Science topic

Snow - Science topic

Snow are frozen water crystals that fall from the ATMOSPHERE.
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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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Have you considered using stereo optical image pairs to extract a DEM of the surface without snow and then a stereo image pair to extract a DEM with snow and then generate a difference image of the two resulting DEMs? Ideally you would like stereo image pairs collected by a forward and downward looking satellite so that the images are collected on the same orbit so they would pretty much be images taken at the same time. Since they will be optical image pairs I realize cloud cover will be an issue, plus I am not sure if existing stereo imaging systems have the vertical resolution needed for this but this would be something to consider.
Also, perhaps if a DEM with high enough resolution already exist for your area of interest you could identify the elevation of points you can see in the 'snow image' and from that get a general idea of the snow depth.
Pat
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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
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Hello,
I am new to survival analysis and need advice on how to correctly analyze some data (with R).
Design
Along an elevation gradient, we have 5 different sites located at 5 different altitudes (600, 1000, 1400, 1600, 2000 m asl). At each site we have 30 different species (belonging to the Brassicaceae family) coming from 3 different altitudes (high-, mid- and low- elevation species). Each species is represented by 20 individuals (10 maternal lines (5 for each population) x 2 replicates).
The plants were germinated in controlled conditions (about 3 weeks before being moved in field) and moved at the different sites when the predicted environmental temperature had similar values (so at 2000m the plants were placed in August, at 600m in October).
For each individual, survival was checked each week (binary, 0/1 where 1 died). In addition, for another study we also recorded flowering (0/1) and fruiting (0/1). Measurements stopped when the sites were inaccessible due to the snow (sites covered and/or road blocked), and started again when sites were are accessible again (i.e. the measurement time is different for each site depending on the snow cover). At the end of the experiment, numerous individuals are still alive.
Finally, at each site we have hourly temperature measurements.
What we want to test
i) if the interaction site_elevation * species_origin influences mortality (e.g., lowland species have a higher mortality ad higher sites) and ii) if mortality is affected by a specific temperature (and if different temperatures affect different elevation class).
The data file structure Site (factor, 5 levels): "600", "1000", "1400", "1600", "2000"; Species (factor, 30 levels): 30 species; phylogeny is available (.nexus); Elev_class (factor, 3 levels): "high", "mid", "low", is the altitudinal class of the 30 species; Elev_m (numeric): median altitude of the 30 species; ID (factor, 3000 levels): unique id for each individual (e.g., 600_1_1_1); Pop (factor): unique id for each population; Fam (factor): a factor (1, 2, 3, ...) indicates the maternal line for each population, it is not unique (e.g., both in pop1 and in pop2 we can have fam 1); Measurement_Date (date): date on which the binary response was measured, in standard format (e.g., 2019-08-31); Week_field (integer): this is a temporal measure assigns 1 to the first week where the plants have been moved to the respective sites (1, 2, 3 ...); Temp (numeric): a variable that represents the temperature (e.g., median) of the week preceding the measurement (e.g., at survival of week 3 is associated the temperature value between week 2 and week 3); Survival: binary variable 0/1 which describes whether the plant is alive or dead on the respective date / week
Problems
i) I need to correct for phylogeny. This prevents me from using models like Kaplan-Meier. I am currently trying with MCMCglmm. Is there another possibility?
ii) From what I understand I have to model the response (survival) using a longitudinal mixed effect model (since each individual is measured weekly, pseudo-replicate). For example, to check if a temperature affects survival, the model is: survival ~ Elev_class * Temp * Week_field random = phylogeny + Site + us (Week_field):ID family = "threshold" However, not all individuals suffer the event (death) during the time of the experiment and from what I understand they are right censored data. Does this also apply to a logistic model or only for a time-to-event model? If yes how should it be corrected?
iii) In lowlands class I have some species that are annual, so mortality after fruiting is not necessarily linked to environmental factors. I am currently trying to make two models, one including only the data up to fruiting (but keeping all the species), and one with the totality of the measures but excluding the annuals. Is it a good procedure? Is there a better way or a way to have a single model that considers this difference?
iv) The temperature varies with time ( in autumn the temperature decreases with the wintering, while in spring this increases with the time). Is this correlation a problem in the model having both time (longitudinal mixed model) and temperature?
Any other suggestions, ideas and / or references are welcome. Thanks in advance!
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Hi Alessio,
I've never gone that much further on survival analysis to solve clearly your problems. For now, I can only recommend these papers:
*How to analyze seed germination data using statistical time­to­event
analysis: non­parametric and semi­parametric methods
James N. McNair, Anusha Sunkara and Daniel Frobish
DOI: 10.1017/S0960258511000547, Published online: 07 February 2012
Cracking the case: Seed traits and phylogeny predict time to
germination in prairie restoration species
Rebecca S. Barak1,2 | Taran M. Lichtenberger1,2 | Alyssa Wellman-Houde3,4 |
Andrea T. Kramer1 | Daniel J. Larkin5
DOI: 10.1002/ece3.4083
*The first one has a really good supplemental material.
I hope it work out for you.
Best regards,
Alex.
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I have multiple miniDOT DO loggers deployed in small mountain streams. Getting data on DO through the winter would be valuable, but at the same time, I don't want to damage our sensors. The PME site suggests that the loggers function in the cold as long as they are in liquid water, but as we weigh whether to keep or remove the sensors this winter, I would be interested to hear about other people's experiences/perspectives on logger and battery performance over long time periods in cold water . Thanks!
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Hi Dana Warren ! We deployed our miniDOTs in the Swiss Alps over the winter with very few problems, even without maintenance given the access restriction in the winter (i.e. avalanche danger). We made sure to put fresh, good batteries in during the final maintenance check and deployment in the fall and set the logging time from 10 minutes to every 30 minutes to ensure that we didn't lose any data due to memory space. The loggers faired well as they were in liquid water or well insulated under the snow. Overall, we left them from early November into March and April and were able to gather data during that entire time frame. Let me know if you want to discuss further -- send me an email!
Amber
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Different moisture source, amount, pattern. landscape and slope are contributing in snow cover of both eastern and western sides of Tibetan plateau. Western side has more glaciated area and relatively less know hydrological cycle as compare to eastern side.
I am looking for reasons of difference beyond above mentioned features in perspective of remote sensing particularly MODIS snow cover.
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Hope the below reference will be a great help.
Molg, T., F. Maussion, and D. Scherer (2014), Mid‐latitude westerlies as a driver of glacier variability in monsoonal High Asia, Nat. Clim. Change, 4(1), 68–73.
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Earth is in more hot water than ever before: Sea levels are rising at an ever-faster rate as ice and snow shrink, and oceans are getting more acidic and losing oxygen, the Intergovernmental Panel on Climate Change said in a report issued as world leaders met at the United Nations. https://www.ipcc.ch/srocc/download-report/
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Ilan Kelman All are very impressive and will be helpful in my research work. thanks
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Can anyone suggest me about the SNOWMOD.
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Estimation of snow/glacier melt contribution in the upper part of the Beas River basin, Himachal Pradesh, using conventional and SNOWMOD modeling approach
Rajeev Saran Ahluwalia ; S. P. Rai ; S. K. Jain ; D. P. Dobhal ; Amit Kumar ---Journal of Water and Climate Change (2015) 6 (4): 880-890.https://doi.org/10.2166/wcc.2015.107
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Why snow an paint are called black bodies though they look white to us..
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Black body (or black body radiation) is a term introduced by physicists in the 19th century, to describe the thermal radiation emitted by a body. The intensity and spectrum of such a radiation is only dependent on the temperature of the body.
The term "black" is to denote the idealized case where the body is opaque (i.e. non-reflective) in all the frequencies of the electromagnetic spectrum.
Even though snow is highly reflective in the visible part of the spectrum, it can be idealized as a "black" (or opaque) body in the infra-red part of the electromagnetic spectrum, only absorbing and emitting radiation, but not reflecting.
To wrap it up, snow is a black body in a thermal-radiation-sense, not in a visible-light-sense.
Hope this helps.
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Hello,
I am interested in using Landsat 5-8 images to map snow and ice cover. I am trying to construct a time series showing how late into the year snow and ice cover lasts. I noticed that for Landsat ARD tiles obtained from USGS Earth Explorer there is a Pixel Quality Assessment band that accompanies surface reflectance products and that this PQA raster includes bit designations for pixels where snow or ice are present (bits 80 and 144 for Landsat 4/5). After reading more I have gathered that this PQA product is generated using the Fmask algorithm which was developed primarily for generating cloud masks. However, I decided to employ these products to see how they perform when generating fractional snow cover rasters.
I noticed that for some images very late into the year (May and June) the Fmask algorithm did classify many pixels as snow or ice, although after generating RGB composites and using the thermal infrared band to look at temperature, I determined that there was no snow or ice cover present in the image although it did look like some clouds were present. After reading more of the literature I found out that the Fmask algorithm has a tendency to sometimes classify cloud pixels as snow or ice, but I could not find an explanation as to why this happens. Is there a particular cloud type that the algorithm classifies as snow or ice, or is it unpredictable? Is there a better algorithm that is designed specifically for generating maps of fractional snow cover?
Thanks for you help,
Best,
Ryan Lennon
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can be isolated using different spectral signature between cloud cover and ice cover
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I have sum of crossponding months of a season and certainly most of values were above 100 than I divide the sum with number of month (typical method of average/mean) because the range of fractional snow cover should 0-100.
So, the mean value is representing monthly mean rather than season, so we can calculate seasonal fractional snow cover?
NOTE; I am using MATLAB for this analysis and data set is MOD10C2 ( MODIS monthly fractional snow cover)
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I have monthly snow cover data in Excel, I want to calculate seasons using the monthly data. SO I've to "SUM" the monthly data for a particular season with corresponding months or "AVERAGE".
Lets say, I want calculate "SPRING season", SO I have SUM (MARCH, APRIL and MAY) or I've to "AVERAGE" all? please suggest. Thanks in advance.
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I have white wasabi meristems that are not dead but they are not green and shooting either. Meristems were placed on media with streptomycin for 5 days under darkness to reduce phenolic compound exudate and endogenous bacteria. 30% remained green chlorophyll 70% white as snow. The good news is there was no phenolic exudate and no bacteria.
The bad news is that there is no growth.
Can anyone explain why this is so and can this be reversed to show green again.
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You are welcome.
Just leave the ex-plants at room temperature without using any media for 2-3 days and then use for the experiments. This will dehydrate them and reduce the phenolics.
Note: It worked for me but differ from plant to plant. Try it on some explants.
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We try to estimate maximal snow depth in selected remote locations on few mountains, far from meteorological stations (gauges).
I wonder how to estimate as accurately as possible "near maximal snow depth" (probably toward the end of January or early February) in these remote areas? Can Sentinel 2 or kind of SAR provide some help, or maybe some other ideas (Landsat etc)? I suppose that the resolution must be better than 100 m as the small localities for which the data is needed. Thank you for any ideas.
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Sentinel data is very fine resolution and should suffice for you needs.
There have been a lot of studies getting water surface elevation using SAR data so snow cover should also be possible.
Please refer to this article
and even this discussion on RG:
Regards,
Samya
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Atmospheric phase is one of the components of the total InSAR phase. In most cases, it is ignored. However, for studies aiming at high precision and reliability, it should be removed. Currently, I am working on estimating snow depth and snow water equivalent using D-InSAR wherein the study area comprises of complex mountainous terrains along with forests (North West Himalayas). So I think atmospheric phase removal would increase the quality of the obtained results.
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I am going for the occupancy and distribution of Brown bear in my research for this I need data like NDVI, snow cover, bare area, cultivated land etc. How can I download these layers? Please someone provide me link and guide me. Thanks to all
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Hi zaman! Go to Glovis, and you can download data layers for the particular area and later calculate NDVI.
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Isn't temperature fall and down related to geological and astronomical dynamics rather than anthropogenic!
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Main drivers for Change in temperature are deterministics.
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My latest results indicate that conceptual models can be improved by warming rain/snow thresholds for higher cloud bases, or lower relative humidity measured at the surface. Both of which are proxy data indicating a drier atmosphere the hydrometer must fall through.
I have found sources that state the microphysics are different in saturated and unsaturated environments, which gets me about 75% of the way to explaining the findings.
What I need to finish the theory is an article that support latent heat exchange rates (evaporation and sublimation) nearing or exceeding sensible heat exchange rates (melting) to allow snow to occur at warmer and drier surface conditions.
If there is a different explanation, I could look into changing the theory.
If I don't find something by the end of the month I could leave it somewhat unexplained, which is not desirable.
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Relative Humidity (RH) means a percentage ration of the water vapour in the air and the amount required for saturation. vapour containing capacity basically depend on temperature scenario.
High RH means the a comparatively higher energy preserved in lower atmosphere. and Higher RH can trap the ground long wave radiation which is a ground cooling process.
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how Greenhouse Gasses Effect on Snow Cover, can some one working on it , which model will use to examine effect on snow cover,
thanks,
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Thanks you So much ...All off you
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Terrestrial water storage (TWS) changes are calculated using GRACE data given as TWS = Surface water + Soil water + Snow.
How to calculate TWS from CMIP data ? what parameters can i use from GCMs?
Is there any possibility to calculate TWS from water balance equation?
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You need to assess the water balance from the climate data that CMIP Models. I recommend using a hydrology model to use the temperature and precipitation data from CMIP. If it is a regional study, VIC model might be ideal. If you are studying a watershed, you can assess TWS using a simulation model. Simple water budgets at a unit scale can also be used to assess water storage using GIS. Without specifics of your research, it is difficult to be specific.
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Many places in Norway we are now getting large amounts of snow on the roofs of our houses. It would be interesting to read articles (if they exist) of alternative ways of getting snow down from the roof. Special focus on safety and effectiveness is of extra interest.
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Christopher Nock Nirmala S.V.S.G Thank you for your input. I have joined my neighbors in testing several methods the last days. One good method for the type of snow we have now has been cutting the snow at the base of the roof with steel wire and making mini avalanches.
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Dear In the article entitled "Mapping Radar Glacier Zones and Dry Snow Line in the Antarctic Peninsula Using Sentinel-1 Images" the normalization of the backscattering coefficient in Sentinel 1C images for the 30 degree angle of incidence was performed, since the raw image can range from 18 to 46 degrees. if there is any procedure that can be done in the Sentinel Application Platform (SNAP) or if the sigma-0 file generated in the processing is already normalized data at a certain angle of incidence. Thanks for listening.
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Fernando!
You can do this normalization in a simple raster calculator in a gis software.