Artificial Neural Networks Application to Predict Wheat Yield Using Climatic Data

The goal of this study was to apply artificial neural networks to predict rain-fed wheat yield using meteorological data a few days to few months before harvesting. The climatic observation data used; were mean of daily minimum and maximum temperature, extreme of daily minimum and maximum temperature, sum of daily rainfall, number of rainy days, sum of daily sun hours, mean of daily wind speed, extreme of daily wind speed, mean of daily relative humidity, and sum of daily water requirements that were collected during 1990-1999 in Sararood Station for wheat phenological stages consisting; sowing, germination, emergence, 3rd leaves, tillering, stem formation, heading, flowering, milk maturity, wax maturity, full maturity, separately for each growing season. Then, they arranged in a matrix whose rows form each of the statistical years and the columns are meteorological factors at each phenological stage. Finally, the obtained model had the following capabilities： Prediction of wheat yield with maximum errors of 45-60 kg/ha at least two months before full maturity stage, determination of the sensitivity of each phenological stage with respect to meteorological factors, and determination of the priority order and importance of each meteorological factor effective in plant growth and crop yield.

A method for extracting the preseismic gravity anomalies over the Tibetan Plateau based on the maximum shear strain using GRACE data

The occurrence of earthquakes is closely related to the crustal geotectonic movement and the migration of mass,which consequently cause changes in gravity.The Gravity Recovery And Climate Experiment(GRACE)satellite data can be used to detect gravity changes associated with large earthquakes.However,previous GRACE satellite-based seismic gravity-change studies have focused more on coseismic gravity changes than on preseismic gravity changes.Moreover,the noise of the north–south stripe in GRACE data is difficult to eliminate,thereby resulting in the loss of some gravity information related to tectonic activities.To explore the preseismic gravity anomalies in a more refined way,we first propose a method of characterizing gravity variation based on the maximum shear strain of gravity,inspired by the concept of crustal strain.The offset index method is then adopted to describe the gravity anomalies,and the spatial and temporal characteristics of gravity anomalies before earthquakes are analyzed at the scales of the fault zone and plate,respectively.In this work,experiments are carried out on the Tibetan Plateau and its surrounding areas,and the following findings are obtained:First,from the observation scale of the fault zone,we detect the occurrence of large-area gravity anomalies near the epicenter,oftentimes about half a year before an earthquake,and these anomalies were distributed along the fault zone.Second,from the observation scale of the plate,we find that when an earthquake occurred on the Tibetan Plateau,a large number of gravity anomalies also occurred at the boundary of the Tibetan Plateau and the Indian Plate.Moreover,the aforementioned experiments confirm that the proposed method can successfully capture the preseismic gravity anomalies of large earthquakes with a magnitude of less than 8,which suggests a new idea for the application of gravity satellite data to earthquake research.

Current Climate Data Rescue Activities in Australia

1. Introduction Recovering historical instrumental climate data is crucial for identifying long-term climate variability and change, putting present climate into context and constraining future climate projections （Brunet and Jones, 2011）. In other words, to understand the future, we need to improve our understanding of the past.

Water storage changes in North America retrieved from GRACE gravity and GPS data

As global warming continues,the monitoring of changes in terrestrial water storage becomes increasingly important since it plays a critical role in understanding global change and water resource management.In North America as elsewhere in the world,changes in water resources strongly impact agriculture and animal husbandry.From a combination of Gravity Recovery and Climate Experiment（GRACE） gravity and Global Positioning System（GPS） data,it is recently found that water storage from August,2002 to March,2011 recovered after the extreme Canadian Prairies drought between 1999 and 2005.In this paper,we use GRACE monthly gravity data of Release 5 to track the water storage change from August,2002 to June,2014.In Canadian Prairies and the Great Lakes areas,the total water storage is found to have increased during the last decade by a rate of 73.8 ± 14.5 Gt/a,which is larger than that found in the previous study due to the longer time span of GRACE observations used and the reduction of the leakage error.We also find a long term decrease of water storage at a rate of-12.0 ± 4.2 Gt/a in Ungava Peninsula,possibly due to permafrost degradation and less snow accumulation during the winter in the region.In addition,the effect of total mass gain in the surveyed area,on present-day sea level,amounts to-0.18 mm/a,and thus should be taken into account in studies of global sea level change.

Climate Data Interpolation for Shkumbini WEAP Model

Albania,like almost every country in the world,is continuously facing challenges in terms of the integrated management of water recourses.Limited access to water resources,the degrading quality of the environment,both being closely related to various policies regarding sustainable development of the water resources,are some of the main issues in this field.In conformity with the requirements of the EU Water Framework Directive Albania has to develop water management plans for seven main river basins(including Shkumbini River Basin),which have been established in the country according to the Decision No.696,date 30.10.2019.The main goal of this study was the development of an integrated hydrological and water management model to evaluate the climate and development scenarios for the Shkumbini River Basin.The study applies the software WEAP(Water Evaluation and Planning)by SEI(Stockholm Environment Institute)to simulate and analyze a set of hydro-ecological and socio-economical scenarios in the Shkumbini River to identify its fundamental vulnerabilities to climate change between the years 2017-2050.Understanding specific vulnerabilities within a basin allows planners to propose and prioritize potential adaptation measures,which can be further examined with cost-benefit analyses.The spatially-based models can incorporate climatic and land use conditions that determine water supply,and this allows the model to investigate diverse changes within the system to consider the various outcomes of uncertain futures,whether climatic,managerial,infrastructural or demographic.

Detecting Climate Change in Using Extreme Data from Two Surface Weather Stations: Case Study Valle of Comitan and La Esperanza, Chiapas, Mexico

The study area is located between the cities of Comitan (16°10'43"N and 92°04'20''W) a city with 150,000 inhabitants and La Esperanza (16°9'15''N and 91°52'5''W) a town with 3000 inhabitants. Both weather stations are 30 km from each other in the Chiapas State, México. 54 years of daily records of the series of maximum (tmax) and minimum temperatures (tmin) of the weather station 07205 Comitan that is on top of a house and 30 years of daily records of the weather station 07374 La Esperanza were analyzed. The objective is to analyze the evidence of climate change in the Comitan valley. 2.07% and 19.04% of missing data were filled, respectively, with the WS method. In order to verify homogeneity three methods were used: Standard Normal Homogeneity Test (SNHT), the Von Neumann method and the Buishand method. The heterogeneous series were homogenized using climatol. The trends of tmax and tmin for both weather stations were analyzed by simple linear regression, Sperman’s rho and Mann-Kendall tests. The Mann-Kendal test method confirmed the warming trend at the Comitan station for both variables with ZMK statistic values equal to 1.57 (statistically not significant) and 4.64 (statistically significant). However, for the Esperanza station, it determined a cooling trend for tmin and a slight non-significant warming for tmax with a ZMK statistic of -2.27 (statistically significant) and 1.16 (statistically not significant), for a significance level α = 0.05.

An Energy Production System Powered by Solar Heat with Biogas Dry Reforming Reactor and Solid Oxide Fuel Cell

In this paper, an energy system consisting of solar collector, biogas dry reforming reactor and solid oxide fuel cell (SOFC) has been proposed. The heat produced from the concentrating solar collector is used to drive a biogas dry reforming reactor in order to produce H2 as a fuel for SOFC, in such as system. The aim of this study is to clarify the impact of climate data on the performance of solar collector with various sizes/designs. The temperature of heat transfer fluid produced by the solar collector is calculated by adopting the climate data for Nagoya city in Japan in 2021. The amount of H2 produced from the biogas dry reforming reactor and the power generated by SOFC were simulated. The results show the temperature of heat transfer fluid (Tfb) and Tfb ratio (a) based on the length of absorber (dx) = 1 m have a peak near the noon following the trend of solar intensity (I). Results also revealed that a increases with increase in dx. It is found that the differences of Tfb and a between dx = 2 m and dx = 3 m are larger than those between dx = 1 m and dx = 2 m. It is revealed that Tfb and a are higher in spring and summer. dx = 4 m is the optimum length of solar absorber. The amount of H2 produced from the biogas dry reforming reactor as well as the power generated by SOFC is the highest in August, resulting that it is prefer to produce H2 and to generate SOFC in summer.

Ontology Based Ocean Knowledge Representation for Semantic Information Retrieval

The drastic growth of coastal observation sensors results in copious data that provide weather information.The intricacies in sensor-generated big data are heterogeneity and interpretation,driving high-end Information Retrieval(IR)systems.The Semantic Web(SW)can solve this issue by integrating data into a single platform for information exchange and knowledge retrieval.This paper focuses on exploiting the SWbase systemto provide interoperability through ontologies by combining the data concepts with ontology classes.This paper presents a 4-phase weather data model:data processing,ontology creation,SW processing,and query engine.The developed Oceanographic Weather Ontology helps to enhance data analysis,discovery,IR,and decision making.In addition to that,it also evaluates the developed ontology with other state-of-the-art ontologies.The proposed ontology’s quality has improved by 39.28%in terms of completeness,and structural complexity has decreased by 45.29%,11%and 37.7%in Precision and Accuracy.Indian Meteorological Satellite INSAT-3D’s ocean data is a typical example of testing the proposed model.The experimental result shows the effectiveness of the proposed data model and its advantages in machine understanding and IR.

Spatial downscaling of climate variables using three statistical methods in Central Iran

Spatial downscaling methods are widely used for the production of bioclimatic variables(e.g. temperature and precipitation) in studies related to species ecological niche and drainage basin management and planning. This study applied three different statistical methods, i.e. the moving window regression(MWR), nonparametric multiplicative regression(NPMR), and generalized linear model(GLM), to downscale the annual mean temperature(Bio1) and annual precipitation(Bio12) in central Iran from coarse scale(1 km × 1 km) to fine scale(250 m ×250 m). Elevation, aspect, distance from sea and normalized difference vegetation index(NDVI) were used as covariates to create downscaled bioclimatic variables. Model assessment was performed by comparing model outcomes with observational data from weather stations. Coefficients of determination(R2), bias, and root-mean-square error(RMSE) were used to evaluate models and covariates. The elevation could effectively justify the changes in bioclimatic factors related to temperature and precipitation. Allthree models could downscale the mean annual temperature data with similar R2, RMSE, and bias values. The MWR had the best performance and highest accuracy in downscaling annual precipitation(R2=0.70; RMSE=123.44). In general, the two nonparametric models, i.e. MWR and NPMR, can be reliably used for the downscaling of bioclimatic variables which have wide applications in species distribution modeling.

Sources of uncertainty in exploring rangeland phenology： A case study in an alpine meadow on the central Tibetan Plateau

Global climate change has been found to substantially influence the phenology of rangeland,especially on the Tibetan Plateau. However, there is considerable controversy about the trends and causes of rangeland phenology owing to different phenological exploration methods and lack of ground validation. Little is known about the uncertainty in the exploration accuracy of vegetation phenology.Therefore, in this study, we selected a typical alpine rangeland near Damxung national meteorological station as a case study on central Tibetan Plateau, and identified several important sources influencing phenology to better understand their effects on phenological exploration. We found man-made land use was not easily distinguished from natural rangelands, and therefore this may confound phenological response to climate change in the rangeland. Change trends of phenology explored by four methods were similar, but ratio threshold method(RTM) was more suitable for exploring vegetation phenology in terms of the beginning of growing season(BGS) and end of growing season(EGS). However, some adjustments are needed when RTM is used in extreme drought years. MODIS NDVI/EVI dataset was most suitable for exploring vegetation phenology of BGS and EGS. The discrimination capacities of vegetation phenology declined with decreasing resolution of remote sensing images from MODIS to GIMMS AVHRR datasets. Additionally, distinct trends of phenological change rates were indicated in different terrain conditions, with advance of growing season in high altitudes but delay of season in lower altitudes. Therefore, it was necessary to eliminate interference of complex terrain and man-made land use to ensure the representativeness of natural vegetation. Moreover, selecting the appropriate method to explore rangelands and fully considering the impact of topography are important to accurately analyze the effects of climate change on vegetation phenology.

>Evaluation of dust activity and climate effects in North China

TOMS/AI data with nearly 20 years are utilized in the paper to evaluate dust activities in North China. Combined with simultaneous NCEP reanalysis climate data, climate effects on dust activities are assessed. The results showed that the whole North China suffers impact by dust aerosols, with three centers standing out in TOMS/AI spring average map that are western three basins, which are characterized by lower annual precipitation and elevation. Gobi deserts in Mongolia Plateau do not attain higher TOMS/AI value due to cloud contamination and relative higher elevation. Spring is the season with the highest TOMS dust aerosol index; within the western three basins, high dust aerosol index appears in both spring and summer, especially in Tarim Basin. Wind speed in spring and precipitation in previous rainy season play important roles in controlling dust activities, higher wind speed and less precipitation than the normal are in favor of dust activities in spring. Temperature in spring and previous winter also affect dust activity to a certain extent, but with contrary spatial distribution. Temperature in winter exert effect principally in west part, contrarily, temperature effect in spring is mainly shown in east part. Both of them have negative correlation with dust activity.

Monthly gravity field solution from GRACE range measurements using modified short arc approach

In this paper we present a series of monthly gravity field solutions from Gravity Recovery and Climate Experiment（GRACE） range measurements using modified short arc approach,in which the ambiguity of range measurements is eliminated via differentiating two adjacent range measurements.The data used for developing our monthly gravity field model are same as Tongji-GRACEOl model except that the range measurements are used to replace the range rate measurements,and our model is truncated to degree and order 60,spanning Jan.2004 to Dec.2010 also same as Tongji-GRACE01 model.Based on the comparison results of the C_（2,0）,C_（2,1）,S_（2,1）,and C_（15,15）,S_（15,15）,time series and the global mass change signals as well as the mass change time series in Amazon area of our model with those of Tongji-GRACE01 model,we can conclude that our monthly gravity field model is comparable with Tongji-GRACE01 monthly model.

Effects of spatial and temporal weather data resolutions on streamflow modeling of a semi-arid basin,Northeast Brazil

One major difficulty in the application of distributed hydrological models is the availability of data with sufficient quantity and quality to perform an adequate evaluation of a watershed and to capture its dynamics.The Soil&Water Assessment Tool(SWAT)was used in this study to analyze the hydrologic responses to different sources,spatial scales,and temporal resolutions of weather inputs for the semi-arid Jaguaribe watershed(73000 km2)in northeastern Brazil.Four different simulations were conducted,based on four groups of weather and precipitation inputs:Group 1-SWAT Weather Generator based on monthly data from four airport weather stations and daily data based on 124 local rain gauges;Group 2-daily local data from 14 weather stations and 124 precipitation gauges;Group 3-Daily values from a global coupled forecast model(NOAA’s Climate Forecast System Reanalysis-CFSR);and Group 4-CFSR data with 124 local precipitation gauges.The four simulations were evaluated using multiple statistical efficiency metrics for four streamflow gauges,using:Nash-Sutcliffe coefficient(NSE),determination coefficient(R2),the ratio of the root mean square to the standard deviation of the observed data(RSR),and the percent bias(PBIAS).The Group 4 simulation performed best overall(provided the best statistical values)with results ranked as“good”or“very good”on all four efficiency metrics suggesting that using CFSR data for weather parameters other than precipitation,coupled with precipitation data from local rain gauges,can provide reasonable hydrologic responses.The second best results were obtained with Group 1,which provided“good”results in three of four efficiency metrics.Group 2 performed worse overall than Groups 1 and 4,probably due to uncertainty related to daily measures and a large percentage of missing data.Groups 2 and 3 were“unsatisfactory”according to three or four of the efficiency metrics,indicating that the choice of weather data is very important.

Long time data series and data stewardship reference model

The need for accessing historical Earth Observation(EO)data series strongly increased in the last ten years,particularly for long-term science and environmental monitoring applications.This trend is likely to increase even more in the future,in particular regarding the growing interest on global change monitoring which is driving users to request time-series of data spanning 20 years and more,and also due to the need to support the United Nations Framework Convention on Climate Change(UNFCCC).While much of the satellite observations are accessible from different data centers,the solution for analyzing measurements collected from various instruments for time series analysis is both difficult and critical.Climate research is a big data problem that involves high data volume of measurements,methods for on-the-fly extraction and reduction to keep up with the speed and data volume,and the ability to address uncertainties from data collections,processing,and analysis.The content of EO data archives is extending from a few years to decades and therefore,their value as a scientific time-series is continuously increasing.Hence there is a strong need to preserve the EO space data without time constraints and to keep them accessible and exploitable.The preservation of EO space data can also be considered as responsibility of the Space Agencies or data owners as they constitute a humankind asset.This publication aims at describing the activities supported by the European Space Agency relating to the Long Time Series generation with all relevant best practices and models needed to organise and measure the preservation and stewardship processes.The Data Stewardship Reference Model has been defined to give an overview and a way to help the data owners and space agencies in order to preserve and curate the space datasets to be ready for long time data series composition and analysis.

Generating high-resolution climate maps from sparse and irregular observations using a novel hybrid RBF network

Sparse and irregular climate observations in many developing countries are not enough to satisfy the need of assessing climate change risks and planning suitable mitigation strategies.The wideused statistical downscaling model(SDSM)software tools use multi-linear regression to extract linear relations between largescale and local climate variables and then produce high-resolution climate maps from sparse climate observations.The latest machine learning techniques(e.g.SRCNN,SRGAN)can extract nonlinear links,but they are only suitable for downscaling low-resolution grid data and cannot utilize the link to other climate variables to improve the downscaling performance.In this study,we proposed a novel hybrid RBF(Radial Basis Function)network by embedding several RBF networks into new RBF networks.Our model can well incorporate climate and topographical variables with different resolutions and extract their nonlinear relations for spatial downscaling.To test the performance of our model,we generated high-resolution precipitation,air temperature and humidity maps from 34 meteorological stations in Bangladesh.In terms of three statistical indicators,the accuracy of high-resolution climate maps generated by our hybrid RBF network clearly outperformed those using a multi-linear regression(MLR),Kriging interpolation or a pure RBF network.

Durum wheat yield forecasting using machine learning

A reliable and accurate forecasting model for crop yields is crucial for effective decision-making in every agricultural sector.Machine learning approaches allow for building such predictive models,but the quality of predictions decreases if data is scarce.In this work,we proposed data-augmentation for wheat yield forecasting in the presence of small data sets of two distinct Provinces in Algeria.We first increased the dimension of each data set by adding more features,and then we augmented the size of the data by merging the two data sets.To assess the effectiveness of data-augmentation approaches,we conducted three sets of experiments based on three data sets:the primary data sets,data sets with additional features and the augmented data sets obtained by merging,using five regression models(Support Vector Regression,Random Forest,Extreme Learning Machine,Artificial Neural Network,Deep Neural Network).To evaluate the models,we used cross-validation;the results showed an overall increase in performance with the augmented data.DNN outperformed the other models for the first Province with a Root Mean Square Error(RMSE)of 0.04 q/ha and R_Squared(R^(2))of 0.96,whereas the Random Forest outperformed the other models for the second Province with RMSE of 0.05 q/ha.