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Droughts cause critical and major risk to ecosystems, agriculture, and social life. While attempts have been made globally to understand drought characteristics, data scarcity in developing countries often challenges detailed analysis, including climatic, environmental, and social aspects. Therefore, this study developed a framework to investigate regional drought analysis (RDA) using regional drought intensity-duration-frequency (RD-IDF) curves and regional drought risk assessment (RDRA) based on the drought hazard indicator (DHI) and drought vulnerability indicator (DVI) for scarce data regions in Afghanistan. The drought characteristics were analyzed using the regional standardized-precipitation-index (SPI), and standardized precipitation-deficit distribution (SPDD). Further, L-moment statistics were used to classify different homogenous regions based on regional frequency analysis (RFA). The historical monthly precipitation data from 23 rainfall stations for the years 1970 to 2016 were collected from the Ministry of Water and Energy of Afghanistan. Based on the analysis performed, the area was classified into six homogeneous regions R-1, R-2, R-3, R-4, R-5, and R-6. The drought was very consistent—almost 50% of the years—irrespective of the homogeneous region classified. R-4, located in the northeast of the country, had a one-year extreme drought with high resiliency and low risk to drought compared to other regions. As R-1, R-3 and R-5 are located in the southwest, center and southeast parts of Afghanistan, they experience moderate drought with low resiliency and high drought risk due to long period of droughts. Moreover, the uniform distribution of precipitation deficit (Dm), was less in arid climate regions. In contrast, the semi-arid climate regions showed higher values of Dm. Furthermore, in the results in all the regions, the IDF curves showed a high drought intensity with increasing drought return periods. In contrast, the intensity significantly decreased when the time scale increased, and fewer were enhanced within the increasing drought return period. However, the outcome of this study may contain essential information for end users to make spatially advanced planning for drought effect mitigation in Afghanistan. [ABSTRACT FROM AUTHOR]

Toxic heavy metals in soil negatively impact soil's physical, biological, and chemical characteristics, and also human wellbeing. The traditional approach of chemical analysis procedures for assessing soil toxicant element concentration is time-consuming and expensive. Due to accessibility, reliability, and rapidity at a high temporal and spatial resolution, hyperspectral remote sensing within the Vis-NIR region is an indispensable and widely used approach in today's world for monitoring broad regions and controlling soil arsenic (As) pollution in agricultural land. This study investigates the effectiveness of hyperspectral reflectance approaches in different regions for assessing soil As pollutants, as well as a basic review of space-borne earth observation hyperspectral sensors. Multivariate and various regression models were developed to avoid collinearity and improve prediction capabilities using spectral bands with the perfect correlation coefficients to access the soil As contamination in previous studies. This review highlights some of the most significant factors to consider when developing a remote sensing approach for soil As contamination in the future, as well as the potential limits of employing spectroscopy data. [ABSTRACT FROM AUTHOR]

Projected climate change is likely to have a significant impact on a range of energy systems. When a building is the centre of that system, a changing climate will affect the energy system in several ways. Firstly, the energy demand of the building will be altered. Taken across the entire building stock, and placed in context of technological and behavioural changes over the same timescale, this can have implications for important parameters such as peak demand and load factors of energy requirement. The performance of demand-side, distribution/transmission and supply-side technologies can also alter as a result of changing temperatures. With such uncertainty, a flexible approach is required for ensuring that this whole energy system is robust for a wide range of future scenarios. Therefore, building design must have a standardised and systematic approach for integrating climate change into the overall energy assessment of a building (or buildings), understanding the implications for the larger energy network. Based on the work of the Low Carbon Futures (LCF) and Adaptation and Resilience In Energy Systems (ARIES) projects, this paper overviews some of the risks that might be linked to a changing climate in relation to provision and use of energy in buildings. The UK is used as a case-study but the outputs are demonstrated to be of relevance, and the tools applicable, to other countries. [ABSTRACT FROM AUTHOR]

Water is essential to all lifeforms including various ecological, geological, hydrological, and climatic processes/activities. With the changing climate, associated El Niño/Southern Oscillation (ENSO) events appear to stimulate highly uncertain patterns of precipitation (P) and evapotranspiration ( E V ) processes across the globe. Changes in P and E V patterns are highly sensitive to temperature (T) variation and thus also affect natural streamflow processes. This paper presents a novel suite of stochastic modelling approaches for associating streamflow sequences with climatic trends. The present work is built upon a stochastic modelling framework (HMM_GP) that integrates a hidden Markov model (HMM) with a generalised Pareto (GP) distribution for simulating synthetic flow sequences. The GP distribution within the HMM_GP model aims to improve the model's efficiency in effectively simulating extreme events. This paper further investigated the potential of generalised extreme value distribution (GEV) coupled with an HMM model within a regression-based scheme for associating the impacts of precipitation and evapotranspiration processes on streamflow. The statistical characteristic of the pioneering modelling schematic was thoroughly assessed for its suitability to generate and predict synthetic river flow sequences for a set of future climatic projections, specifically during ENSO events. The new modelling schematic can be adapted for a range of applications in hydrology, agriculture, and climate change. [ABSTRACT FROM AUTHOR]

According to the India Energy Security Scenario 2047, the number of residential air conditioner (A/C) units may increase seven-fold by 2037 as compared to 2017. Also, the related energy consumption might increase four times in the next two decades, according to India's National Cooling Action Plan. Therefore, the study of occupant cooling behaviour is essential to reduce and manage the significant electricity demand, helping to formulate and implement climate-specific cooling policies, and to adopt low-energy and low-cost technologies at mass-market scale. The study aims to analyse residential electricity consumption in order to investigate occupant behaviour, especially for thermal comfort by using space cooling and mechanical ventilation technologies. Among the five climate zones in India, this study focuses on the occupant behaviour in a warm-humid climate using Auroville as a case study, where climate analysis of the past 30 years demonstrated progression towards unprecedented warmer weather in the last five years. In this study, electricity consumption data from 18 households (flats) were monitored for seven months (November 2018–June 2019). The study also elaborated the limitations faced while monitoring and proposed a data filling methodology to create a complete daily profile for analysing occupant behaviour through electricity consumption. The results of the data-driven approach demonstrated the characteristics and complexities in occupant behaviour and insight on the operation of different technologies to attain thermal comfort in residential buildings in an increasingly warming climate. [ABSTRACT FROM AUTHOR]

Climate change represents a major threat to lotic freshwater ecosystems and their ability to support the provision of ecosystem services. England's chalk streams are in a poor state of health, with significant concerns regarding their resilience, the ability to adapt, under a changing climate. This paper aims to quantify the effect of climate change on hydroecological response for the River Nar, south-east England. To this end, we apply a coupled hydrological and hydroecological modelling framework, with the UK probabilistic climate projections 2009 (UKCP09) weather generator serving as input (CMIP3 A1B high emissions scenario, 2021 to the end-of-century). The results indicate a minimal change in the long-term mean hydroecological response over this period. In terms of interannual variability, the median hydroecological response is subject to increased uncertainty, whilst lower probability extremes are virtually certain to become more homogeneous (assuming a high emissions scenario). A functional matrix, relating species-level macroinvertebrate functional flow preferences to functional food groups reveals that, on the baseline, under extreme conditions, key groups are underrepresented. To date, despite this limited range, the River Nar has been able to adapt to extreme events due to interannual variation. In the future, this variation is greatly reduced, raising real concerns over the resilience of the river ecosystem, and chalk ecosystems more generally, under climate change. [ABSTRACT FROM AUTHOR]