Science topic

Snow - Science topic

Snow are frozen water crystals that fall from the ATMOSPHERE.
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Since i am going for, Glacier studies optical based data for correlation. it is imperative to select suitable band combination your replies would be highly appreciated.
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Hi Gurnam,
For the Snow, Glacier and Ice studies using optical data, the reflectance of snow is high in Green band (0.53 - 0.59 µm), while in SWIR band (1.57 - 1.65 µm) snow reflects less in compared to other surfaces. Also it will help you to differentiate snow from clouds.
Use these bands along with other bands like blue, red and NIR for Visual Analysis of optical satellite imageries for your study. Also NDSI is significantly best for isolating snow, ice from other land cover like rocks and vegetations. This will enhance your ability to accurately map and analyze snow cover and glacier area.
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Dear colleagues
Could someone send to me an already validated protocol for microplastic analysis in fresh snow that includes sampling and analysis in the lab?
I suppose there is no need to digest it, but I would greatly appreciate it if someone has a validated protocol to send me.
greetings
Dr. JM Rios
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The paper by Bergmann et al has quite a detailed description of the protocol for sampling and processing: ,Melanie Bergmann et al.
White and wonderful? Microplastics prevail in snow from the Alps to the Arctic.Sci. Adv.5,eaax1157(2019).DOI:10.1126/sciadv.aax1157
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I am thinking that Skiing done by people in upper reaches is causing early melting of snow, that is causing later on scarcity of water in lower regions. If we will not disturb with snow it will took time to melt.
What is your suggestion regarding this Question?
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what are you thinking
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Dear All,
I am currently in the process of learning the Spatial Processes in Hydrology (SPHY) model, specifically working with the preprocessor module in the Sikkim Himalayan region, which is part of the High Mountain Asia (HMA) region.
I have encountered an issue in the "Module" tab of the preprocessing tool. I selected and saved data for glaciers, snow, groundwater, and routing, but after running this step, only the GlacFrac map appears in the table of contents. In the Python console, it indicates that "temp2" and "temp3.tif" have been removed.
I am seeking assistance in understanding the cause of this problem and how to resolve it. Any guidance on this matter would be greatly appreciated.
Thank you in advance for your help.
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Hi ,
It seems like you're experiencing an issue with the Spatial Processes in Hydrology (SPHY) model while working with the preprocessor module in the Sikkim Himalayan region, which is part of the High Mountain Asia (HMA) region. Specifically, you've encountered a problem where only the GlacFrac map appears in the table of contents after running the preprocessing step, and it indicates that "temp2" and "temp3.tif" have been removed in the Python console.
This issue might be related to several factors, so let's troubleshoot it step by step:
  1. Check Data Availability: First, ensure that you have all the necessary input data for glaciers, snow, groundwater, and routing in the correct file formats and locations. Check if "temp2" and "temp3.tif" are present in the specified directories.
  2. Data Compatibility: Verify that the data you're using is compatible with the SPHY model. Ensure that the data is in the right projection, coordinate system, and format that the model expects. Any inconsistencies could lead to issues during preprocessing.
  3. Layer Ordering: In some GIS software, the order of layers in the table of contents can affect the processing. Make sure that the layers for glaciers, snow, groundwater, and routing are listed in the correct order, with the glaciers layer on top if applicable.
  4. Naming Conventions: Double-check that your data layers have appropriate and consistent naming conventions. Sometimes, naming discrepancies can lead to data not being recognized or processed correctly by the model.
  5. Layer Properties: Review the properties of the layers you're working with, such as data type, compression, and spatial resolution. Ensure they match the requirements of the SPHY model.
  6. Error Logs: Examine the Python console logs carefully. Look for any specific error messages or warnings related to "temp2" and "temp3.tif." These messages may provide clues about what went wrong.
  7. Documentation and Support: Consult the SPHY model documentation and user guides for troubleshooting tips specific to the version you are using. Additionally, consider reaching out to the SPHY model's support or user community for assistance. They may have encountered and solved similar issues.
  8. Software Version Compatibility: Ensure that the version of the SPHY model you are using is compatible with your GIS software. Sometimes, updates or changes in either the model or the GIS software can cause compatibility issues.
  9. File Permissions: Check that you have the necessary permissions to access and modify the files in the specified directories.
  10. Reinstallation or Update: If none of the above steps resolve the issue, consider reinstalling or updating the SPHY model or your GIS software to the latest compatible versions.
By systematically checking these aspects and addressing any discrepancies or errors, you should be able to identify and resolve the problem with the SPHY model's preprocessing tool.
If you encounter specific error messages or have further details, please provide them for more targeted assistance.
If you finf my reply useful , please recommend it . Thanks
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Hello, everyone!recently we are collecting ground hyperspectral measurement data ( such as soil, vegetation, snow ) with a wavelength range of 400-2500 nm. We have collected some spectral data, but it is still not enough for our research. Therefore, we would like to ask you where there is a free open spectral library ? Or we can cooperate together. Thank you very much.
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When I did a google search using these keywords, several sources appeared: agriculture hyperspectral download.
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Does snow cover play an important role at the global and regional level? So that it reflects a large part of the incoming radiation and maintains the earth's radiation balance?
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Yes, snow cover plays a significant role at both global and regional levels, impacting various aspects of the Earth's climate, ecosystems, and water resources.
Here are some of the key ways in which snow cover is important:
  1. Climate Regulation: Snow cover acts as a reflector, or "albedo," which means it reflects sunlight back into space. This reflective property helps cool the Earth's surface by reducing the amount of solar energy absorbed. Changes in snow cover can therefore influence local and global temperature patterns.
  2. Water Resource Management: Snowpack serves as a natural reservoir of freshwater in many regions. As snow melts during the warmer months, it contributes to river flows and groundwater recharge, providing a crucial source of water for agriculture, drinking water, and hydropower generation in both mountainous and downstream areas.
  3. Ecosystems: Many ecosystems in high-latitude and mountainous regions are adapted to seasonal snow cover. Plants and animals have evolved specific strategies to survive and thrive in snowy conditions. Changes in snow cover duration and depth can disrupt these ecosystems, affecting species distributions, food availability, and habitat suitability.
  4. Sea Level Rise: Snow cover also contributes to sea level rise. When glaciers and ice caps that are covered by snow melt, the resulting runoff contributes to rising sea levels. This is a significant concern in the context of climate change and its impact on polar ice sheets and glaciers.
  5. Weather Patterns: Snow cover can influence local weather patterns by modifying temperature and humidity conditions. Snow-covered areas tend to have cooler temperatures, which can impact weather systems and atmospheric circulation patterns, potentially affecting weather events in nearby regions.
  6. Societal Impact: In regions where snowfall is a regular occurrence, snow cover can have profound impacts on daily life, transportation, and infrastructure. Snowstorms and heavy snowfall can disrupt travel, cause power outages, and lead to economic losses.
  7. Climate Change Indicator: Changes in snow cover patterns are considered indicators of climate change. Rising temperatures can lead to shifts in snowfall patterns, earlier spring melts, and reductions in snow cover extent and duration. Monitoring these changes provides valuable information about the state of the climate.
  8. Mountain Glaciers: Snowfall contributes to the growth of glaciers in mountainous regions. Glaciers act as reservoirs of freshwater and can influence regional water availability. Changes in snowfall patterns can impact glacier mass balance and contribute to the overall retreat of glaciers due to warming temperatures.
Overall, snow cover is a dynamic and important component of the Earth's climate system, with far-reaching implications for ecosystems, water resources, weather patterns, and the global climate. Changes in snow cover patterns are closely monitored by scientists to better understand the impacts of climate change and to inform decision-making for resource management and adaptation strategies.
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It would be a project report conducte by Dobbertin M, Jüngling E (2009) Totholzverwitterung und C-Gehalt. Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf, 3 p which is currently not available in the internet anymore.
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Dear Steffen,
to my knowledge, the "Scientific contributions by x [while affiliated with y]"-pages are created automatically, i.e., by the RG algorithms. Of course, the system can only pick up publications which are available or at least fully referenced somewhere on the web. So, as always, the AI is only as good as the data it is fed with...
With best wishes,
Julius
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I am working on Snow hazards and Remote sensing. I want to calculate SWE from D-InSAR image. How can I calculate equation ⑸ in Rott et al. (2003) from Fig. 1?
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The propagation delay of microwave signals due to snow can be calculated based on the refractive index of snow and the thickness of the snow layer. The refractive index of a material is a measure of how much the speed of light is reduced when passing through that material. It depends on the dielectric constant of the material.
The formula to calculate the microwave propagation delay (Δt) due to snow is:
Δt = (2 * d * n) / c
where:
  • Δt is the propagation delay (in seconds)
  • d is the thickness of the snow layer (in meters)
  • n is the refractive index of snow
  • c is the speed of light in a vacuum (approximately 3.00 x 10^8 meters per second)
The refractive index of snow can vary with its density, temperature, and other factors. It is generally greater than 1, which means that the speed of light is reduced when passing through snow compared to its speed in a vacuum. As a result, microwave signals passing through snow experience a delay.
The thickness of the snow layer (d) is the distance the microwave signal travels through the snow. It's important to measure or estimate the thickness accurately for precise calculations.
Keep in mind that this formula provides an approximate calculation of the propagation delay due to snow. In reality, the refractive index of snow can be affected by its structure and other environmental factors. For more accurate measurements, specialized instruments and models are often used in scientific studies and engineering applications.
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Snow accumulation on road surfaces will turn into ice by over-passing traffic, especially heavy traffic. Ice will move by a small amount every time a vehicle passes. Why will it end up in pointed, thorn-like peaks?
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I don't think it is result of any extra weight on snow. I guess it could be some mineral from the ground oozing or precipitating and forming such shapes as in the case of stalagmites.
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hello
I am using ESRI LULC for a glacierized watershed in ArcSWAT.
What will be the classification of flooded vegetation in the database file?
Similarly, what should I use for snow/ice to make a look-up table?
The description of ESRI classification is attached herewith.
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Thank You so much for your Answer !
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What are the factors that cause the formation of snow? Does snowfall in a region generally depend on climatic and geographical factors such as latitude, altitude, distance from the main water sources and the type of air mass cycle in the region? What other factors are involved in your opinion?
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Dear Abbas,
in addition to the factors you mention (altitude, lattitude, ambient air temperature etc.), seed crystals usually constitute another precondition for snow to form. However, even under the presence of suitable seed crystals in the atmosphere, snow formation usually only sets in below - 12 °C. And if there are no seed crystals (at all), fluid water may in some instances not even freeze/cristalize up to almost - 50 °C.
And, yes, as you say, cloud formation will also depend on the prevailing weather systems in the given area. Mid-lattitude dynamic low-pressure systems with their warm and cold fronts are a typical example. Proximity to large water bodies can also enhance or enable snow formation as does the combined effect of winds and orography (mountains).
Finally, actual snowfall observed at ground level of course requires the snow crystals not to melt on their (usually quite slow!) way to the ground. So the temperature gradient between ground level and the snow cloud formation level will be important, too.
Best,
Julius
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I have computed the evapotranspiration in MATLAB using the FAO PM method and found negative values especially in January month. The study site was in Sicily, Italy ( very rare for snow and uncommon the temperature less than 0 degree temperature) Could yo suggest me why the value is negative, please?
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Dear Tagele,
Negative values of evapotranspiration are not physically meaningful and suggest that there may be a problem with your calculation or input data. There are several possible reasons why you may be getting negative values, including:
1: Incorrect input data: Check that you have entered the correct values for temperature, humidity, wind speed, solar radiation, and other inputs. Make sure that the units are consistent and appropriate for the FAO PM method.
2: Errors in the code: Check your MATLAB code to ensure that there are no errors or typos in the equations or formulas. Check that you have used the correct constants and parameters for the FAO PM method.
3: Physical limitations: Negative values of evapotranspiration are not physically meaningful, as it represents a loss of water from the system. It may be that the FAO PM method is not appropriate for the conditions at your study site, or that there are physical limitations to the system that are not captured by the model.
4: Data quality: Check the quality of your input data to ensure that it is accurate and representative of the conditions at your study site. If your data is incomplete or unreliable, it may lead to negative values of evapotranspiration.
It is also worth noting that the FAO PM method may not be appropriate for all conditions and locations, and there may be other models or methods that are better suited for your study site. You may want to consider consulting with an expert in the field or seeking additional guidance from the literature to ensure that your evapotranspiration calculations are accurate and meaningful.
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There it remains unspecified about, among those two legged beings, before snow ball earth of ice age era, whether there were human beings and their traces not yet discovered due to having no knowledge of whereabouts if there were any human living beings on the earth?
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Hello Fatima; A brief Google search suggests that the genus Homo originated sometime between 3 million and 2 million years ago. Of course, this depends on how the genus is defined and there seems to be some disagreement about that. Do you include in your "human beings" term only H. sapiens? Or, are the other species in the genus also human? Your question will turn out to have a complicated answer. Best regards, Jim Des Lauriers
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weather forecasting, the prediction of the weather through application of the principles of physics, supplemented by a variety of statistical and empirical techniques. In addition to predictions of atmospheric phenomena themselves, weather forecasting includes predictions of changes on Earth’s surface caused by atmospheric conditions—e.g., snow and ice cover, storm tides, and floods. Measurements and ideas as the basis for weather prediction weather conditions
source: Weather forecasting | Methods, Importance, & History | Britannica
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There are several recent projects that connect computer and electrical engineering, computer science, mathematics, and information sciences with weather forecasting. Here are some examples:
  1. Deep Learning for Weather Forecasting: This project explores the use of deep learning techniques to improve weather forecasting accuracy. The project uses convolutional and recurrent neural networks to predict the movement of storms and improve the accuracy of weather forecasting.
  2. High-Performance Computing for Weather Forecasting: This project focuses on developing high-performance computing systems to process and analyze large weather datasets. The project uses advanced computing techniques to improve the speed and accuracy of weather forecasting models.
  3. Data Assimilation for Weather Forecasting: Data assimilation is the process of combining multiple sources of data to create a more accurate forecast. This project focuses on developing algorithms for data assimilation in weather forecasting models. The project combines computer science, mathematics, and information science to develop algorithms that can efficiently assimilate large volumes of data.
  4. Internet of Things (IoT) for Weather Forecasting: IoT refers to a network of connected devices that can collect and share data. This project explores the use of IoT devices to collect weather data and improve weather forecasting accuracy. The project combines computer and electrical engineering with weather science to develop IoT devices that can collect and transmit weather data in real-time.
  5. Computational Fluid Dynamics (CFD) for Weather Forecasting: CFD is a branch of engineering that focuses on simulating fluid flow. This project explores the use of CFD techniques to simulate weather patterns and improve weather forecasting accuracy. The project combines computer science and engineering with weather science to develop advanced simulations of weather patterns.
These projects demonstrate the interdisciplinary nature of weather forecasting research and the important role that computer and electrical engineering, computer science, mathematics, and information science play in improving the accuracy of weather forecasting models.
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Dear All,
I want to know any available article of statistical method to estimate - Human activities (Anthropogenic), which can accelerate snow melting. Please help to find any suitable method to any published article on this topic.
There are several human activities (GHGs emission, CO2 release, urbanization etc.), which resulted massive snow melting now a days. But to quantify the percentage (%) of share coming from Human activities, which causing SCA change.
Thanks in advance.
Abhishek Banerjee
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You can give SAR coherence for snow cover mapping a try.
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Throwing projectiles on mountains to let down accumulated snow and falling rocks;
The north part of earth is filled with snow; and many people have been stuck in snow & avalanches; do you agree with using projectiles for prevention?
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No, I do not agree with using projectiles for prevention in the north part of the earth. The risk of throwing projectiles into the snow and avalanches is too great, and can potentially cause more harm than good. Instead, I believe that the best way to prevent avalanches and snow accumulation is to promote avalanche awareness and education, as well as to invest in better avalanche prevention infrastructure.
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It is said that due to high surface reflectance(bright surface) of snow covered region, estimation of AOD using spectral channels of sensors onboard satellites like MODIS, SUOMI-NPP and Sentinel-3, is limited. I am studying snow melt in Himalayan region where Aerosol Optical depth is crucial for correct estimation of Radiation processes. Are there any methods (or) alternative satellite sensors (or) indirect methods to capture the spatio-temporal variability of AOD over Himalayan snow covered regions?
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I agree with Michel M. Verstraete about MISR data. In addition, you may also consider looking at POLDER aerosol products. As polarized reflectance from snow surface can be small enough, which is an advantage for retrieving aerosols over snow. This is one article about this:
Also, CALIOP lidar may provide aerosol retrievals over snow as well, although with limited spatial coverage.
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I am working on loads such as snow load, and I was wondering how it is achievable to assign a uniform horizontal projected load to a spherical shell (i.e. not applied to the reference surface)?
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Can you share your Abaqus model (.inp)?
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I am working on a study of sample size 100 collected through snow ball technique using referral method. Can you please guide that in order to achieve my objectives, should I apply  parametric techniques ( as sample size is 100) or should apply non- parametric techniques ( as samples are not selected randomly) ??
Please pour your valuable suggestions 
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In my opinion,
:::> Size of sample is relative, based on the population size the sample size is determined.
::::> Sample size of 350 is considered to be enough for a larger population.
Incase of a non parametric study which is used incase of a small sample size such as 50 or 10. Non parametric tests are conducted when the data is not normally distributed and the sample size is small like 10 or 50.
Parametric tests are conducted when sample size is large say 250-350 or more.
If you as a researcher can get responses till 250-350 through snowball sampling then go ahead.
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Hello Dear Users!
I have a problem with low value of surface runoff. In my output.std is:
PRECIP =    620.9 MM
SNOW FALL =   52.53 MM
SNOW MELT =    52.25 MM
SUBLIMATION =     0.28 MM
SURFACE RUNOFF Q =     5.90 MM
LATERAL SOIL Q =   11.97 MM
TILE Q =     0.00 MM
GROUNDWATER (SHAL AQ) Q =   138.97 MM
GROUNDWATER (DEEP AQ) Q =    52.86 MM
REVAP (SHAL AQ => SOIL/PLANTS) =   55.96 MM
DEEP AQ RECHARGE =    53.14 MM
TOTAL AQ RECHARGE =  192.12 MM
TOTAL WATER YLD =   210.15 MM
PERCOLATION OUT OF SOIL =  193.78 MM
ET =    415.9 MM
PET =    621.6MM
My calibration NS is 0.73. How parameters can increase the value for surface runoff?
Best regards
Damian
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Dear Damian,
I read your question and comments of our good colleagues Christoph and Reza. So, you received instructions and clarifications.
On the other hand, I am kindly inviting you to have a look at the application of my IntErO model for the calculation of soil erosion intensity and runoff (link): www.geasci.org/IntErO.
- Free download of the software;
- Simple installation;
- Examples of applications available on the same web page (link): www.geasci.org/IntErO;
- Examples of applications available on my profile on Research Gate;
- Examples of applications available on web page (link): www.geasci.org/Publications;
- I am, as a model author, available to demonstrate how it is working (with using ZOOM, email-s, but also internal messages by Viber/WhatsApp). Could be interesting to use it and to compare with some other models results.
Sincerely,
Velibor Spalevic
Mob/Viber/WhatsApp: +382 67 201 222
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I am working on building a snow melt runoff model using satellite based Snowcover, Snow albedo, Snow temperature, AOD, etc.,. Also using global forecasted air temperature datasets. I am looking for a test basin for my model where Snow water equivalent, Snow depth, Snow temperature, Snowmelt runoff and air temperature records for multiple years are available over multiple snow melt seasons. Any suggestions?
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Hi, there is station in the French Alps that has recorded such data for more than 50 years, and still going.
it is the Col de Porte station
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Hello, everyone. I don't quite understand some physical mechanisms when I am doing research on snow depth of lidar. I hope I can get help.
When I read the previous literature, I learned that the longer the wavelength of electromagnetic wave, the stronger the ability to "bypass" snow and the higher the penetration depth.  But why is 532 more penetrating than 1064nm in lidar?(artile:Lidar measurement of snow depth: a review."doi:10.3189/2013JoG12J154)
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In the visible wavelengths (e.g. 532 nm lidar), absorption is much lower, resulting in higher snow reflectance but greater penetration of the incident laser light into the snowpack. In the shortwave infrared (e.g. 1550 nm lidar), absorption by ice is much stronger and the snow reflectance is <10%.
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Hello everyone,
  • I use RIGEL's miniVUX-1UAV sensor, which has a wavelength of 905nm. For this wavelength, the drone receives the reflected signal from the top 10cm of snow(DOI:10.3189/2013JoG12J154). Therefore, I wonder if it is possible to use Lidar classification method as mentioned in the paper (doi:https://doi.org/10.5194/tc-2020-37)to classify the point as ground and non-ground point , i.e., to get the snow depth using the data at once? Is the physical mechanism reasonable for snow (Snow reflectivity is high and penetration is low)?
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Tianwen Feng On a clear day, LiDAR can provide a high-resolution 3D image by bouncing laser beams off nearby objects, but it cannot see in fog, dust, rain, or snow.
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Hello,
I am working on a precipitation analysis and have data for two rain gauges. I also have air temperature. What is the best way to determine precipitation type? What is a good temperature to assume anything above is rain and anything below is snow? I assume there is also an uncertainty range (-1C to 4C?)?
Any help would be much appreciated!
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>>I do not understand how to determine Psat(Ti) when I am solving for Ti.<<
In order to solve for Ti you have to you use a Newton-Raphson method as a closed form analytical solution is most likely too difficult to find in the general case. The details of this numerical solution are provided by Harder and Pomeroy (2013) in Appendix A of the paper: Estimating Precipitation Phase Using a Psychrometric Energy Balance Method (the text is available on the Research Gate)
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Any downscaled reanalysis/satellite based high resolution (less than 10km) temperature data available for whole HKH region which can be used for snow research for time domain 2000-2015?
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Dear you can download it from USGS freely
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Hello everyone,
I am currently working on my masters thesis to maximize water system performance in the Chilean Laja River basin under climate change. I need historical observed data for streamflow of the rivers Tucapel and Puente Perales. Similarly, I need snow accumulation data for the basin as well.
Could someone please kindly suggest, where/how I could find these data? Has anyone downloaded these data from internet. I would be grateful for any suggestions.
Thank you in advance!
Cheers
AS
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Dear Asmita Subedi , maybe you can find you the data you are looking for in the CR2 database: https://www.cr2.cl/.
Cheers
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Hi. I want to research about geothermal and find relationship between geothermal and snow cover ( snow melting) and vegetation stress. i am looking for an idea about how can i do this? and the important qustion is can i do this with optical sensors?
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There are some famous Snow cover datasets derived from remote sensing,such as IMS. When the ground was covered by snow, it is hard to estimate soil temperature by sensors . So land data assimilation datasets and ERA5 data are optical data.
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Hello!
I'm looking to use this model to get the Snow Water Equivalence (SWE) from my snow height data but I'm having a hard time understanding how to actually start using this model/where to put the inputs etc. If anyone has some experience using this model it would be greatly appreciated!
Alternatively if you have another suggestion on how to obtain SWE from snow height (no snow density available, however climate data is) it would be greatly appreciated!
Thanks in advanced!
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SWE data from ERA5Land or GlobSnow is a good source.
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Hi Everyone!
I have already calculated the original NDSI index, and I would like to use PCA analysis for more accurate results. I read in a study that I need the brightest and the darkest component for the index calculation (NDPCSI). I also read that this corresponds to the PCA component 1 and component 2. That is also mean Band 1 and Band 2? (if not, which bands should I use) Because I did the PCA analysis with Landsat 8 Bands (1-8), but I'm not sure what I supposed to use. I am a little bit confused about it.
Somebody could help me in this case, please?
I would be very glad if you tell me.
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PCA normally use to remove the redundancy of satellite bands and to select appropriate bands for image classification.
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I am looking to get snowfall data or temporal snow cover data from non-modeled data platforms. If anyone has some information then please share it.
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Prashant Rawat, You can get snowfall or temporal snow cover data from European countries, moreover, you can get it from Kashmir in winter.
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Dear all,
Is there a literature review or a comprehensive study on the unsaturated of various soils under the influence of snow that is gradually accumulated on the ground over winter?
Thank you in advance.
Ali Ahmadi
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Dear Ali
There is a recent study on the general topic of soil moisture dynamics and snow accumulation in Hydrological Processes:
Perhaps you can use it as a starting point?
John
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I'm troubleshooting Fv/Fm measurements on dark-adapted snow algae samples in the field. We have a field season coming up in the North Cascade Mountains here in Washington State. I have an Opti-Sciences OS1p flourometer. If you have insight into the process, I would love to hear about your successes and struggles.
The unknown factors I forsee coming up are:
- Does the probe need to be submerged in the sample?
- What is the ideal "slushiness" of the sample? i.e. how much water vs. snow?
- How long to dark-adapt samples?
- How much algal tissue is necessary for accurate readings?
Thanks for your thoughts.
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Thanks for the thoughts! I agree that the PAM technology is deceptive at first. It's easy to get a number. The trick is getting those numbers to actually mean something.
I have a phytoplankton cuvette that our optical probe rests on, so maintaing distance from the sample is feasible. We have a bloom starting up just this week, so we'll put it to the test in a couple of days!
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I am modeling the impact of snow events on traffic flow to extract the relationship between snow events and traffic flow. Known the pattern of the relationship between these 2 variables will be used in the prediction model.
Giving an assumption of snow value, I want to predict the traffic flow value.
I have historical spatiotemporal weather & traffic data, and I was thinking to use trajectories modeling approach to analyze the relationship between these 2 variables in my data.
Then using the trajectory model outcome to train the prediction model in order to predict the traffic flow value.
Not sure if my methodology would achieve my goal or if there's any research work similar to this approach.
I would greatly appreciate it if you share your thoughts.
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You may want to consider using STIFF (Spatio Temporal Integrated Forecasting Framework) by using a divide-and-conquer methodology to discover the hidden spatial correlation to obtain the overall integrated forecasting?
Good luck.
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Dear experts,
I have snow parameters calculated using MODIS snow products and I'm trying to validation with ERA5-Land monthly averaged - ECMWF climate reanalysis images using Pearson correlation. MODIS snow products have spatial resolution of 500 m while ERA-5 has 0.1 degree (11.1 km) of spatial resolution, thus terrible for me to correlation, even resizing back to 500m doesn't make sense. Could one of you be so kind for any advices, please?
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Dear sir, your last part of the reply is the answer I was looking for. Much appreciations to you.
PS. Again, thank you for your clarification. I was having unclear between validation and calibration while I posted this. Thank you so much.
With due respect,
Swun
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There's any software able to efficiently simulate snowmelt runoff paths on a specific surface (also looking at the geomorphology, e.g. considering available DEMs), starting from local snowmelt runoff measures?
>> More details: I have punctual values of snowmelt runoff (m3/s), crossing the end section of a watershed, but I would like to find out a solution so as to spatially simulate snowmelt runoff flows on the entire watershed, looking at the values obtained to its end section.
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Good morning/evening
in the years I've used R extensively (mostly the package ggplot2) in order to produce high resolution plots of my data. Since I'm about to start a new project that involves analyzing snow samples across Antarctica, I want to plot the different locations on the continent... problem is, I mostly plot with R, and I'd like to keep using it, but when I search for how to plot coordinates in R, most people focuses on the rest of the world and discard the Antarctic continent. Does anyone know any way to quickly plot coordinates in R? Or direct me to some sources I can study in order to solve this problem?
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You may also use "leaflet" package and leaflet() %>% addTiles( your data, lat, lng)
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The simulation of the snowmelt runoff using the SRM model seems to be characterized by some issues regarding the inclusion of wind data in input setting. Since wind data have a primary role in snow accumulation, how can I efficiently include this factor in order to preserve the accuracy of SRM simulations?
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not sure perhaps need to find answer in the software manual
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I am looking for literature suggestions on the topic of snow aggregation. My main interest is on the aggregation process itself (rather than papers describing aggregate snowflakes characteristics) and its driving factors
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I would like to suggest a thesis of MICHAEL J. KRAUS prepared at MIT In 1968: Snowflake aggregation - a numerical model.
The text is available at
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Dear all,
I'm doing my time-series research using MODIS snow products' snow albedo (version 6) band to calculate daily snow water equivalent. However, my study area is heavily cloud-contaminated. Therefore, the majority of the pixels have been masked out due to the MODIS algorithm. The pixels are not enough to analyze for seasonal behavior of snow water equivalent. I found MCD43A1.006 MODIS BRDF-Albedo Model Parameters Daily 500m. But it is wavelength-specified albedos. Will it be possible to apply the bands corresponding to the snow reflectance curve? Thank you in advance.
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Swun Wunna Htet this data is in netcdf format. You can transfer it to tiff format.That may help.
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I am working on a risk assessment and management, integrating physical and social sciences, for a high altitude mountainous region. The area is prone to multiple natural hazards, particularly snow storm and avalanche. There are literatures on social vulnerability to earthquake, flood, drought, cyclone and hurricane; but, could not find any on avalanche. So, I would like to know if there are similar research relating to avalanche and what indicators are good for understanding social vulnerability to avalanche.
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hello,
I need the albedo value on the ground to be able to calculate the solar radiation for a location. I know that the albedo value is not the same for oceans, snow, ... etc.
I would like to know is the albedo value for a site varies every hour or remains constant?
thank you in advance
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The albedo by definition is the reflection coefficient of the incident solar radiation.
It depends on the properties of the land surface. So, if the land surface does not change, one can assume that the albedo is consatnt with time.
So, you can assume the albedo consatnt with time if the surface does not change.
Best wishes
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Dear all,
I am looking for any information about the relationships between the snowfall or snow cover with an altitude over the Tibetan plateau. I know that in general, the precipitation is lower at a higher altitude, And the temperature trend is opposite but I am mainly interested in how long the snow cover lasts at different altitudes. Probably saying that the temperatures are lower and the cause of that snow lasts longer is far too simple.
Do you have any information about that or know published papers?
Greetings
Lukasz
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In addition to the variables of temperature (daily max and min), humidity and snow density, slope aspect and wind speed and direction will also impact snow depth and snowpack duration.
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Is there a high spatial and temporal resolution snow depth dataset available?
I would like to inquire if there is a recommended snow depth dataset covering China with high spatial and temporal resolution in long term. The resolution should preferably be higher than daily 25km×25km. Thanks!
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I don't know the actual resolution but NASA made the production regarding global snow depth. Also, these addresses have some snow data: http://westdc.westgis.ac.cn/zh-hans/
Hope it works.
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Hi everyone, is there any paper or model which relates the factors which control snowfall rates? The idea is to quantify the effect of climatic/geographical conditions to justify the existence of snow over highlands.
The logistic functions relate the occurrence according to the temperature, however, they do not take into account the effect of altitude or vapor pressure directly. Please suggest to me any research that integrates these issues.
Thanks a lot for your help
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Froidurot, S., Zin, I., Hingray, B., & Gautheron, A. (2014). Sensitivity of precipitation phase over the Swiss Alps to different meteorological variables. Journal of Hydrometeorology, 15(2), 685-696.
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Welcome, all Suggestions for mitigation of the impact of Spatio-temporal change on Snow, Vegetation, and Timberline in the Indian Himalaya.
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D. S. Parihar , good day! For many people, the Himalayas are a mountainous region where all natural and anthropogenic processes and factors are interrelated. The Himalayas! There are few places in the world that can match the beauty of the Himalayas! As a geographer, it seems necessary for me to address the following issues: 1) further study of high-altitude terrain; 2) organization of monitoring of dangerous natural processes; 3) development of integrated geographical zoning; 4) solution of environmental problems. Perhaps there is still a group of unresolved issues and problems to be resolved. Good luck!
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What are the best loggers for obtaining changes in snow cover temperature at different depths with changes in air temperature? What are the long-term data loggers without recharging? What loggers come with a great software product?
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HOBO mini loggers are very good and easy to use.
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I am doing my project work in snowmelt runoff modelling. I have collected a Matlab code which I found very difficult to understand due to its complexity.
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Any help on compilation of SWAT source code into SWAT.exe would be much appreciated
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Interested
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We obtained the global results using remote sensing reanalysis dataset. But the snow-coverd areas should be removed (like the blank area in the picture below, sources: Purdy et al., 2016). Does anyone know where we could obtain the snow data as mask? Thanks a lot!
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Hello Wen,
The following links might be helpful to you for fining the snow cover dataset:
Also, you can use the GIS mask extraction option for a particular threshold value to remove those snow cover regions.
Cheers
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We know that heating happens due to mid-infrared region of solar irradiation.
Ice melts due to absorption of which specific range of wavelengths of electromagnetic spectrum?
Can we relate this to the vibration states of hydrogen bonds inside ice crystals and due to water molecules?
And is all the energy absorbed in UV, Infrared and Microwave region used for heating the ice mass or could it be used for breaking OH bond and just lead to ionisation?
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You can use, for example, measurements of spectral albedo from Perovich et al. (2002) for sea ice:
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Hi,
Could you pleas tell me if I used snow ball sample (nonrandom sample) to achieve the following objectives by using SMART-PLS.
1- to determine the relationship between I.V1 and D.V. , I have used path coefficient (path analysis) only.
2- to determine the effect of I.V1 and I.V2 on D.V, I used path coefficients , R2 and effect size (f2) only (without applying construct validity, decrement validity).
is it correct steps?
Thank you so much.
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That's right.
Thank you so much.
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Hi
Do you any publicly accessible ground observations which have both solid and liquid precipitation rate? Preferably for many sites in different regions.
Assuming that ground measured data is the most reliable for the snowfall rate.
Thank you.
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Trying to get details
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Hello scholars, 
Can the Variable Infiltration (VIC) model be applied to a large Tropical Basin (e.g of Africa) where there is no occurrence or history of snow?
a quick response is needed. thanks
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Dear Amobichukwu,
I know it a bit late but you may find it useful if you are still working on it. Yes of course VIC can be applied in large tropical river basins. We have done a few studies in the tropical river basin of India by using the VIC model and it provides good results. I have attached the links for some of the papers using VIC in tropical river basins:
Thanks
Ankur
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How does Tibetan plateau snow cover influence summer rainfall and extremes over Africa?
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Dear Asaminew,
According to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC, 2013), global mean surface temperature shows warming of 0.85 °C during 1880–2012. However, the TP has warmed much more rapidly. In situ observations, reanalyses and climate models show a clear amplification of the warming rate over the TP in recent decades.
Some important works in this context are highlighted below. For your reference:
Tan, X., Zhenni, W., Xingmin, M., Peng, G., Guangju, Z., Wenyi, S., Chaojun, G., 2019.
Spatiotemporal changes in snow cover over China during 1960–2013. Atmos. Res. 218, 183–194.
Smith, T., Bookhagen, B., 2018. Changes in seasonal snow water equivalent distribution in High Mountain Asia (1987 to 2009). Sci. Adv. 4 (1), e1701550. https://doi.org/10.1126/sciadv.1701550
Zhu, Y., Liu, H., Ding, Y., Zhang, F., Li, W., 2015. Interdecadal variation of spring snow depth over the Tibetan Plateau and its influence on summer rainfall over East China in the recent 30 years. Int. J. Climatol. 35 (12), 3654–3660.
Wang, Z., Wu, R., Zhao, P., Yao, S.-L., Jia, X., 2019. Formation of snow cover anomalies over the Tibetan Plateau in cold seasons. J. Geophys. Res. 124 (9), 4873–4890. https://doi.org/10.1029/2018JD029525.
Wang, X., Wu, C., Wang, H., Gonsamo, A., Liu, Z., 2017b. No evidence of widespread decline of snow cover on the Tibetan Plateau over 2000–2015. Sci. Rep. 7 (1), 14645. https://doi.org/10.1038/s41598-017-15208-9.
Wang, Z., Wu, R., Huang, G., 2017c. Low-frequency snow changes over the Tibetan
Plateau. Int. J. Climatol. 38 (2), 949–963. https://doi.org/10.1002/joc.5221.
Wang, A., Xu, L., Kong, X., 2018a. Assessments of the Northern Hemisphere snow cover response to 1.5 and 2.0 °C warming. Earth Syst. Dynam. 9 (2), 865–877. https://doi. org/10.5194/esd-9-865-2018.
Pu, Z.X., Xu, L., 2009. MODIS/Terra observed snow cover over the Tibet Plateau: distribution, variation and possible connection with the East Asian Summer Monsoon (EASM). Theor. Appl. Climatol. 97 (3–4), 265–278. https://doi.org/10.1007/s00704- 008-0074-9.
Pu, Z.X., Xu, L., Salomonson, V.V., 2007. MODIS/Terra observed seasonal variations of snow cover over the Tibetan Plateau. Geophys. Res. Lett. 34 (6), L06706.
These will be helpful for you.
Thank you
Ankur
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Hi everyone,
I am using HBV model with daily temperature and precipitation as its input. Accurate and reliable winter precipitation is hard to measure in prairie and other cold regions, but weather station data is still only sources to run hydrological models. So I have snow survey data as supplements, where snow survey is usually conducted right before snow melt beginning.
How could I incorporate snow survey data into daily precipitation record so that I can use HBV model to simulate spring snow melt runoff? Or you have other options, such as using re-analyzed data, to go forward?
Kangsheng
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My two cents ...snow depth of one cm is considered to be equivalent to one mm rain. Standard meteorology. Will do as a first proxy in a model.
However, snow comes in many varieties. Even in the temperate lowland we know and name about ten varieties. As former winter off-piste tour skier, I now even more varieties and these vary with elevation in glaciated mountains.
A further confounding issue is the lack of long-term, standardized measurements of "precipitation"/snow across elevations in the major mountain chains of the world. On the empirical reliability of the latter please refer to
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I hope to exchange information about this phenomenon caused by a species of algae
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Unlike Microwave data, optical data is not sensitive to snow depth and internal snow pack properties. But Is there any other way with which it is possible to estimate snow volume using optical remote sensing data. I could not find any relevant publications on this topic. Kindly share links of articles, or any ideas in this connection. I appreciate the time and effort taken for answering this query.
Thank you.
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As rightly suggested by Pat S. Chavez, Jr. A recent study amplifies use of stereo DEM data to study 40 years of ice loss across the Himalayas. This study could be regarded as an excellent example how DEM alongwith supplementary data can be utilized to analyze environmental degradation/restoration processes over time.
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Hello Researchers,
I faced a problem while using Landsat 8 OLI surface reflectance product. The images of the same scene captured on different dates show very different contrast in colour. Any particular Radiometric/Atmospheric corrections required?
Sun angle, cloud/snow cover are not there, I am sure for that. Because it is a plain topograhy and the selected images are with less than 10% scene cloud cover.
Please provide technical answers only matching with the question.
Regards.
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As the the topoography is important in your area, Try to do an othortectification first on the 1st Landsat 8 product (the DN values), using a DTM (SRTM/USGS).
On ENVI software : Map > Ortho> Ortho using Generic RPC
Once orthorectified, convert it to reflectance , i think it will be more coherent.
On ENVI : Basic Tools>Preprocessing>Calibration> Landsat calibration
Good luck
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Snow fences are using in cold regions of different countries (US, Canada etc.) to prevent the road casualties along the roads. I would like to study for the feasibility of using snow fence in Nepal. Can you please let me know if you have any idea about it or if you have done any research about it in the context of Nepal?
- Keshav Basnet
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Thanks @Sarvat! This is helpful.
-Keshav Basnet
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Dear coworkers,
my questions are about HRLDAS. I am runing HRLDAS with GLDAS-based forcing data.
For running offline LSM such HRLDAS, we know we need atmospheric forcing and initial data.
In case of HRLDAS, we need forcing data such as
Rainf/ Snowf/ Wind/ Tair/ Qair/ Psurf/ SWdown/ LWdown/ SWdown24/ Precip/ U/ V/
and initial data such as
SWE/ Canopint/ AvgSurfT/ SoilM/ Tsoil
from Reanalysis data or other land assimilation data. (my case, GLDAS)
My question in this step is, why it need combine_precipitation and hourly interpolation of solar radiation?
I mean, from forcing data, we extracted snow and rain separately, but next step is to combine them as a precipitation. Is there any reason for this?
And why should we need to do hourly interpolation of solar radiation via zenith angle using 3hrly solar radiation? why only this parameter?
Thanks for time and for reading my question!
Thanks in advance.
Arim
My email if necessary : arimyun@yonsei.ac.kr
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Thank you so much! As I understand, Snow is the key parameter in Noah MP and it needs total precipitation to reinitialize and produce the snow related parameters. Am I understand correctly?
Thanks again :))
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I’m exploring monitoring of snow cover on the river, I used the NDSI NDWI index, but they show me that snow is water and water is snow, how can I distinguish them?
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Thank you all very much for your advice! I shifted the range for determining water, from 0.1 and higher, water began to be detected better, but still there are errors (
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A lot of people suggested to use Water and change the CN value afterwards but I already have Water and Barren land as other classes.
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I recommend you to use a proxy land use type (eg: the land use code for may be tomato, agriculture land) and then provide the appropriate CN for ice or snow
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I have a datasheet of snowpack depth by using a Campbell SR50 Sonic sensor. But even after read the manual, it's not clear for me what's the depth of the snowpack. Can someone help me with this?
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For several weeks, the ice surrounding Vernadsky's research base in Antarctica has looked like a bloodbath.
In recent weeks, the ice surrounding the Ukrainian research base in Vernadsky, Antarctica, has been covered with what researchers call "raspberry snow". A sight that may sound scary but has a simple scientific explanation, says British media The Sun.
What looks like a bloodbath is actually linked to the presence of a red-pigmented alga, called Chlamydomonas nivalis. The latter develops in icy water and generally remains dormant under snow and ice until summer melts some of the snow. Once in the sun, its red pigment helps the algae absorb heat and thrive. As a result, the presence of this organism has the effect of causing faster melting of snow and ice since the more it absorbs heat, the faster the surrounding ice melts.
A phenomenon that worries some scientists. On Facebook, the Ministry of Education and Science of Ukraine believes that these algae "contribute to global warming".
A vicious circle
"Because of its crimson red color, snow reflects less sunlight and melts faster. As a result, more and more algae are produced", the ministry said. The more algae, the more the ice melts and the less ice there is, the more the algae spread. A vicious circle exacerbated by high temperatures.
This phenomenon is not unusual. However, if global temperatures continue to rise, these strange events will eventually become more frequent and help melt snow and ice faster.
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It should be a matter of grave concern for all humanity.
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From physics perspective the ice or snow are good in reflecting the solar radiation and the land is a good absorber of solar radiation. Let consider scientist who climb the top on mountain covered with glacier for ice core drilling, hope they may expose the land covered by ice to solar radiation and this may accelerate the melting of ice over the mountain. I would like to know is this could explain the melting of glacier for example over mountain Kilimanjaro. Or what are the impact of ice core drilling over tropical mountains?
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The vast majority of tropical mountains that have glaciers or permanent snowpack (at least throughout the year) are volcanoes. Considering the above, the composition of the substrate is lavas (basalt among others). One of the characteristics of volcanic rocks is the high absorption of solar radiation and conductivity. Therefore, once the rock emerges between the smell and the snow, its fusion accelerates almost exponentially.
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I am currently doing literature review on snow depth retrieval methods using passive remote sensing. While the algorithm in Chang (1987) says it uses brightness temperature difference at 18 and 37 GHz frequencies (Horizontal)(https://nsidc.org/sites/nsidc.org/files/technical-references/amsr-atbd-supp12-snow.pdf) in few other papers it is mentioned as vertical polarisation( ). Which is correct representation.
I would be grateful for all the responses and time taken to answer this query.
Thankyou.
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Srinivasarao Tanniru i have read lot of literature for snow cover monitoring and depth analysiss. According to many studies VV polarisation is best suitable for snow depth analysis.
FOr more information please visit this page: https://forum.step.esa.int/t/snow-depth-estimation/13517
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I am looking for the techniques used in SAR data processing to differentiate the pixels of snow, ice, debris and waterbody.
Thank you
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Masoom Reza the processing technique for ice or snow classification depends upon the user, but you have to perform basic processing S-1 data. Attached are documents with pre-processing steps of SAR data in SNAP toolbox.
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I am not sure which projection system would be best fit to study snow cover of Himalayan mountains through MODIS snow cover data.
How much differ the result of WGS84 and EPSG projection system?
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Ali Fanos Yeah, I used geographical coordinate system and it gave me less snow cover over high elevation thank lower elevation which is against the natural truth. I belief that projection system effects on the result remote sensing.
Moh. Dede thank you, I will check the details
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I have two raster one is elevation class map from DEM reclassification and another is snow cover fraction map. I need to calculate elevation class wise Snow cover fraction(%). I do not want to convert the input raster format to vector format because in the later stage I need the output file in raster format.
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Which software are you using?
In ArcGIS for this purpose you may use zonal statistics: the elevation zones define your zone raster, the snow raster your values. You will end up in a table sowing the snow "statistics" like sum, average, max, min...
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Topographic effect, data scarcity and complex atmospheric system makes the precipitation of Himalaya mountains very dynamic. Down scaling of precipitation in such area without considering orographic, laps rate, rain shadow, snow, seasonal and annual shift of precipitation phenomena is not much promising.
So, what are best possible ways to down scale the remotely sensed, reanalysis and model output precipitation product over Himalaya mountains by considering the above factors?
Thank you in advance
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thank you Yanqiang Chen yes that is the more suitable way to fill the gap
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Dear colleagues,
My question is regarding suggested methodologies for snow sampling in, for example, mountains or peeks. Some ice sampling techniques for these environments would also be appreciated. Must consider these samples are going to be processed to identify microplastics in the snowy mountain ecosystems.
Thanks in advance,
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Hi Gabriel! Not sure what scale you're hoping to work on -- here are some links to various snow and ice core methods:
https://www.wcc.nrcs.usda.gov/factpub/ah169/SnowSurveySamplingGuideHandout.pdf
Most of the snow sampling methods are for estimating the amount of water in snowpack (in dry states in the US, these methods are used to project drinking water supply/shortage). If you use the SWE methods alongside your microplastic analyses, it will give you a rudimentary method of estimating the mass loads of microplastics in the snowpack of the region you're sampling. The NSF ice cores are probably overkill for what you want to do, but I still thought the info might be interesting to you.
Good luck!
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I would like to use Snowmelt Runoff Model (SRM) for my research, I need to interpolate MODIS 8-Day snow cover data in to daily. although based on the basin characteristics I have extracted 9 elevation zones, therefore I need to extrapolate temperature and precipitation data considering 9 zones.
Please help me by your suggestions.
Thanks in advanced.
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Should I simply subtract the temperature at a particular station from the altitude adjustment to get the temperature at that particular elevation?
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A place near Quetta about 40km west to quetta called jangal pir alizai has salt underground water. And in winter ,this region earth surface change their surface colour as white as like snow. But it is due to salinity at the region. So what is the reason of this region plate?
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Hi Sadam:
One of the major sources is seawater intrusion to make groundwater saline. however, Quetta is about 600 km away from the sea which means saltwater intrusion is not possible.
Another potential reason could be the dissolution of minerals in the saturated and unsaturated zones. Salt deposits, such as halite or gypsum, can be found in many sedimentary basins and are highly soluble. Other deposits are less soluble but can contribute dissolved solids when in contact with water over longer periods of time. This means it is important to investigate the characteristics of rocks of aquifer or groundwater zone to understand if it is caused by rocks of the aquifer.
It is also possible to movement of saline groundwater from adjacent aquifers. It is also possible to have leached from saline soils or road salts. Salts at the land surface can leach to shallow groundwater. Evapotranspiration can concentrate dissolved salts in the soil where it subsequently can infiltrate to shallow groundwater, especially where precipitation rates are low.
Overall, it is important to conduct some tests to better understand the potential causes of groundwater being saline. In addition, whatever (snowy) you see on the ground surface is indeed a salt accumulation that has been deposited after surface salty water gets evaporated or as Bayan said saline groundwater appeared at the surface through capillary action and then water gets evaporated leaving salt on the surface. I hope it helps.
Thank you