Air Temperature Time Series Research Articles

Long-term variability of ice regime characteristics in river mouth areas of the western coast of the White Sea on the background of climatic changes

The paper presents results of the comprehensive study of the long-term variability of the main elements of the ice regime for the period 1955–2020 in mouth areas of the rivers Gridina, Kuzema, Pongoma, Kem, Shuya, Nizhny Vyg, Suma, Nyukhcha and Maloshuika flowing into the White Sea on its western coast (Republic of Karelia, Arkhangelsk region). The average daily air temperatureы in sites of the State observation network of Roshydromet – marine hydrometeorological coastal stations Gridino, Kem and Onega - were used as initial information for this work. Information about the main characteristics of the ice regime (times of coming of characteristic dates of ice phenomena) of the rivers was presented by data from nine hydrological posts. The mean values of characteristics of the ice regime (average statistical dates and durations of the ice regime phases) of the rivers under consideration (except those regulated by the cascade of hydroelectric power stations). were calculated. Statistical analysis of time series of the mean air temperatures obtained for the cold season on the western coast of the White Sea and the duration of periods with ice phenomena on the above rivers made possible to reveal two quasi-homogeneous periods with a turning point in 1990. This analysis shows that the temperature background in years 1991–2020 is significantly higher (by 1.4 °C) than the similar one in 1956–1990, and at the same time the average duration of ice phenomena decreased to almost two weeks (shorter by 11 days). The regression analysis allowed finding the presence of a statistically significant negative trend in the duration of ice phenomena for the whole period (1956–2020), which is −3.3 days/10 years. At the same time, the shortening of the duration of the periods with ice phenomena is due equally to both a shift in the time of the beginning of the stable ice formations towards later dates (1.6 days/10 years), and the earlier dates of the ice phenomena end (−1.7 days/10 years).

Correlation dimension for temperature and other micrometeorological variables in different ground cover from six biomes of the Amazon basin

Correlation dimension () for embedding dimensions, varying between 3 to 11, were estimated for a 1- or 2-month time series of air temperature and other micrometeorological variables, such as energy fluxes, solar radiation and concentration, in six ecosystems in Amazon and the central region of Brazil. Dc remained within the 1.5 - 3.0 range, for variables locally coupled to ecosystems, and outside the range for variables not locally coupled, such as precipitation and wind velocity. Results indicate that the feature of the variables does not have an important stochastic component. There is a small amount of data available concerning estimates of correlation dimension () of micrometeorological variables. The measurement of for several variables from six Brazilian biomes indicates that this parameter is sensitive to environmental conditions as the rainfall. The obtained results also indicates that the natural processes in the ecosystems remain in a non-chaotic regime. In certain circumstances, as in conditions of high humidity, natural ecosystems may stay inside a limit which is close but lower than chaos. Moreover, an important stochastic component was not observed.

Advanced Forecasting of Drought Zones in Canada Using Deep Learning and CMIP6 Projections

This study addresses the critical issue of drought zoning in Canada using advanced deep learning techniques. Drought, exacerbated by climate change, significantly affects ecosystems, agriculture, and water resources. Canadian Drought Monitor (CDM) data provided by the Canadian government and ERA5-Land daily data were utilized to generate a comprehensive time series of mean monthly precipitation and air temperature for 199 sample locations in Canada from 1979 to 2023. These data were processed in the Google Earth Engine (GEE) environment and used to develop a Convolutional Neural Network (CNN) model to estimate CDM values, thereby filling gaps in historical drought data. The CanESM5 climate model, as assessed in the IPCC Sixth Assessment Report, was employed under four climate change scenarios to predict future drought conditions. Our CNN model forecasts CDM values up to 2100, enabling accurate drought zoning. The results reveal significant trends in temperature changes, indicating areas most vulnerable to future droughts, while precipitation shows a slow increasing trend. Our analysis indicates that under extreme climate scenarios, certain regions may experience a significant increase in the frequency and severity of droughts, necessitating proactive planning and mitigation strategies. These findings are critical for policymakers and stakeholders in designing effective drought management and adaptation programs.

Brief communication: Significant biases in ERA5 output for the McMurdo Dry Valleys region, Antarctica

Abstract. The ERA5 climate reanalysis dataset plays an important role in applications such as monitoring and modeling climate system changes in polar regions, so the calibration of the reanalysis to ground observations is of great relevance. Here, we compare the 2 m air temperature time series of the ERA5 reanalysis and the near-surface bias-corrected reanalysis to the near-ground air temperature measured at 17 automatic weather stations (AWSs) in the McMurdo Dry Valleys, Antarctica. We find that the reanalysis data have biases that change with the season of the year and do not clearly correlate with elevation. Our results show that future work should rely on secondary observations to calibrate when using the ERA5 reanalysis in polar regions.

Применение фазового анализа для прогнозирования многолетней динамики температуры атмосферного воздуха

Long-term average values of atmospheric air temperature (annual, autumn, winter, spring and summer) are one of the key points used to make scientifically valid decisions on the adaptation of crops to changing climate and environmental conditions. Despite their widespread use for the analysis and forecasting of various meteorological parameters of the atmosphere, the nature of such series has not yet been sufficiently studied. This determines the relevance of detailed analysis and forecasting of time series of atmospheric air temperature. At the same time, they are considered to be formed under the influence of linear and cyclic factors. To identify cyclical components, one of the most powerful and adequate time series analysis tools called phase analysis is used. In this study, a phase portrait of the time series of average air temperatures for the autumn period at the Nalchik weather station from 1961 to 2022 is constructed for preforecast analysis. To select the most significant periods of quasi-cycles, the theory of fuzzy logic is used. On its basis an algorithm for the formation of a fuzzy set of quasi-cycle lengths is implemented. Further prediction of the values of the time series in the retrospective section is carried out by the least square method. Further forecasting of time series values in the retrospective section is carried out using the least squares method. According to the results of the conducted research, it was found that the proposed model makes it possible to predict the values of average autumn air temperatures with high accuracy (5%). In the time series of average autumn air temperatures, a cycle characteristic of the 11-year cycle of solar activity and its phases is traced. All the quality criteria of the proposed model meet the requirements for the quality and adequacy of forecast models. This model can be applied to the analysis and forecasting of the average values of atmospheric air temperatures for the spring, summer and winter periods, as well as the average annual temperatures in general. The research has shown that the time series characterizing the temperature regime of atmospheric air, which is very complex in nature, can be predicted using phase analysis.

Assessment of Typical Meteorological Year Data in Photovoltaic Geographical Information System 5.2, Based on Reanalysis and Ground Station Data from 147 European Weather Stations

The Photovoltaic Geographical Information System (PVGIS) is a web application that provides free access to solar radiation and temperature data, typical meteorological year (TMY) data, and to photovoltaic performance assessment tools for any place in most parts of the world. The PVGIS was originally developed over 20 years ago, and since then, it has been under continuous development. At present, there are two versions of the PVGIS online—the older version 5.1 and the newest version 5.2. PVGIS 5.2 includes substantial improvements compared to the previous version, e.g., the update of the underlying data sets both in terms of quality, resolution, and geographical coverage and the extension of the time period used. This paper focuses on comparing the TMYs (and more specifically the TMY time series of air temperature), coming from both PVGIS 5.1 and 5.2, with the TMY produced on the basis of ground station meteorological data and with the ground station data itself. The results show that whereas overall the errors and biases for most locations are within the expected range (mean stationRMSE 4.27), these differences increase in places with complicated topography, e.g., in the Alps (maximum stationRMSE 9.50).

Quality-controlled meteorological datasets from SIGMA automatic weather stations in northwest Greenland, 2012–2020

Abstract. In situ meteorological data are essential to better understand ongoing environmental changes in the Arctic. Here, we present a dataset of quality-controlled meteorological observations from two automatic weather stations in northwest Greenland from July 2012 to the end of August 2020. The stations were installed in the accumulation area on the Greenland Ice Sheet (SIGMA-A site, 1490 m a.s.l.) and near the equilibrium line of the Qaanaaq Ice Cap (SIGMA-B site, 944 m a.s.l.). We describe the two-step sequence of quality-controlling procedures that we used to create increasingly reliable datasets by masking erroneous data records. Those datasets are archived in the Arctic Data archive System (ADS) (SIGMA-A – https://doi.org/10.17592/001.2022041303, Nishimura et al., 2023f; SIGMA-B – https://doi.org/10.17592/001.2022041306, Nishimura et al., 2023c). We analyzed the resulting 2012–2020 time series of air temperature, surface height, and surface albedo and histograms of longwave radiation (a proxy of cloudiness). We found that surface height increased, and no significant albedo decline in summer was observed at the SIGMA-A site. In contrast, high air temperatures and frequent clear-sky conditions in the summers of 2015, 2019, and 2020 at the SIGMA-B site caused significant albedo and surface lowering. Therefore, it appears that these weather condition differences led to the apparent surface height decrease at the SIGMA-B site but not at the SIGMA-A site. We anticipate that this quality-controlling method and these datasets will aid in climate studies of northwest Greenland and will contribute to the advancement of broader polar climate studies.

Methodology to measure the energy flux captured by a submerged U-OWC by using temperature sensors

The estimation of the power captured by a wave energy converters (WEC) device, needs to calculate the plant efficiency. In general, it is necessary to measure both the pressure and the discharge fluctuations of the fluid motion inside the plant. Unfortunately, gauges for the direct measurement of the velocity are bulky and provide punctual measures and, especially on converters having a U-duct, the presence of the velocity sensor produces a relevant disturbance on the motion field. To overcome this issue, an alternative method to evaluate the captured energy flux, using the pressure fluctuation and the air temperature inside the plenum, was proposed by [1] and [2]. However, no information about the accuracy of the temperature sensors and consequently about the errors in estimation of the energy flux were provided.
 In this work, following the procedure described by [1] and [2], we have analysed the influence of the time response of the temperature sensor in evaluating the variation of the air volume inside the chamber and, consequently the energy captured by the plant. To this aim, the submerged U-OWC, tested directly at sea in [1], has been simulated numerically. The aim of the numerical experiment is having the actual estimation of the air temperature inside the plenum and trough it, the captured energy flux. The computational domain is constituted by a wave-flume, with a piston-type wavemaker, placed at the left extremity and a submerged breakwater embedded a U-OWC plant, in the middle. The numerical 2D unsteady simulation is based on the Eulerian approach, using the commercial code Ansys Fluent v17.0, Academic Version.
 Starting from the knowledge of the pressure fluctuation at the upper opening of the vertical duct and, of both pressure and temperature variations of the air in the plenum, we have evaluated the energy flux absorbed by the plant and we have calibrated the mathematical model used in [1] and [2], using as input the time series of the pressure fluctuations at the upper opening of the vertical duct, and the variation of both temperature and pressure of the air inside the chamber. Then, using the time series of the actual air temperature, we have simulated the input of several first order temperature sensors characterized by different time constant t, and we have analysed the percentage differences in term of energy flux as a function of t.
 We have observed that the measurements of the temperature inside the plenum are strongly affected by time constant of the sensor, which produce large errors in the evaluation of the captured energy flux.
 Finally, we have proposed a method for conditioning the measure of the air temperature, obtaining an excellent estimation of the energy flux.
 
 Boccotti, P. (2003) "On a new wave energy absorber"Ocean Engineering 30(9), pp. 1191-1200.
 Arena, F., Filianoti, P. (2007),"Small-scale field experiment on a submerged breakwater for absorbing wave energy", Journal of Waterway, Port, Coastal and Ocean Engineering, 2007, 133(2), pp. 161–167.

Temporal variability of precipitation isotopes in Damascus, Syria — implications for regional climate change

Records of stable isotopes (2H and 18O), deuterium excess (d-excess) and tritium (3H) values in precipitation (P) during 1990–2022, together with long-term time series (1919–2022) of air temperature (T) and P-amount values at the Damascus station, were analysed to explore the seasonal and annual variability patterns of those parameters in P and assess the vulnerability to climate change in this area. Variation of the annual average air T values over the period 1919–1969 shows an increase gradient of ≈ + 0.1 °C/decade. However, a remarkable much higher gradient (+ 0.64 °C/decade) is calculated for the period 1990–2022. The average P-amount value calculated for the last three decades (≈185 mm) was lower by ≈28 mm, compared to the value (≈213 mm), relative to the reference period (1919–1969). This significant decline in the annual P-amount value by ≈11–13%, accompanied by an annual heating of 0.2–0.6 °C/decade in the annual air T, is likely the result of the climate change affecting this area. The linear relationships between annual average δ18O and annual average δ2H values versus time over the period 1990–2019 indicate increased gradients in both stable isotopes (≈0.3–0.5‰ and ≈1.1–3.2‰ per decade for δ18O and δ2H, respectively), accompanied with a decrease gradient of ≈0.9–1.1‰ per decade in d-excess values. Variability of annual 3H concentrations towards low levels (< 6 TU) during the later years strongly suggests the return back towards the cosmogenic production of this radioisotope in the upper atmosphere. Information gained from this work would offer new insights to improve the understanding of the temporal variability of P isotopes and assess the risks associated with climate change on the natural water resources in the Eastern Mediterranean region.

Recovering of gaps in the time series of CO2 concentration and air temperature using methods of mathematical statistics

The article is devoted to the problem of recovering gaps in the data series of experimental long-term continuous high-frequency observations of carbon dioxide concentration and air temperature. The study was carried out on the example of the results of observations of an automatic ecological and climatic station located in a tropical monsoon forest on the territory of south Vietnam (Dong Nai Nature Reserve). Omissions in the series of observations, as a rule, are random and are caused by technical malfunctions of the instrument base. Correctly recovered series of observations allow us to estimate the temporal variability of the observed parameters on different time scales. Within the framework of this study, options for recovering the continuity of time series based on the methods of mathematical statistics - autoregression (ARIMA) and the method of linear prediction were considered. A comparative analysis of the accuracy of restoring omissions by various methods is given.

Homogeneity and change point detection of hydroclimatic variables: A case study of the Ghba River Subbasin, Ethiopia

In most studies on hydroclimatic variability and trend, the notion of change point detection analysis of time series data has not been considered. Understanding the system is crucial for managing water resources sustainably in the future since it denotes a change in the status quo. If this happened, it is difficult to distinguish the time series data’s rising or falling tendencies in various areas when we look at the trend analysis alone. This study’s primary goal was to describe, quantify, and confirm the homogeneity and change point detection of hydroclimatic variables, including mean annual, seasonal, and monthly rainfall, air temperature, and streamflow. The method was employed using the four-homogeneity test, i.e., Pettitt’s test, Buishand’s test, standard normal homogeneity test, and von Neumann ratio test at 5% significance level. In order to choose the homogenous stations, the test outputs were divided into three categories: “useful”, “doubtful”, and “suspect”. The results showed that most of the stations for annual rainfall and air temperature were homogenous. It is found that 68.8% and 56.2% of the air temperature and rainfall stations respectively, were classified as useful. Whereas, the streamflow stations were classified 100% as useful. Overall, the change point detection analyses timings were found at monthly, seasonal, and annual time scales. In the rainfall time series, no annual change points were detected. In the air temperature time series except at Edagahamus station, all stations experienced an increasing change point while the streamflow time series experienced a decreasing change point except at Agulai and Genfel hydro stations. While alterations in streamflow time series without a noticeable change in rainfall time series recommend the change is caused by variables besides rainfall. Most probably the observed abrupt alterations in streamflow could result from alterations in catchment characteristics like the subbasin’s land use and cover. These research findings offered important details on the homogeneity and change point detection of the research area’s air temperature, rainfall, and streamflow necessary for the planers, decision-makers, hydrologists, and engineers for a better water allocation strategy, impact assessment and trend analyses.

Data quality control and homogenization of daily precipitation and air temperature (mean, max and min) time series of Ukraine

AbstractIn this paper we present the results of quality control and homogenization procedures applied to long time series of daily atmospheric precipitation sums (Rr) and daily mean (Tm), maximum (Tx) and minimum (Tn) air temperature collected in Ukraine. The daily data from 178 meteorological stations covering the period of 1946–2020 were analysed. In order to perform a thorough quality assurance check, we used the R package INQC, while the Climatol homogenization software was used to detect and remove breaks from the time series. The INQC quality assurance tests revealed a relatively small number of erroneous records (around 0.01% for each variable) and suspicious values (up to 0.09%). The application of Climatol resulted in 195, 296, 355 and 359 break points, detected for Rr, Tm, Tx and Tn, respectively. These quantities coincide roughly with the results of the HOMER homogenization procedure applied to monthly time series for the same stations and almost the same period (performed in the previous works of the authors). To verify the homogenization results, statistical comparison of the raw and homogenized time series was performed. The verification demonstrated that the quality control and homogenization procedures detected and removed errors and breaks very well, and air temperature and precipitation fields after the homogenization are more self‐consistent compared to the original raw data.

A Methodology for Air Temperature Extrema Characterization Pertinent to Improving the Accuracy of Climatological Analyses

The suggested methodology for the characterization of temperature extrema presents a multistep preprocessing procedure intended to derive extrema time series of correctly identified and thermally defined daily air temperature extrema pairs. The underlying conceptual framework for this approach was developed in response to the existing gaps in the current state of daily extrema identification and the development of extrema-based synthetic air temperature time series. A code consisting of a series of algorithms was developed to establish four-parameter criteria for a more accurate representation of daily variability that allows easy replication of temperature distribution based on the correct characterization of daily temperature patterns. The first preprocessing step consists of subjecting the high-frequency temperature time series to a theoretical diurnal observing window that imposes latitudinally and seasonally crafted limits for the individual identification of daily minima and maxima. The following pre-processing step involves the supplementation of air temperature extrema with the information on the occurrence of extrema timing deemed as vital information for the reconstruction of the temperature time series. The subsequent step involves the application of an innovative temperature pattern recognition algorithm that identifies physically homogeneous air temperature populations based on the information obtained in previous steps. The last step involves the use of a metric for the assessment of extrema temperature and timing parameters’ susceptibility to climate change. The application of the presented procedure to high-frequency temperature data yields two strains of physically homogeneous extrema time series with the preserved characteristics of the overall temperature variability. In the present form, individual elements of this methodology are applicable for correcting historical sampling and air temperature averaging biases, improving the reproducibility of daily air temperature variation, and enhancing the performance of temperature index formulae based on daily temperature extrema. The objective of this analysis is the eventual implementation of the presented methodology into the practice of systematic temperature extrema identification and preprocessing of temperature time series for the configuration of physically homogeneous air temperature subpopulations.

Trend Analysis and Fluctuations of Winter Temperature over Saudi Arabia

The aim of this work is to study the variability of winter (monthly mean of DJF) air temperatures in Saudi Arabia. The study of the coefficient of variation (CV) of winter air temperature time series shows that the CV is a function of latitude, decreasing generally gradually from north to south. The highest values of CV during the winter season are mainly because of the migrating extratropical cyclones (Mediterranean cyclones) from west to east, and active subtropical jet, as well as the polar jet. The trend analysis illustrates that all stations have positive trends for the wintertime series. The study of decadal fluctuations in the behavior of winter temperature shows that the period from 1982 to 2010 is characterized in general by negative trend values in most northern stations of Saudi Arabia. In the middle of Saudi Arabia, negative trend values also appear but for the period 1983 to 2003. The southern and middle stations are distinguished by a positive trend during the period from 2003 to 2018.

Development and validation of a pavement temperature profile prediction model in a mechanistic-empirical design framework

An accurate temperature prediction tool is an important part of any mechanistic-empirical (M-E) pavement design and performance prediction method. In this paper, a one-dimensional finite control volume (FCV) model is introduced that predicts the temperature within a pavement structure as a function of time and depth. The main input data required for the model are continuous time series of air temperature for conductive heat transfer, solar radiation for radiative heat transfer, and wind speed for convective heat transfer. The heat balance equation for each control volume of the FCV model is solved using an implicit scheme. To validate the numerical model, comparisons were made to measured temperature data from four test sections in Sweden located in regions with different climatic conditions. A good agreement was obtained between the calculated and measured temperature values within the asphalt layer, and temperature in the granular layers with the values of the coefficient of determination R2 ranging from 0.866 to 0.979. The model is therefore suitable to be implemented as a pavement temperature prediction tool in M-E design.

Application of Advanced Optimized Soft Computing Models for Atmospheric Variable Forecasting

Precise Air temperature modeling is crucial for a sustainable environment. In this study, a novel binary optimized machine learning model, the random vector functional link (RVFL) with the integration of Moth Flame Optimization Algorithm (MFO) and Water Cycle Optimization Algorithm (WCA) is examined to estimate the monthly and daily temperature time series of Rajshahi Climatic station in Bangladesh. Various combinations of temperature and precipitation were used to predict the temperature time series. The prediction ability of the novel binary optimized machine learning model (RVFL-WCAMFO) is compared with the single optimized machine learning models (RVFL-WCA and RVFL-MFO) and the standalone machine learning model (RVFL). Root mean square errors (RMSE), the mean absolute error (MAE), the Nash–Sutcliffe efficiency (NSE), and the determination coefficient (R2) statistical indexes were utilized to access the prediction ability of the selected models. The proposed binary optimized machine learning model (RVFL-WCAMFO) outperformed the other single optimized and standalone machine learning models in prediction of air temperature time series on both scales, i.e., daily and monthly scale. Cross-validation technique was applied to determine the best testing dataset and it was found that the M3 dataset provided more accurate results for the monthly scale, whereas the M1 dataset outperformed the other two datasets on the daily scale. On the monthly scale, periodicity input was also added to see the effect on prediction accuracy. It was found that periodicity input improved the prediction accuracy of the models. It was also found that precipitation-based inputs did not provided very accurate results in comparison to temperature-based inputs. The outcomes of the study recommend the use of RVFL-WCAMFO in air temperature modeling.

Recent Trends of Mean Maximum and Minimum Air Temperatures in Bangladesh

The annual, seasonal, and monthly maximum and minimum air temperature time series for 34 stations in Bangladesh have been examined during the period 1981– 2020 in this study. Three statistical tests were used in the analysis namely Mann–Kendall, Sen’s slope estimator, and linear regression, respectively. 47% of stations experienced significantly rising maximum air temperature trends during the pre-monsoon, 100% during the monsoon, 74% during the post-monsoon, and 24% during the winter season, respectively. Similar findings have been made about minimum air temperatures during the pre-monsoon, monsoon, post-monsoon, and winter seasons in 41, 91, 41, and 35% of stations over Bangladesh. According to the linear regression technique, the significant increasing trends of the maximum air temperatures at Patuakhali and Mongla stations are 0.83°C/decade and 0.82°C/decade in February and October, respectively. Furthermore, at Sandwip and M. Court, the significant increasing minimum air temperature trends in July and March are 0.26°C/decade and 0.66°C/decade, respectively. The highest monthly maximum and minimum air temperature time series value falls (decreases) were seen in January over the stations at Mongla (-0.58°C/decade) and Sandwip (-0.63°C/decade), respectively. This information will be useful in developing adaptation plans to mitigate the harmful effects of climate change. Vulnerability assessments, disaster management, enhanced structure design, institutional reform, and antiextreme climate engineering are some of the viable climate change adaptation approaches in Bangladesh due to rising temperatures.

GeoTh: An Experimental Laboratory Set-Up for the Measurement of the Thermal Conductivity of Granular Materials

GeoTh is a new, simple, efficient, flexible, low-cost experimental laboratory apparatus. These features make it an excellent technological tool for measuring the thermal conductivity of granular materials, e.g., soils, sand, silt, clay or artificial composites. In particular, a configuration based on the one-dimensional heat conduction in steady-state regime was designed, built, and assembled to determine the thermal conductivity of the samples. In addition, we developed two user-friendly codes; the first for acquisition of the technical data (time series of air temperature; samples temperatures; and heat power); and the second relative to the elaboration of collected data and the calculation of the physical and thermophysical parameters of each analyzed sample (porosity, bulk density, % of water saturation, thermal conductivity, thermal diffusivity). The developed apparatus allows us to temporarily carry out measurements from one to six samples either in ambient conditions or at constant temperature. The robustness of the system has been tested by analyzing and measuring numerous materials in different conditions. We also performed several tests by varying the water content (between 0% and 100% water saturation) for sandy and silty-sand samples to calibrate and test the robustness of the system as well as for verifying the repeatability of the analytical data. Finally, the obtained thermal conductivity values are compared with other dataset proposed in the literature, showing a good fit.

A Review of Extreme Air Temperature Analysis in Croatia

A historical review of extreme air temperature analysis in Croatia is presented. Two capital works on the subject were published in the 1970s by the Croatian Meteorological and Hydrological Service (DHMZ) and Faculty of Science University of Zagreb (PMF-Zagreb), respectively. The first is a monography on extreme value theory or extreme value analysis (EVA) with an application on more than a century-long time series of annual minima air temperature for Zagreb Grič weather station (Croatia) for the period 1862–1969. It is just a case study, with a lot of instructions regarding how to estimate the generalized extreme value (GEV) distribution parameters. The second is a master’s thesis with an application of the EVA on maxima air temperature time series for 41 weather stations from Croatia for the period 1950–1969. The shortness of the time series of the presented data caused instability in the estimation of GEV distribution parameters in transition areas from continental to maritime climate, but in general, the results are acceptable after a reduction of the 1950–1969 time series data on a ‘normal climate period’ 1910–1969. Both works were pioneering for that time in the South-Eastern Europe scale. A routine application of GEV distribution on the extreme air temperature (both minimum and maximum) for ten representative weather stations from Croatia is represented in Climate atlas of Croatia for the period 1961–1990, published by DHMZ in 2008. Theoretically estimated results fit well with empirical data. A review of long-term “warm” and “cold” indices of extreme air temperature for 41 weather stations from Croatia for the period 1951–2010 is represented in the Sixth National Communication Report of Croatia under the UNFCCC published by the Ministry for Environment and Nature Protection of Croatia (MZOIP) in 2014, showing a positive trend of “warm” and a negative trend of “cold” indices during the period 1951–2010 which tackled the non-stationarity of extreme air temperature time series. That topic of non-stationarity is more extensively considered using the results of a series of scientific papers published in the international journals which conducted a study of extreme air temperature of the wider Western Europe territory, including Croatia and other countries close to Croatia. Some authors of these papers stated that the GEV distribution parameters have to be considered as a function of time rather than fixed in time using covariates like North Atlantic Oscillation (NAO), coherent atmospheric blocking regions, linear trends in data caused by global warming and others covariates. The EVA results, connected with the global climate warming, could contribute to the national Natural Disaster Risk Reduction (NDRR) efforts.

A data-driven high spatial resolution model of biomass accumulation and crop yield: Application to a fragmented desert-oasis agroecosystem

Information on crop yield is important for food security, in particular under the conditions of climate change and growing population worldwide. We developed a new fully distributed, high spatial resolution, model of biomass accumulation and crop yield applicable to a highly heterogeneous desert-oasis agroecosystem. The bulk of required input data is obtained by retrieving pixel-wise biogeophysical variables from a suite of very diverse satellite data. Both temperature and water stress conditions at field-scale are given full consideration, while the model was designed to strike a balance between model applicability and satisfactory characterization of the heterogeneous desert-oasis system to estimate field-scale yield. The development of this model relies on three main innovations. First, the start and end of the growing season were estimated for each pixel by calibrating the high spatial and temporal resolution observations of Normalized Difference Vegetation Index (NDVI) by Sentinal-2 (S2) MSI (Multi-Spectral Instrument) against limited local phenological information. Second, to monitor crop water stress, account taken of irrigation, a process-based water and energy balance model was applied to estimate the actual evapotranspiration (ET). This requires knowledge of soil water availability, which is characterized by downscaling the ASCAT (Advanced SCATterrometer) soil moisture data product. To capture the dominant features of the eco-hydrological conditions in the desert and oasis agroecosystem, ET was further downscaled from the 1 km resolution. Third, likewise the water stress indicator, the air temperature stress indicator was mapped after characterizing the thermal contrast and heterogeneity of the desert-oasis system, by generating time series of air temperature at 1 km spatial resolution using the MODIS (Moderate Resolution Imaging Spectroradiometer) Land Surface Temperature (LST) data product. In the temporal dimension, gaps were mitigated by applying time series analysis techniques to reconstruct cloud-free time series of LST, NDVI, fAPAR and albedo. These innovations add up to a high resolution characterization of crop response to the geospatial variability of weather and climate forcing in the desert-oasis agroecosystem. The model was applied to the dominant crops, i.e., spring wheat, maize, sunflower, and melon, in the oases of the Shiyang River Basin (northwestern China) characterized by a rather fragmented land use. The high resolution of pixel-wise ecohydrological parameters, i.e., crop phenology, temperature stress and water stress factors successfully reflect differences of crops with different phenology and location in the oases. The relative errors for wheat and maize yields compared to the census data are less than 5% at district level. At the county level, the relative errors of wheat yields of Liangzhou, Minqin, Gulang, Jinchuan, and Yongchang equal to 0.87%, 24.2%, 9.7%, 12.5%, and 7.2%. For maize, the dominant crop, the error on estimated yields was less than 5%, except in Gulang. The relative error on estimated yield for sunflower was less than 10% compared to agricultural census data. The relative error on estimated melon yield was 16%. This performance highlights the applicability of the model to estimate field-scale yields in agroecosystems characterized by fragmented land use.