Local Meteorological Station Research Articles

Evaluation of planetary boundary layer schemes in WRF model for simulating sea-land breeze in Shanghai, China

The applicability of the planetary boundary layer (PBL) scheme in the WRF model varies in different regions due to the influences of topography, underlying surface, and atmospheric background. As a secondary circulation, when a sea-land breeze (SLB) process is simulated, the numerical result is very sensitive to the choice of the WRF PBL scheme. In the present study, the meteorological characteristics of a typical SLB event in Shanghai are simulated by employing eleven PBL schemes of the WRF model. The simulations are validated against the near-surface meteorological data from five local meteorological stations as well as enhanced sounding observation data and then compared with the hourly near-surface meteorology during the SLB event. The results show that, except for the YSU, MYNN2.5, and SH schemes, the statistical values of the simulation results of the other schemes can only partially match the near-surface observations and sounding observation data. Although the statistical results of the MYNN2.5 scheme are consistent with the observational data, the results differ significantly from the hourly meteorological data during the occurrence of the SLB, which leads to a risk of overestimating the sea breeze intensity and consequently underestimating the inland temperature. Therefore, only the YSU and SH schemes are applicable to simulate the SLB in Shanghai. When the two schemes are applied to simulate the variations of sea breeze intensity and near-surface temperature, the results are in good agreement with the hourly observations from the coastal and inland meteorological stations. Moreover, YSU and SH schemes are appropriate for simulating the coupled structure of sea-land circulation and heat island circulation.

Assessment of Daily of Reference Evapotranspiration Using CLDAS Product in Different Climate Regions of China

Reference Crop evapotranspiration (ET0) datasets based on reanalysis products can make up for the time discontinuity and the spatial insufficiency of surface meteorological platform data, which is of great significance for water resources planning and irrigation system formulation. However, a rigorous evaluation must be conducted to verify if reanalysis products have application values. This study first evaluated the ability of the second-generation China Meteorological Administration Land Data Assimilation System (CLDAS) dataset for officially estimating ET0 (the local meteorological station data is used as the reference dataset). The results suggest that the temperature data of CLDAS have high accuracy in all regions except the Qinghai Tibet Plateau (QTP) region. In contrast, the global solar radiation data accuracy is fair, and the relative humidity and wind speed data quality are poor. The overall accuracy of ET0 is acceptable other than QTP, but there are also less than 15% (103) of stations with significant errors. In terms of seasons, the error is largest in summer and smallest in winter. Additionally, there are inter-annual differences in the ET0 of this data set. Overall, the CLDAS dataset is expected to have good applicability in the Inner Mongolia Grassland area for estimating ET0, Northeast Taiwan, the Semi Northern Temperate zone, the Humid and Semi Humid warm Temperate zone, and the subtropical region. However, there are certain risks in other regions. In addition, of all seasons, summer and spring have the slightest bias, followed by autumn and winter. From 2017 to 2020, bias in 2019 and 2020 are the smallest, and the areas with large deviation are south of climate zone 3, the coastal area of climate zone 6, and the boundary area of climate zone 7.

Development of a low-cost iot platform for data collection

Precision agriculture in the Internet of Things (IoT) integrates different technologies able to raise crop productivity, optimize resource efficiency, and accelerate decision making. However, the adoption of this technology is usually costly, affecting the acquisition by the farmers. Thus, the objective of this work was to develop and evaluate low-cost hardware to obtain data in a hydroponic system via IoT. The experiment was conducted at the Pici Campus of the Federal University of Ceará and split into three distinct stages. Firstly, the DS18B20 temperature sensors were calibrated in water, using the KR380 infrared thermometer as a comparison method. For the second step, when the hydroponic system was installed, the water temperature was monitored in the channel and not in the solution reservoir. In this same phase, the quality of data sending and receiving was investigated. In the third step, the sensory data were analyzed with those obtained by the local Meteorological Station. The calibration results revealed that the DS18B20 sensor has reasonable accuracy and excellent agreement and reliability between data. As for receiving and storing, only 6% of the total data was lost.

The relationship between rising temperatures and malaria incidence in Hainan, China, from 1984 to 2010: a longitudinal cohort study

The influence of rising global temperatures on malaria dynamics and distribution remains controversial, especially in central highland regions. We aimed to address this subject by studying the spatiotemporal heterogeneity of malaria and the effect of climate change on malaria transmission over 27 years in Hainan, an island province in China. For this longitudinal cohort study, we used a decades-long dataset of malaria incidence reports from Hainan, China, to investigate the pattern of malaria transmission in Hainan relative to temperature and the incidence at increasing altitudes. Climatic data were obtained from the local meteorological stations in Hainan during 1984-2010 and the WorldClim dataset. A temperature-dependent R0 model and negative binomial generalised linear model were used to decipher the relationship between climate factors and malaria incidence in the tropical region. Over the past few decades, the annual peak incidence has appeared earlier in the central highland regions but later in low-altitude regions in Hainan, China. Results from the temperature-dependent model showed that these long-term changes of incidence peak timing are linked to rising temperatures (of about 1·5°C). Further, a 1°C increase corresponds to a change in cases of malaria from -5·6% (95% CI -4·5 to -6·6) to -9·2% (95% CI -7·6 to -10·9) from the northern plain regions to the central highland regions during the rainy season. In the dry season, the change in cases would be 4·6% (95% CI 3·7 to 5·5) to 11·9% (95% CI 9·8 to 14·2) from low-altitude areas to high-altitude areas. Our study empirically supports the idea that increasing temperatures can generate opposing effects on malaria dynamics for lowland and highland regions. This should be further investigated and incorporated into future modelling, disease burden calculations, and malaria control, with attention for central highland regions under climate change. Scientific and Technological Innovation 2030: Major Project of New Generation Artificial Intelligence, National Natural Science Foundation of China, Beijing Natural Science Foundation, National Key Research and Development Program of China, Young Elite Scientist Sponsorship Program by CAST, Research on Key Technologies of Plague Prevention and Control in Inner Mongolia Autonomous Region, and Beijing Advanced Innovation Program for Land Surface Science.

Growth ring response of two Atlantic Forest tree species pre- and post-dam operation in Southern Brazil

Hydropower plants are important sources of renewable energy, but the climatic impacts of their constructions remain poorly explored. Considering that tree growth analysis is a useful tool to identify environmental impacts, this study aimed at using climate records and tree-ring chronologies to understand possible local climate changes caused by the construction of a hydropower plant in the 1980s in the State of Paraná, Southern Brazil. Historical climatic data were obtained from the local meteorological station and surrounding municipalities and analyzed using ANOVA and means tests. The Pettitt test was additionally used to identify change-points in the meteorological data. Wood samples from a total of 60 trees from Araucaria angustifolia (Bertol.) Kuntze (Araucariaceae) and Cedrela fissilis Vell. (Meliaceae) were collected, and tree-ring chronologies were built using dendrochronological standard procedures. Chronologies for A. angustifolia and C. fissilis represented time periods from 1800 to 2016 and 1899–2015, respectively. Tree-ring growth responses to climatic variables were evaluated by adjusting generalized mixed linear models and the Spearman correlations. Our results evidenced that the hydropower plant altered the local climate, mostly influencing the hydrological cycle by increasing local rainfall, with monthly rain volumes being statistically higher than in other meteorological stations. Significant responses in the growth of A. angustifolia were found to be associated with the water level increase caused by the dam and of C. fissilis due to the increase in cloud cover.

Thermodynamic and mathematical analysis of geothermal power plants operating in different climatic conditions

This study presents the thermodynamic and mathematical analysis of the different cooling systems for geothermal power plants at various climatic conditions. The existing binary geothermal power plant data, located in Turkey, were processed in the commercial software Ebsilon Professional to model the power plant and cooling systems. The dry-bulb temperature and relative humidity values from data obtained from local meteorological stations in the analyzed region are used as variables in the model. The reference air-cooled cooling system, the wet-tower cooling system, the additional dry cooling system and three different hybrid cooling systems are modelled and analyzed separately in the reference geothermal power plant model. Accordingly, mathematical equations are developed to evaluate the power production and water consumption characteristics of cooling systems on the thermodynamic performance of geothermal power plants depending on weather conditions. The power production and water consumption of geothermal power plants can be calculated using hourly ambient temperature and relative humidity data at different climatic conditions with these equations. Considering water reserves in the area, operation periods of the hybrid cooling systems can be determined. Moreover, power plant operators can compare the performance and water consumption characteristics of hybrid cooling systems for different conditions with these equations.

Reliability assessment of existing RC bridges with spatially-variable pitting corrosion subjected to increasing traffic demand

Bridges are critical for transportation networks. Temporary closures due to poor maintenance may trigger adverse cascading events, affecting economic and societal well-being. Two main factors play a key role in bridge health conditions: ageing and wear-and-tear due to the increasing traffic. This paper proposes a comprehensive framework to quantify the combined phenomena in a holistic approach. The reliability assessment of an existing reinforced concrete bridge subjected to increasing traffic demand and spatially-variable pitting corrosion is investigated. Empirical data are used to develop probabilistic models for cracking initiation, pitting factor, severe cracking, and cover spalling.Statistical distributions of temperatures from a local meteorological station are used to investigate environmental effects on corrosion initiation. For the traffic, national highway databases are used to model the vehicular flow. Ductile and brittle failure mechanics are considered for the structural capacity assessment. Coupled biaxial bending-axial loading domain is adopted for the ductile structural checks. The shear capacity is assessed through the response limit surface from the modified compression field theory results. Finally, Monte Carlo simulations are performed at intervals of 10 years to derive a time-dependent reliability profile compared against standard thresholds to determine the health conditions of the bridge.

Strong El Niño reduces fruit production of Brazil-nut trees in the eastern Amazon

ABSTRACT The Brazil-nut tree (Bertholletia excelsa) is native to the Amazon rainforest, and its fruit production varies naturally with climatic conditions. Our aim was to evaluate the temporal variation in Brazil-nut production associated with climatic variables, including the strong El Niño of 2015/2016. The study was carried out in two 9-ha permanent plots in the northeastern Brazilian Amazon from 2007 to 2018: one in forest (12-year monitoring) and the other in savannah/forest transition (eight years). Overall, we monitored fruit production of 205 trees with diameter at breast height ≥ 50 cm. Annual fruit production was related to temporal series (2005-2018) of climatic data (the Oceanic Niño Index; and precipitation and air temperature from two local meteorological stations). Average fruit production per tree in 2017 was eight times lower than in 2015 and two times lower than the general average for both sites, and was significantly associated to the El Niño of 2015/2016, that increased average maximum monthly temperature and reduced the precipitation in the region, extending the dry season from three to six months. Years with higher and lower fruit production per tree coincided in both sites. Annual fruit production was significantly and negatively correlated with thermal anomalies that occurred in the third semester prior to harvest monitoring. Years with higher production were related with predominance of neutrality or the La Niña phenomenon at the global scale, and higher rainfall at the local scale. The relationship of fruit production with climate was independent of the local habitat.

Testing gas dispersion modelling: A case study at La Soufrière volcano (Guadeloupe, Lesser Antilles)

Volcanic gas dispersal can be a serious threat to people living near active volcanoes since it can have short- and long-term effects on human health, and severely damage crops and agricultural land. In recent decades, reliable computational models have significantly advanced, and now they may represent a valuable tool to make quantitative and testable predictions, supporting gas dispersal forecasting and hazard assessments for public safety. Before applying a specific modelling tool into hazard quantification, its calibration and its sensitivity to initial and boundary conditions should be carefully tested against available data, in order to produce unbiased hazard quantifications. In this study, we provided a number of prototypical tests aimed to validate the modelling of gas dispersal from a hazard perspective. The tests were carried out at La Soufrière de Guadeloupe volcano, one of the most active gas emitters in the Lesser Antilles.La Soufrière de Guadeloupe has shown quasi-permanent degassing of a low-temperature hydrothermal nature since its last magmatic eruption in 1530 CE, when the current dome was emplaced. We focused on the distribution of CO2 and H2S discharged from the three main present-day fumarolic sources at the summit, using the measurements of continuous gas concentrations collected in the period March–April 2017. We developed a new probabilistic implementation of the Eulerian code DISGAS-2.0 for passive gas dispersion coupled with the mass-consistent Diagnostic Wind Model, using local wind measurements and atmospheric stability information from a local meteorological station and ERA5 reanalysis data. We found that model outputs were not significantly affected by the type of wind data but rather upon the relative positions of fumaroles and measurement stations. Our results reproduced the statistical variability in daily averages of observed data over the investigated period within acceptable ranges, indicating the potential usefulness of DISGAS-2.0 as a tool for reproducing the observed fumarolic degassing and for quantifying gas hazard at La Soufrière. The adopted testing procedure allows for an aware application of simulation tools for quantifying the hazard, and thus we think that this kind of testing should actually be the first logical step to be taken when applying a simulator to assess (gas) hazard in any other volcanic contexts.

Estimación de la evapotranspiración a partir de datos satelitales para la región de Alto Beni, Norte de La Paz

La evapotranspiración (ET), resulta de un proceso simultáneo de evaporación y la transpiración. En una superficie de referencia, que ocurre sin restricciones de agua, se denomina como evapotranspiración del cultivo de referencia (ETo). Para este estudio se utilizó ecuación de Penman-Monteith FAO, propuesta por Allen, Pereira, Raes y Smith (2006); Thornthwaite et al. (1944); Hargreaves et al. (1985); Blaney y Criddle (1950) y Turc (1961). La información climática necesaria para la estimación de la ETo, no es suficiente debido a que las estaciones meteorológicas locales son muy escasas adema dejaron de funcionar, en ese sentido se utilizaron datos de los simuladores recomendados por la FAO LocClim (2005) y NASA (power larc). La región de Alto Beni se extiende; 14º 56' 55'' a 15º 56' 14'' Sur y longitud 66º 48' 04'' a 67º 36' 42'' una altitud de 300 a 1 800 m s.n.m. ocupan una extensión de 4 836.62 km2. Es una zona agrícola de importante, debido a que proporciona a la ciudad una buena parte de sus productos alimenticios. Los resultados de la ETo obtenidos en la modelación espacial, muestran diferentes valores distribuidos en la región, el cual varía en cada estación del año. Se asevera que, en la mayoría de los métodos, el déficit de agua inicia a medio mes de abril hasta septiembre, y los picos más altos de octubre a marzo. El análisis estadístico entre los resultados de la ETo, verifica que el modelo de Thornthwaite es el más aceptado con un coeficiente de correlación R2 de 0.966 seguida de Blaney-Criddle con 0.969, en último lugar los métodos de Hargreaves y Turc. El modelo más aceptado y aplicado para la zona es el Thornthwaite el cual tiene un comportamiento muy similar al PM-FAO el mismo no sobre estima las demandas de agua y sus cálculos son reflejados con los eventos de la región.

An effect of 24-hour temperature change on outpatient and emergency and inpatient visits for cardiovascular diseases in northwest China.

Some studies suggested that 24-h temperature change (TC24) was one of the potential risk factors for human health. However, evidence of the short-term effect of TC24 on outpatient and emergency department (O&ED) visits and hospitalizations for cause-specific cardiovascular diseases (CVDs) is still limited. The aim of this study is to explore the short-term effects of TC24 on O&ED visits and hospitalizations for CVDs in northwest China which is an area with large temperature variation. The O&ED visits records for CVDs of 3 general hospitals and the inpatient records for CVDs of 4 general hospitals were collected from January 1, 2013, to December 31, 2016, in Jinchang City, northwest China. Meteorological and air pollution data were also obtained during the same study period from local meteorological monitoring station and environmental monitoring station, respectively. A generalized additive model (GAM) with Poisson regression was employed to analyze the effects of TC24 on O&ED visits and hospitalizations for CVDs. V-shaped relationship were found between TC24 and O&ED visits and hospitalizations for CVDs, including total CVD, hypertension, coronary heart disease (CHD) and stroke. Stratified analysis showed that men and patients over 65 years old were more susceptible to temperature changes. The estimates in non-heating monthswerehigher than infullyear. TC24 can affect the O&ED visits and hospitalizations for CVDs in this study. This study provides useful data for policy makers to better prepare local responses to the impact of changes in temperature on population health.

Short-Term Solar Power Forecasting: A Combined Long Short-Term Memory and Gaussian Process Regression Method

Solar power is considered a promising power generation candidate in dealing with climate change. Because of the strong randomness, volatility, and intermittence, its safe integration into the smart grid requires accurate short-term forecasting with the required accuracy. The use of solar power should meet requirements proscribed by environmental law and safety standards applied for consumer protection. First, time-series-based solar power forecasting (SPF) model is developed with the time element and predicted weather information from the local meteorological station. Considering the data correlation, long short-term memory (LSTM) algorithm is utilized for short-term SPF. However, the point prediction provided by LSTM fails in revealing the underlying uncertainty range of the solar power output, which is generally needed in some stochastic optimization frameworks. A novel hybrid strategy combining LSTM and Gaussian process regression (GPR), namely LSTM-GPR, is proposed to obtain a highly accurate point prediction with a reliable interval estimation. The hybrid model is evaluated in comparison with other algorithms in terms of two aspects: Point prediction accuracy and interval forecasting reliability. Numerical investigations confirm the superiority of LSTM algorithm over the conventional neural networks. Furthermore, the performance of the proposed hybrid model is demonstrated to be slightly better than the individual LSTM model and significantly superior to the individual GPR model in both point prediction and interval forecasting, indicating a promising prospect for future SPF applications.

Does surrounding greenness moderate the relationship between apparent temperature and physical activity? Findings from the PHENOTYPE project

BackgroundPhysical activity can be affected by both meteorological conditions and surrounding greenness, but few studies have evaluated the effects of these environmental factors on physical activity simultaneously. This multi-city comparative study aimed to assess the synergetic effects of apparent temperature and surrounding greenness on physical activity in four European cities. Specifically, we aimed to identify an interaction between surrounding greenness and apparent temperature in the effects on physical activity. MethodsData were collected from 352 adult residents of Barcelona (Spain), Stoke-on-Trent (United Kingdom), Doetinchem (The Netherlands), and Kaunas (Lithuania) as part of the PHENOTYPE study. Participants wore a smartphone for seven consecutive days between May–December 2013 and provided additional sociodemographic survey data. Hourly average physical activity (Metabolic Equivalent of Task (MET)) and surrounding greenness (NDVI) were derived from the Calfit mobile application collecting accelerometer and location data. Hourly apparent temperature was calculated from temperature and relative humidity, which were obtained from local meteorological stations along with other meteorological covariates (rainfall, windspeed, and sky darkness). We assessed the interaction effects of apparent temperature and surrounding greenness on hourly physical activity for each city using linear mixed models, while adjusting for meteorological, demographic, and time-related variables. ResultsWe found significant interactions between apparent temperature and surrounding greenness on hourly physical activity in three of four cities, aside from the coastal city of Barcelona. Significant quadratic effects of apparent temperature were found in the highest level of surrounding greenness for Stoke-on-Trent and Doetinchem, with 4% decrease in median MET observed for a 10°C departure from optimal temperature (15.2°C and 14.6°C, respectively). Significant linear effects were found for higher levels of surrounding greenness in Kaunas, whereby an increase of 10°C was associated with ∼4% increase in median MET. ConclusionApparent temperature and surrounding greenness interacted in the effect on hourly physical activity across three of four European cities, with varying effect between cities. While quadratic effects of temperature suggest diminishing levels of physical activity in the highest greenness levels in cities of temperate climates, the variation in surrounding greenness between cities could be further explored, particularly by looking at indoor-outdoor locations. The study findings support the need for evidence-based physical activity promotion and urban design.

Photovoltaic Power Forecasting: Assessment of the Impact of Multiple Sources of Spatio-Temporal Data on Forecast Accuracy

The efficient integration of photovoltaic (PV) production in energy systems is conditioned by the capacity to anticipate its variability, that is, the capacity to provide accurate forecasts. From the classical forecasting methods in the state of the art dealing with a single power plant, the focus has moved in recent years to spatio-temporal approaches, where geographically dispersed data are used as input to improve forecasts of a site for the horizons up to 6 h ahead. These spatio-temporal approaches provide different performances according to the data sources available but the question of the impact of each source on the actual forecasting performance is still not evaluated. In this paper, we propose a flexible spatio-temporal model to generate PV production forecasts for horizons up to 6 h ahead and we use this model to evaluate the effect of different spatial and temporal data sources on the accuracy of the forecasts. The sources considered are measurements from neighboring PV plants, local meteorological stations, Numerical Weather Predictions, and satellite images. The evaluation of the performance is carried out using a real-world test case featuring a high number of 136 PV plants. The forecasting error has been evaluated for each data source using the Mean Absolute Error and Root Mean Square Error. The results show that neighboring PV plants help to achieve around 10% reduction in forecasting error for the first three hours, followed by satellite images which help to gain an additional 3% all over the horizons up to 6 h ahead. The NWP data show no improvement for horizons up to 6 h but is essential for greater horizons.

Evaluation of Substitution of Meteorological Data from the Korea Meteorological Administration for Data from a Cattle Farm in Calculation of Temperature-Humidity Index

Relative humidity (RH%) data are inaccessible in the majority of cattle farms; however, such data often are available at local meteorological stations. Thus, this study purposed use of dry bulb temperature (DBT℃) data from a cattle barn and RH% data of Korea Meteorological Administration (KMA) for calculation of temperature-humidity index (THI). The scope of research was analysis of three cattle barns, categorized as barn 1 (sidewalls-closed and adjustable with windows, 1,071 ㎡), barn 2 (sidewalls-opened and adjustable with winch curtain, 1,067 ㎡), and barn 3 (sidewalls half opened, 544 ㎡) located at a livestock research farm at Kangwon National University. The microclimate data of the barns were recorded by Hobo loggers and compared with KMA data from June to September 2019. Comparison of maximum DBTºC between KMA and the barns showed no difference in the study period. On the other hand, minimum RH% at KMA was lower than those of barn1 and barn3 in June and September, and minimum RH% at KMA was lower than that of barn1 in August (p < 0.05). The maximum THI based on KMA data was not different from that based on barns data. Moreover, the maximum THI based on KMA data was not different from that of modified THI (combination of maximum DBT℃ of barns and minimum RH% of KMA). Furthermore, maximum THI based on barns data was not different from modified THI of barns. The THI calculation in barns was correlated with KMA data. Overall, microclimate information from KMA can be used as effective barn microclimate data for THI calculation. We recommend using RH% data provided by KMA in the absence of microclimatic RH% data from a cattle barn as an effective way of regulating THI inside the cattle farm.

Solar Energy Potential in the Yangtze River Delta Region—A GIS-Based Assessment

Decarbonization of electrical power generation is an essential necessity in the reduction of carbon emissions, mitigating climate change and attaining sustainable development. Solar energy as a substitution for fossil fuel-based energy sources has the potential to aid in realizing this sustainable future. This research performs a geographic information systems (GIS)-based assessment of the solar energy potential in the Yangtze River Delta region (YRDR) of China using high-resolution solar radiation data combined with geographical, social, environmental and cultural constraints data. The solar energy potential is evaluated from the geographical and technical perspective, and the results reveal that the YRDR is endowed with rich solar energy resources, with the geographical potential in the suitable areas ranging from 1446 kWh/m2 to 1658 kWh/m2. It is also estimated that the maximum solar capacity potential could be up to 4140.5 GW, illustrating the high potential available for future capacity development in this region. Realizing this significant potential as an alternative for fossil fuel-based electricity generation would result in a substantial mitigation of CO2 emissions in this region, where air pollution is severe. Potential evaluations found that Jiangsu and Anhui provinces provide the most optimal areas for the development of solar photovoltaics (PV) installations, as they have the highest geographical and technological solar energy potential. Further, findings of the case study undertaken at a solar PV plant show disparities between actual generated power and technical solar potential, highlighting the significance of utilizing solar radiation data from local ground-based meteorological stations. This study provides policy makers and potential investors with information on solar energy potential in the Yangtze River Delta region that would contribute to solar power generation development.

Significance of local meteorological stations in research planning

The goal of research planning is to optimize human and material resources while maximizing efficiency. If there are databases that can be used as a substitute for own data collection, effective research can be facilitated by reallocating resources. In the case of environmental studies, the knowledge of the climatic conditions of the study period is known to be a key factor in research planning process. In the present study, the data of our meteorological station in our research area (known as “Szamárháti Tanya”, Kesznyéten, Hungary) was compared with the measurements of meteorological stations operated by the competent water authority. Stations were taken into the study within a 10 and 20 km radius over a period of 21 months, to determine which provided more relevant data from the area. During the evaluation of results, the relationship between the local and regional weather stations were determined, and deductions were made from the obtained results to support the decision which of the targeted investigations could be of greater benefit.

IrrigaSys: A web-based irrigation decision support system based on open source data and technology

IrrigaSys is a decision support system (DSS) for irrigation water management based on online, open source tools. The aim of this paper is to describe the structure of IrrigaSys and how it is implementation at the plot scale. The DSS includes remote access to local meteorological stations for weather conditions, a meteorological model for weather forecast, the MOHID-Land model for the computation of the soil water balance and irrigation scheduling, and a database for data repository. Despite its complexity, the data necessary to run IrrigaSys is minimal, and include as mandatory input information on the location of field plots, crop type, sowing and harvest dates, soil texture, irrigation method, and daily/weekly applied irrigation depths. Based on this information, the system automatically downloads the weather data from the meteorological station located closest to the agricultural plot, as well as the weather forecast for the seven incoming days. The soil water balance is then computed from sowing to the present date (updating always the system with newly acquired information) as well as the recommended irrigation schedule for the incoming week. Results are made available via a web interface, a mobile app, a SMS, and email. The IrrigaSys further provides the Normalized Difference Vegetation Index (NDVI) computed from the most recent Sentinel-2 imagery available with a resolution of 10 m. The IrrigaSys was developed in close cooperation with the Water Board from the Sorraia Valley irrigation district, southern Portugal, supporting 103 plots of 30 farmers over the last 5 years. This stakeholder has been fundamental for successfully running the system. This paper further discusses the main strengths and limitations of IrrigaSys, with the latter being naturally associated to difficulties in providing reliable estimates for all field plots based on limited data.

Irrigation Influencing Farmers’ Perceptions of Temperature and Precipitation: A Comparative Study of Two Regions of the Tibetan Plateau

Farmers are among the most vulnerable groups that need to adapt to climate change. Correct perception is a prerequisite for farmers to adopt adaptation strategies, which plays a crucial guiding role in the development of adaptation plans and the improvement of the security of livelihoods. This study aimed to compare farmers’ perceptions of temperature and precipitation change with meteorological data in two regions of the Tibetan Plateau, analyzed how irrigation affects farmers’ perceptions. Data were obtained from local meteorological stations and household questionnaires (N = 1005). The study found that, since 1987, the climate warming trend was significant (p &lt; 0.01), and the temperature increase was faster in winter. Precipitation had no significant change trend, but the seasonal variations indicated that the precipitation concentration period moved forward in the Pumqu River Basin and was delayed a month in the Yellow River-Huangshui River valley. The farmers’ perception of temperature change was consistent with meteorological data, but there was an obvious difference in precipitation perception between the two regions. We noticed that irrigation facilities played a mediating role on precipitation perception and farmers having access to irrigation facilities were more likely to perceive increased precipitation. Finally, this study suggested that meteorological data and farmers’ perceptions should be integrated when developing policies, rather than just considering actual climate trends. Simultaneously, while strengthening irrigation investment, the government should also pay attention to publicizing the consequences of climate change and improving farmers’ abilities of risk perception.

Land cover affects microclimate and temperature suitability for arbovirus transmission in an urban landscape.

The emergence of mosquito-transmitted viruses poses a global threat to human health. Combining mechanistic epidemiological models based on temperature-trait relationships with climatological data is a powerful technique for environmental risk assessment. However, a limitation of this approach is that the local microclimates experienced by mosquitoes can differ substantially from macroclimate measurements, particularly in heterogeneous urban environments. To address this scaling mismatch, we modeled spatial variation in microclimate temperatures and the thermal potential for dengue transmission by Aedes albopictus across an urban-to-rural gradient in Athens-Clarke County GA. Microclimate data were collected across gradients of tree cover and impervious surface cover. We developed statistical models to predict daily minimum and maximum microclimate temperatures using coarse-resolution gridded macroclimate data (4000 m) and high-resolution land cover data (30 m). The resulting high-resolution microclimate maps were integrated with temperature-dependent mosquito abundance and vectorial capacity models to generate monthly predictions for the summer and early fall of 2018. The highest vectorial capacities were predicted for patches of trees in urban areas with high cover of impervious surfaces. Vectorial capacity was most sensitive to tree cover during the summer and became more sensitive to impervious surfaces in the early fall. Predictions from the same models using temperature data from a local meteorological station consistently over-predicted vectorial capacity compared to the microclimate-based estimates. This work demonstrates that it is feasible to model variation in mosquito microenvironments across an urban-to-rural gradient using satellite Earth observations. Epidemiological models applied to the microclimate maps revealed localized patterns of temperature suitability for disease transmission that would not be detectable using macroclimate data. Incorporating microclimate data into disease transmission models has the potential to yield more spatially precise and ecologically interpretable metrics of mosquito-borne disease transmission risk in urban landscapes.