Average Monthly Precipitation Research Articles

Estimation and analysis of the effective rainfall In Tabuk area – Saudi Arabia

this study examined the effective rainfall in Tabuk area by applying the Lang rainfall factor, De Martonne Index and the (UNEP) arid index. The study methodology was based on a statistical tests using the Normality test (Shapiro-Wilk), the Homogeneity test (Leven test), the ANOVA (LSD test) of the monthly mean of rainfall, the daily maximum rainfall and the total number of rainfall days. The multiple comparison (Tehmane’s Test) had been applied between the studied stations. The results of the Shapiro-Wilk test shows that the distribution of monthly rainfall averages follows normal distribution in all stations except stations Tabuk and Al Bad’ and that the maximum daily rainfall distribution at Duba, Shuw?q, Al Kurr stations is also normal distribution. The Leven test results showed that the significance level was greater than 0.05 and the Leven Test was greater than 0.05 for the average monthly precipitation, for the daily maximum rainfall and for the number of rain days and it indicates the homogeneity of the rainfall variances in the studied stations.The ANOVA analysis of the averages and maximum daily rainfall, shows that the significance level is greater than 0.05 and the LSD test is greater than 0.05 indicating that the differences are not significant. While the LSD was greater than 0.05 for the number of rain days, indicating that the differences between the number of rain days and their distribution at the studied stations are statistically significant differences, and therefore there is no homogeneity in their differences. The results of the Tamhane’s Test of multiple comparison confirmed that the mean level of monthly mean rainfall and daily maximum rainfall is greater than 0.05 indicates that the differences between the monthly rain averages of and the daily maximum rainfall amounts are not significant for 22.2% of total comparisons.The effective rainfall estimate of Lang’s rainfall index shows that the threshold of effective mean rainfall is between 1.3 and 27.4 mm and that the threshold of maximum daily rainfall is 154.1 mm at the station (Al Bad’). Also, the effective rainfall estimate by the DeMartonne index shows that the threshold of average effective rainfall is between 1.2 and 15.2 mm and that the threshold of maximum daily rainfall is 80.4 mm at the station (Al Bad’). Therefore, the effective rainfall estimate by the UNEP index shows that the threshold of average effective rainfall is between 1.8 and 30.3 mm and that the threshold of maximum daily rainfall is 130.5 mm at the station (Al Bad’). Finally, the effective rainfall estimate by the difference between the rainfall and Pan Class“A” Evaporation shows that the threshold of average effective rainfall is between 1.2 and 25.8 mm and that the threshold of maximum daily rainfall is 137.1 mm at the station (Al Bad’).- Key Words:Effective rainfall average, Effective maximum daily rainfall , Number of rainy days, Statistical tests, Lang rainfall factor, DeMartonne index, UNEP arid index, Tabuk area, Saudi Arabia.

An integrated simulation-optimization framework to optimize the reservoir operation adapted to climate change scenarios

In order to optimize the operation rule curve of the Shahrchay reservoir in the north-west of Iran under climate change, a new integrated simulation-optimization framework under different climate change scenarios was introduced in the current study. The proposed framework contains several steps: First, the more accurate general circulation models (GCMs) were chosen among several available GCMs and the predictors screening based on decision tree model (M5) was applied to select the most important predictors among enormous number of potential large-scale climate variables of GCMs. Then, the artificial neural networks (ANNs) were trained by the observed temperature and precipitation time series and the selected dominant predictors to downscale the precipitation and temperature parameters of GCMs for the base period (1951–2000) over the case area and the future monthly predictions of temperature and precipitation were implemented by the trained ANNs under A1B, B1, RCP4.5, and RCP8.5 climate change scenarios to assess the future changes of precipitation and temperature between 2020 and 2060. Thereafter, the estimated precipitation values were imposed to the hybrid Wavelet-M5 model to simulate inflow (runoff) to the Shahrchay reservoir and finally, genetic algorithm (GA) was used in order to optimize the operation rule curves of the reservoir system using the predicted average monthly precipitation, evaporation, inflow and considering water supply for agricultural and municipal targets and minimization of total squared deficiencies for future. The Wavelet-M5 model could result in a reliable performance in the one-step-ahead runoff prediction as the obtained correlation coefficient (CC) for training and verifying data sets were 98% and 97.7%, respectively. The results showed that the average long-term annual runoff volume may be decreased between 0.08% and 2.27% in the future with regard to the base period, and the simulation results for present and future conditions indicated a decrease in water availability. Also, the estimated optimal rule curves for the reservoir showed a different shape for all scenarios employed in this study.

Regionalização de vazões mínimas da bacia hidrográfica do Rio Mearim no estado do Maranhão

Métodos de regionalização vem sendo utilizados para estimar as vazões em regiões que apresentam carência ou mesmo inexistência de dados hidrológicos. Dessa forma, o presente trabalho teve como objetivo desenvolver um modelo de regionalização de vazões mínimas de referência Q90 para a bacia hidrográfica do Rio Meari, no estado do Maranhão. Foram utilizados dados consistidos de 28 estações fluviométricas e de 28 estações pluviométricas. As variáveis área de drenagem, precipitação anual média e precipitação mensal média foram empregadas como características físico-climáticas da bacia em estudo. Os modelos regionais foram desenvolvidos por intermédio do Método Tradicional. Parâmetros de validação, como o coeficiente de Pearson (r), o coeficiente de determinação (R2), o coeficiente de Nash-Sutcliffe (NASH), a raiz do erro quadrático médio (RMSE), o erro médio absoluto (BIAS) e o índice de desempenho “c”, foram empregados para verificar o ajuste e o desempenho dos modelos. A equação regional linear de vazão mínima Q90, explicada a partir da área de drenagem e precipitação anual média, foi o modelo que mostrou o melhor desempenho no ajuste dos valores observados e simulados, classificando-se como bom, uma vez que foram encontrados valores de R2 e NASH superiores a 0,60, do mesmo modo que RMSE inferiores a 2, BIAS igual a 0,01 e índice de desempenho (c) superior a 0,65.

Investigation of Climatological Components on Runoff Modeling using SWAT

It was planned most hydraulic projects, for instance barriers, it was defined the overflow of the rivers. If the river absences any position to measure the yield, the hydraulic models can be utilized to estimate it. SWAT is widely-used computerized mockups. It was required to feed such influential climatological information as precipitation, temperature, wind speed, solar radiation and relative humidity, in addition to, watershed information with the curve number and roughness constant to compute the watershed runoff. Watershed contain few climate positions and it was dangerous that the registered data in a position was not characterized the entire watershed. Consequently, the amount of the runoff estimation fault wants to be defined. This research considers the sensitivity of the runoff estimation for rivers, Using the SWAT prototypical, based on differences in such climatological components as precipitation, solar radiation, wind, humidity and temperature. The obtained consequences specify that with a 30.46% decline in the average monthly precipitation, sunshine, relative humidity, wind and temperature, it was set ermined 64.73% decline, 115.14% rise, 45.99% reduce, 126.58% rise and 40.15% rise in modeled runoff, individually. The wind speed and the solar radiation are the most sensitive and temperature is the least sensitive parameters in the runoff estimation.

Projecting the Impact of Climate Change on Temperature, Precipitation, and Discharge in the Bago River Basin

Climate change affects both the temperature and precipitation, leading to changes in river runoff. The Bago River basin is one of the most important agricultural regions in the Ayeyarwady Delta of Myanmar, and this paper aims to evaluate the impact of climate change on it. Linear scaling was used as the bias-correction method for ten general circulation models (GCMs) participating in the fifth phase of the Coupled Model Intercomparison Project. Future climate scenarios are predicted for three 27-year periods: the near future (2020–2046), middle future (2047–2073), and far future (2074–2100) with a baseline period of (1981–2005) under two Representative Concentration Pathway (RCP) scenarios: RCP4.5 and RCP8.5 of the IPCC Assessment Report 5 (AR5). The Hydrologic Engineering Center-Hydrologic Modeling System model is used to predict future discharge changes for the Bago River considering future average precipitation for all three future periods. Among the GCMs used to simulate meteorological data in the Ayeyarwady Delta zone, the Model for Interdisciplinary Research on Climate-Earth System is the most suitable. It predicts that average monthly precipitation will fluctuate and that average annual precipitation will increase. Both average monthly and annual temperatures are expected to increase at the end of the 21st century under RCP4.5 and RCP8.5 scenarios. The simulation shows that the Bago River discharge will increase for all three future periods under both scenarios.

Forecasting severe grape downy mildew attacks using machine learning.

Grape downy mildew (GDM) is a major disease of grapevine that has an impact on both the yields of the vines and the quality of the harvested fruits. The disease is currently controlled by repetitive fungicide treatments throughout the season, especially in the Bordeaux vineyards where the average number of fungicide treatments against GDM was equal to 10.1 in 2013. Reducing the number of treatments is a major issue from both an environmental and a public health point of view. One solution would be to identify vineyards that are likely to be heavily attacked in spring and then apply fungicidal treatments only to these situations. In this perspective, we use here a dataset including 9 years of GDM observations to develop and compare several generalized linear models and machine learning algorithms predicting the probability of high incidence and severity in the Bordeaux region. The algorithms tested use the date of disease onset and/or average monthly temperatures and precipitation as input variables. The accuracy of the tested models and algorithms is assessed by year-by-year cross validation. LASSO, random forest and gradient boosting algorithms show better performance than generalized linear models. The date of onset of the disease has a greater influence on the accuracy of forecasts than weather inputs and, among weather inputs, precipitation has a greater influence than temperature. The best performing algorithm was selected to evaluate the impact of contrasted climate scenarios on GDM risk levels. Results show that risk of GDM at bunch closure decreases with reduced rainfall and increased temperatures in April-May. Our results also show that the use of fungicide treatment decision rules that take into account local characteristics would reduce the number of treatments against GDM in the Bordeaux vineyards compared to current practices by at least 50%.

Estimating Current and Future Rainfall Erosivity in Greece Using Regional Climate Models and Spatial Quantile Regression Forests

A future variation of precipitation characteristics, due to climate change, will affect the ability of rainfall to precipitate soil loss. In this paper, the monthly and annual values of rainfall erosivity (R) in Greece are calculated, for the historical period 1971–2000, using precipitation records that suffer from a significant volume of missing values. In order to overcome the data limitations, an intermediate step is applied using the calculation of monthly erosivity density, which is more robust to the presence of missing values. Spatial Quantile Regression Forests, a data driven algorithm that imitates kriging without the need of strict statistical assumptions, was utilized and validated, in order to create maps of R and its uncertainty using error propagation. The monthly average precipitation for the historical period 1971–2000 estimated by five (5) Global Circulation Models-Regional Climatic Models were validated against observed values and the one with the best performance was used to estimate projected changes of R in Greece for the future time period 2011–2100 and two different greenhouse gases concentration scenarios. The main findings of this study are: (a) the mean annual R in Greece is 1039 MJ·mm/ha/h/y, with a range between 405.1 and 3160.2 MJ·mm/ha/h/y. The highest values are calculated at the mountain range of Pindos and the lowest at central Greece; (b) the monthly R maps adhere to the spatiotemporal characteristics of precipitation depth and intensities over the country; (c) the projected R values, as an average over Greece, follow the projected changes of precipitation of climatic models, but not in a spatially homogenous way.

Evaluating the Adaptation of Chinese Torreya Plantations to Climate Change

Studying the capacity of some plant species to adapt to climate change is essential for ecological research and agricultural policy development. Chinese Torreya (Torreya grandis ‘Merrillii’) has been an important crop tree in subtropical China for over a thousand years. It is necessary to characterize its adaptation to climate change. In this study, the average monthly temperature and precipitation from 1901 to 2017 in the six regions with Chinese Torreya plantations at different provinces were analyzed. The results indicated that the average annual air temperature across these regions had increased by about 2.0 °C, but no general trend in the annual precipitation and incidence of drought was found. The annual air temperature that Chinese Torreya plantations had experienced was 12.96–18.23 °C; the highest and the lowest average monthly air temperatures were 30.1 °C and −0.8 °C, respectively. The lowest and the highest annual precipitation were 874.56 mm and 2501.88 mm, respectively. Chinese Torreya trees endured a severe drought period in the 1920s. The monthly air temperature at Zhuji, which is the central production region, showed a significant correlation with the air temperature in the other five regions. The monthly precipitation in Hunan and Guizhou had no significant correlation with that of Zhuji. Chinese Torreya plantations have been grown in the regions with a similar climate distance index of air temperatures but different precipitation. This tree has a high capacity to adapt to climate change based on the climate dynamics across its range. This approach may provide a way to evaluate climate adaptation in other tree species. These results may provide helpful information for the development of Chinese Torreya plantations.

Effects of Tillage Methods and Vegetation Coverage on Reducing Runoff and Soil Erosion

To study the characteristics of runoff and soil erosion of natural rainfall conditions, five standard runoff plots were set up in our experiment, and different tillage methods and vegetation coverage types were set up. The 58-month monthly precipitation data and 44-month runoff plot observation data from 2013 to 2017 were analysed. The results show that: 1) The monthly precipitation fluctuates significantly, ranging from 13mm to 683.5mm. The precipitation is unevenly distributed over the year. The largest average monthly precipitation is in June and the smallest is in January. Rainfall is mainly concentrated in the spring and summer. The precipitation from March to June accounts for 58.0% of the annual rainfall. 2) There is a positive correlation between runoff depth and precipitation in each runoff plot (R2= 0. 5101~0. 6676, Sig.<0.01); 3) There is also a positive correlation between soil loss and precipitation (R2=0. 424~0. 558, Sig.<0.01); 4) The amount of soil loss and the runoff depth increase with increasing rainfall. The runoff plot without any vegetation cover or farming measures increase the most. While the one with horizontal steps and shrubs, or a combination of arbor and grass increase the slowest, indicating that they have the best effect of reducing runoff and soil loss.

Türkiye’nin İller Bazında Kuraklık Değerlendirmesi

İklim değişikliğinin ülkemizde ne gibi sonuçlar doğurduğunun ve gelecekte ne gibi sonuçları olacağının bilinmesi bütün sektörlerde olduğu gibi tarım sektöründe de son derece önemlidir. Kurak dönemlerin belirlenmesi; tarımsal uygulamalar, sulama zamanının planlanması ile çölleşme ve erozyonla mücadele gibi konularda yapılacak çalışmalarda oldukça yararlı olacaktır. Ayrıca bir bölgede kuraklık periyodunun belirlenmesi o bölgede yaşanacak olası kuraklık riskine karşı önceden tedbir alınması açısından yarar sağlayacaktır. Bu amaçla yapılan bu çalışmada Türkiye geneli 81 ildeki meteoroloji istasyonlarına ait ortalama olarak aylık toplam yağış miktarları ve aylık ortalama sıcaklık verileri kullanılarak Bagnouls-Gaussen kuraklık indisleri hesaplanmış, illere göre kurak dönemler belirlenmiş ve il bazında Türkiye kuraklık haritası hazırlanmıştır. Elde edilen verilerle son 15 yıldaki değişim belirlenmeye çalışılmıştır. Buna göre son 15 yılda ülkemizde nemli alan %0.01 ve kurak alanlar %1.79 oranında azalma gösterirken yarı nemli alanlarda %1.25 ve çok kurak alanlarda %0.56 oranında artış olmuştur. Kuzeyden güneye doğru kuraklık artış gösterirken; Ege, Akdeniz ve Doğu Anadolu Bölgelerinin bir bölümü ile Güneydoğu Anadolu Bölgesi çok kurak, Trakya’nın bir bölümü ile Karadeniz kıyıları ise yarı nemli sınıfta yer almıştır. Nemli sınıf sadece Karadeniz kıyılarında yer alırken iç bölgeler kurak özellik göstermiştir.

Numerical investigation of the effects of causeway opening configurations on horizontal currents of Lake Urmia

Unlike Lake Urmia, its two neighbor lakes with similar climate and size have experienced stabilized condition over the past decades. Actually, greater impact of anthropological activities, compared to climatological parameters, on hydrological and ecological systems of Lake Urmia has been stated in the literature. In this paper, the impact of a recently constructed causeway on this lake is studied using environmental impact assessment approach. This study evaluates different alternatives for future re-development plans and clearly proves the importance of preliminary environmental studies during the design development phase of infrastructure projects. The depth-averaged flow module of NASIR finite volume solver was utilized to simulate the horizontal flow patterns in Lake Urmia. Considering hydrological and meteorological parameters, different scenarios regarding location and number of causeway openings (current opening location, opening in the middle of causeway, and 2 openings) were compared to the condition before causeway construction. Discharge values of 21 streams, average monthly evaporation of 5.93 mm/day and average monthly precipitation of 0.4 m/day were considered as input data of the developed model. The simulation results showed that horizontal flow circulations occur mostly on the southern part of the lake and the causeway opening variations generally affect the form of these circulations. In the scenario with causeway and only one opening in the middle, flow circulations present the most comparability to the lake’s flow circulation pattern before causeway construction, whereas the scenario with 2 openings has the least similarities.

Stima degli effetti delle variazioni climatiche attese sul bilancio idrico dell'area del distretto vulcanico dei Colli Albani

In the present work it is proposed, in light of the data obtained from recent available climate projections, a rereading of the water balance of the area of the volcanic district of the Alban Hills (Lazio) and neighboring sectors, including the city of Rome and the delta of the Tiber River. The water balance of this area has been the subject of extensive studies in the past (Capelli et al., 2005; Mazza et al., 2009,Gazzetti, 2011) which allowed the calibration of the inverse water balance for the period 1996-2000. These budget analyzes formed the basis for the Safeguarding rules for aquifers issued by the Authority of the Regional Basins of Lazio and the Basin Authority of the Tiber River. The monthly average precipitation and temperature values of two decades, 1991-2001 and 2046-2055, have been extrapolated from the selected climate projection model. To these data, statisticaldownscaling methods were applied to obtain monthly precipitation and temperature maps, with a spatial resolution of 250 m x 250 m, used in the estimates of the effects on the water balance of the expected climatic variation.

Impact of solar activity on precipitation in the United States

In this paper we analyze the relationship between the sunspot numbers and the average monthly precipitation measured at meteorological stations in the US. The results indicate that there is a significant correlation between the solar activity and the monthly average precipitation data for selected months and time delays. In order to confirm the relationship, for the precipitation data we check the forecasting abilities of a popular time series model with and without additional information on the sun activity. Namely, first we fit the autoregressive moving average (ARMA) time series model to the US precipitation data and calculate one-step ahead forecasts. Next, we repeat the same steps with the ARMA model with exogenous input (ARMAX) which is the sunspot number data. We show that the forecast errors are essentially lower for the ARMAX model for months and time delays found in the correlation analysis.

Assessment of Groundwater Recharge, Evaporation, and Runoff in the Drava Basin in Hungary with the WetSpass Model

The assessment of spatial and temporal distribution of groundwater recharge is required as an input to develop the regional groundwater model in the Drava flood plain for more accurate simulations of different management scenarios. WetSpass-M, a GIS-based spatially-distributed water balance model, was implemented to assess monthly, seasonal, and the annual averages of groundwater recharge, surface runoff and actual evapotranspiration in the Drava basin, Hungary for the period between 2000–2018. The basic relevant input-data for the Wetspass-M model is prepared in grid-maps using the tool ARCGIS tool. It comprises monthly climatological recordings (e.g., rainfall, temperature, wind speed), distributed land cover, soil map, groundwater depth, topography, and slope. The long-term temporal and spatial average monthly precipitation (58 mm) is distributed as 29% (17 mm) surface runoff, 27% (16 mm) actual evapotranspiration, and 44% (25 mm) groundwater recharge. The mean annual groundwater recharge, actual evapotranspiration, and surface runoff were 307, 190, and 199 mm, respectively. The findings of the WetSpass-M model are intended to support integrated groundwater modeling. The analysis of simulation results shows that WetSpass-M model works properly to simulate hydrological water budget components in the Drava basin. Moreover, a better understanding of the simulated long-term average spatial distribution about water balance components is useful for managing and planning the available water resources in the Drava basin.

Landslide Hazard Zonation and Slope Instability Assessment using Optical and InSAR Data: A Case Study from Gidole Town and its Surrounding Areas, Southern Ethiopia

The present study was carried out in and around Gidole Town in Southern Ethiopia which is about 580km from Addis Ababa. The main objective of the study was to prepare a landslide hazard zonation (LHZ) map by using Bivariat statistical information value model and to assess the slope instability in the area by using InSAR approach. For LHZ six causative factors such as; slope, land-use/land-cover, slope-material, elevation, aspect, and Normalized Difference Vegetation Index (NDVI) were considered. For the sub-classes of these causative factors weights were obtained from the information value model. The results showed that very high hazard zones and high hazard zones covers 6.63% (14.12 km2) and 15.36% (32.72 km2) of the area, respectively. Whereas, moderate hazard, low hazard and very low hazard zones covers 7.47% (15.9 km2), 34.2% (72.85 km2) and 36.34% (77.4 km2) of the area, respectively. Further, validation of the LHZ map showed that 92.3% of the past landslides fall in very high hazard and high hazard zones. Thus, the hazard zones delineated in the present study has reasonably validated with the past landslide data and the potential zones depicted in the prepared LHZ map can be applied for the safe planning of the area. Further, the results of the PS-InSAR processing indicates that the average downward displacement in the study area is gradually increasing from 15.3mm/yr (2014) to −19.2 mm/yr (2018) and the rate of displacement in general increases with increase in the average monthly precipitation at all selected persistence scattered points.

The use of hierarchical cluster analysis for grouping atmospheric precipitation in Poland

The aim of this study is to present the application of statistical methods to assess the possibility of grouping precipitation according to their elevation above sea level and precipitation levels in the temperate climate of Poland. The country was divided into regions with similar levels of precipitation using cluster analysis by Ward’s method. The study was performed with meteorological data on average monthly precipitation of 53 meteorological stations from 1981 to 2010. The selection of stations was dictated by the need to consider the variability in amounts of annual precipitation throughout the country. The data was used to calculate average annual precipitation total as well as average precipitation total in the summer half-year (from May to October) and the winter half-year (from November to April) for Poland, from 1981 to 2010. Statistical method was performed and six clusters were generated, for the elevation of the stations and average annual precipitation as well as average precipitation for hydrological half-years. The average annual precipitation for the clusters ranged from 530 mm (cluster 5) to 820 mm (cluster 2). The average precipitation for the winter hydrological half-year ranged from 190.1 mm (cluster 1) to 288.8 mm (cluster 5). The average precipitation for the summer hydrological half-year ranged from 326.3 mm (cluster 5) to 605 mm (cluster 2). The conclusions from the analysis carried out in the study are that: (1) grouping by means of Ward’s method can be used to distinguish homogeneous areas with the same levels of precipitation; (2) both precipitation and the elevation, at which meteorological stations are located, are the basis for distinguishing clusters in Ward’s method.

Temporal Variations in Tree-Ring Growth Response to Climate of European Larch (Larix Decidua Mill.): An Example from Sudetes, Poland

The temporal stability of climate-growth relationship is a basic assumption and requirement of climate reconstruction using tree rings. However, many recent studies showed change in tree-ring growth response to climate for the last decades. The temporal stability of relationship mentioned above can depend on the local factors, e.g. slope aspect. In presented study the tree-ring growth response to climate of larch from Polish part of Sudetes was analysed over time. The aim was to investigate its temporal variability, existence of trends and influence of local factors. The relation between tree-ring growth of larches from six sites located at comparable altitudes (about 700-750 m.a.s.l.) and climatic factors that mainly control the tree-ring growth of larches in the study area was analysed over time. The Pearson correlation between tree-ring width site chronologies and climatic data for the study area (the average monthly air temperature and total monthly precipitation series from high-resolution gridded dataset CRU TS v.4.01) was calculated in 40-years moving windows for the period 1901-2010. The results showed variations in the tree-ring growth response to climate over the analysed time period. Although in general, similar trends were observed for all sites, some differences in trends and magnitudes among sites were also spotted. Interesting is, that the response of sites became more consistent and similar for the last decades. The discussion of the received results with respect to currently observed climatic trends in the region and local factors that can explain the differences in response between particular sites is presented.

Using of GIS Software for Mapping the Climatic Data Obtained from Internet Network: Iraq Case Study

Nowadays, the long-term climatic data supplied by the famous internet sites, plays an important role in analyzing and mapping of various climatic features (temperature, precipitation, solar radiation … etc.). Precipitation is one of the important elements in agriculture. It is a major field in climatological studies. Studying precipitation is important to identify precipitation characteristics; temporal and spatial variability. In the present study, extract for each (0.5*0.5) degree grid cell of the monthly precipitation data for the year 2016 except the summer months (June - July - August) using the GPCC Visualizer website is applied for Iraq country. Kriging interpolation method within the environment of ArcGIS10.3 program, the average monthly and seasonal precipitation maps are drawn. The study revealed the possibility to use the more popular website sources of historical climate datasets to compensate the lack of climate data available from the meteorological stations in Iraq, which is often difficult to obtain recent data..

Disparity in Performance of the Czech Construction Sector: Evidence from the Markov-Switching Model

Activities in the construction sector are assumed to be influenced by inflow of mortgage funding in the private housing sector and public finances targeted at large infrastructure projects, apart from climate variables. In this study, we modeled seasonal time series representing monthly output in the Czech construction sector in CZK mil. during 2000:1 through 2016 : 12 (T = 204) adjusted for calendar variations and seasonal movements via TRAMO‑SEATS and then transformed to natural logarithms of gross returns. A Markov‑Switching model with two states, no intercept, average monthly temperature, average monthly precipitation and parameters of first‑order autoregression process was specified and estimated by the Expectation‑Maximization. In State 1 of regular performance, the log‑differenced returns were significantly and positively influenced by precipitation levels, but not by ambient outdoor temperature. In State 2 of non‑standard operation of the construction sector, the transformed series was unaffected by precipitation levels, but instead by ambient outdoor temperatures. First‑order autocorrelation dependency in both regimes was established. Changes in legal and macroeconomic environment pertinent to tax law amendments affecting VAT or corporate tax, country’s accession to EU or large construction project deadlines were shown to induce nonstandard regime in the construction sector (State 2). The model classified 91 % observations in the first state, while only 9 % data belonged to the State 2. Transition probability matrix indicates that change from model State 1 to State 2 is difficult to attain. At the same time, once State 2 was established, it tends to persist or change to State 1 with near equal probability. Ability of the Markov‑Switching model to identify both states is reasonably good.

Climate change evidence in Brazil from Köppen's climate annual types frequency

This article proposes to use the Köppen classification to evidence climate change in Brazil. To do so, the average monthly temperature and precipitation data were computed for a set of 208 stations, representative of the climatic diversity of Brazil for the period from 1964 to 2015. The originality of our approach is not only to look at the shift between the averages of two references periods but also to observe how each year can be classified using Köppen's classification; thus, we defined “annual climate types” (ACT) and established the frequency of ACTs for each station and each period of 26 years: 1964–1989 and 1990–2015. The statistical and cartographic treatment of this information makes it possible to define the limits of the climates of Brazil and look at its evolution showing the diversity of regional situations in Brazil. The results show that 35 stations (17%) changed from the average Köppen's type, with significant regression of wet tropical types (Af and Am) and temperate types (C). On the other hand, the tropical (Aw), arid and semi‐arid (B) types progress between the two periods showing significant modifications of the climatic limits in Brazil: extension of aridity in the northeast of Brazil and increasing extinction of tropical types upon temperate climates in the south of the country and upon rainy types in the south of the Amazon. These results are consistent with other studies and illustrate the potential for climate change and variability monitoring using a simple classification method.