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Stretchable, nanocarbon heaters were screen-printed onto a stretchable film to create a passive heat maintenance device for elite sport. The heat uniformity and the temperature performance of these lightweight, large area electrothermal heaters were evaluated over a range of applied voltages using thermal imaging. The heaters provided a uniform heat over the 15 × 4 cm area with temperatures of 39°C, 54°C, and 72°C at 10, 15, and 20 V, respectively, within 150 s of being switched on. Tensile testing was used to examine the performance of the heaters under strain. At 20% nominal strain, the heaters gave a uniform heat output and a temperature of 44°C at 15 V, making it a promising candidate for wearable applications. The heaters were capable of maintaining temperatures of 40°C over 10 cyclic strains up to 10% nominal strain. The heaters were integrated into a proof-of-concept stretchable base-layer garment, with the effect of the heaters on skin temperature measured and thermal sensation evaluated during a simulated training session in an environmental chamber at an ambient temperature 0°C. The printed heaters maintained skin temperature and thermal sensation when compared with an unheated control.

Reliable flood forecasts are dependent on accurate quantitative precipitation forecasts. Despite improvements in the resolution and schematisation of Numerical Weather Prediction (NWP) models, there are still substantial biases in their precipitation forecasts. Biases are present at a range of time scales and correctly representing the multi-temporal scale properties of precipitation including its persistence and variability is vital. In this presentation a new method for post-processing NWP model precipitation forecasts is developed. The new method is based on continuous wavelet transforms (CWT) which correct the statistical characteristics of the precipitation forecasts across a range of time scales. The precipitation amounts are corrected using a simple quantile mapping of the amplitude of each time scale of the wavelet decomposition. To account for uncertainty in precipitation timing, we also adjust the phase of the CWT randomly to create an ensemble of post-processed forecasts. Spatial correlations are preserved by maintaining the same phase adjustments at each different precipitation forecast location. The new method is demonstrated using hourly forecast data from the ACCESS model over the period March 2018 to September 2021 for a network of 158 gauges around Sydney, in eastern Australia. The new method improves the correlation of the forecasts and reduces the root mean square error. The spatial correlation structure of the post-processed forecasts is also improved. Correctly representing spatial patterns of precipitation is vital to ensure that catchment averaged precipitation and the resulting flood forecasts are correct.

Droughts are a natural occurrence in many small Pacific Islands and can have severe impacts on local populations and environments. The El Niño-Southern Oscillation (ENSO) is a well-known driver of drought in the South Pacific, but our understanding of extreme ENSO events and their influence on island hydroclimate is limited by the short instrumental record and the infrequency of ENSO extremes. To address this gap, we present the South Pacific Drought Atlas (SPaDA), a multi-proxy, spatially resolved reconstruction of the November-April Standardised Precipitation Evapotranspiration Index for the southwest Pacific islands. The reconstruction integrates coral proxies, which provide local information on the South Pacific hydroclimate but are limited in number and length, with a network of continental tree-ring chronologies targeting Pacific climate variability through remote teleconnections. The reconstruction demonstrates the benefits of multi-proxy reconstructions incorporating tree rings, which allow for the alignment of other proxy records without chronological error.The SPaDA provides a 350-year, continuous dataset of climate information, which can be used to explore the occurrence of extreme events in the pre-instrumental period. The SPaDA closes the gap between existing paleo-reconstructions of point ENSO indices, and a spatially resolved drought atlas, allowing both the hydroclimate of individual islands and regional patterns of drought to be assessed. The benefit of a spatially resolved dataset to assess climate extremes in small Pacific islands is highlighted in the case of extreme El Niño events, which can have substantially different hydroclimatic impacts than more moderate events.We used an Isolation Forest, an unsupervised machine learning algorithm, to identify anomalous hydroclimatic states in the SPaDA that may indicate the occurrence of an extreme event. Extreme El Niño events characterised by very strong southwest Pacific drought anomalies and a zonal South Pacific Convergence Zone orientation are shown to have occurred semi-regularly throughout the reconstruction interval, providing a valuable baseline to compare to climate model projections. By identifying the spatial patterns of drought resulting from extreme events, we can better understand the impacts these events may have on individual Pacific Islands in the future.

Improving lead time for forecasting floods is important to minimize property damage and ensure the safety of the public and emergency services during flood events. Numerical weather prediction (NWP) models are important components of flood forecasting systems and have been vital in extending forecasting lead-time under complex weather and terrain conditions. However, NWP forecasts still have significant uncertainty associated with the precipitation fields that are the main inputs of the hydrologic models and thus the resulting flood forecasts. An issue often overlooked is the importance of correctly representing variability over a range of different temporal scales. To address this gap, here a new wavelet-based method for post-processing NWP precipitation forecasts is proposed. First, precipitation forecasts are decomposed into the frequency domain using a wavelet transform, providing estimates of the amplitudes and phases of the time series at different frequencies. Quantile mapping is then used to correct bias in the amplitudes of each frequency. Randomized phases are used to generate an ensemble of realizations of the precipitation forecasts. The post-processed precipitation forecasts are reconstructed by taking the inverse of adjusted time-frequency decompositions with the corrected amplitudes and randomized phases. The proposed method was used to post-process NWP precipitation forecasts in the Sydney region, Australia. There is a significant improvement in post-processed precipitation forecasts across multiple time scales in terms of bias and temporal and spatial correlation structures. The post-processed precipitation fields can be used for the modeling of fully distributed hydrologic systems, improving runoff stimulation, flood depth estimation, and flood early warning.

The Australian Bureau of Meteorology has developed a national hydrological projections (NHP) service for Australia. With the focus on hydrological change assessment, the NHP service aims at being complementary to climate projections work carried out by many federal and state governments, universities, and other organisations across Australia. The projections comprise an ensemble of application-ready bias-corrected climate model data and derived hydrological projections at daily temporal and 0.05° × 0.05° spatial resolution for the period 1960–2099 and two emission scenarios (RCP 4.5 and RCP 8.5). The spatial resolution of the projections matches that of gridded historical reference data used to perform the bias correction and the Bureau's operational gridded hydrological model. Three bias correction techniques were applied to four CMIP5 global climate models (GCMs) and one to output from a regional climate model forced by the same four GCMs, resulting in a 16-member ensemble of bias-corrected GCM data for each emission scenario. The bias correction was applied to fields of precipitation, minimum and maximum temperature, downwelling shortwave radiation and surface winds. These variables are required inputs to the Bureau's landscape water balance hydrological model (AWRA-L) which was forced using the bias-corrected GCM and RCM data to produce a 16-member ensemble of hydrological output. The hydrological output variables include root-zone soil moisture (moisture in the top 1 m soil layer), potential evapotranspiration and runoff. Here we present an overview of the production of the hydrological projections, including GCM selection, bias correction methods and their evaluation, technical aspects of their implementation and examples of analysis performed to construct the NHP service. The data are publicly available on the National Computing Infrastructure (https://dx.doi.org/10.25914/6130680dc5a51) and a user interface is accessible at https://awo.bom.gov.au/products/projection/.

Water colour can reflect waterbody health and environmental conditions of inland waterbodies. As a result, changes in water colour may be associated with environmental dynamics resulting from either climate or catchment changes or both. In the absence of long term, spatially comprehensive global databases of lake water quality, satellite-derived water colour can be used to understand water quality variability. Here we analyse the spatial-temporal variability of water colour for nearly 200,000 inland waterbodies around the world using Landsat 5, 7, and 8 images for 35 years. We investigated the spatial variability in the baseline water colour and temporal patterns of water colour across 41 climate reference regions. We found that the mean dominant wavelengths of small waterbodies were constantly higher (less blue) than larger waterbodies. We also found that most waterbodies became increasingly bluer, with an average change of -14.5 nm ± 0.15nm over the 35-year study period. The exception were waterbodies in north-eastern Russia which tended to shift towards red wavelengths. The waterbodies were grouped based on their inter-annual water colour change and intra-annual variation (standard deviation) in water colour. Intra-annual variability increased for two thirds of the waterbodies over the study period. Around 20% of the waterbodies have tended to shift to redder wavelengths accompanied by increased intra-annual variation, which may indicate they are more vulnerable to environmental stresses. The findings from this work provide a strong baseline understanding of historical global lake water quality variability with which future projected climate change impacts can be compared.

Appetitive traits are associated with weight and could be managed using behavioural strategies. Personalised approaches to weight loss could use a person's appetitive trait profile to tailor weight management advice. This study aimed to explore participants’ experiences of a brief Appetitive Trait Tailored Intervention (ATTI) based on participants’ Adult Eating Behaviour Questionnaire (AEBQ) scores. The ATTI was developed using strategies from modified Cognitive Behavioural Therapy and behaviour change techniques. Acceptability testing of the ATTI was carried out with participants (body mass index ≥25) who completed the AEBQ online and were sent their appetitive trait profile and corresponding weight loss tips via e-mail. Participants were asked to follow the tips for 8 weeks and following the tips, perceived helpfulness, barriers, and initial and final body weight. Qualitative interviews explored their experiences. Thirty-seven participants provided feedback and reported the majority of the tips to be helpful. Thirty-two participants (92.5% female) provided their final weight; 10 reported weight loss ≥5% of initial weight. Qualitative interviews (n = 21) revealed that tailoring was seen as novel and participants felt that the ATTI increased their self-awareness and encouraged behavioural changes. The low intensity of the ATTI limited engagement for some. The ATTI is an acceptable, novel approach to weight management.

Background: Removing unhealthy products from checkouts will arguably reduce impulse purchasing, but evidence is lacking. In 2015, Tesco Express stores implemented so-called healthy checkouts; products high in sugar, fat, or salt (defined by national Nutrient Profiling Model criteria) were removed from in-queue areas. We aimed to compare the purchasing of unhealthy foods before and after its introduction. Methods: Tesco provided store-level sales data for 1151 Tesco Express stores in England over two 8 week periods (May–July in 2014 and 2015). We used paired t tests to examine whether spend on unhealthy foods (biscuits, cakes, crisps, confectionary) as a proportion of total spend changed in 2015 versus 2014. Analyses were repeated for the quantity of unhealthy products sold. Unannounced store visits (n=41) were conducted by three researchers (in London, the South East, the North West, and Yorkshire and Humber) to measure compliance (ie, whether prohibited products were displayed in-queue). Findings: Complete sales data were available for 1101 stores (96%). Mean overall spend increased in 2015 compared with 2014 (£666 079·70 [SD 406 385·00] vs £653 786·59 [SD 447 580·77], p

Satellite-based estimates of rainfall are frequently used to complement scarce networks of gauges. Understanding uncertainties is an important step, but it is often hindered by a lack of validation data or misrepresented by spatial-scale-related uncertainties, which are especially important in spatially variable regions such as mountains. This study evaluates the Integrated Multisatellite Retrievals for GPM (IMERG) V05B 30-min estimates for all three runs (Early, Late, Final) over the high tropical Andes. A unique dataset containing 15 rain gauges located within one IMERG grid at elevations ranging from 3800 to 4600 m provides a first evaluation opportunity in this topographical context. The evaluation was based on categorical, statistical, and graphical methods. Error dependencies on precipitation characteristics and data source of the IMERG estimate were investigated. We show that IMERG severely underdetects precipitation events, thus underestimating precipitation depths. Poor detection is partially attributable to the low-intensity nature of precipitation over the region. However, tracing the error to the data source highlights limitations in passive microwave retrievals over the full range of intensities. No IMERG run has best overall performance, emphasizing that run suitability is application specific. The impact of gauge density on performance metrics was also evaluated and showed that subdaily IMERG accuracy is overestimated by sparse networks. A minimum of six gauges was required at the 30-min increment so that performance metrics are within 0.1 points of their true scores. We provide the first comprehensive assessment of 30-min IMERG in a mountainous setting, highlighting the importance of high-density networks for accurate subdaily evaluations.

Athletes report poor breast/bra knowledge, breast pain, sports bras causing lacerations and chafing, negatively affecting sports performance. No bra interventions to address these issues are reported. Working with 142 UK female athletes preparing for Tokyo Olympics/Paralympics (27 sports), this multi-phase intervention assessed breast/bra knowledge, preferences, issues via surveys and breast/bra assessments. Data were used to develop two sports bras. A total of 112 athletes were prescribed one of the new bras through individual assessments. After four weeks, wear athletes completed evaluations. Pre-intervention breast/bra knowledge was low (83% ≤average), multiple breast/bra issues were reported and most athletes wore ill-fitting, loose bras, offering limited support. Post-intervention, 63% reported improved knowledge and 97% reported their prescribed bra as better than their original bra. Eighty-seven per cent reported benefitting from this intervention, with 17% reporting improved performance. This intervention effectively assessed sports bra needs, developed and implemented solutions, which improved knowledge and potentially performance for some UK athletes.

Much of our knowledge about the impacts of volcanic eruptions on climate comes from proxy records. However, little is known about their impact on the low to mid-latitudes of the Southern Hemisphere. Using superposed epoch analysis, we investigated whether volcanic signals could be identified in annual tree-ring series from eight New Zealand dendrochronological species. We found that most species are reliable recorders of volcanic cooling and that the magnitude and persistence of the post-event response can be broadly linked to plant life history traits. Across species, site-based factors, particularly altitude and exposure to prevailing conditions, are more important determinants of the strength of the volcanic response than species. We then investigated whether chronology selection impacts the magnitude of post-volcanic cooling in tree-ring-based temperature reconstructions by developing two new multispecies reconstructions of New Zealand summer (December–February) temperature with one reconstruction from the pool of all available chronologies, and the other from a selected subset shown to be sensitive to volcanic eruptions. Both reconstructions record temperature anomalies that are remarkably consistent with studies based on instrumental temperature and the ensemble mean response of climate models, demonstrating that New Zealand ring widths are reliable indicators of regional volcanic climate response. However, we also found that volcanic response can be complex, with positive, negative, and neutral responses identified – sometimes within the same species group. Species-wide composites thus tend to underestimate the volcanic response. This has important implications for the development of future tree-ring and multiproxy temperature reconstructions from the Southern Hemisphere.

It is common to test hydrologic models under contrasting historical periods as an indicator of likely performance under climate change. For example, a model calibrated under average conditions may be tested under increasingly dry subsets of the observational record. Any decline in performance as the testing conditions deviate further from the calibration conditions is then assumed to represent likely performance degradation under climate change scenarios with comparable rainfall decreases. Many studies have inherently applied the assumption that past rainfall variability can be used as a proxy for future climate change, but the analogy may be flawed for three main reasons:Due to lagged hydrologic response to meteorological shifts, catchment behaviour under long-term wetting or drying may not be fully represented over shorter wet or dry periods. Subsets of the past record selected based on rainfall are unlikely to reflect future temperature increases. Past observations do not include expected increases in carbon dioxide levels. If any of these factors substantially impacts catchment response, subsets of the historical record with equivalent rainfall will not be accurate proxies for future climate scenarios. We tested the impact of each factor using the ecohydrologic model RHESSys. RHESSys dynamically simulates vegetation growth, subsurface flow and nutrient cycling and is thus able to capture the key processes that could drive nonstationary catchment response in the future. We found that all three future climate factors (rainfall change persistence, temperature, and carbon dioxide) altered catchment response substantially, especially for drier future scenarios. For our study catchment, persistence of dry conditions over many decades led to different subsurface water storage levels than the same rainfall experienced over shorter timeframes, leading to different streamflow. The impacts of increased temperature and carbon dioxide concentrations on vegetation further altered runoff behaviour. This means that long-term climate change effects will not necessarily emerge over short historical periods with equivalent rainfall. In our example, ignoring persistence in rainfall changes, rising temperatures, and higher carbon dioxide levels could lead us to underestimate model performance degradation in terms of Nash-Sutcliffe efficiency by as much as 0.41. Therefore, the uncertainty introduced in hydrologic models by future climate change has probably been underestimated in the current literature.

Much of our knowledge about the impacts of volcanic events on climate comes from proxy records. However, little is known about the impact of volcanoes on trees from the Southern Hemisphere. We investigated whether volcanic signals could be identified in ring widths from eight New Zealand dendrochronological species, using superposed epoch analysis. We found that most species are good recorders of volcanic dimming and that the magnitude and persistence of the post-event response can be broadly linked to plant life history traits. Across species, site-based factors, particularly altitude and exposure to prevailing conditions, are more important determinants of the strength of the volcanic response than the species. We then investigated whether proxy selection impacts the magnitude of post-volcanic cooling in tree-ring based temperature reconstructions by developing two new multispecies reconstructions of New Zealand summer (December–February) temperature. Both reconstructions showed temperature anomalies remarkably consistent with studies based on instrumental temperature, and with the ensemble mean response of climate models, demonstrating that New Zealand ring widths are reliable indicators of regional volcanic climate response. However, we also found that volcanic response is complex, with positive, negative, and neutral responses identified – sometimes within the same species group. Species-wide composites thus tend to underestimate the volcanic response. The has important implications for the development of future tree ring and multiproxy temperature reconstructions from the Southern Hemisphere.

PURPOSE A taxonomy of behaviour change techniques has been developed to help specify the active ingredients of behaviour change interventions. Its potential for rehabilitation research is significant, however, reliable use among allied health professionals has not yet been explored. This article describes the content of a conversation therapy for post-stroke aphasia using the taxonomy and investigates inter-rater reliability among Speech and Language Therapists. METHODS AND MATERIALS Two Speech and Language Therapists undertook the same half day, self-led training programme in the behaviour change technique taxonomy and independently coded all materials in the "Better Conversations with Aphasia" programme. Inter-rater reliability was evaluated using the kappa coefficient and percentage agreement. Reliably agreed techniques were categorised according to the speaker and type of behaviour they targeted. RESULTS Sixteen behaviour change techniques were reliably agreed to be present. Inter-rater reliability was moderate (K = 0.465), and in line with satisfactory percentage agreement (79.8%). More techniques were used to target the adoption of new behaviours (15) than the termination of old ones (3). People with aphasia received fewer behaviour change techniques (10) than their communication partners (16). CONCLUSIONS Describing the content of conversation therapy with the taxonomy of behaviour change techniques offers clinically useful insights with potential to enhance both research and practice. The intervention is shown to target different types of behaviour in different ways, and offer different speaker groups different content. Non-psychologist users of the taxonomy may encounter challenges working with unfamiliar concepts and terminology, which may impact on reliable use.IMPLICATIONS FOR REHABILITATIONIn order to change communicative behaviours within conversation, feedback should focus not only on performance but also on the immediate social and emotional consequences of a behaviour.This study adds to the evidence that unhelpful conversational behaviours can be reduced by providing speakers with information about any unwanted consequences, and then agreeing on a supportive behaviour to use instead.People with aphasia should be offered the same range of behaviour change techniques as their communication partners.

Climate change is expected to increase waterborne diseases especially in developing countries. However, we lack understanding of how different types of water sources (both improved and unimproved) are affected by climate change, and thus, where to prioritize future investments and improvements to maximize health outcomes. This is due to limited knowledge of the relationships between source water quality and the observed variability in climate conditions. To address this gap, a 20‐month observational study was conducted in Tanzania, aiming to understand how water quality changes at various types of sources due to short‐term climate variability. Nine rounds of microbiological water quality sampling were conducted for Escherichia coli and total coliforms, at three study sites within different climatic regions. Each round included approximately 233 samples from water sources and 632 samples from households. To identify relationships between water quality and short‐term climate variability, Bayesian hierarchical modeling was adopted, allowing these relationships to vary with source types and sampling regions to account for potentially different physical processes. Across water sources, increases in E. coli/total coliform levels were most closely related to increases in recent heavy rainfall. Our key recommendations to future longitudinal studies are (a) demonstrated value of high sampling frequency and temporal coverage (a minimum of 3 years) especially during wet seasons; (b) utility of the Bayesian hierarchical models to pool data from multiple sites while allowing for variations across space and water sources; and (c) importance of a multidisciplinary team approach with consistent commitment and sharing of knowledge., Key Points We present a longitudinal study in a developing country on water quality changes at a range of water sources under climate variabilityIncreases in E. coli and Total Coliform levels were most closely related to recent heavy rainfallRecommendations for future cross disciplinary studies on drinking water quality and relationships with climate variability are made

Background Reducing youth (15-17 years) smoking uptake is critical to tobacco control; accordingly, youth smoking prevalence is a key monitoring and evaluation outcome. Many nationally representative surveys collect youth smoking behaviour data from, or in the presence of, the youth’s parent or caregiver. We aimed to quantify the potential bias conferred by this. Methods We compared youth smoking prevalence when reported by parent proxy, with parent present or by private self-report, in Australian Bureau of Statistics Health Surveys. National youth current smoking prevalence if all data were collected by youth self-report was estimated. Results Smoking behaviour data for over 75% of youth participants in the health survey were collected by proxy or with parent present. Ever-smoking prevalence using private self-report versus report by proxy was 1.29 (95%CI:0.96-1.73) to 1.99 (1.39-2.85) times as high in Aboriginal and Torres Strait Islander youth, and 1.83 (0.92-2.63) to 2.72 (1.68-4.41) times as high in total population youth. Predicted national current smoking prevalence if all youth were to self-report alone was substantively higher than the estimated national prevalence based on actual responses, but still reveals a decline over time. Conclusions Youth smoking estimates drawn from data collected by proxy/with parent present are unlikely to be accurate. Increased youth self-report is crucial to ensure data accuracy to inform effective tobacco control. Key messages The accuracy of youth smoking data collected by proxy/with parent present should be further scrutinised before it is used to inform assessment of national prevalence and trends.

Constructed shallow waterbodies are often designed and built to limit harmful algal blooms in urban regions. Efforts to reduce algal bloom occurrence in these waterbodies have largely focused on waterbody design, catchment criteria and onsite engineering options. However, many constructed shallow waterbodies that comply with design guidelines still experience harmful algal blooms. Identifying the knowledge gaps in current guidelines and examining their recommended design criteria can improve their effectiveness to reduce algal outbreaks. Here, we reviewed 66 global guidelines and identified common design criteria. The use of a ‘one size fits all' empirical approach and dated literature are common issues associated with the design criteria recommended. Further, only approximately one third of the guidelines that were analyzed directly mentioned harmful algal bloom-related design criteria. To test the validity of these design values in a real-world setting, the suitability of design factors in limiting harmful algal blooms was assessed by analyzing 222 shallow waterbodies monitored over a 9 year period in southeastern Australia. The site analysis indicated that macrophyte area to surface area ratio, shoreline development index, and fetch are the three most influential single design factors associated with harmful algal bloom reduction. The analyses highlighted the ineffectiveness of the existing design criteria globally, with blooms occurring even though some waterbodies were designed in accordance with recommended parameters. The analysis suggested that understanding interactions between multiple design factors may be a useful approach, for example, when considering the macrophyte area to surface area ratio in combination with the shoreline development index.

We are excited to provide the guest editorial for this issue of the Journal of Humanitarian Engineering as the new co-Editors in Chief of the journal. Our sincere thanks and appreciation go to Dr Dani Barrington-Francis for her leadership and work on the journal for the last xxx years. It needed two people to fill her big shoes! We join the journal at a time of global heartache and upheaval as COVID-19 is fundamentally changing the ways we live and work. From a Humanitarian Engineering perspective, it poses a double-edged sword. While making the value and importance of our work more apparent than ever, it also brings disruption and threats. Like most crises, COVID-10 amplifies societal inequities and highlights the important role humanitarian engineers play in eliminating these. Pandemics do not affect all equally. Vulnerable groups once again bear the brunt of the burden, not least because COVID-19 disproportionally affects older people and people with low immunity. But vulnerable groups are also more likely to live in tight quarters, making social distancing challenging, and are more likely to lack access to health services and information. They are more likely to lose their jobs, may be unable to buy in bulk, and may find home-schooling more challenging. For us, as humanitarian engineers, COVID-19 especially brings to light the disparities in access to technology we, as a global society, are still facing. It is impossible to wash your hands for 20 seconds to halt the spread of disease if you do not have access to clean water or soap. Approximately 2.3 billion people globally are in this situation. Furthermore more than 20% of health services in least developed countries have no water and/or no sanitation and/or waste management services. How can medical workers save lives under these conditions? The COVID-19 pandemic also highlights the important role engineers play in urgently managing humanitarian emergencies. Engineers have, for example, responded to COVID-19 by rapidly designing low-cost face masks, face shields, and ventilators, and promptly converting manufacturing facilities to fulfil the growing demands for PPE. Engineers have also provided crucial data to support policy making by modelling and tracking the spread of the disease. One example is the dashboard developed by the Centre for System Science and Engineering at John Hopkins University in the US, which has had over 1 billion usage requests per day. These, and many other remarkable efforts by engineers, will continue to play a crucial role in the long battle against COVID-19. While the current crisis highlights the importance of humanitarian engineering, it also disrupts our work, and makes it more challenging. So much of what we do every day, whether internationally or locally, requires us to build strong relationships and work directly with vulnerable and marginalised groups to co-design solutions that improve life and well-being. COVID-19 threatens to weaken or even break those relationships by replacing face-to-face collaboration with online interactions, which the world is now discovering is a poor substitute. We must find new and innovative ways of empowering our collaborators from afar and continue to support them through the crisis. COVID-19 also threatens the important work the Humanitarian Engineering community has done to transform engineering education over the past decade. Engineers Australia, and other engineering peak bodies, have long recognised the need for engineers to apply their practice ethically and collaboratively. However, until recently, this has not been reflected in the way engineering students have been educated. Engineers have often paid lip service to community consultation as another tick that gets big infrastructure projects approved. Humanitarian Engineering educators have, in collaboration with Engineers without Borders Australia, played an important role in changing this. Over the last 10 years, we have seen a number of courses and programs developed at universities across Australia and New Zealand that aim to train the next generation of engineers with the skills to lead our profession in human-centred design. What will happen to these efforts as engineering education moves online and universities across the region are looking to cut budgets? In these uncertain times, we should, as a community, band together to take advantage of this unique opportunity to demonstrate the value of our work, while finding ways of collectively navigating the current and potential future challenges and threats. We believe the Journal of Humanitarian Engineering provides an important avenue for doing this and warmly invite you to join the conversation.

Event-based hydrologic models are frequently used for flood design and assessment. These models generally require the specification of loss values that should relate to antecedent conditions in the catchment. The loss values are key calibration parameters, usually defined by matching model output with recorded or derived streamflow information. It is now widely recognized that climate change will impact the hydrologic cycle and affect catchment conditions. Therefore, loss values calibrated using historical observations may not be appropriate for simulating future flooding. We use the method of bottom-up climate change assessment to understand the potential for future performance changes in a calibrated event-based model due to changing antecedent conditions. This is achieved by comparing the results against those of a continuous hydrologic model that accounts for differences in antecedent catchment storages. We find that event-based model performance diverges substantially from the continuous model results under the climate change scenarios, which account for increased dryness and greater extreme rainfall intensities. The results indicate that there is greater uncertainty in event-based model results when simulating drier climatic states, attributed to greater variability in antecedent conditions. However, when simulating increased extreme rainfall intensity (giving rise to larger rainfall events and generally wetter antecedent conditions), the impact of changing antecedent conditions was less important than the models’ representations of catchment nonlinearity. This suggests that changing antecedent conditions are not always the key source of potential model performance degradation. Therefore, applying continuous simulation will not necessarily offer an advantage in characterizing future floods. This study highlights the uncertainty facing practicing engineers and hydrologists wanting to account for climate change in flood modelling and design. Large scale changes in engineering practice may be required to ensure that the robustness of flood modelling is maintained in a changing climate.

Digital Elevation Models (DEMs) such as Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Models (ASTER GDEM), or Shuttle Radar Topography Mission DEM (SRTM) are widely used in remote areas and non-industrial countries because of their availability rather than their accuracy. Although a global DEM can be considerably enhanced using additional reference information such as higher resolution DEMs or ground truth points, improving accuracy in areas without reference data remains a challenge. This paper develops an approach to improve the accuracy of the estimated topography by combining two complementary DEMs (ASTER GDEM 1 arc-second and SRTM DEM 1 arc-second) in regions missing reference data. The combination approach is based on formulating relationships between slopes and weights in sites with reference data. Then the developed relationships are applied to sites with similar geomorphology to determine the combination weight for each DEM without using reference data. The results indicate that combined DEMs offer significant improvements of 47% and 20% in mean bias over a mountainous site, and 16% and 58% at a low-relief site when compared with the SRTM and ASTER GDEM products, respectively. DEM-derived drainages were also found to be more accurate for the combined DEMs than the near-global DEMs in areas where reference data is not available.

Space borne radar altimeters often complement in-situ water levels to provide greater insights to hydrodynamic models in sparsely gauged catchments. However, 10-day or 35-day water levels derived from satellite radar altimetry are generally too infrequent for flood forecasting or hydrodynamic modelling purposes. This paper proposes a new approach to find daily water levels for areas where in-situ river heights are not available. The new approach is based on the relationship between river height and difference between daytime and night time land surface temperatures (LST). This relationship is first demonstrated using in-situ gauge data to explore appropriate statistical models to predict river height using LST differences. The approach is then applied to a number of virtual stations at the intersections between the Mekong River and the ground-tracks of Jason-2 satellite altimetry which gives 10-day water level time series. The LST difference from the thermal infrared (TIR) observations of Moderate Resolution Imaging Spectro-radiometer (MODIS) is shown to have a strong relationship with in-situ water levels and a good relationship with the Jason-2 water levels. The models included a simple linear regression model which was then extended to firstly include seasonal terms and secondly assimilate satellite altimetry data. A regression model tree (M5) was also considered but was found to be inferior to the seasonal linear model. The developed regression models were used to predict daily water levels to infill the 10-day Jason-2 altimeters. RMSE of modelled daily water levels at gauges is between 0.3 m to 0.6 m, while RMSE of modelled daily water levels without using in-situ data is higher varying from 1.4 m to 1.9 m. The temporal correlation between the modelled water levels using satellite altimeters at virtual stations and in-situ water levels at adjacent gauges ranged from 0.72 to 0.86. These results show the potential of the proposed approach to produce high temporal resolution water levels for flood models or other applications using only remotely sensed data.

Simulations from climate models require bias correction prior to use in impact assessments or for statistical or dynamic downscaling to finer scales. There are a number of different approaches to bias correction, although most of these focus on a single variable for a particular location. Another limitation is that often corrections are only applied for one time scale of interest, for example daily or monthly aggregated simulations despite evidence of different bias structures existing at different time scales. Recent works have sought to address each of these limitations and have led to the development of the Multivariate Recursive Nesting Bias Correction (MRNBC) and Multivariate Recursive Quantile-matching Nested Bias Correction (MRQNBC) methods. An open-source software toolkit in the R statistical computing environment has been developed to provide access to these methods. Several applications of the software are demonstrated in this paper along with information about the capabilities of the software.

Study region: Australia. Study focus: Design rainfalls, in the form of Intensity Duration Frequency curves, are the standard input for most flood studies. Methods to combine rainfall data across space are required to provide optimal estimates of design rainfalls and constrain their uncertainty. This paper robustly investigates the use of a variety of regionalization methods to provide Australia wide design rainfall estimates using 8619 high quality rainfall stations. The influence of an individual large rainfall event in March 2012 on the regionalised design rainfalls is also investigated to guide decisions on when design rainfall data should be updated following record-breaking rainfall events. New hydrological insights for the region: The optimum approach was found to be circular regions of influence with a size of 500 station-years. The regions of influence are least biased when defined using spatial proximity and including elevation in the calculating spatial proximity can help to reduce the bias in design rainfall estimates in areas of strong topographic relief. The March 2012 rainfall event increased site rainfall quantiles by 14% on average. When regionalisation is used the increases were substantially moderated and were within the uncertainty bounds of the design rainfalls. It is still not routine to provide uncertainty estimates with design rainfall data, but such information is particularly important given likely future changes in extreme rainfalls due to anthropogenic climate change. Keywords: Design rainfall, Regionalisation, Region of influence

Soil moisture (SM) plays an important role in determining the antecedent condition of a watershed, while topographic attributes define how and where SM and rainfall interact to create floods. Based on this principle, we present a method to identify flood risk at a location in a watershed by using remotely sensed SM and open-access information on rainfall, soil properties, and topography. The method consists of three hydrologic modules that represent the generation, transfer, and accumulation of direct runoff. To simplify the modeling and provide timely warnings, the flood risk is ascertained based on frequency of exceedance, with warnings issued if above a specified threshold. The simplicity of the method is highlighted by the use of only three parameters for each watershed of interest, with effective regionalization allowing use in ungauged watersheds. For this proof-of-concept study, the proposed model was calibrated and tested for 65 hydrologic reference stations in the Murray–Darling Basin in Australia over a 35-year study period by using satellite-derived surface SM. The three model parameters were first estimated using the first ten-year data and then the model performance was evaluated through flood threshold exceedance analyses over the remaining 25-year study period. The results for estimated parameters and skill scores showed promise. The three model parameters can be regionalized as a function of watershed characteristics, and/or representative values estimated from neighboring watersheds, allowing use in ungauged basins everywhere.

Summary Addressing systematic biases in regional climate model simulations of extreme rainfall is a necessary first step before assessing changes in future rainfall extremes. Commonly used bias correction methods are designed to match statistics of the overall simulated rainfall with observations. This assumes that change in the mix of different types of extreme rainfall events (i.e. convective and non-convective) in a warmer climate is of little relevance in the estimation of overall change, an assumption that is not supported by empirical or physical evidence. This study proposes an alternative approach to account for the potential change of alternate rainfall types, characterized here by synoptic weather patterns (SPs) using self-organizing maps classification. The objective of this study is to evaluate the added influence of SPs on the bias correction, which is achieved by comparing the corrected distribution of future extreme rainfall with that using conventional quantile mapping. A comprehensive synthetic experiment is first defined to investigate the conditions under which the additional information of SPs makes a significant difference to the bias correction. Using over 600,000 synthetic cases, statistically significant differences are found to be present in 46% cases. This is followed by a case study over the Sydney region using a high-resolution run of the Weather Research and Forecasting (WRF) regional climate model, which indicates a small change in the proportions of the SPs and a statistically significant change in the extreme rainfall over the region, although the differences between the changes obtained from the two bias correction methods are not statistically significant.

Forecasting of hydrologic extremes across a range of timescales is critical for minimizing the socio-economic costs of these events. Regression-based prediction is commonly adopted even in operational forecasting systems, often necessitating the use of distributional transformations to improve model specifications. One of the issues in such predictions, however, is the marked differences that distinguish the frequency spectrum of the hydrologic response from the predictor variables used. This raises the question of whether there exists an optimal predictor variable transformation that can mimic the frequency spectrum embedded in the observed response variable series. The present paper discusses the need to transform predictor variables to improve hydrologic forecasts, and specifically focuses on the frequency domain of the variables involved. A number of alternatives using wavelet-based approaches are presented as a means of transforming the variance associated with different frequency bands in each predictor variable. The limitations and advantages of these transformations are summarized and demonstrated using synthetic examples. A stepwise variance transformation framework is further proposed that facilitates transformations of the residual error from a given predictor variable conditioned on existing (or pre-identified) predictor variables. Results of the stepwise framework demonstrate that the response is better characterized using both synthetic case studies and when applied to forecasting the El Nino–Southern Oscillation (ENSO) over long lead times.

Satellite altimetry helps to monitor continental water levels with global coverage and near-real time accessibility. However, a main drawback of satellite altimetry is its low acquisition frequency that hinders the use of altimeters in flood forecasting or reservoir operation processes. This study provides a new method to estimate daily river heights from multisource hydro-climate satellite products without using ground-based data. First, the approach develops a simple statistical model, a seasonal linear model, using satellite altimetry, land surface temperature (LST), satellite precipitation and satellite soil moisture products. Second, a Monte Carlo simulation is used to understand the model accuracy by attributing the model output errors to uncertainties from the multiple satellite products. For the first aim, the study shows that using high-frequency satellite data to improve the temporal resolution of the satellite radar altimetry is better than an approach merging multiple satellite altimetry data. With respect to the second objective, it is found that higher variances are found in seasonal transition periods where there are significant changes in satellite observations. The overall error of the model is attributed to both uncertainties of satellite input data and the interactions among their uncertainties. The results demonstrate the capability of using multisource satellite data to predict daily time series of river heights in data-poor areas. The results also highlight the importance of quantifying uncertainty to increase the reliability of flood forecasting models using satellite products.

Water supply management for hydroclimatic extremes has commonly been analysed using the cumulative difference between inflow and demand, a concept known as the Residual Mass Curve. This paper extends the residual mass concept to develop a new method to quantify water availability, termed the Residual Mass Severity Index (RMSI). The RMSI improves on the Residual Mass Curve by allowing extreme deficit periods to be easily identified without the need for manual inspection of the Residual Mass Curve. Another advantage of the RMSI is that it does not require a temporal window to be specified to characterise extreme deficit periods, which is needed for many drought indices such as the Standardized Precipitation Index (SPI). This paper develops the method for calculating the RMSI using precipitation time series and compares it to the SPI for different drought durations across Australia. Major hydroclimatic extremes during the instrumental period are assessed using RMSI, SPI, and by comparing the RMSI estimates with streamflow data from 467 catchments with varying sizes and climate across Australia. RMSI is a useful index for water managers interested in assessing water availability with potential applicability in summarising the features of historical hydroclimatic extremes as well as future changes in water availability due to anthropogenic climate change.

Warmer temperatures are expected to increase extreme short-duration rainfall due to the increased moisture-holding capacity of the atmosphere. While attention has been paid to the impacts of climate change on future design rainfalls at daily or longer time scales, the potential changes in short duration design rainfalls have been often overlooked due to the limited availability of sub-daily projections and observations. This study uses a high-resolution regional climate model (RCM) to predict the changes in sub-daily design rainfalls for the Greater Sydney region in Australia. Sixteen methods for predicting changes to sub-daily future extremes are assessed based on different options for bias correction, disaggregation and frequency analysis. A Monte Carlo cross-validation procedure is employed to evaluate the skill of each method in estimating the design rainfall for the current climate. It is found that bias correction significantly improves the accuracy of the design rainfall estimated for the current climate. For 1 h events, bias correcting the hourly annual maximum rainfall simulated by the RCM produces design rainfall closest to observations, whereas for multi-hour events, disaggregating the daily rainfall total is recommended. This suggests that the RCM fails to simulate the observed multi-duration rainfall persistence, which is a common issue for most climate models. Despite the significant differences in the estimated design rainfalls between different methods, all methods lead to an increase in design rainfalls across the majority of the study region.

Considerable variability exists in simulations of the future climate. This variability is caused by differences in the parameterisations across general circulation models (GCMs), the initial conditions used and the different assumptions made as to how emissions will evolve in the future. As a result, there is considerable disagreement between available projections of climate variables, which can be used to quantify the uncertainty each variable exhibits. This leads to the question—which variables (or set of variables) are more reliable for use in climate change impact assessments. This research presents a framework to quantify the relative reliability amongst a range of upper air atmospheric variables. This is made possible by pooling simulations across multiple models, trajectories (scenarios) and initial conditions in a rank-transformed space. A metric named the variable reliability score (VRS) assesses the relative reliabilities across different atmospheric variables on a common scale. The VRS has been applied to calculate the total reliability as well as reliability from each source of uncertainty, namely model, scenarios and initial conditions. This comparison helps to decide if more models, scenarios or ensembles are required for uncertainty analysis of climate change impact assessment. The variables compared include geopotential height and its north-south difference, specific humidity, eastward wind and northward wind, all at the 500 and 850 hPa pressure levels. These variables were chosen based on availability of data and their documented use in previous climate change impact assessment studies worldwide. A regional assessment of VRS over 21 regions around the world shows that though the magnitude of VRS varies spatially, the ranked reliability of the variable rank remains relatively similar. On average, the lowest reliability is associated with geopotential height, whilst wind speeds and the north-south difference of geopotential height have higher reliability.

A novel approach is presented to spatially disaggregate coarse soil moisture (SM) by only using remotely sensed vegetation index. The approach is based on the conditional relationship of vegetation with time-aggregated SM, allowing the coarse-scale SM to be disaggregated to the spatial resolution of the vegetation product. The method was applied to satellite-derived SM over January 2010–December 2011, using the high-resolution normalized difference vegetation index (NDVI). The results were evaluated against ground measurements during the two-year period over the contiguous United States and Spain, and also compared with an existing disaggregation method that also requires land surface temperature observations. It is shown that the proposed approach can provide fine-resolution SM with reasonable spatial variability.