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Climate change in infographics
\"Readers will learn about the causes and effects of climate change through colorful and clear graphics, such as maps, charts, and infographics. Book also includes a glossary, index, suggested books and websites, and a bibliography\"-- Provided by publisher.
Book
Support-Vector-Machine-Based Models for Modeling Daily Reference Evapotranspiration With Limited Climatic Data in Extreme Arid Regions
Evapotranspiration is a major factor that controls hydrological process and its accurate estimation provides valuable information for water resources planning and management, particularly in extremely arid regions. The objective of this research was to evaluate the use of a support vector machine (SVM) to model daily reference evapotranspiration (ET₀) using limited climatic data. For the SVM, four combinations of maximum air temperature (T ₘₐₓ), minimum air temperature (T ₘᵢₙ), wind speed (U ₂) and daily solar radiation (R ₛ) in the extremely arid region of Ejina basin, China, were used as inputs with T ₘₐₓ and T ₘᵢₙ as the base data set. The results of SVM models were evaluated by comparing the output with the ET₀calculated using Penman–Monteith FAO 56 equation (PMF-56). We found that the ET₀estimated using SVM with limited climatic data was in good agreement with those obtained using the conventional PMF-56 equation employing the full complement of meteorological data. In particular, three climatic parameters, T ₘₐₓ , T ₘᵢₙ , and R ₛ were enough to predict the daily ET₀satisfactorily. Moreover, the performance of SVM method was also compared with that of artificial neural network (ANN) and three empirical models including Priestley-Taylor, Hargreaves, and Ritchie. The results showed that the performance of SVM method was the best among these models. This offers significant potential for more accurate estimation of the ET₀with scarce data in extreme arid regions.
Journal Article
Relative Homogenization of Climatic Time Series
Homogenization of the time series of observed climatic data aims to remove non-climatic biases caused by technical changes during the history of the climate observations. The spatial redundancy of climate information helps to recognize station-specific inhomogeneities with statistical methods, but the correct detection and removal of inhomogeneity biases is generally not easy for the combined effects of individual inhomogeneities. In a homogenization procedure, several time series of a given climatic variable observed in one climatic region are usually homogenized together via a large number of spatial comparisons between them. Such procedures are called relative homogenization. A relative homogenization procedure may include one or more homogenization cycles where a cycle includes the steps of time series comparison, inhomogeneity detection and corrections for inhomogeneities, and they may include other steps like the filtering of outlier values or spatial interpolations for infilling data gaps. Relative homogenization methods differ according to the number and content of the individual homogenization cycles, the procedure for the time series comparisons, the statistical inhomogeneity detection method, the way of the inhomogeneity bias removal, among other specifics. Efficient homogenization needs the use of tested statistical methods to be included in partly or fully automated homogenization procedures. Due to the large number and high variety of homogenization experiments fulfilled in the Spanish MULTITEST project (2015–2017), its method comparison test results are still the most informative about the efficiencies of homogenization methods in use. This study presents a brief review of the advances in relative homogenization, recalls some key results of the MULTITEST project, and analyzes some theoretical aspects of successful homogenization.
Journal Article
Real‐Climatic Microcontroller‐in‐the‐Loop (RCMIL) Framework: A Novel, Rapid, and Cost‐Effective Approach for Verifying Photovoltaic Control Systems
In the search for sustainable energy solutions, photovoltaic (PV) systems have emerged as a primary focus of innovation, attracting substantial worldwide interest in recent decades. Among the essential study topics, the development of control techniques, such as maximum power point tracking (MPPT), is a thriving and quickly growing discipline. Despite multiple advances, many control systems remain confined to simulation settings, far from practical deployment due to obstacles such as prohibitive cost (especially for researchers from developing countries) and system complexity. This disparity highlights the critical need for cost‐effective, dependable verification systems that can bridge the gap between theory and real‐world application. This paper therefore proposes the Real‐Climatic Microcontroller‐in‐the‐Loop (RCMIL) architecture, a pioneering platform for quick and realistic verification of MPPT controllers. By harnessing real‐climatic functionalities with Microcontroller‐in‐the‐Loop (MIL) execution, the RCMIL provides a practical‐like environment, allowing the implementation of PV control systems in real‐world situations, mimicking the nonlinear and stochastic character of weather patterns in any geographic region. Experimental results demonstrate the framework's efficacy, with the real‐climatic efficiency (RCE) reaching 97.15% under slowly changing conditions, while the real‐climatic mean absolute error (RCMAE) and real‐climatic mean absolute percentage error (RCMAPE) for voltage were measured at 4.55 V and 18.77%, respectively. Furthermore, the MIL feature allows the system to work on commercial microcontrollers, which reflects the real‐world problems that control algorithms would encounter in actual deployment circumstances. The study describes a clear, reproducible process for constructing the RCMIL platform and illustrates its efficacy through a series of experiments with proven MPPT controllers. The findings demonstrate the RCMIL's remarkable applicability for quick, low‐cost verification of PV control schemes, making it a valuable tool for researchers, engineers, and industry experts alike. By pushing the frontiers of PV system research, this paper provides a vital resource that speeds up the transition from simulation to real‐world applications. The Real‐Climatic Microcontroller‐in‐the‐Loop (RCMIL) Framework provides a novel, rapid, and cost‐effective platform for verifying photovoltaic (PV) control systems under real‐world climatic conditions. By integrating real climatic data with Microcontroller‐in‐the‐Loop execution, this approach bridges the gap between simulations and practical deployment, enhancing the reliability and accessibility of MPPT controller validation.
Journal Article
Daily Climatic Data Better Explain the Radial Growth of Swiss Stone Pine (Pinus cembra L.) in High-Elevation Cliffs in the Carpathians
Information about climate–growth relationships is crucial for predicting the potential climatic impact on tree species, especially those growing on the edges of their distribution range, for instance, in high-elevation forests. This study aimed to determine changes in the relationships between tree-ring widths and daily climatic data in high-elevation forests in the Western Carpathians over time. Climate–growth relationships were calculated to obtain the TRWI (tree-ring-width index) chronology (based on 104 trees) and day-wise aggregated data for temperature (mean, minimum, and maximum) and sums of precipitation. The radial growth of stone pine was mostly determined by the mean temperature in the period between mid-June (21st) and the beginning of July (4th) for the critical 14-day window width (r = 0.44). The negative influence of precipitation on the radial growth occurred in summer (r = −0.35) and overlapped with the period of the positive influence of temperature. Dendroclimatic studies based on daily data may define the exact periods (expressed in calendar days) that influence the radial growth of trees much better than the commonly used monthly means. This is particularly important in analysing the growth of trees at high elevations, where the climatic factor strongly limits radial growth.
Journal Article
Simulating reference crop evapotranspiration with different climate data inputs using Gaussian exponential model
Obtaining accurate data on reference crop evapotranspiration (
ET
0
) is important for agricultural water management. A novel Gaussian exponential model (GEM) was developed in this study to predict
ET
0
with limited climatic data. The GEM was further compared with the M5 model tree (M5T), extreme learning machine (ELM), and boosted trees (BT) model under local and regional scenarios. Daily meteorological data during 1997–2016 from four stations in Northeast China were used to develop and validate the model. The results showed that the models considering solar radiation and relative humidity demonstrated considerably higher accuracy than those using other inputs. The GEM demonstrated higher accuracy among the four machine learning models for different stations. The accuracy of GEM under local scenarios was higher than that under regional scenarios with the root mean square error (RMSE) reducing by 0.025–0.046 mm/d, relative root mean square error (RRMSE) reducing by 0.879–2.022%, coefficient of efficiency (E
ns
) increasing by 0.008–0.026, the coefficients of determination (
R
2
) increasing by 0.008–0.026, and mean absolute error (MAE) reducing by 0.015–0.033 mm/d. The GEM considering solar radiation had the highest accuracy with the global performance indicator (GPI) of 1.876. It can also be seen from the Taylor diagrams that the GEM has the the lowest standard deviation and mean square error and the highest correlation coefficient with the standard values. In general, the GEM considering solar radiation had the lowest error and the highest consistency and could be recommended for
ET
0
simulation for Northeast China.
Journal Article
Deep Learning for Flash Drought Detection: A Case Study in Northeastern Brazil
Flash droughts (FDs) pose significant challenges for accurate detection due to their short duration. Conventional drought monitoring methods have difficultly capturing this rapidly intensifying phenomenon accurately. Machine learning models are increasingly useful for detecting droughts after training the models with data. Northeastern Brazil (NEB) has been a hot spot for FD events with significant ecological damage in recent years. This research introduces a novel 2D convolutional neural network (CNN) designed to identify spatial FDs in historical simulations based on multiple environmental factors and thresholds as inputs. Our model, trained with hydro-climatic data, provides a probabilistic drought detection map across northeastern Brazil (NEB) in 2012 as its output. Additionally, we examine future changes in FDs using the Coupled Model Intercomparison Project Phase 6 (CMIP6) driven by outputs from Shared Socioeconomic Pathways (SSPs) under the SSP5-8.5 scenario of 2024–2050. Our results demonstrate that the proposed spatial FD-detecting model based on 2D CNN architecture and the methodology for robust learning show promise for regional comprehensive FD monitoring. Finally, considerable spatial variability of FDs across NEB was observed during 2012 and 2024–2050, which was particularly evident in the São Francisco River Basin. This research significantly contributes to advancing our understanding of flash droughts, offering critical insights for informed water resource management and bolstering resilience against the impacts of flash droughts.
Journal Article
Impact of climate change and man-made irrigation systems on the transmission risk, long-term trend and seasonality of human and animal fascioliasis in Pakistan
Large areas of the province of Punjab, Pakistan are endemic for fascioliasis, resulting in high economic losses due to livestock infection but also affecting humans directly. The prevalence in livestock varies pronouncedly in space and time (1-70%). Climatic factors influencing fascioliasis presence and potential spread were analysed based on data from five meteorological stations during 1990-2010. Variables such as wet days (Mt), water-budget-based system (Wb-bs) indices and the normalized difference vegetation index (NDVI), were obtained and correlated with geographical distribution, seasonality patterns and the two-decade evolution of fascioliasis in livestock throughout the province. The combined approach by these three indices proved to furnish a useful tool to analyse the complex epidemiology that includes (i) sheep-goats and cattlebuffaloes presenting different immunological responses to fasciolids; (ii) overlap of Fasciola hepatica and F. gigantica; (iii) co-existence of highlands and lowlands in the area studied; and (iv) disease transmission following bi-seasonality with one peak related to natural rainfall and another peak related to man-made irrigation. Results suggest a human infection situation of concern and illustrate how climate and anthropogenic environment modifications influence both geographical distribution and seasonality of fascioliasis risks. Increased fascioliasis risk throughout the Punjab plain and its decrease in the northern highlands of the province became evident during the study period. The high risk in the lowlands is worrying given that Punjab province largely consists of low-altitude, highly irrigated plains. The importance of livestock in this province makes it essential to prioritise adequate control measures. An annual treatment scheme to control the disease is recommended to be applied throughout the whole province.
Journal Article
The impact of heritage area development on the microclimate of Borobudur Temple
The extensive development of the Borobudur Art Village and Concourse near the UNESCO-listed Borobudur Temple has raised concerns about potential microclimate effects on the temple’s preservation. This study uses ENVI-met simulations and future climate scenarios to evaluate the microclimate impact over the next 25 years as part of the temple’s heritage impact assessment. Future climate conditions are projected based on 25-year estimates derived from a linear regression of 10 years of historical climate data. The findings show that while the developments have minimal impact on the temple’s overall microclimate, localized increases in air and mean radiant temperatures are evident, particularly in the Borobudur Art Village. The study underscores the need for climate-sensitive planning to address rising temperatures and potential visitor discomfort, emphasizing the importance of integrating these strategies into heritage conservation to protect the site’s Outstanding Universal Value.
Journal Article