Investigating the Associations between Drought, Poverty, High-Risk Sexual Behaviours, and HIV Incidence in Sub-Saharan Africa: A Cross-Sectional Study.

Climate change is increasing the likelihood of drought in sub-Saharan Africa, where HIV prevalence is high. Drought could increase HIV transmission through various mediating mechanisms; we investigated these associations. We used data on people aged 15-59 from Population-Based HIV Impact Assessment surveys from 2016 in Eswatini, Lesotho, Tanzania, Uganda, and Zambia. Survey data were geospatially linked to precipitation data for 2014-2016, with local droughts defined as cumulative rainfall between 2014 and 2016 being in < 15th percentile of all 2-year periods over 1981-2016. Using multivariable logistic regression, stratified by sex and rural/urban residence, we examined associations between (a) drought and poverty, (b) wealth quintiles and sexual behaviours (transactional, high-risk, and intergenerational sex), (c) sexual behaviours and recently acquiring HIV, and (d) drought and recent HIV. Among 102,081 people, 31.5% resided in areas affected by drought during 2014-2016. Experiencing drought was positively associated with poverty for women and men in rural, but not urban, areas. For each group, increasing wealth was negatively associated with transactional sex. For rural women, intergenerational sex was positively associated with wealth. Women reporting each sexual behaviour had higher odds of recent HIV, with strong associations seen for high-risk sex, and, for urban women, intergenerational sex, with weaker associations among men. Women in rural areas who had been exposed to drought had higher odds of having recently acquired HIV (2.10 [95%CI: 1.17-3.77]), but not women in urban areas, or men. Droughts could potentially increase HIV transmission through increasing poverty and then sexual risk behaviours, particularly among women in rural areas.

Associations of inter-annual rainfall decreases with subsequent HIV outcomes for persons with HIV on antiretroviral therapy in Southern Africa: a collaborative analysis of cohort studies.

BACKGROUND: Periods of droughts can lead to decreased food security, and altered behaviours, potentially affecting outcomes on antiretroviral therapy (ART) among persons with HIV (PWH). We investigated whether decreased rainfall is associated with adverse outcomes among PWH on ART in Southern Africa. METHODS: Data were combined from 11 clinical cohorts of PWH in Lesotho, Malawi, Mozambique, South Africa, Zambia, and Zimbabwe, participating in the International epidemiology Databases to Evaluate AIDS Southern Africa (IeDEA-SA) collaboration. Adult PWH who had started ART prior to 01/06/2016 and were in follow-up in the year prior to 01/06/2016 were included. Two-year rainfall from June 2014 to May 2016 at the location of each HIV centre was summed and ranked against historical 2-year rainfall amounts (1981-2016) to give an empirical relative percentile rainfall estimate. The IeDEA-SA and rainfall data were combined using each HIV centre's latitude/longitude. In individual-level analyses, multivariable Cox or generalized estimating equation regression models (GEEs) assessed associations between decreased rainfall versus historical levels and four separate outcomes (mortality, CD4 counts < 200 cells/mm3, viral loads > 400 copies/mL, and > 12-month gaps in follow-up) in the two years following the rainfall period. GEEs were used to investigate the association between relative rainfall and monthly numbers of unique visitors per HIV centre. RESULTS: Among 270,708 PWH across 386 HIV centres (67% female, median age 39 [IQR: 32-46]), lower rainfall than usual was associated with higher mortality (adjusted Hazard Ratio: 1.18 [95%CI: 1.07-1.32] per 10 percentile rainfall rank decrease) and unsuppressed viral loads (adjusted Odds Ratio: 1.05 [1.01-1.09]). Levels of rainfall were not strongly associated with CD4 counts < 200 cell/mm3 or > 12-month gaps in care. HIV centres in areas with less rainfall than usual had lower numbers of PWH visiting them (adjusted Rate Ratio: 0.80 [0.66-0.98] per 10 percentile rainfall rank decrease). CONCLUSIONS: Decreased rainfall could negatively impact on HIV treatment behaviours and outcomes. Further research is needed to explore the reasons for these effects. Interventions to mitigate the health impact of severe weather events are required.

Flood hazard potential reveals global floodplain settlement patterns.

Flooding is one of the most common natural hazards, causing disastrous impacts worldwide. Stress-testing the global human-Earth system to understand the sensitivity of floodplains and population exposure to a range of plausible conditions is one strategy to identify where future changes to flooding or exposure might be most critical. This study presents a global analysis of the sensitivity of inundated areas and population exposure to varying flood event magnitudes globally for 1.2 million river reaches. Here we show that topography and drainage areas correlate with flood sensitivities as well as with societal behaviour. We find clear settlement patterns in which floodplains most sensitive to frequent, low magnitude events, reveal evenly distributed exposure across hazard zones, suggesting that people have adapted to this risk. In contrast, floodplains most sensitive to extreme magnitude events have a tendency for populations to be most densely settled in these rarely flooded zones, being in significant danger from potentially increasing hazard magnitudes given climate change.

A mechanistic approach to include climate change and unplanned urban sprawl in landslide susceptibility maps.

Empirical evidence shows that climate, deforestation and informal housing (i.e. unregulated construction practices typical of fast-growing developing countries) can increase landslide occurrence. However, these environmental changes have not been considered jointly and in a dynamic way in regional or national landslide susceptibility assessments. This gap might be due to a lack of models that can represent large areas (>100km2) in a computationally efficient way, while simultaneously considering the effect of rainfall infiltration, vegetation and housing. We therefore suggest a new method that uses a hillslope-scale mechanistic model to generate regional susceptibility maps under changing climate and informal urbanisation, which also accounts for existing uncertainties. An application in the Caribbean shows that the landslide susceptibility estimated with the new method and associated with a past rainfall-intensive hurricane identifies ~67.5 % of the landslides observed after that event. We subsequently demonstrate that the hypothetical expansion of informal housing (including deforestation) increases landslide susceptibility more (+20 %) than intensified rainstorms due to climate change (+6 %). However, their combined effect leads to a much greater landslide occurrence (up to +40 %) than if the two drivers were considered independently. Results demonstrate the importance of including both land cover and climate change in landslide susceptibility assessments. Furthermore, by modelling mechanistically the overlooked dynamics between urban growth and climate change, our methodology can provide quantitative information of the main landslide drivers (e.g. quantifying the relative impact of deforestation vs informal urbanisation) and locations where these drivers are or might become most detrimental for slope stability. Such information is often missing in data-scarce developing countries but is key for supporting national long-term environmental planning, for targeting financial efforts, as well as for fostering national or international investments for landslide mitigation.

Ground truthing global-scale model estimates of groundwater recharge across Africa.

Groundwater is an essential resource for natural and human systems throughout the world and the rates at which aquifers are recharged constrain sustainable levels of consumption. However, recharge estimates from global-scale models regularly disagree with each other and are rarely compared to ground-based estimates. We compare long-term mean annual recharge and recharge ratio (annual recharge/annual precipitation) estimates from eight global models with over 100 ground-based estimates in Africa. We find model estimates of annual recharge and recharge ratio disagree significantly across most of Africa. Furthermore, similarity to ground-based estimates between models also varies considerably and inconsistently throughout the different landscapes of Africa. Models typically showed both positive and negative biases in most landscapes, which made it challenging to pinpoint how recharge prediction by global-scale models can be improved. However, global-scale models which reflected stronger climatic controls on their recharge estimates compared more favourably to ground-based estimates. Given this significant uncertainty in recharge estimates from current global-scale models, we stress that groundwater recharge prediction across Africa, for both research investigations and operational management, should not rely upon estimates from a single model but instead consider the distribution of estimates from different models. Our work will be of particular interest to decision makers and researchers who consider using such recharge outputs to make groundwater governance decisions or investigate groundwater security especially under the potential impact of climate change.

Risk of groundwater contamination widely underestimated because of fast flow into aquifers.

Groundwater pollution threatens human and ecosystem health in many regions around the globe. Fast flow to the groundwater through focused recharge is known to transmit short-lived pollutants into carbonate aquifers, endangering the quality of groundwaters where one quarter of the world's population lives. However, the large-scale impact of such focused recharge on groundwater quality remains poorly understood. Here, we apply a continental-scale model to quantify the risk of groundwater contamination by degradable pollutants through focused recharge in the carbonate rock regions of Europe, North Africa, and the Middle East. We show that focused recharge is the primary reason for widespread rapid transport of contaminants to the groundwater. Where it occurs, the concentration of pollutants in groundwater recharge that have not yet degraded increases from <1% to around 20 to 50% of their concentrations during infiltration. Assuming realistic application rates, our simulations show that degradable pollutants like glyphosate can exceed their permissible concentrations by 3 to 19 times when reaching the groundwater. Our results are supported by independent estimates of young water fractions at 78 carbonate rock springs over Europe and a dataset of observed glyphosate concentrations in the groundwater. They imply that in times of continuing and increasing industrial and agricultural productivity, focused recharge may result in an underestimated and widespread risk to usable groundwater volumes.

Discovering environmental management opportunities for infectious disease control.

Climate change and emerging drug resistance make the control of many infectious diseases increasingly challenging and diminish the exclusive reliance on drug treatment as sole solution to the problem. As disease transmission often depends on environmental conditions that can be modified, such modifications may become crucial to risk reduction if we can assess their potential benefit at policy-relevant scales. However, so far, the value of environmental management for this purpose has received little attention. Here, using the parasitic disease of fasciolosis in livestock in the UK as a case study, we demonstrate how mechanistic hydro-epidemiological modelling can be applied to understand disease risk drivers and the efficacy of environmental management across a large heterogeneous domain. Our results show how weather and other environmental characteristics interact to define disease transmission potential and reveal that environmental interventions such as risk avoidance management strategies can provide a valuable alternative or complement to current treatment-based control practice.

Matlab/R workflows to assess critical choices in Global Sensitivity Analysis using the SAFE toolbox.

Global Sensitivity Analysis (GSA) is a set of statistical techniques to investigate the effects of the uncertainty in the input factors of a mathematical model on the model's outputs. The value of GSA for the construction, evaluation, and improvement of earth system models is reviewed in a companion paper by Wagener and Pianosi (2019). The present paper focuses on the implementation of GSA and provides a set of workflow scripts to assess the critical choices that GSA users need to make before and while executing GSA. The workflows proposed here can be adopted by GSA users and easily adjusted to a range of GSA methods. We demonstrate how to interpret the outcomes resulting from these different choices and how to revise the choices to improve GSA quality, using a simple rainfall-runoff model as an example. We implement the workflows in the SAFE toolbox, a widely used open source software for GSA available in MATLAB and R. •The workflows aim to contribute to the dissemination of good practice in GSA applications.•The workflows are well-documented and reusable, as a way to ensure robust and reproducible computational science.

A mechanistic hydro-epidemiological model of liver fluke risk.

The majority of existing models for predicting disease risk in response to climate change are empirical. These models exploit correlations between historical data, rather than explicitly describing relationships between cause and response variables. Therefore, they are unsuitable for capturing impacts beyond historically observed variability and have limited ability to guide interventions. In this study, we integrate environmental and epidemiological processes into a new mechanistic model, taking the widespread parasitic disease of fasciolosis as an example. The model simulates environmental suitability for disease transmission at a daily time step and 25 m resolution, explicitly linking the parasite life cycle to key weather-water-environment conditions. Using epidemiological data, we show that the model can reproduce observed infection levels in time and space for two case studies in the UK. To overcome data limitations, we propose a calibration approach combining Monte Carlo sampling and expert opinion, which allows constraint of the model in a process-based way, including a quantification of uncertainty. The simulated disease dynamics agree with information from the literature, and comparison with a widely used empirical risk index shows that the new model provides better insight into the time-space patterns of infection, which will be valuable for decision support.

Enhanced groundwater recharge rates and altered recharge sensitivity to climate variability through subsurface heterogeneity.

Our environment is heterogeneous. In hydrological sciences, the heterogeneity of subsurface properties, such as hydraulic conductivities or porosities, exerts an important control on water balance. This notably includes groundwater recharge, which is an important variable for efficient and sustainable groundwater resources management. Current large-scale hydrological models do not adequately consider this subsurface heterogeneity. Here we show that regions with strong subsurface heterogeneity have enhanced present and future recharge rates due to a different sensitivity of recharge to climate variability compared with regions with homogeneous subsurface properties. Our study domain comprises the carbonate rock regions of Europe, Northern Africa, and the Middle East, which cover ∼25% of the total land area. We compare the simulations of two large-scale hydrological models, one of them accounting for subsurface heterogeneity. Carbonate rock regions strongly exhibit "karstification," which is known to produce particularly strong subsurface heterogeneity. Aquifers from these regions contribute up to half of the drinking water supply for some European countries. Our results suggest that water management for these regions cannot rely on most of the presently available projections of groundwater recharge because spatially variable storages and spatial concentration of recharge result in actual recharge rates that are up to four times larger for present conditions and changes up to five times larger for potential future conditions than previously estimated. These differences in recharge rates for strongly heterogeneous regions suggest a need for groundwater management strategies that are adapted to the fast transit of water from the surface to the aquifers.

Model-based analysis of the influence of catchment properties on hydrologic partitioning across five mountain headwater subcatchments.

Ungauged headwater basins are an abundant part of the river network, but dominant influences on headwater hydrologic response remain difficult to predict. To address this gap, we investigated the ability of a physically based watershed model (the Distributed Hydrology-Soil-Vegetation Model) to represent controls on metrics of hydrologic partitioning across five adjacent headwater subcatchments. The five study subcatchments, located in Tenderfoot Creek Experimental Forest in central Montana, have similar climate but variable topography and vegetation distribution. This facilitated a comparative hydrology approach to interpret how parameters that influence partitioning, detected via global sensitivity analysis, differ across catchments. Model parameters were constrained a priori using existing regional information and expert knowledge. Influential parameters were compared to perceptions of catchment functioning and its variability across subcatchments. Despite between-catchment differences in topography and vegetation, hydrologic partitioning across all metrics and all subcatchments was sensitive to a similar subset of snow, vegetation, and soil parameters. Results also highlighted one subcatchment with low certainty in parameter sensitivity, indicating that the model poorly represented some complexities in this subcatchment likely because an important process is missing or poorly characterized in the mechanistic model. For use in other basins, this method can assess parameter sensitivities as a function of the specific ungauged system to which it is applied. Overall, this approach can be employed to identify dominant modeled controls on catchment response and their agreement with system understanding.