Series Of Meteorological Data Research Articles

Spatiotemporal change characteristics of NDVI and response to climate factors in the Jixi Wetland, Eastern China.

Exploring the spatiotemporal variation characteristics of vegetation in the confluent area of water systems in western Jinan and its response mechanism to climatic factors is of great significance for the scientific evaluation of the benefits of the water system connectivity project and eco-environmental protection and can provide a reference for ecotourism development in the Jixi wetland park. Based on the Landsat series of images and meteorological data, this study used ENVI to interpret the normalized difference vegetation index (NDVI) of the confluent area from 2010 to 2021, and the spatiotemporal change characteristics and trends of NDVI were quantitatively analyzed. The response of the growing-season NDVI (GSN) to climate factors and its time-lag effect were explored. The results showed that the overall change in the interannual NDVI in the confluent area from 2010 to 2021 was stable. The GSN in the confluent area was significantly positively correlated with precipitation, average temperature, and relative humidity in 37.64%, 25.52%, and 20.87% of the area respectively, and significantly negatively correlated with sunshine hours in 15.32% of the area. There was a time-lag effect on the response of the GSN to climate factors; the response to precipitation and sunshine hours lagged by 1month, and the response to average temperature and relative humidity was longer.

SENSITIVITY OF THE KOLYMA RIVER RUNOFF TO MODERN CLIMATE CHANGE

The Kolyma River is a large river in the continuous permafrost zone. Its basin is subject to significant climatic changes. An information-modeling complex ECOMAG is employed for the study of water regime features and dynamics. ECOMAG calculates daily water flow rates at specified points based on meteorological data with a one-day resolution.This study analyzes the actual dynamics of water runoff and runoff-forming factors during the period from 1979 to 2013. A physical-mathematical model is adapted for the Kolyma River basin. Furthermore, the study examines the sensitivity of water regime characteristics to changing climate parameters. Calibration and verification demonstrated the ECOMAG’s ability to accurately reproduce observed water discharges for two points on the Kolyma River and the satisfactory performance on its tributary, the Bolshoy Anyui River. The ECOMAG model was for the first time employed for the Kolyma River basin, thus allowing the investigation of climate-related runoff changes. The preliminary statistical analysis revealed that if the average hydrological characteristics in the Kolyma River basin from 2000 to 2013 are compared with those from 1979 to 1999 a 5,1% rise of annual river runoff is evident for the Kolymskoye river section 283 km upstream the Kolyma River mouth (from 99,4 km3 in 1979-1999 up to 104,5 km3 in 2000-2013). The ECOMAG model also effectively captures changes in the annual water runoff and its intra-annual distribution.A rise in the mean annual air temperature by 1,3°C is characteristic of the modern time period. According to weather station data, the air temperature in March, April, November, and December increased by more than 2,5°C. The total annual precipitation increased by 8,3%, with September and March experiencing the most pronounced growth.To gain a more comprehensive understanding of the mechanisms governing the Kolyma River’s response to climate changes, the sensitivity of water runoff characteristics to air temperature and precipitation changes was analyzed using the ECOMAG model to generate artificial meteorological data series. The study demonstrates that water discharge is more sensitive to a 5% increase in precipitation than to a 1ºС rise in air temperature.

The Applicability of the Drought Index and Analysis of Spatiotemporal Evolution Mechanisms of Drought in the Poyang Lake Basin

Poyang Lake, the largest freshwater lake in China, is an important regional water resource and a landmark ecosystem. In recent years, it has experienced a period of prolonged drought. Using appropriate drought indices to describe the drought characteristics of the Poyang Lake Basin (PLB) is of great practical significance in the face of severe drought situations. This article explores the applicability of four drought indices (including the precipitation anomaly index (PJP), standardized precipitation index (SPI), China Z-index (CPZI), and standardized precipitation evapotranspiration index (SPEI)) based on historical facts. A systematic study was conducted on the spatiotemporal evolution patterns of meteorological drought in the PLB based on the optimal drought index. The results show that SPI is more suitable for the description of drought characteristics in the PLB. Meteorological droughts occur frequently in the summer and autumn in the PLB, with the frequency of mild drought being 17.29% and 16.88%, respectively. The impact range of severe drought or worse reached 22.19% and 28.33% of the entire basin, respectively. The probability of drought occurrence in the PLB shows an increasing trend in spring, while in most areas, it shows a decreasing trend in other seasons, with only a slight increase in the upper reaches of the Ganjiang River (UGR). One of the important factors influencing drought in the PLB is atmospheric circulation. The abnormal variation of the Western Pacific Subtropical High was one of the key factors contributing to the severe drought in the PLB in 2022. This study is based on a long-term series of meteorological data and selects the drought index for the PLB. It describes the spatiotemporal distribution characteristics and evolution patterns of drought and investigates the developmental path and influencing factors of drought in typical years. This study provides a reliable scientific basis for similar watershed water resource management.

Using a Combination of High-Frequency Coastal Radar Dataset and Satellite Imagery to Study the Patterns Involved in the Coastal Countercurrent Events in the Gulf of Cadiz

This study exploits the combination of High-Frequency Coastal Radar (HFR) information with satellite-derived observations to characterize the patterns involved in the coastal countercurrents (CCCs) events in the Gulf of Cadiz (GoC), which is situated in the SW of the Iberian Peninsula. The westward alongshore currents are observed throughout the year, but the main drivers necessary to develop this flow and its extension in both parts of the basin are not fully clear. In order to identify the main physical processes (both local and remote) that induce the development of these countercurrents and to evaluate the connection of the circulation patterns between the eastern and the western part of the GoC, we make use of several data sources available for the region. First of all, a land-based system of HFR antennas located at four different sites of the GoC provides the velocity field of the surface circulation of the basin. To achieve a significant characterization of the CCCs in the Gulf, the dataset analyzed is processed by means of a series of operations, including the Empirical Orthogonal Functions (EOFs) analysis used to identify spatial and temporal variability of the flow, a low-pass filter used to isolate the sub-inertial signal of the current and temporal interpolation to fill in the missing values. Secondly, given the known importance of the zonal component of the local winds combined with the variations in the mean pressure at sea level over the Western Mediterranean during these events, time series of meteorological data are processed and correlated with the current velocity series via a statistical analysis. Finally, sea surface temperature fields and chlorophyll-a distribution patterns are used as tracers to obtain information on the extension of the countercurrents where HFR data are missing in four cases studied during the year 2017. The conducted analysis revealed the consistent occurrence of westward coastal currents throughout the year, driven in the most intense cases by a combination of the zonal component of the local wind and atmospheric pressure fluctuations over the Western Mediterranean Sea. During those events, CCCs reached the Portuguese side of the Gulf and facilitated the advection of biological material and warmer waters.

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

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

Boruta extra tree-bidirectional long short-term memory model development for Pan evaporation forecasting: Investigation of arid climate condition

In this study, two deep learning approaches, bidirectional long short-term memory (BiLSTM) and long short-term memory (LSTM), were used along with adaptive boosting and general regression neural network to forecast multi-step-ahead pan evaporation in two arid climate stations in Iran (Ahvaz and Yazd). Lagged time series of meteorological data and pan evaporation data were input to the machine learning models. Two feature selection methods, i.e., the Boruta extra tree and XGBoost, were used to select significant inputs to reduce the number of inputs and model complexity. Different statistical metrics were used to investigate the model performance. The results demonstrated that Boruta-extra-tree-based models were more accurate than XGBoost-based models. Compared with the machine learning techniques, the combination of Boruta extra tree and BiLSTM enabled more accurate one-day-ahead forecasting of pan evaporation for both sites (Root Mean Square Error (RMSE) = 1.6857, for the Ahvaz station, and RMSE = 1.3996 for the Yazd station). The proposed model was used to forecast up to 30 days ahead of pan evaporation in both stations. The results showed that the Boruta-BiLSTM model could accurately forecast the pan evaporation for up to 30 days in both stations.

Climate risk zoning for wheat crops in the southeastern region of Brazil.

Wheat (Triticum aestivum L.) is the second most consumed food in the world. One way to meet this demand is the expansion of wheat cultivation to the Brazilian Cerrado in the southeastern region. However, one of the major limitations is that there are few studies related to wheat climate risk zoning. Thus, this study aimed to determine the agroclimatic zoning of wheat by estimating the water needs satisfaction index (ISNA) in the southeastern region of Brazil. For this purpose, a 60-year historical series of meteorological data was used to calculate the potential evapotranspiration, crop evapotranspiration, and climatological water balance values. To define the agroclimatic zones of wheat and sowing date, the ISNA method was used. The data were analyzed using descriptive statistics to determine the variations. To obtain the agroclimatic zoning of wheat, the geostatistical method of kriging interpolation was used. The regions with the highest rainfall are the south of Minas Gerais and the coast of São Paulo. The sowing period directly impacts the development of the crop, the available water capacity and the ISNA values indicated the spring and summer had better cultivation conditions, and the best window for wheat cultivation is concentrated in the fall due to the limitation of biotic factors. In terms of altitude (>700 m), Minas Gerais has 39.4% of the area suitable for wheat cultivation. Thus, climatic variations within and between the states of the southeastern region should be considered for the positioning of wheat cultivars in these regions to obtain the maximum yield. © 2023 Society of Chemical Industry.

Calibration of alternative equations to estimate the reference evapotranspiration in Nova Venécia, Espírito Santo, Brazil

The estimation of the reference evapotranspiration is fundamental in defining irrigation projects. However, an estimation using the standard equation requires climate variables that are difficult to measure and are not very accessible. Thus, the objective of this study was to calibrate and validate alternative methods to estimate evapotranspiration that use simple variables and to compare performance with the standard Penman-Monteith method for the municipality of Nova Venécia, Espírito Santo, Brazil. For this, a 12-year time series (2008-2019) of meteorological data from the Instituto Nacional de Meteorología was used. The standard FAO-56 Penman–Monteith method was used to evaluate alternative methods: Hargreaves and Samani, Benevides and Lopes, Linacre, Hamon and Camargo. Method performance was analyzed by correlation coefficient, Willmott index, root mean square of normalized error, and performance index. Calibration improved the statistical indices, increasing the performance of the Hargreaves and Samani, Benevides and Lopes, and Linacre methods to “very good” in the rainy season and to “intermediate” in the dry season. They were superior to the Hamon and Camargo methods, which continued to show “tolerable” to “very poor” performance in both periods.

Short-Term Net Load Forecasting for Regions with Distributed Photovoltaic Systems Based on Feature Reconstruction

Short-term load forecasting is the guarantee for the safe, stable, and economical operation of power systems. Deep learning methods have been proven effective in obtaining accurate forecasting results. However, in recent years, the large-scale integration of distributed photovoltaic systems (DPVS) has caused changes in load curve fluctuations. Current deep learning models generally train with historical load series and load-related meteorological data series as input features, which limits the model’s ability to recognize the load fluctuations caused by DPVS. In order to further improve the accuracy of load forecasting models, this paper proposes an input feature reconstruction method based on the maximum information coefficient (MIC). Firstly, the load curves with DPVS are classified by Gaussian mixture model (GMM) clustering. Then, considering the coupling relationship between the load and input features at different times, the load data and input features are reordered. Finally, the MIC between different features and loads at different times is calculated to select the relevant features at those different times and construct new input features. The case analysis shows that the feature reconstruction strategy proposed in this paper effectively improves the prediction performance of deep neural networks.

ГРОЗЫ И ЛЕСНЫЕ ПОЖАРЫ В СОВРЕМЕННЫХ КЛИМАТИЧЕСКИХ УСЛОВИЯХ СРЕДНЕЙ СИБИРИ

The paper presents the results of an analysis of the spatial distribution of forest fires and their causes in the forest areas of Central Siberia in 1987-2020. An increase in the share of fires from lightning discharges has been recorded (R2 = 0.78, p < 0.05). A spatiotemporal extrapolation of long-term meteorological data series has been performed. An increase in thunderstorm activity by at least 30% relative to the average annual norm for the end of the 20th century has been revealed for the entire territory of Central Siberia. It is shown that if current trends are maintained until 2100, the number of lightning discharges during the fire season (March-September) will determine up to 10-40% increase in the number of fires caused by lightning. Thunderstorm activity can be one of the significant factors in the toughening of fire regimes in Central Siberia.

Thermal simulations on periglacial soils of the Central Andes, Argentina

AbstractRock glaciers are the most common landforms of the Andean periglacial landscape in the Central Andes of San Juan, Argentina. Their active layer is gravelly with a typical openwork structure. The upper parts of these rock glaciers are coarse‐grained Turbic Cryosols, with no vegetation cover. Since March 2018, coarse soils in the active layer of the “Candidato” rock glacier have been monitored (31.9°S, 70.18°W). Three trenches, 4,000 m a.s.l. and down to a depth of 90 cm, were equipped with sensors to measure soil temperature and volumetric water content. We also measured particle size distributions and calculated thermal properties from soil samples. The mean thermal conductivities for unfrozen and frozen soils were 0.69 and 0.54 W m−1 K−1, respectively, and the mean thermal diffusivities were 2.05 × 10−7 and 1.64 × 10–7 m2 s−1, respectively. Analysis of the seasonal thermal and hydrological fluxes in the active layer is challenging, as the physical properties change cyclically, thus controlling processes such as water storage, infiltration and seepage, heat balance, mechanical behavior, and kinematic response. We used the Coupled Heat and Mass Transfer Model for the Soil–Plant–Atmosphere System (COUP) numerical computerized model, performing a site‐specific calibration, to simulate soil temperatures, active layer thicknesses, and seasonal freezing–thawing depths. The model implemented, in combination with a reanalysis of the meteorological data series, performed very well to reproduce the data from thermo‐sensors placed in the ground. This proposed methodology is viable for areas with limited instrumentation or low accessibility. The “Candidato” rock glacier can be used as a pilot model for thermal modeling purposes on rhyolitic rock glaciers in the region.

Interannual rainfall variability and soybean yields in Mata Roma municipality, Maranhão

Among the activities affected by rainfall variability, agriculture is one of the most vulnerable. In this sense, this work aimed to relate the interannual rainfall variability with the soybean yields variability in Mata Roma municipality, Maranhão, considering dry and rainy events. For this, for the period from 2003 to 2019, data on average soybean yield and rainfall in this producing region, which is one of the main ones in the State, were analyzed. Monthly rainfall data for the period 1985-2019 were also analyzed and the dry and rainy events were identified in these two meteorological data series. The results showed that rainfall from January to May explains about 99%, on average, of the interannual variation of yields and that occurrence of dry and very humid periods disfavor the soybean crop, causing, in most cases, yields and productions far below the average for the study region. Based on the regression analysis, it was found that maximum average yield of 2942.52 kg×ha?1 was reached with an average rainfall of 1709 mm.

Integrated effects of co-evolutions among climate, land use and vegetation growing dynamics to changes of runoff quantity and quality

Climates, Land use/land cover (LULC) and vegetation growing dynamics have been regarded as the main factors affecting terrestrial hydrological process. However, the mechanisms underlying their integrated effects on terrestrial runoff and nutrient dynamics are not understood well. Here, we constructed a framework to disentangle and quantify the independent and coupled contributions of climate, LULC and vegetation leaf area index (LAI) changes to watershed runoff and nutrient yields changes. Long series of changing meteorological, LULC and LAI data between 1990 and 2020 were integrated into a factor-controlled simulation protocol in a distributed hydrological model, to quantify their comprehensive contributions (individual contribution of single factor change and coupling contribution of multiple factor synchronous changes) to runoff and nutrient changes. The results showed that changes of runoff and nutrient yields are more induced by climate change, rather than LULC and LAI transformations. Increase in annual precipitation significantly elevated runoff and nutrient yields. TP yield was more sensitive to climate change than runoff and TN yields. LULC transformation and climate change have synergistic effects on runoff and nutrient yields. Shift of vegetation areas to construction lands will amplify the effect of climate change on runoff and nutrient yields. Single LAI change has weak effect on runoff and nutrient yields, but it can significantly alter the hydrological effects derived from climate change and the synergistic effects between climate change and LULC transformation. This study considered the coupling and potential synergistic effects among climate change, LULC conversion and LAI variation, which elucidated the comprehensive effects of changing environment on runoff and nutrients evolutions in a more systematic and integrated perspective.

Climate change in the Debrecen area in the last 50 years and its impact on maize production

The average yield of maize is significantly dependent on the meteorological conditions of the growing year. Both the most favorable weather conditions and the weather anomalies that tend to cause damage depend on the given phenophase. The aim of this research is to analyze the climatic changes that are important in maize production in the Hajdúság region. For the climatological study of the area, homogenized temperature and precipitation data from the Hungarian Meteorological Service was used for the Debrecen region, which are freely available for download from the data repository of the institution. Trend analysis was performed for the last 50-year (1973–2022) and 30-year (1993–2022) periods. In total, 40 meteorological data series matching the study objective were analyzed. Linear regression calculations were performed using the SPSS 27 statistical software. For the non-parametric procedure, the MAKESENS Excel application was used, based on the Mann-Kendall (MK) test and Sen's slope estimation. This research shows that the choice of the length of the study period affects the results of trend analysis. The numerical values of the trend slope for the 30-year vs. 50-year period differ, and for some parameters there are also substantial differences (e.g., trend sign). The results of the parametric and non-parametric trend analyses differed only marginally for the temperature variables included. Also, for precipitation data that do not follow a normal distribution (e.g., monthly), there were only a few significant differences. The trend in mean annual temperature shows an increase of 0.39 and 0.52 °C in 10 years, and an increase of around 2 °C in 50 years and 1.5 °C in 30 years. There is a significant warming in both the summer and winter half-years, with the summer half-year showing a steeper upward trend in the 50-year data series and the winter half-year in the 30-year data series. There is a clear pattern of large, highly significant warming in the summer months and less significant changes in the two spring and two autumn months that were observed. A negative, non-significant trend in annual precipitation is observed. The decreases of 17 mm and 24 mm/10 years obtained for the 50- and 30-year time series are not negligible from a practical point of view. For the summer half-year, the precipitation amount is decreasing, with a slope of -27 mm/10 years for the last 30 years, but even this value is not significant due to the high variability. There is no significant change in the amount of precipitation in the winter half-year over the last decades. Significant trends cannot be detected from monthly or even semi-annual or annual precipitation data. The Mann-Kendall test showed a trend decrease only in the 30-year April data series at the p=0.1 significance level. Overall, the changes are negative for maize production. It should be highlighted that the obvious warming, combined with a slight decrease in precipitation, is leading to a deterioration in crop water availability and a reduction in crop yields. The impact of the identified adverse climatic changes can be compensated to a significant extent by the proposed agrotechnical responses.

Effects of Environmental and Socioeconomic Inequalities on Health Outcomes: A Multi-Region Time-Series Study.

The gradual increase in temperatures and changes in relative humidity, added to the aging and socioeconomic conditions of the population, may represent problems for public health, given that future projections predict even more noticeable changes in the climate and the age pyramid, which require analyses at an appropriate spatial scale. To our knowledge, an analysis of the synergic effects of several climatic and socioeconomic conditions on hospital admissions and deaths by cardiorespiratory and mental disorders has not yet been performed in Brazil. Statistical analyses were performed using public time series (1996-2015) of daily health and meteorological data from 16 metropolitan regions (in a subtropical climate zone in South America). Health data were stratified into six groups according to gender and age ranges (40-59; 60-79; and ≥80 years old) for each region. For the regression analysis, two distributions (Poisson and binomial negative) were tested with and without zero adjustments for the complete series and percentiles. Finally, the relative risks were calculated, and the effects based on exposure-response curves were evaluated and compared among regions. The negative binomial distribution fit the data best. High temperatures and low relative humidity were the most relevant risk factors for hospitalizations for cardiovascular diseases (lag = 0), while minimum temperatures were important for respiratory diseases (lag = 2 or 3 days). Temperature extremes, both high and low, were the most important risk factors for mental illnesses at lag 0. Groups with people over 60 years old presented higher risks for cardiovascular and respiratory diseases, while this was observed for the adult group (40-59 years old) in relation to mental disorders. In general, no major differences were found in the results between men and women. However, regions with higher urbanization levels presented risks, mainly for respiratory diseases, while the same was observed for cardiovascular diseases for regions with lower levels of urbanization. The Municipal Human Development Index is an important factor for the occurrence of diseases and deaths for all regions, depending on the evaluated group, representing high risks for health outcomes (the value for hospitalization for cardiovascular diseases was 1.6713 for the female adult group in the metropolitan region Palmas, and the value for hospitalization for respiratory diseases was 1.7274 for the female adult group in the metropolitan region Campo Mourão). In general, less developed regions have less access to adequate health care and better living conditions.

Livestock farmers' perception of climate change and adaptation strategies in the Gera district, Jimma zone, Oromia Regional state, southwest Ethiopia

The aim of this study is to assess livestock farmers' perception of climate change (CC)/variability and adaptation strategies in the Gera district. Rainfall and temperature were the variables taken in the CC perception study. A total of 190 smallholder livestock farmers were sampled for the survey. Primary data were collected through semi-structured questionnaire interviews, focus group discussions (FGDs) and meteorological data series of 2001–2020. The Statistical Package for Social Sciences (SPSS) version 20.0 was used to analyze the data. The results revealed that 79.17% of respondents perceived climate change over the past 20 years. About 84.9% and 82.9% of respondents perceived increasing temperature and decreasing rainfall over the past 20 years, respectively. Farmers' perception was consistent with meteorological data of the area, which also showed increasing trend in temperature and decreasing trend in rainfall. Farmers' perceived that anthropogenic action and natural processes, anthropogenic action, natural processes, and God's anger against human sins were the main causes of CC, in decreasing order. No statistical difference (p > 0.05) was found between AEZs regarding effects of CC except for incidence of trypanosomiasis. Decreased quality and quantity of feeds, water availability, milk production, and animal fertility, and increased calving interval, number of services per conception, incidence of animal disease, and parasite were perceived as the major impacts (indicators) of CC on livestock production and productivity in their order of importance. Diversification of mixed crop-livestock, diversification of livestock species, feed conservation, reducing herd sizes, water harvesting, provision of supplementary feeds, and forage production were the most practiced adaptation strategies. Lack of technical know-how about water harvesting, shortage of land for forage production, lack of improved forage seeds, lack of supplementary feed, poor livestock management skill, lack of feed conservation practices and poor access to market were the most important barriers to CC adaptation. It is concluded that there is a need for policy makers and livestock development stakeholders to formulate and implement intervention that promote farmers' perception and adaptation abilities to CC impacts and address the identified barriers for improving livestock productivity in the study area.

Solar Irradiance Probabilistic Forecasting Using Machine Learning, Metaheuristic Models and Numerical Weather Predictions

Solar-power-generation forecasting tools are essential for microgrid stability, operation, and planning. The prediction of solar irradiance (SI) usually relies on the time series of SI and other meteorological data. In this study, the considered microgrid was a combined cold- and power-generation system, located in Tahiti. Point forecasts were obtained using a particle swarm optimization (PSO) algorithm combined with three stand-alone models: XGboost (PSO-XGboost), the long short-term memory neural network (PSO-LSTM), and the gradient boosting regression algorithm (PSO-GBRT). The implemented daily SI forecasts relied on an hourly time-step. The input data were composed of outputs from the numerical forecasting model AROME (Météo France) combined with historical meteorological data. Our three hybrid models were compared with other stand-alone models, namely, artificial neural network (ANN), convolutional neural network (CNN), random forest (RF), LSTM, GBRT, and XGboost. The probabilistic forecasts were obtained by mapping the quantiles of the hourly residuals, which enabled the computation of 38%, 68%, 95%, and 99% prediction intervals (PIs). The experimental results showed that PSO-LSTM had the best accuracy for day-ahead solar irradiance forecasting compared with the other benchmark models, through overall deterministic and probabilistic metrics.

Use of Data Mining to Predict the Influx of Patients to Primary Healthcare Centres and Construction of an Expert System

In any productive sector, predictive tools are crucial for optimal management and decision-making. In the health sector, it is especially important to have information available in advance, as this not only means optimizing resources, but also improving patient care. This work focuses on the management of healthcare resources in primary care centres. The main objective of this work is to develop a model capable of predicting the number of patients who will demand health care in a primary care centre on a daily basis. This model is integrated into a decision support system that is accessible and easy to use by the manager through a web application. In this case, data from a primary care centre in the city of Jaén, Spain, were used. The model was estimated using spatial-temporal training data, the daily health demand data in that centre for five years, and a series of meteorological data. Different regression algorithms have been employed. The workflow requires selecting the parameters that influence the health demand prediction and discarding those that distort the model. The main contribution of this research is the daily prediction of the number of patients attending the health centre with absolute errors better than 3%, which is crucial for decision-making on the sizing of health resources in a primary care health centre.

Anticipating the Climate Change Impacts on Madeira’s Agriculture: The Characterization and Monitoring of a Vine Agrosystem

Climate—Madeira Strategy (CMS) foresees two models to describe the climate scenarios for the Madeira region in 2050 and 2070. These scenarios anticipate an average temperature rise of 1.4 to 3.7 °C and a decrease in precipitation by 30 to 40%. Consequently, Madeira’s agriculture will suffer the impacts of climate change. To understand these impacts, a baseline of major agrosystem components needs to be established, with the ultimate goal to monitor its consequences in its functioning. CASBio project used the 1961–1991 and 2010–2020 meteorological data series to modulate climate conditions and characterize and monitor six agrosystems for 2 years. One of them was a vineyard, Quinta das Vinhas, representing a typical agrosystem in the Mediterranean climate. The annual and seasonal variation in climatic parameters, soil conditions, microbiological communities, floristic and insect diversity, and crop production was assessed, using a total of 50 parameters. The results were used to establish a baseline of the agrosystem components and their seasonal and annual variation. The major findings are: (i) winter and summer extreme events show a trend in temperature and precipitation supporting a fast change in climate; (ii) a critical imbalance between nitrogen-fixing and denitrifying bacteria was identified, especially in summer, that could be determined by the rise in temperature and drought; (iii) among floristic diversity, the therophytes and geophytes confirm to be the most suitable indicators for the rise in temperature and reduction in precipitation in the agrosystems; (iv) an imbalance in favor of C. capitata plague was observed, associated with the summer rise in temperature and decrease in precipitation; (v) despite an increase in most of the grape varieties production, the Madeiran wine local varieties were shown to be less stable in productivity under observed climate conditions. The agrosystem baseline is a starting point for long-term monitoring and allows for further quantifying the influence of climate change on agrosystem productivity, resilience, and sustainability.

Multi-modal temporal CNNs for live fuel moisture content estimation

Live fuel moisture content (LFMC) is an important environmental indicator used to measure vegetation conditions and monitor for high fire risk conditions. However, LFMC is challenging to measure on a wide scale, thus reliable models for estimating LFMC are needed. Therefore, this paper proposes a new deep learning architecture for LFMC estimation. The architecture comprises an ensemble of temporal convolutional neural networks that learn from year-long time series of meteorological and reflectance data, and a few auxiliary inputs including the climate zone. LFMC estimation models are designed for two training and evaluation scenarios, one for sites where historical LFMC measurements are available (within-site), the other for sites without historical LFMC measurements (out-of-site). The models were trained and evaluated using a large database of LFMC samples measured in the field from 2001 to 2017 and achieved an RMSE of 20.87% for the within-site scenario and 25.36% for the out-of-site scenario.