Monthly Precipitation Dataset Research Articles

Development of high‐resolution monthly precipitation datasets for geomorphological applications in the Tianshan Mountains from 2000 to 2050

AbstractPrecipitation has a fundamental influence on landscape evolution and erosion rates. Accurate predictions of precipitation changes are crucial for landscape evolution modelling and erosion modelling. The Tianshan Mountains are known as the ‘Water Tower in Central Asia’, where precipitation is the main water supply for the surrounding lakes and rivers and also supports the population and economic development in the plain areas. This study developed a global climate model (GCM) and digital elevation model (DEM)‐based downscaling method and simulated high‐resolution monthly precipitation in Tianshan from 2000 to 2050, with a resolution of 90 m. The datasets developed from 30 surface observation stations had a high degree of precision and simulated the precipitation processes well in the historical period. The simulation results indicated that precipitation will increase at a rate of 11 mm/decade under the SSP245 scenario and 5 mm/decade under the RCP4.5 scenario from 2022 to 2050. Spatially, precipitation was projected to increase in the central and eastern regions and decrease in the western region. In the Kaidu, Urumqi and Manas river basins in the central Tianshan Mountains, precipitation was projected to increase at a rate of 20 mm/decade. The datasets developed here have important theoretical and practical implications for water resource management, and sustainable development in Central Asia.

Long‐Term Alpine Precipitation Reconstruction (LAPrec): A Gridded Monthly Data Set Dating Back to 1871

AbstractSpatial climate data sets that extend back in time over many decades are an important resource for climate monitoring. The long‐term consistency of such data sets is, however, compromised by changes in the measurement systems over time. In this paper, we introduce a data set of monthly precipitation on a 5‐km grid over the European Alps that extends back to the late 19th century. In deriving the “long‐term Alpine precipitation reconstruction” (LAPrec), special care is taken of variations in the station network, in order for the data set to satisfy high standards in long‐term consistency. LAPrec builds on a reconstruction method that integrates the available information in two portions: The first is a set of high‐quality homogenized station series, taken from the HISTALP data archive, covering the entire period almost continuously. The second is a high‐resolution gridded precipitation analysis, taken from the “Alpine Precipitation Grid Data Set,” constructed from thousands of rain‐gauges but covering a few decades only. We demonstrate how the reconstruction approach successfully introduces mesoscale structures that are not resolved by the available long‐term station series, more plausibly so than a predecessor data set using conventional interpolation. We also illustrate that LAPrec reveals long‐term precipitation trends that are spatially more consistent and more detailed than the trends in popular climate monitoring data sets. Over the period 1871–2017 a statistically significant increase is found in winter over the northern parts of the Alps (1%–2% per 10 years). LAPrec is available in two versions (back until 1871 and 1901 respectively) from the Copernicus climate data store.

Drought Atlas of India, 1901–2020

India has been considerably affected by droughts in the recent past. Despite the considerable impacts of droughts on agriculture and water resources, long-term datasets to examine droughts and their consequences at appropriate spatial and temporal scales have been lacking in India. Datasets that provide drought information are mostly available for a short period and at coarser resolutions, therefore, these do not comprehend the information regarding the major droughts that occurred in the distant past at administrative scales of decision-making. To fill this critical gap, we developed the high-resolution (0.05°) and long-term monthly precipitation and temperature datasets for the 1901–2021 period. We used long-term high-resolution precipitation and temperature to estimate droughts using standardized precipitation and evapotranspiration index (SPEI). As SPEI considers the role of air temperature in drought estimation, it can be used to examine meteorological, agricultural, and hydrological droughts. Using high-resolution SPEI, we developed drought atlas for India (1901–2020) that can provide comprehensive information on drought occurrence, impacts, and risks in India.

Constructing a high-precision precipitation dataset on the data-limited Qinghai-Tibet Plateau

The primary constraint hindering a comprehensive understanding of the atmospheric, hydrological, and ecological dynamics on the Qinghai-Tibet Plateau (QTP) is the absence of highly accurate precipitation datasets. Given the limited availability of climate observations and the pronounced spatial disparities in this region, traditional interpolation techniques are ill-suited, necessitating the development of novel methods. In this study, we formulated a random forest model leveraging precipitation data from 209 climate stations and multisource satellite datasets spanning the period from 1960 to 2020. These datasets encompassed low spatial resolution TRMM precipitation products and high spatial resolution NDVI data. This model facilitated the creation of a monthly precipitation dataset at a 1 km resolution for the QTP. Our findings showed that (1) the prevailing precipitation datasets for the QTP exhibited notable deficiencies, characterized by low spatial precision (R² = 0.592, p = 0.015) and incongruent temporal trends when compared to observational data (NSE = 0.398); (2) by incorporating multisource satellite data, we achieved a substantial enhancement in precipitation accuracy. For instance, the introduction of NDVI led to a reduction in precipitation estimation errors by 7.9 mm and 15.4 mm in desert and nondesert regions, respectively. The inclusion of TRMM satellite precipitation data not only improved accuracy in areas with limited climate stations by more than 16 % but also reduced cumulative monthly errors by 46.8 mm, and (3) the precipitation dataset generated through this method displayed a 24.8 % increase in accuracy (R² = 0.84) compared to existing datasets for the QTP, aligning well with actual precipitation trends (NSE = 0.806). This study offers an effective approach for acquiring high-precision precipitation datasets in regions with limited meteorological stations and substantial spatial heterogeneity in precipitation patterns. Furthermore, our method significantly enhances the spatial heterogeneity of precipitation products, potentially serving as a model for the development of datasets in data-scarce areas.

Observed Precipitation Trends Inferred from Canada’s Homogenized Monthly Precipitation Dataset

Abstract This study first developed a comprehensive semiautomatic data homogenization procedure to produce gap-infilled and homogenized monthly precipitation data series for 425 long-term/critical stations in Canada, which were then used to assess Canadian historical precipitation trends. Data gaps in the 425 series were infilled by advanced spatial interpolation of a much larger dataset. The homogenization procedure repeatedly used multiple homogeneity tests without and with reference series to identify changepoints/inhomogeneities, the results from which were finalized by manual analysis using metadata and visual inspection of the multiphase regression fits. As a result, 298 out of the 425 data series were found to be inhomogeneous. These series were homogenized using quantile matching adjustments. The homogenized dataset shows better spatial consistency of trends than does the raw dataset. The improved gridding and regional mean trend estimation methods also provide more realistic trend estimates. With these improvements, Canadian historical precipitation trends were found to be dominantly positive and significant, except in central-south Canada where the trends are generally insignificant and small with mixed directions. For annual precipitation, the largest increases are seen in southeastern Canada and along the Pacific coast; however, the largest relative increases (in percent of the 1961–90 mean) are seen in northern Canada. The largest trend difference between northern and southern Canada is seen in winter, in which significant increases in the north were matched with significant decreases in the south. Significance Statement This study aims to produce a homogenized long-term monthly precipitation dataset for Canada, which is then used to assess Canadian historical precipitation trends. The work is important because it developed a comprehensive algorithm for homogenization of precipitation data, and the results provide better representation of precipitation climate and more robust estimates of precipitation trends. It also identified the causes for large biases in the published estimates of precipitation trends over Canada.

Spatial Downscaling of GPM Satellite Precipitation Data Using Extreme Random Trees

Obtaining precise and detailed precipitation data is crucial for analyzing watershed hydrology, ensuring sustainable water resource management, and monitoring events such as floods and droughts. Due to the complex relationship between precipitation and geographic factors, this study divides the entire country of China into eight vegetation zones based on different vegetation types. Within each vegetation zone, we employ a seasonally adjusted Extreme Random Trees approach to spatially downscale GPM (Global Precipitation Measurement) satellite monthly precipitation data. To validate the effectiveness of this method, we compare it with kriging interpolation and traditional global downscaling methods. By increasing the spatial resolution of the GPM monthly precipitation dataset from 0.1° to 0.01°, we evaluate the downscaled results and validate them against ground-level rain gauge data and GPM satellite precipitation data. The results indicate that the partitioned area prediction method outperforms other approaches, resulting in a precipitation dataset that not only achieves high accuracy but also offers finer spatial resolution compared to the original GPM precipitation dataset. Overall, this approach enhances the model’s capability to capture complex spatial features and demonstrates excellent generalization. The resulting higher-resolution precipitation dataset enables the creation of more accurate precipitation distribution maps, providing data support for regions lacking hydrological information. These data can be used to analyze seasonal precipitation patterns and reveal differences in precipitation across different seasons and geographic regions.

A dataset of monthly precipitation with resolution of 1 km in China from 1960 to 2020

Precipitation data are greatly valuable in theoretical research and practical application, and have been widely used in hydrological forecasting, agricultural production, ecological protection and other fields. China has a vast territory with large topographic fluctuations, and most meteorological stations are located in densely populated plain areas. Due to the low density of stations, there is a lack of precipitation observation data in some areas such as mountainous areas. Therefore, it is necessary to use the interpolation method to establish high-precision precipitation datasets. Based on the precipitation monitoring data from more than 2,400 meteorological stations on the ground from 1960 to 2020, we used the climate data spatial interpolation software ANUSPLIN to obtain a dataset of monthly precipitation interpolation with a resolution of 1 km in China. According to the comparison between the dataset and the measured precipitation data as well as the hydrological yearbook data in China released by the CRU and Northwest University of agriculture and forestry science and technology, this dataset has the characteristics of high accuracy, high resolution and long time series, with great potential in scientific research and application.

Anthropogenic problems threatening major cities: Largest surface deformations observed in Hatay, Turkey based on SBAS-InSAR

The surface deformation caused by tectonic activities and anthropogenic factors poses a great threat to cities worldwide. The investigation and monitoring of these deformations are crucial in order to create risk analysis for the future. The problem in this case is to investigate the surface deformations and their negative effects caused by groundwater use and to identify possible landslide areas. In this study, the surface deformations in Hatay province were analyzed using SBAS-InSAR. The results from these analyses were evaluated by field observations. Sentinel-1 descending (183 datasets) and ascending (147 datasets) track geometries were selected to determine the surface deformation and its temporal evolution. Both east-west and vertical surface deformations were calculated, and the surface deformation profiles, surface 3D models and time series were created. These time series were associated with monthly precipitation data. The deformation area was interpreted with regard to available well-log data and geological setting of the study area. As a result of the study, a surface deformation resembling a bowl like structure was observed in the industrial zone located in the city center of Hatay-Güzelburç. The deformation rates are approximately 22.3 cm/year in the form of subsidence, 3.6 cm/year in the form of eastern movement and 10.1 cm/year in the form of western movement. The deformation of this bowl-like structure decelerated in the winter and accelerated in the summer due to excessive water use. The average monthly precipitation dataset supports these results. The stratigraphic data from water wells and the presence of limestone outside the eastern boundary of the deformation area show a thick clay layer in the eastern block of the bowl-shaped deformation structure. The difference between these two units, which causes a sharp anomaly at the eastern border of the deformation area, is interpreted as a probable normal fault. The second study area where surface deformations are observed is the landslide zone. The deformation was found to be 7.5 cm/year in a westward direction and 1.5 cm/year as subsidence.

A New Agricultural Drought Disaster Risk Assessment Framework: Coupled a Copula Function to Select Return Periods and the Jensen Model to Calculate Yield Loss

China is one of the regions with the most frequent drought disasters and serious social and economic losses. Agricultural drought is the most serious natural disaster. Due to climate change, the regional agricultural drought risk assessment has always been the focus of the academic circle. This study takes Zunyi City as an example, which is the most typical city of karst landform development. The monthly precipitation data set of ground meteorological observation stations in Zunyi City from 1956 to 2020 was selected, and the drought characteristic variables were extracted by the coupled use of the precipitation anomaly percentage (Pa) index and the theory of runs. A copula function was applied to establish the joint distribution model of characteristic variables, obtaining the drought frequency and drought return periods. Combined with the Jensen model, the agricultural drought loss rate under different drought return periods in the target year (2020) was calculated and evaluated. The results showed that the Gumbel-Hougaard copula function was suitable for the joint distribution of drought joint variables in Zunyi City. From 1956 to 2020, fewer droughts occurred in Zhengan and Wuchuan, and the most droughts took place in Fenggang, Meitan, and Yuqing. The average drought duration in each county was about 1.5 months, and the average drought severity was about 0.35 in spatial distribution. Crop loss rate caused by drought increased and the affected area expanded with the increase of drought return periods (5, 10, 20, 50, and 100 years) in temporal distribution. Meanwhile, the drought disaster was most drastic in the eastern region, followed by the south, north, west, and central area. The results were highly consistent with the historical drought in Zunyi City, which verified the validity of the model. This study could provide scientific knowledge for drought resistance and reasonable mitigation programing for the security of the regional agricultural production and the sustainability of social and economic development.

Assessing the use of ERA5-Land reanalysis and spatial interpolation methods for retrieving precipitation estimates at basin scale

Precipitation data availability plays a crucial role in many climatic, hydrological and agricultural-related applications. In this study, the use of alternative data sources (i.e. interpolation methods and ERA5-Land reanalysis data) was combined for improving the spatially distributed precipitation estimates at the Simeto river basin, located in Eastern Sicily (Italy). A total of 51 rain gauges were used to generate a monthly precipitation dataset for the reference period 2002–2019. Among the 6 tested interpolation methods, Natural Neighbour was the method that predicted precipitation the best at monthly level with a Distance between Indices of Simulation and Observation (DISO) of 0.51. Radial Basis Functions and Inverse Distance Weighting provided the highest precipitation accuracies, respectively, for winter and autumn (with DISO values of 0.44 and 0.50, respectively), and for spring and summer seasons (with DISO values of 0.50 and 0.63, respectively). Underestimations on the ERA5-Land precipitation estimates were observed when compared to the most accurate interpolation methods both at monthly (25%) and seasonal temporal scales (21% in winter and summer, 36% in autumn), with the exception for spring. The performance was significantly improved when the interpolation estimates were corrected with local observations (with RMSD values ranging from 35.29 mm to 26.46 mm at monthly scale, and from 23.33–55.34 mm to 23.15–37.88 mm at seasonal level). The spatial distribution of the estimation errors associated to precipitation obtained from ERA5-Land reanalysis revealed a significant positive correlation (p value <0.05) with the altitude variation in each ERA5-Land cell within the basin under study. These results confirm the good performance on the combined use of alternative precipitation data sources, while adjustments are required to reduce site-specific uncertainties due to local microclimatic conditions occurring at the basin scale.

Spatial Modeling of Precipitation Based on Data-Driven Warping of Gaussian Processes.

Modeling and forecasting spatiotemporal patterns of precipitation is crucial for managing water resources and mitigating water-related hazards. Globally valid spatiotemporal models of precipitation are not available. This is due to the intermittent nature, non-Gaussian distribution, and complex geographical dependence of precipitation processes. Herein we propose a data-driven model of precipitation amount which employs a novel, data-driven (non-parametric) implementation of warped Gaussian processes. We investigate the proposed warped Gaussian process regression (wGPR) using (i) a synthetic test function contaminated with non-Gaussian noise and (ii) a reanalysis dataset of monthly precipitation from the Mediterranean island of Crete. Cross-validation analysis is used to establish the advantages of non-parametric warping for the interpolation of incomplete data. We conclude that wGPR equipped with the proposed data-driven warping provides enhanced flexibility and—at least for the cases studied– improved predictive accuracy for non-Gaussian data.

Construction of fine resolution bioclimatic variables for Central Highlands and Southern Central Coast of Vietnam

In this study, 19 surface bioclimatic variables of high spatial resolution 0.00226o (~ 250 m) are generated in a Geographic Information System by the combination of (1) the raster dataset of monthly temperature and precipitation obtained from the global WorldClim database at 0.00833o spatial resolution for the period of 1960–2000; and (2) the climate data (temperature and precipitation) of the Central Highlands and Southern Central Coast collected from the 31 temperature and 97 precipitation recording sites for the period of 1991–2015. The statistical downscaling method is applied, using multiple linear regression analysis, in which elevation, geographic coordinates, and distance from the coast are treated as independent variables, to estimate the distribution of temperature; and the B-Spline interpolation method combined with multiple linear regression analysis is employed on precipitation over the study area. The outcomes of the two main analyses are computed to create 19 high spatial resolution bioclimatic variables. While using only local climate data on analyzing the regression models results in high fluctuation of estimated temperature, the combination of the two datasets is more informative. The spatial distribution of our interpolated precipitation is similar to the WorldClim data but has a smaller difference in the mean annual precipitation. The results, which shows higher spatial resolution and are closer to the observed data than those from the WorldClim, could be better applied for predicting species distribution in the region.

A framework for assessing compound drought events from a drought propagation perspective

Compared with a single drought event, compound drought (i.e., two or more droughts occurring simultaneously) has more substantial effects on economic growth, water resources, and the ecological environment. A useful framework for assessing compound drought events from a drought propagation perspective was proposed. The framework includes the definition, identification, and risk assessment of compound drought. We defined compound drought as a drought event from a drought propagation perspective when meteorological drought and hydrological drought occur simultaneously. The Standardized Streamflow Index (SSI) and Standardized Precipitation Index (SPI) were used to represent hydrological and meteorological drought, respectively. By applying a certain truncation threshold of run theory, the compound drought events in the time series were identified and characterized according to their duration and severity. Five univariate distribution functions (i.e., Log-normal, Exponential, Gamma, Weibull, and Pareto) and four bivariate copula models (i.e., Gaussian, Clayton, Frank, and Gumbel) were used for fitting both the duration and severity of compound drought and their joint return periods. A 40-year monthly streamflow and precipitation dataset from the Dongjiang River Basin, which is located on the southern coast of China, was used as a case study. The characteristics of compound drought, including duration and severity, were captured by considering the time continuity of meteorological and hydrological droughts from a drought propagation perspective based on run theory. The most severe compound droughts in the study area were observed in 1963 and 2004–2005. The characteristics of compound drought duration and severity were related to the timescale of the drought index (i.e., SSI and SPI). For the study basin, the SPI at a 2–3-month timescale better matched the SSI on the monthly timescale compared with other timescales. The Gaussian-copula function better characterized the joint return periods between the duration and severity of compound drought compared with three other copula models. Although reservoir regulation played an important role in decreasing the duration (lower by 8.25%) and severity (lower by 9.27%) of compound drought, it had little effect on extreme compound drought events. Overall, the results indicated that the proposed method provides a useful tool for statistical assessments of compound drought from a drought propagation perspective. This framework could also be applied to other regions.

A spatiotemporal framework to calibrate high‐resolution global monthly precipitation products: An application to the Urmia Lake Watershed in Iran

AbstractImproving precipitation accuracy over a watershed is one of the highest priorities in water resources studies and management. Several global precipitation datasets are available for estimating precipitation over any region in the world. However, local or regional application of these datasets should account for and correct potential errors in the original products. This article presents a novel spatiotemporal calibration framework to improve the accuracy (bias and correlation) of global precipitation datasets in regional applications. The proposed methodology consists of two steps. First, gridded global precipitation datasets are regressed pointwise against rain gauge data. This yields downscaled and bias‐corrected precipitation values at the point scale. Second, the resulting point‐scale regression parameters are used to build a geostatistical model that predicts the regression parameters across the region of interest, allowing for bias‐correcting the precipitation datasets at the regional scale. The framework is applied to the Urmia Lake Watershed in northwestern Iran. Eight global high‐resolution monthly precipitation datasets (CHIRPS, ERA‐5, IMERG, PERSIANN, PERSIANN‐CCS, PERSIANN‐CDR, TRMM and Terra) are evaluated and three downscaling approaches including linear, Q‐Q and Linear Scaling (LS) regression methods are used to calibrate the precipitation datasets based on a regional network of rain gauge observations. Ordinary kriging is subsequently used to predict the regression parameters at ungauged locations. Out of all combinations (i.e., eight datasets and three methods), downscaled IMERG using linear and Q‐Q regression methods showed the best performance in estimating the spatiotemporal variations of monthly precipitation across the watershed of interest. The original IMERG dataset overestimated the monthly precipitation by approximately 20% compared to the precipitation from rain gauges. After applying the proposed methodology in this article, the IMERG bias was reduced by 93%, with an additional 26% decrease in the RMSE.

Water Quality and Phytoplankton Distribution of the Lower Kinabatangan River Catchment, Sabah

A study on water quality and phytoplankton distribution was carried out at the Lower Kinabatangan River Catchment, Sabah in November 2013, January 2014 and March 2014. The objectives were to study the surface water quality of the Lower Kinabatangan River Catchment; to identify the composition of phytoplankton in three different types of land use in Sukau, Kinabatangan; and to determine spatial and temporal variations of water quality in Sukau, Kinabatangan. Three sampling stations were selected to represent different types of land use, consisting of oil palm plantation (OP), secondary forests (SF) and oxbow lake (OB). Based on Interim National Water Quality Standards (INWQS) for Malaysia, the parameters were categorized within Class I to Class IV. Statistical analyses ANOVA one-way, paired sample t-test and discriminant analysis have been carried out to both water quality and total monthly precipitation data sets. The distribution of phytoplankton in Kinabatangan River consisted of 5 divisions: the Bacillariophyta, Chlorophyta, Cyanophyta, Cryptophyta and Euglenophyta. Chlorophyta recorded the highest diversity, with 10 species recorded out of 17 species found of the Lower Kinabatangan River Catchment. Discriminant analysis suggested that UV-visible absorption coefficients at 254 and 340 nm were dominant in samples from OP and SF. Temporal variations showed that parameters suspended sediment, UV-visible absorption coefficients at 254 and 340 nm were dominant in samples from collected in January 2014.

A universal Standardized Precipitation Index candidate distribution function for observations and simulations

Abstract. The Standardized Precipitation Index (SPI) is a widely accepted drought index. Its calculation algorithm normalizes the index via a distribution function. Which distribution function to use is still disputed within the literature. This study illuminates that long-standing dispute and proposes a solution that ensures the normality of the index for all common accumulation periods in observations and simulations. We compare the normality of SPI time series derived with the gamma, Weibull, generalized gamma, and the exponentiated Weibull distribution. Our normality comparison is based on a complementary evaluation. Actual compared to theoretical occurrence probabilities of SPI categories evaluate the absolute performance of candidate distribution functions. Complementary, the Akaike information criterion evaluates candidate distribution functions relative to each other while analytically punishing complexity. SPI time series, spanning 1983–2013, are calculated from the Global Precipitation Climatology Project's monthly precipitation dataset, and seasonal precipitation hindcasts are from the Max Planck Institute Earth System Model. We evaluate these SPI time series over the global land area and for each continent individually during winter and summer. While focusing on regional performance disparities between observations and simulations that manifest in an accumulation period of 3 months, we additionally test the drawn conclusions for other common accumulation periods (1, 6, 9, and 12 months). Our results suggest that calculating SPI with the commonly used gamma distribution leads to deficiencies in the evaluation of ensemble simulations. Replacing it with the exponentiated Weibull distribution reduces the area of those regions where the index does not have any skill for precipitation obtained from ensemble simulations by more than one magnitude. The exponentiated Weibull distribution maximizes also the normality of SPI obtained from observational data and a single ensemble simulation. We demonstrate that calculating SPI with the exponentiated Weibull distribution delivers better results for each continent and every investigated accumulation period, irrespective of the heritage of the precipitation data. Therefore, we advocate the employment of the exponentiated Weibull distribution as the basis for SPI.

High-resolution terrestrial climate, bioclimate and vegetation for the last 120,000 years

The variability of climate has profoundly impacted a wide range of macroecological processes in the Late Quaternary. Our understanding of these has greatly benefited from palaeoclimate simulations, however, high-quality reconstructions of ecologically relevant climatic variables have thus far been limited to a few selected time periods. Here, we present a 0.5° resolution bias-corrected dataset of global monthly temperature, precipitation, cloud cover, relative humidity and wind speed, 17 bioclimatic variables, annual net primary productivity, leaf area index and biomes, covering the last 120,000 years at a temporal resolution of 1,000–2,000 years. We combined medium-resolution HadCM3 climate simulations of the last 120,000 years with high-resolution HadAM3H simulations of the last 21,000 years, and modern-era instrumental data. This allows for the temporal variability of small-scale features whilst ensuring consistency with observed climate. Our data make it possible to perform continuous-time analyses at a high spatial resolution for a wide range of climatic and ecological applications - such as habitat and species distribution modelling, dispersal and extinction processes, biogeography and bioanthropology.

Precipitation trends in the island of Ireland using a dense, homogenized, observational dataset

AbstractA dense monthly precipitation dataset of Ireland and Northern Ireland was homogenized with several modern homogenization methods. The efficiency of these homogenizations was tested by examining the similarity of homogenization results both in the real data homogenization and in the homogenization of a simulated dataset. The analysis of homogenization results shows that the real dataset is characterized by a large number of, but mostly small, non‐climatic biases, and a moderate reduction of such biases can be achieved with homogenization. Finally, a combination of the ACMANT and Climatol homogenization results was applied to improve the data accuracy before the trend calculations. These two methods were selected for their proven high accuracy, missing data tolerance and ability to complete time series via the infilling of missing values before the trend calculations. Metadata were used within the Climatol method. To facilitate this analysis the study area was split into smaller climatic regions by using the Ward clustering method. Five climatic zones consistent with the known spatial patterns of precipitation in Ireland were established. Linear regression fitting and the Mann‐Kendall test were applied. Low frequency fluctuations were also examined by applying a Gaussian filter. The results show that the precipitation amount generally increases in the study area, particularly in the northwestern region. The most significant increasing trends for the whole study period (1941–2010) are found for late winter and spring precipitation, as well as for the annual totals. In the period from the early 1970s the increase of precipitation is general in all seasons of the year except in winter, but the statistical significance of this increase is weak.

Phenological behavior of Atlas cedar (Cedrus atlantica) forest to snow and precipitation variability in Boutaleb and Babors Mountains, Algeria

Abstract. Missaoui K, Gharzouli R, Djellouli Y, Messner F. 2020. Phenological behavior of Atlas cedar (Cedrus atlantica) forest to snow and precipitation variability in Boutaleb and Babors Mountains, Algeria. Biodiversitas 21: 239-245. Understanding the changes in snow and precipitation variability and how forest vegetation response to such changes is very important to maintain the long-term sustainability of the forest. However, relatively few studies have investigated this phenomenon in Algeria. This study was aimed to find out the response of Atlas cedar (Cedrus atlantica (Endl.) G.Manetti ex Carrière) forest in two areas (i.e Boutaleb and Babors Mountains) and their response to the precipitation and snow variability. The normalized difference vegetation index (NDVI) generated from satellite images of MODIS time series was used to survey the changes of the Atlas cedar throughout the study area well as dataset of monthly precipitation and snow of the province of Setif (northeast of Algeria) from 2000 to 2018. Descriptive analysis using Standarized Precipitation Index (SPI) showed the wetter years were more frequent in the past than in the last two decades. The NDVI values changes in both areas with high values were detected in Babors Mountains with statistically significant differences. Our findings showed important difference in Atlas cedar phenology from Boutaleb mountains to Babors Mountains which likely related to snow factor.

Decadal change in summer precipitation over the east of Northwest China and its associations with atmospheric circulations and sea surface temperatures

AbstractAs global warming has progressed, precipitation patterns over arid Northwest China have undergone significant change. In this study, changes in summer (JJA) precipitation over the eastern part of Northwest China (ENWC) from 1980 to 2014 were investigated using the China gridded monthly precipitation dataset (CN05.1). The results showed that summer precipitation over the ENWC experienced a decadal wet‐to‐dry shift in 1998. Westerlies played an important role in the upper atmospheric levels in terms of water vapour transport; the decadal variations in summer precipitation were principally controlled by the water vapour input from the ENWC's western boundary. In addition, the decadal variations in summer precipitation in the ENWC appear to be associated with a meridional teleconnection around 110°E and a zonal pattern over 45–60°N in the lower troposphere. These two teleconnections led to cyclonic anomalies in the ENWC and enhanced water vapour transport into the ENWC, resulting in above‐normal precipitation during the 1989–1998 decadal period. Further, the warmer (colder) sea surface temperatures (SSTs) observed in the tropical Eastern Pacific correspond to the southward (northward) displacement of the Asian jet stream and a negative (positive) phase of the Silk Road pattern, resulting in a wet (dry) ENWC. Moreover, the SST anomalies in the North Atlantic and Northwest Pacific may affect summer precipitation over the ENWC via a zonal teleconnection in the middle troposphere.