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Building energy policy, such as the Energy Performance of Buildings Directive (EPBD), has a direct effect on use of building models and related parameters. Modelling annual energy consumption of a building is a different task to characterizing the demand of that building at a transient level; to do so at scale requires additional complexity. With the ubiquity of Energy Performance Certificates (EPC) across Europe, there is a tendency to use these to communicate building energy demand to policy. However, there is growing evidence of EPCs being applied to areas which they were not designed to serve. By comparing alternative techniques with current methodologies, this study proposes future directions for standardized energy assessment of dwellings, proposing a framework for critiquing such techniques. New methods are formulated that make use of simulation and statistical techniques developed by the authors, and allow for urban-scale modelling that is consistent with traditional energy assessment. [ABSTRACT FROM AUTHOR]

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]

Modeling hydrological processes for managing the available water resources effectively is often complex due to the existence of high nonlinearity, and the associated prediction uncertainty mainly arising from model inputs, parameters, and structure. Despite several attempts to quantify the model prediction uncertainty, reducing the same for improving the reliability of models is indispensable for their wider acceptance. This paper presents a novel modeling framework for minimizing the prediction uncertainty in the streamflow simulation of the conceptual hydrological model (HBV) by integrating with the Bayesian‐based Particle Filter technique (PF) and machine learning algorithm (Random Forest algorithm, RF). Initially, the streamflow prediction interval (PI) is derived from the stochastically estimated parameters of the HBV model through the PF technique (HBV‐PF model). As the HBV‐PF model quantifies only parametric uncertainty, the RF algorithm was employed (HBV‐PF‐RF model) for further minimizing the prediction uncertainty by inherently taking care of different sources of uncertainty. The RF algorithm inherently combines the physics of the hydrological system (i.e., process‐based variables) with machine learning‐based approach to minimize the overall prediction uncertainty. The proposed framework was analyzed on Nepal and India's Sunkoshi and Beas River basins, through several statistical performance indices for assessing the accuracy and uncertainty of the model prediction. The framework was observed to be consistently improving the model performance minimizing the uncertainty in both watersheds. Therefore, the proposed framework can be considered to be more reliable in improving the prediction capability of hydrological models. Key Points: Development of a physics‐aware machine learning based hydrological model for streamflow simulationThe proposed framework characterizes the model prediction uncertainty, accounting different sources of uncertainty implicit/explicitlySignificant reduction in overall prediction uncertainty is achieved [ABSTRACT FROM AUTHOR]

Occurrences of extreme events, especially floods, have become more frequent and severe in the recent past due to the global impacts of climate change. In this context, possibilities for generating a near‐accurate streamflow forecast at higher lead times, which could be utilized for developing a reliable flood warning system to minimize the effects of extreme events, are highly important. This paper aims to investigate the potential of a novel hybrid modeling framework that couples the random forest algorithm, particle filter, and the HBV model for improving the overall accuracy of forecasts at higher lead times through the dynamic error correction schematic. The new framework simulates an ensemble of streamflow for estimating uncertainty associated with the predictions and is applied across two snow‐fed Himalayan rivers: the Beas River in India and the Sunkoshi River in Nepal. Several statistical indices along with graphical performance indicators were used for assessing the accuracy of the model performance and associated uncertainty. The modeling framework achieved the Nash Sutcliffe Efficiency of 0.94 and 0.98 in calibration and 0.95 and 0.99 in validation for the Beas and Sunkoshi river basin respectively for a 7‐day ahead forecast. Thus, the proposed framework can be considered as a promising tool having reasonably good performance in forecasting streamflow at a higher lead time. Key Points: Hybrid hydrological model integrates process‐based model with machine learning algorithm through data assimilation techniqueDynamic error correction framework capable of improving the streamflow forecast at longer lead time is proposedOverall the developed framework improves the forecast accuracy along with quantifying the model prediction uncertainty [ABSTRACT FROM AUTHOR]

The rapid growth of urban development, industrialization, mining, farming, and biological activities has resulted in potentially toxic metal pollution of the soil all over the world. This has caused degradation of soil quality, lower crop production, and risk to human health. For this work, two study sites were selected to evaluate metal concentrations in the agricultural as well as the recreational soil around the Cerrito Blanco in Matehuala, San Luis Potosi, Mexico. The concentrations of eight metals, namely As, Ca, Mg, Na, K, Sr, Mn, and Fe were analysed in order to determine the level of contamination risk as well as their spatial distributions. However, this study is mainly focused on toxic metals, e.g. As, Sr, Mn, and Fe. The contamination indices techniques were used to evaluate the risk assessment of soil. Additionally, the positive matrix factorization (PMF) model as well as the geostatistical analysis was used to identify the contamination sources based on 64 surface soil samples. After implementing PMF to analyze the soils, it was possible to differentiate the variations in factors linked to the contaminants, farming impacts, and the reference soil geochemistry. The soil in the two studied locations included high concentrations of As, Ca, Mg, K, Sr, Mn, and Fe, including variations in their spatial compositions, which were caused by direct mining activities, the movement and deposition of smelting waste, and the extensive use of irrigated contaminated groundwater for irrigation. The four possible factors were identified for soil pollution including industrial, transportation, agricultural, and naturogenic based on the PMF and geostatistical analysis. The spatial distribution of metal concentrations in the soil was also presented using a geographical information system (GIS) interpolation technique. The identification of metal sources and contamination risk mapping presents a significant role in minimizing pollution sources, and it may be performed in regions with high levels of soil contamination risk. [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]

This paper presents a novel system of data-driven approaches for simulating the dynamics of electricity demand profiles. Demand profiles of individual dwellings are decomposed into deterministic (e.g. 'Trends' and 'Seasonal') and stochastic ('remainder') components using the STL (a Seasonal-Trend decomposition procedure based on Loess) approach. Stochastic components are modelled using a Hidden Markov Model (HMM) and combined with deterministic components to generate synthetic demand profiles. To simulate extreme (peak) demand, the synthetic profiles were post-processed using a Generalised Pareto (GP) distribution, and a percentile-based bias-correction scheme. All the techniques are systematically coupled into a hybrid system, referred to as 'STL_HMM_GP'. The STL_HMM_GP system is thoroughly accessed and validated by comparing a range of statistical characteristic of observed and simulated profiles for three case study communities. The potentials of the STL_HMM_GP system is demonstrated for simulating aggregated demand profiles, generated using an accessible small sample of observed individual demand profiles. [ABSTRACT FROM AUTHOR]

This paper presents a new data-driven modelling framework for forecasting probabilistic flood inundation maps for real-time applications. The proposed end-to-end (rainfall–inundation) method combines a suite of machine learning (ML) algorithms to forecast discharge and deliver probabilistic flood inundation maps with a 3 h lead time. To classify wet/dry cells, the method applies rainfall–discharge models based on random forest technique on top of classifiers based on multi-layer perceptron. The hybrid modelling framework was tested using two subsets of data created from an observed fluvial flood event in a small flood-prone town in the UK. The results showed that the model can effectively emulate the outcomes of a hydrodynamic model (Flood Modeller (FM)) with considerably high accuracy measured in terms of flood arrival time error and classification accuracy. The mean arrival time difference between the proposed model and the hydrodynamic model was 1 h 53 min. The classification accuracy was measured against a synthetic aperture radar image, producing accuracies of 88.22% and 86.58% for the proposed data-driven model and FM, respectively. The key features of the proposed modelling framework are that it is simple to implement, detects flooded cells effectively and substantially reduces computational time. [ABSTRACT FROM AUTHOR]

This paper presents a systematic investigation into modelling capacities of three conventional data-driven modelling techniques, namely, wavelet-based artificial neural network (WANN), support vector regression (SVR) and deep belief network (DBN) for multi-step ahead stream flow forecasting. To evaluate the effectiveness of these modelling techniques, hydro-meteorological hourly datasets from three case-study rivers located in the UK have been used. A heuristic performance analysis of the modelling schemes has been conducted by systematically analysing the key statistics that measure magnitude, scatter and density of model errors. Finally, for each of the modelling techniques, the performance deterioration rate in time was estimated. The results show that the SVR model can forecast quite accurately up to one to two hours ahead but its performance deteriorates gradually from three hours onwards. Further it has been found that the WANN model performs better when the overall non-linearity of the system increases, whereas the DBN model appeared to show consistently poor predictive capabilities when compared to the other models presented herein. The authors conclude by stating that, for any selected model, it is possible to use an identical model structure for up to two steps ahead forecasting. Models need to be re-configured beyond that limit. [ABSTRACT FROM AUTHOR]

Hydrological models can be used to assess the impact of hydrologic alteration on the river ecosystem. However, there are considerable limitations and uncertainties associated with the replication of ecologically relevant hydrological indicators. Vogel and Sankarasubramanian's 2003 (Water Resources Research) covariance approach to model evaluation and parameterization represents a shift away from algorithmic model calibration with traditional performance measures (objective functions). Using the covariance structures of the observed input and simulated output time series, it is possible to assess whether the selected hydrological model is able to capture the relevant underlying processes. From this plausible parameter space, the region of parameter space which best captures (replicates) the characteristics of a hydrological indicator may be identified. In this study, a modified covariance approach is applied to five hydrologically diverse case study catchments with a view to replicating a suite of ecologically relevant hydrological indicators identified through catchment-specific hydroecological models. The identification of the plausible parameter space (here n≈20) is based on the statistical importance of these indicators. Evaluation is with respect to performance and consistency across each catchment, parameter set, and the 40 ecologically relevant hydrological indicators considered. Timing and rate of change indicators are the best and worst replicated respectively. Relative to previous studies, an overall improvement in consistency is observed. This study represents an important advancement towards the robust application of hydrological models for ecological flow studies. [ABSTRACT FROM AUTHOR]

Hydrological models can be used to assess the impact of hydrologic alteration on the river ecosystem. However, there are considerable limitations and uncertainties associated with the replication of the required, ecologically relevant hydrological indicators. Vogel and Sankarasubramanian's covariance approach to model parameterisation represents a shift away from the traditional calibration-validation goodness-of-fit paradigm. Using the covariance structures of the observed input and simulated output time-series, the region of parameter space which best captures (replicates) the characteristics of a hydrological indicator may be identified. Through a case study, a modified covariance approach is applied with a view to replicating a suite of seven ecologically relevant hydrological indicators. Model performance and consistency are assessed relative to four comparative studies. The ability of the approach to address the limitations associated with traditional calibration-validation is further considered. Benefits of the approach include an overall reduction in model uncertainty whilst also reducing overall time-demands. Difficulties in the replication of complex indicators, such as rate of change, are in line with prior work. Nonetheless, the study illustrates that consistency in the replication of hydrological indicators is achievable; additionally, the replication of magnitude indices is markedly improved upon. [ABSTRACT FROM AUTHOR]

Abstract: Understanding of the hydroecological relationship is vital to maintaining the health of the river and thus its ecosystem. Stepwise selection is widely used to develop numerical models which represent these processes. Increasingly, however, there are questions over the suitability of the approach, and coupled with the increasing complexity of hydroecological modelling, there is a real need to consider alternative approaches. In this study, stepwise selection and information theory are employed to develop models which represent two realizations of the system which recognizes increasing complexity. The two approaches are assessed in terms of model structure, modelling error, and model (statistical) uncertainty. The results appear initially inconclusive, with the information theory approach leading to a reduction in modelling error but greater uncertainty. A Monte Carlo approach, used to explore this uncertainty, revealed modelling errors to be only slightly more distributed for the information theory approach. Consideration of the philosophical underpinnings of the two approaches provides greater clarity. Statistical uncertainty, as measured by information theory, will always be greater due to its consideration of two sources, parameter and model selection. Consequently, by encompassing greater information, the measure of statistical uncertainty is more realistic, making an information theory approach more reflective of the complexity in real‐world applications. [ABSTRACT FROM AUTHOR]

Typically, the analysis and design of fluvial flood defence schemes is based on a single N year extreme flow event using a single survey of the river channel and flood plains. Adopting this approach assumes that the channel capacity is identical for all subsequent N year events. If one assumes that the typical design life for a flood defence scheme is of the order of 50 years, then such an approach is flawed because river channel morphology, and hence flood conveyance, may change considerably over this time scale. Therefore, to provide a more robust estimate of future flood inundation, a sensitivity analysis of these changes should be undertaken. This paper proposes a modeling methodology that combines a stochastic model, for estimating streamflow throughout the design period, and a 1D sediment transport model (HEC-RAS), to enable this sensitivity to be included in flood inundation modeling and defence scheme design. The methodology is demonstrated through conceptual implementation to evaluate the change in water surface elevation (WSE) along an alluvial river (River Caldew, England) reach after 50 years of sediment transport. Changes in WSE are assessed when the reach is natural (no flood defences) and modified (with idealized flood defences). Results show that the construction of the flood defence scheme does not alter the overall morphological pattern of the reach but can significantly increase (260%) local aggradation. Additionally, 50 years of morphological change have the potential to increase WSE such that high flows, previously confined within the channel, can overtop the banks and become flood events; and that, the standard freeboard levels of the flood defence scheme may be insufficient to prevent overtopping when morphological change is considered. The method can be considered as a semiquantitative modeling methodology to account for the sediment-related sensitivity of flood risk management; and provides valuable insights into the potential magnitude that this has on future flood inundation. [ABSTRACT FROM AUTHOR]

Estimation of daily streamflow time series is of paramount importance for the design and implementation of river engineering and management projects (e.g., restoration, sediment-transport modelling, hydropower). Traditionally, indirect approaches combining stochastic simulation of rainfall with hydrological rainfall-runoff models are used. However, these are limited by uncertainties in model calibration and computational expense. Thus, this paper demonstrates an alternative, direct approach, for stochastic modelling of daily streamflow data, specifically seeking to address wellknown deficiencies in model capability to capture extreme flow events in the simulated time series. Combinations of a hidden Markov model (HMM) with the generalised extreme value (HMM-GEV) and generalised Pareto (HMM-GP) distributions were tested for four hydrologically contrasting catchments in the UK (Rivers Dee, Falloch, Caldew and Lud), with results compared to recorded flow data and estimations obtained from a simpler autoregressive-moving-average (ARMA) model. Results show that the HMM-GP method is superior in performance over alternative approaches (relative mean absolute differences (RMAD) of <2% across all catchments), appropriately captures extreme events and is generically applicable across a range of hydrological regimes. In contrast, the ARMA model was unable to capture the flow regime successfully (average RMAD of 14% across all catchments). [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]

Abstract Rivers are among the ecosystems most sensitive to climate change. Whilst methods quantifying the impact and uncertainty of climate change on flow regime are well-established, the impact on hydroecological response is not well understood. Typically, investigative methods are qualitative in nature or follow quantitative methods of limited scope, whilst the effect of uncertainty is frequently minimised. This paper proposes a coupled hydrological and hydroecological modelling framework to assess the impact of climate change on hydroecological response quantitatively. The characterisation and reduction of modelling uncertainties was critical to the development of the framework. The ability of the framework is illustrated through application to a case study river, the River Nar, Norfolk, England, using the UKCP09 probabilistic climate projections (high emissions scenario, SRES A1F1). The results show that, by the 2050s, a reduction in instream biodiversity is virtually certain if future emissions follow the assumptions of SRES A1F1. Disruption to the natural low flow processes, essential to ecosystem functioning, is also indicated. These findings highlight the importance of the framework in water resources adaptation, particularly with respect to future environmental flows management. Highlights • We propose a modelling framework to assess hydroecological response to climate change. • Characterisation and reduction of uncertainty considered throughout. • Provides quantifiable measure of the uncertainty in the hydroecological projections. • Case study sees reduced instream biodiversity and disruption to flow processes. [ABSTRACT FROM AUTHOR]

The condition of the soil environment is critical for human health and agricultural sustainability. As a result, the environmental and ecological issues impacting the soils throughout the world are receiving more attention. This research focuses on local site-specific studies in Cerrito Blanco, Matehuala municipality, San Luis Potosi, Mexico, and describes different types of GIS interpolation techniques, multivariate statistical analysis, and various contamination indices to investigate the relationship between predictive accuracy, levels of contamination risk, and soil toxic metal elements variation. Inductively coupled plasma optical emission spectroscopy (ICP-EOS) used to test 39 digested surface soil samples for significant toxic metals (Ag, Cd, Co, Cr, Li, and Ni) after suitable dilution with deionised water. According to the results, we found that only the mean value of cadmium (Cd) exceeded the permissible standard value. After evaluating the four types of interpolation techniques, the Inverse Distance Weighting (IDW) was determined to be the optimal interpolation model for assessing the spatial distribution patterns of toxic metal concentration in the research area. The calculated contamination risk indices showed no significant high contamination risk due to soil-borne toxic metals. These results provide a comprehensive analysis of the impact of past mining activities on toxic metal concentrations in non-cultivated surface soil. • The study addresses the research of different approaches of contamination assessment techniques to identify the toxic metal contaminations in the surface soil. • The study exploits the estimation of the contamination risk level of toxic heavy metals. • The study discusses GIS interpolation techniques as well as multivariate statistical analysis which were used for spatial distribution. [ABSTRACT FROM AUTHOR]