Atmospheric and Oceanic Sciences

This research was conducted in the Yarlung River watershed (YRW) on the southeastern Tibetan Plateau (TP), a representative alpine cold region catchment. The focus of this study is to investigate the spatial heterogeneity of surface-groundwater (SW–GW) interactions and their driving environmental factors in this alpine basin. An integrated approach combining field investigations, physical measurements, and radon isotope (²²²Rn) tracing was employed to quantify groundwater contributions (GC)—the proportion of groundwater relative to surface runoff, and to elucidate key interaction patterns controlled by geomorphology, sediment composition, and climatic forcing. The results demonstrate significant spatial heterogeneity in SW-GW interactions along the river's main stem: (1) Groundwater discharge is limited in the upstream reaches, (2) Intensive SW-GW exchange occurs in the midstream, and (3) Discharge is subsequently reduced in the downstream. Additionally, climate warming-induced changes in permafrost and vegetation cover are exerting an increasing influence on hydrological connectivity, particularly in the middle and lower reaches. The observed hydrological patterns, with minimal anthropogenic disturbance, provide a natural baseline for understanding SW-GW interactions under evolving climatic conditions. This study underscores that combining field data with isotopic process-based modeling is crucial for identifying dominant controls on SW-GW interactions in alpine regions. The findings enhance the understanding of cryosphere-hydrosphere linkages and offer a scientific basis for water resource assessment in vulnerable alpine basins. • SW-GW exchange is highly heterogeneous, with groundwater contribution (GC) ranging from 0.4% to 43.3% • Downstream sediment fining ( D 50 from 210.5 to 142.0 μm) and gradient regulate riverbed permeability • Human impact is negligible with only ∼0.7% expansion of impervious surfaces. • Steppe-to-meadow conversion and 1.15℃/decade warming enhance connectivity. • A projected 1.1 ℃ water temperature rise by 2050 will intensify leakage via permafrost degradation.

Abstract Surface soil moisture (SSM) is essential to the hydrological cycle and land–atmosphere interactions, and its accurate simulation is crucial for climate prediction and resource management. This study developed an innovative modeling framework for global SSM prediction by integrating physics‐guided deep learning (PGDL) and clustering‐based regionalization. The PGDL model combines the physical knowledge from the Terrestrial Ecosystem Model (TEM) and the temporal learning capacity of long short‐term memory (LSTM) networks. By introducing a clustering strategy based on multi‐source features, the global land was divided into subregions with consistent characteristics. Within this framework, cluster‐specific models were trained using in situ observations and evaluated at the global scale against independent satellite observations. This clustering approach enhanced model generalization across diverse climatic and geographic conditions, yielding more robust predictions based on environmentally consistent samples. Results show that the PGDL model (RMSE: 0.081, : 0.55) outperformed both the process‐based (PB) model (RMSE: 0.167, : 0.43) and the purely deep learning (DL) model (RMSE: 0.085, : 0.40) at the global scale, while also exhibiting stronger physical consistency with water balance diagnostics. After excluding regions with high uncertainty in SSM observations, the performance of all models improved, with PGDL maintaining the best performance (masked RMSE: 0.064, masked : 0.58). Overall, this study demonstrates the superiority of the PGDL model and highlights the importance of clustering strategies in model construction and evaluation for achieving more accurate and robust SSM predictions across heterogeneous environments.

Abstract The Southern Annular Mode (SAM) represents the dominant pattern of climate variability in the extratropical Southern Hemisphere. However, its signature in coastal East Antarctic ice core records is unclear. A daily synoptic typing data set for the southern Indian Ocean constructed using self‐organizing maps on 500 hPa daily geopotential height anomalies was used to investigate the relationship between the SAM and ice core records from Law Dome and Mount Brown South. Results indicate that synoptic weather regimes can either enhance or suppress the signature of the SAM on surface weather and ice core records. Weather regimes that are more likely during negative SAM and are associated with positive temperature anomalies over East Antarctica support the observed relationship between the SAM and the Law Dome δ 18 O record since 1979. In contrast, weather regimes that describe meridional moisture flux toward Antarctica explain more snowfall accumulation variability at the ice core sites compared to the SAM, which contributes to suppressing the SAM signal in snowfall accumulation records. Results highlight the importance of considering the synoptic‐scale dynamics on the relationship and stationarity of the SAM influence on surface weather and interpretation of ice core records. Given the limited robust relationships between the SAM and the ice core records investigated, caution should be taken before using these records to reconstruct past SAM variability. Instead, the coastal East Antarctic ice core records provide insight into the occurrence of meridional weather regimes, which are often associated with extreme temperature and precipitation conditions.

Abstract The Low‐Earth Orbit Space Radiation Dosimeter (LEO‐DOS) onboard Next‐Generation Satellite II (NEXTSat‐2) measured absorbed dose rate variations during the May 2024 geomagnetic superstorm. The observations show deep storm‐time penetration of solar energetic particle (SEP) spanning L ≈ 2–10, a pronounced enhancement near L ≈ 3 consistent with storm‐related electron belt formation, and a rapid decrease in inner‐belt proton dose rates near L ≈ 2 following storm onset. L‐shell‐resolved profiles, geographic dose rate maps, and the dose‐to‐flux (D/F) ratio collectively track the temporal evolution of source‐dependent contributions throughout the event. The superstorm drove a sustained and global reorganization of the LEO radiation environment, with deep SEP access, enhanced outer‐belt electrons, and rapid inner belt proton loss occurring nearly simultaneously. Because absorbed dose rate directly reflects energy deposition in shielding and tissue‐equivalent materials, LEO‐DOS measurements complement flux‐based analyses and enable a radiation‐hazard‐oriented interpretation of storm‐time belt restructuring.

Abstract Karachi, Pakistan’s largest city, faces serious challenges in the fair and equitable distribution of water among its seven administrative divisions. To address the growing scarcity of water, fairly and equitably, this study develops an integrated Nash equilibrium and agent-based simulation for sustainable residential demand management. The approach is divided into two parts. In the first part, we present an agent-based model (ABM) developed in NetLogo, which simulates water allocation dynamics across the seven administrative divisions of the city. The model incorporates both demand-driven (e.g., water-saving behaviour, theft, and adaptive demand reduction) and supply-driven (e.g., infrastructure expansion) scenarios to evaluate system responses under varying scarcity conditions. It implements a proportional allocation strategy while simulating government interventions when water stress exceeds a certain threshold. In the second part, the Nash Bargaining Solution (NBS) is applied to address situations where supply falls short of demand in selected scenarios characterized by extreme water shortages. The scenarios, representing extreme cases of supply-demand gap, and NBS are used to reallocate the water between the seven districts fairly and efficiently. The inclusion of NBS, which is a game-theoretic approach, ensures that agents (in this case, the city divisions or districts) are allocated water based on negotiated outcomes that reflect their vulnerabilities and needs, resulting in greater fairness and equity of access to scarce water resources. We demonstrate how an agent-based model can be coupled with the cooperative bargaining solution to offer insights onto adaptive water governance, supporting equitable water distribution in heavily populated water-stressed urban cities.

Abstract An accurate medium-term streamflow forecast is one of the significant functions for managing and planning water resources. Considering the characteristics of trend, periodicity, and stochasticity of streamflow into account. So, this research aims to develop a new strategy, including a singular spectrum analysis (SSA) technique and a linear autoregressive (AR) model to predict monthly Tigris River streamflow data with three scenarios. The first scenario applies the SSA to decompose the normalised and cleaned time series into different signals (i.e., trend, seasonal, stochastic, and noise), then reconstruct the signals without noise and use the AR model to simulate the new time series. The second scenario employs the AR model to forecast each signal of streamflow without noise separately. The simulated time series was obtained by summing each predicted signal. The third scenario uses the AR model to simulate the raw data. Based on several statistical tests, the comparative analysis reveals that the first and second scenarios were much more accurate than the third ones. The second scenario is the best, reaching RMSE = 0.1223 (m 3 /s) and MAE = 0.0913 (m 3 /s) in the testing phase. The novelty of this study lies in the comparative evaluation of three SSA-AR modelling scenarios and the finding that forecasting decomposed components individually leads to superior accuracy compared to conventional approaches. The results are of substantial significance to the Ministry of Water Resources in managing and planning freshwater resources amid growing water demand.

Abstract Landscape Metrics (LMs) have an important role in linking spatial configuration with runoff to characterize hydrological response of a watershed. However, the effects of LMs on runoff parameters have not been investigated quantitatively, and predictive accuracy has not been examined in detail yet. To address this gap, runoff parameters are regionalized by LMs using simple linear and log-linear regressions, Catchment Similarity Approach (CSA), and a sound method the Ordered Weighted Averaging (OWA). Curve number method and Nash’s instantaneous unit hydrograph concept are used for lumped hydrological modelling at Kocaırmak and Darıören stream gauging stations located in Türkiye. In the watershed, 25 sub-basins are delineated, LMs are extracted, hydrological parameters are computed and regionalized by LMs. Prediction results are assessed by Nash–Sutcliffe Efficiency coefficient (E NS ), Peak Error (PE), Peak–Weighted Root Mean Square Error (PW–RMSE). E NS , PE, and PW–RMSE are computed using the extracted runoff parameters as 0.943, 0.792, 2.20%, 0.84%, 68.30 and 9.10 for Kocaırmak and Darıören, respectively. Regression-based methods reveal that number of patches, landscape shape index, area-weighted patch area, mesh and various statistics of shape indexes estimate more enhanced predictions than the extracted runoff parameters. CSA contributes to estimate satisfactory runoff hydrographs. Outcomes from regression-based methods and CSA provide satisfactory predictions by collaboration of sound decision strategy coefficients and threshold values of catchments similarity index in the OWA method. The methodology adapted in this study may initiate attempts encouragingly to link LMs with hydrological parameters to develop sustainable land use strategies by controlling runoff parameters.

This study examines the synergies between urban heat islands (UHIs) and heat waves (HWs) and their quantitative effect on urban heat stress in Dhaka, Bangladesh under HWs. For this, total 51 days in the pre-monsoon seasons of 2020, 2021, and 2022 are simulated using the Weather Research and Forecasting model. During the HW days, the synergies between UHIs and HWs are weak in the daytime (0.07 °C) and pronounced in the nighttime (1.28 °C). Due to HWs, the heat storage and the release of stored heat increase greatly in the urban area, which is responsible for the pronounced synergies in the nighttime. To quantitatively evaluate the effect of the synergies on urban heat stress, individual effects of HWs, UHIs, and their synergies on urban heat stress are quantified and the normalized relative impact index is defined. The HW effect plays the most important role in daytime urban heat stress under HWs, and the UHI effect is most important for aggravating nighttime urban heat stress under HWs. The synergistic effect on urban heat stress is minor in the daytime, whereas it plays roles in worsening nighttime urban heat stress, its influence being 37–44% of the UHI effect. • Synergies between urban heat islands (UHIs) and heat waves (HWs) are studied. • The synergies are pronounced in the nighttime, being 1.28 °C. • The increase in urban heat storage due to HWs is important for the synergies. • The HW (UHI) effect is most important for daytime (nighttime) urban heat stress. • The synergistic effect worsens urban heat stress particularly in the nighttime.

• Unsmooth time series data works well in data-restricted area. • Binary transformer model yield average 91.39% overall accuracy. • A probability thresholding method is reliable to generate multi-class result. • Final multi-class map products have overall accuracy higher than 94%. • Staple crop in Nepal increased 3.91% from 2021 to 2023. Accurate mapping of staple crops using satellite imagery is essential for sustainable agricultural management, particularly in data-limited regions. Despite recent advances in Earth observations and deep learning, using multi-year time series satellite data and binary deep learning models to produce national-scale multi-class crop type map remains underexplored. In this study, we attempt to map staple crops, other crops, and non-crop at 10 m resolution that leverages multi-year, unsmoothed Sentinel-2A/B MSI Level-2A time series imagery, binary transformer models, and a probability-based thresholding method. Nepal was selected as the case study due to its complex topography and heterogeneous landscapes. We developed six experimental setups, and models achieved an average overall accuracy of 91.39%, with class-wise F1-scores ranging from 0.81 to 0.96. The best-performing model was then applied to 28 tiles covering Nepal, and a probability-based thresholding approach was used to assemble binary results into a multi-class map. The map overall accuracy was higher than 94%, when validated with field data. Sensitivity analyses further confirmed the robustness and spatial consistency of the proposed framework across diverse land-cover and agroecological zones. Finally, the produced national-scale maps indicated 3.91% increase in staple crops areas from 2021 to 2023 in Nepal.

Abstract In this paper, we deal with simulation of surface rain-runoff in urban areas, in particular densely built ones, where impermeable surfaces prevail and prevent rainwater infiltration to the ground. Our aim is to check whether simple computational methods and freely available computational tools can adequately describe rain-runoff along streets. The basic idea is that streets can be simulated as open channels and adjacent neighbourhoods as hydrological basins, feeding them. Then, the Modified Rational method can be combined with HEC-RAS to calculate water depths at selected street cross-sections. To test the procedure, we have selected an area of the historical centre of Thessaloniki, Greece, which is densely built. Two streets have been considered as the main open channels. Using detailed topographical maps and findings of in situ survey, we delineated the respective drainage areas. Special care was needed in street junctions, to decide on inflows and outflows. Moreover, to highlight the importance of using low environmental impact techniques in the urban fabric, application of rain gardens in suitable locations was proposed, with the aim of reducing the runoff volume and, consequently, local inundation problems during the peaks of extreme rain events. Finally, we have compared the calculated water depths with those obtained using the Manning formula for open channel flows. Our results show that the free hydraulic model HEC-RAS, although developed for simulation of flows in natural watercourses, can be used successfully in the simulation of stormwater runoff at the urban street scale and for planning local flood alleviation measures.

Abstract The cellular plains on Sputnik Planitia (SP) are thought to originate from convection in the nitrogen ice layer that fills the basin. Whereas the cells toward the center of SP's cellular plains are wider and interconnected—features explained by vigorous convection—the cells near the margins are smaller and less contiguous, with some appearing completely isolated. We propose that these isolated cells are surface expressions of localized convection—a rare planform in which stable thermal plumes can form in isolation from one another. Numerical simulations of convection using experimentally determined flow laws for power‐law creep in nitrogen ice show that for typical parameters of SP, the viscosity contrasts across the layer are sufficiently high for localization to occur. The diameter of the isolated cells provides constraints on the thickness of the nitrogen ice layer, the surface topographic anomaly, surface heat flow, and surface velocities associated with the localized cells.