Rock glaciers manifest the creep of mountain permafrost occurring in the past or at present. Their presence and dynamics are indicators of permafrost distribution and changes in response to climate forcing. Knowledge of rock glaciers is completely lacking in the West Kunlun, one of the driest mountain ranges in Asia, where widespread permafrost is rapidly warming. In this study, we first mapped and quantified the kinematics of active rock glaciers based on satellite Interferometric Synthetic Aperture Radar (InSAR) and Google Earth images. Then we trained DeepLabv3+, a deep learning network for semantic image segmentation, to automate the mapping task. The well-trained model was applied for a region-wide, extensive delineation of rock glaciers from Sentinel-2 images to map the landforms that were previously missed due to the limitations of the InSAR-based identification. Finally, we mapped 413 rock glaciers across the West Kunlun: 290 of them were active rock glaciers mapped manually based on InSAR and 123 of them were newly identified and outlined by deep learning. The rock glaciers are categorized by their spatial connection to the upslope geomorphic units. All the rock glaciers are located at altitudes between 3,389 m and 5,541 m with an average size of 0.26 km2 and a mean slope angle of 17°. The mean and maximum surface downslope velocities of the active ones are 24 cm yr-1 and 127 cm yr-1, respectively. Characteristics of the rock glaciers of different categories hold implications on the interactions between glacial and periglacial processes in the West Kunlun.

Freeboardelevation of a structure above the base flood elevation (BFE)is a critical component in mitigating or avoiding flood losses. However, the unrevealed benefits and savings of freeboard installation have prevented communities from adopting this approach. To improve decision-making for flood-vulnerable communities and enhance flood risk mitigation strategies, this study presents the methodology underlying a new webtool, FloodSafeHome, that estimates comprehensively the economic benefits and savings of freeboard installation for new construction of residential buildings. Specifically, the proposed evaluation framework has been designed to calculate monthly savings for individual buildings by assessing freeboard cost, insurance savings per year, and expected annual flood loss. This new evaluation method is built into a web-based, decision-making tool for use by the public and community leaders in three southeastern Louisiana parishes, to identify expected future benefits of building residences with freeboard and enhance their decision-making processes with interactive risk/benefit analysis features. For example, results indicate the levels of freeboard that optimize the costbenefit ratio for flood-insured homes in the study area. This approach is expected to improve long-term flood resilience and provide cost-efficient flood mitigation strategies particularly in disaster vulnerable regions.

Manganese (Mn) is an essential element for photosynthetic life, yet concentrations in Southern Ocean open waters are very low, resulting from biological uptake along with limited external inputs. At southern latitudes, waters overlying the Antarctic shelf are expected to have much higher Mn concentrations due to their proximity to external sources such as sediment and sea ice. In this study, we investigated the potential export of Mn-rich Antarctic shelf waters toward depleted open Southern Ocean waters. Our results showed that while high Mn concentrations were observed over the shelf, strong biological uptake decreased dissolved Mn concentrations in surface waters north of the Southern Antarctic Circumpolar Current Front (< 0.1 nM), limiting export of shelf Mn to the open Southern Ocean. Conversely, in bottom waters, mixing between Mn-rich Antarctic Bottom Waters and Mn-depleted Low Circumpolar Deep Waters combined with scavenging processes led to a decrease in dissolved Mn concentrations with distance from the coast. Subsurface dissolved Mn maxima represented a potential reservoir for surface waters (0.3 – 0.6 nM). However, these high subsurface values decreased with distance from the coast, suggesting these features may result from external sources near the shelf in addition to particle remineralization. Overall, these results imply that the lower-than-expected lateral export of trace metal-enriched waters contributes to the extremely low (< 0.1 nM) and potentially co-limiting Mn concentrations previously reported further north in this Southern Ocean region.

Heatwaves occurred frequently in summers, severely harming natural environment and human society. While a few long-term spatiotemporal heatwave studies have been conducted in China at the grid scale, their shortcomings involve discrete distribution and poor spatiotemporal continuity. We used daily data of 691 meteorological stations to obtain torridity index (TI) and heatwave index (HWI) datasets (0.01°), to evaluate the spatiotemporal distribution of heatwaves in Chinese mainland for 1990-2019. The results were as follows: (1) TI rose but with fluctuations. The largest increase occurred in North China in July. Areas with hazard levels of medium and above accounted for 22.16%, mainly in the eastern and southern provinces of China, South Tibet, East and South Xinjiang, and Chongqing. The hazard indicators in Chongqing and central Zhejiang were at especially high levels, which is concerning. (2) Average heatwave frequency, intensity, and duration reached relatively high levels of 6-8, 20-25, and 11-16, respectively, in East and South Xinjiang and Southeast Tibet. (3) The study areas were divided into four categories according to the spatiotemporal distribution of hazards. The “high hazard and rapidly increasing” and “low hazard and keep increasing” areas accounted for 8.71% and 41.33%, respectively. (4) The proportions of units with significantly increased average hazard (AH) at city and county levels were 57% and 68%, respectively. Jinhua, Zhengzhou, Nanchang, Wuhan, Shaoxing, Changsha, Shijiazhuang, Nanjing, Wuxi, and Changzhou accounted for the top 10 AH among the 49 first-tier, new first-tier, and second-tier cities. “Ten Furnaces” at the top of the provincial capitals were Zhengzhou, Nanchang, Wuhan, Changsha, Shijiazhuang, Nanjing, Hangzhou, Haikou, Chongqing, and Hefei. Suzhou’s AH rose the fastest. While the strategy of west development and of revitalizing northeast China progressed, and the urbanization level and population aging of developed areas were further developed, the continuously increasing heatwave hazard should be fully considered.

The distribution of a mosaic of biological soil crusts (BSCs) and shrubs is a common landscape surface feature in temperate deserts. With the continued climatic change, the desert shrub experiences varying rates of mortality which has serious negative impacts on soil structure and functions. However, it is not clear whether BSCs, which develop extensively in areas under shrub canopies, can mitigate the effects of shrub mortality on soil nutrient multifunctionality. Therefore, in this study, the Gurbantungut Desert, a typical temperate desert in northern China, was selected as the study area, and the dominant shrubs, Ephedra przewalskii shrub, and the moss crust were used as the study objects. Soil samples were collected from the bare sand and moss crusts under the living shrub and the dead shrub and analyzed to determine their carbon, nitrogen, phosphorus, and potassium contents. The results showed that the shrub mortality reduced the soil moisture content, pH, electric conductivity, and carbon, nitrogen, phosphorus, and potassium contents in the bare sand compared with the bare sand under the living shrub. The presence of the moss crust greatly mitigated the negative impacts of shrub mortality on soil carbon, nitrogen, phosphorus, and potassium contents, and the nutrient multifunctionality of the moss crust was only reduced by 4.01% compared with the reduction by bare sand (67.42%) after shrub mortality. The results of SEM analysis showed that with the coexistence of shrubs and crust, the effect of shrubs on soil multifunctionality was much stronger than that of the moss crust; compared with available nutrients, the total nutrient content was the most important factor driving changes in soil nutrient multifunctionality. In conclusion, in desert ecosystems with degraded shrubs, moss crusts can mitigate the reduction in soil nutrient contents caused by shrub degradation and, therefore, maintain the soil stability and nutrient multifunctionality as a “substitute”.

Arctic-Boreal lakes emit methane (CH₄), a powerful greenhouse gas. Recent studies suggest ebullition may be a dominant methane emission pathway in lakes but its drivers are poorly understood. Various predictors of lake methane ebullition have been proposed, but are challenging to evaluate owing to different geographical characteristics, field locations, and sample densities. Here we compare large geospatial datasets of lake area, lake perimeter, permafrost, landcover, temperature, soil organic carbon content, depth, and greenness with remotely sensed methane ebullition estimates for 5,143 Alaskan lakes. We find that lake wetland fraction (LWF), a measure of lake wetland and littoral zone area, is a leading predictor of methane ebullition (adj. R² = 0.211), followed by lake surface area (adj. R² = 0.201). LWF is inversely correlated with lake area, thus higher wetland fraction in smaller lakes may explain a commonly cited inverse relationship between lake area and methane ebullition. Lake perimeter (adj. R² = 0.176) and temperature (adj. R² = 0.157) are moderate predictors of lake ebullition, and soil organic carbon content, permafrost, lake depth, and greenness are weak predictors. The low adjusted R² values are typical and informative for methane attribution studies. A multiple regression model combining LWF, area, and temperature performs best (adj. R² = 0.325). Our results suggest landscape-scale geospatial analyses can complement smaller field studies, for attributing Arctic-Boreal lake methane emissions to readily available environmental variables.

The propagating muons deposit their energies in the volume-of-interest (VOI) within the tomographic configurations, and this energy loss directly indicates that there is a difference in terms of the kinetic energy between the incoming muons and the the outgoing muons. In this study, by using the GEANT4 simulations, we first elaborate this energy difference over the nuclear waste barrels that contain cobalt, strontium, caesium, uranium, and plutonium. We show that the deposited energy through these VOIs is not negligible for the initial energy bins. Then, we suggest a correction factor for the image reconstruction codes where the initial kinetic energy of the entering muons is coarsely predicted in accordance with the deflection angle through the hodoscope sections, thereby renormalizing the deflection angle in the bottom hodoscope depending on the intrinsic properties of the corresponding VOIs. This correction factor encompasses useful information about the target volume traversed by the muons since it is related to the intrinsic features of the VOI. Therefore, it might be utilized in order to complement the scattering information as an input to the image reconstruction.

Dust devils play an important role in dust transport by carrying it from surface into the atmosphere, especially in summer. However, information on how dust devil changed in the past decades and what caused these changes remains lacking. Based on thermodynamic criterion and ERA5 reanalysis dataset, this study investigated long-term variation of dust devil in East Asia over the past 60 years. We found the annual mean frequency of dust devil in East Asia was approximately 8.2×105 hours for 1959 to 2021. In particular, it is shown that an overall downward trend in dust devil frequency over the study period in East Asia, among which strong dust devils declined significantly at the fastest rate while the weak ones dropped insignificantly. Moreover, dust devil frequency in the Gobi Desert (GD) increased obviously but they decreased in the Taklimakan Desert (TD). It is represented that there was a peak for monthly variation of dust devil, shifting from June to July, over the past decades. For the diurnal changes, dust devils mainly occurred between 10:00 and 16:00 at Local Time (LT), with over 60% of them happening between 12:00 LT and 14:00 LT. We also found a negative correlation between precipitation and dust devil frequency. This study provides a comprehensive understanding of dust devils in East Asia over the past decades, which is of great importance to further evaluate its impact on climate, environment as well as ecosystem.

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.

Urban flooding is caused due to poor drainage design, extreme weather, and excessive rain. Such flooding severely affects the road infrastructure. While there are a number of hydrologic software (e.g., TR-55, HydroCAD, TR-20, HEC-RAS, StreamStats, L-THIA, SWMM, WMOST, MAST, HY-8) available to examine extent of urban flooding, the softwares primarily require walking through a series of manual steps and address each study area individually preventing a collective view of an urban area in an efficient manner for hydrologic analysis. Furthermore, the softwares have no ability to recommend optimal culver pipe sizes to minimize flooding. In this paper, we develop a non-linear optimization formulation to minimize urban flooding using underdrain pipe size as a decision variable. We propose a solution algorithm in an integrated GIS and Python environment. Monte Carlo Simulation is used to simulate rainfall intensity by using empirical data on extreme weather from the National Oceanic and Atmospheric Administration. An example using the storm-drain system for the Baltimore County is performed. The results show that the model is effective in identifying storm-drain deficiencies and correcting them by choosing appropriate storm-drain inlet types to minimize flooding. The proposed method eliminates the need to examine each study area manually using existing hydrologic tools. Future works may include expanding the methodology for large datasets. They may also include a more sophisticated modeling approach for estimating rainfall intensity based on extreme weather patterns.

Vegetation acts as a critical link between the geosphere, biosphere, and atmosphere, regulating the flux of water to the atmosphere via transpiration (E) and the input of carbon from the atmosphere to plants and soil via photosynthetic carbon assimilation (A). The rate of A is known to be seasonally dynamic, however, few studies have investigated how the ratio between E and A, known as the water use efficiency (WUE), changes with phenology. WUE directly impacts regional to global carbon and water cycles and lack of knowledge regarding the dynamics of WUE remains among the largest uncertainties in current earth system model (ESM) projections of carbon and water exchange in temperate forests. Here we attempt to reduce this knowledge gap by studying these dynamics across a range of eight deciduous tree species common to temperate forests of North America. Using gas exchange and spectroscopic measurements, we investigated seasonal patterns in leaf level physiological, biochemical, and anatomical properties, including the seasonal progress of WUE and foliar capacity for carbon assimilation, which corollate with seasonal leaf phenology. We incorporate these findings into a modeling framework that contains the same representation of A, E, and canopy scaling found in ESMs to explore the impact of parameterization, which tracks phenological status, on model forecasts. Our results indicate that both photosynthetic capacity and WUE are seasonally dynamic processes which are not synchronized. WUE increased from a minimum at leaf out toward a more conservative behavior at the mid-summer growth peak. This pattern was explained by a decreased stomatal aperture and a decrease in cuticular leakage with leaf aging. We also observed a seasonal increase in maximum carboxylation capacity, with maximum rates of A and modeled tree net primary productivity (NPP) occurring later toward the end of the summer. This change was primarily driven by an increase in foliar nitrogen content, and a shift in the ratio of Vcmax to Jmax between expanding and mature leaves. By applying our revised parameterization, which captures seasonal dynamics of gas exchange, into our model framework we aim to improve the process representation of leaf function in a temperate forest, and more faithfully represent dynamics of NPP and E in the early and late growth season.

[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.

Nowcasting is a term originating from economics, finance and meteorology. It refers to the process of determining the uncertain state of the economy, markets or the weather at the current time by indirect means. In this paper we describe a simple 2-parameter data analysis that reveals hidden order in otherwise seemingly chaotic earthquake seismicity. One of these parameters relates to a mechanism of seismic quiescence arising from the physics of strain-hardening of the crust prior to major events. We observe an earthquake cycle associated with major earthquakes in California, similar to what has long been postulated. An estimate of the earthquake hazard revealed by this state variable timeseries can be can be optimized by the use of machine learning in the form of the Receiver Operating Characteristic skill score. The ROC skill is used here as a loss function in a supervised learning mode. Our analysis is conducted in the region of 5o x 5o in latitude-longitude centered on Los Angeles, a region which we used in previous papers to build similar timeseries using more involved methods (Rundle and Donnellan, 2020; Rundle et al., 2021). Here we show that not only does the state variable timeseries have forecast skill, the associated spatial probability densities have skill as well. In addition, use of the standard ROC and Precision (PPV) metrics allow probabilities of current earthquake hazard to be defined in a simple, straightforward and rigorous way.

Most of Earth’s mosquito-borne illnesses are transmitted by mosquitoes in one of three genera: Anopheles, Aedes, and Culex. Mosquitos of such genera are located in all continents but Antarctica and infect millions of humans with parasitic viruses yearly. However, a special concern is reserved for Anopheles mosquitoes for their unique ability to carry and transmit Malaria, a disease that, according to WHO, infects more than 200 million and kills over 500,000 humans annually (Malaria, 2022). While it is most prevalent in Africa, Southeast Asia, and Central America, Malaria could soon spread to northern and southern latitudes with a changing global climate. Therefore, it is crucial to track the extent of the Anopheles range and identify any changes that could have detrimental consequences on public health. One way this can be done is using the NASA GLOBE Observer Mosquito Habitat Mapper (MHM) tool, which allows global users free access to photograph mosquito larvae, attempt to identify their genus, and upload said images to a worldwide database that records the location at which they were taken. While citizen science data is extremely helpful for mosquito research, it can be difficult for citizens with minimal training to classify the genus of their discovered larva correctly. A large portion of mosquito photos uploaded to the GLOBE MHM database are either unidentified or misidentified. Therefore, this research paper aims to devise and assess how the MHM database can be appropriately classified to create an accurate dataset with all Anopheles larvae photos classified by their proper genus.Besides being a vector of Malaria, another unique characteristic of theAnopheles mosquito is the absence of a siphon, so by scanning for this trait among MHM larvae photographs and noting positive matches, researchers created a dataset of mosquito larvae that could become vectors of Malaria as adults (Image Reference #1). This data set could then be used to train AImodels utilizing Convolutional Neural Networks (CNN) or VisionTransformers (ViT) to classify the MHM database autonomously in the near future.

Here, we explore the importance of export productivity versus anoxia in the formation of sedimentary layers with enhanced total organic carbon (TOC) content. We use geochemical, sedimentological and micropaleontological records from two SW Sicily outcropping successions, Lido Rosello (LR) and Punta di Maiata (PM), over three mid-Pliocene precession-forced climate cycles (4.7 – 4.6 million years ago [Ma]). Grey marls, deposited during precession minima, show enhanced TOC in both records. We suggest that basin-wide, low-oxygenated bottom-waters, resulting from freshwater-induced stratification during precession minimum, was integral to preserving grey marl TOC. Furthermore, prolonged eastern Mediterranean stratification may have produced a deep chlorophyll maximum (DCM), leading to ‘shade-flora’ dominated productivity. The LR succession displays two unique laminated layers containing enhanced TOC. These laminations do not occur at specific times in the precession cycle or in time-equivalent PM samples. They are likely to have been produced by an intermittent dysoxic/anoxic pool at LR, caused by a local depression, which enhanced TOC preservation. Consequently, the laminations provide a rare window into ‘true’ eastern Mediterranean productivity conditions during precession maxima, as organic matter is typically poorly preserved during these period due to enhanced ventilation. The laminated ‘windows’ indicate that eastern Mediterranean export productivity may not have been significantly lower during precession maxima compared to precession minima, as previously thought. During these periods, productivity conditions are likely to have been comparable to the modern eastern Mediterranean, with a spring-bloom caused by enhanced winter/spring deep-water mixing preceding a summer ‘shade-flora’ bloom caused by a summer-stratification induced DCM.

The current narrative of artificial upwelling (AU) is to use ocean pipes to pump nutrient rich deep water to the ocean surface, thereby stimulating the biological carbon pump. This simplistic concept of AU does not take the response of the solubility pump or the CO2 emission scenario into account. Using global ocean-atmosphere model experiments and several idealized model tracers we show that the effectiveness of almost globally applied AU from the year 2020 to 2100 to draw down CO2 from the atmosphere is strongly dependent on the CO2 emission scenario and ranges from 1.01 Pg C / year under RCP 8.5 to 0.32 Pg C / year under RCP 2.6. The solubility pump becomes equally effective compared to the biological carbon pump under the highest emission scenario (RCP 8.5), but responds with CO2 outgassing under low CO2emission scenarios.

Canada’s boreal forests and tundra ecosystems are responding to unprecedented climate change with implications for the global carbon (C) cycle and global climate. However, our ability to model the response of Canada’s terrestrial ecosystems to climate change is limited and there has been no comprehensive, process-based assessment of Canada’s terrestrial C cycle. We tailor the Canadian Land Surface Scheme Including Biogeochemical Cycles (CLASSIC) to Canada and evaluate its C cycling performance against independent reference data. We utilize skill scores to assess model performance against reference data alongside benchmark scores that quantify the level of agreement between the reference data sets to aid in interpretation. Our results demonstrate CLASSIC’s sensitivity to prescribed vegetation cover. They also show that the addition of five region-specific PFTs improves CLASSIC’s skill at simulating the Canadian C cycle. CLASSIC performs well when tailored to Canada, falls within the range of the reference data sets, and meets or exceeds the benchmark scores for most C cycling processes. New region-specific land cover products, well-informed plant functional type (PFT) parameterizations, and more detailed reference data sets will facilitate improvements to the representation of the terrestrial C cycle in regional and global land surface models (LSMs). Incorporating a parameterization for boreal disturbance processes and explicitly representing peatlands and permafrost soils will improve CLASSIC’s future performance in Canada and other boreal regions. This is an important step toward a comprehensive process-based assessment of Canada’s terrestrial C cycle and evaluating Canada’s net C balance under climate change.

Antarctic landfast sea ice (fast ice) is stationary sea ice that is attached to the coast, grounded icebergs, ice shelves, or other protrusions on the continental shelf. Fast ice forms in narrow (generally up to 200 km wide) bands, and ranges in thickness from centimeters to tens of meters. In most regions, it forms in autumn, persists through the winter and melts in spring/summer, but can remain throughout the summer in particular locations. Despite its relatively limited horizontal extent (comprising between about 4 and 13 \% of overall sea ice), its presence, variability and seasonality are drivers of a wide range of physical, biological and biogeochemical processes, with both local and far-ranging ramifications for various Earth systems. Antarctic fast ice has, until quite recently, been overlooked in studies, likely due to insufficient knowledge of its distribution, leading to its reputation as a “missing piece of the Antarctic puzzle”. This review presents a synthesis of current knowledge of the physical, biogeochemical and biological aspects of fast ice, based on the sub-domains of: fast ice growth, properties and seasonality; remote-sensing and distribution; interactions with the atmosphere and the ocean; biogeochemical interactions; its role in primary production; and fast ice as a habitat for grazers. Finally, we consider the potential state of Antarctic fast ice at the end of the 21st Century, underpinned by Coupled Model Intercomparison Project model projections. This review also gives recommendations for targeted future work to increase our understanding of this critically-important element of the global cryosphere.

The Gravity Recovery and Climate Experiment (GRACE) data help to determine the total water storage anomalies (TWS) across the global scale. The various other important components such as Groundwater storage (GWS) and evapotranspiration for the region of South –East Asia have been determined. With the study of the gravity variation across the globe the long-term changes in the hydrological cycle can be determined which can be related to climate science or the influence of anthropogenic activities. The variation between the Groundwater storage (GWS) and the Total water storage (TWS) of the study area has been calculated for the pre and post-monsoon season of the study area. The variation between groundwater storage and total water storage can be visualized through geospatial analysis. Therefore, the regions with a substantial decrease in water storage can be related to various climate and anthropogenic factors hence implying a sustainable use of groundwater as a resource. Keywords: Machine Learning, Remote Sensing, Groundwater Recharge, Climate science.

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.

Machine Learning and Remote sensing method to determine the relationship between Climate and Groundwater Recharge. Adya Aiswarya Dash1, Abhijit Mukherjee1,2,3. 1Department of Geology and Geophysics, Indian Institute of Technology Kharagpur, West Bengal 721302, India 2School of Environmental Science and Engineering, Indian Institute of Technology Kharagpur, West Bengal 721302, India 3Applied Policy Advisory for Hydrogeoscience (APAH) Group, Indian Institute of Technology Kharagpur, West Bengal 721302, India Abstract 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.

Sustainably managing resources in a transboundary freshwater basin is a complex problem, particularly when considering the compounding impacts of climate change, hydropower development, and evolving water governance paradigms. In this study, we used a mixed methods approach to analyze potential impacts of climate change on regional hydrology, the ability of dam operation rules to keep downstream flow within acceptable limits, and the present state of water governance in Laos, Vietnam, and Cambodia. Our results suggest that future river flows in the 3S river system could move closer to natural (i.e., pre-development) conditions during the dry season and experience increased floods during the wet season. This anticipated new flow regime in the 3S region would require a shift in the current dam operations, from maintaining minimum flows to reducing flood hazards. Moreover, our Governance and Stakeholders survey assessment results revealed that existing water governance systems in Laos, Vietnam, and Cambodia are ill-prepared to address such anticipated future water resource management problems. Our results indicate that the solution space for addressing these complex issues in the 3S river basins will be highly constrained unless major deficiencies in transboundary water governance, strategic planning, financial capacity, information sharing, and law enforcement are remedied in the next decade. This work is part of an ongoing research partnership between the National Aeronautical and Space Agency (NASA) and the Conservation International (CI) dedicated to improving natural resources assessment for conservation and sustainable management.

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.

Feasibility of observing near-inertial waves with a single cast of a lowered Acoustic Doppler Current Profiler is quantitatively assessed in simulated Garrett-Munk internal waves. Because the inertial period is shorter in higher latitudes and the interval between the upand downcasts is longer in shallower depths, the performance of the estimator is better in higher latitudes at shallower depths. Even in the best conditions, however, the estimates are contaminated by relative uncertainties greater than 100%. It is not feasible to estimate nearinertial waves accurately using a LADCP cast. Nevertheless, repeated casts at one station are capable of resolving typical near-inertial waves.