate the trend of precipitation and temperature series. To clarify the influence of wetland areas on runoff, the Gini coefficient and SRI of runoff were used to calculate runoff temporal inhomogeneity. The results showed that the area of the MWNRB obviously decreased, with 74.5 × 106 m2/year from 1993 to 2008, and increased slowly from 2008 to 2015, with 27.7 × 106 m2/year. From 1993 to 2008, 50.74% and 38.87% of wetlands were transformed into paddy fields and dry fields, respectively. From 2008 to 2015, 61.69% and 7.76% of wetlands were transformed from paddy fields and dry fields, respectively. The temperature of the MWNRB increased slowly by 0.04 °C/year from 1993 to 2008 and increased obviously by 0.16 °C/year from 2008 to 2015. The precipitation decreased by 5.6–8.1 mm/year and increased by 16.6–41.2 mm/year in 1993–2008 and 2008–2015, respectively. Compared with precipitation and temperature, land use change caused by human activities is the main cause of wetland area change. The area change of the MWNRB has a certain influence on the runoff regulation and storage capacity. The Gini coefficient and SRI index increased from 0.002/year (0.008) to 0.023/year from 1993 to 2008 and decreased from 0.046/year (0.045) to 0.161/year from 2008 to 2015, respectively.

Precipitation homogenization and trends in the Usumacinta River Basin (Mexico‐Guatemala) over the period 1959–2018

AbstractThe precipitation variability and trends were investigated in the Usumacinta River Basin (URB) for the period 1959–2018, based on imputed and homogenized data records from 60 meteorological stations in Mexico and Guatemala. The homogenization process played a crucial role in enhancing the quality of the original precipitation series, reducing regional inconsistencies and improving temporal and spatial coherence. The dataset reliably captured large‐scale climate variations, revealing three regions with similar precipitation variability and trends in the URB. Notably, maximum precipitation occurred at 636 m a.s.l., while minimum precipitation was at 1531 m a.s.l., indicating an orographic effect in the region. Extreme precipitation events were linked to El Niño–Southern Oscillation. Although the Mann–Kendall test showed statistically significant negative trends in only 18% of the stations, integration of Sen's slope analysis and 30‐year normals and dry year occurrences highlighted a progressive shift towards dryer conditions throughout the study period in the URB. These drier conditions could notably affect regions with higher precipitation, requiring special attention due to possible socioeconomic impacts associated with drought events. By identifying these vulnerable regions, policymakers and stakeholders can proactively plan and execute adaptive measures to mitigate the potential impacts of droughts on communities, ecosystems, and economic activities within the basin.

Efficacious Extraction and Purification Technique of a Potential Antimycobacterial Bacteriocin Produced by Bacillus subtilis (MK733983) of Ethnomedicinal Origin.

The rapid proliferation of antibiotic-resistant microbes has imposed an urgent need for development of novel antimicrobial agents with diverse mechanisms. This study reports a novel extraction method with salting-in and salting-out method for obtaining potential bacteriocin from Bacillus subtilis (MK733983) of ethnomedicinal origin. This technique extracted bacteriocin with desired antimicrobial peptide moieties that showed creditable minimum inhibitory concentrations, thermostability and efficacy compared to all other extraction protocols attempted. Further study used a unique scheme of steps in RP-HPLC purification process using methanol-water as solvents for the bacteriocin that achieved an outstanding antimicrobial activity against Staphylococcus aureus (MTCC 737). The bacteriocin is sensitive to proteases, confirming its proteinaceous nature and showed promising heat stability up to 70°C for 10min. Bacteriocin extracted from a series of ammonium sulphate precipitation showed MIC values 350µg and 300µg for Mycobacterium smegmatis and Staphylococcus aureus respectively. On the other hand, bacteriocin extracted by using chloroform showed MIC values 400µg and 300µg for M. smegmatis and Staphylococcus aureus. All the results implicate the efficacy of bacteriocin and future prospect as an effective antimicrobial agent.

Deep learning precipitation prediction models combined with feature analysis.

Precise rainfall forecasting modeling assumes a pivotal role in water resource planning and management. Conducting a comprehensive analysis of the rainfall time series and making appropriate adjustments to the forecast model settings based on the characterization results of the rainfall series significantly enhance the accuracy of the forecast model. This paper employed the Mann-Kendall and sliding T mutation tests to identify the mutational components in rainfall between 1961 and 2013 at four meteorological stations located in Henan Province. Wavelet analysis was utilized to determine the periodicity of the precipitation series. The model parameters were adjusted based on the mutation and periodicity findings, and appropriate training and test sets were selected accordingly. Rainfall simulation in Henan Province, China, was conducted using a combination of complementary ensemble empirical mode decomposition (CEEMD) and bi-directional long short-term memory (BiLSTM) networks. The integrated approach aimed at predicting rainfall in the region. The findings of this study demonstrate that the CEEMD-BiLSTM model, coupled with feature analysis, yielded favorable results in terms of prediction accuracy. The model achieved a mean MAE (mean absolute error) of 131.210, a mean MRE (mean relative error) of 0.637, a mean RMSE (root mean square error) of 187.776, and an NSE (Nash-Sutcliffe efficiency) above 0.910. The data processed according to the feature analysis results and then predicted; Zhengzhou, Anyang, Lushi, and Xinyang stations improved by 39.548%, 14.478%, 11.548%, and 19.037% respectively compared with the original prediction model.

Regional variation of climatological cloudburst frequency estimated from historical observations of daily precipitation sums

AbstractThis study aims to estimate climatological cloudburst frequencies from daily precipitation sums for a dense network of 161 historical Danish stations covering the period 1914–2010, in order to reveal any systematic regional differences. We define a cloudburst day for a given station as a day when the maximum 1‐h precipitation sum exceeds a predefined threshold; we use the thresholds 15, 20 and 25 mm, corresponding to a cloudburst frequency around 0.42, 0.16 and 0.06 year−1, respectively. Supplementing the historical data, we use hourly data from a modern network and relate the daily probability of cloudburst occurrence to the corresponding daily precipitation using binary regression. This allows an estimation of the cloudburst frequency from a series of daily sums. To validate the methods, we use 33 stations that have been operating for 30 years or longer. For these stations, we demonstrate significant skill by comparing observed and estimated cloudburst frequencies with a jackknife procedure. By applying the model to the 161 historical series, we estimate climatological cloudburst frequencies throughout Denmark. We find large systematic regional variations across Denmark and these variations are much larger, but with a similar regional pattern, than obtained by previous work. We suggest applying the approach to the historical daily precipitation series that exist across the world.

Identification of the Representative Point for Soil Moisture Storage Using a Precipitation History Model

Soil water storage is an essential variable in hydrological processes at the hillslope scale. This study proposed models for predicting soil water based on the precipitation history. According to the mathematical analysis of soil water storage on the hillslope scale, hydrological fluxes can be effectively expressed in terms of the weighted time series of precipitation and evapotranspiration. Moreover, the impact of evapotranspiration on soil water storage was incorporated into the model structure as an autoregressive process. A new soil water prediction model was developed through the integration of the soil moisture stochastic process into the structure of a precipitation-based model for the hillslope scale. Intensive soil moisture and rainfall data collected over two years were then used to test the performance of the developed models for two different hillslopes. The proposed model exhibited a better ability to find representative points for soil water storage than either existing precipitation-based models or the temporal stability method.

Trends, forecasting and adaptation strategies of climate change in the middle and west regions of Iraq

Climate change has placed considerable pressure on the residential environment, agricultural, and water supplies in different areas of the world, especially arid places such as Iraq. Iraq is one of the five most vulnerable countries in the world to climate change, where it has been encountering extremes heat waves during the most recent decades resulted in drought, desertification, and rivers dried up, which led to thousands of hectares to turn dry and yellow. This study aims to investigate the trends of climate change in the middle and western regions of Iraq and future expectations. The daily maximum temperature, minimum temperature, and precipitation are downscaled using the Long Ashton Research Station Weather Generator (LARS-WG) model. Five General Circulation Models (GCMs) from Coupled Model Intercomparison Project Phase 5 (CMIP5) are employed for three future periods: the near future (2021–2040), medium future (2051–2070), and far future (2081–2100), based on two scenarios of the Representative Concentration Pathways (RCP4.5 and RCP8.5) for four selected meteorological stations representing the study area. The outcomes of the calibration and validation of the model supported its skill and reliability to downscale precipitation and temperature time series for statistical indices (R2, RMSE and MBE) ranging between (0.894–0.998), (0.1270–1.9274) and (− 0.6158 to 0.0008), respectively. The results showed that the average minimum and maximum annual temperatures will increase at all selected stations across the three future periods by between 0.94 and 4.98 °C by the end of the twenty-first century. Annual changes in precipitation tend generally towards increase for the study area by average (6.09–14.31%) for RCP4.5 and (11.25–20.97%) for RCP8.5 Compared to the historical data (1990–2020). These findings can contribute to become more acquainted with the effects of climate change on the environment and encourage managers and planners to come up with plans for mitigating and adapting to these effects. They can also serve as a guide for future management of water and agricultural resources in the study region.

Understanding the Contributions of Paleo‐Informed Natural Variability and Climate Changes to Hydroclimate Extremes in the San Joaquin Valley of California

AbstractTo aid California's water sector to better understand and manage future climate extremes, we present a method for creating a regionally consistent ensemble of plausible daily future climate and streamflow scenarios that represent natural climate variability captured in a network of tree‐ring chronologies, and then embed anthropogenic climate change trends within those scenarios. We use 600 years of paleo‐reconstructed weather regimes to force a stochastic weather generator, which we develop for five subbasins in the San Joaquin Valley of California. To assess the compound effects of climate change, we create temperature series that reflect projected scenarios of warming and precipitation series that have been scaled to reflect thermodynamically driven shifts in the distribution of daily precipitation. We then use these weather scenarios to force hydrologic models for each of the five subbasins. The paleo‐forced streamflow scenarios highlight periods in the region's past that produce flood and drought extremes that surpass those in the modern record and exhibit large non‐stationarity through the reconstruction. Variance decomposition is employed to characterize the contribution of natural variability and climate change to variability in decision‐relevant metrics related to floods and drought. Our results show that a large portion of variability in individual subbasin and spatially compounding extreme events can be attributed to natural variability, but that anthropogenic climate changes become more influential at longer planning horizons. The joint importance of climate change and natural variability in shaping extreme floods and droughts is critical to resilient water systems planning and management in the San Joaquin.

The effect of homogenization when constructing long‐term gridded monthly precipitation and temperature data

AbstractWhen assessing long‐term climate trends and variability, it is important to analyse data that are not disturbed by external factors that might lead to misleading trends. It is also urgent to analyse consistent series that cover the entire time period. Since observation networks are continuously changing not too many complete series are available for a centennial long analysis. In this paper an approach to construct long‐term homogenized climate series to be used to derive a long‐term gridded dataset is presented. A gridded dataset of monthly climate anomalies based on all available observations was used to construct 165 raw temperature series and 323 raw precipitation series covering the entire period 1901–2020. These series are homogenized by using the automatic procedure in the R‐package climatol. The gridded data based on the homogenized data show the same regional trends and variability as the data derived from non‐homogenized data. The local spatial variance is smaller in the gridded data based on homogenized series than those based on the raw data series. Both the construction and homogenization of data series to provide spatial and temporal consistent data for gridding lead to better and more reliable gridded datasets for analysing climate trends and variability.

Assessing the Return Periods and Hydroclimatic Parameters for Rainwater Drainage in the Coastal City of Cotonou in Benin under Climate Variability

Cotonou, the economic capital of Benin, is suffering from the impacts of climate change, particularly evident through recurrent floods. To effectively manage these floods and address this issue, it is crucial to have a deep understanding of return periods and hydroclimatic parameters (such as intensity-duration-frequency (IDF) curves and related coefficients), which are essential for designing stormwater drainage structures. Determining return periods and these parameters requires statistical analysis of extreme events, and this analysis needs to be regularly updated in response to climate change. The objective of this study was to determine the necessary return periods and hydroclimatic parameters to improve stormwater drainage systems in the city and its surroundings areas. This required annual maximum precipitation series of 1, 2, 3, 6, 12, and 24 h for 20 years length (1999–2018) as well as flood record data. The intensity series, derived by dividing the amount of rainfall by its duration, was adjusted using Gumbel’s law. IDF curves were constructed based on Montana and Talbot models, and their coefficients were determined according to the corresponding return periods. In 2010, which witnessed devastating floods in the country, the return period for the most intense rainfall events was 40 years, followed by 2013 with a return period of 13.4 years. Consequently, the commonly used 10-year return period for the design of stormwater drainage structures in Cotonou is insufficient. The Talbot model produced the lowest mean square errors for each quantile series and coefficients of determination closest to one, indicating that the parameters obtained from this model are well suited for designing hydraulic structures in Cotonou. The hydroclimatic parameters presented in this study will contribute to the improved design of hydraulic structures in the city of Cotonou.

Variability of Extreme Air Temperatures and Precipitation in Different Natural Zones in Late XX and Early XXI Centuries According to ERA5 Reanalysis Data

Global temperature rise on the planet is accompanied by an increase in the frequency of extreme weather events, which can lead to significant disturbances in plant metabolic processes and the functioning of plant communities. Depending on the type of vegetation and climatic conditions, the extent and nature of this impact can vary significantly. Therefore, the aim of this work was to identify areas with different types of plant communities that have been most affected by extreme temperatures and precipitation in recent decades. To identify regions with temperature and precipitation extremes, we estimated the frequency of exceeding a given threshold (less than 5% quantile or greater than 95% quantile) for selected theoretical probability density functions for the air temperature and precipitation series. The number of cases of exceeding the extreme threshold is calculated for the whole globe, their spatial and temporal variability in different seasons of the year and in the first decades of the XXI century compared to the end of the XX century is analysed. The regions with different types of plant communities with the most pronounced amplification of extreme temperature and precipitation regimes under changing climate conditions were identified.

Electron microscopic investigation of precipitation hardening in Al-Si based alloys

The present work was performed on Al-8.5%Si-1.75%Cu-0.55%Mg-0.2%Ti-0.32%Zr-0.015%Sr alloy (coded as 354 alloy) following T6 (at 160 °C) and T7 (at 240 °C) artificial aging conditions using high resolution transmission electron microscopy (HR-TEM). The main results inferred from the present study are the precipitation of GP zones in samples aged for 20 h at 160 °C along with S-Al2CuMg phase particles. Aging at 240 °C was carried out for different times, ranging between 30 min and 200 h. The quenched structures revealed a series of precipitations mainly GP zones at low aging times to Θ’-Al2Cu after 200 h together with the S-phase. No coherency was observed between the S-phase and the aluminum (Al) matrix. The growth of the S- phase takes place by precipitation of S2 type phase on the advancing S1-type phase. Mismatching was clearly observed at particle/matrix interfaces. High magnification images indicated the dissolution of some of the Θ” and Θ’ particles to enrich the nearby particles (coarsening). The start of incoherency is observed when the alloy is aged at 240 °C for 200 h.

Developing the Actual Precipitation Probability Distribution Based on the Complete Daily Series

The defense against urban pluvial flooding relies on the prediction of rainfall frequency, intensity, and long-term trends. The influence of the choice of the complete time series or the wet-day series on the rain analyses remains unclear, which affects the adaptive strategies for the old industrial cities such as Changchun in Northeastern China, with the outdated combined sewer systems. Based on the data from the two separate weather stations, four types of distributions were compared for analyzing the complete daily precipitation series, and their fitting accuracy was found in decreasing order of Pearson III, Pareto–Burr–Feller distribution (PBF), generalized extreme value (GEV), and Weibull. The Pearson III and the PBF probability distribution functions established based on the complete time series were found to be at least 458% and 227%, respectively, more accurate in fitting with the consecutive observations than those built from the wet-day-only series, which did not take account of the probability of the dry periods between the rain events. The rain depths of the return periods determined from the wet-day-only series might be over-predicted by at least 76% if the complete daily series were regarded as being more closely representative of the real condition. A clear threshold of 137 days was found in this study to divide the persistent or autocorrelated time series from the antipersistent or independent time series based on the climacogram analysis, which provided a practical way for independence determination. Due to the significant difference in the rain analyses established from the two time series, this work argued that the complete daily series better represented the real condition and, therefore, should be used for the frequency analysis for flood planning and infrastructure designs.

Geosystemic Impacts of the Extreme Rainfall Linked to the El Niño 2015/2016 Event in Northern Paraná, Brazil

The El Niño episode between September 2015 and February 2016 caused abundant rainfall in the state of Paraná in southern Brazil. In this study, we map the greatest magnitude of this climatic event and the respective geosystem responses in the landscape. For this purpose, 32 precipitation series were analyzed (1981–2016). The parameter most indicative of extreme rainfall events during the El Niño period was the six-month accumulated precipitation. The return period (RP) of this parameter was calculated and spatialized using kriging. The results show that a longitudinal strip of 450 km by 140 km had rainfall with RP > 30 years, reaching maximums greater than 150 years. Surveys performed in the field, with aerial images, official sources and media news show a great diversity of effects caused by rainfall, such as road blockages, interruption of water and power supply, and erosion in urban and rural areas. It is concluded that the stability of regional geosystems was disrupted because of extreme rainfall values, so that the northwest region of Paraná had less resilience compared to the north-central and Pioneer North regions, due to the differences between the lithologies and soils of these regions.

Precipitation prediction based on CEEMDAN–VMD–BILSTM combined quadratic decomposition model

Abstract Accurate prediction of monthly precipitation is crucial for effective regional water resources management and utilization. However, precipitation series are influenced by multiple factors, exhibiting significant ambiguity, chance, and uncertainty. In this research, we propose a combined model that integrates adaptive noise-complete ensemble empirical mode decomposition (CEEMDAN), variational modal decomposition method (VMD), and bidirectional long- and short-term memory (BILSTM) to enhance precipitation prediction. We apply this model to forecast precipitation in Fuzhou City and compare its performance with existing models, including CEEMD–long and short-term memory (LSTM), CEEMD–BILSTM, and CEEMDAN–BILSTM. Our findings demonstrate that the combined CEEMDAN–VMD–BILSTM quadratic decomposition model yields more accurate predictions and captures the real variation in precipitation series with greater fidelity. The model achieves an average relative error of 1.69%, at a lower level, and an average absolute error of 1.32 m, with a Nash–Sutcliffe efficiency coefficient of 0.92. Overall, the proposed quadratic decomposition model exhibits excellent applicability, stability, and superior predictive capabilities in monthly precipitation forecasting.