Climatic Regions Research Articles

ANOVA (Benova) correction in relative homogenization: Why it is indispensable

AbstractThis paper reviews the role of ANOVA correction model in the homogenization of climatic time series. In the present context ANOVA has only weak connection to its original meaning (analysis of variance), so we propose the new name “Benova” to replace the confusing old name. In the linear model of Benova corrections (hereafter Benova) the information of statistically detected inhomogeneities and metadata are jointly considered for all time series of a given climatic region. Benova has indisputable advantages on the accuracy of homogenization results, and this has both theoretical and practical evidence. The study presents two principal versions of Benova: in simple Benova the climate signal is presumed to be spatially invariant, while in weighted Benova the spatial variation of climate is considered. In Benova models usually only breaks (i.e., sudden shifts of section mean) are considered, but this restriction has practical reasons, rather than theoretical limits, and the study shows an extended version of the method with which trend‐like inhomogeneity biases can also be removed. Benova can be used for a group of time series covering varied time periods, the operations can be performed in any time resolution, and statistical characteristics others than climatic means can also be homogenized by the method. Benova can be used together with any break detection method. The study discusses the likely reasons of the relatively slow spread in practical application. PRODIGE was the first homogenization method which used Benova corrections. Until now, all homogenization methods including Benova corrections include also the same kind break detection method, i.e., penalized maximum likelihood method with step function fitting. The brief descriptions of two modern methods of this method family, that is, ACMANT and Bart methods, are also provided.

A novel decision support system for designing fixed shading systems in the early design stage: A case study in Egypt

Solar shading is a crucial element that can significantly impact lighting conditions and energy consumption, especially in hot regions. However, selecting the best shading design that achieves optimal daylight performance while meeting architects' requirements is very challenging. This is because it involves considering many design parameters and balancing multiple objectives. The paper introduces a decision support system for designing fixed shading systems to enhance interior environments and reduce energy consumption. Parametric simulation, a meta-model based on automatic selection of regression machine learning algorithms (RMLA), and non-dominated sorting genetic algorithm II (NSGA-II) are used to prepare the designs for selection. Three multiple criteria decision-making (MCDM) methods are employed: the analytic hierarchy process (AHP), entropy, and the technique for order preference by similarity to the ideal solution (TOPSIS) in two combinations (AHP-TOPSIS and entropy-TOPSIS) to adapt to different expert availability scenarios. The proposed method is coded and automated with an easy-to-use interactive interface for flexibility and easy application. The method is applied to help design a fixed shading system for hot climate regions in Cairo, Egypt, which involves considering five objectives: useful day light illuminance (UDI), energy use intensity (EUI), solar gains (SG), daylight autonomy (DA), and continuous daylight autonomy (CDA). The proposed method can provide an efficient design that significantly improves daylight performance. Different dataset sizes are used for training the meta model to study their effect on the final selected design. A sensitivity analysis is done to explore the impact of changes in experts’ opinions on the decision-making process.

A bioinspired hierarchical gradient structure to maximize resilience and enhanced cooling performance in polymeric radiative cooling coatings

Despite the extensive advancements in recent years, polymeric daytime radiative cooling (PDRC) coatings face certain challenges, encompassing restricted spectral performance, susceptibility to aging, poor mechanical strength, and so forth. Herein, we proposed a facile biomimetic 500 μm thick PDRC coating, featuring a gradient distribution of pore sizes throughout the cross-section. The proposed functional structure demonstrates spectral characteristics of a near-ideal broadband emitter by attaining over 0.95 emissivity in the main atmospheric window and 0.99 reflectance in the visible spectrum. During the outdoor experiment, it achieved an 8.5 °C subambient temperature drop along with a cooling power of ∼106 W/m2 at the solar irradiance of ∼800 W/m2 and ∼650 W/m2, respectively. Experimental findings highlight that the bioinspired design results in a tensile strength of ∼9 MPa along with a tensile strain of over 200%, which is more than twice that of non-gradient porous PDRC coatings. In addition, it offers tunable surface contact angle and manifests its resilience in anti-ultraviolet and water resistance tests. Furthermore, a building energy model reveals a decrease in cooling load of between 34 and 119 kWh/(m2.year), establishing its real-world application under broader climatic regions.

Adaptation of Rhizobium leguminosarum sv. trifolii strains to low temperature stress in both free-living stage and during symbiosis with clover

Legume-rhizobial symbiosis plays an important role in agriculture and ecological restoration. This process occurs within special new structures, called nodules, formed mainly on legume roots. Soil bacteria, commonly known as rhizobia, fix atmospheric dinitrogen, converting it into a form that can be assimilated by plants. Various environmental factors, including a low temperature, have an impact on the symbiotic efficiency. Nevertheless, the effect of temperature on the phenotypic and symbiotic traits of rhizobia has not been determined in detail to date. Therefore, in this study, the influence of temperature on different cell surface and symbiotic properties of rhizobia was estimated. In total, 31 Rhizobium leguminosarum sv. trifolii strains isolated from root nodules of red clover plants growing in the subpolar and temperate climate regions, which essentially differ in year and day temperature profiles, were chosen for this analysis. Our results showed that temperature has a significant effect on several surface properties of rhizobial cells, such as hydrophobicity, aggregation, and motility. Low temperature also stimulated EPS synthesis and biofilm formation in R. leguminosarum sv. trifolii. This extracellular polysaccharide is known to play an important protective role against different environmental stresses. The strains produced large amounts of EPS under tested temperature conditions that facilitated adherence of rhizobial cells to different surfaces. The high adaptability of these strains to cold stress was also confirmed during symbiosis. Irrespective of their climatic origin, the strains proved to be highly effective in attachment to legume roots and were efficient microsymbionts of clover plants. However, some diversity in the response to low temperature stress was found among the strains. Among them, M16 and R137 proved to be highly competitive and efficient in nodule occupancy and biomass production; thus, they can be potential yield-enhancing inoculants of legumes.

Towards real-time monitoring of insect species populations

Insect biodiversity and abundance are in global decline, potentially leading to a crisis with profound ecological and economic consequences. Methods and technologies to monitor insect species to aid in preservation efforts are rapidly being developed yet their adoption has been slow and focused on specific use cases. We propose a computer vision model that works towards multi-objective insect species identification in real-time and on a large scale. We leverage an image data source with 16 million instances and a recent improvement in the YOLO computer vision architecture to present a quick and open-access method to develop visual AI models to monitor insect species across climatic regions.

Understanding Public Attention to Heat Wave Information

Excessive heat warnings issued by the U.S. National Weather Service (NWS) were compared to their public attention using data provided by Google Trends. Results show that 50 percent of heat wave attention occurred while the excessive heat warning was active, rather than prior to the event. Heat wave attention correlated positively with NWS excessive heat warnings but varied between climate regions, with the highest significant correlation value for the Southwest region of the United States. Severity was also shown to affect the amount of attention an event receives. There was a positive correlation between heat wave severity and heat wave attention. The Southwest’s attention was most affected by severity, and the South’s attention was least affected. The results of this research suggest that when there is an excessive heat warning active within the United States, the odds of searching for heat waves are 5.39 times greater than when there are no active warnings. When there is a warning issued within the same state of search origin, the odds of searching for heat waves are 3.45 times greater than when there are no NWS-issued warnings.

Predictive Control Modeling of Regional Cooling Systems Incorporating Ice Storage Technology

Due to the hot climate, energy consumption for refrigeration is significantly higher in the subtropical monsoon climate region. Combined with renewable energy and ice-storage technology, a model predictive control model of the regional cooling system was proposed, which was conducive to improving the flexibility of the regional cooling system and the ability of peak shifting and valley filling. In this model, an artificial bee colony (ABC) optimized back propagation (BP) neural network was used to predict the cooling load of the regional cooling system, and the model parameter identification method was adopted, combining utilizing a river-water-source heat pump and ice-storage technology. The results showed that the load prediction algorithm of the ABC-BP neural network had a high accuracy, and the variance coefficient of load prediction root-mean-square error (RMSE) was 16.67%, which was lower than BP, support vector regression (SVR), and long short-term memory (LSTM). In addition, compared with the three control strategies of chiller priority, ice-storage priority, and fixed proportion, the operation strategy optimized by the comprehensive model can reduce the average daily cost by 19.20%, 4.45%, and 5.10%, respectively, and the maximum daily energy consumption by 30.02%, 18.08%, and 8.90%, respectively.

Catalytic Heaters at Oil and Gas Sites May be a Significant yet Overlooked Seasonal Source of Methane Emissions.

Successful reduction of oil and gas sector methane emissions to meet near-zero intensity targets requires the identification and mitigation of all possible sources. One potentially important source is catalytic heaters, which have largely escaped attention in regulatory and mitigation efforts despite being ubiquitous at upstream production sites in cold climate regions. This study reports direct in situ measurements of the exhaust streams of 38 natural gas-fired catalytic heaters at upstream production sites in British Columbia, Canada. All heaters in the sample showed consistently poor methane conversion with mean destruction efficiencies of 61 ± 5% while releasing 235 [+31/-28] g of methane per cubic meter of fuel. Although individual units are generally small methane sources (mean of 0.28 ± 0.04 kg/h), their prevalence means they could represent 6% of the total provincial upstream methane inventory and as an aggregate methane source could be 5× more significant than abandoned wells. Notably, these heaters are seasonal sources whose emissions would be missed in measurement campaigns occurring solely in summer months. However, additional measurements from a small number of heat medium burners demonstrate that, where feasible, methane emissions can be reduced by approximately 425× by replacing catalytic heaters with centralized heat systems.

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.

Effects of Low-Temperature Stress on Physiological Characteristics and Microstructure of Stems and Leaves of Pinus massoniana L.

Pinus massoniana L. is one of the most important conifer species in southern China and is the mainstay of the forest ecosystem and timber production, yet low temperatures limit its growth and geographical distribution. This study used 30-day-old seedlings from families of varying cold-tolerance to examine the morphological traits of needles and stems, chlorophyll fluorescence characteristics, protective enzymes, and changes in starch and lignin under different low-temperature stresses in an artificial climate chamber. The results showed that the seedlings of Pinus massoniana exhibited changes in phenotypic morphology and tissue structure under low-temperature stress. Physiological and biochemical indexes such as protective enzymes, osmoregulatory substances, starch, and lignin responded to low-temperature stress. The cold-tolerant family increased soluble sugars, starch grain, and lignin content as well as peroxidase activity, and decreased malondialdehyde content by increasing the levels of actual photochemical efficiency (ΦPSII), electron transport rate (ETR), and photochemical quenching (qP) to improve the cold tolerance ability. This study provides a reference for the selection and breeding of cold-tolerant genetic resources of Pinus massoniana and the mechanism of cold-tolerance, as well as the analysis of the mechanism of adaptation of Pinus massoniana in different climatic regions of China.

Estimation of wood biomass boiler use in cold climate regions on CO2 emissions of light-frame timber structure

Considering Nagano City, as the cold climate region of Japan, the annual heating load was simulated using a model of a two-story timber structure. The results of simulations were used to compare the CO2 emissions according to the energy consumption of five methods of heating as well as primary and secondary energy sources. Actual data from power and gas companies in Nagano and Japanese energy standards were used for the estimations of CO2 emissions of electric heat pump air conditioners, LNG-powered city gas boilers, LPG boilers, kerosene stoves, and wood biomass boilers using pellets as an energy source. Wood biomass boilers showed CO2 emission savings of up to 85 %, 75 %, 75 %, and 76 %, respectively. The operating and equipment costs were estimated based on secondary energy calculations. The kerosene stove had the lowest cost, but CO2 emissions were lowest in the case of using pellet boilers for 30 years. These results clearly show the potential and merit of pellets used in wood biomass boilers as a renewable energy source when compared to fossil fuels. In addition to the CO2 reduction capabilities, the low operating costs of wood biomass heating systems incentivize their use in cold climates.

Thermal comfort in a two-storey malaysian terrace house: Are passive cooling methods sufficient in present and future climates?

Rising temperatures due to climate change will impact both the indoor thermal comfort of naturally-ventilated housing and the cooling energy needs of air-conditioned housing in tropical regions. This study aims to evaluate the impact of rising temperatures based on the IPCC A2 scenario on indoor thermal comfort and cooling energy in a typical Malaysian terrace house. It addresses three key questions: 1) Can passive cooling (PC) achieve future thermally acceptable conditions? 2) How will PC impact cooling energy with air conditioning (AC)? 3) What is the effectiveness of various PC methods, individually and combined, in controlling indoor climate and saving energy under present and future conditions? We evaluated the effectiveness of eight PC measures—including shading, insulation, solar reflectivity, and nighttime natural ventilation—using EnergyPlus simulations. Results indicate that without PC and AC, indoor temperatures range from 26 to 31 °C, rising to 29–36 °C by the 2050s, often exceeding ASHRAE 55 standards. Currently, combined PC and nighttime ventilation can improve thermal comfort time fraction by up to 87 %. However, by the 2050s, this drops to 38 %, indicating PC alone will be insufficient, and AC systems will be essential. Additionally, in an AC scenario, PC measures reduce cooling energy by 6 % compared to the baseline under future conditions, yet demand is 67 % higher than current conditions. These findings underscore the need for integrating both PC measures and efficient AC systems to mitigate future greenhouse gas emissions from residential sectors in Malaysia and other developing hot climate regions.

Natural ventilation potential of teaching building complexes with different block shapes and layout patterns

Natural ventilation significantly enhances building energy efficiency and indoor air quality, thereby reducing virus transmission risks, especially crucial in teaching buildings on university campuses frequently occupied by large numbers of students. This study investigates the design impacts of teaching building complexes on indoor and outdoor wind environments, aiming to propose practical design recommendations. Firstly, a typological analysis of teaching building complexes from ten universities in Guangzhou University Town (GUT) was conducted, screening out several archetypes representing diverse design features. Computational fluid dynamics (CFD) simulations were then performed on 16 hypothetical teaching building complexes derived from these archetypes. The wind velocity distributions and area-weighted wind velocities across various hypothetical scenarios were compared and analyzed, allowing for a thorough assessment of the natural ventilation performance associated with different block shapes and layout patterns. The findings reveal that canyons between buildings have a marginal impact on natural ventilation within scenarios characterized by aligned layouts. In contrast, staggered layouts notably enhance natural ventilation, particularly with orthogonal prevailing winds. Furthermore, semi-enclosed courtyard blocks outperform fully enclosed courtyard blocks in natural ventilation, especially when their openings align with prevailing wind directions. Finally, several practical recommendations were summarized for achieving sustainable teaching building design in similar climate regions. Key contributions include demonstrating the significant influence of block shape and layout on ventilation performance and offering novel insights into optimizing building designs for improved natural ventilation.

Buckling prediction based on joint opening and safe temperature estimation in jointed plain concrete pavements

Concrete pavement buckling is a significant distress that disrupts traffic flow and results in costly repairs, issues that are increasingly exacerbated by global warming. Addressing these issues, this study develops a systematic framework for assessing jointed plain concrete pavement (JPCP) by integrating temperature and moisture gradients, pavement structures, and material properties into mechanical-based models for predicting buckling potential. Specifically, a two-stage analysis is developed to analyze buckling development based on the slab deformation and the slab-base interface, including joint closure assessment and upheaval buckling prediction. Through the established model, parametric studies are conducted to quantitatively identify key factors influencing buckling. Moreover, the developed models are applied to pavement sections in various climate regions and validated through comparative analysis with field data from the Long-Term Pavement Performance (LTPP) database to confirm model accuracy. Additionally, a case study using pavement buckling data from Wisconsin between 2014 and 2020 demonstrates the model's practical application. As the results, it is found that temperature increase, the percentage of incompressible materials, and the coefficient of thermal expansion are critical for joint opening changes, while the coefficient of thermal expansion, elastic modulus, and slab thickness are crucial to post-buckling performance. Regression equations are provided to estimate safe temperature increases as a recommended buckling temperature limit. Furthermore, the predicted joint opening shows reasonable alignment with joint gauge measurement in the field recordings from the LTPP database. Finally, the predicted buckling events are reasonably compared with field recordings for a Wisconsin pavement section from 2014 to 2020. In summary, by considering pavement structural parameters, material properties, and weather conditions, this comprehensive model provides a reasonable tool for predicting and mitigating JPCP buckling in different climate conditions.

Effects of ambient heat exposure on risk of all-cause mortality in children younger than 5 years in Africa: a pooled time-series analysis

Reducing child mortality is a Sustainable Development Goal, and climate change constitutes numerous challenges for Africa. Previous research has shown an association between leading causes of child mortality and climate change. However, few studies have examined these effects in detail. We aimed to explore the effects of ambient heat on neonate, post-neonate, and child mortality rates. For this pooled time-series analysis, health data were obtained from the International Network for the Demographic Evaluation of Populations and Their Health (INDEPTH) Health and Demographic Surveillance System. We included data from 29 settlements from 13 countries across Africa, collected via monthly surveys from Jan 1, 1993, to Dec 31, 2016. Climate data were obtained from ERA5, collected from Jan 1, 1991, to Dec 31, 2019. We pooled these data for monthly mean daily maximum wet bulb globe temperature (WBGT) and downscaled to geolocations. Due to data heaping, we pooled our health data on a monthly temporal scale and a spatial scale into six different climate regions (ie, Sahel [ie, Burkina Faso and northern Ghana], Guinea [ie, southern Ghana, Côte d'Ivoire, and Nigeria], Senegal and The Gambia, eastern Africa [ie, Kenya, Malawi, Tanzania, Mozambique, and Uganda], South Africa, and Ethiopia). Our outcomes were neonate (ie, younger than 28 days), post-neonate (ie, aged 28 days to 1 year), and child (ie, older than 1 year and younger than 5 years) mortality. To assess the association between WBGT and monthly all-cause mortality, we used a time-series regression with a quasi-Poisson, polynomial-distributed lag model. Between Jan 1, 1993, and Dec 31, 2016, there were 44 909 deaths in children younger than 5 years across the 29 sites in the 13 African countries: 10 078 neonates, 14 141 post-neonates, and 20 690 children. We observed differences in the association of heat with neonate, post-neonate, and child mortality by study region. For example, for Ethiopia, the relative risk ratio of mortality at the 95th percentile compared with median heat exposure during the study period was 1·14 (95% CI 1·06-1·23) for neonates, 0·99 (0·90-1·07) for post-neonates, and 0·79 (0·73-0·87) for children. Across the whole year, there was a significant increase in the relative risk of increased mortality for children in eastern Africa (relative risk 1·27, 95% CI 1·19-1·36) and Senegal and The Gambia (1·11, 1·04-1·18). Our results show that the influence of extreme heat on mortality risk in children younger than 5 years varies by age group, region, and season. Future research should explore potentially informative ways to measure subtleties of heat stress and the factors contributing to vulnerability. EU Horizons as part of the Heat Indicators for Global Health (HIGH) Horizons project.

Performance of data-driven models based on seasonal-trend decomposition for streamflow forecasting in different climate regions of Türkiye

This study examines the ability of different methods such as machine learning, ensemble models, and metaheuristic algorithms to predict streamflow. For this purpose, five different methods were used: Artificial Neural Networks (ANN), Support Vector Machines (SVM), Adaptive Boosting, Particle Swarm Optimization (PSO), and BSPSO hybridized with Band Similarity (BS), a relatively new method. Additionally, the impact of seasonality and trend components obtained through Seasonal-Trend decomposition using LOESS (locally weighted regression and scatterplot smoothing) (STL) data decomposition technique on prediction success was investigated. Models were developed in three basins with three different climate characteristics: continental, temperate, and arid. The results showed higher prediction success in input structures including seasonality and trend components. While higher prediction successes were achieved at Karasu in the continental climate class and Körkün in the temperate climate class, model performances were lower at Küçük Muhsine in the arid climate class. While the most successful modeling for Küçük Muhsine (NSE = 0.696) and Karasu stations (NSE = 0.811) was obtained with the BSPSO method, the SVM method produced the best results for Körkün station (NSE = 0.818). Moreover, BSPSO models outperformed the prediction successes obtained by using PSO alone for each scenario at all three stations. The percentages of the BSPSO method improving the prediction success according to the NSE metric ranged from 3.53% to 17.40% at Küçük Muhsine, 0.49%–3.72% at Karasu, and 1.24%–7.24% at Körkün. The competitive results achieved by the BSPSO approach compared to ANN and SVM in flow prediction constitute the innovative aspect of this study.

Nutritional convergence in plants growing on gypsum soils in two distinct climatic regions.

Soil endemics have long fascinated botanists due to the insights they can provide about plant ecology and evolution. Often, these species have unique foliar nutrient composition patterns that reflect potential physiological adaptations to these harsh soil types. However, understanding global nutritional patterns to unique soil types can be complicated by the influence of recent and ancient evolutionary events. Our goal was to understand whether plant specialization to unique soils is a stronger determinant of plant nutrient composition than climate or evolutionary constraints. We worked on gypsum soils. We analyzed whole-plant nutrient composition (leaves, stems, coarse roots and fine roots) of 36 native species of gypsophilous lineages from the Chihuahuan Desert (North America) and the Iberian Peninsula (Europe) regions, including widely distributed gypsum endemics, as specialists, and narrowly distributed endemics and non-endemics, as non-specialists. We evaluated the impact of evolutionary events and soil composition on the whole-plant composition, comparing the three categories of gypsum plants. Our findings reveal nutritional convergence of widely distributed gypsum endemics. These taxa displayed higher foliar Sulfur and higher whole-plant Magnesium than their non-endemic relatives, irrespective of geographic location or phylogenetic history. Sulfur and Magnesium concentrations were mainly explained by non-phylogenetic variation among species related to gypsum specialization. Other nutrient concentrations were determined by more ancient evolutionary events. For example, Caryophyllales usually displayed high foliar Calcium, whereas Poaceae did not. In contrast, plant concentrations of Phosphorus was mainly explained by species-specific physiology not related to gypsum specialization or evolutionary constraints. Plant specialization to a unique soil may strongly influence plant nutritional strategies, as we described for gypsophilous lineages. Taking a whole-plant perspective (all organs) within a phylogenetic framework has enabled us to gain a better understanding of plant adaptation to unique soils when studying taxa from distinct regions.

Combining global precipitation data and machine learning to predict flood peaks in ungauged areas with similar climate

Increasing flood risk due to urbanization and climate change poses a significant challenge to societies at global scale. Hydrologic information that is required for understanding flood processes and for developing effective warning procedures is currently lacking in most parts of the world. Procedures that can combine global climate dataset from satellite and reanalysis with fast and low computational cost prediction systems, are attractive solutions for addressing flood predictions in ungauged areas. This work develops and tests a prediction framework that relies on two fundamental components. First, meteorological data from global datasets (IMERG and ERA5-Land) provide key input variables and second, ML models trained in the data-rich contiguous US, are applied in climatically similar regions in other parts of the world. Catchments in Australia, Brazil, Chile, Switzerland, and Great Britain were used as pseudo-ungauged regions for testing. Results indicate acceptable performance for both IMERG and ERA5-Land forced models with relative difference in flood peak prediction within 30 % and similar overall performance to locally trained ML models. Specific climate regions for which ML models have revealed good performance include Mediterranean climates like the US West Coast, subtropical areas like the Southern Atlantic Gulf, and mild temperate regions like the Mid-Atlantic Basin. This work highlights the potential of combining global precipitation dataset with pre-trained ML models in data-rich areas, for flood prediction in ungauged areas with similar climate.

Sustainable wastewater recovery system employing geothermal energy – Performance and feasibility study

In this study, a sustainable wastewater recovery system employing geothermal energy has been proposed. The efficiency of the suggested sustainable system has been simulated using heat transfer. Then, component-wise validation has been carried out with the existing data available in the literature wherever applicable and estimated an error of ± 8 %. The proposed sustainable system’s parametric and performance analyses have employed the developed vapor. From the parametric study, it is observed that hot spring temperatures have a significant influence on sustainable systems. Further, through performance investigation, it is understood that low dam/river/sea water temperatures and enhancing the water condenser effectiveness and flow rate ratio simultaneously improve the system performance. For different climatic regions of India, explored the feasible geothermal hotspots near dams/rivers/seawater and analyzed that among the hot and dry, hot and humid, composite and cold regions, the proposed sustainable system is best suitable at a composite region with a maximum possible water extraction/freshwater generation rate of 0.78 L/h for the given specifications and operating range. Moreover, it is assessed that hot spring temperature is the deciding factor for sustainable system performance in different climatic regions. Further, the current study can be used as a reference for understanding the phenomenon of potential geothermal hotspots for wastewater recovery in various climatic areas

Assessing GNSS hydrological monitoring capability across different climatic settings in China

Vertical position changes of ground-based Global Navigation Satellite System (GNSS) stations have been used to study regional terrestrial water storage (TWS) changes. However, the feasibility is still unclear in many areas due to diverse local effects. This study aims to evaluate the capability of GNSS vertical displacements in monitoring hydrological variations in four climate settings over Chinese mainland. The spatial and temporal variations of hydrological load-induced (HYDL) vertical displacements at 208 GNSS sites during 2011–2020 were analyzed by comparing with Gravity Recovery and Climate Experiment (GRACE)/GRACE Follow-On (GFO) and Global Land Data Assimilation System (GLDAS) derived TWS changes. The results indicate that GNSS vertical positions show different capabilities in capturing seasonal and non-seasonal hydrological dynamics in different climate regions. Among the four climatic settings, the subtropical monsoon climate (SMC) region, with the largest deformation fluctuation (the regional mean root mean square (RMS) is 7.97 mm), has the highest regional mean HYDL-GRACE and HYDL-GLDAS anti-correlation coefficients (CCs) of −0.47 and −0.45 at the seasonal scale, respectively. For the individual GNSS site, the number of the sites with CC <−0.40 between HYDL and GRACE/GLDAS-derived TWS changes accounts for 55.1% and 55.1% (SMC), 13.0% and 7.4% (temperate monsoon climate, TMC), 6.7% and 13.3% (temperate continental climate, TCC), 32.3% and 38.7% (plateau climate, PC), respectively. For the non-seasonal term, although the proportion with CC <−0.40 in each climate type decreases mainly due to the influence of local geodynamic and human activities, especially in the SMC and PC regions, GNSS site vertical deformations still show good capability in monitoring hydrological extremes. The results provide valuable information for better application of GNSS to hydrology.