Atmospheric and Oceanic Sciences

Study region The Daqing River basin in China has been frequently affected by floods. Flood risk exhibits both temporal and spatial interdependence, driven by hydrological memory effects and cascading flood processes within spatially connected river systems. Study focus The study elucidated spatiotemporal propagation patterns of flood risk and developed a novel spatiotemporal propagation function for risk prediction. Initially, flood risk in twenty-two subbasins of the Daqing River was evaluated by integrating hazard, exposure, vulnerability and mitigation capacity. Markov-based state transition diagrams were subsequently employed to calculate flood risk propagation probabilities, distinguishing temporal and spatial risk propagation patterns. Moreover, a spatiotemporal risk propagation function incorporating risk propagation effect and flood control infrastructure interference was proposed to predict flood risk. New hydrological insights Results indicate pronounced spatial heterogeneity in flood risk across the Daqing River basin during 2000–2020, with high-risk areas concentrated in northern region and eastern edge. Temporally, interannual flood risk variation in each subbasin was driven by mid-high and high, and low and medium grade transitions. Spatial propagation between adjacent subbasins was dominated by risk attenuation across 44.8% of adjacent subbasins. The spatiotemporal propagation function achieved an average prediction accuracy of 90.5% and disentangled temporal and spatial propagation contributions to flood risk. Additionally, five reservoirs and flood detention areas in the central-eastern basin reduced risk propagation by 26.4% and 30.3% via their buffering capacities.

Atopic dermatitis (AD) and allergic contact dermatitis (ACD) are common inflammatory skin diseases influenced by environmental factors, but disease-specific environmental pathways remain poorly defined. This study developed a machine learning model to predict monthly disease prevalence and characterize distinct environmental conditions associated with each disease. We analyzed nationwide health insurance claims data for AD, ACD, and corns (control) from six major South Korean cities from 2012 to 2017, constituting 432 city-month records per disease. The M5P model tree algorithm predicted relative monthly prevalence based on meteorological data (temperature, humidity, precipitation, diurnal temperature range) and air pollutants (SO₂, NO₂, CO, PM10), with performance evaluated using Pearson Correlation Coefficient (CC) and Mean Absolute Error (MAE). Analysis of 3,990,692 AD and 16,890,182 ACD cases showed that the combined weather-pollution model achieved high accuracy for AD (CC = 0.839, MAE = 0.038) and ACD (CC = 0.932, MAE = 0.049). Mean temperature was the primary splitting variable for both diseases, but with different thresholds and secondary modulators. For AD, the initial split occurred at 17.4°C; above this, high PM10 (>44μg/m³) was associated with higher prevalence. For ACD, a notable split was identified at 11.65°C; below this, low humidity (<62%) appeared to be a key contributing factor. PM10 was a consistent predictor for both diseases. While temperature is a universal primary driver for both AD and ACD, the diseases follow distinct environmental pathways. AD is modulated by air pollution in warmer conditions, whereas ACD is sensitive to humidity in cooler conditions. This data-driven approach provides insights into disease-specific environmental triggers for public health interventions.

This study aims to maximize the informational return of mobile environmental data acquisition systems operating on road networks. We propose a two-step survey design framework that fuses spatially dense auxiliary data describing landscape heterogeneity with road network information and formulates route planning as a combinatorial optimization problem. An information-rich initial route is constructed using fuzzy clustering and orienteering optimization, followed by an economization step that preserves information content through convex hull analysis in auxiliary data space. The framework is evaluated using a large-scale (4,500 km 2 ) case study of mobile Cosmic Ray Neutron Sensing (CRNS) soil moisture measurements in central Germany. The optimized route yields more representative spatial coverage and reduced extrapolation requirements when regionalizing sparse CRNS measurements into gravimetric soil moisture maps, thereby lowering ontological uncertainty in regression-based soil moisture mapping compared to an empirically designed route of similar length. The results demonstrate that explicitly information-driven route design substantially improves survey efficiency and data quality. The proposed framework offers a general and transferable alternative to convenience-based mobile sampling strategies in Earth and environmental sciences.

Abstract We demonstrate the combined utility of distributed acoustic sensing (DAS) and repeat conductivity‐temperature‐depth (CTD) profiling for observing internal tide dynamics over a sloping seafloor. While DAS has been widely proposed as a method to infer ocean temperature variability from seafloor cables, quantitative in situ validation has been limited. Here we present the first published detailed comparison between DAS‐inferred bottom temperature fluctuations and near‐bottom temperature measured from high‐resolution yoyo‐CTD time series reaching 5 m above the seafloor, conducted over the EllaLink/GeoLab cable on the slope of Madeira. Linear regressions yield an empirical DAS temperature sensitivity of 21–37 µstrain , a factor 2.5–4 greater than commonly used theoretical estimates. The calibrated DAS record reveals semidiurnal internal tide bores propagating upslope to 1,300 m depth, where propagation halts and transitions to a complex interference pattern. These observations highlight the potential of combining seafloor fiber sensing with targeted in situ profiling to observe near‐bottom internal tide breaking processes.

Study region The Mediterranean region, identified as a global climate change hotspot, faces a critical hydrological paradox: while forest expansion is promoted for carbon sequestration and soil conservation, it potentially threatens water availability in water-scarce catchments. Study focus This article presents a systematic synthesis of peer-reviewed literature (2010–2024) to evaluate the mechanistic trade-offs between Mediterranean forest recovery and hydrological fluxes. Using a PRISMA-compliant protocol, we analysed 59 studies to disentangle the effects of spontaneous reforestation, afforestation, and land abandonment on runoff, infiltration, and evapotranspiration. New hydrological insights for the region The synthesis reveals that forest expansion consistently reduces annual water yield by approximately 20–30% in headwater catchments. This decline is primarily driven by increased interception and transpiration rates that override infiltration gains. Unlike in temperate regions, the "sponge effect" in Mediterranean environments is frequently compromised by high evaporative demand, leading to significant baseflow reductions during summer droughts. However, forests play an irreplaceable role in stabilizing sediment fluxes and buffering flood peaks. The analysis highlights that these hydrological responses are non-linear, scale-dependent, and increasingly amplified by climate warming. We identify a critical gap in current modelling approaches, which often fail to couple dynamic vegetation responses with extreme climatic events. Consequently, we argue that passive reforestation policies must be replaced by adaptive forest management strategies, such as targeted density reduction, to reconcile ecosystem services.

Abstract. Disturbances to the magnetopause location driven by upstream pressure variations or flow shear instabilities may be described as surface waves, which act as localised sources of field-aligned currents coupling the magnetosphere to the ionosphere. However, their impacts on the ionosphere and ground across representative ranges of wave and system properties are poorly understood. We, therefore, develop a simple numerical model for dispersionless mesoscale magnetopause surface waves within the coupled magnetosphere–ionosphere–ground system to gain insight into how their amplitudes and spatial scales throughout the system might vary with conditions. In general, the impacts of finite wave packets can be decomposed into periodic fluctuations (with matching wavelength to that directly above in the magnetosphere) along with slowly-varying trends that result from finite wave effects. Finite wave packets act in the far-field like a string of alternating field-aligned currents well described both in the ionosphere and on the ground as a two-dimensional current dipole. In the ionosphere, near-field periodic fluctuations exponentially decay over the reduced wavelength latitudinally away from the projected magnetopause boundary layer flux tubes, which may limit how well they can be resolved by radar. The relationship between the magnetic field above and below the ionosphere becomes more complicated for surface waves than infinite plane Alfvén waves due to the additional spatial structure, which introduces interference across the spectrum of wavenumbers present. This modifies how the ionosphere screens, rotates, and spatially smears magnetic field perturbations across all three components in different ways. For mesoscale wavelengths this importantly results in latitudinal scales of amplitude and polarisation variation smaller than typical ground magnetometer spacings, motivating the need for denser networks. A range of effective skin depths in the ground are applicable to surface waves, meaning ground induction can vary between a near-perfect insulator to a good conductor, affecting both observable ground magnetic fields and resulting geoelectric fields. The predicted peak amplitudes of surface waves' impacts suggest they may act as significant sources of ionospheric/thermospheric Joule heating and geoelectric fields in the ground, thereby contributing to space weather impacts. These are, however, highly localised latitudinally when considering typical mesoscale waves. Our results provide key insight into interpreting ground-based observations, of particular timeliness with the rollout of new digital ionospheric radars and the SMILE mission's planned conjugate ground–space campaigns.

Days with high diurnal temperature range (DTR) are commonly linked to morbidity and mortality, yet these health impacts are rarely connected with the underlying meteorological factors. To provide health researchers with much-needed context, we examine the climatology of DTR in the continental United States from 1950 to 2022 using the gridded ERA5 Land reanalysis archive. DTR has declined significantly over the period of record over much of the United States, with stronger signals in the east and north in summer and autumn. Extreme positive DTR values (95th percentile), which are commonly linked to morbidity, are also declining significantly. DTR declines can be ascribed to maximum temperatures increasing at a slower rate than minima, with minimum temperatures rising in response to increasing atmospheric humidity, cloud cover, and precipitation. Given that DTR trends are clearly linked to anthropogenic warming, this would imply reduced health risks, if DTR is a direct causal exposure. Because the most extreme DTR days occur under calm, low-humidity and cloud-free conditions, clinical evidence is needed to demonstrate how and why these weather conditions produce intra-day changes in exposure that are physiologically harmful.

Reliable fisheries catch data are essential for robust stock assessments, ecosystem modelling, and the testing of ecological hypotheses, thereby informing policy decisions that support sustainable ecosystem-based fisheries management. However, official Greek marine fisheries data have long suffered from underreporting, taxonomic inconsistencies, and the exclusion of discards, small-scale, and recreational catches. This study presents an updated, spatially disaggregated reconstruction of Greek marine fisheries catches in the Aegean and eastern Ionian Seas from 1950 to 2022. Using official datasets from the Hellenic Statistical Authority (HELSTAT), augmented with discard estimates from the EU Data Collection Framework and recreational catch data from extensive field surveys, we corrected for the presentist bias and inconsistencies in reporting protocols. We applied species- and fleet-specific adjustments, retroactively incorporated landings from small-scale vessels with engines&amp;lt;20 HP, and resolved key taxonomic ambiguities, highlighting the importance of continuous methodological refinement in fisheries data. Our results reveal that total catches have historically been underestimated by over 25%, with significant implications for stock assessments and ecosystem modelling, and therefore for sustainable fisheries management. The reconstructed dataset reveals a prolonged recession in Greek fisheries, contradicting official statistics that suggested a phantom recovery after 2016. This work will support more accurate assessments and ecocentric management of marine resources in the eastern Mediterranean Sea, a region increasingly vulnerable to climate, fisheries, and other anthropogenic pressures. It is important, however, that the GFCM/FAO databases be updated with the reconstructed datasets for the period 1950-2016, as the recently improved catch records of Greece from 2016 onward represent a case of presentist bias. Without retroactive correction, this leads to inconsistencies and erroneous applications.

Accurate maize distribution information is critical for crop-area statistics, food-security assessment, and agricultural monitoring, but large-scale maize-mapping remains difficult in regions with limited reference samples, heterogeneous crop calendars, and frequent optical data gaps. This study proposes a phenology-adaptive maize mapping framework based on Sentinel-2 time-series imagery and an Enhanced Red-edge NDVI (ENDVIre). ENDVIre was constructed from the Sentinel-2 red-edge 4 and red-edge 2 bands to enhance the spectral response of maize during the silking-to-grain-filling stage, when maize develops a dense canopy and high chlorophyll content but is often confused with soybean. The framework first reconstructed the NDVI time series using an upper-envelope-constrained Whittaker smoother to identify key phenological stages, including sowing–emergence, vigorous growth, and maturity–harvest. NDVI, ENDVIre, and LSWI were then integrated into an interpretable decision-tree model with phenology-aligned time windows to distinguish maize from soybean, rice, wheat, and other non-maize backgrounds. The method was evaluated in six representative maize-growing regions across the United States, Brazil, China, Kenya, and Ukraine, covering different crop calendars, field sizes, and agricultural systems. The mean overall accuracy, F1-score, and Kappa coefficient across the six regions reached 93.27%, 93.14%, and 0.8652, respectively. Cross-year experiments in a winter-wheat–summer-maize rotation region from 2020 to 2024 achieved overall accuracies of 89.80–96.80%, while spatial-transfer experiments in six independent regions achieved overall accuracies of 87.40–95.40%. A comparison with existing high-resolution maize products in the Huang-Huai-Hai Plain further showed that the proposed method better balanced omission and commission errors. These results indicate that ENDVIre-based phenology rules provide an interpretable and transferable solution for maize mapping under limited-sample conditions, although persistent cloud contamination and fragmented smallholder landscapes remain important challenges.

Abstract Raindrop size distribution (RDSD) is crucial for radar-based precipitation estimation and validating numerical simulation models, especially in mountainous regions where accurate rainfall data is vital for water resource management and disaster mitigation. Previous studies suggest that orographic rainfall is characterized by raindrops with a small mean diameter, yet observations using instruments sensitive to drizzle are scarce. In this study, we investigated the characteristics of RDSDs, including the drizzle mode, on Mount Tsukuba in Japan, using a combination of Meteorological Particle Spectrometer (MPS), Thies Clima Laser Precipitation Monitor (LPM), and METEK Micro Rain Radar (MRR). We constructed a composite RDSD from the MPS (0.1–0.5 mm) and LPM (0.5–5 mm) data and fitted the observations to the generalized gamma distribution model to clarify their features. Our observations revealed that sixty percent of the one-minute RDSDs exhibited a "drizzle mode" characterized by high concentrations of small raindrops. Drizzle modes were observed in both moderate stratiform and deep convective rainfall. They occurred more frequently at higher rainfall intensities ( R ) and larger mass-weighted mean raindrop diameters, indicating that the number concentration of large raindrops also increased when the RDSD exhibited a drizzle mode. Furthermore, the relationship between radar reflectivity and R differed markedly depending on whether the RDSD had a drizzle mode.

Abstract Z-R relationships are widely used in radar meteorology and hydrology, as rainfall estimation plays a key role in understanding hydro-meteorological processes and supporting operational applications such as flood forecasting. Although many studies determine coefficients for these Z-R relationships using regression-based methodologies with direct reflectivity observations and rainfall intensity measurements, the determination process is often not described in detail. In particular, the choice of the dependent variable is frequently left implicit, despite its influence on the outcome. This study investigates how the choice of the dependent variable affects rainfall estimate performance using X-band radar data, rain gauges, and disdrometers. We conducted analyses over 30 events (22 convective and 8 stratiform) in 2023 over the Seveso-Olona-Lambro river basin and the Milan metropolitan area (northern Italy). The results show that treating rainfall intensity as the dependent variable in least-squares regressions with logarithmic transformation consistently improves rainfall estimate performance, as assessed with multiple skill scores, including bias, mean absolute error, root mean square error, coefficient of determination, Nash-Sutcliffe efficiency, and ratio of valid pairs. We also compared the resulting relationships with commonly used standard Z-R relationships in terms of mean areal accumulated rainfall. The relationships determined using rainfall intensity as the dependent variable exhibit better performance, highlighting the relevance of properly determining coefficients in hydrological modeling. The findings underscore the importance of explicitly reporting regression setups and variable roles in Z-R coefficient determination studies. Such transparency enables reassessment of standard operational coefficients, enhances rainfall estimate reliability, and improves reproducibility across instrumentation configurations and event types.

This study develops a systematic framework for assessing the temporal dynamics of tropical cyclone (TC) risk in the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) from 2013 to 2023. A unified composite index was constructed by integrating hazard, vulnerability, and mitigation capacity, allowing for the quantification of the interannual evolution of TC risk. The analysis showed that maximum storm surge and extreme precipitation drove hazard variability, with distinct peaks during the super typhoons of 2017 and 2018. In the vulnerability dimension, GDP and population density together accounted for over 50% of the total weight, and the vulnerability index shows an upward trend, though its growth slowed in 2020. Mitigation capacity improved steadily, accelerating after 2020 and partly offsetting the risk pressure from growing vulnerability. The risk index broadly mirrored the hazard index, peaking in 2018. Notably, in the two TC-free years (2014 and 2019), the risk index was higher in 2019 than in 2014, reflecting an increased vulnerability-to-mitigation ratio over the intervening period. A coherence check against three disaster loss indicators (2018–2023) yielded a correlation of r = 0.8, indicating broad consistency in the interannual patterns of the risk index and observed losses. This study provides a temporally explicit baseline for understanding recent TC risk dynamics and offers methodological support for resilience planning in coastal megaregions facing climate-related hazards.

Abstract. During rain, water infiltrates the ground, where it flows as groundwater toward nearby rivers. There, its emergence can entrain sediments, triggering seepage erosion and thereby influencing the development and expansion of river networks. To investigate this process, we construct an experimental aquifer, made of erodible plastic sediments. A reservoir beneath the aquifer supplies water at a controlled recharge rate. We find that seepage erosion, driven by the resulting groundwater flow, is sufficient to initiate the formation and growth of a drainage network. For a given recharge rate, network growth eventually ceases as the drainage system reaches a steady-state morphology, in which sediments are everywhere at the threshold of motion. This observation indicates that the recharge rate of the aquifer selects the size of the network. In our experiment, the depth of the aquifer is small compared to its lateral extent, so that the flow of groundwater obeys the Dupuit-Boussinesq equation. As in natural systems, the water table in our experiment intersects the drainage network at the elevation of the streams. This condition provides the necessary boundary conditions to solve for the Dupuit-Boussinesq equation and reconstruct the shape of the water table around the river network. The resulting numerical solution agrees well with piezometric measurements carried out in the experimental aquifer and reveals that groundwater flow converges toward channel tips, where its flux is maximal.

High-precision WTC is essential for satellite altimetry and ocean dynamic environment monitoring. Existing WTC approaches often rely on globally unified statistical frameworks, which inadequately represent wind-speed-dependent nonlinear sea-surface microwave radiative responses and are prone to systematic bias under uneven observation distributions. To address these limitations, this study proposes an adaptive WTC method integrating overlapping wind-regime modeling, multi-scale collaborative sample balancing, and a model soft-fusion strategy. Firstly, a modeling framework with overlapping transition zones for low-, moderate-, and high-wind-speed regimes is established according to wind-speed-driven variations in sea-surface radiative responses, and sub-models are trained independently. Subsequently, a multi-scale sample balancing, combining global and local weights, is designed to enhance learning from sparse samples. Finally, a soft-fusion strategy based on a trapezoidal membership function is applied to dynamically weight sub-model outputs, ensuring retrieval continuity across transition zones. Using HY-2C Calibration Microwave Radiometer (CMR) observations, the proposed method is developed, trained, and evaluated against model-derived WTC and collocated Jason-3 AMR-2 measurements. Results show that the proposed method improves overall WTC retrieval accuracy and stability while effectively reducing systematic biases under wind-speed regimes with sparse observations, providing an effective and robust approach for high-accuracy WTC retrieval under various wind-speed conditions.

This study presents a systematic uncertainty quantification of the two-track InSAR three-dimensional (3D) deformation field of the 2025 Dingri earthquake (Mw 7.1). Using Sentinel-1 ascending and descending track data, a 3D coseismic displacement field was constructed via least-squares inversion. The results revealed that the earthquake produced a north–south-striking normal fault rupture, with the vertical component reaching a maximum subsidence of −403.3 mm and a maximum uplift of +621.1 mm, and the east–west component reaching a maximum westward displacement of −592.3 mm and an eastward displacement of +332.1 mm. Uncertainty analysis reveals a divergence between formal errors and actual accuracy: formal error propagation yields 1σ uncertainties of 1.09 mm and 1.38 mm for the vertical and east–west components, respectively; a realistic error budget based on Monte Carlo simulations indicates that the actual errors are approximately 13.8 mm for the vertical component and 17.2 mm for the east–west component, with systematic error contributions far exceeding random noise. Cross-validation against an independent Sentinel-1 processing chain supports the above error assessment: the correlation coefficient R for ascending track line-of-sight (LOS) displacement is 0.88, whereas it is 0.62 for the descending track; for the three-dimensional components, R reaches 0.88 for the vertical component and 0.59 for the east–west component, with discrepancies arising primarily from the greater sensitivity of the east–west component to processing strategies and observation geometry. This study demonstrates that formal error propagation underestimates the actual uncertainty of two-track InSAR inversion and that systematic error sources contribute far more than random noise does.

CO 2 injection into subsurface reservoirs is an effective method for CO 2 storage and higher geological resources recovery in low-permeability reservoirs. However, substantial CO 2 coproduction during conventional CO 2 flooding limits storage efficiency. In this study, a natural amphiphilic molecule (ARGS) is introduced to enhance oil recovery and CO 2 storage by regulating CO 2 -oil–water-rock interfacial and mass-transfer properties under saline conditions, achieving a 9.7% increase in oil recovery compared with conventional CO 2 -enhanced oil recovery (CO 2 -EOR) and a CO 2 storage ratio of up to 85% simultaneously. Under high-salinity formation-water conditions, ARGS increased CO 2 solubility by 4-fold and reduced the interfacial tension of the CO 2 -formation water system by 36%. At the water–rock interface, ARGS shifted pore-surface wettability from neutral-wet to water-wet. During ARGS-assisted CO 2 injection, oil displacement efficiency in micro- and mesopores increased by 10%–15%, expanding the CO 2 sweep range and providing more pore space for CO 2 storage. By enhancing the water-wetness of pore walls, ARGS increased CO 2 flow resistance and consequently promoted both dissolved and trapped CO 2 storage. Ultimately, the proportion of dissolved CO 2 can reach 70%. This study provides an environmentally friendly approach for the efficient CO 2 geological storage and utilization in low-permeability reservoirs.

Abstract. Solar eclipses offer a unique natural experiment to probe ionospheric responses to sudden reductions in solar radiation. This study reports the recovery of historical ionogram records to analyze the ionospheric response to solar eclipses spanning several decades over Concepción (36.79° S, 73.03° W)/Chillán (36.64° S, 71.99° W). Out of 21 identified events between 1958 and 2024, data from 16 (76 %) cases were rescued, many originally on fragile or hazardous 35 mm film, emphasizing the scientific value of long-term datasets. Critical frequencies (foE, foF1, foF2) and virtual heights (h'E, h'F1, h'F/F2) were extracted from digitized and scaled ionograms to quantify eclipse-induced perturbations. Diurnal variations show typical dips in the E- and F1-layer critical frequencies, while F2-layer responses are more complex and variable. Regression analysis was performed exclusively on critical frequencies, revealing a nearly linear decrease of foE and foF1 while the maximum obscuration percentage of the eclipse is higher, whereas inconsistent behavior was observed on foF2. High-cadence observations, available for select events, provided a significantly clearer depiction of the response to the eclipses than 1 h resolution historical data. Only the 2 July 2019 and 14 December 2020 eclipse responses had been previously published. Predictions for the 6 February 2027 eclipse indicate an expected %ΔfoE decrease of ∼ 28 % and a %ΔfoF1 decrease of ∼ 24 % at Chillán, offering a timely opportunity to validate the regression models and assess predictive skill.

Leucine aminopeptidase (LAP) plays a crucial role in the hydrolysis of proteinaceous nitrogen in soils. However, existing studies use varying conditions in the fluorimetric assay of soil LAP, which hinders cross-study comparison of the measured activities. Using one purified enzyme and three soils of contrasting properties, we examined how LAP activity responded to variations in assay conditions including buffer pH, substrate (L-leucine-7-amido-4-methylcoumarin) concentration, temperature, incubation time, soil amount, and metal ion concentrations. We found that: (1) the optimal pH for LAP activity ranged from 7 to 9; (2) a substrate concentration of 250 μM was necessary to achieve zero-order reaction kinetics; (3) LAP activity increased as the temperature rose from 10 to 40 °C, with a Q10 value between 1.63 and 2.45; (4) the rate of the enzymatic reaction remained stable for at least 3 hours; (5) measured activity decreased as the amount of soil used for homogenate preparation increased from 0.5 to 2.0 g; and (6) the activity of LAP was not substantially stimulated by the addition of metal ions, suggesting that a metal cofactor is not needed for the fluorimetric assay of LAP. We also compared the behaviors of LAP with those of the colorimetrically measured arylamidase that catalyze the release of an N-terminal amino acid from peptides, amides, or arylamides, and found that they may represent the same group of soil enzymes. Our findings may help standardize the assay protocol for soil LAP, which is essential for conducting meta-analysis of enzyme activities measured across different studies.

Tesla valves are passive hydraulic devices capable of producing directional flow resistance without moving components, making them attractive for applications in microfluidics, thermal systems, and high-reliability hydraulic circuits. Despite extensive experimental and numerical studies, an analytical formulation capable of describing the hydrodynamic behavior of Tesla valves under varying operating and geometric conditions remains limited. In this work, a comprehensive analytical model is developed to describe the pressure losses, flow redistribution, and diodicity behavior of Tesla valves through a physics-based formulation derived from conservation laws, dimensional analysis, and inertial scaling principles. The proposed model incorporates the influence of Reynolds number, flow partition, geometric ratios, branch inclination angle, and number of diode stages within a unified nonlinear framework. A closed structural equation is obtained that relates hydraulic losses and directional asymmetry to the internal geometry of the valve. The formulation reveals the existence of geometric and energetic constraints governing rectification efficiency, including bounds associated with stage number, channel scaling, and angular momentum exchange. The results show that Tesla valve performance emerges from a delicate balance between inertial amplification and dissipative mechanisms, providing an analytical framework for the design and optimization of Tesla-type hydraulic systems across multiple scales.

Mining of sulfide-rich deposits enhances the oxidation of sulfide minerals, generating acid mine drainage (AMD) characterized by high sulphate and dissolved metal concentrations and the formation of secondary iron minerals (hematite, goethite, and jarosite). As these minerals display diagnostic features in the visible–near-infrared (VNIR) region, multispectral satellite data provide a cost-effective means of monitoring. Here, the performances of Sentinel-2 and the VNIR bands from WorldView-3 are assessed and compared for the mapping and discrimination of secondary iron minerals in Sierra Minera de Cartagena–La Unión (SE Spain). Both datasets were analyzed using a band ratio and a parabola fitting technique focused on reflectance maxima. Band ratio results were interpreted as broad spectral patterns rather than definitive mineral identifications. Mineral maps were validated by applying X-ray diffraction on 74 surface soil samples. Although both sensors were able to reproduce the main spatial patterns of iron mineral distribution, Sentinel-2 data better discriminated hematite, goethite, and jarosite, especially when using the parabola fitting approach, whereas WorldView-3 VNIR data distinguished mainly hematite from the combined goethite–jarosite group. The better performance of Sentinel-2 is attributed to its red-edge and near-infrared band configuration. These findings indicate that freely available Sentinel-2 imagery can support systematic monitoring of oxidation processes in mining environments and contribute to environmental risk assessment in degraded landscapes.

Landslide detection from satellite imagery has advanced through deep learning, yet most models rely on large, highly correlated spectral-topographic inputs whose contributions remain poorly understood. The question of which channels are actually necessary has received surprisingly little attention. This matters: redundant or correlated inputs obscure physical interpretability, inflate computational overhead, and can actively degrade model performance through the Hughes Phenomenon. We present a systematic, explainable channel-selection framework for the Landslide4Sense benchmark, combining Sentinel-2 multispectral and ALOS PALSAR terrain data with 16 engineered spectral and structural indices. Rather than relying on conventional single-band drop tests, which evaluate channels in isolation and miss interaction effects, we apply Sequential Forward Floating Selection (SFFS) to iteratively build and prune a candidate feature pool using a lightweight U-Net++ proxy model. Beyond identifying a compact 8-channel subset that matches or exceeds the segmentation F1 of configurations using up to 30 channels, we use the selection process itself to interrogate which spectral and topographic features landslide models genuinely rely on, and what this reveals about the physical cues driving their predictions. We argue that SFFS represents a principled feature selection approach to input design in Earth observation, in contrast to the prevailing practice of appending every available band and hoping the model learns what to ignore.