Total Monthly Precipitation Research Articles

Drought Analysis with Two Different Indices in Yeşilırmak Basin

Reducing the negative effects of drought disaster, which is one of the most important parameters affecting the planning and management of water resources, has become very important today. Many methods have been proposed in the literature for the calculation of drought, which is used to express the periods when precipitation is significantly less than the average, and two of these methods, "Standardized Precipitation Index" and "Reconnaissance Drought Index" were applied to the Yeşilırmak basin in this study.
 12-month standardized precipitation index and reconnaissance drought index analyzes were made and compared by using the monthly precipitation and evapotranspiration totals of 8 stations in the Yeşilırmak Basin for the period 1991-2020. The results show us that standardized precipitation index and reconnaissance drought index methods generally give similar signals for wet periods, but reconnaissance drought index method defines more extreme dry periods than standardized precipitation index method in extreme dry periods. While the reconnaissance drought index method defines more extreme dry periods than the standardized precipitation index method; It was observed that the standardized precipitation index method defined more extreme wet periods than the reconnaissance drought index method. In drought analysis, using an index based on more than one meteorological parameter (such as reconnaissance drought index) will give more reliable results instead of using an index based on a single parameter (i.e, standardized precipitation index). It has shown in this study that the effect of evapotranspiration values in drought calculation is very important by comparing it with reconnaissance drought index analysis and standardized precipitation index analysis.

Entropy Based Regional Precipitation Prediction in the Case of Gediz River Basin

The Shannon’s entropy concept is defined to measure the information content of hydrological processes in hydrology and water resources. Entropy concept has also provided an opportunity to solve several related topics in water resources engineering. The presented study aims to define the regional distribution of the expected long-term annual total precipitation, by using the entropy concept. For this purpose, the frequency analysis of the observed long-term total monthly precipitation data for each station is analyzed, and entropy values are calculated using the frequency histogram called " Intensity Entropy – IE". By using the IE values, it is possible to define the regional information of long-term expected precipitation even if the gauging stations have different observation periods without missing any information of available data. In addition, there is no need to complete the data of the missing observation years (months) to define the precipitation-elevation relations for producing an isometric map. In regional analysis, it is possible to create isoentropy map, by using the determined IE values for each gauging station. The IE method is performed and isoentropy map is created for Gediz Basin as a case study, and the a priori conditions of using IE method results for regional information are discussed in the paper.

Climate change and child malnutrition: A Nigerian perspective

Erratic temperatures and precipitation influence nutrition, human capital investment, and living standards, particularly for children. This study investigates the effect of climate change (changes in the monthly maximum average near-surface temperature and total monthly precipitation) on children’s health outcomes, particularly stunting and underweight, in Nigeria. We combine Living Standards Measurement Study - Integrated Surveys on Agriculture (LSMS-ISA) data with high resolution gridded climate data. We find that the rise in temperature is associated with higher levels of stunting – even more so in rural areas. The paper’s findings highlight the need for climate-friendly policies to mitigate the long-term effect of climate change on malnourishment. Without such policies, climate change could reverse years of progress in lowering children’s malnutrition.

Feasibility and Zoning of Establishing Solar Power Stations to Produce Sustainable Energy From the Environment in Northwestern Iran

Attention to climatic and environmental potentials for exploitation and meeting human needs has been considered for many years. But the optimal and correct use of this potential requires accurate knowledge and subsequent careful planning. Today, the use of climate and productive energy from the environment is more of a concern for researchers. This investigation aims to appraise the feasibility of establishing solar thermal power plants (solar panels) by examining eight climatic and atmospheric parameters in the environment based on fuzzy logic in northwestern Iran. In this study, the weather datum of 22 Urban Climate Synoptic stations were prepared to evaluate the climatic conditions for the feasibility of establishing a thermal solar power plant for sustainable development using fuzzy logic in the north-west of Iran for each synoptic station during a statistical period of 31 years (1990–2020). This research used eight climatic parameters: average max temperature, average temperature, average min relative moisture, mean relative moisture, total monthly precipitation, and a daily average of Sunny hours, altitude, and wind speed in this systemic model. MATLAB software was used to combine the input data from fuzzy logic and ANFIS Adaptive Neural Network. The climate zoning map for establishing a thermal solar power plant with the scores obtained for each of the parameters and finally the final map was drawn using the ArcGIS. The results show that the fuzzy logic method showed a great variety of options for the establishment of solar thermal power plants using climatic parameters. In total, after obtaining the final rating for each of the 22 stations, Bonab station, with a coefficient score of (0.544–0.589), and Miyaneh station with a score (0.543–0.577), located in East Azarbaijan Province, in the north-west of Iran are appropriate for the establishment of a solar power plant. The Northwest of Iran is suitable to meet the needs of various energies, including domestic electricity, due to the potential stemming from its favorable climatic conditions climate; therefore, it is necessary to take steady steps to develop this important industry. Also, the accuracy of MATLAB and ArcGIS software with a high level of 0.96% confidence in finding a suitable place for the establishment of solar power stations was confirmed. According to the final results obtained from the integration and fuzzification of 8 climatic parameters and zoning of suitable and unsuitable areas for prioritization, Ardabil station with a score of 0.345% in the priority of inconvenient location, and Bonab station with a score of 0.589% in the priority of where the best places were for the establishment of solar power stations. Considering the findings of the present study, the central region and distant locales in mountainous areas were suitable places for the establishment of solar panels. The valuable results of the present study can be given more attention by the sectors that use a lot of sustainable energy. The results of this research in the field of sustainable energy production from the environment are more in the spotlight.

Inter-comparison on the Suitability of Rain-Based Meteorological Drought in Johor River Basin, Malaysia

Drought is identified and quantified using a variety of methods. This study aims to evaluate the performance of several rain-based meteorological droughts, namely, Rainfall Anomaly Index (RAI), Standard Precipitation Index (SPI), China-Z Index (CZI), Percent of Normal (PN), Deciles Index (DI), and Z-Score Index (ZSI) on 1-, 3-, 6-, 9-, 12-, 18-, 24-, 48-, and 72-month time scales based on monthly precipitation totals for 24 Johor River Basin (JRB) stations from 1970 to 2015. After imputation of missing data with Multiple Imputation by Chained Equations (MICE), homogeneity test for the annual time series found that 21 stations were ‘useful’, 1 station ‘doubtful’ and 2 stations ‘suspect’, meanwhile monthly time series found 17 stations ‘useful’, 1 station ‘doubtful’ and 6 stations ‘suspect’. The performance of CZI, RAI, and SPI were found to be higher compared to others based on the correlation between drought indices, average correlation of a combination of drought indices for all stations (correlation plot and heatmap), scatter plot linearity between drought indices in capturing the wet/dry conditions, and simulating the historical drought events. However, based on the ability to simulate the historical event, spatial assessment, and frequency analysis, it was found that RAI is more responsive in identifying the ‘drought prone’ area to enable a better spatial mapping of the 3- and 6-months drought condition in JRB. ‘Moderately dry’ condition is expected to be temporally and spatially widespread as North East (NE) monsoon becomes drier. Meanwhile, a condition of ‘very dry’ and ‘extremely dry’ is expected to be more prevalent in the South East area of the basin, particularly due to the drier South West (SW) monsoon period. Study results indicate that RAI can perform comparatively better than other studied indices, and because the calculations of RAI are relatively simple, they can be used as better tools to monitor the conditions of drought in JRB.

Rainfall Thresholds of the 2014 Smutná Valley Debris Flow in Western Tatra Mountains, Carpathians, Slovakia

An extensive debris flow occurred, in the Smutná valley, Western Tatra Mts. in Slovakia on 15 May 2014. The aim of this study is to describe the morphology of the observed debris flow and to evaluate the conditions that preceded its formation as well as the previous activity of debris flows on this path. The observed debris flow is among the most extensive ones in terms of morphometric characteristics in the Roháčská valley and its tributaries (e.g., the length of the erosion-accumulation zone of ~600 m, volume >1200 m3). However, compared to previous studies from the Western Tatra Mts., it belongs to the average sized debris flows, or a minor event in terms of the general size classification based on the volume of debris flows. A similarly extensive debris flow was recorded on this track in the early 1970s after which only two additional minor events have been recorded there until 2014. The monthly precipitation totals in the 2013/2014 winter season were low compared to the long-term average. The main triggering factor for debris flow initiation was continuous rainfall that lasted 29 hours resulting in ~120–135 mm of precipitation. Most of the derived global empirical thresholds for debris flow initiation were exceeded as well as rainfall thresholds suggested by the published studies for the Western Tatra Mts.

Spring Precipitation Deficiency in Poland and Its Temporal and Spatial Variability in the Context of Agricultural Needs

Deficient precipitation (dPr) in the growing season, especially in critical periods, affects plant condition and determines the quality and quantity of obtained yields. Knowledge about the variability and distribution of dPr is essential to mitigate its effect on agricultural soils and on crop and livestock production. The goal of the work is to determine the spatial and temporal distribution of spring precipitation deficiency and also to indicate the zones of risk and variability of its occurrence in Poland. It was assumed that dPr occurred when total monthly precipitation in a given year accounted for ≤75% of the total multi-year mean (1951–2018). In the spring season, the multi-year mean of the area covered by deficient precipitation (ACDP) amounted to 33% and fluctuated between approximately 31% in May and approximately 35% in March. The study distinguished four zones in Poland that vary in terms of the risk and variability of spring precipitation deficiency. The obtained results may be used, for example, to assess the needs for irrigation in the changing climate conditions, to model the growing season and yields of cultivated plants, and to select adaptation measures for agriculture in response to climate change.

Effects of consecutive dry and wet days on the forest–savanna boundary in north‐west South America

AbstractAimMultiple abiotic and biotic factors, mainly the amount and seasonality of precipitation, fire regime, soil properties and species adaptation strategies, have been invoked to explain the existence of the tropical savanna–humid forest transition. We explored the rainfall variables that influence the probability of forest or savanna occurring in north‐west South America, specifically monthly precipitation and the mean and maximum duration of wet and dry spells, defined as the number of consecutive wet (dry) days.LocationNorth‐west South America.Time period1980–2017.Major taxa studiedSavanna and humid forest.MethodsWe used a set of logistic generalized linear models to assess the effect of multiple variables on the probability of occurrence of forest. Predictor variables included monthly precipitation, mean monthly dry and wet spells and maximum monthly dry and wet spells.ResultsThe probability of forest (savanna) occurrence depends mostly on the average duration of dry and wet spells and the interaction between these two variables, whereas the effect of total monthly precipitation is marginal. Dry spell duration had the largest effect on the probability of forest occurrence, which decreased by 7.5% for each additional consecutive dry day. For wet spells, the probability of forest occurrence increased by 4.1% for each additional consecutive wet day. Our model shows that the savanna–forest transition takes place under an average combination of dry spells lasting 8–10 days and wet spells lasting 1–3 days during the dry season (December–February). This combination changes to dry spells lasting 1–3 days and wet spells lasting 4–6 days during the wet season (May–July).Main conclusionsThis study reveals the importance of using precipitation data with a fine temporal scale to investigate forest–savanna distribution and indicates the limited explanatory power of aggregated precipitation variables (e.g., monthly and annual values). Models that assess the effect of climate change should therefore incorporate precipitation variables acquired at finer temporal resolution.

The Innovative Polygon Trend Analysis (IPTA) as a Simple Qualitative Method to Detect Changes in Environment—Example Detecting Trends of the Total Monthly Precipitation in Semiarid Area

Precipitation is a crucial component of the water cycle, and its unpredictability may dramatically influence agriculture, ecosystems, and water resource management. On the other hand, climate variability has caused water scarcity in many countries in recent years. Therefore, it is extremely important to analyze future changes of precipitation data in countries facing climate change. In this study, the Innovative Polygon Trend Analysis (IPTA) method was applied for precipitation trend detection at seven stations located in the Wadi Sly basin, in Algeria, during a 50-year period (1968–2018). In particular, the IPTA method was applied separately for both arithmetic mean and standard deviation. Additionally, results from the IPTA method were compared to the results of trend analysis based on the Mann–Kendall test and the Sen’s slope estimator. For the different stations, the first results showed that there is no regular polygon in the IPTA graphics, thus indicating that precipitation data varies by years. As an example, IPTA result plots of both the arithmetic mean and standard deviation data for the Saadia station consist of many polygons. This result means that the monthly total precipitation data is not constant and the data is unstable. In any case, the application of the IPTA method showed different trend behaviors, with a precipitation increase in some stations and decrease in others. This increasing and decreasing variability emerges from climate change. IPTA results point to a greater focus on flood risk management in severe seasons and drought risk management in transitional seasons across the Wadi Sly basin. When comparing the results of trend analysis from the IPTA method and the rest of the analyzed tests, good agreement was shown between all methods. This shows that the IPTA method can be used for preliminary analysis trends of monthly precipitation.

The development of long temperature and precipitation series for Ascension Island

AbstractAscension Island has had a long intermittent record of instrumental weather recording. Here, we develop monthly records of mean temperature and precipitation totals (for 1924–2020 when an almost complete record is available) from the three principal recording sites: the capital Georgetown and two gauges at Wideawake Airfield. Although some of the data are in global climate databases, we have sourced as much data as possible from the primary sources in the United Kingdom, the United States and from the island itself. Air temperature shows statistically significant warming since 1950 of 0.54°C and since 1979 of 0.40°C, and agrees closely with sea surface temperatures, taken from the seas around the island, back to the start of the island series in 1924. Although the island is too small to be in Reanalyses, the warming trends of air temperatures from these products also agree, but the absolute air temperature values are about 1°C cooler than measured on the island. Annual precipitation on the island indicates it is very arid, with a long‐term average of only 165 mm. Occasionally, heavy monthly precipitation totals occur (always between February and June) which bring severe damage to the island's infrastructure and ecosystems. The heaviest monthly total was 334 mm for April 1985.

Time Series Analysis of Climatic Variables in Peninsular Spain. Trends and Forecasting Models for Data between 20th and 21st Centuries

Time series of mean monthly temperature and total monthly precipitation are two of the climatic variables most easily obtained from weather station records. There are many studies analyzing historical series of these variables, particularly in the Spanish territory. In this study, the series of these two variables in 47 stations of the provincial capitals of mainland Spain were analyzed. The series cover time periods from the 1940s to 2013; the studies reviewed in mainland Spain go up to 2008. ARIMA models were used to represent their variation. In the preliminary phase of description and identification of the model, a study to detect possible trends in the series was carried out in an isolated manner. Significant trends were found in 15 of the temperature series, and there were trends in precipitation in only five of them. The results obtained for the trends are discussed with reference to those of other, more detailed studies in the different regions, confirming whether the same trend was maintained over time. With the ARIMA models obtained, 12-month predictions were made by measuring errors with the observed data. More than 50% of the series of both were modeled. Predictions with these models could be useful in different aspects of seasonal job planning, such as wildfires, pests and diseases, and agricultural crops.

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.

Glacial changes over the Himalayan Beas basin under global warming

We simulated and analyzed the glacier dynamics over the Beas basin (situated in the north-western Himalayas) for the present (1980–2015) and future climates (2006–2100) under RCP4.5 and RCP8.5 global warming scenarios. We first calibrated the Open Global Glacier Model over the study region and then conducted simulations for the present (forced by ERA-Interim) and future (forced by CMIP5 models) climates. For the present climate, the model simulations show that 50% of the total glacier volume (compared to 1980) is lost by 2011, with glacier area and volume showing a significantly decreasing trend, with higher fluctuations in the glacial area during recent decades. Future projections suggest 75% loss by 2040 ± 2.5 years and ~90% loss by 2094 ± 3.5 years under RCP4.5. Under RCP8.5, 75% loss is expected to occur by 2040 ± 3 years and ~90% loss by 2084 ± 8 years. Ensemble mean of the near-surface air temperature (both monthly mean and annual mean) shows a significantly increasing trend under both RCP4.5 and RCP8.5 for the entire 21st century. Ensemble mean of the total monthly precipitation shows no trend under RCP4.5, however, it shows a decreasing trend for months ODJFMA and an increasing trend for months JJ under RCP8.5. An increase in JJ precipitation does not increase glacier mass since this region does not receive snowfall during these months. Under RCP4.5, snowfall does not show any significant trend during NDJF, however, it shows a decreasing trend during October and March. Under RCP8.5, snowfall shows a significant decreasing trend for October through March. Overall, we find similar melting rates under RCP4.5 and RCP8.5 until ~2050, but the latter shows a higher rate afterward.

Trend Analysis of Precipitation Datas Observed for Many Years (1970-2019) in Niğde Center and Ulukisla District of Turkey

This research situated in Turkey's Anatolia region, Nigde center and Ulukışla district were conducted. Long annual precipitation data were subjected to statistical analysis on a monthly basis. Within the scope of the research, 50 years and 600 months long total monthly precipitation and maximum monthly precipitation values between 1970-2019 were examined. Spearman Rho test, Mann-Kendall Correlation test and Sen's slope methods were used in trend analysis. According to the total precipitation values; The highest total precipitation was observed in 2009 as 460,8 mm. The lowest total precipitation was calculated as 192,9 mm in 1973. Total rainfall average was calculated as 120,5 mm in spring and 98,4 mm in winter. The total rainfall average in the summer months was determined as 38,2 mm. In general, the average total precipitation for many years in the center of Niğde was found to be 325,9 mm. The highest total precipitation in Ulukışla district was determined as 428,2 mm in 1997. The lowest total precipitation for many years was 182,1 mm in 1989. The average total precipitation for long years was determined to be 318,5 mm. Total rainfall average was determined as 132,8 mm in spring and 80,7 mm in winter. Total rainfall average in the summer months was calculated as 42,4 mm. According to the trend analysis results; It is concluded that there is no significant trend in total precipitation values for many years in the center of Niğde and Ulukışla district. The maximum annual precipitation in the center of Niğde is calculated as 10,5 mm and in the Ulukışla district as 10,3 mm. The average maximum precipitation in spring was found to be 13,2 mm in the center of Niğde and 14 mm in the town of Ulukışla. The highest maximum precipitation was observed in 1978 in Niğde city center as 27 mm in winter. The highest maximum precipitation in Ulukışla district was found as 33,8 mm in 2003. According to the long annual maximum precipitation values; It has been determined that there is an increasing trend in Niğde center in the general average of winter, summer and all months. It is observed that there is an increasing trend in the Ulukışla district in the winter months and in general average

Innovative trend pivot analysis method (ITPAM): a case study for precipitation data of Susurluk Basin in Turkey

Throughout the geological history of the earth, there have been many climate changes due to natural and external factors. In the past, the changes in climate were caused by natural causes, and today it is primarily caused by human activities. Besides being different climate types, Turkey is among countries that will be affected by climate change induced by global warming. Climate changes in the regions will be affected differently and degrees due to the country’s surroundings by seas, fragmented topography and orographic features. Trend analysis methods are used in many areas such as on various engineering, agriculture, environmental and water resources, especially in climate change impact studies resulting from global warming. When data are analyzed with classical trend analysis methods, forward-looking predictions are generally made as low, medium, high, decreasing and increasing. However, risk classes showing changes between available data sets are not known. Innovative Trend Pivot Analysis Method (ITPAM) determines risk classes by establishing a relationship between data. Furthermore, in this method, increasing and decreasing trend regions are separated into five classes more clearly than classical/traditional trend methods. In this study, Susurluk Basin’s total monthly precipitation data (2006–2017) were analyzed by using ITPAM which the newest trend method. When arithmetic mean analysis results are examined, a significant change is observed between first data set and second data set at two stations (Bandirma and Uludag). When examined at other stations, it is observed that at least one month of almost every station is in 1st degree risk group. When standard deviation analysis results of each station are examined, a significant change is observed between first data set and second data set at many stations. Because while trend class of a point in developed IPTA graph is the medium degree, this point is in 1st risk class in the risk graph.

Intensification scenarios in projected precipitation using stochastic weather generators: a case study of central Oklahoma

Previous studies have investigated the character and distribution of intense precipitation events across the United States. Increasing trends in intense, daily precipitation events at heavy (90–< 95th percentile), very heavy (95–< 99th percentile), and extreme (≥ 99th percentile) thresholds have all been reported. However, no previous studies have investigated the potential application of stochastic weather generators in determining future, site-specific distributions of such intense precipitation occurrences. In this study, two scenarios of future changes in intense precipitation for Weatherford, Oklahoma were examined through the use of a specific weather generator, SYNTOR, and by examination of heavy, very heavy, and extreme precipitation categories. All precipitation events across the three categories were increased multiple times by eight different percentages ranging from 5% to 75%, while precipitation events within the three categories were simultaneously increased by 14%, 20%, and 30%, respectively. Projected changes in the occurrence and categorical thresholds of intense precipitation events, as well as total monthly and annual precipitation and wet-dry transition probabilities, were assessed. The findings of this study show that projected increases in intense precipitation ranging from 5% to 40% are plausible and within the margin of error, based on the application of the two intensification scenarios to the synthetically generated weather data. Overall, the precipitation intensification scenarios markedly impacted estimates of intense precipitation, as well as average annual and monthly precipitation totals, but did not markedly impact the temporal distribution of precipitation annually or across seasons, nor the transition probabilities of projected precipitation between wet and dry days. Precipitation intensification scenarios can ultimately benefit in simulating erosion, runoff, and crop productivity responses to future precipitation distributions in agricultural watersheds of the Southern Great Plains as well as other locations.

Performance comparison of filtering methods on modelling and forecasting the total precipitation amount: a case study for Muğla in Turkey

Abstract Condensed water vapor in the atmosphere is observed as precipitation whenever moist air rises sufficiently enough to produce saturation, condensation, and the growth of precipitation particles. It is hard to measure the amount and concentration of total precipitation over time due to the changes in the amount of precipitation and the variability of climate. As a result of these, the modelling and forecasting of precipitation amount is challenging. For this reason, this study compares forecasting performances of different methods on monthly precipitation series with covariates including the temperature, relative humidity, and cloudiness of Muğla region, Turkey. To accomplish this, the performance of multiple linear regression, the state space model (SSM) via Kalman Filter, a hybrid model integrating the logistic regression and SSM models, the seasonal autoregressive integrated moving average (SARIMA), exponential smoothing with state space model (ETS), exponential smoothing state space model with Box-Cox transformation-ARMA errors-trend and seasonal components (TBATS), feed-forward neural network (NNETAR) and Prophet models are all compared. This comparison has yet to be undertaken in the literature. The empirical findings overwhelmingly support the SSM when modelling and forecasting the monthly total precipitation amount of the Muğla region, encouraging the time-varying coefficients extensions of the precipitation model.

Observed changes in precipitation during recent warming: The Czech Republic, 1961–2019

AbstractThis paper presents a comprehensive spatiotemporal analysis of precipitation patterns over the territory of the Czech Republic for the 1961–2019 period. Monthly, seasonal and annual series of precipitation totals and numbers of precipitation days were calculated for four altitudinal groups and the entire Czech Republic, based upon the daily precipitation totals recorded by 531 rain‐gauge stations run by the Czech Hydrometeorological Institute. Analysis of series of monthly, seasonal and annual precipitation totals revealed relatively stable fluctuations, while linear trends remained largely insignificant. However, wavelet analysis indicated significant interannual variability on a timescale of 4–8‐years in seasonal and annual series. The minimum in annual variation tended to appear in February (also in January and, at higher altitudes in particular, in April) with the maximum favouring July (but also June and August). The relative proportions of annual totals taken up by winter precipitation increased with altitude, while the proportions for summer precipitation decreased with altitude. Linear trends in the numbers of precipitation days exhibited the most pronounced decreases from April to June, reflected in negative precipitation trends in April–June and positive in July–September. The results obtained are also presented in the broader (central) European context.

Influence of climate change on water partitioning in agricultural watersheds: Examples from Sweden

Future climate change is predicted to increase precipitation volume in Sweden, and also to modify precipitation patterns and produce more intense rainfall events. This study examined the impact of such changes in three small Swedish watersheds dominated by agricultural land. The Soil and Water Assessment Tool (SWAT) was used to investigate the relationship between changes in precipitation (monthly and daily) and monthly water partitioning between runoff, soil water content, and evapotranspiration. A climate ensemble produced from Representative Concentration Pathway (RCP) scenarios 4.5 and 8.5 and five different global climate models, regionalized by the Swedish regional climate model RCA4, was used to feed the SWAT model. The results showed similar outcomes for the three sites, with an increase in total monthly precipitation often associated with an increase in high daily rainfall events. Increased intensity of rainfall, particularly events > 15 mm/day, was associated with an increase in runoff, but not in soil water content. In the few cases where monthly precipitation decreased, soil water content also decreased. The main impact on all hydrological components of the system appeared to be more significant during the middle of the cropping season (May–August) than at the beginning (April) or end (September). These conclusions emerge despite the climate model ensemble underestimating the heaviest daily rainfall events. Overall, this study showed that the projected increasing trend in seasonal rainfall in southern Sweden would not result in more soil water being available for crop production.

Climate Signals in Earlywood, Latewood and Tree-Ring Width Chronologies of Sessile Oak (Quercus petraea (Matt.) Liebl.) from Majdanpek, North–Eastern Serbia

In this article, the dependence of the sessile oak (Quercus petraea (Matt.) Liebl.) radial growth (tree-ring, earlywood, and latewood widths) on climate (the mean monthly temperature and precipitation totals) was studied in the Majdanpek area, north-eastern Serbia. The growth response of the oak trees to the prevailing climate conditions was dendroecologically investigated, by applying the correlation and response function, as well as by pointer years analysis. The site chronology covered 159 years (1855-2013). We found that latewood and total tree-ring width contain the imprinted positive response to the amount of precipitation in summer months (June and July) of the current growing season. The earlywood width showed no direct dependence on climate data, but it was significantly affected by the previous-year latewood width. Moreover, 40 % of the variation in the latewood width is explained by the earlywood variation in the same season. The temperature was not found to have any significant effect on the growth of oak at the study site. The use of pointer years, determined by applying several calculation procedures, has highlighted previous results, indicating that the precipitation in summer months was the deciding climate factor leading to the occurrence of the years with exceptionally wide or narrow tree-rings and latewood. To enhance our understanding of the response of the sessile oak growth at south-oriented sites with a shallow soil profile to precipitation and temperature variations, and expand the current database and knowledge, future studies should be undertaken.