Earth and Space Science最新文献

In a warming climate, an intensifying fire regime and higher likelihood of extreme rain are expected to increase watershed sediment yield in many regions. Understanding regional variability in landscape response to fire and post-fire rainfall is essential for managing water resources and infrastructure. We measured sediment yield resulting from sequential wildfire and extreme rain and flooding in the upper Carmel River watershed (116 km²), on the central California coast, USA, using changes in sediment volume mapped in a reservoir. We determined that the sediment yield after fire and post-fire flooding was 854–1,100 t/km²/yr, a factor of 3.5–4.6 greater than the long-term yield from this watershed and more than an order of magnitude greater than during severe drought conditions. In this first large-scale field validation test of the WEPPcloud/wepppy framework for the Water Erosion Prediction Project (WEPP) model on a burned landscape, WEPP predicted 81%–106% of the measured sediment yield. These findings will facilitate assessing and predicting future fire effects in steep watersheds with a Mediterranean climate and indicate that the increasingly widespread use of WEPP is appropriate for evaluating post-fire hillslope erosion even across 100-km² scales under conditions without debris flows.

This study examines the accumulation, distribution, and mobility of Potentially Toxic Elements (PTEs) in the sediments of a low-lying coastal drainage network (Ravenna, Italy). The aim is to understand the geochemical processes occurring between drainage water and canal bed sediments and assess factors affecting and driving PTE distribution and enrichment in these environments. A geochemical database resulting from the analysis of 203 drainage sediment samples was analyzed using Principal Component Analysis and compared to undisturbed near-surface sediment samples from the same depth and depositional environment. The results reveal PTEs exceeding national regulation limits. Distance from the sea, electrical conductivity of drainage water, and fertilizer use were identified as the main driving factors. The primary mechanisms for PTE precipitation (As, Co, Mo) and subsequent enrichment in the sediments is attributed to the absorption on Fe- and Mn-oxyhydroxides (HFO and HMO), particularly in high salinity areas near the coast. While Cu, Zn, Pb, Cr, and V also have affinity for HFO and HMO, their adsorption efficiency decreases due to the competition with salt-derived cations during ongoing salinization processes. Anthropogenic sources, including agriculture, hunting activities, traffic dust, and railways, contribute to the local abundance of other elements (Cr, Ni, Cu, Zn, Pb, and Sn). This paper's significant progress lies in assessing the concurrent interactions of chemical and physical processes that drive PTE distribution and accumulation in reclaimed low-lying coastal plains. The findings are significant for assessing PTE accumulation risks and sediment toxicity in coastal areas affected by water salinization, drainage, and subsidence, providing valuable information to water management institutions globally.

As a key water quality parameter, dissolved oxygen (DO) concentration, and particularly changes in bottom water DO is fundamental for understanding the biogeochemical processes in lake ecosystems. Based on two machine learning (ML) models, Gradient Boost Regressor (GBR) and long-short-term-memory (LSTM) network, this study developed three ML model approaches: direct GBR; direct LSTM; and a 2-step mixed ML model workflow combining both GBR and LSTM. They were used to simulate multi-year surface and bottom DO concentrations in five lakes. All approaches were trained with readily available environmental data as predictors. Indices of lake thermal structure and mixing provided by a one-dimensional (1-D) hydrodynamic model were also included as predictors in the ML models. The advantages of each ML approach were not consistent for all the tested lakes, but the best one of them was defined that can estimate DO concentration with coefficient of determination (R²) up to 0.6–0.7 in each lake. All three approaches have normalized mean absolute error (NMAE) under 0.15. In a polymictic lake, the 2-step mixed model workflow showed better representation of bottom DO concentrations, with a highest true positive rate (TPR) of hypolimnetic hypoxia detection of over 90%, while the other workflows resulted in, TPRs are around 50%. In most of the tested lakes, the predicted surface DO concentrations and variables indicating stratified conditions (i.e., Wedderburn number and the temperature difference between surface and bottom water) are essential for simulating bottom DO. The ML approaches showed promising results and could be used to support short- and long-term water management plans.

Satellite observations of column-averaged water isotopes are relatively new retrieval products that are in need of further in situ evaluation. Such evaluation studies are generally difficult to perform due to the wide mismatch in temporal and spatial scales between the satellite observations based on instantaneous pixel averages during an overpass and airborne in situ measurements ranging up to several hours over a km-scale. In addition, topography, weather conditions and in particular cloudiness impose severe constraints on an exact collocation between satellite and airborne in situ measurement platforms. Here we present a new method that allows a comparison between in situ measurements and satellite observations of δD on a broader statistical basis. We use regional isotope-enabled model simulations as intermediate information to identify the area for best comparisons. Applying our methodology to TROPOMI total column δD retrievals for the L-WAIVE campaign in Annecy, France, during June 2019 increases the number of satellite pixels for comparison despite widespread cloudiness on average by a factor of 20. In addition, the comparison of simulated and observed δD revealed a dependency of the satellite evaluation on the structure of the middle and upper troposphere. We conclude that our method provides a more robust statistic basis for in situ evaluation of δD satellite retrievals. The method will thus be useful in planning and executing forthcoming validation and evaluation campaigns, and can potentially be used for the evaluation of other satellite products.

This study evaluates the coded aperture imaging method for pitch angle observations of magnetospheric energetic electrons in the solar, Earth, and planetary space environments. We present a review of key previous energetic electron instruments with pitch angle-resolved observations across a range of electron energies. We describe the coded aperture imaging method, typically used for high angular resolution X-ray and gamma ray observations, and evaluate design parameters in the context of energetic electron observations. We present the results of simulations of energetic electrons in Geant4 and evaluate the method's ability to resolve sources with high angular and temporal resolution. We also evaluate the impact of secondary radiation produced from electron interactions in the tungsten coded aperture, as well as the impact of artifacts from the decoding process. With these simulated results, we identify key areas in magnetospheric science that would benefit from high angular resolution observations of energetic electrons. We find that coded aperture imaging may be well-suited for high-resolution observations of intense localized structures, such as low energy (tens of eV to several keV) field-aligned electron beams or the electron strahl wind.

During the first part of the Martian year (L_s = 50°–160°) a phenomenon occurs on Mars in the tropical and equatorial regions (30°N–10°S) known as the Aphelion Cloud Belt (ACB). During this time, there is prominent formation and diurnal variability of water ice clouds. Limited empirical attempts have been made to characterize the magnitude of radiative flux contributions from clouds to nighttime surface temperatures. In this work, we estimated the infrared (IR) flux contribution at ground level from the clouds by comparing surface temperature data from the Thermal Emission Spectrometer (TES) onboard Mars Global Surveyor (MGS) to calculated temperatures using the KRC numerical thermal model. We then generated a database of IR fluxes at the ground contributed by clouds spanning the entirety of the tropical and equatorial regions as a function of Solar Longitude (L_s) on Mars in one degree bins. We compared results with work presented elsewhere in the literature and found good agreement. We also found that temporal trends followed the general established range for the ACB but our analysis demonstrated the peak ACB values occurred at later times (L_s = 100°–140°) than previously published data sets using water ice opacity retrievals (L_s = 90°–110°). This database may be used in comparison to calculated Global Climate Model fluxes as well as a lookup tool for more precise estimation of surface and subsurface thermal environments in these regions.

Solar irradiance received at the lunar surface is crucial for interpreting brightness temperatures detected by orbiters and for understanding the thermal, physical, and dielectric properties of the lunar regolith. We developed a real-time effect solar irradiance (ESI) model that accounts for the influence of surface relief and terrain shading. This model was integrated with a standard thermal model to examine ESI fluctuations and their impacts on the diurnal physical temperature variations. To assess the effects of spatial resolution, we selected four locations with significant ESI disparities for simulation, then compared lunar surface temperatures at various spatial scales, ranging from 20 m to 25 km. Utilizing brightness temperature data obtained from the Chang'E-2 (CE-2) microwave radiometer (MRM), we integrated the shallow physical temperature profiles with the radiative transfer equation to simulate brightness temperatures and determine dielectric loss at different frequencies. In the Von Kármán crater, the received ESI exhibits a cyclical pattern of approximately 18 years and areas with rugged topography may exhibit larger ESI variations (∼7%). We found that the spatial resolution of ESI has a minimal effect on the physical and brightness temperatures at resolutions of 10 km or coarser. At the shallow layer, the average dielectric loss values are 0.0128–0.0170, 0.0083–0.0110, 0.0055–0.0073, and 0.0061–0.0081 for the CE-2 frequencies of 3, 7.8, 19.35, and 37 GHz, respectively. The integration of real-time ESI modeling, thermal dynamics, radiative transfer equations, and observational data enhances our comprehension of the physical temperature profile and thermal characteristics of shallow regolith.

Far infrared (FIR) emission from the Earth's polar regions has become an area of increasing scientific interest and value. FIR emission is important for understanding Earth's radiative balance and improving global climate models, especially in rapidly changing Arctic conditions. Far-infrared emission from Earth is not currently being monitored from space, except as part of broadband emission channels of Earth radiation budget measurements like those from the CERES project, and only limited measurements in the FIR spectrum exist. The Absolute Radiance Interferometer (ARI), developed as a prototype of the infrared spectrometer for CLARREO at the University of Wisconsin-Madison, Space Science and Engineering Center, measures absolute spectrally resolved infrared (IR) radiance from 200 to 2,000 cm⁻¹ (or 5–50 μm) at 0.5 cm⁻¹ resolution with high accuracy (<0.1 K 3-sigma brightness temperature at scene temperature). This instrument was taken into the field in Madison, Wisconsin, USA, during the winters of 2021 and 2022, where the weather can reach polar-like conditions to measure high spectral resolution radiances of various sample types. Sample materials included water, snow, ice, evergreen leaves, dry grass, and sand, all characteristic of high latitude regions. Radiances collected from both a sky view and the sample view in clear-sky conditions were used to retrieve FIR emissivity. This paper describes the ARI instrument configuration and capability for ground-based measurements in the FIR region, and documents retrieved emissivities of various analyzed samples. The retrieved emissivity results are publicly available, and comparisons are made to simulated emissivity estimates.

Satellite-based sensors of ocean color have become the primary tool to infer changes in surface chlorophyll, while BGC-Argo floats are now filling the information gap at depth. Here we use BGC-Argo data to assess depth-resolved information on chlorophyll-a derived from an ocean biogeochemical model constrained by the assimilation of surface ocean color remote sensing. The data-assimilating model replicates well the general seasonality and meridional gradients in surface and depth-resolved chlorophyll-a inferred from the float array in the Southern Ocean. On average, the model tends to overestimate float-based chlorophyll, particularly at times and locations of high productivity such as the beginning of the spring bloom, subtropical deep chlorophyll maxima, and non-iron limited regions of the Southern Ocean. The highest model RMSE in the upper 50 m with respect to the float array is of 0.6 mg Chl m⁻³, which should allow the detection of seasonal changes in float-based biomass (varying between 0.01 and >1 mg Chl m⁻³) but might hinder the identification of subtle changes in chlorophyll at narrow local scales. Both model and float profiling data show good agreement with in situ data from station ALOHA, with model estimates showing a slight accuracy edge in inferring depth-resolved observations. Uncertainties in float bio-optical estimates impede their use as a reliable benchmark for validation, but the general qualitative agreement between model and float data provides confidence in the ability of model to replicate biogeochemical features below the surface, where data is not directly constrained by the assimilation of satellite ocean color.

Convective initiation (CI) nowcasting in subtropical regions often faces challenges, such as complex physical processes and imbalanced samples of CI events, resulting in a high false alarm ratio (FAR). In this paper, we propose a Storm Warning System with Physics-Augmentation (SWASP) based on the random forest algorithm and cloud physical conditions, using Himawari-8 Advanced Himawari Imager data from April to September 2019 in South China. The cloud physical conditions (e.g., cloud-top cooling rates) were investigated to establish regional thresholds for convection occurrence. Ancillary information, including elevation, satellite zenith angle, and latitude, was also incorporated into the SWASP model. Compared to conventional methods, the SWASP model exhibits an improved probability of detection by 0.11 and 0.08 and a decreased FAR by 0.38 and 0.44 for daytime and nighttime forecasts. Moreover, the SWASP model enables the detection of local convective storm systems about 30 min to 1 hr ahead of radar detection in typical convective storm cases. This study contributes to further advancements of the SWASP model by incorporating physical conditions and emphasizes the potential application of geostationary satellites in convective early warnings.