The U.S. Climate Reference Network (USCRN) has been engaged in ground-based soil water and soil temperature measurements since 2009. As a nationwide climate network, the network stations are distributed across vast complex terrains. Due to the expansive distribution of the network and the related variability in soil properties, obtaining site-specific calibrations for sensors is a significant and costly endeavor. Presented here are three commercial-grade electromagnetic sensors, with built-in thermistors to measure both soil water and soil temperature, including the SoilVUE10 Time Domain Reflectometry (TDR) probe (hereafter called SP, for SoilVUE Probe) (Campbell Scientific, Inc., Logan, UT), the 50 MHz coaxial impedance dielectric sensor (model HydraProbe (hereafter called HP), Stevens Water Monitoring Systems, Inc., Portland, OR), and the TDR-315L Acclima Probe (hereafter called AP) sensor (model TDR-315L, Acclima, Inc., Meridian, ID), which were evaluated in a nonconductive loam soil in Oak Ridge, Tennessee, USA from 2021 to 2022. The manufacturer-supplied calibration equation for loam soils was successfully used in this study. Measurements of volumetric water content by SP were much lower than gravimetric measurements in the top 20-cm soil horizon, where soil water showed relatively large spatial variability. Study results highlight that the SP may be an important alternative to reduce soil disturbances that usually ensue when HP and AP sensors are installed; however, in-situ calibrations are essential for the SP for xeric soil water conditions.

This paper presents the first scientific application of local time-stepping (LTS) schemes in the Model for Prediction Across Scales-Ocean (MPAS-O). We use LTS schemes in a single-layer, global ocean model that predicts the storm surge around the eastern coast of the United States during Hurricane Sandy. The variable-resolution meshes used are of unprecedentedly high resolution in MPAS-O, containing cells as small as 125 meters wide in Delaware Bay. It is shown that a particular, third-order LTS scheme (LTS3) produces sea-surface height (SSH) solutions that are of comparable quality to solutions produced by the classical four-stage, fourth-order Runge-Kutta method (RK4) with a uniform time step on the same meshes. Furthermore, LTS3 is up to 35% faster in the best cases, showing that LTS schemes are viable for use in MPAS-O with the added benefit of substantially less computational cost. The results of these performance experiments inform us of the requirements for efficient mesh design for LTS schemes. In particular, we see that for LTS to be efficient on a given mesh, it is important to have enough cells using the coarse time-step relative to those using the fine time-step, typically at least 1:5.

Understanding how soil thickness and bedrock weathering vary across ridge and valley topography is needed to constrain the flowpaths of water and sediment within a landscape. Here, we investigate how soil and weathered bedrock properties vary across a ridge-valley system in the Northern California Coast Ranges where topography varies with slope aspect such that north facing slopes, which are more densely vegetated, are steeper. In this study, we use seismic refraction surveys to extend observations made in boreholes and soil pits to the hillslope scale and identify that while soils are thicker on north facing slopes, the thickness of weathered bedrock does not vary with slope aspect. We estimate the porosity of the weathered bedrock and find that it is several times the annual rainfall, indicating that water storage is not limited by the available pore space, but rather the amount of precipitation delivered. Bedding-parallel and bedding-perpendicular seismic refraction surveys reveal weathering profiles that are thickest upslope and taper downslope to channels. We do not find a clear linear scaling relationship between depth to bedrock and hillslope length, which may be due to local variation in incision rate or bedrock hydraulic conductivity. Together, these findings, which suggest that the aspect-independent weathering profile structure is a legacy of past climate and vegetation conditions and that weathering varies strongly with hillslope position, have implications for hydrologic processes across this landscape.

Infiltration and channel roughness, two major factors that govern stream discharge were studied between ephemeral streams (ES) and similar-sized perennial streams (PS) for two ephemeral flow conditions: with surface flow (wet season) and with ceased flow (dry season). The highest infiltration was observed at the low flow areas around the thalweg of ES in the dry season. Also, the infiltration in the high flow areas close to the channel margin was higher in ES than PS in the wet season but was similar in the dry season. Similar infiltration rates in ES and PS were rather unexpected and was attributed to the vegetation mat formed by air-dried litter because of the rapid decrease in sediment moisture. In high flow areas of both stream types in the wet season, negative and positive correlations were observed for infiltration with biomass and sediment organic content, respectively. Also, in a few cases sediment moisture showed a positive correlation with infiltration. ES were two to three times rougher than PS and standing crop biomass and/or litter content increased stream roughness and decreased with herb diversity. Impact of vegetation parameters on roughness was more prominent in PS, whereas mean particle size had equally strong importance on roughness for both streams other than perennials in the dry season. Modelled (via HEC-HMS) and observed discharges had a better agreement for PS. The field observations, analytical solutions as well as hydrological modelling revealed ES to have a lower unit discharge than PS.

Typhoon Faxai hit Japan in 2019 and severely damaged the Tokyo metropolitan area. To mitigate such damages, a good track forecast is necessary even before the typhoon formation. To investigate the predictability of the genesis and movement of a precursor vortex and its relationship with the synoptic-scale flow, 1600-member ensemble simulations of Typhoon Faxai were performed using a 14-km mesh global nonhydrostatic atmospheric model, which started from 16 different initial days (i.e., 1600 members in total). The results show that the model could predict an enhanced risk of a Faxai-like vortex heading toward Japan two weeks before landfall, which was up to 70%. The reason for the enhancement was a rapid increase in the members reproducing a precursor vortex from 15 to 12 days before landfall in Japan. In addition, the upper-tropospheric trough played an essential role in the track simulation of Faxai.

This study examines the association between the perception of water scarcity and support for alternative water sources in general, and specifically desalination and recycled water. It also examines the mediating role that perception of climate change has on the aforementioned association. A 46-item survey (n=588) was conducted in the Geelong region of Australia. Logistic regression was used to determine the independent association between perceived water scarcity and socio-demographic factors, with support for alternative water sources, desalination and recycled water. 82% of respondents supported undefined ‘alternative water sources’. However, support for specific alternatives was lower (desalination: 65%; recycled water: 40.3%). Perception of water scarcity was significantly associated with increased odds of support for alternative water sources (OR 1.94, 95% CI: 1.25–3.00) and support for recycled water (OR 2.32, 95% CI: 1.68–3.31). There was no significant relationship between perception of water scarcity and support for desalination (OR 0.959 95% CI: 0.677–1.358). Climate change was found to mediate perceived water scarcity and support for alternative sources (OR 1.360, 95% CI: 0.841–2.198). The mediation of the relationship between perceived water scarcity and support for recycled water by climate change was not strong. These results facilitate enhanced community engagement strategies.

Boreal-wintertime hindcasts in the Unified Forecast System with the tropics nudged toward reanalysis improve United States (U.S.) West Coast precipitation forecasts at Weeks 3-4 lead times when compared to those without nudging. To diagnose the origin of these improvements, a multivariate k-means clustering method is used to group hindcasts into subsets by their initial conditions. One cluster characterized by an initially strong Aleutian Low demonstrates larger improvements at Weeks 3-4 with nudging compared to others. The greater improvements with nudging for this cluster are related to the model error in simulating the interaction between the Aleutian Low and the teleconnection patterns associated with the Madden-Julian oscillation (MJO) and El Niño-Southern Oscillation (ENSO). Improving forecasts of tropical intraseasonal precipitation, especially during early MJO phases under non-cold ENSO, may be important for producing better Weeks 3-4 precipitation forecasts for the U.S. West Coast.

Nearly one million Rohingya refugees of Myanmar have fled to neighboring Bangladesh for seeking shelter from systematic oppression since 25 August 2017. The speed and scale of the influx has led to unprecedented growth of the Kutupalong refugee camp of eastern coast of Bangladesh within few months. There is a few study focused on environmental degradation of the refugee camp, however, no study has been done so far to estimate the impact of camp expansion on ecosystem services loss. This study, therefore, made an attempt to estimate the changes of Ecosystem Services Value (ESV) in response to camp expansion for the year July 2017 (pre-camp) and July 2018 by GIS technique and corresponding global value coefficient developed by Costanza et.al. (1997). Land cover map of the study area was prepared by using Landsat 8 satellite data. Results show an overall decrease of vegetation of 2486 hectares, of which 20% were used to expand the camp and 80% were deforested. Total ecosystem service values of the study area reduced dramatically, from 51.53×106 US$ to 49.12×106 US$ in the study period. This represents a 4.63% net decline in annual value of ecosystem services in the study area. In terms of 2 km buffer of the camp, the net decline rate is found 32.58%. The significant changes are also recorded in individual ecosystem services function of the area. Hence, the findings of this study may motivate the Bangladesh government to develop better plans to protect the ecologically sensitive forested land and wildlife habitats surrounding the refugee camps and assist in more sustainable resource mobilization for the Rohingya refugees.

Landscape evolution is controlled by tectonic strain, bedrock lithology, and climatic conditions, and is expressed in the spatial and temporal variations in river channel networks. In response to tectonic and climatic disturbance, river networks shift both laterally and vertically to achieve a steady state. Several metrics are available to assess the nature of river network disequilibrium, upon which the direction of drainage divide migration can be interpreted. However, to link this information to other observational, theoretical, and experimental data requires the knowledge of the rate of migration, which is still lacking. Here we develop a modified method based on Gilbert metrics to calculate the transient direction and rate of drainage divide migration from topography. By choosing a high base level, linear or quasi-linear χ-plots are obtained for rivers on both sides of the drainage divide, and the elevation-χ gradient is proportional to the average normalized steepness index (ksn). In turn, the velocity of divide migration can be quantified theoretically from the cross-divide comparison of χ. We applied this method to eastern Tibet and obtained a uniform, westward migration pattern for 29 points along two drainage divides with rates between 0.02 and 0.66 mm/yr, which is consistent with the great river capture events in the region. The ongoing reorganization of the river network in eastern Tibet is caused by the Cenozoic growth and eastward expansion of the Tibetan Plateau, the strengthening of the precipitation and regional extension throughout East Asia, and the local fault activities.

Using authentic and locally-relevant environmental data is a valuable teaching tool for providing students with opportunities for self-directed exploration of the natural world. We will present a set of instructional material that focus on climate science and data literacy, prepared by the Lawrence Hall of Science, Berkeley, CA, (https://mare.lawrencehallofscience.org/curriculum/climate-data-aclipse-activities). We will demonstrate how these materials can be used in formal (6-12 or university) and informal settings (professional development workshops for teachers and scientists, after school clubs and summer programs). Students engaging in these activities gain a deeper understanding of carbon cycling, and other phenomena related to climate change. The materials guide students through data orientation, interpretation, and synthesis as well as developing and using models and solving problems, constructing explanations, and engaging in argument based on scientific evidence. By solving problems in local contexts, the learners gain knowledge of science content, view science as relevant to their lives and future, and engage in science in socially relevant and transformative ways. Such authentic learning experiences are shown to be an effective approach to improving understanding, confidence, awareness, and interest in geoscience and STEM careers (NRC, 2007).

The poor across the world is very vulnerable to floods and drought disasters and have a detrimental effect on the lives and livelihoods of the poor. Weather based index insurance is one of the ways of dealing with these disasters. Protecting against floods and providing risk cover against losses due to floods has been a major area of concern for any government. Risk transfer through insurance is an important component in managing agricultural risks from extreme flood events. The study developed the first of its kind of designing and implementation of an index-based flood insurance (IBFI) product with the advanced use of satellite data and flood models to estimate crop losses due to floods. IBFI insurance product uses two different data elements, the first one is based on the flood model using HEC-HMS and HEC-RAS that uses inputs from NASA GPM bias-corrected satellite rainfall estimates, observed water level and discharge data, river characteristics, and digital elevation model to generate flood depth and flood duration to develop pre-determined thresholds based on the historical flood events between 1991 to 2015 and the second IBFI product uses only satellite data from NASA MODIS Terra and Aqua satellite data and the Copernicus Sentinel-1 SAR data to generate flood depth and flood duration to develop pre-determined thresholds based on the historical flood events and economic losses. More than 7,000 farming households in Bihar (India) and northern Bangladesh have signed up for a pilot IBFI scheme, which went live in 2017. The participating farmers have received insurance compensation for crop losses of over USD 160,000. In addition to the insurance product implementation, the research evaluated the farming willingness to pay, developing business models for scaling; social equity, and economic benefits of derisk disasters. IBFI initiative promotes a closer linkage between risk transfer and risk reduction that could make this more sustainable and robust financial instruments for flood-affected communities and reducing the burden of post-disaster relief funds for the government. In summary, the index insurance using open-access satellite imagery is a win-win opportunity as it brings down the data development cost, lower insurance premium, quick settlement, and greater transparency among various users.

We present the first comparative statistical study of subauroral arc detachment from the main auroral oval at Athabasca (magnetic latitude = 61.5°N), Canada, for three different types of subauroral arcs: pure red arc, red arc with simultaneous emission in green-line (red+green arc), and STEVE (strong thermal emission velocity enhancement). Based on 15-years (2006-2020) of all-sky imaging observations, this study not only uncovers the occurrence characteristics of different arcs but also provides important insights into the specific geomagnetic conditions under which STEVE develops. Red arc was the most common subauroral arc (139 events), followed by red+green arc (42 events), and STEVE (26 events) was a rare phenomenon. The detachment rate of red and red+green arcs exhibits dependence on both the solar flux and geomagnetic activity. The detachment rate of STEVE was higher during premidnight, whereas red and red+green arcs were higher around the midnight sector. The geomagnetic activity was relatively higher for STEVE, the decrease in the AL index and local X-component magnetic variations were ~2-3 times higher for STEVE as compared to other arcs. STEVE shows a strong association with asymmetric ring current in terms of prominent bay-like enhancement in ASY-H index prior to the STEVE detachment. Such bay-like enhancement was ~4 times higher for STEVE as compared to other arcs. STEVE events were accompanied by dispersionless injection for both electron and proton flux at the geosynchronous orbit. These results unambiguously suggest that STEVE develops after the substorm associated energy injection and significant intensification of asymmetric ring current.

Solar flares may be the best-known examples of the explosive conversion of magnetic energy into bulk motion, plasma heating, and particle acceleration via magnetic reconnection. The energy source for all flares is the highly sheared magnetic field of a filament channel above a polarity inversion line (PIL). During the flare, this shear field becomes the so-called reconnection guide field (i.e., the non-reconnecting component), which has been shown to play a major role in determining key properties of the reconnection including the efficiency of particle acceleration. We present new high-resolution, three-dimensional, magnetohydrodynamics simulations that reveal the detailed evolution of the magnetic shear/guide field throughout an eruptive flare. The magnetic shear evolves in three distinct phases: shear first builds up in a narrow region about the PIL, then expands outward to form a thin vertical current sheet, and finally is transferred by flare reconnection into an arcade of sheared flare loops and an erupting flux rope. We demonstrate how the guide field may be inferred from observations of the sheared flare loops. Our results indicate that initially the guide field is larger by about a factor of 5 than the reconnecting component, but it weakens by more than an order of magnitude over the course of the flare. Instantaneously, the guide field also varies spatially over a similar range along the three-dimensional current sheet. We discuss the implications of our results for understanding observations of flare particle acceleration.

Monitoring nuclear reactors is an important safety and security task with growing requirements. We explore the possibility of using seismic and acoustic data for inferring the power level of an operating reactor. Continuous data recorded at a single seismo-acoustic station that is located about 50 m away from a research reactor was visualized and analyzed. The data show a clear correlation between seismo-acoustic features and reactor main operational states. We designed a workflow that includes two machine learning models to classify the reactor operational states (off, transition, and on) and estimate reactor power levels (10%, 30%, 50%, 70% and 90%). We applied and compared five machine learning algorithms for the reactor off-transition-on and four approaches for the power level classification. We also compared the performance of machine learning models trained with seismic-only, acoustic-only, and both types of data. Five-fold cross-validations were implemented to assure a thorough evaluation of the model performances. The results show the extreme boosting gradient algorithm worked best for the first model, while random forests performed best for the second model. Combining seismic and acoustic data leads to better performance than using a single type of data. Seismic data contributed more than acoustic data for both models. We reached an accuracy of 0.98 for reactor off and on. The accuracies for the transition state and power levels are less optimal. However, our results suggest seismic and acoustic data contain useful information about the transition state as well as power levels. Seismic and acoustic data could be integrated with other observations to improve monitoring performance.

The retreat of Arctic sea ice is enabling increased ocean surface wave activity at the sea ice edge, yet the physical processes governing interactions between waves and sea ice are not fully understood. Here, we use a collection of in situ observations of waves in ice to evaluate a recent global climate model experiment that includes coupled interactions between ocean waves and the sea ice floe size distribution. Observations come from subsurface moorings and free-drifting buoys spanning 2012-2019 in the Beaufort Sea, and we group the data based on distance inside the ice edge for comparison with model results. Locally generated wind waves are relatively prevalent in observations beyond 100 km inside the ice but are absent in the model. Low-frequency swell, however, is present in the model, while subsurface moorings located more than 100 km inside the ice do not report any swell with significant wave height exceeding the instruments' detection limits. These results motivate further model development and future observing campaigns, suggesting that local wave generation inside the ice edge may play a significant role for floe fracture while demonstrating a need for more robust constraints on wave attenuation by sea ice.

The ratio between slow or thermal (<2.2 km/s) and fast (>2.2 km/s) neutrons is known to be a good measure of the amount of water present in a radius of about 300m from the measurement. COSMOS detectors use this principle and measure neutrons by means of the helium isotope 3He. COSMOS has been in use for some time now and its large-scale observations are central to bridging the scaling gap between direct gravimetric observation of soil moisture (<<1m2) and the scale at which soil moisture is represented in hydrological models and satellite observations (>100m2). The main sources of 3He were nuclear warheads. The fortunate demise of nuclear weapons has had the less fortunate consequence that 3He has become expensive, leading to a search for more affordable alternatives. Here, we present laboratory results of a boron-based neutron detector called BLOSM. About 20% of naturally occurring boron is 10B, which has a large cross-section for thermal neutrons. When 10B absorbs a neutron, it decays into lithium and alpha particles. Alpha particles can then be detected by ZnS(Ar), which sends out UV photons. Because real-estate is at a premium for most neutron detection applications, most boron detectors are based on relatively expensive enriched boron with >99% 10B. In hydrology, space is usually less of an issue, so one innovation here is that we use natural boron in a detector that is simply a bit larger than one based on enriched boron but much cheaper. A second innovation, put forward by Jeroen Plomp of the Delft Reactor Institute, are wavelength shifting fibers that capture UV photons by downshifting the wavelength to green. Green photons have a wider angle of total internal reflection and tend to stay in the fiber until they exit at the end. Here, a third innovation comes into play, inspired by Spencer Axani’s $100 muon detector, namely the use of simple electronics and silicon photon multipliers (SiPMs). Because we want to know the ratio between fast and slow neutrons, we need two detectors, one that just counts the thermal neutrons that continuously zap around and through us, and one covered by a moderator that slows down faster neutrons to thermal levels, so that they can be detected. Presently, we can build two detectors for about EU 1000. We expect that after the development of some custom electronics, this will come down to around EU 500. Ideally, we would like to build a network of these detectors in Africa in conjunction with the TAHMO network (www.tahmo.org).

Although geologic carbon sequestration projects have yet to induce – or may never induce – a damaging earthquake, experiences from other deep injection industries such as hydraulic fracturing, enhanced geothermal systems, and saltwater disposal suggest that effective quantitative seismic risk assessment is necessary for deep saline carbon capture and sequestration (CCS) projects. One such imminent CCS project is the San Juan Basin CarbonSAFE Phase III program. The study detailed in this paper utilizes Monte Carlo probabilistic geomechanical analyses combined with observations of the geological and operational parameters of the San Juan Basin site and suggests that this project is of low induced seismic risk. The primary analysis is split into four sections. First, we assessed the literature for faults and past seismicity, and at least five faulting scenarios are directly relevant. Second, we developed and calibrated an integrated earth model for the project site. Third, we performed Monte Carlo simulations that considered reasonable uncertainties of the geomechanical parameters. Only the Hogback flexural faulting scenario presented high Coulomb failure functions, but fourth, we determined the risk to be low based on the combined lack of historical seismicity, the geological framework of the flexural faults, and the presence of saltwater injection at the same depth as the proposed supercritical carbon dioxide injection. The most sensitive parameters in the geomechanical calculations were the fault dip and the coefficient of friction. The least sensitive were the fault strike and the orientation of the maximum horizontal principal stress.

Urban areas (i.e. cities, towns and suburbs) provide a home to over 70% of the EU‑population, and this number is expected to exceed 80% by 2050 (Tapia et al., ECOL INDIC, 2017). The increase in frequency and intensity of extreme precipitation events caused by the changing climate (e.g. cloudbursts, rainstorms, heavy rainfall, hail, heavy snow) combined with the high population density and concentration of assets in urban areas makes them particularly vulnerable to pluvial flooding, hence, assessing their vulnerability under current and future climate scenarios is of paramount importance. Detailed hydrologic-hydraulic numerical modelling is resource intensive and therefore scarcely suitable for a consistent hazard assessment across large urban settlements. Given the steadily increasing availability of LiDAR (Light Detection And Ranging) high-resolution DEMs (Digital Elevation Models), several studies highlighted the potential for consistent pluvial flood hazard characterization of fast-processing DEM-based methods, such as the Hierarchical Filling and Spilling or Puddle-to-Puddle Dynamic Filling and Spilling (see e.g. Zhang et al., J HYDROL, 2014; Chu et al., WATER RESOUR RES, 2013). As part of the activities of the EIT Climate-KIC Demonstrator project SAFERPLACES (https://saferplaces.co/), we developed a fast-processing algorithm, named Safer_RAIN, that enables one to map pluvial flooding in large urban areas by implementing a filling and spilling procedure that accounts for spatially distributed rainfall input and infiltration processes (Green Ampt method). We present the first applications of the algorithm to model recent urban inundations occurred in Northern Italy. These preliminary applications, compared against ground evidence and detailed output from a two-dimensional hydrologic and hydraulic numerical model, highlight limitations and potential of Safer_RAIN for identifying pluvial-hazard hotspots across large urban environments.