Search Results (23)

Snow-dominated watersheds experience a snowmelt-driven peak in streamflow that occurs in the spring or early summer. Some of the headwater basins in Colorado, USA have two or more peaks in streamflow, including the Uncompahgre River, a Colorado River tributary. The timing of peak streamflow is important for water management and recreational planning. As such, we examined the connection between the timing of each streamflow peak and readily available snow measurement information in the forest and alpine zones. These station data are the date of the initiation of snowmelt, 50% melt-out, and complete melt-out or the snow disappearance date (SDD). When it occurs before mid-June (14 of 20 years), the timing of the first peak is well correlated with the forested snow measurement station SDD. The second streamflow peak timing is well correlated with SDD from the alpine station except for very early (3 years) and very late (2 years) SDD. We also examine the spatial variability of snow disappearance and peak snow water equivalent (SWE) across the four seasonally snow-covered headwater sub-basins using a dataset from a coupled meteorological–snowpack model. Full article

Snow cover variations significantly affect the stability of regional water supply and terrestrial ecosystems in arid northwest China. This study comprehensively evaluates snow resource changes since 2000 by integrating multisource remote sensing datasets and analyzing four key indicators: snow cover area (SCA), snow phenology (SP), snow depth (SD), and snow water equivalent (SWE). The results reveal a slight downtrend in SCA over the past two decades, with an annual decline rate of 7.13 × 10³ km². The maximum SCA (1.28 × 10⁶ km²) occurred in 2010, while the minimum (7.25 × 10⁵ km²) was recorded in 2014. Spatially, SCA peaked in December in the north and January in the south, with high-altitude subregions (Ili River Basin (IRB), Tarim River Region (TRR), North Kunlun Mountains (NKM), and Qaidam Basin (QDB)) maintaining stable summer snow cover due to low temperatures and high precipitation. Analysis of snow phenology indicates a significant shortening of snow cover duration (SCD), with 62.40% of the study area showing a declining trend, primarily driven by earlier snowmelt. Both SD and SWE exhibited widespread declines, affecting 75.09% and 84.85% of the study area, respectively. The most pronounced SD reductions occurred in TRR (94.44%), while SWE losses were particularly severe in North Tianshan Mountains (NTM, 94.61%). The total snow mass in northwest China was estimated at 108.95 million tons, with northern Xinjiang accounting for 66.24 million tons (60.8%), followed by southern Xinjiang (37.44 million tons) and the Hexi Inland Region (5.27 million tons). Consistency analysis revealed coherent declines across all indicators in 55.56% of the study area. Significant SD and SCD reductions occurred in TRR and Tuha Basin (THB), while SWE declines were widespread in NTM and IRB, driven by rising temperatures and decreased snowfall. The findings underscore the urgent need for adaptive strategies to address emerging challenges for water security and ecological stability in the region. Full article

Background/Objectives: Postmortem changes in tissue stiffness and organ morphology are critical for forensic medicine and pathology. Shear wave elastography (SWE) has emerged as a non-invasive tool to assess tissue stiffness, yet its potential for postmortem interval estimation remains underexplored. While previous studies have demonstrated early postmortem alterations in tissue elasticity, the temporal progression of these changes in different organs is not fully understood. This study aims to investigate the temporal changes in liver and spleen stiffness during the postmortem period using SWE and to evaluate the predictive potential of elastographic parameters for postmortem interval estimation. Methods: Twelve male Sprague–Dawley rats were sacrificed via cervical dislocation following deep anesthesia. Postmortem liver and spleen measurements, including longitudinal and short diameters and SWE values (kPa), were recorded at 0, 2, 4, 6, 9, 12, 18, 24, and 36 h. All elastographic measurements were obtained using a 5 mm circular region of interest (ROI) for the liver and a 3 mm ROI for the spleen. Changes over time were analyzed using repeated measures ANOVA, with post hoc Bonferroni corrections applied where necessary. Additionally, Receiver Operating Characteristic (ROC) curve analysis and binary logistic regression analysis were performed to assess the predictive accuracy of SWE parameters in estimating postmortem time. Results: Postmortem liver and spleen stiffness exhibited a significant declining trend over time (p < 0.001, η² = 0.749 and η² = 0.810, respectively). Liver and spleen dimensions initially increased, reaching peak values around 6 h, followed by a gradual reduction. ROC analysis demonstrated that spleen SWE (AUC = 0.917) and liver SWE (AUC = 0.845) were the strongest predictors of early postmortem time. Binary logistic regression further confirmed that liver and spleen SWE were statistically significant predictors of postmortem time (p = 0.006 and p = 0.020, respectively). Conclusions: This study provides evidence that postmortem liver and spleen stiffness decline progressively over time, while organ dimensions exhibit a biphasic pattern. Elastographic parameters, particularly SWE values, demonstrated strong predictive accuracy in estimating early postmortem intervals. These findings suggest that SWE may serve as a valuable imaging modality for forensic applications, providing objective insights into postmortem biomechanical changes and time-of-death estimation. Further research should explore the applicability of SWE in different tissue types and under varying environmental conditions. Full article

Quantifying the water budget in the Great Salt Lake (GSL) basin is a nontrivial task, especially under a changing climate that contributes to increasing temperatures and a shift towards more rainfall and less snowfall. This study examines the potential impacts of temperature thresholds on the water budget in the GSL, emphasizing the influence on snowmelt, evapotranspiration (ET), and runoff under varying climate warming scenarios. Current hydrological models such as the Variable Infiltration Capacity (VIC) model use a universal temperature threshold to partition snowfall and rainfall across different regions. Previous studies have argued that there is a wide range of thresholds for partitioning rainfall and snowfall across the globe. However, there is a clear knowledge gap in quantifying water budget components in the Great Salt Lake (GSL) basin corresponding to varying temperature thresholds for separating rainfall and snowfall under the present and future climates. To address this gap, the study applied temperature thresholds derived from observation-based data available from National Center for Environmental Prediction (NCEP) to the VIC model. We also performed a suite of hydrological experiments to quantify the water budget of the Great Salt Lake basin by perturbing temperature thresholds and climate forcing. The results indicate that higher temperature thresholds contribute to earlier snowmelt, reduced snowpack, and lower peak runoff values in the early spring that are likely due to increased ET before peak runoff periods. The results show that the GSL undergoes higher snow water equivalent (SWE) values during cold seasons due to snow accumulation and lower values during warm seasons as increased temperatures intensify ET. Projected climate warming may result in further reductions in SWE (~71%), increased atmospheric water demand, and significant impacts on water availability (i.e., runoff reduced by ~20%) in the GSL basin. These findings underscore the potential challenges that rising temperatures pose to regional water availability. Full article

Climate change is profoundly impacting snow-dependent regions, altering hydrological cycles and threatening water security. This study examines the relationships between snow water equivalent (SWE), snow cover, temperature, and wind speed in Jammu and Kashmir, India, over five decades (1974–2024). Using ERA5 reanalysis and Indian Meteorological Department (IMD) datasets, we reveal significant declines in SWE and snow cover, particularly in high-altitude regions such as Kupwara and Bandipora. A Sen’s slope of 0.0016 °C per year for temperature highlights a steady warming trend that accelerates snowmelt, shortens snow cover duration, and reduces streamflow during critical agricultural periods. Strong negative correlations between SWE and temperature (r = −0.7 to −0.9) emphasize the dominant role of rising temperatures in SWE decline. Wind speed trends exhibit weaker correlations with SWE (r = −0.2 to −0.4), although localized effects on snow redistribution and evaporation are evident. Temporal snow cover analyses reveal declining winter peaks and diminished summer runoff contributions, exacerbating water scarcity. These findings highlight the cascading impacts of climate variability on snow hydrology, water availability, and regional ecosystems. Adaptive strategies, including real-time snow monitoring, sustainable water management, and climate-resilient agricultural practices, are imperative for mitigating these challenges in this sensitive Himalayan region. Full article

After peak snowmelt, baseflow is the primary contributor to streamflow in snow-dominated watersheds. These low flows provide important water for municipal, agricultural, and recreational purposes once peak flows have been allocated. This study examines the correlation between peak snow water equivalent (SWE), post-peak SWE precipitation, and baseflow characteristics, including any yearly lag in baseflow. To reflect the hydrologic processes that are occurring in snow-dominated watersheds, we propose using a melt year (MY) beginning with the onset of snowmelt contributions (the first deviation from baseflow) and ending with the onset of the following year’s snowmelt contributions. We identified the beginning of an MY and extracted the subsequent baseflow values using flow duration curves (FDCs) for 12 watersheds of varying sizes across Colorado, USA. Based on the findings, peak SWE and summer rain both dictate baseflow, especially for the larger watersheds evaluated, as identified by higher correlations with the MY-derived baseflow. Lags in the correlation between baseflow and peak SWE are best identified when low-snow years are investigated separately from high-snow years. The MY is a different and more effective approach to calculating baseflow using FDCs in snow-dominated watersheds in Colorado. Full article

Phytochemical profiling of six natural populations of Gentianella austriaca was performed by HPLC identification and quantification of a number of secondary metabolites, and evaluation of time series of peak areas by chemometric analysis. Phytochemical analysis of G. austriaca revealed the presence of iridoids, flavone-C-glucosides and xanthones. Twelve secondary metabolites were identified in the aerial parts, roots and seeds, including swertiamarin (SWM), gentiopicrin (GP), sweroside (SWZ), isoorientin (ISOOR), swertisin (SWE), demethylbellidifolin-8-O-glucoside (DMB-8-O-glc), bellidifolin-8-O-glucoside (BDF-8-O-glc), mangiferin (MGF), corymbiferin (CBF), corymbiferin-1-O-glucoside (CBF-1-O-glc), bellidifolin (BDF) and campestroside. Multivariate statistical analyses showed relatively low variability among populations according to secondary metabolite content. However, some pharmacologically important compounds were found in higher amounts in a few populations, which could be useful for conservation and future biotechnological procedures. Full article

A correct differentiation between hepatocellular carcinoma (HCC) and intracellular cholangiocarcinoma (ICC) is essential for clinical management and prognostic prediction. However, non-invasive differential diagnosis between HCC and ICC remains highly challenging. Dynamic contrast-enhanced ultrasound (D-CEUS) with standardized software is a valuable tool in the diagnostic approach to focal liver lesions and could improve accuracy in the evaluation of tumor perfusion. Moreover, the measurement of tissue stiffness could add more information concerning tumoral environment. To explore the diagnostic performance of multiparametric ultrasound (MP-US) in differentiating ICC from HCC. Our secondary aim was to develop an US score for distinguishing ICC and HCC. Between January 2021 and September 2022 consecutive patients with histologically confirmed HCC and ICC were enrolled in this prospective monocentric study. A complete US evaluation including B mode, D-CEUS and shear wave elastography (SWE) was performed in all patients and the corresponding features were compared between the tumor entities. For better inter-individual comparability, the blood volume-related D-CEUS parameters were analyzed as a ratio between lesions and surrounding liver parenchyma. Univariate and multivariate regression analysis was performed to select the most useful independent variables for the differential diagnosis between HCC and ICC and to establish an US score for non-invasive diagnosis. Finally, the diagnostic performance of the score was evaluated by receiver operating characteristic (ROC) curve analysis. A total of 82 patients (mean age ± SD, 68 ± 11 years, 55 men) were enrolled, including 44 ICC and 38 HCC. No statistically significant differences in basal US features were found between HCC and ICC. Concerning D-CEUS, blood volume parameters (peak intensity, PE; area under the curve, AUC; and wash-in rate, WiR) showed significantly higher values in the HCC group, but PE was the only independent feature associated with HCC diagnosis at multivariate analysis (p = 0.02). The other two independent predictors of histological diagnosis were liver cirrhosis (p < 0.01) and SWE (p = 0.01). A score based on those variables was highly accurate for the differential diagnosis of primary liver tumors, with an area under the ROC curve of 0.836 and the optimal cut-off values of 0.81 and 0.20 to rule in or rule out ICC respectively. MP-US seems to be a useful tool for non-invasive discrimination between ICC and HCC and could prevent the need for liver biopsy at least in a subgroup of patients. Full article

Objective: To evaluate the stiffness of the sciatic nerve by shear wave elastography (SWE) and to determine whether SWE can be used to predict diabetic foot ulcer (DFU) in a patient with diabetic peripheral neuropathy (DPN). Methods: Sixteen patients (thirty-two lower limbs) with unilateral DFU were studied retrospectively. The ultrasonographic parameters including cross-sectional area (CSA) of sciatic nerve, intraneural blood flow, peak systolic velocity (Vmax) and resistive index (RI) in the intraneural artery of the sciatic nerve, and the SWE stiffness value of the sciatic nerve were measured. The examinations of arteries of the lower limbs were also performed by ultrasound. According to the presence or absence of DFU, the 32 lower limbs were divided into two groups: the DFU group and the non-DFU group. The ultrasonographic parameters were compared between these two groups. Results: There was no significant difference (p > 0.05) between the two groups for CSA, intraneural blood flow, Vmax and RI in the intraneural artery of the sciatic nerve, and numbers of severe artery stenosis or full occlusion of the artery in the lower limbs. However, SWE stiffness values in the sciatic nerve in the DFU group are higher than the non-DFU group (p < 0.05). When the SWE stiffness values were used for prediction of DFU in patients with DPN, the area under the ROC curve (AUC) was 0.727 (95% CI: 0.541–0.868). When the best SWE stiffness value of 24.48 kPa was taken as a cutoff for prediction of DFU, the sensitivity was 62.50% (95% CI: 35.4–84.8%), and the specificity was 75% (95% CI: 47.6–92.7%). Conclusions: Sciatic nerve stiffness is significantly higher in lower limbs with DFU. SWE is a noninvasive imaging method that may be used to evaluate sciatic nerve stiffness, then potentially predict DFU in patients with DPN. Full article

Since the middle of the 20th century, the peak snowpack in the Upper Rio Grande (URG) basin of United States has been decreasing. Warming influences snowpack characteristics such as snow cover, snow depth, and Snow Water Equivalent (SWE), which can affect runoff quantity and timing in snowmelt runoff-dominated river systems of the URG basin. The purpose of this research is to investigate which variables are most important in predicting naturalized streamflow and to explore variables’ relative importance for streamflow dynamics. We use long term remote sensing data for hydrologic analysis and deploy R algorithm for data processing and synthesizing. The data is analyzed on a monthly and baseflow/runoff basis for nineteen sub-watersheds in the URG. Variable importance and influence on naturalized streamflow is identified using linear standard regression with multi-model inference based on the second-order Akaike information criterion (AICc) coupled with the intercept only model. Five predictor variables: temperature, precipitation, soil moisture, sublimation, and SWE are identified in order of relative importance for streamflow prediction. The most influential variables for streamflow prediction vary temporally between baseflow and runoff conditions and spatially by watershed and mountain range. Despite the importance of temperature on streamflow, it is not consistently the most important factor in streamflow prediction across time and space. The dominance of precipitation over streamflow is more obvious during baseflow. The impact of precipitation, SWE, sublimation, and minimum temperature on streamflow is evident during the runoff season, but the results vary for different sub-watersheds. The association between sublimation and streamflow is positive in the runoff season, which may relate to temperature and requires further research. This research sheds light on the primary drivers and their spatial and temporal variability on streamflow generation. This work is critical for predicting how warming temperatures will impact water supplies serving society and ecosystems in a changing climate. Full article

Snow influences land–atmosphere interactions in snow-dominated areas, and snow melt contributes to basin streamflows. However, estimating snowpack properties such as the snow depth (SD) and snow water equivalent (SWE) from land surface model simulations remains a challenge. This is, in part, due to uncertainties in the atmospheric forcing variables, which propagate into hydrological model predictions. This study implements the Weather Research and Forecasting (WRF)-Hydro framework with the Noah-Multiparameterization (Noah-MP) land surface model in the NOAA’s National Water Model (NWM) version 2.0 configuration to estimate snow in a single column and subsequently the streamflow across the Aroostook River’s sub-basins in Maine for water years (WY) 2014–2016. This study evaluates how differences between two atmospheric forcing datasets, the North American Land Data Assimilation version 2 (NLDAS-2) and in situ (Station), translate into differences in the simulation of snow. NLDAS-2 was used as the meteorological forcing in the retrospective NWM 2.0 simulations. The results from the single-column study showed that differences in the simulated SWE and SD were linked to differences in the 2 m air temperature (T2m), which influenced the precipitation partitioning of rain and snow, as parameterized in Noah-MP. The negative mean bias of −0.7 K (during the accumulation period) in T2m for NLDAS-2, compared to the Station forcing, was a major factor that contributed to the positive mean bias of +52 mm on average in the peak SWE in the NLDAS-2-forced Noah-MP simulation during the study period. The higher T2m values at the Station led to higher sensible heat fluxes towards the snowpack, which led to a higher amount of net energy at the snow’s surface and melt events during the accumulation season in Station-forced Noah-MP simulations. The results from the retrospective NWM version 2.0′s simulation in the basin showed that the streamflow estimates were closer to the United States Geological Survey gage observations at the two larger sub-basins (NSE = 0.9), which were mostly forested, compared to the two smaller sub-basins (NSE ≥ 0.4), which had more agricultural land-use. This study also showed that the spring snowmelt timing was captured quite well by the timing of the decline in the simulated SWE and SD, providing an early indication of melt in most sub-basins. The simulated fractional snow cover area (fSCA) however provided less information about the changes in snow or onset of snowmelt as it was mostly binary (full snow cover in winter), which differed from the more realistic fSCA values shown by the Moderate Resolution Imaging Spectroradiometer. Full article

Snow plays a major role in the hydrological cycle. Variations in snow duration and timing can have a negative impact on water resources. Excluding predicted changes in snowmelt rates and amounts could result in deleterious infrastructure, military mission, and asset impacts at military bases across the US. A change in snowpack can also lead to water shortages, which in turn can affect the availability of irrigation water. We performed trend analyses of air temperature, snow water equivalent (SWE) at 22 SNOTEL stations, and streamflow extremes for selected rivers in the snow-dependent and heavily irrigated Yakima River Basin (YRB) located in the Pacific Northwest US. There was a clear trend of increasing air temperature in this study area over a 30 year period (water years 1991–2020). All stations indicated an increase in average air temperatures for December (0.97 °C/decade) and January (1.12 °C/decade). There was also an upward trend at most stations in February (0.28 °C/decade). In December–February, the average air temperatures were 0.82 °C/decade. From these trends, we estimate that, by 2060, the average air temperatures for December–February at most (82%) stations will be above freezing. Furthermore, analysis of SWE from selected SNOTEL stations indicated a decreasing trend in historical SWE, and a shift to an earlier peak SWE was also assumed to be occurring due of the shorter snow duration. Decreasing trends in snow duration, rain-on-snow, and snowmelt runoff also resulted from snow modeling simulations of the YRB and the nearby area. We also observed a shift in the timing of snowmelt-driven peak streamflow, as well as a statistically significant increase in winter maximum streamflow and decrease in summer maximum and minimum streamflow trends by 2099. From the streamflow trends and complementary GEV analysis, we show that the YRB basin is a system in transition with earlier peak flows, lower snow-driven maximum streamflow, and higher rainfall-driven summer streamflow. This study highlights the importance of looking at changes in snow across multiple indicators to develop future infrastructure and planning tools to better adapt and mitigate changes in extreme events. Full article

Purpose: There is a clinical need to better non-invasively characterize the tumor microenvironment in order to reveal evidence of early tumor response to therapy and to better understand therapeutic response. The goals of this work are first to compare the sensitivity to modifications occurring during tumor growth for measurements of tumor volume, immunohistochemistry parameters, and emerging ultrasound parameters (Shear Wave Elastography (SWE) and dynamic Contrast-Enhanced Ultrasound (CEUS)), and secondly, to study the link between the different parameters. Methods: Five different groups of 9 to 10 BALB/c female mice with subcutaneous CT26 tumors were imaged using B-mode morphological imaging, SWE, and CEUS at different dates. Whole-slice immunohistological data stained for the nuclei, T lymphocytes, apoptosis, and vascular endothelium from these tumors were analyzed. Results: Tumor volume and three CEUS parameters (Time to Peak, Wash-In Rate, and Wash-Out Rate) significantly changed over time. The immunohistological parameters, CEUS parameters, and SWE parameters showed intracorrelation. Four immunohistological parameters (the number of T lymphocytes per mm² and its standard deviation, the percentage area of apoptosis, and the colocalization of apoptosis and vascular endothelium) were correlated with the CEUS parameters (Time to Peak, Wash-In Rate, Wash-Out Rate, and Mean Transit Time). The SWE parameters were not correlated with the CEUS parameters nor with the immunohistological parameters. Conclusions: US imaging can provide additional information on tumoral changes. This could help to better explore the effect of therapies on tumor evolution, by studying the evolution of the parameters over time and by studying their correlations. Full article

Flows originating from cold and mountainous watersheds are highly dependent on temperature and precipitation patterns, and the resulting snow accumulation and melt conditions, affecting the magnitude and timing of annual peak flows. This study applied a multiple linear regression (MLR) modelling framework to investigate spatial variations and relative importance of hydroclimatic drivers of annual maximum flows (AMF) and mean spring flows (MAMJflow) in 25 river basins across western Canada. The results show that basin average maximum snow water equivalent (SWEmax), April 1st SWE and spring precipitation (MAMJprc) are the most important predictors of both AMF and MAMJflow, with the proportion of explained variance averaging 51.7%, 44.0% and 33.5%, respectively. The MLR models’ abilities to project future changes in AMF and MAMJflow in response to changes to the hydroclimatic controls are also examined using the Canadian Regional Climate Model (CanRCM4) output for RCP 4.5 and RCP8.5 scenarios. The results show considerable spatial variations depending on individual watershed characteristics with projected changes in AMF ranging from −69% to +126% and those of MAMJflow ranging from −48% to +81% by the end of this century. In general, the study demonstrates that the MLR framework is a useful approach for assessing the spatial variation in hydroclimatic controls of annual maximum and mean spring flows in the western Canadian river basins. However, there is a need to exercise caution in applying MLR models for projecting changes in future flows, especially for regulated basins. Full article

The aim of this paper was to investigate the ability of some 2D and 3D numerical models to simulate flood waves in the presence of an isolated building or building array in an inundated area. Firstly, the proposed 2D numerical model was based on the finite-volume method (FVM) to solve 2D shallow-water equations (2D-SWEs) on structured mesh. The flux-difference splitting method (FDS) was utilized to obtain an exact mass balance while the Roe scheme was invoked to approximate Riemann problems. Secondly, the 3D commercially available CFD software package was selected, which contained a Flow 3D model with two turbulent models: Reynolds-averaged Navier-Stokes (RANs) with a renormalized group (RNG) and a large-eddy simulation (LES). The numerical results of an impact force on an obstruction due to a dam-break flow showed that a 3D solution was much better than a 2D one. By comparing the 3D numerical force results of an impact force acting on building arrays with the existence experimental data, the influence of velocity-induced force on a dynamic force was quantified by a function of the Froude number and the water depth of the incident wave. Furthermore, we investigated the effect of the initial water stage and dam-break width on the 3D-computed results of the peak value of force intensity. Full article