Coastal Land-Use and Land-Cover Change Trajectories: Are They Sustainable?

In this study, past and current land-use and land-cover (LULC) change trajectories between 1947 and 2018 were analysed in terms of sustainability using a unique set of nine detailed, high-precision LULC thematic maps for the municipality of Portimão (Algarve region), Portugal. Several Geographic Information System (GIS)-based spatial analysis techniques were used to process LULC data and assess the spatiotemporal dynamics of LULC change processes. The dynamics of LULC change were explored by analysing LULC change trajectories. In addition, spatial pattern metrics were introduced to further investigate and quantify the spatial patterns of such LULC change trajectories. The findings show that Portimão has been experiencing complex LULC changes. Nearly 52% of the study area has undergone an LULC change at least once during the 71-year period. The analysis of spatial pattern metrics on LULC change trajectories confirmed the emergence of more complex, dispersed, and fragmented shapes when patches of land were converted from non-built categories into artificial surface categories from 1947 to 2018. The combined analysis of long-term LULC sequences by means of LULC change trajectories and spatial pattern metrics provided useful, actionable, and robust empirical information that can support sustainable spatial planning and smart growth, which is much needed since the results of this study have shown that the pattern of LULC change trajectories in Portimão municipality has been heading towards unsustainability.

The exploratory value of cross-sectional partial correlation networks: Predicting relationships between change trajectories in borderline personality disorder

Objective Within the network approach to psychopathology, cross-sectional partial correlation networks have frequently been used to estimate relationships between symptoms. The resulting relationships have been used to generate hypotheses about causal links between symptoms. In order to justify such exploratory use of partial correlation networks, one needs to assume that the between-subjects relationships in the network approximate systematic within-subjects relationships, which are in turn the results of some within-subjects causal mechanism. If this assumption holds, relationships in the network should be mirrored by relationships between symptom changes; if links in networks approximate systematic within-subject relationships, change in a symptom should relate to change in connected symptoms. Method To investigate this implication, we combined longitudinal data on the Borderline Personality Disorder Severity Index from four samples of borderline personality disorder patients (N = 683). We related parameters from baseline partial correlation networks of symptoms to relationships between change trajectories of these symptoms. Results Across multiple levels of analysis, our results showed that parameters from baseline partial correlation networks are strongly predictive of relationships between change trajectories. Conclusions By confirming its implication, our results support the idea that cross-sectional partial correlation networks hold a relevant amount of information about systematic within-subjects relationships and thereby have exploratory value to generate hypotheses about the causal dynamics between symptoms.

Prenatal Weight Change Trajectories Among Women With Obesity

Objectives Current gestational weight change (GWC) recommendations were established by the Institute of Medicine (IOM) with limited evidence for pattern and timing of weight change across pregnancy. Likewise, the recommendation of 5–9kg for women with obesity does not differentiate by severity of obesity. We sought to describe GWC trajectory patterns among a large, contemporary cohort of women with obesity. Methods Electronic medical records were abstracted for 22,987 women with obesity (BMI ≥ 30.0) who delivered a singleton pregnancy at Kaiser Permanente Northern California between 2008–2013. We modeled GWC for women with a measured prepregnancy weight, at least three prenatal visit weights, and a normal oral glucose tolerance test at 20 wk gestation. Weight change trajectories were modeled using flexible latent class mixed modeling (package LCMM) in R. Results Before pregnancy, 60.4% of women had class 1 obesity (BMI 30.0–34.9kg/m2), 24.8% class 2 (35.0–39.9), and 14.8% class 3 (≥40.0). Most women identified as White (37.3%) or Hispanic (37.3%), with 12.4% Black and 8.5% Asian. Five GWC trajectory groups across pregnancy were identified, each with a distinct pattern of weight change before 15 wk followed by weight gain thereafter (<15 wk/>15 wk): 12.2% exhibited HighLoss/SteadyGain with mean GWC at delivery of 0.9 ± 3.1kg (mean ± sd); 25.8% LowLoss/SteadyGain (6.4 ± 2.4); 30.4% LowGain/ModerateGain (11.4 ± 2.5); 23.0% ModerateGain/HighGain (16.8 ± 3.1); and 8.6% HighGain/HighGain (24.5 ± 5.0). Prepregnancy obesity classes were represented across GWC trajectory groups but were predominantly in two groups: those with class 1 obesity exhibited LowGain/ModerateGain (32.1%), class 2 was split between LowGain/ModerateGain (29.0%) and LowLoss/SteadyGain (28.8%), and class 3 exhibited LowLoss/SteadyGain (31.8%). Only the LowLoss/SteadyGain (25.8%) group demonstrated total GWC at delivery within the IOM recommendation. Conclusions Among women with obesity, GWC was not linear or uniform, and current recommendations do not reflect nuances of the prenatal period. Gestational weight change in women with obesity can be flexibly modeled using latent classes, an important next step for prenatal weight change guidance. Funding Sources ANDF, KPNC.

Future Land-use Changes in the transboundary Sio-Malaba-Malakisi Basin of East Africa: Simulations using the CLUE-S model and Classified Satellite Land Cover Datasets

A comprehensive undertanding of land-use/cover(LUC) change processes, their trends and future trajectories is essential for the development of sustainable land-use management plans. While contemporay tools can today be employed to monitor historical land-cover changes, prediction of future change trajectories in most rural agro-ecological landscapes remains a challenge. This study evaluated potential LUC changes in the transboundary Sio-Malaba-Malakisi River Basin of Kenya and Uganda for the period 2017-2047. The land use change drivers were obtained through a rigorous fieldwork procedure and the Logistic Regression Model (LGM) to establish key factors for the simulation. The CLUE-S model was subsequently adapted to explore future LUC change trajectories under different scenarios. The model was validated using historical land cover maps for the period of 2008 and 2017, producing overall accuracy result of 85.7% and a Kappa coefficient of 0.78. The spatial distribution of vegetation cover types could be explained partially by proximate factors like soil cation exchange capacity, soil organic carbon and soil pH. On the other hand, built-up areas were mainly influenced by population density. Under the afforestation scenario, areas under forest cover expanded further occupying 54.7% of the basin. Conversely, under the intense agriculture scenario, cropland and pasture cover types occupied 78% of the basin. However, in a scenario where natural forest and wetlands were protected, cropland and pasture only expanded by 74%. The study successfully outlined proximate land cover change drivers, including potential future changes and could be used to support the development of sustainable long-term transboundary land-use plans and policy.

Impact of groundwater – soil moisture interaction on evolution of evapotranspiration and air temperature under climate change

<p>Groundwater (GW) constitutes by far the largest volume of liquid freshwater on Earth. The most active part is soil moisture (SM), which plays a key role on land/atmosphere interactions. But GW is often stored in deep reservoirs below the soil as well, where it presents slow horizontal movements along hillslopes toward the river network. They end up forming baseflow with well-known buffering effects on streamflow variability, but they also contribute to sustain higher SM values, especially in the lowland areas surrounding streams, which are among the most frequent wetlands.  As a result, GW-SM interactions may influence the climate system, in the past but also in the future, with a potential to alleviate anthropogenic warming, at least regionally, owing to enhanced evapotranspiration rate (ET) or higher soil thermal inertia for instance.<br>To assess where, when, and how much GW-SM interaction affects the climate change trajectories, we use coupled land-atmosphere simulations with the IPSL-CM6 climate model, developed by the Institut Pierre Simon Laplace for CMIP6.  We contrast the results of two long-term simulations (1979-2100), which share the same sea surface temperature and radiative forcing, using the SSP5-8.5 scenario (i.e. the most pessimistic) for 2015-2100. The two simulations differ by their configuration of the land surface scheme ORCHIDEE: in the default version, there is no GW-SM interaction, while this interaction is permitted in the second simulation, within a so-called lowland fraction, fed by surface and GW runoff from the rest of the grid-cell. For simplicity, this lowland fraction is set constant over time, but varies across grid-cells based on a recently designed global scale wetland map. <br>Within this framework, we analyse the impact of the GW-SM interaction on climate change trajectories, focusing on the response of evapotranspiration rates and near-surface air temperatures. The GW-SM interaction can modulate the response to climate change by amplifying, attenuating, or even inverting the climate change trend. Based on yearly mean values over land, we find that the GW-SM interaction amplifies the response of evapotranspiration to climate change, as the mean evapotranspiration rate increases 50% faster over 1980 - 2100 in the simulation with GW-SM interaction. In contrast, the mean warming over land is 1% weaker, shifting from 6.4 to 6.3 °C/100 years; thus attenuated, if the GW-SM interaction is accounted for. In both cases, these values hide important differences across climates and seasons, with mitigation or amplification for both variables, indicating the need for regional and seasonal assessment. We will also further explore how GW-SM interaction impacts the future evolution of heatwaves, in terms of duration and frequency. </p>