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Freshwater is considered an essential natural resource that must be monitored effectively. The Surface Water and Ocean Topography (SWOT) satellite mission scheduled for launch in December 2022 will enable river discharge estimation from surface water extents, elevations, and slopes. However, the SWOT-based discharges will be inconsistent due to infrequent temporal sampling (TS) of SWOT orbit. This study explores the potential of unique TS of SWOT to determine whether it influences the calibration of hydrological model. The Weather Research and Forecast Hydrological system (WRF-Hydro) has been used to simulate river discharge. Further WRF-Hydro model has been calibrated using proxy SWOT data based on a dynamically dimensioned search algorithm. Results show that more than 90% of parameter iterations have KGE values with less than 20% absolute percent difference when only SWOT TS alone has been considered. The results suggest that model calibration using SWOT-based discharge performs similarly to daily in-situ discharge.

River discharge information is crucial for various applications, but the measurement process often remains impeded by factors that hinder near real-time (NRT) data availability in India. However, leveraging telemetry-based water surface elevation (WSE) data across the country provides an opportunity to convert it into river discharge. This conversion is made possible by utilizing rating curves (RCs) derived from historical collocated measurements of WSE and discharge. In this study, NRT WSE from the Central Water Commission (CWC) flood portal is obtained via the web-scraping tool. Through the application of RCs, discharge data is extended across 210 gauging stations in India from the year 2020 to the present, encompassing sub-daily discharge during the non-monsoon and hourly discharge series during monsoon (June-September) across the Indian rivers. Annually, the study generated over 800,000 discharge data points for Indian rivers, accounting for more than 4000 discharge measurements per station annually. These comprehensive datasets provide valuable insights for water resource and flood management research, offering NRT access to WSE, and discharge, along with the local RCs. [ABSTRACT FROM AUTHOR]

Purpose: To compare the surgical outcomes of twin-site phacotrabeculectomy with Mitomycin C (MMC) in primary open-angle glaucoma (POAG) versus primary angle-closure glaucoma (PACG). Methods: Prospective, comparative, observational study. Patients with visually significant cataract and primary glaucoma were divided into two groups: POAG and PACG. They underwent twin-site phacotrabeculectomy with MMC and followed up on days 1, 15, 1 month, 6 months, and 12 months. Baseline and follow-up visits were compared to find the differences in intraocular pressure (IOP), antiglaucoma medications (AGM), success rates, anterior chamber depth (ACD), and axial length (AXL). Results: There were 50 eyes each in POAG and PACG groups. Mean IOP reduction from baseline to 12 months (21.22 ± 6.0 to 11.40 ± 2.8—POAG group vs 24.16 ± 7.6 to 12.42 ± 3.2—PACG group) was statistically significant in both groups (P < 0.001), but no significant difference between groups (P = 0.095). There was a statistically significant decline in the number of AGM in POAG [1.66 (0.7) to 0.38 (0.7), P < 0.001] and PACG [2.10 (0.7) to 0.70 (0.8), P < 0.001]; the decline was more in POAG (P = 0.012) at last visit. Probability of overall (complete and qualified) success at 12 months postop was 72.0% [95% confidence interval (CI): 57.4–82.4] in PACG and 84.0% (95% CI: 70.5–91.7) in POAG group. There was a significant increase in ACD and a decrease in AXL in both groups (P < 0.001). More interventions were required in the PACG group (38, P = 0.012). Conclusion: Phacotrabeculectomy with MMC causes a significant reduction in IOP and improvement in biometric parameters in both POAG and PACG. Patients with PACG required more postoperative interventions, while a lesser number of antiglaucoma medications were needed in POAG patients.

We present a preliminary study on using the Surface Water and Ocean Topography (SWOT) mission to extend water surface elevation (WSE) measurements from a sparsely distributed in situ gauge network along a one-dimensional (1D) study reach within the Mahanadi River basin in India. We use the Centre National d'Etudes Spatiales (CNES) simulator to simulate 3 years of synthetic measurements of SWOT WSE, which are assimilated into a 1D stochastic model based on six independent configurations. For each configuration, the model is derived using historical measurements of in situ WSE to simulate daily WSE estimates every 200 m along the study reach. Assimilated outputs showed high Nash-Sutcliffe efficiency (0.697–0.811), high correlation (0.838–0.901) and low root mean square error (0.242–0.513 m) with respect to in situ validation data. Results indicate consistent performance of the SWOT-assimilated model framework and are relevant considering the declining availability of in situ monitoring stations. [ABSTRACT FROM AUTHOR]

BackgroundVaccine and sufficient food availability are key factors for reducing pneumonia outbreaks in sub-Saharan Africa.MethodsIn this study, the 10-valent pneumococcal conjugate vaccine (Synflorix® or PCV10) was administered to a child cohort (5–7 years old, n = 237) in Msambweni, Kenya, to determine relationships between dietary intake, nutritional/socioeconomic status of mothers/caregivers, and vaccine response. 7-day food frequency questionnaire (FFQ), dietary diversity score (DDS) and single 24-h dietary recall were used to address participants' dietary assessment and nutritional status. Individual food varieties were recorded and divided into 9 food groups as recommended by Food and Agriculture Organization. Anthropometric measurements, nasopharyngeal swabs and vaccine administration were performed at the initial visit. Participants were followed 4–8 weeks with a blood draw for pneumococcal IgG titers assessed by Luminex assay.FindingsChronic malnutrition was prevalent in the cohort (15% stunting, 16% underweight). Unbalanced dietary intake was observed, with mean energy intake 14% below Recommended Dietary Allowances (1,822 Kcal) for 5–7 years age range. 72% of the daily energy was derived from carbohydrates, 18% from fats and only 10% from proteins. Poor anthropometric status (stunting/underweight) was associated with low socioeconomic/educational status and younger mother/caregiver age (p < 0.002). Limited intake of essential micronutrients (vitamins A, E, K) and minerals (calcium, potassium) associated with low consumption of fresh fruits, vegetables, and animal source foods (dairy, meat) was observed and correlated with poor vaccine response (p < 0.001). In contrast, children who consumed higher amounts of dietary fiber, vitamin B1, zinc, iron, and magnesium had adequate vaccine response (p < 0.05). Correlation between higher dietary diversity score (DDS), higher Vitamin E, K, Zinc intake and adequate vaccine response was also observed (p < 0.03).InterpretationOverall, this study highlights ongoing food scarcity and malnutrition in Kenya and demonstrates the links between adequate socioeconomic conditions, adequate nutrient intake, and vaccine efficacy.

Purpose: To present a case of posterior lenticular surface neovascularization in the absence of any ocular or systemic pathology. Observations: A 29-year-old asymptomatic male was detected with the right eye (OD) posterior lenticular surface neovascularization extending for six-clock hours in the temporal portion along with the presence of mild posterior capsular haze. His best-corrected visual acuity was 20/25 in OD and 20/20 in the left eye (OS). No additional abnormality was identified in the remainder of the OD anterior segment and the fundus respectively. OS evaluation was unremarkable. OD imaging including B-scan ultrasonography and ultrasound biomicroscopy (UBM) were essentially normal. The anterior segment optical coherence tomography (AS-OCT) thickened lens capsule with a dense hyperreflective layer adhered to its posterior surface and separating from the capsule in the periphery. Few hyperreflective dots were visible posterior to the ciliary body although no CB thickening was noted. No systemic abnormality was detected. The patient is being managed conservatively and has shown no signs of progression of the neovascularization over six months. Conclusions and importance: This is the first reported case of an isolated idiopathic posterior lenticular surface neovascularization occurring in an otherwise healthy patient. Although an unspecified breach in the posterior capsule or undetermined cyclitis can trigger such neovascularization, further histopathological studies of the capsular biopsy and ciliary body can provide better insight into its etiopathogenesis. Additionally, considering the asymptomatic nature of the condition, these patients can be observed and closely monitored.

Purpose: To compare the surgical outcomes of twin-site phacotrabeculectomy with Mitomycin C (MMC) in primary open-angle glaucoma (POAG) versus primary angle-closure glaucoma (PACG). Methods: Prospective, comparative, observational study. Patients with visually significant cataract and primary glaucoma were divided into two groups: POAG and PACG. They underwent twin-site phacotrabeculectomy with MMC and followed up on days 1, 15, 1 month, 6 months, and 12 months. Baseline and follow-up visits were compared to find the differences in intraocular pressure (IOP), antiglaucoma medications (AGM), success rates, anterior chamber depth (ACD), and axial length (AXL). Results: There were 50 eyes each in POAG and PACG groups. Mean IOP reduction from baseline to 12 months (21.22 ± 6.0 to 11.40 ± 2.8--POAG group vs 24.16 ± 7.6 to 12.42 ± 3.2--PACG group) was statistically significant in both groups (P < 0.001), but no significant difference between groups (P = 0.095). There was a statistically significant decline in the number of AGM in POAG [1.66 (0.7) to 0.38 (0.7), P < 0.001] and PACG [2.10 (0.7) to 0.70 (0.8), P < 0.001]; the decline was more in POAG (P = 0.012) at last visit. Probability of overall (complete and qualified) success at 12 months postop was 72.0% [95% confidence interval (CI): 57.4-82.4] in PACG and 84.0% (95% CI: 70.5-91.7) in POAG group. There was a significant increase in ACD and a decrease in AXL in both groups (P < 0.001). More interventions were required in the PACG group (38, P = 0.012). Conclusion: Phacotrabeculectomy with MMC causes a significant reduction in IOP and improvement in biometric parameters in both POAG and PACG. Patients with PACG required more postoperative interventions, while a lesser number of antiglaucoma medications were needed in POAG patients. [ABSTRACT FROM AUTHOR]

Soil moisture is a key variable that contributes for flux partitioning into sensible and latent heat components. There is a pressing need to study the impacts of shallow groundwater table dynamics on antecedent soil moisture and corresponding feedbacks to atmosphere. In this study, we evaluate WRF‐NoahMP simulated soil moisture using two experimental configurations: one with the default, free drainage approach (CTL) and with Miguez‐Macho groundwater scheme (GW). Results are presented for the study conducted over Ganga River basin, India between the years 2008 and 2014 for all the three seasons (pre‐monsoon, monsoon, post‐monsoon). It was observed that the GW model runs, improve soil moisture in topmost and bottom‐most layers. We discovered that the simulations improved temporal negative bias of seasonal accumulation during monsoon by around 91 mm. In addition, the average negative bias in latent heat flux improved by around 28 W/m2. Compared to CTL, GW was found to contribute additional atmospheric moisture content ranging between 3% and 5%. Comparison with in‐situ ground water showed that model overestimates the ground water table depth. This can be attributed to the ability of the model to only simulate natural occurring variabilities and lack of inclusion of anthropogenic factors such as ground water pumping in the model. The groundwater trend in Ganga is declining at alarming rate. In addition, if over exploitation of groundwater is not monitored properly, water and food scarcity is an imminent threat to the country's economy. To the best of authors knowledge, this is the first study of its kind to be implemented over Ganga basin, which evaluates the role of shallow ground water table on regional climate using coupled atmosphere‐land surface‐groundwater model. In summary, we highlight that the groundwater component along with WRF‐NoahMP coupled model improves soil moisture and precipitation representation over the Ganga basin. [ABSTRACT FROM AUTHOR]

The Surface Water and Ocean Topographic (SWOT) mission is aimed to improve flood mapping by providing high-resolution data of water surface elevation, slope and water extent for rivers wider than 100 m and water bodies as small as 62500 m². Although, SWOT's data latency and huge data size of rawest point cloud data make it impracticable for real or near-real-time flood forecasting but the SWOT data will provide new insight in the first-hand information of flood mapping. In this study, we explore the potential of SWOT mission for flood mapping over India. It is observed that the spatio-temporal resolution of SWOT allows the observation of 0.67%, 15.79%, 29.24%, 45.54%, and 8.06% of Indian districts with one, two, three, four and more than four observations per cycle of SWOT respectively. It is observed that SWOT would have observed 49.6% of flood events at least once if the mission were launched earlier. The SWOT could also observe 30.6% of small flood events with flood duration <4 at least once. Using the information, this study proposes a flood severity map by combining geomorphological and physical flood parameters for a reach of Brahmaputra river. From the map different flood severity zones can be observed. [ABSTRACT FROM AUTHOR]

Data assimilation (DA) offers immense potential for uncertainty identification, improving the initial estimates for hydrological and atmospheric modelling. This paper reviews the studies in hydrological DA using Kalman filters. Recent applications of Kalman filters in hydrological and atmospheric DA are summarized. Existing challenges for DA studies are briefly described. In addition, three case study examples are presented highlighting the effects of: (a) soil moisture DA in the Noah land surface model; (b) variational assimilation for improving precipitation forecasts in the WRF (Weather Research Forecast) model; and (c) simulating AMSR-2 (Advanced Microwave Scanning Radiometer-2) radiances towards DA. Although there are many unresolved issues in DA that warrant further research, it has immense potential for predicting variables at a better lead time for hydrometeorology. [ABSTRACT FROM AUTHOR]

This study explores machine learning for estimating soil moisture at multiple depths (0–5 cm, 0–10 cm, 0–20 cm, 0–50 cm, and 0–100 cm) across the coterminous United States. A framework is developed that integrates soil moisture from Soil Moisture Active Passive (SMAP), precipitation from the Global Precipitation Measurement (GPM), evapotranspiration from the Ecosystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS), vegetation data from the Moderate Resolution Imaging Spectroradiometer (MODIS), soil properties from gridded National Soil Survey Geographic (gNATSGO), and land cover information from the National Land Cover Database (NLCD). Five machine learning algorithms are evaluated including the feed-forward artificial neural network, random forest, extreme gradient boosting (XGBoost), Categorical Boosting, and Light Gradient Boosting Machine. The methods are tested by comparing to in situ soil moisture observations from several national and regional networks. XGBoost exhibits the best performance for estimating soil moisture, achieving higher correlation coefficients (ranging from 0.76 at 0–5 cm depth to 0.86 at 0–100 cm depth), lower root mean squared errors (from 0.024 cm3/cm3 at 0–100 cm depth to 0.039 cm3/cm3 at 0–5 cm depth), higher Nash–Sutcliffe Efficiencies (from 0.551 at 0–5 cm depth to 0.694 at 0–100 cm depth), and higher Kling–Gupta Efficiencies (0.511 at 0–5 cm depth to 0.696 at 0–100 cm depth). Additionally, XGBoost outperforms the SMAP Level 4 product in representing the time series of soil moisture for the networks. Key factors influencing the soil moisture estimation are elevation, clay content, aridity index, and antecedent soil moisture derived from SMAP. [ABSTRACT FROM AUTHOR]

The Western Himalayas receive significant snowfall, and it's essential to monitor the snow cover for various purposes like managing water resources, conducting hydrological research, and predicting avalanches using remote sensing techniques. With the pan-sharpening of different satellite sensor images, the essential features can be obtained to understand the snow cover dynamics. The resampling algorithms play a very important role in the pan-sharpening procedure to highlight the important features of the pan-sharpened dataset. However, the performance of various resampling algorithms for pan-sharpening is very rarely validated with optical and microwave datasets. In this article, the spatial and spectral analysis of different resampling algorithms, i.e., Nearest Neighbour (NN), Bilinear (BI), and Cubic Convolution (CC) resampling, have been performed using the fusion of optical and microwave satellite images. In this study, two datasets, i.e., MODIS (optical) and SCATSAT-1 (microwave) were used over the western Himalayas (i.e., Ladakh, Jammu and Kashmir, Himachal Pradesh, and Uttrakhand). To evaluate the performance of each resampled pan-sharpened dataset, the output is classified using a Support Vector Machine (SVM) classifier and a Spectral Angle Mapper (SAM) classifier. Root Mean Square Error values were computed to quantify the level of agreement between the pan-sharpened datasets and the ground truth data. The result of statistical analysis showed that NN-based pan-sharpened performed better than BI-based and CC-based pan-sharpened classified images with both classifiers i.e., SVM and SAM. This study is important in terms of the effective utilization of the resampling techniques along with pan-sharpening algorithms. [ABSTRACT FROM AUTHOR]

Aged hydrocarbon wells, if proper care is not ensured, can crack, get corroded, and leak subsurface fluids. Permian Basin in Texas, home to thousands of such wells, has seen numerous blowouts and wastewater leaks. Our study employs surface deformation derived from satellite observations, and injection well records to investigate these events. The results reveal an over‐pressurized wastewater aquifer producing a surface uplift of 20 cm/yr, likely due to wastewater being injected tens of kilometers away. Focusing on a January 2022 blowout resulting in 3 cm subsidence in 2 weeks, our geophysical model suggests aquifer over‐pressurization as the cause. With an excess pressure of over 3 MPa in the aquifer, several more such blowouts are possible in the near future. This research highlights the urgent need to better understand the impact of subsurface fluid injection and calls for prompt action to mitigate the environmental effects of oil and gas production. Plain Language Summary: Wastewater, a byproduct during oil extraction, is generally injected back into the ground. Permian Basin has witnessed several incidents of leakage of wastewater in the last 3 years. Recently in January 2022, huge amounts of wastewater were expelled at a high pressure from an old well. We used satellite data and wastewater injection data to understand the cause of these events. We found that the wastewater injection happening nearly several kilometers away is responsible for these leakages. We also discovered a highly pressurized wastewater lake below the surface in this region using geophysical modeling. Due to high pressures, the land in this region rose by 40 cm in just 2 years. Meanwhile, a part of this region sunk by 3 cm because of the leakage in January 2022. Our findings raise concerns about the potential for more leakages in the near future if action is not taken. Key Points: We utilized satellite observations of land surface deformation to explore the subsurface anomalies in the Permian BasinWe established a significant link between wastewater injection and oil well blowouts in the Permian BasinWe discovered an over‐pressurized aquifer responsible for the reported blowouts, with a potential for more blowouts in the near future [ABSTRACT FROM AUTHOR]

Intermittent rivers and ephemeral streams are crucial for the water cycle and ecosystem services, yet they are often neglected by managers and researchers, especially in headwater areas. This oversight has caused a lack of comprehensive basemaps for these vital river systems. In headwater regions, water bodies are typically sparse and disconnected, with narrow and less distinct channels. Therefore, we propose a novel hybrid method that integrates topographic data and remote sensing imagery to delineate river networks. Our method reestablishes connectivity among sparsely distributed water bodies through topographic pairs, enhances less distinct channel features using the gamma function, and converts topographic and water indices data into a weighted graph to determine optimal channels with the A* algorithm. The topographic and water indices data are derived from the Multi-Error-Removed Improved-Terrain DEM (MERIT DEM) and an average composite of the Modified Normalized Difference Water Index (MNDWI), respectively. In the upper Lancang-Mekong River basin, our method outperformed five publicly available DEM datasets, achieving over 91% positional accuracy within a 30 m buffer. This hybrid method enhances positional accuracy and effectively connects sparse water bodies in headwater areas, offering promising applications for delineating intermittent rivers and ephemeral streams and providing baseline information for these river systems. [ABSTRACT FROM AUTHOR]

In the current biodiversity crisis, populations of many species have alarmingly declined, and insects are no exception to this general trend. Biodiversity monitoring has become an essential asset to detect biodiversity change but remains patchy and challenging for organisms that are small, inconspicuous or make (nocturnal) long-distance movements. Radars are powerful remote-sensing tools that can provide detailed information on intensity, timing, altitude and spatial scale of aerial movements and might therefore be particularly suited for monitoring aerial insects and their movements. Importantly, they can contribute to several essential biodiversity variables (EBVs) within a harmonized observation system. We review existing research using small-scale biological and weather surveillance radars for insect monitoring and outline how the derived measures and quantities can contribute to the EBVs 'species population', 'species traits', 'community composition' and 'ecosystem function'. Furthermore, we synthesize how ongoing and future methodological, analytical and technological advancements will greatly expand the use of radar for insect biodiversity monitoring and beyond. Owing to their long-term and regional-to-large-scale deployment, radar-based approaches can be a powerful asset in the biodiversity monitoring toolbox whose potential has yet to be fully tapped. This article is part of the theme issue 'Towards a toolkit for global insect biodiversity monitoring'. [ABSTRACT FROM AUTHOR]

The photodegradation of azithromycin present was carried out in water using H2O2 under UV irradiation. The reaction variables considered in this study were the amount of H2O2 solution and the initial concentration of azithromycin to evaluate the performance of the photodegradation process. The azithromycin degradation was not observed in the dark during stirring for 20 min. The study showed an efficient photodegradation of azithromycin using H2O2 as an oxidant in the presence of UV irradiation. The azithromycin degradation was altered significantly by the pH of the irradiated solution. The degradation was low at an acidic pH and showed an increasing trend as the pH changed to basic. The azithromycin degradation increased with a higher amount (higher concentration) of H2O2. The degradation of azithromycin decreased with a higher concentration of azithromycin in the reacting solution. The highest degradation of AZT was achieved in 1 h using a 1.0 ppm AZT solution containing 3 mL of H2O2. The experimental data obtained were well-fitted to zero-order reaction kinetics. The results of this study were found quite excellent. They showed 100% degradation in 1 h when compared with those reported in the literature, both with photocatalysis using nanomaterials and photolysis using light irradiation and/or H2O2. The UV/H2O2 system was found to be quite efficient for the photodegradation of azithromycin, and this system can be applied to degrade other organic pollutants present in industrial wastewater. [ABSTRACT FROM AUTHOR]

With the increasing availability of satellite monitoring data, the demand for storage and computational resources for updating the results of monitoring the surface subsidence in a mining area continues to rise. Sequential adjustment (SA) models are considered effective for rapidly updating time series interferometry synthetic aperture radar (TS-InSAR) measurements. However, the accuracy of surface subsidence values estimated through traditional sequential adjustment is highly sensitive to abnormal observations or prior information on anomalies. Moreover, the surface subsidence associated with mining exhibits nonlinear and large gradient characteristics, making general InSAR methods challenging for obtaining reliable monitoring results. In this study, we employ the phase unwrapping network (PUNet) to obtain unwrapped values of differential interferograms. To mitigate the impact of abnormal errors in the near real-time small baseline subset InSAR (SBAS-InSAR) sequential updating process in mining areas, a robust sequential adjustment method based on M-estimation is proposed to estimate the temporal deformation parameters by using the equivalent weight model. Using a coal backfilling mining face in Shanxi, China, as the study area and the Sentinel-1 SAR dataset, we comprehensively evaluate the performance of unwrapping methods and subsidence time series estimation techniques and evaluate the effect of filling mining on surface subsidence control. The results are validated using leveling measurements within the study area. The relative error of the proposed method is less than 5%, which can meet the requirements of monitoring the surface subsidence in mining areas. The method proposed in this study not only enhances computational efficiency but also addresses the issue of underestimation encountered by InSAR methods in mining area applications. Furthermore, it also mitigates unwrapping phase anomalies on the monitoring results. [ABSTRACT FROM AUTHOR]

Process-oriented observational constraints for the anthropogenic effective radiative forcing due to aerosol–cloud interactions (ERFaci) are highly desirable because the uncertainty associated with ERFaci poses a significant challenge to climate prediction. The contoured frequency by optical depth diagram (CFODD) analysis supports the evaluation of model representation of cloud liquid-to-rain conversion processes because the slope of a CFODD, generated from joint MODerate Resolution Imaging Spectroradiometer (MODIS)-CloudSat cloud retrievals, provides an estimate of cloud droplet collection efficiency in single-layer warm liquid clouds. Here, we present an updated CFODD analysis as an observational constraint on the ERFaci due to warm rain processes and apply it to the U.S. Department of Energy's Energy Exascale Earth System Model version 2 (E3SMv2). A series of sensitivity experiments shows that E3SMv2 droplet collection efficiencies and ERFaci are highly sensitive to autoconversion, i.e., the rate of mass transfer from cloud liquid to rain, yielding a strong correlation between the CFODD slope and the shortwave component of ERFaci (ERFaci SW ; Pearson's R=-0.91). E3SMv2's CFODD slope (0.20 ± 0.04) is in agreement with observations (0.20 ± 0.03). The strong sensitivity of ERFaci SW to the CFODD slope provides a useful constraint on highly uncertain warm rain processes, whereby ERFaci SW , constrained by MODIS-CloudSat, is estimated by calculating the intercept of the linear association between the ERFaci SW and the CFODD slopes, using the MODIS-CloudSat CFODD slope as a reference. [ABSTRACT FROM AUTHOR]