An open and extensible framework for spatially explicit land use change modelling: the lulcc R package

We present the lulcc software package, an object-oriented framework for land use change modelling written in the R programming language. The contribution of the work is to resolve the following limitations associated with the current land use change modelling paradigm: (1) the source code for model implementations is frequently unavailable, severely compromising the reproducibility of scientific results and making it impossible for members of the community to improve or adapt models for their own purposes; (2) ensemble experiments to capture model structural uncertainty are difficult because of fundamental differences between implementations of alternative models; and (3) additional software is required because existing applications frequently perform only the spatial allocation of change. The package includes a stochastic ordered allocation procedure as well as an implementation of the CLUE-S algorithm. We demonstrate its functionality by simulating land use change at the Plum Island Ecosystems site, using a data set included with the package. It is envisaged that lulcc will enable future model development and comparison within an open environment

CR1 Knops blood group alleles are not associated with severe malaria in the Gambia

The Knops blood group antigen erythrocyte polymorphisms have been associated with reduced falciparum malaria-based in vitro rosette formation (putative malaria virulence factor). Having previously identified single-nucleotide polymorphisms (SNPs) in the human complement receptor 1 (CR1/CD35) gene underlying the Knops antithetical antigens Sl1/Sl2 and McC(a)/McC(b), we have now performed genotype comparisons to test associations between these two molecular variants and severe malaria in West African children living in the Gambia. While SNPs associated with Sl:2 and McC(b+) were equally distributed among malaria-infected children with severe malaria and control children not infected with malaria parasites, high allele frequencies for Sl 2 (0.800, 1,365/1,706) and McC(b) (0.385, 658/1706) were observed. Further, when compared to the Sl 1/McC(a) allele observed in all populations, the African Sl 2/McC(b) allele appears to have evolved as a result of positive selection (modified Nei-Gojobori test Ka-Ks/s.e.=1.77, P-value <0.05). Given the role of CR1 in host defense, our findings suggest that Sl 2 and McC(b) have arisen to confer a selective advantage against infectious disease that, in view of these case-control study data, was not solely Plasmodium falciparum malaria. Factors underlying the lack of association between Sl 2 and McC(b) with severe malaria may involve variation in CR1 expression levels

Effects of winter and summer-time irrigation over Gangetic Plain on the mean and intra-seasonal variability of Indian summer monsoon

The decreasing trend in rainfall in the last few decades over the Indo-Gangetic Plains of northern India as observed in ground-based observations puts increasing stress on groundwater because irrigation uses up to 70% of freshwater resources. In this work, we have analyzed the effects of extensive irrigation over the Gangetic Plains on the seasonal mean and intra-seasonal variability of the Indian summer monsoon, using a general circulation model and a very high-resolution soil moisture dataset created using extensive field observations in a state-of-the-art hydrological model. We find that the winter-time (November–March) irrigation has a positive feedback on the Indian summer monsoon through large scale circulation changes. These changes are analogous to a positive North Atlantic Oscillation (NAO) phase during winter months. The effects of the positive NAO phase persist from winter to spring through widespread changes in surface conditions over western and central Asia, which makes the pre-monsoon conditions suitable for a subsequent good monsoon over India. Winter-time irrigation also resulted in a reduction of low frequency intra-seasonal variability over the Indian region during the monsoon season. However, when irrigation is practiced throughout the year, a decrease in June–September precipitation over the Gangetic Plains, significant at 95% level, is noted as compared to the no-irrigation scenario. This decrease is attributed to the increase in local soil moisture due to irrigation, which results in a southward shift of the moisture convergence zone during the active phase of monsoon, decreasing its mean and intraseasonal variability. Interestingly, these changes show a remarkable similarity to the long-term trend in observed rainfall spatial pattern and low-frequency variability. Our results suggest that with a decline in the mean summer precipitation and stressed groundwater resources in the Gangetic Plains, the water crisis could exacerbate, with irrigation having a weakening effect on the regional monsoon

Process-informed subsampling improves subseasonal rainfall forecasts in Central America

Subseasonal rainfall forecast skill is critical to support preparedness for hydrometeorological extremes. We assess how a process-informed evaluation, which subsamples forecasting model members based on their ability to represent potential predictors of rainfall, can improve monthly rainfall forecasts within Central America in the following month, using Costa Rica and Guatemala as test cases. We generate a constrained ensemble mean by subsampling 130 members from five dynamic forecasting models in the C3S multimodel ensemble based on their representation of both (a) zonal wind direction and (b) Pacific and Atlantic sea surface temperatures (SSTs), at the time of initialization. Our results show in multiple months and locations increased mean squared error skill by 0.4 and improved detection rates of rainfall extremes. This method is transferrable to other regions driven by slowly-changing processes. Process-informed subsampling is successful because it identifies members that fail to represent the entire rainfall distribution when wind/SST error increases

Dhaka city water logging hazards: area identification and vulnerability assessment through GIS-remote sensing techniques

Water logging is one of the most detrimental phenomena continuing to burden Dhaka dwellers. This study aims to spatio-temporarily identify the water logging hazard zones within Dhaka Metropolitan area and assess the extent of their water logging susceptibility based on informal settlements, built-up areas, and demographical characteristics. The study utilizes integrated geographic information system (GIS)-remote sensing (RS) methods, using the Normalized Difference Vegetation Water and Moisture Index, distance buffer zone from drainage streams, and built-up distributions to identify waterlogged zones with a temporal extent, incorporating social and infrastructural attributes to evaluate water logging effects. These indicators were integrated into an overlay GIS method to measure the vulnerability level across Dhaka city areas. The findings reveal that south and south-western parts of Dhaka were more susceptible to water logging hazards. Almost 35% of Dhaka belongs to the high/very highly vulnerable zone. Greater number of slum households were found within high to very high water logging vulnerable zones and approximately 70% of them are poorly structured. The built-up areas were observed to be increased toward the northern part of Dhaka and were exposed to severe water logging issues. The overall findings reveal the spatio-temporal distribution of the water logging vulnerabilities across the city as well as its impact on the social indicators. An integrated approach is necessary for future development plans to mitigate the risk of water logging

Association of Antenatal COVID-19-Related Stress with Postpartum Maternal Mental Health and Negative Affectivity in Infants

IMPORTANCE Antenatal stress is a significant risk factor for poor postpartum mental health. The association of pandemic-related stress with postpartum outcomes among mothers and infants is, however, less well understood. OBJECTIVE To examine the association of antenatal COVID-19-related stress with postpartum maternal mental health and infant outcomes. DESIGN, SETTING, AND PARTICIPANTS This cohort study was conducted among 318 participants in the COVID-19 Risks Across the Lifespan study, which took place in Australia, the UK, and the US. Eligible participants reported being pregnant at the first assessment wave between May 5 and September 30, 2020, and completed a follow-up assessment between October 28, 2021, and April 24, 2022. MAIN OUTCOMES AND MEASURES COVID-19-related stress was assessed with the Pandemic Anxiety Scale (score range, 0-4, with higher scores indicating greater COVID-19-related stress). The 8-item Patient Health Questionnaire (score range, 0-3, with higher scores indicating more frequent symptoms of depression) was used to measure maternal depression at each time point, and the 7-item General Anxiety Disorder scale (score range, 0-3, with higher scores indicating more frequent symptoms of anxiety) was used to measure generalized anxiety at each time point. At follow-up, postpartum distress was assessed with the 10-item Postpartum Distress Measure (score range, 0-3, with higher scores indicating greater postpartum distress), and infant outcomes (negative and positive affectivity and orienting behavior) were captured with the Infant Behavior Questionnaire (score range, 1-7, with higher scores indicating that the infant exhibited that affect/behavior more frequently). RESULTS The study included 318 women (mean [SD] age, 32.0 [4.6] years) from Australia (88 [28%]), the US (94 [30%]), and the UK (136 [43%]). Antenatal COVID-19-related stress was significantly associated with maternal postpartum distress (β = 0.40 [95%CI, 0.28-0.53]), depression (β = 0.32 [95%CI, 0.23-0.41]), and generalized anxiety (β = 0.35 [95%CI, 0.26-0.44]), as well as infant negative affectivity (β = 0.45 [95%CI, 0.14-0.76]). The findings remained consistent across a range of sensitivity analyses. CONCLUSIONS AND RELEVANCE The findings of this cohort study suggest that targeting pandemic-related stressors in the antenatal period may improve maternal and infant outcomes. Pregnant individuals should be classified as a vulnerable group during pandemics and should be considered a public health priority, not only in terms of physical health but also mental health

A velocity map ion imaging study of difluorobenzene-water complexes: Binding energies and recoil distributions

Susan M. Bellm, Rebecca J. Moulds, Matthew P. van Leeuwen, and Warren D. Lawranc

Localizing hydrological drought early warning using in situ groundwater sensors

Drought early warning systems (DEWSs) aim to spatially monitor and forecast risk of water shortage to inform early, risk-mitigating interventions. However, due to the scarcity of in situ monitoring in groundwater-dependent arid zones, spatial drought exposure is inferred using maps of satellite-based indicators such as rainfall anomalies, soil moisture, and vegetation indices. On the local scale, these coarse-resolution proxy indicators provide a poor inference of groundwater availability. The improving affordability and technical capability of modern sensors significantly increases the feasibility of taking direct groundwater level measurements in data-scarce, arid regions on a larger scale. Here, we assess the potential of in situ monitoring to provide a localized index of hydrological drought in Somaliland. We find that calibrating a lumped groundwater model with a short time series of groundwater level observations substantially improves the quantification of local water availability when compared to satellite-based indices. By varying the calibration length, we find that a 5-week period capturing both wet and dry season conditions provides most of the calibration capacity. This suggests that short monitoring campaigns are suitable for improving estimations of local water availabilities during drought. Short calibration periods have practical advantages, as the relocation of sensors enables rapid characterization of a large number of wells. These well simulations can supplement continuous in situ monitoring of strategic point sources to setup large-scale monitoring systems with contextualized and localized information on water availability. This information can be used as early warning evidence for the financing and targeting of early actions to mitigate impacts of hydrological drought

Isolating the impacts of anthropogenic water use within the hydrological regime of north India

The effects of anthropogenic water use play a significant role in determining the hydrological cycle of north India. This paper explores anthropogenic impacts within the region's hydrological regime by explicitly including observed human water use behaviour, irrigation infrastructure and the natural environment in the CHANSE (Coupled Human And Natural Systems Environment) socio‐hydrological modelling framework. The model is constrained by observed qualitative and quantitative information collected in the study area, along with climate and socio‐economic variables from additional sources. Four separate scenarios, including business as usual (BAU, representing observed irrigation practices), groundwater irrigation only (where the influence of the canal network is removed), canal irrigation only (where all irrigation water is supplied by diverted surface water) and rainfed only (where all human interventions are removed) are used. Under BAU conditions the modelling framework closely matched observed groundwater levels. Following the removal of the canal network, which forces farmers to rely completely on groundwater for irrigation, water levels decrease, while under a canal‐only scenario flooding occurs. Under the rainfed‐only scenario, groundwater levels similar to current business‐as‐usual conditions are observed, despite much larger volumes of recharge and discharge entering and leaving the system under BAU practices. While groundwater abstraction alone may lead to aquifer depletion, the conjunctive use of surface and groundwater resources, which includes unintended contributions of canal leakage, create conditions similar to those where no human interventions are present. Here, the importance of suitable water management practices, in maintaining sustainable water resources, is shown. This may include augmenting groundwater resources through managed aquifer recharge and reducing the impacts on aquifer resources through occasional canal water use where possible. The importance of optimal water management practices that highlight trade‐offs between environmental impact and human wellbeing are shown, providing useful information for policy makers, water managers and user