Although adequately detailed kerosene chemical-combustion Arrhenius reaction-rate suites were not readily available for combustion modeling until ca. the 1990’s (e.g., Marinov [1998]), it was already known from mass-spectrometer measurements during the early Apollo era that fuel-rich liquid oxygen + kerosene (RP-1) gas generators yield large quantities (e.g., several percent of total fuel flows) of complex hydrocarbons such as benzene, butadiene, toluene, anthracene, fluoranthene, etc. (Thompson [1966]), which are formed concomitantly with soot (Pugmire [2001]). By the 1960’s, virtually every fuel-oxidizer combination for liquid-fueled rocket engines had been tested, and the impact of gas phase combustion-efficiency governing the rocket-nozzle efficiency factor had been empirically well-determined (Clark [1972]). Up until relatively recently, spacelaunch and orbital-transfer engines were increasingly designed for high efficiency, to maximize orbital parameters while minimizing fuels and structural masses: Preburners and high-energy atomization have been used to pre-gasify fuels to increase (gas-phase) combustion efficiency, decreasing the yield of complex/aromatic hydrocarbons (which limit rocket-nozzle efficiency and overall engine efficiency) in hydrocarbon-fueled engine exhausts, thereby maximizing system launch and orbital-maneuver capability (Clark; Sutton; Sutton/Yang). The combustion community has been aware that the choice of Arrhenius reaction-rate suite is critical to computer engine-model outputs. Specific combustion suites are required to estimate the yield of high-molecular-weight/reactive/toxic hydrocarbons in the rocket engine combustion chamber, nonetheless such GIGO errors can be seen in recent documents. Low-efficiency launch vehicles also need larger fuels loads to achieve the same launched mass, further increasing the yield of complex hydrocarbons and radicals deposited by low-efficiency rocket engines along launch trajectories and into the stratospheric ozone layer, the mesosphere, and above. With increasing launch rates from low-efficiency systems, these persistent (Ross/Sheaffer [2014]; Sheaffer [2016]), reactive chemical species must have a growing impact on critical, poorly-understood upper-atmosphere chemistry systems.

This work presents a comparison of the meteorology and the surface energy and mass fluxes of the clean ice and debris-covered ice surfaces of the Djankuat Glacier, a partly debris-covered valley glacier situated in the Caucasus. A 2D spatially distributed and physically-based energy and mass balance model at high spatial and temporal resolution is used, driven by meteorological data from two automatic weather stations and ERA5-Land reanalysis data. Our model is the first that attempts to assesses the spatial variability of meteorological variables, energy fluxes, mass fluxes, and the melt-altering effects of supraglacial debris over the entire surface of a (partly) debris-covered glacier during one complete measurement year. The results show that the meteorological variables and the surface energy and mass balance components are significantly modified due to the supraglacial debris. As such, changing surface characteristics and different surface temperature/moisture and near-surface wind regimes persist over debris-covered ice, consequently altering the pattern of the energy and mass fluxes when compared to clean ice areas. The eventual effect of the supraglacial debris on the energy and mass balance and the surface-atmosphere interaction is found to highly depend upon the debris thickness and area: for thin and patchy debris, sub-debris ice melt is enhanced when compared to clean ice, whereas for thicker and continuous debris, the melt is increasingly suppressed. Our results highlight the importance of the effect of supraglacial debris on glacier-atmosphere interactions and the corresponding implications for the changing melting patterns and the climate change response of (partly) debris-covered glaciers.

A document by Lukas Pilz. Click on the document to view its contents.

The challenge of reconstructing air temperature for environmental applications is to accurately estimate past exposures even where monitoring is sparse. We present XGBoost-IDW Synthesis for air temperature (XIS-Temperature), a high-resolution machine-learning model for daily minimum, mean, and maximum air temperature, covering the contiguous US from 2003 through 2021. XIS uses remote sensing (land surface temperature and vegetation) along with a parsimonious set of additional predictors to make predictions at arbitrary points, allowing the estimation of address-level exposures. We built XIS with a computationally tractable workflow for extensibility to future years, and we used weighted evaluation to fairly assess performance in sparsely monitored regions. The weighted root mean square error (RMSE) of predictions in site-level cross-validation for 2021 was 1.89 K for the minimum daily temperature, 1.27 K for the mean, and 1.72 K for the maximum. We obtained higher RMSEs in earlier years with fewer ground monitors. Comparing to three leading gridded temperature models in 2021 at thousands of private weather stations not used in model training, XIS had at most 49% of the mean square error for the minimum temperature and 87% for the maximum. In a national application, we report a stronger relationship between minimum temperature in a heatwave and social vulnerability with XIS than with the other models. Thus, XIS-Temperature has potential for reconstructing important environmental exposures, and its predictions have applications in environmental justice and human health.

The urban morphology determined by urban canopy parameters (UCPs) plays an important role in simulating the interaction of urban land surface and atmosphere. The impact of urbanization on a typical summer rainfall event in Hangzhou, China, is investigated using the integrated WRF/urban modelling system. Three groups of numerical experiments are designed to assess the uncertainty in parameterization schemes, the sensitivity of urban canopy parameters (UCPs), and the individual and combined impacts of thermal and dynamical effects of urbanization, respectively. The results suggest that the microphysics scheme has the highest level of uncertainty in simulating precipitation, followed by the planetary boundary layer scheme, whereas the land surface and urban physics schemes have minimal impacts. The choices of the physical parameterization schemes for simulating precipitation are much more sensitive than those for simulating temperature, mixing ratio, and wind speed. Of the eight selected UCPs, changes in heat capacity, thermal conductivity, surface albedo, and roughness length have a greater impact on temperature, mixing ratio, and precipitation, while changes in building height, roof width, and road width affect the wind speed more. The total urban impact could lead to higher temperature, less mixing ratio, lower wind speed, and more precipitation in and around the urban area. Comparing the thermal and dynamical effects of urbanization separately, both of them contribute to an increase in temperature and precipitation and the thermal effect plays a major role. However, their impacts are opposite in changes of mixing ratio and wind speed, and each play a major role respectively.

A weather station in Nukuʻalofa (NUKU), Tonga, ~68km away from the epicenter of the 2022 Tonga eruption, recorded exceptional pressure, temperature, and wind data representative of the eruption source hydrodynamics. These high-quality data are available for further source and propagation studies. In contrast to other barometers and infrasound sensors at greater ranges, the NUKU barometer recorded a decrease in pressure during the climactic stage of the eruption. A simple fluid dynamic explanation of the depressurization is provided, with a commentary on near- vs far-field pressure observations of very large eruptions.

This paper is a contribution to the exploration of the parametric Kalman filter (PKF), which is an approximation of the Kalman filter, where the error covariance are approximated by a covariance model. Here we focus on the covariance model parameterized from the variance and the anisotropy of the local correlations, and whose parameters dynamics provides a proxy for the full error-covariance dynamics. For this covariance mode, we aim to provide the boundary condition to specify in the prediction of PKF for bounded domains, focusing on Dirichlet and Neumann conditions when they are prescribed for the physical dynamics. An ensemble validation is proposed for the transport equation and for the heterogeneous diffusion equations over a bounded 1D domain. This ensemble validation requires to specify the auto-correlation time-scale needed to populate boundary perturbation that leads to prescribed uncertainty characteristics. The numerical simulations show that the PKF is able to reproduce the uncertainty diagnosed from the ensemble of forecast appropriately perturbed on the boundaries, which show the ability of the PKF to handle boundaries in the prediction of the uncertainties. It results that Dirichlet condition on the physical dynamics implies Dirichlet condition on the variance and on the anisotropy.

The Central Highlands of Vietnam is the biggest Robusta coffee (Coffea canephora Pierre ex A.Froehner) growing region in the world. This study aims to identify the most important climatic variables that determine the current distribution of coffee in the Central Highlands and build a “coffee suitability” model to assess changes in this distribution due to climate change scenarios. A suitability model based on neural networks was trained on coffee occurrence data derived from national statistics on coffee-growing areas. Bias-corrected regional climate models were used for two climate change scenarios (RCP8.5 and RCP2.6) to assess changes in suitability for three future time periods (i.e., 2038-2048, 2059-2069, 2060-2070) relative to the 2009-2019 baseline. Average expected losses in suitable areas were 62% and 27% for RCP8.5 and RCP2.6, respectively. The loss in suitability due to RCP8.5 is particularly pronounced after 2060. Increasing mean minimum temperature during harvest (October-November) and growing season (March-September) and decreasing precipitation during late growing season (July-September) mainly determined the loss in suitable areas. If the policy commitments made at the Paris agreement are met, the loss in coffee suitability could potentially be compensated by climate change adaptation measures such as making use of shade trees and adapted clones.

Extreme heat waves beset western North America during 2021, including a 46.7°C (116°F) observation in Portland, Oregon, an astonishing 5°C above the previous record. Using Portland as an example we provide evidence for a latent risk of extreme heat waves in the Pacific Northwest (PNW) and along the west coast of the United States where a maritime climate and its intrinsic variations yield a positive skewness in summertime daily maximum temperatures. A generalized Pareto extreme value analysis yields a heavy tailed distribution with a return period of 300-1000 years, indicating that, while rare, the event was possible, contrary to prior claims that the event was “virtually impossible”. We demonstrate that the extreme temperatures can be explained by the coincident extreme values of geopotential heights, and that the relationship between heights and extreme temperatures has not materially changed over the observational record. The dynamical nature of the event along with recent developments in stochastic theory justifies the use of skewed and heavy-tailed distributions which may provide the basis for a more proactive approach to managing the risk of future events.

Using the global and coupled ICON-Sapphire model with a grid spacing of \SI{5}{\kilo\meter}, we describe seasonal and diurnal features of the tropical rainbelt and assess the limits of ICON-Sapphire in representing tropical precipitation. Aside from the meridional migration, the tropical rainbelt exhibits a seasonal enlargement and a zonal migration. Surprisingly, ICON-Sapphire reproduces these characteristics with better performance over land than over ocean and with a very high degree of agreement to observations. ICON-Sapphire especially struggles in capturing the seasonal features of the tropical rainbelt over the oceans of the Eastern Hemisphere, an issue associated with a cold SST bias at the equator. ICON-Sapphire also shows that a perfect representation of the diurnal cycle of precipitation over land is not a requirement to capture the seasonal features of the rainbelt over land, while over the ocean, 5km is sufficient to adequately represent the diurnal cycle of precipitation.

The study investigates how sea surface temperature (SST) anomalies surrounding the Maritime Continent (MC) modulate the impact of developing El Niño events on Indian Summer Monsoon (ISM) rainfall. Using a climate model we find that the ISM rainfall response to tropical Pacific SST anomalies of eastern and central Pacific El Niño events is sensitive to the details of cold SST anomalies surrounding the MC. Furthermore, the remote rainfall responses to regions of SST anomalies do not combine linearly and depend strongly on gradients in the SST anomaly patterns. The cold SST anomalies around the MC have a significantly larger impact on the ISM response to eastern Pacific events than to central Pacific events. These results show the usefulness of idealised modelling experiments, which offer insights into the complex interactions of the ISM with modes of climate variability.

To integrate temporal and spatial dimensions of seasonal cycles, we combine two conceptual frameworks: ecological calendars and the “3Hs” model of the biocultural ethic. The latter values the vital links between human and other-than-human co-inhabitants, their life habits (e.g., cultural practices of human communities or life cycles of other-than-human species) and the structure, patterns and processes of their shared habitats. This integration enhances an understanding of core links between cultural practices and the life cycles of biocultural keystone species. As a synthesis, we use the term biocultural calendars to emphasize the co-constitutive nature of calendars that result from continuous interactions between dynamic biophysical and cultural processes. We apply biocultural calendars to examine cultural practices and socio-environmental changes in southwestern South America, specifically in Chile, spanning from (1) Cape Horn at the southern of the Americas in sub-Antarctic habitats inhabited by the Yagan indigenous community, (2) artisanal fisher communities in Chiloe; archipelagoes, (3) coastal regions of central-southern Chile inhabited by Lafkenche and Williche indigenous communities, to (4) high Andean habitats in northern Chile co-inhabited by Aymara communities along with domesticated camelids and a rich biodiversity. To illustrate biocultural calendars, we designed analemma diagrams that show the position of the Sun in the sky as seen from a fixed time and location, and linked to continuous renewal of astronomical, biological and cultural, seasonal cycles that sustain life. These biocultural calendars enhance the integration of indigenous and scientific knowledge to confront complex challenges of climate change faced by local communities and global society.

[This presentation is published at https://doi.org/10.1111/1440-1703.12317] Dead organic matter (DOM), which consists of leaf litter, fine woody debris (FWD; < 3 cm diameter), downed coarse woody debris (CWDlog), and standing or suspended coarse woody debris (CWDsnag), plays a crucial role in forest carbon cycling. However, the contributions of each DOM type on stand-scale carbon storage (necromass) and stand-scale CO2 efflux (Rstand) estimates are not well understood. In addition, there is little knowledge of the effect of each DOM type on the accuracy of stand-scale estimates of total necromass and Rstand. This study investigated characteristics of necromass and Rstand from DOM in a subtropical forest in Okinawa island, Japan, to quantify the effect of each DOM type on total necromass, total Rstand, and estimate error of total necromass and Rstand. The CWDsnag accounted for the highest proportion (54%) of total necromass (1499.7 g C m–2), followed by CWDlog (24%), FWD (11%), and leaf litter (11%). Leaf litter accounted for the highest proportion (37%) of total Rstand (340.6 g C m–2 yr–1), followed by CWDsnag (25%), CWDlog (20%), and FWD (17%). The CWDsnag was distributed locally with 173% of the coefficient of variation for necromass, which was approximately two times higher than those of leaf litter and FWD (72–73%). Our spatial analysis revealed, for accurate estimates of CWDsnag and CWDlog necromass, sampling areas of ≥ 28750 m2 and ≥ 2058‒42875 m2 were required, respectively, under the condition of 95% confidence level and 0.1 of accepted error. In summary, CWD considerably contributed to stand-scale carbon storage and efflux in this subtropical forest, resulting in a major source of errors in the stand-scale estimates. In forests where frequent tree death is likely to occur, necromass and Rstand of CWD are not negligible in considering the carbon cycling as in this study, and therefore need to be estimated accurately.

The Terrestrial Gamma-ray Flashes (TGF) to lightning ratio, computed over the 3 tropical chimneys, presents a paradox: African thunderstorms produce the most lightning but yield the lowest fraction of TGF when compared to American and Southeast Asian thunderclouds. To understand the physical insights into this asymmetry, TRMM Precipitation Radar measurements are used to depict the vertical precipitation structure of the observed thunderstorms in the 3 regions and the thunderstorms during TGF occurrences detected by AGILE, Fermi-GBM and RHESSI sensors. African thunderstorms are taller, smaller and have higher concentration of dense ice particles above the freezing level. TGF thunderstorms are taller and less intense (0.5-2dBZ), besides presenting similar radar reflectivity decay with height independent of the region. In addition, these storms show thicker electrical charge layers separated by 4.7-5.2 km and also a positive charge fraction reduction between -20 o C and -40 o C and enhancement above -50 o C when compared to the overall thunderstorms.

Spectral-based vegetation indices (VI) have been shown to be good proxies of grapevine stem water potential (Ψstem), potentially assisting in irrigation-decision making of commercial vineyards. However, VI-Ψstem correlations are mostly reported at the leaf or canopy scales using sensors attached to leaves or very-high-spatial resolution images derived from sensors mounted on small airplanes or drones. Here, for the first time, we take advantage of the high spatial resolution (3-m), near-daily images acquired from Planet’s nano-satellites constellation to derive VI-Ψstem correlations at the vineyard scale. Weekly Ψstem were measured along the growing season of 2017 in six vines in 81 commercial vineyards and in 60 pairs of vines in a 2.4 ha experimental vineyard in Israel. The clip application programming interface (API), provided by Planet, and Google Earth Engine platform were used to derive spatially continuous time series of four VIs: GNDVI, NDVI, EVI, and SAVI in the 82 vineyards. Results show that per-week multivariable linear models using variables extracted from VI time series successfully tracked spatial variations in Ψstem across the experimental vineyard (Pearson’s-r = 0.45–0.84: N=60). A simple linear regression model enabled monitoring seasonal changes in Ψstem along the growing season in the vineyard (r = 0.80–0.82). Planet VIs and seasonal Ψstem data from the 82 vineyards were used to derive a ‘global’ model for in-season monitoring of Ψstem at the vineyard-level (r = 0.81: RMSE = 17.5%: N=970). The ‘global’ model, which requires only a few VI variables extracted from Planet images, may be used for real-time weekly assessment of Ψstem in Mediterranean vineyards, substantially reducing expenses of conventional monitoring efforts.

Markus Sebastian Gross passed away on 25 January 2022, due to the injuries he sustained during a household accident on 8 January 2022, and unexpected complications at the treating hospital. In this obituary we honor his character and his contributions to science and engineering.

A document by Sakib Imtiaz. Click on the document to view its contents.

The variability of the Southern Hemisphere (SH) extratropical large-scale circulation is dominated by the Southern Annular Mode (SAM), whose timescale is extensively used as a key metric in evaluating state-of-the-art climate models. Past observational and theoretical studies suggest that the SAM lacks any internally generated (intrinsic) periodicity. Here, we show, using observations and a climate model hierarchy, that the SAM has an intrinsic 150-day periodicity. This periodicity is robustly detectable in the power spectra and principal oscillation patterns (aka dynamical mode decomposition) of the zonal-mean circulation, and in hemispheric-scale precipitation and ocean surface wind stress. The 150-day period is consistent with the predictions of a new reduced-order model for the SAM, which suggests that this periodicity is tied with a complex interaction of turbulent eddies and zonal wind anomalies, as the latter propagate from low to high latitudes. These findings present a rare example of periodic oscillations arising from the internal dynamics of the extratropical turbulent circulations. Based on these findings, we further propose a new metric for evaluating climate models, and show that some of the previously reported shortcomings and improvements in simulating SAM’s variability connect to the models’ ability in reproducing this periodicity. We argue that this periodicity should be considered in evaluating climate models and understanding the past, current, and projected Southern Hemisphere climate variability.

In the atmosphere, there is an intimate relationship between clouds, atmospheric radiative cooling/heating, and radiatively induced circulations at various temporal and spatial scales. This coupling remains not well under- stood, which contributes to limiting our ability to model and predict clouds and climate accurately. Cloud liquid and ice particles interact with both shortwave (SW) and longwave (LW) radiation, leading to cloud radiative effect (CRE). The CRE includes perturbations of the radiative fluxes at the top of the atmosphere (TOA) and the surface, as well as perturbations of the radiative cooling pro- file within the atmosphere. The effect of clouds that results in atmospheric radiative heating or cooling that is distinct from the clear-sky radiative cooling profile will be termed the CRE on atmospheric heating, or CRE-AH. The CRE-AH can significantly modify the horizontal and vertical gradients of the diabatic heating profile, inducing circulations at various scales in the atmosphere. In turn, circulations govern cloud formation and evolution processes and therefore the properties and distribution of clouds.

Through machine learning and remote sensing, a high-end model with a finer resolution for groundwater recharge has been developed for the region of South-East Asia. The groundwater recharge coefficient can be found by the application of Random Forest regression followed by the implication of the water budget method to calculate the Groundwater Recharge values. Climatic factors such as precipitation and actual evapotranspiration to map Groundwater Recharge has been framed with a sophisticated machine learning method to be considered as a scale predicting model. A comprehensive visualization of the dataset has been done; the accuracy of the model is noted through random forest regression. Thus, the model can be used for various regions of the dataset specifically for the area where there is a lack of reach for data. It can be successfully used to form a sophisticated end-to-end ML model. Keywords: Machine Learning, Remote Sensing, Groundwater Recharge, Climate science.

Earth Observations (EO) systems aim to monitor nearly all aspects of the global Earth environment. Observations of Essential Water Variables (EWVs) together with advanced data assimilation models, could provide the basis for systems that deliver integrated information for operational and policy level decision making that supports the Water-Energy-Food-Nexus (EO4WEF), and concurrently the UN Sustainable Development Goals (SDGs), and UN Framework Convention on Climate Change (UNFCCC). Implementing integrated EO for GEO-WEF (EO4WEF) systems requires resolving key questions regarding the selection and standardization of priority variables, the specification of technologically feasible observational requirements, and a template for integrated data sets. This paper presents a concise summary of EWVs adapted from the GEO Global Water Sustainability (GEOGLOWS) Initiative and consolidated EO observational requirements derived from the GEO Water Strategy Report (WSR). The UN-SDGs implicitly incorporate several other Frameworks and Conventions such as The Sendai Framework for Disaster Risk Reduction; The Ramsar Convention on Wetlands; and the Aichi Convention on Biological Diversity. Primary and Supplemental EWVs that support WEF Nexus & UN-SDGs, and Climate Change are specified. The EO-based decision-making sectors considered include water resources; water quality; water stress and water use efficiency; urban water management; disaster resilience; food security, sustainable agriculture; clean & renewable energy; climate change adaptation & mitigation; biodiversity & ecosystem sustainability; weather and climate extremes (e.g., floods, droughts, and heat waves); transboundary WEF policy.

Low marine clouds are a major source of uncertainty in cloud feedbacks across climate models and in forcing by aerosol-cloud interactions. The evolution of these clouds and their response to aerosol are sensitive to the ambient environmental conditions, so it is important to be able to determine different responses over a representative set of conditions. Here, we propose a novel approach to encompassing the broad range of conditions present in low marine cloud regions, by building a library of observed environmental conditions. This approach can be used, for example, to more systematically test the fidelity of Large Eddy Simulations (LES) in representing these clouds. ERA5 reanalysis and various satellite observations are used to extract and derive macrophysical and microphysical cloud-controlling variables (CCVs) such as SST, estimated inversion strength (EIS), subsidence, and cloud droplet number concentrations. A few locations in the stratocumulus (Sc) deck region of the Northeast Pacific during summer are selected to fill out a phase space of CCVs. Thereafter, Principal Component Analysis (PCA) is applied to reduce the dimensionality and to select a reduced set of components that explain most of the variability among CCVs in order to efficiently select cases for LES simulations that encompass the observed CCV phase space. From this phase space, 75-100 cases with distinct environmental conditions will be selected and used to initialize 2-day LES modeling to provide a spectrum of aerosol-cloud interactions and Sc-to-Cumulus transition under observed ambient conditions. Such a large number of simulations will help create statistics to assess how well the LES can simulate the cloud lifecycle when constrained by the ‘best estimate’ of the environmental conditions, and how sensitive the modeled clouds are to changes in these driving fields.

Earth Observations (EO) systems aim to monitor nearly all aspects of the global Earth environment. Observations of Essential Water Variables (EWVs) together with advanced data assimilation models, could provide the basis for systems that deliver integrated information for operational and policy level decision making that supports the Water-Energy-Food-Nexus (EO4WEF), and concurrently the UN Sustainable Development Goals (SDGs), and UN Framework Convention on Climate Change (UNFCCC). Implementing integrated EO for GEO-WEF (EO4WEF) systems requires resolving key questions regarding the selection and standardization of priority variables, the specification of technologically feasible observational requirements, and a template for integrated data sets. This paper presents a concise summary of EWVs adapted from the GEO Global Water Sustainability (GEOGLOWS) Initiative and consolidated EO observational requirements derived from the GEO Water Strategy Report (WSR). The UN-SDGs implicitly incorporate several other Frameworks and Conventions such as The Sendai Framework for Disaster Risk Reduction; The Ramsar Convention on Wetlands; and the Aichi Convention on Biological Diversity. Primary and Supplemental EWVs that support WEF Nexus & UN-SDGs, and Climate Change are specified. The EO-based decision-making sectors considered include water resources; water quality; water stress and water use efficiency; urban water management; disaster resilience; food security, sustainable agriculture; clean & renewable energy; climate change adaptation & mitigation; biodiversity & ecosystem sustainability; weather and climate extremes (e.g., floods, droughts, and heat waves); transboundary WEF policy.

Earth Observations (EO) systems aim to monitor nearly all aspects of the global Earth environment. Observations of Essential Water Variables (EWVs) together with advanced data assimilation models, could provide the basis for systems that deliver integrated information for operational and policy level decision making that supports the Water-Energy-Food-Nexus (EO4WEF), and concurrently the UN Sustainable Development Goals (SDGs), and UN Framework Convention on Climate Change (UNFCCC). Implementing integrated EO for GEO-WEF (EO4WEF) systems requires resolving key questions regarding the selection and standardization of priority variables, the specification of technologically feasible observational requirements, and a template for integrated data sets. This paper presents a concise summary of EWVs adapted from the GEO Global Water Sustainability (GEOGLOWS) Initiative and consolidated EO observational requirements derived from the GEO Water Strategy Report (WSR). The UN-SDGs implicitly incorporate several other Frameworks and Conventions such as The Sendai Framework for Disaster Risk Reduction; The Ramsar Convention on Wetlands; and the Aichi Convention on Biological Diversity. Primary and Supplemental EWVs that support WEF Nexus & UN-SDGs, and Climate Change are specified. The EO-based decision-making sectors considered include water resources; water quality; water stress and water use efficiency; urban water management; disaster resilience; food security, sustainable agriculture; clean & renewable energy; climate change adaptation & mitigation; biodiversity & ecosystem sustainability; weather and climate extremes (e.g., floods, droughts, and heat waves); transboundary WEF policy.