Search Results (28)

Climate warming has led to changes in floods in snow-packed mountain areas, but how snowmelt contributes to floods in the high-altitude Tibetan Plateau remains to be studied. To solve this problem, we propose a more reasonable method for evaluating snowmelt’s contributions to floods. We use a distributed hydrological model with the capability to track snowmelt paths in different media, such as snowpack, soil, and groundwater, to assess snowmelt’s contribution to peak discharge. The study area, the Xiying River basin, is located northeast of the Tibetan Plateau. Our results show that in the past 40 years, the average annual air temperature in the basin has increased significantly at a rate of 0.76 °C/10a. The annual precipitation (precipitation is the sum of rainfall and snowfall) decreased at a rate of 5.59 mm/10a, while the annual rainfall increased at a rate of 11.01 mm/10a. These trends were not obvious. The annual snowfall showed a significant decrease, at a rate of 14.41 mm/10a. The contribution of snowmelt to snowmelt-driven floods is 85.78%, and that of snowmelt to rainfall-driven floods is 10.70%. Under the influence of climate change, the frequency of snowmelt-driven floods decreased significantly, and flood time advanced notably, while the intensity and frequency of rainfall-driven floods slowly decreased in the basin. The causes of the change in snowmelt-driven floods are the significant increase in air temperature and the noticeable decrease in snowfall and snowmelt runoff depth. The contribution of snowmelt to rainfall-driven floods slowly weakened, resulting in a slight decrease in the intensity and frequency of rainfall-driven floods. The results also indicate that rising air temperature could decrease snowmelt-driven floods. In snow-packed mountain areas, rainfall and snowmelt together promote the formation of and change in floods. While rainfall dominates peak discharge, snowpack and snowmelt play a significant role in the formation and variability of rainfall-driven floods. The contributions of snowmelt and rainfall to floods have changed under the influence of climate change, which is the main cause of flood variability. The changed snowmelt adds to the uncertainties and could even decrease the size and frequency of floods in snow-packed high mountain areas. This study can help us understand the contributions of snowmelt to floods and assess the flood risk in the Tibetan Plateau under the influence of climate change. Full article

A novel self-heating technique is proposed to clear snow from photovoltaic panels as a solution to the issue of winter snow accumulation in photovoltaic (PV) power plants. This approach aims to address the shortcomings of existing methods. It reduces PV cell wear, resource loss, and safety risks, without the need for additional devices. A self-heating current is applied to the solar panel to melt the snow covering its surface, which is then allowed to slide off the panel due to gravity. The proposed system consists of a bidirectional DC-DC converter, which removes the snow cover by heating the solar PV modules using electricity from the grid or electric vehicle (EV) batteries. It also charges the EV battery pack and/or supplies the DC bus when no EV is plugged into the charging station. For each mode of operation, a current-controlled system was implemented using a PI controller and a model predictive controller (MPC). The MPC approach achieved a faster rise time, shorter settling time, very low current ripples, and high stability for the proposed system. Specifically, the settling time decreased from 9 ms and 155 ms when using the PI controller at 20 µs and 35 µs with the MPC controller for both the buck and boost modes, respectively. Full article

Climate change is affecting the snow cover conditions on a global scale, leading to changes in the extent and duration of snow cover as well as variations in the start and end of snow cover seasons. These changes can have a paramount impact on runoff and water availability, especially in catchments that are characterized by nival runoff regimes, e.g., the Syr Darya in Central Asia. This time series analyses of daily MODIS snow cover products and in situ data from hydrological stations for the time series from 2000 through 2022 reveal the influences of changing snow cover on the runoff regime. All catchments showed a decrease in spring snow cover duration of −0.53 to −0.73 days per year over the 22-year period. Catchments located farther west are generally characterized by longer snow cover duration and experience a stronger decreasing trend. Runoff timing was found to be influenced by late winter and spring snow cover duration, pointing towards earlier snowmelt in most of the regions, which affects the runoff in some tributaries of the river. The results of this study indicate that the decreasing snow cover duration trends lead to an earlier runoff, which demands more coordinated water resource management in the Syr Darya catchment. Further research is recommended to understand the implications of snow cover dynamics on water resources in Central Asia, crucial for agriculture and hydropower production. Full article

Caribou (Rangifer tarandus) undergo exceptionally large, annual synchronized migrations of thousands of kilometers, triggered by their shared environmental stimuli. The proximate triggers of those migrations remain mysterious, though snow characteristics play an important role due to their influence on the mechanics of locomotion. We investigate whether the snow melt–refreeze status relates to caribou movement, using previously collected Global Positioning System (GPS) caribou collar data. We analyzed 117 individual female caribou with >30,000 observations between 2007 and 2016 from the Bathurst herd in Northern Canada. We used a hierarchical model to estimate the beginning, duration, and end of spring migration and compared these statistics against snow pack melt characteristics derived from 37 GHz vertically polarized (37V GHz) Calibrated Enhanced-Resolution Brightness Temperatures (CETB) at 3.125 km resolution. The timing of migration for Bathurst caribou generally tracked the snowmelt onset. The start of migration was closely linked to the main melt onset in the wintering areas, occurring on average 2.6 days later (range −1.9 to 8.4, se 0.28, n = 10). The weighted linear regression was also highly significant (p-value = 0.002, $R^2=0.717$). The relationship between migration arrival times and the main melt onset on the calving grounds ($R^2$ = 0.688, p-value = 0.003), however, had a considerably more variable lag (mean 13.3 d, se 0.67, range 3.1–20.4). No migrations ended before the main melt onset at the calving grounds. Thawing conditions may provide a trigger for migration or favorable conditions that increase animal mobility, and suggest that the snow properties are more important than snow presence. Further work is needed to understand how widespread this is and why there is such a relationship. Full article

Human schistosomiasis is one of neglected tropical diseases that remain highly prevalent in sub-Saharan Africa (SSA). Human schistosomiasis is mainly caused by two species, Schistosoma haematobium and S. mansoni, leading to urogenital and intestinal schistosomiasis, respectively. The World Health Organization (WHO) recommends mass drug administration (MDA) with praziquantel as the primary method of global intervention. Currently, MDA with praziquantel covers over half of the target population in endemic SSA countries. However, an accurate diagnosis is crucial for monitoring and evaluating the effectiveness of MDA. The standard diagnosis of both urogenital and intestinal schistosomiasis relies on the microscopic identification of eggs. However, the diagnostic sensitivity of this approach is low, especially for light or ultra-light infections. This is because Schistosoma eggs are laid inside of the venous plexus of the urinary bladder or mesenteric vein, where the adult flukes live. Approximately half of the eggs circulate in the blood vessels or are packed in neighboring tissues, while the remaining half are expelled into the lumen of the urinary bladder or intestine intermittently when the blood vessels are ruptured. In the field setting, the accuracy of any diagnostic method is critical for proper management of the intervention. The present article reviews the recent prevalence of urogenital schistosomiasis in SSA and highlights the practical limitations of diagnostic methods such as urine microscopy, urine reagent strips, molecular diagnosis, and ultrasound scanning in the field setting. Despite continuous global efforts to eliminate schistosomiasis over the past 20 years, many areas still remain endemic in SSA. No single diagnostic approach achieves acceptable sensitivity and specificity in the field setting. Therefore, any field survey should employ a combination of these methods based on the purpose of the study to accurately monitor and evaluate urogenital schistosomiasis. Based on diagnostic values and a cost–benefit analysis, a urine reagent strip test can replace urine microscopy in the field setting. The WHO criteria by ultrasound diagnosis should be updated including the echogenic snow sign and contour distortion. Full article

Research on solid precipitation and snow cover, especially in mountainous areas, suffers from problems related to the lack of on-site observations and the low reliability of measurements, which is often due to instruments that are not suitable for the environmental conditions. In this context, the study area is the Monti Sibillini National Park, and it is no exception, as it is a mountainous area located in central Italy, where the measurements are scarce and fragmented. The purpose of this research is to provide a characterization of the snow cover with regard to maximum annual snow depth, average snow depth during the snowy period, and days with snow cover on the ground in the Monti Sibillini National Park area, by means of ground weather stations, and also analyzing any trends over the last 30 years. For this research, in order to obtain reliable snow cover data, only data from weather stations equipped with a sonar system and manual weather stations, where the surveyor goes to the site each morning and checks the thickness of the snowpack and records, it were collected. The data were collected from 1 November to 30 April each year for 30 years, from 1991 to 2020; six weather stations were taken into account, while four more were added as of 1 January 2010. The longer period was used to assess possible ongoing trends, which proved to be very heterogeneous in the results, predominantly negative in the case of days with snow cover on the ground, while trends were predominantly positive for maximum annual snow depth and distributed between positive and negative for the average annual snow depth. The shorter period, 2010–2022, on the other hand, ensured the presence of a larger number of weather stations and was used to assess the correlation and presence of clusters between the various weather stations and, consequently, in the study area. Furthermore, in this way, an up-to-date nivometric classification of the study area was obtained (in terms of days with snow on the ground, maximum height of snowpack, and average height of snowpack), filling a gap where there had been no nivometric study in the aforementioned area. The interpolations were processed using geostatistical techniques such as co-kriging with altitude as an independent variable, allowing fairly precise spatialization, analyzing the results of cross-validation. This analysis could be a useful tool for hydrological modeling of the area, as well as having a clear use related to tourism and vegetation, which is extremely influenced by the nivometric variables in its phenology. In addition, this analysis could also be considered a starting point for the calibration of more recent satellite products dedicated to snow cover detection, in order to further improve the compiled climate characterization. Full article

Globally, the seasonal snow cover is the areal largest, the most short-lived and the most variable part of the cryosphere. Remote sensing proved to be a reliable tool to investigate their short-term variations worldwide. The medium-resolution sensor MODIS sensor has been delivering daily snow products since the year 2000. Remaining data gaps due to cloud coverage or polar night are interpolated using the DLR’s Global SnowPack (GSP) processor which produces daily global cloud-free snow cover. With the conclusion of the hydrological year 2022 in the northern hemisphere, the snow cover dynamics of the last 23 hydrological years can now be examined. Trends in snow cover development over different time periods (months, seasons, snow seasons) were examined using the Mann–Kendall test and the Theil–Sen slope. This took place as both pixel based and being averaged over selected hydrological catchment areas. The 23-year time series proved to be sufficient to identify significant developments for large areas. Globally, an average decrease in snow cover duration of −0.44 days/year was recorded for the full hydrological year, even if slight increases in individual months such as November were also found. Likewise, a large proportion of significant trends could also be determined globally at the catchment area level for individual periods. Most drastic developments occurred in March, with an average decrease in snow cover duration by −0.16 days/year. In the catchment area of the river Neman, which drains into the Baltic Sea, there is even a decrease of −0.82 days/year. Full article

Range anxiety is a significant contributor to consumer reticence when purchasing electric vehicles (EVs). To alleviate this concern, new commercial EVs readily achieve over 200 miles of range, as found by the United States Environmental Protection Agency (EPA). However, this range, measured under idealized conditions, is often not encountered in real-world conditions. As a result, this effort describes the simplest model that incorporates all key factors that affect the range of an EV. Calibration of the model to EPA tests found an average deviation of 0.45 and 0.57 miles for highway and city ranges, respectively, among seven commercial EVs. Subsequent predictions found significant losses based on the impact of road grade, wind, and vehicle speed over a Kansas interstate highway. For cabin conditioning, up to 57.8% and 37.5% losses in range were found when simulating vehicles at 20 °F and 95 °F, respectively. Simulated aging of the vehicle battery pack showed range losses up to 53.1% at 100,000 miles. Model extensions to rain and snow illustrated corresponding losses based on the level of precipitation on the road. All model outcomes were translated into an Excel spreadsheet that can be used to predict the range of a generic EV over Kansas-centric roads. Full article

The freeze–thaw process controls several hydrologic processes, including infiltration, runoff, and soil erosion. Simulating this process is important, particularly in cold and mountainous regions. The Soil and Cold Regions Model (SCRM) was used to simulate, study, and understand the behavior of twelve homogenous soils subject to a freeze–thaw process, based on meteorological data at a snow-dominated forest site in Laramie, WY, USA, from 2010 and 2012. The relationships of soil pore size, soil particle contact, and meteorological data were varied. Our analysis of the model compared simulations using metrics such as soil frost depth, days with ice, and maximum ice content. The model showed that the freeze–thaw process was strongest in the period with a shallow snowpack, with particle packing within the soil profile being an important factor in this process; that soil texture and water content control soil thermal properties; and that water movement towards the freezing front was especially important in fine-textured soils, where water and ice were concentrated in the upper layers. Based on these results, future research that combines a broader set of soil conditions with an extended set of field meteorology and real soil data could elucidate the influence of soil texture on the thermal properties related to soil frost. Full article

Different factors affect solar photovoltaic (PV) systems by decreasing input energy and reducing the conversion efficiency of the system. One of these factors is the effect of snow cover on PV panels, a subject lacking sufficient academic research. This paper reviews and compares current research for snow removal in solar PV modules. Additionally, this paper presents the design, analysis and modelling of a smart heating system for solar PV Electric Vehicle (EV) charging applications. The system is based on a bidirectional DC-DC converter that redirects the grid/EV-battery power into heating of the solar PV modules, thus removing snow cover, as well as providing the function of MPPT when required to charge the EV battery pack. A control scheme for each mode of operation was designed. Subsequently, a performance evaluation by simulating the system under various conditions is presented validating the usefulness of the proposed converter to be used in solar PV systems under extreme winter conditions. Full article

Accurate knowledge of snow cover extent, depth (SD), and water equivalent is essential for studying the global water cycle, climate, and energy–mass exchange in the Earth–atmosphere system, as well as for water resources management. Ratio between SAR cross- and co-polarization backscattering (${\sigma }_{VH}/{\sigma }_{VV}$) was used to monitor SD during snowy months in mountain areas; however, published results refer to short periods and show lack of correlation during non-snowy months. We analyze Sentinel-1A images from a study area in Central Pyrenees to generate and investigate (i) time series of ${\sigma }_{VH}/{\sigma }_{VV}$ spatial dispersion, (ii) spatial distribution of pixelwise ${\sigma }_{VH}/{\sigma }_{VV}$ temporal standard deviation, and (iii) fundamental modes of ${\sigma }_{VH}/{\sigma }_{VV}$ evolution by non-negative matrix factorization. The spatial dispersion evolution and the first mode are highly correlated (correlation coefficients larger than 0.9) to SD evolution during the whole seven-year-long period, including snowy and non-snowy months. The local incidence angle strongly affects how accurately ${\sigma }_{VH}/{\sigma }_{VV}$ locally follows the first mode; thus, areas where it predominates are orbit-dependent. When combining ascending- and descending-orbit images in a single data matrix, the first mode becomes predominant almost everywhere snow pack persists during winter. Capability of our approach to reproduce SD evolution makes it a very effective tool. Full article

Recently, the reconfigurable intelligent surface (RIS)-aided communication system has emerged as a promising candidate for future millimeter-wave wireless communications. Due to the short wavelength of millimeter wave, the antennas on the base station (BS) and the elements on the RIS can be densely packed. It usually causes the BS and RIS to be blocked by rain, snow, or dust, which will change the channel’s characteristics and decrease the performance of communication system. In order to solve this problem, we propose an iterative compressed sense based algorithm for joint estimating the blockage coefficients of RIS and BS. Then, for the complete blockage scenario, we propose a low complexity algorithm for estimating the blockage coefficients. Our simulation results demonstrate the superior performance of the proposed algorithm to existing ones. Full article

The boreal winter 2019/2020 was very irregular in Europe. While there was very little snow in Central Europe, the opposite was the case in northern Fenno-Scandia, particularly in the Arctic. The snow cover was more persistent here and its rapid melting led to flooding in many places. Since the last severe spring floods occurred in the region in 2018, this raises the question of whether more frequent occurrences can be expected in the future. To assess the variability of snowmelt related flooding we used snow cover maps (derived from the DLR’s Global SnowPack MODIS snow product) and freely available data on runoff, precipitation, and air temperature in eight unregulated river catchment areas. A trend analysis (Mann-Kendall test) was carried out to assess the development of the parameters, and the interdependencies of the parameters were examined with a correlation analysis. Finally, a simple snowmelt runoff model was tested for its applicability to this region. We noticed an extraordinary variability in the duration of snow cover. If this extends well into spring, rapid air temperature increases leads to enhanced thawing. According to the last flood years 2005, 2010, 2018, and 2020, we were able to differentiate between four synoptic flood types based on their special hydrometeorological and snow situation and simulate them with the snowmelt runoff model (SRM). Full article

As drought occurs in a region it can have cascading effects through the water cycle. In this study, we explore the temporal co-evolution of various components of the hydrologic cycle in California from 2002 to 2018. We combine information from the Gravity Recovery and Climate Experiment (GRACE) satellites, the North American Land Data Assimilation System (NLDAS) suite of models, and the California Department of Water Resources (DWR) reservoir levels to analyze dynamics of Total Water Storage (TWS), soil moisture, snow pack, large reservoir storage, and ultimately, groundwater. For TWS, a trend of −2 cm/yr is observed during the entire time period of our analysis; however, this rate increases to about −5 cm/yr during drought periods (2006−2010 and 2012−2016). Results indicate that the majority of the loss in TWS is caused by groundwater depletion. Using proper error accounting, we are able to identify the start, the peak, and the ending of the drought periods for each individual water state variable in the study domain. We show that snow and soil moisture are impacted earlier and recover faster than surface water and groundwater. The annual and year-to-year dynamics shown in our results portray a clear cascading effect of the hydrologic cycle on the scale of 8−16 months. Full article

The vertical diffuse attenuation coefficient for downward plane irradiance ( $K_d$ ) is an apparent optical property commonly used in primary production models to propagate incident solar radiation in the water column. In open water, estimating $K_d$ is relatively straightforward when a vertical profile of measurements of downward irradiance, $E_d$ , is available. In the Arctic, the ice pack is characterized by a complex mosaic composed of sea ice with snow, ridges, melt ponds, and leads. Due to the resulting spatially heterogeneous light field in the top meters of the water column, it is difficult to measure at single-point locations meaningful $K_d$ values that allow predicting average irradiance at any depth. The main objective of this work is to propose a new method to estimate average irradiance over large spatially heterogeneous area as it would be seen by drifting phytoplankton. Using both in situ data and 3D Monte Carlo numerical simulations of radiative transfer, we show that (1) the large-area average vertical profile of downward irradiance, $\overline{E_d}\left(z\right)$ , under heterogeneous sea ice cover can be represented by a single-term exponential function and (2) the vertical attenuation coefficient for upward radiance ( $K_{Lu}$ ), which is up to two times less influenced by a heterogeneous incident light field than $K_d$ in the vicinity of a melt pond, can be used as a proxy to estimate $\overline{E_d}\left(z\right)$ in the water column. Full article