Exploring interactions between socioeconomic context and natural hazards on human population displacement.

Climate change is leading to more extreme weather hazards, forcing human populations to be displaced. We employ explainable machine learning techniques to model and understand internal displacement flows and patterns from observational data alone. For this purpose, a large, harmonized, global database of disaster-induced movements in the presence of floods, storms, and landslides during 2016-2021 is presented. We account for environmental, societal, and economic factors to predict the number of displaced persons per event in the affected regions. Here we show that displacements can be primarily attributed to the combination of poor household conditions and intense precipitation, as revealed through the interpretation of the trained models using both Shapley values and causality-based methods. We hence provide empirical evidence that differential or uneven vulnerability exists and provide a means for its quantification, which could help advance evidence-based mitigation and adaptation planning efforts.

Soil and vegetation water content identify the main terrestrial ecosystem changes.

Environmental change is a consequence of many interrelated factors. How vegetation responds to natural and human activity still needs to be well established, quantified and understood. Recent satellite missions providing hydrologic and ecological indicators enable better monitoring of Earth system changes, yet there is no automatic way to address this issue directly from observations. Here, we develop an observation-based methodology to capture evidence of changes in global terrestrial ecosystems and attribute these changes to natural or anthropogenic activity. We use the longest time record of global microwave L-band soil moisture and vegetation optical depth as satellite data and build spatially explicit maps of change in soil and vegetation water content and biomass reflecting large ecosystem changes during the last decade, 2010-20. Regions of prominent trends (from [Formula: see text] to 9% per year) are observed, especially in humid and semi-arid climates. We further combine such trends with land cover change maps, vegetation greenness and precipitation variability to assess their relationship with major documented ecosystem changes. Several regions emerge from our results. They cluster changes according to human activity drivers, including deforestation (Amazon, Central Africa) and wildfires (East Australia), artificial reforestation (South-East China), abandonment of farm fields (Central Russia) and climate shifts related to changes in precipitation variability (East Africa, North America and Central Argentina). Using the high sensitivity of soil and vegetation water content to ecosystem changes, microwave satellite observations enable us to quantify and attribute global vegetation responses to climate or anthropogenic activities as a direct measure of environmental changes and the mechanisms driving them.

Influence of atmospheric patterns on soil moisture dynamics in Europe.

Soil moisture (SM) plays a key role in the water cycle, and its variability is intimately linked to coupled land-atmosphere processes. Having a good knowledge of soil-atmospheric interactions is thus essential to assess the impact of climate change on SM; however, many aspects of how water and energy exchanges occur in the soil-atmosphere continuum are still uncertain. In particular, it is known that atmospheric circulation patterns influence climate conditions over Europe but their impact on SM has only rarely been studied. This study provides insight into how atmospheric patterns influence soil moisture dynamics in Europe, where an increase in temperature and agricultural droughts are expected as an impact of climate change. To do so, we analysed the influence of the North Atlantic Oscillation (NAO), the Arctic Oscillation (AO), and the El Niño Southern Oscillation (ENSO) on European SM, including lagged responses, for the period 1991-2020 at a monthly scale. Two methods have been used: a lagged correlation analysis and a more sophisticated causality approach using the PCMCI (PC method combined with the momentary conditional independence (MCI) test). SM series from two different databases were considered: the hydrological model LISFLOOD and the reanalysis dataset ERA5-Land. The results from the correlation analysis showed a significant, predominantly negative relationships of SM with NAO and AO over almost all of Europe and no significant relation with ENSO. With the causality analysis, similar patterns are obtained for NAO and AO; however, the PCMCI analysis revealed clear patterns of ENSO influencing SM with a delayed response of one-to-two months in central and northwest Europe. The results obtained in this work highlight that there are causal relations between the main modes of interannual climate oscillations and SM variations in Europe, underlining the importance of accounting for global atmospheric circulations to study current changes in regional soil water-related processes.

Inferring causal relations from observational long-term carbon and water fluxes records.

Land, atmosphere and climate interact constantly and at different spatial and temporal scales. In this paper we rely on causal discovery methods to infer spatial patterns of causal relations between several key variables of the carbon and water cycles: gross primary productivity, latent heat energy flux for evaporation, surface air temperature, precipitation, soil moisture and radiation. We introduce a methodology based on the convergent cross-mapping (CCM) technique. Despite its good performance in general, CCM is sensitive to (even moderate) noise levels and hyper-parameter selection. We present a robust CCM (RCCM) that relies on temporal bootstrapping decision scores and the derivation of more stringent cross-map skill scores. The RCCM method is combined with the information-geometric causal inference (IGCI) method to address the problem of strong and instantaneous variable coupling, another important and long-standing issue of CCM. The proposed methodology allows to derive spatially explicit global maps of causal relations between the involved variables and retrieve the underlying complexity of the interactions. Results are generally consistent with reported patterns and process understanding, and constitute a new way to quantify and understand carbon and water fluxes interactions.

Explicit Granger causality in kernel Hilbert spaces.

Granger causality (GC) is undoubtedly the most widely used method to infer cause-effect relations from observational time series. Several nonlinear alternatives to GC have been proposed based on kernel methods. We generalize kernel Granger causality by considering the variables' cross-relations explicitly in Hilbert spaces. The framework is shown to generalize the linear and kernel GC methods and comes with tighter bounds of performance based on Rademacher complexity. We successfully evaluate its performance in standard dynamical systems, as well as to identify the arrow of time in coupled Rössler systems, and it is exploited to disclose the El Niño-Southern Oscillation phenomenon footprints on soil moisture globally.

Synergistic integration of optical and microwave satellite data for crop yield estimation.

Developing accurate models of crop stress, phenology and productivity is of paramount importance, given the increasing need of food. Earth observation (EO) remote sensing data provides a unique source of information to monitor crops in a temporally resolved and spatially explicit way. In this study, we propose the combination of multisensor (optical and microwave) remote sensing data for crop yield estimation and forecasting using two novel approaches. We first propose the lag between Enhanced Vegetation Index (EVI) derived from MODIS and Vegetation Optical Depth (VOD) derived from SMAP as a new joint metric combining the information from the two satellite sensors in a unique feature or descriptor. Our second approach avoids summarizing statistics and uses machine learning to combine full time series of EVI and VOD. This study considers two statistical methods, a regularized linear regression and its nonlinear extension called kernel ridge regression to directly estimate the county-level surveyed total production, as well as individual yields of the major crops grown in the region: corn, soybean and wheat. The study area includes the US Corn Belt, and we use agricultural survey data from the National Agricultural Statistics Service (USDA-NASS) for year 2015 for quantitative assessment. Results show that (1) the proposed EVI-VOD lag metric correlates well with crop yield and outperforms common single-sensor metrics for crop yield estimation; (2) the statistical (machine learning) models working directly with the time series largely improve results compared to previously reported estimations; (3) the combined exploitation of information from the optical and microwave data leads to improved predictions over the use of single sensor approaches with coefficient of determination R ≥ 2 0.76 ; (4) when models are used for within-season forecasting with limited time information, crop yield prediction is feasible up to four months before harvest (models reach a plateau in accuracy); and (5) the robustness of the approach is confirmed in a multi-year setting, reaching similar performances than when using single-year data. In conclusion, results confirm the value of using both EVI and VOD at the same time, and the advantage of using automatic machine learning models for crop yield/production estimation.

Reactivity of neutral Mo(S2C6H4)3 in aqueous media: an alternative functional model of sulfite oxidase.

The kinetics of the reaction of neutral [Mo(S2C6H4)3] with hydrogen sulfite to produce the anionic Mo(V) complex, [Mo(S2C6H4)3]-, and sulfate have been investigated. It has been shown that [Mo(S2C6H4)3] acts as the electron-proton sink in the oxygenation reaction of HSO3(-) by water. Reaction rates, monitored by UV/vis stopped-flow spectrometry, were studied in THF/water media as a function of the concentration of HSO3(-) and molybdenum complex, pH, ionic strength, and temperature. The reaction exhibits pH-dependent HSO3(-) saturation kinetics, and it is first-order in complex concentration. The kinetic data and MS-ESI spectra are consistent with the formation of [Mo O(S2C6H4)2(S2C6H5)]- (1) adduct as a crucial intermediate that transfers the oxygen atom to HSO3(-) yielding the Mo(V) species quantitatively.

Unusual oxidation of phosphines employing water as the oxygen atom source and tris(benzene-1,2-dithiolate)molybdenum(VI) as the oxidant. A functional molybdenum hydroxylase analogue system.

The kinetics of the reaction of Mo(VI)(S2C6H4)3 with organic phosphines to produce the anionic Mo(V) complex, Mo(V)(S2C6H4)3-, and phosphine oxide have been investigated. Reaction rates, monitored by UV-vis stopped-flow spectrophotometry, were studied in THF/H2O media as a function of the concentration of phosphine, molybdenum complex, pH, and water concentration. The reaction exhibits pH-dependent phosphine saturation kinetics and is first-order in complex concentration. The water concentration strongly enhances the reaction rate, which is consistent with the formation of Mo(VI)(S2C6H4)3(H2O) adduct as a crucial intermediate. The observed pH dependence of the reaction rate would arise from the distribution between acid and basic forms of this adduct. Apparently, the electrophilic attack by the phosphine at the oxygen requires the coordinated water to be in the unprotonated hydroxide form, Mo(VI)(S2C6H4)3(HO)-. This is followed by the concerted abstraction of 2e-, H+ by the Mo(VI) center to give Mo(IV)(S2C6H4)3(2-), H+, and the corresponding phosphine oxide. However, this Mo(IV) complex product is oxidized rapidly to Mo(V)(S2C6H4)3- via comproportionation with unreacted Mo(VI)(S2C6H4)3. The Mo(V) complex thus formed can be oxidized to the starting Mo(VI) complex upon admission of O2. Consequently, Mo(VI)(S2C6H4)3 is a catalyst for the autoxidation of phosphines in the presence of water. Additionally, there was a detectable variation in the reactivity for a series of tertiary phosphines. The rate of Mo(VI) complex reduction increases as does the phosphine basicity: (p-CH3C6H4)3P > (C6H5)3P > (p-ClC6H4)3P. Oxygen isotope tracing confirms that water rather than dioxygen is the source of the oxygen atom which is transferred to the phosphine. Such reactivity parallels oxidase activity of xanthine enzyme with phosphine as oxygen atom acceptor and Mo(VI)(S2C6H4)3 as electron acceptor.

Reduction of tris(benzene-1,2-dithiolate)molybdenum(VI) by water. A functional mo-hydroxylase analogue system.

Mo(VI)(S(2)C(6)H(4))(3) reacts cleanly and completely with H(2)O in THF to afford [H(3)O](+)[Mo(V)(S(2)C(6)H(4))(3)](-). Kinetic data were fit by the rate equation -d[Mo(VI)(S(2)C(6)H(4))(3)]/dt = k[Mo(VI)(S(2)C(6)H(4))(3)]/[H(3)O(+)], which is consistent with a coupled electron-proton transfer mechanism involving a coordinated H(2)O molecule. The Mo(VI)(S(2)C(6)H(4))(3) reduction is accelerated by the presence of PPh(3) and affords OPPh(3). (18)O isotope tracing shows that H(2)O is the source of oxygen transferred to PPh(3).

The reduction of tris-dithiolene complexes of molybdenum(VI) and tungsten(VI) by hydroxide ion: kinetics and mechanism.

The kinetic study of the spontaneous reduction of some neutral tris-dithiolene complexes [ML3] of molybdenum(VI) and tungsten(VI), (L = S2C6H4(2-), S2C6H3CH3(2-) and S2C2(CH3)2(2-); M = Mo or W) by tetrabutylammonium hydroxide in tetrahydrofuran-water solutions demonstrates that OH- is an effective reductant. Their reduction is fast, clean and quantitative. Depending upon both the molar ratio in which the reagents are mixed and the amount of water present, one- or two-electron reductions of these tris-dithiolene complexes were observed. If Bu4NOH is present in low concentration or/and at high concentrations of water, the total transformation of the neutral M(VI) complex into the monoanionic M(V) complex is the only observed process. Stopped-flow kinetic data for this reaction are consistent with the rate law: -d[ML3]/dt = d[ML3-]/dt = k[ML3][Bu4NOH]. The proposed mechanism involves nucleophilic attack of OH- to form a mono-anionic seven-coordinate intermediate [ML3OH]-, which interacts with another molecule of [ML3] to generate the monoanionic complex [ML3]- transfering the oxygen from coordinated OH- to water. Hydrogen peroxide was identified as the reaction product. The molybdenum complexes are more difficult to reduce than their corresponding tungsten complexes, and the values of k obtained for the molybdenum and tungsten series of complexes increase as the ene-1,2-dithiolate ligand becomes more electron-withdrawing (S2C6H4(2-) > S2C6H3CH3(2-) > S2C2(CH3)2(2-)). This investigation constitutes the only well-established interaction between hydroxide ion and a tris(dithiolene) complex, and supports a highly covalent bonding interaction between the metal and the hydroxide ion that modulates electron transfer reactions within these complexes.