Agro-meteorological Stations Research Articles

Improving the simulation accuracy of summer maize growth and yield by pixel-based parameterization based on assimilating upscaled MODIS LAI

Accurately simulating maize (Zea mays L.) yield at the regional scale is of paramount importance for making informed policy adjustments and for ensuring food security. Sobol sensitivity analysis was used in this study to screen sensitive parameters of a process-based crop model (Chinese Agricultural Meteorological Model, CAMM). An upscaling approach was utilized to reduce the intra-pixel heterogeneity error of MODIS LAI. Then upscaled MODIS LAI data were assimilated into the CAMM model through a 4DVar assimilation algorithm to optimize pixel-level sensitive parameters, thereby improving the simulation accuracy of the summer maize growth process and yield. Specific leaf area (SLATB_0, SLATB_1) during the period from emergence to tasseling of summer maize exhibited the strongest impact on summer maize yield. Additionally, the average LAI value within the 85–95 % range of ordered LAI values for small pixels within large pixels effectively reduced the intra-pixel heterogeneity error of MODIS LAI, and pixel-based parameterization of SLATB_0 and SLATB_1 at a larger pixel scale (0.0625°) was achieved. Based on yields recorded at agrometeorological stations from 2015 to 2020, assimilated yields in both data assimilation scheme 1 (DA1, optimization of only the sensitive parameter SLATB_0) and scheme 2 (DA2, simultaneous optimization of sensitive parameters SLATB_0 and SLATB_1) exhibited higher accuracy than schemes without data assimilation (with r values of 0.41–0.72 and NRMSE values of 19–30 %). Furthermore, DA2 showed greater simulation accuracy (r: 0.64–0.93, NRMSE: 9–21 %) than DA1 (r: 0.61–0.91, NRMSE: 12–23 %). Upscaling remotely sensed LAI products can significantly reduce the uncertainties of LAI data at a larger pixel scale, and assimilating these LAI data into crop models can effectively increase the simulation accuracy of crop growth and development processes at the regional scale.

Comparing Remote and Proximal Sensing of Agrometeorological Parameters across Different Agricultural Regions in Croatia: A Case Study Using ERA5-Land, Agri4Cast, and In Situ Stations during the Period 2019–2021

The paper evaluates the usability of remote satellite-based and proximal ground-based agrometeorological data sources for precision agriculture and crop production in Croatia. The compared agrometeorological datasets stem from the open-access data sources Copernicus CDS and the Agri4Cast portal, and commercial in situ agrometeorological stations (PinovaMeteo) which monitor environmental parameters relevant to the physiological state of crops. The study compares relevant parameters for 10 different locations in Croatia for three consecutive years (2019, 2020, and 2021) to investigate whether model-based data from ERA5-Land and Agri4Cast are well-correlated with ground measurements from independent in situ stations (PinovaMeteo) for specific agrometeorological parameters (air and soil temperature, and precipitation). Our results indicate the following: both the ERA5-Land and Agri4Cast datasets show mostly strong positive correlations with ground observations for air temperature, modest correlations for soil temperature, but modest or even low correlations for precipitation. Analysis of the residuals indicates higher overall residual values, especially in areas with complex topography and near large bodies of water or the sea, and deviations of residuals that may limit the usability of satellite- and model-based data for decision-making in agriculture.

Development of Synthetic Unit Hydrographs Using Spatial Proximity Regionalization for the Makiling Forest Reserve, Philippines

There is a dearth of streamflow data in the Philippines to generate hydrographs needed for flood forecasting and water resources assessment. A method of generating and calibrating synthetic hydrographs using model simulations and spatial proximity regionalization is presented. Synthetic unit hydrographs of four storm events in the gauged watershed of Makiling Forest Reserve were generated using Soil Conservation Service Unit Hydrograph, Clark Unit Hydrograph, and Snyder’s Unit Hydrograph methods. The generated synthetic hydrographs for each runoff modeling technique were calibrated with the actual hydrographs of the watershed. Snyder’s Unit Hydrograph model results were the most acceptable based on the Nash-Sutcliffe Efficiency, Index of Volumetric Fit, and Relative Error of Peak Flow. The weighted values of the calibrated watershed parameters computed using spatial proximity regionalization technique and the hyetographs derived from the Rainfall Intensity Duration Frequency Curve of the University of the Philippines Los Baños-National Agrometeorological Station were used to generate the synthetic unit hydrographs for three neighboring ungauged watersheds at 2-, 5-, 10-, 15-, 20-, 25-, 50, 100-year return periods. Total runoff volume, the magnitude of peak flows, and time to peak derived from the generated hydrographs can be used in watershed planning, water resources management, flood forecasting and the design of various water control structures.

Impact of climate change on wheat production in China

Against the background of rising international disputes, numerous countries have placed great importance on ensuring self-sufficiency of crop production. Weather, as a notably variable factor than others, has a considerable influence on agricultural productivity, production variability and, in turn, on food security. Our primary objectives were to: (1) evaluate the relative importance of climate, soil, and field management factors on wheat production across major wheat-producing regions in China, (2) explore the direct and indirect ways of the effect of climate on wheat production at different phenological stages, and (3) forecast future trends in wheat production levels and stability based on historical insights. Initially, we analyzed the spatiotemporal variations in production and phenology across six subregions using linear regression. This analysis was based on data from 124 agro-meteorological stations, spanning the period from 1999 to 2013. Subsequently, we used the Random Forest algorithm to discern the relative importance of climate, soil, and field management on wheat production. Moreover, we utilized a structural equation model to elucidate the direct and indirect pathways through which climatic factors affected production during different phenological stages, incorporating the first difference method. Finally, with a Support Vector Machine algorithm, the production level and stability under RCP4.5 and RCP8.5 were predicted using 33 Global Climate Models. Over the 1999–2013 period, among 124 wheat stations, 33 stations showed a significant (P < 0.05) increase in wheat production, while 53 exhibited an insignificant (P > 0.05) increase. Conversely, 4 stations exhibited a significant (P < 0.05) decrease, and 34 showed an insignificant (P > 0.05) decrease in wheat production. Climate, soil, and field management all played significant roles in wheat production in each of the six subregions, with relative importance ranging from 19.0% to 44.2%, 30.4% to 35.9%, and 25.4% to 45.1%, respectively. Climate factors, predominantly temperature-related, influenced wheat production by altering key growth period lengths, while other factors directly impacted production. Finally, our findings suggested that the wheat production level in China is expected to increase in the future, while its production stability could decrease. These findings hold the potential to guide policymakers in different regions towards ensuring food security and enhancing agricultural system resilience in the context of climate change.

AgDataBox-IoT - application development for agrometeorological stations in smart

Currently, Brazil is one of the world's largest grain producers and exporters. Agriculture has already entered its 4.0 version (2017), also known as digital agriculture, when the industry has entered the 4.0 era (2011). This new paradigm uses Internet of Things (IoT) techniques, sensors installed in the field, network of interconnected sensors in the plot, drones for crop monitoring, multispectral cameras, storage and processing of data in Cloud Computing, and Big Data techniques to process the large volumes of generated data. One of the practical options for implementing precision agriculture is the segmentation of the plot into management zones, aiming at maximizing profits according to the productive potential of each zone, being economically viable even for small producers. Considering that climate factors directly influence yield, this study describes the development of a sensor network for climate monitoring of management zones (microclimates), allowing the identification of climate factors that influence yield at each of its stages.•Application of the internet of things to assist in decision making in the agricultural production system.•AgDataBox (ADB-IoT) web platform has an Application Programming Interface (API).•An agrometeorological station capable of monitoring all meteorological parameters was developed (Kate 3.0).

Quantifying Multi-hazards and Impacts Over Different Growth Periods of Maize: A Study Based on Index Construction

AbstractOwing to the complexity and variability of global climate, the study of extreme events to ensure food security is particularly critical. The standardized precipitation requirement index (SPRI) and chilling injury index (ICi) were introduced using data from agrometeorological stations on the Songliao Plain between 1981 and 2020 to identify the spatial and temporal variability of drought, waterlogging, and low-temperature cold damage during various maize growth periods. Compound drought and low-temperature cold damage events (CDLEs) and compound waterlogging and low-temperature cold damage events (CWLEs) were then identified. To measure the intensity of compound events, the compound drought and low-temperature cold damage magnitude index (CDLMI), and compound waterlogging and low-temperature cold damage magnitude index (CWLMI) were constructed by fitting marginal distributions. Finally, the effects of extreme events of various intensities on maize output were examined. The findings demonstrate that: (1) There were significant differences in the temporal trends of the SPRI and ICi during different maize growth periods. Drought predominated in the middle growth period (MP), waterlogging predominated in the early growth period (EP) and late growth period (LP), and both drought and waterlogging tended to increase in intensity and frequency. The frequency of low-temperature cold damage showed a decreasing trend in all periods. (2) The CDLMI and CWLMI can effectively determine the intensity of CDLEs and CWLEs in the study area; these CDLEs and CWLEs had higher intensity and frequency in the late growth period. (3) Compared to single events, maize relative meteorological yield had a more significant negative correlation with the CDLMI and CWLMI.

Apple (Malus domestica Borkh.) phenology in relation to topoclimate in Central Macedonia, Greece

Plant phenological behavior is strongly dependent on climate conditions. In the current research, the apple (Malus domestica Borkh.) phenology response to topoclimate conditions, as mainly determined by air temperature, was investigated at two agricultural regions of the Region of Central Macedonia, Greece, Giannitsa Pellas and Rachi Pierias. In this regard, phenological observations were collected in order to evaluate the timing of appearance of the following phenophases: bud burst-green leaf tips (BBCH 53), flower bud visible-still closed (BBCH 55), pink bud stage-flower petals elongating-visible sepals slightly open (BBCH 57), and full flowering at least 50% (BBCH 65). Also, air temperature data were taken from agrometeorological stations which covered the study regions. Results showed that higher maximum and lower minimum air temperatures prevailed during almost the whole year at the plain of Giannitsa than those of Rachi. Earlier appearance of the examined apple phenophases was confirmed at Giannitsa, in comparison to Rachi, increasing the damage risk of vulnerable plant tissues during the bud development and flowering from the late spring frosts. It seems that the maximum and minimum air temperatures of March are decisive for the appearance o BBCH 53, 55 and 57 phenophases while those of April are considered as crucial factors for the onset of BBCH 65. The knowledge of relation between apple tree phenology and topoclimate, in terms of air temperature, may provide valuable information for farmers' decision-making regarding the rational planning of cultivation techniques.

Changing maize hybrids helps adapt to climate change in Northeast China: revealed by field experiment and crop modelling

Adopting new cultivars is an effective strategy to partially offset the negative effects of climate change on crop yields. We integrated the regional agro-meteorological observations, site-level field experiment data, and a process-based crop model to assess the effects of maize hybrid management on grain yield at meteorological sites across Northeast China. On a regional scale, for per 1 ℃ increase in Tmin (Tmax) at the agro-meteorological stations, the length of the entire actual growing period for maize was shortened by 3.6 (2.3) days per year. Without changing maize hybrids, maize grain yield could be in jeopardy. Adopting hybrids with relatively longer growing period could take full advantage of the regional warming trend and hence improve the maize grain yield. In this study, we found that adopting novel hybrids (bred through trait selection and propagation during the past four decades) could improve maize grain yield in Northeast China. This conclusion was supported by the two-year field experiment that used nine prevailing newly-bred hybrids and the well-validated crop modeling at 74 locations covering a 60-year period (from 1961 to 2020). Compared with using the same hybrid, changing hybrid adapted the maize production to climate change in Northeast China; and the regional maize grain yield was increased by 5.4%, 7.4%, 6.8%, and 10.3%, respectively, due to changes in, respectively, solar radiation, Tmax, Tmin, and precipitation. The improvement in maize grain yield became more evident as latitude increased, especially at locations to the north of 42.5ºN. In the future, changing hybrid may increase the region-simulated maize grain yield by 6.2 and 7.2% under the scenarios of SSP-126 and SSP-585, respectively. In sum, adopting newly-bred maize hybrids, especially those with relatively long growing periods, would be an effective measure to adapt regional maize production to climate change in Northeast China.

Soybean phenological changes in response to climate warming in three northeastern provinces of China

ContextSoybean plays an important role in agricultural production in three northeastern provinces of China; furthermore, climate change, particularly temperature and rainfall changes, can significantly regulate soybean phenological periods. Research QuestionCurrent research on the spatial and temporal characteristics of soybean phenology and its relationship with climate change in the three northeastern provinces of China is still inadequate. MethodsIn this study, phenological and meteorological data from 26 agrometeorological stations in northeastern China were used to evaluate temporal and spatial changes and their correlations with soybean temperature, rainfall, phenological stage, and growth duration over the long term in three northeastern provinces. ResultsOn average, 22 of the 26 stations showed increases in temperature, and 15 stations showed increases in rainfall during the survey stage. All of soybean growth nodes were delayed, except R8, which advanced by 0.28 days, and all soybean growth durations were shortened except for during the V3-R1 period. In addition, temperature was negatively correlated with growth duration, while rainfall was positively correlated with growth duration. Further, the spatial and temporal distribution of temperature and rainfall showed that an increase in temperature led to an increase in thermal conditions and a significant northward and eastward expansion of the cumulative temperature zone, with the most significant increase in temperature occurring in most areas of Heilongjiang, followed by Liaoning. ConclusionsSoybean planting practices in the three eastern provinces should be adjusted to medium- and late-maturing varieties, which can adapt to climate change and mitigate its effects during the phenological period, due their tolerance to high heat. SignificanceThese findings fill a knowledge gap for understanding the relationship between soybean phenological changes and climate change in the three northeastern provinces.

Evaluation of BOLAM Fine Grid Weather Forecasts with Emphasis on Hydrological Applications

The evaluation of weather forecast accuracy is of major interest in decision making in almost every sector of the economy and in civil protection. To this, a detailed assessment of Bologna Limited-Area Model (BOLAM) seven days fine grid 3 h predictions is made for precipitation, air temperature, relative humidity, and wind speed over a large lowland agricultural area of a Mediterranean-type climate, characterized by hot summers and rainy moderate winters (plain of Arta, NW Greece). Timeseries that cover a four-year period (2016–2019) from seven agro-meteorological stations located at the study area are used to run a range of contingency and accuracy measures as well as Taylor diagrams, and the results are thoroughly discussed. The overall results showed that the model failed to comply with the precipitation regime throughout the study area, while the results were mediocre for wind speed. Considering relative humidity, the results revealed acceptable performance and good correlation between the model output and the observed values, for the early days of forecast. Only in air temperature, the forecasts exhibited very good performance. Discussion is made on the ability of the model to predict major rainfall events and to estimate water budget components as rainfall and reference evapotranspiration. The need for skilled weather forecasts from improved versions of the examined model that may incorporate post-processing techniques to improve predictions or from other forecasting services is underlined.

Determining the critical threshold of meteorological heat damage to tea plants based on MODIS LST products for tea planting areas in China

Tea (Camellia sinensis (L.) Kuntze) is a daily drink in modern life throughout the world. Global warming increases the exposure of tea plants to elevated heat conditions during the summer picking period. Therefore, it is a fundamental function of the meteorological service to determine the critical temperature threshold that triggers heat damage (HD) to tea plants. In this study, MODIS daytime land surface temperature (LST) products obtained from the Aqua satellite (MYD11A1), annual land cover type products (MCD12Q1), and actual HD records were jointly used to determine the critical LST threshold resulting in HD to tea planting areas in China. An LST dataset of HD and HD-free samples was constructed based on actual HD records. The box-plot method was then used to delineate the range of LST outliers in HD to tea plants. The cumulative frequency distribution (CFD) method was adopted to determine the critical LST threshold by searching for the maximum slope of CFD within the range of LST outliers based on 2901 actual HD samples from 42 agrometeorological stations in 11 provinces in China. Results showed that the critical LST threshold triggering HD to tea plants was 30.1 °C. Two different HD data sources (i.e., 68 counties in eight provinces from the Meteorological Disaster Management System (MDMS) and 37 counties in six cities from a published book) were used as HD validation samples. Validation accuracy analysis revealed that a critical LST threshold of 30.1 °C was reasonable, with an average accuracy of between 60% and 90% for the published-book dataset, and 75.7% in the northern Yangtze River area and 86.3% in the southern Yangtze River area for the MDMS dataset. This study confirmed that using remotely sensed LST products is an effective method for identifying temperature-related agrometeorological disasters owing to the method's ability to intuitively capture the surface temperature distribution of the disaster-affected object. The findings will be helpful for identifying the timing and location of HD to tea plants, and thereby potentially preventing HD to tea plants.

Characteristics of the Water Consumption Components of Winter Wheat Fields and Their Effects on the Loess Plateau under Climate Change: An Example at Xifeng Station, Gansu, China

Understanding the components of water consumption plays a critical role in agricultural management in arid regions. This study aimed to analyze the characteristics of the components of the water consumption of winter wheat on the Loess Plateau in China to investigate their effects on yield and water use efficiency (WUE). Winter wheat observation data were collected from 1981 to 2020 at the Xifeng Agrometeorological Station on the Loess Plateau. The results show that over the past 40 years, the average water consumption of the winter wheat fields was 315 mm, but there were large differences between years. The soil water was first converted from precipitation (P) during the growing season, accounting for 69.4%, and then consumed via soil water storage (ΔW) in the fallow period, accounting for 30.6%. The yield of winter wheat varied from 1057 to 6914 kg·ha−1. The correlation between winter wheat yield and P during the growth period was stronger than the correlation between winter wheat yield and ΔW. The average WUE was 11.0 kg·ha−1·mm−1 from 1981 to 2020, with the highest value of 13.7 kg·ha−1·mm−1 occurring in the 2010s and the lowest value of 9.6 kg·ha−1·mm−1 occurring in the 2000s. The WUE was positively correlated with P (p &lt; 0.01) during the growth period and negatively correlated with ΔW (p &lt; 0.05). Therefore, P can increase yield and WUE more effectively than ΔW. These findings provide a theoretical basis for the efficient utilization of water resources on the Loess Plateau and the sustainable development of dry farming.

Growing degree-hours and degree-days in two management zones for each phenological stage of wheat (Triticum aestivum L.).

Monitoring the climatic conditions of crops is essential for smart agriculture development and adaptation of agricultural systems in the era of global change. Thereby, it is possibly better to understand the stages of development of the crop, thus adopting management practices more efficiently and planning the harvest with greater accuracy. This study was developed to analyze the growing degree-hours and degree-days in two management zones (MZs) for each phenological stage of wheat (Triticum aestivum L.) and the application of low-cost agroclimatological stations to monitor the climatic conditions of the field production. The study was developed in a Ferralsol in Céu-Azul/Brazil. Ten low-cost agrometeorological stations were installed in two MZs delineated based on elevation data using the web platform AgDataBox. Data on solar radiation, atmospheric pressure, wind speed, precipitation, relative humidity, air, and soil temperature were evaluated over two wheat crop seasons. Our results showed different climatic conditions, especially humidity and temperature, between MZs and crop seasons, which could probably cause yield variability. By the low-cost agroclimatological stations, it is possible to collect data on the thermal accumulation by the culture in growing degree-hours, which is a more accurate parameter than the growing degree-days (commonly used in similar studies). With the growing degree-hours data, it was possible to follow the development of the phenological stages of wheat. In conclusion, the results obtained suggest the importance of evaluating agroclimatological parameters in monitoring wheat crops. However, more studies are needed in regions with greater slopes, which may have microclimates that intensely influence the crop.

Mid-season lodging modulates photosynthesis, evapotranspiration, and dry matter accumulation and distribution simulated by the optimized model in maize

IntroductionMid-season lodging in maize (Zea mays L.) often restricts grain yield potential and is a great hurdle in improving production efficiency. The aim of this research was to study the effects of lodging on photosynthesis, evapotranspiration, dry matter accumulation, and distribution in a maize population.MethodsWe examined the effects of lodging on photosynthesis, dry matter accumulation, and distribution of maize in Zhengzhou Agro-meteorological station in August 2016, following a strong wind lodging process. Based on observational data of crops, meteorology, and CO2/H2O flux from milk maturity to maturity of maize in a normal growth year (2017), a model of population photosynthesis and evapotranspiration of maize under normal growth conditions was constructed. The validated model was used to simulate the theoretical value of photosynthesis and evapotranspiration in lodging years (2016), then the measured value of population photosynthesis and evapotranspiration after lodging was calculated based on the measured data of CO2/H2O flux from milk maturity to maturity of maize, and the difference between the simulated value and the measured value of population photosynthesis and evapotranspiration in lodging years (2016) was compared and analyzed. The correlation between dry matter accumulation and population photosynthetic accumulation was examined in order to estimate the reduction of dry matter accumulation after lodging. The effect of lodging on dry matter accumulation, distribution, and yield was analyzed using field biomass data. The population photosynthesis model and evapotranspiration model could accurately simulate the characteristics of normal growth conditions of summer maize.ResultsThe population photosynthesis model absolute error between the simulated value and the measured values in 2017 was −0.43 mg m−2 s−1, and the relative error was −3.3%; the evapotranspiration model absolute error between the simulated value and the measured values in 2017 was −0.005 mm·30 min−1, and the relative error was −10.7%. In 2016, the measured value of photosynthesis after lodging was significantly lower than the simulated value, and the daily average population photosynthesis rate decreased by 13.99 mg m−2 s−1 or 53%. Under the same condition, the daily average evapotranspiration decreased by 1.03 mm d−1 or 28%. The lodging process altered the accumulation and distribution of dry matter in maize. The dry weight of the stem and sheath increased by 5.5% and the ear weight decreased by 10.9% after lodging, compared to without lodging but there was no significant effect on leaf dry weight. After lodging, the proportion of stem sheath distribution increased by 3.0%, while the proportion of ear distribution decreased by 3.0%. After lodging, 100-grain weight and plant grain weight decreased by 2.8 and 10.8%, respectively. According to the lodging rate and density theory of computation yield, the yield of the entire field was reduced by 5.0%.

Calibration of MODIS-Derived Cropland Growing Season Using the Climotransfer Function and Ground Observations

The global environment experienced notable changes in the recent past of planet Earth. Satellite remote sensing has played an increasingly important role in monitoring and characterizing these changes. Being recognized as a sensitive indicator of global climate change, land surface phenology (LSP) observations by satellite remote sensing have received much attention in recent years; however, much less attention has been paid to the calibration of these observations using standardized procedures. Here, we propose a new approach to calibrating the satellite LSP products by developing a climotransfer function (CTF) based on a polynomial regression of the satellite-ground observation difference in key crop phenophases against climatic factors. We illustrate the model development and evaluation process with a case study of the cropland growing season in Northeast China (NEC) from 2001 to 2010 using the MODIS LSP product MCD12Q2 Collection 6 and the ground-observed crop phenology and climatic data from 98 agrometeorological stations across the region. Our results showed that the start of the cropland growing season (SOS) derived from MODIS data compared well to the ground-observed SOS, whereas the MODIS-derived season end (EOS) was delayed by 15.5 d, relative to ground observation. The MODIS-derived EOS was, therefore, spatiotemporally calibrated using a CTF model fitted to the satellite-ground difference in EOS (∆EOS) versus two climatic factors, namely, the growing degree-days on the base temperature of 10 °C (GDD10) and cloud cover (CL). The calibrated MODIS data revealed that the cropland growing season in NEC tended to shorten at 4.5 d decade−1 during 2001–2010, mainly driven by a significant delay in SOS at a similar rate, whereas no trend was detected for EOS. The calibrated data also revealed a significant shortening gradient of 1.7 d degree−1 of latitude northward. These spatiotemporal patterns would have been erroneously characterized if calibration had not been applied. More attention is therefore called to the proper calibration of satellite LSP products prior to any meaningful applications.

Daily Estimation of Global Solar Irradiation and Temperatures Using Artificial Neural Networks through the Virtual Weather Station Concept in Castilla and León, Spain.

In this article, the interpolation of daily data of global solar irradiation, and the maximum, average, and minimum temperatures were measured. These measurements were carried out in the agrometeorological stations belonging to the Agro-climatic Information System for Irrigation (SIAR, in Spanish) of the Region of Castilla and León, in Spain, through the concept of Virtual Weather Station (VWS), which is implemented with Artificial Neural Networks (ANNs). This is serving to estimate data in every point of the territory, according to their geographic coordinates (i.e., longitude and latitude). The ANNs of the Multilayer Feed-Forward Perceptron (MLP) used are daily trained, along with data recorded in 53 agro-meteorological stations, and where the validation of the results is conducted in the station of Tordesillas (Valladolid). The ANN models for daily interpolation were tested with one, two, three, and four neurons in the hidden layer, over a period of 15 days (from 1 to 15 June 2020), with a root mean square error (RMSE, MJ/m2) of 1.23, 1.38, 1.31, and 1.04, respectively, regarding the daily global solar irradiation. The interpolation of ambient temperature also performed well when applying the VWS concept, with an RMSE (°C) of 0.68 for the maximum temperature with an ANN of four hidden neurons, 0.58 for the average temperature with three hidden neurons, and 0.83 for the minimum temperature with four hidden neurons.

Simulation of Urban Heat Island during a High-Heat Event Using WRF Urban Canopy Models: A Case Study for Metro Manila

This present study aims to determine the performance of using the Weather Research and Forecasting (WRF) Model, coupled with the urban canopy models (UCMs), in simulating the 2 m air temperature and 2 m relative humidity in Metro Manila. The simulation was performed during a high heat event on 22–29 April 2018, which coincided with the dry season in the Philippines. The four urban canopy model options that were used in this study include, the bulk (no urban), SLUCM, BEP, and BEM. The results of the simulations were compared with the hourly observations from three weather stations over Metro Manila from the National Oceanic and Atmospheric Administration Integrated Surface Dataset (ISD) and one agrometeorological station in Naic, Cavite. After model validation, the urban heat island (UHI) was then characterized to determine the spatial-temporal variations in the cities of Metro Manila. Statistical results show that the WRF simulation for 2 m air temperature agrees with measurements with an RMSE of &lt;3.0 °C, mean bias error of &lt;2.0 °C, and index of agreement of &gt;0.80. WRF simulation for relative humidity still presents a challenge where simulation errors are higher than the acceptable range. The addition of UCMs does not necessarily improve the simulation for 2 m air temperature, while the use of BEP improved the 2 m relative humidity simulation. The results suggest the importance of using actual urban morphology values in WRF to accurately simulate near-surface variables. On the other hand, WRF simulation shows the presence of urban heat islands, notably in the northwest and central area of Metro Manila during daytime, extending throughout Metro Manila during nighttime. Lower air temperature was consistently observed in areas near Laguna Lake, while higher air temperature due to stagnant winds was observed in the northwest area of Metro Manila. High heat index was also observed throughout Metro Manila from daytime until nighttime, especially in areas near bodies of water like Manila Bay and Laguna Lake due to high humidity.

A Comparison of Some Methods for Determination of Potential Evapotranspiration for Rogerm Basin, Mangesh, Kurdistan Region of Iraq

Potential evapotranspiration is an important component of the hydrological cycle at various spatial scales that impacts the runoff quantity and the irrigation water requirements. Potential evapotranspiration is a projectile worker in the ecosystem of the operation of evapotranspiration. The aims of this is to find the most suitable method for calculating monthly potential evapotranspiration in Rogerm basin area by comparing several methods. The climatic data for the period 2012-2021 were used in the models to estimate the potential evapotranspiration. The performance index was applied by using statistical criteria including R2, RMSE, MBE, and MAPE were used to compare the FAO-56-PM into four temperature-based methods and four radiation-based methods of PET at Mangesh Agrometeorological station. The study found that the Hargraves method is the most accurate one compared with other

A Comparison of Some Methods for Determination of Potential Evapotranspiration for Rogerm Basin, Mangesh, Kurdistan Region of Iraq

Potential evapotranspiration is an important component of the hydrological cycle at various spatial scales that impacts the runoff quantity and the irrigation water requirements. Potential evapotranspiration is a projectile worker in the ecosystem of the operation of evapotranspiration. The aims of this is to find the most suitable method for calculating monthly potential evapotranspiration in Rogerm basin area by comparing several methods. The climatic data for the period 2012-2021 were used in the models to estimate the potential evapotranspiration. The performance index was applied by using statistical criteria including R2, RMSE, MBE, and MAPE were used to compare the FAO-56-PM into four temperature-based methods and four radiation-based methods of PET at Mangesh Agrometeorological station. The study found that the Hargraves method is the most accurate one compared with other

Response and Adaptation of Single-Cropping Rice Growth Periods to Sowing Date, Cultivar Shift and Extreme Temperatures in China from 1981 to 2010

Crop growth period responses to climate change directly affect yield and quality; these changes have been investigated, but few studies have researched the quantitative effects of the sowing date (SD), cultivar shift and extreme temperatures on the vegetative growth period (VGP) and reproductive growth period (RGP) of crops. Based on the observed growth periods and weather data from 30 agro-meteorological stations in Northeast China (NEC), the middle and lower reaches of the Yangtze River (MLYR), the Sichuan Basin (SB) and on the Yunnan-Guizhou Plateau (YGP) during 1981–2010, we found that (1) advancing the SD resulted in the extension of the VGP but had no significant effect on the RGP. (2) Cultivar shift mainly prolonged the RGP, and its mean contribution to the trend in the RGP (68.4%) was greater than that of climate change (31.6%). (3) Increases in growing degree days (GDD) tended to lengthen the VGP and RGP, and their mean relative contribution to the VGP (65.9%) was higher than that of the RGP (58.4%). (4) Increases in killing degree days (KDD) and decreases in cold degree days (CDD) tended to shorten the VGP and RGP, and the mean relative contribution of KDD to the VGP (− 18.8%) was lower than that of the RGP (− 29.8%), while the mean relative contribution of CDD to the VGP (− 15.2%) was higher than that of the RGP (− 10.9%). The findings highlight the adverse effects of KDD and CDD on the growth periods of single-cropping rice and show how a reasonable SD scientifically matched with suitable cultivar types can make full use of climate resources and mitigate the adverse effects of extreme temperatures.