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# Evapotranspiration - Science topic

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I downloaded the latent heat flux from fluxnet, the latent heat flux is per hour in W m-2. I summed each value per hour of the day to get the daily sum.
Now I have the daily sum of latent heat flux in W m-2 and want to daily evapotranspiration in mm.
You can calculate this with the formula ET= λ LE/λ. First you calculate the heat of vaporization; λ = 2.501 – (2.361 x 10^-3) x Ta. Where Ta is temperature air in celcius, I also have this daily data.
My calculations in my code (df stands just for dataframe and double ** is ^):
df['heat_of_vaporization'] = 2.501-(2.361*10**-3)*df['TA_daily_average']
df['ET'] = df['LE_daily_sum']/(df['heat_of_evaporation']*1000 *1000)*3600
Is the calculation right? It think I get the good data. I did * 3600 because of the seconds in an hour, added all the hours so not 3600*24 I think. Are the *1000 *1000 also right?
Thanks a lot!
Thank you! Super! I understand :)
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We are working on a formula to calculate irrigation needs for agricultural soil, to help farmers (and the planet) with saving water. We work with water sensors in the agri fields and want to assist the farmers with a time sheet for telling them when they would need to start irrigation. Let's say you need to irrigate field A in 4 days and field B in 9, so they would have a timetable. We have many different factors at the moment, and we do not think it is realistic to account for them all because it would make the formula too complicated and maybe inaccurate. We have the following factors in mind:
Absolute and relative humidity of ambient air. (plus minus 2 meter)
Near surface soil humidity.
Soil humidity at the lower basis A(h) horizon (plus minus 30 cm), both absolute and relative.
Air temperature.
Soil temperature at the depths given above (1-5 cm and plus minus 30 cm).
Amount of precipitation over the last 24-48 hours.
Evaporation + transpiration= evapotranspiration rate.
Duration of sunshine exposure over last 12-24 hours.
Amount of water required by the crop for healthy growing circumstances (statistical data).
Stage of plant growth
Wind
Irrigation practice.
Climate
Type of soil (to account for soil drainage)
Plant density
They all affect the irrigation needs but some are not as important, please assist me with your expertise in telling me which are not as impactful/ important. And which are absolutely crucial to account for. Maybe we even have missed some?
With kind regards, Morris la Crois
Hi,
One of the factors that affects water need is underground water pipeline leakage.
please take a look on my paper entitled "A Novel Technique for Detecting Underground Water Pipeline Leakage Using the Internet of Things" published in Journal of Universal Computer Science.
Hope this can help.
Regards,
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With the help of standardized Precipitation Evapotranspiration Index how we get return period of drought?
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We are working on a formula to calculate irrigation needs for agricultural soil, to help farmers (and the planet) with saving water. We work with water sensors in the agri fields and want to assist the farmers with a time sheet for telling them when they would need to start irrigation. Let's say you need to irrigate field A in 4 days and field B in 9, so they would have a timetable. We have many different factors at the moment, and we do not think it is realistic to account for them all because it would make the formula too complicated and maybe inaccurate. We have the following factors in mind:
Absolute and relative humidity of ambient air. (plus minus 2 meter)
Near surface soil humidity.
Soil humidity at the lower basis A(h) horizon (plus minus 30 cm), both absolute and relative.
Air temperature.
Soil temperature at the depths given above (1-5 cm and plus minus 30 cm).
Amount of precipitation over the last 24-48 hours.
Evaporation + transpiration= evapotranspiration rate.
Duration of sunshine exposure over last 12-24 hours.
Amount of water required by the crop for healthy growing circumstances (statistical data).
Stage of plant growth
Wind
Irrigation practice.
Climate
Type of soil (to account for soil drainage)
Plant density
They all affect the irrigation needs but some are not as important, please assist me with your expertise in telling me which are not as impactful/ important. And which are absolutely crucial to account for. Maybe we even have missed some?
With kind regards, Morris la Crois
Morris La Crois Plant characteristics in your model includes mostly statistical or pre-measured data
1) Evaporation + transpiration= evapotranspiration rate.
2) Amount of water required by the crop for healthy growing circumstances (statistical data).
3) Stage of plant growth
4) Plant density
But, real water useage will depend more on plant leaf area, architecture of plants, physiological state of plants (stressed/healthy), and some other crop type/cultivar specific traits.
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relationship of evapotranspiration fraction (ETf ) with stress coefficient (Ks ) due to effect the salinity of water, are found paper and researches please
I share your deep interest in this field, as it holds the key to understanding and mitigating the widespread impact of corruption on our planet. The devastating consequences of corruption ripple through ecosystems, affecting not only our crops and plant life but also our animal life. By delving into the relationship between evapotranspiration fraction (ETf) and stress coefficient (Ks) in the context of water salinity, we gain valuable insights into how these corruption-driven disruptions manifest in the natural world. This knowledge equips us with the tools to safeguard our environment and work towards sustainable solutions that counteract the negative effects of corruption on our fragile ecosystems. Join the Dailyplanet.Club
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effecting the stress on evapotranspiration by salt and deficit water
En plantas herbáceas C4 el déficit hídrico y aplicaciones de fertilizantes químicos de mala calidad, falta de precipitaciones, entre otros pueden causar retrasos en los ciclos vegetativos, así como perdida de turgencia celular, lo cual deriva en problemas fisiológicos graves que merman la producción y ponen a los cultivos vulnerables a plagas y enfermedades
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can give you equation Standardized Precipitation Evapotranspiration Index (SPEI)
Comparative analyses of SPI and SPEI and drought index.
You can view this website
It contains the required dehydration equation
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Kindly any experts shared the procedure details. Here, I need with blockwise (taluk/tehsil) field measured hydrometereological data (2000 to 2022) for Southern India especially.
Even I know their limited mointoring facility in Southern India. But there will be possible in few mointoring facility case at Metropolitan cities like Bengaluru, Coimbatore, Chennai, Madurai etc..
Note: Rainfall (daily), Max and min Temperature (daily), relative humidity (daily), Solar radiation (daily), evapotranspiration (Monthly), Soil moisture (Monthly) etc.
This is for supply of meteorological data .
or for more details call to help desk +91 (20) 25572-254, 255
from (Monday to Friday 10 AM to 5 PM )
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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?
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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Hi,
I am trying to understand the limitations of the Simplified Surface Energy Balance (SSEB) approach and Landsat Collection 2 (C2) Provisional ETa Science Products to estimate actual evapotranspiration of different crops in various locations.
These would be used by an agribusiness to monitoring water consumption and water availability for crops (wheat, rice and corn) grown in 14 different countries
I am struggling to understand if and how these can be applied to different crop / locations couples as Landsat Collection 2 (C2) Provisional ETa Science Products are yet to be validated.
Best regards.
while satellite remote-sensing techniques can provide valuable insights into crop water use and water availability, there are several limitations to their accuracy and reliability. These limitations must be carefully considered when using satellite-derived ETa estimates for monitoring and managing crop water use in different locations and for different crops.
There are several limitations to estimating actual crop evapotranspiration (ETa) using satellite remote-sensing techniques such as the Simplified Surface Energy Balance (SSEB) approach and Landsat Collection 2 (C2) Provisional ETa Science Products.
Spatial resolution: The spatial resolution of satellite imagery may not be fine enough to capture small-scale variations in ETa, particularly in heterogeneous landscapes with multiple crop types, varying topography, and soil characteristics.
Atmospheric interference: Atmospheric conditions such as cloud cover, haze, and aerosols can interfere with satellite measurements of ETa, particularly in regions with high levels of atmospheric pollution.
Sensor limitations: The accuracy of ETa estimates can be affected by sensor limitations, such as saturation of sensor values, band-to-band misregistration, and sensor noise.
Surface characteristics: The accuracy of ETa estimates can also be affected by surface characteristics, such as the presence of vegetation canopies, soil moisture, and surface temperature variations.
Crop variability: Crop variability, including differences in planting dates, crop management practices, and genetic traits, can result in variations in crop growth and water use that are difficult to capture using satellite remote-sensing techniques.
Calibration and validation: Accurate calibration and validation of satellite-derived ETa estimates is critical to ensure the accuracy and reliability of the data. This requires ground-based measurements of ETa, which can be difficult and expensive to obtain, particularly in remote or inaccessible areas.
Cost and accessibility: The cost of acquiring, processing, and analyzing satellite imagery can be prohibitive, particularly for small-scale farmers or resource-limited agribusinesses. Additionally, satellite imagery may not be readily accessible in some regions, particularly in areas with limited internet connectivity or data infrastructure.
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I have a monthly dataset of GLDAS evapotranspiration (version 3.6 a with 3-hourly temporal resolution). The conversions I have come across suggests multiplying the dataset values with 86400*(Number of days in the month) which sounds reasonable however the dataset values are all greater than 1 thus, this conversion will result in unrealistic ET values (>86400 mm at least).
So, I was wondering if the monthly dataset are actually daily ET values averaged over the month, making the actual units of the dataset kg/m2/day (mm/day) which require multiplication only with the number of days in the month.
Given the density of water, 1000 kg/m^3, the evapotranspiration (ETA) in the units of kg/m2/sec with 3 hours intervals, can be expressed in mm/month as
ETA [mm/month] = ETA [kg/m2/sec] x 10800 [sec/3hr] x 8 [3hr/day] * 30 [days]
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I want to quantify the impacts of LAI changes (or “greening”) and stomatal closure on ET changes in FLUXNET sites. Are there any recommended statistical methods？
Evapotranspiration (ET) is a key component of the water cycle, and its measurement and analysis are important for understanding hydrological processes. Statistical methods can be used to analyze evapotranspiration at the site scale, such as fluxnet data. Fluxnet data is a network of micrometeorological towers that measure energy and water exchange between the land surface and atmosphere. This data can be used to calculate ET rates at different timescales, from hourly to seasonal. Other statistical methods such as regression analysis can also be used to examine relationships between ET and other environmental variables such as temperature, humidity, wind speed, and precipitation. Additionally, remote sensing techniques such as satellite imagery can be used to estimate ET over large areas.
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Hello, so I am trying to carry out a very simple and high-level approach for calculating the effect of building height on green roof cooling performance, and have been trying to use a certain building height-cooling decay rate I found in the literature, but am unsure if I am actually using it correctly and wanted to ask the research experts here for perhaps some guidance. I will further explain my project below with an example.
Let’s say I have 3 buildings, building A, B, and C in a city, each with a green roof. I also have a land surface temperature map of that city. I then want to find the cooling intensity/performance of each green roof. Here, this is defined as the difference in temperature between the LST of the green roof site itself, and the LST at the distance where the cooling effect ends. This means I need to calculate the cooling extent of each green roof. To accomplish this, I draw buffer doughnut rings around the green space site, and take the average LST value of each buffer ring. I will expect the ring averages to gradually increase moving away from the green roof site, as the cooling effect fades away. However, once I get to a ring distance where the cooling actually drops, then I will mark that as the point where the cooling extent ends. And so then, to find the cooling intensity/performance of each green roof, I take the absolute value of the difference between the LST at the green roof itself and the average LST at the distance where the cooling effect ends. For example, if the green roof itself has an LST of 25C and the distance where the cooling effect is marked as ending is 31C, then I take the difference and say that green roof has a cooling intensity of 31-25 = 6C. Ok great, done.
This buffer ring method to determine cooling extents is proposed in this paper “Quantifying the local cooling effects of urban green spaces: Evidence from Bengaluru, India” by Shah, Garg, and Mishra (2021):
Now I want to find, how is the cooling intensity of a green roof affected by the height of the building? I am basing this on the assumption that the higher a green roof is off the ground, the more the cooling performance will decay. I want to find what this building height to cooling performance decay relationship is. From this paper “Modeling the outdoor cooling impact of highly radiative “super cool” materials applied on roofs” by Sinsel et al. (2021):
I see that for every increase in 1-meter height, the cooling performance decreases by .003C (the paper says .003 K, but just converting to C since C and K have the same magnitude). I am trying to apply this decay rate to my example to see if I can understand how cooling intensity will decrease with building height.
And so for my example: Building A is 15 meters tall, Building B is 25 meters tall, and Building C is 42 meters tall. I want to account for how severely the height of each building will damped/lessen its cooling intensity/performance. And so, for the cooling intensities I would have calculated using the previously mentioned technique, this would assume that the green roof was 0 meters off the ground. And so to actually find how that cooling intensity would be lowered, I will apply the .003C/meter rate. Calculating cooling intensity for each green roof I get: Building A is 6C, Building B is 4C, and Building C is 3.4C. And so I calculate:
Building A: .003C x (15 meters) = .045 C, and so 6C – 0.045C = 5.955 C cooling intensity
Building B: .003C x (25 meters) = .075C, and so 4C - .075C = 3.925 C cooling intensity
Building C: .003C x (42 meters) = 0.126C, and so 3.4C - 0.126C = 3.274C cooling intensity
However, this would assume that the relationship is linear, while the paper says the relationship is nonlinear. Since this paper does not say what “nonlinear” means here, I turn to this paper “The impact of building height on urban thermal environment in summer: A case study of Chinese megacities” by Wang and Xu (2021):
which says the relationship between building height and LST is “negative logarithmic”, and so I use the natural log now and calculate:
Building A: .003C x ln(15 meters) = 0.00812C, and so 6C – 0.00812C = 5.99188 C cooling intensity
Building B: .003C x ln(25 meters) = 0.00966C, and so 4C – 0.00966C = 3.99034C cooling intensity
Building C: .003C x ln(42 meters) = 0.01121C, and so 3.4C - 0.01121C = 3.38879C cooling intensity
But wait, that’s barely anything! It’s almost as if the height of the building has no effect on cooling intensity at all now!
Just to sanity check this, let’s imagine a building D. Treating this green roof as if it were 0 meters above ground, I calculate a cooling intensity of 5.6C. Now let’s say this building is really tall, 200 meters tall. Now at that height, I think it would be reasonable to say that the green roof at the top would likely have a very, very small effect on cooling ground surface temperature for nearby pedestrians, if any effect at all. So let’s try this out with the linear approach and the nonlinear approach:
Building D: .003C x (200 meters) =  0.6C, and so 5.6C – 0.6C = 5C cooling intensity
I was expecting there to be no cooling intensity as all, given how high above the ground the green roof is, but OK.
Building D: .003C x ln(200 meters) =  0.01589 C, and so 5.6C – 0.01589 C = 5.58411C cooling intensity
And so a green roof being 200 meters above the ground have just about the same cooling intensity/performance as if it were 0 meters above ground?? That doesn’t make sense! How can a green roof 200 meters in the sky provide the same cooling relief to nearby pedestrians as a green roof (or technically a green space I suppose if it were 0 meters off the ground) 0 meters off the ground?? It just doesn’t make sense to me! And so while I recognize this building height-cooling decay rate is justified and credited in the literature, I am just not understanding how these results seem reasonable. And so I would like to ask, am I approaching the use of the rate correctly or is my math totally wrong here? Also, I completely recognize understanding green roof cooling performance is far more complex than what I have here, accounting for variables such as evapotranspiration rates and albedo, however, I am trying to keep this very simple and high level for the moment, just so I can understand the role of building height here. I would really appreciate any guidance and feedback on my approach here! Sorry for the very long post, but I wanted to fully explain my example problem! Thank you!
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I have been applying SIMHYD model in the southeast Australian catchments with the daily rainfall, streamflow and potential evapotranspiration data. Unfortunately, the calibration result varies within the negative range. I have tried 100s of combinations in changing calibration periods, parameters value etc but none of these are working. Has anyone tried SINHYD? Please share your experience. Thanks
Dear Barry Croke & Takuya Iwanaga thank you so much for your suggestions. The modelling results have eventually increased after using satellite derived evapotranspiration data. I have also recalculated the catchments area for the study catchments. The calibration results produced more than 0.8 NSE values. I appreciate your support and time.
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I'm going to apply the FAO's equation for the wind climate erosivity of the Tibetan Plateau, but the potential evapotranspiration (PET) is sometimes less than precipitation (P) in this region. I'm wondering if the negative value calculated in this case has any particular meaning. Obviously, it will affect the aggregation of monthly data to annual data.
I think your explanation makes a lot of sense, and it is common for PET to be less than P. However, this has caused me confusion in applying this equation. I chose this particular equation to calculate the index of land sensitivity to wind erosion, and this index can help me to continue analyzing other ecological issues (e.g., exploring the community characteristics of wind erosion sensitive areas), i.e., I may not need quantitative erosion values. Since I am not an expert in soil erosion, I wanted to find a suitable equation to simplify the effect of climate on wind erosion. If other conditions are consistent, the land will be sensitive to wind erosion in drier times and insensitive to wind erosion in wetter times, which is what this equation is trying to convey. I am not sure if this equation is applicable to my study area, and am also looking for a better solution.
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Hi all,
Using HEC-HMS I have established a model and am able to successfully run simulations in a basin for the data set range I have (precipitation, evapotranspiration and river gauge for comparison) from 2008-2020, however I'm struggling to confirm how I move past the simulation and transition into forecasting over long periods (say 10 years for example) to determine what type of flows could potentially be harvested from a basin.
I understand I want to perform continuous modelling, however I I'm having a problem with the precipitation values past the date range I have. When I use the specified hyetograph method, the precipitation data doesn't carry into my forecast period so I end up with no outflow after this period. When I try other precipitation methods (i.e standard project storm) it only provides a storm event at the very beginning of the simulation within the first 24 hours.
I can't find anything in the meteorological model or forecast alternative where it's obvious to direct HEC-HMS to future forecasting based on predicted rainfall. Is this something that's available in HEC-HMS? Is there a certain precipitation model that I need to use? Any help would be greatly appreciated.
Quite a late reply but still, been working on the same and I found that future projection simulation can be done using climate change scenario datasets. user needs to download the GCM datasets (suitable model) and downscale them using either software or programming languages like (Python, R, or MATLAB) then these downscaled datasets are used in HEC HMS to run the simulation (continuous simulations). And for just return periods (event-based simulations) one needs to use the Hypothetical storm method for precipitation where the user uses IDF curves, the precipitation distribution method to calculate return periods (10, 20 ... 100 or more). This is however based on empirical formulas used on peak rainfalls.
I request the scientific community to correct me if I am wrong as I am still new to this.
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Hello how is calculate evopotranspiration from raster data using GIS ?
For instance, Worldclim data or like it use calculation for evopotranspiration .
If you have a suggestion about question and usefull data source could you share with me ?
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There are many commonly used methods for esimtation of evapotranspiration which are based on temperature, radiation, mass and energy transfer etc. or a compbination of thse. Methods such as Penman-Monetieth, Pendman, Hargreaves, Priestley-Taylor, Thronthwaite etc. fall under these categories. What I want to know is the applicability of these methods primarily based on the climatic conditions of the area. Like whichmethod is suitable for humid conditions or arid climate? If someone asks me what method should I use to estimate the ETo for an area in Mumbai, which method should I use?
The standard ET measurement method is the so called FAO 56-PM method. But different studies indicated the preference of other empirical methods according to the specific climatic conditions they studied, For example, based on the study of Gao et al. (2017) the Priestley-Taylor performed better in Dry climate, Hargreaves performed better in semi dry climate and Makkink methods performed best in humid climates. So it depends. It is also important to note that now a days ET could be predicted easily by machine learning algorithms.
F. Gao, G. Feng, Y. Ouyang, H. Wang, D. Fisher, A. Adeli, J. Jenkins Evaluation of reference evapotranspiration methods in arid, semiarid, and humid regions
JAWRA J. Am. Water Resour. Assoc., 53 (2017), pp. 791-808,
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i want to calculate crop water requirement of different fodders crop before experiments but i don't have such data to calculate evapotranspiration.
is there any other method to calculate crop water requirement ?
#cropwater requirement
plz see, the procedure. May be helpful.
Regards,
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Lot of methods are available for estimation of evapotranspiration.pl. suggest any instrument which will directly give the ET at a particular location.
How to Measure Evapotranspiration | CivilMint.Com
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Whether river flow or actual evapotranspiration is more useful for calibrating hydrological models.
We tested the added value of gridded evaporation products in hydrological model calibration and found that they have a good potential to improve the model performance if they are used adequately (in fact, we tested various calibration settings).
The following papers will tell you more about our work:
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Good morning,
Can anyone suggest a dataset presenting historic reference evapotranspiration in the different provinces?
Thanks a lot !
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I come to know that water content in plant or crop can be measured using remote sensing techniques or using evapotranspiration mechanism. I am interested to know how to measure this information in order to know the crop water consumed in plant or in field level.
One such technique is measure evapotranspiration thru imaging using satellite imagery or using UAV imagery I am not sure. I am interested to know which technology is used to measure such data. what is the method/technology to implement in satellite or in Software in ground computer after the satellite imagery is received.
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how can I get this data ? It is related to hydrus 1d
Yes, you may find it in AquaCrop model.
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I am developing an intelligent irrigation system. I have automatic solenoid valves capable of irrigating at the value of the daily evapotranspiration. and I have soil sensors that measure soil moisture. Is there a simple study to find a correlation between evapotranspiration and soil moisture. I propose to use evapotranspiration value for water quantity prediction and humidity value for exact quantity correction and adjustment. are there any other avenues.
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Hi all, I am searching for some values of Crop Evapotranspiration Coefficient (Kc) for natural forest habitats (coniferous forests, broadleaves forests...). On FAO database they only refer to crops like beans, rice, and wheat, but no data on forests. Thank you very much to anyone who can help
Yes, it can be done Using Satellite Data and Eddy Covariance Stations.
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I want to downscale time-series imagery data using precipitation and evapotranspiration (temporal) and possibly even topography (static) using Google Earth Engine (GEE) Random Forest Regression. I have processed the remote sensing products to the same temporal and spatial resolution and joined them.
Typically the code would be something like:
// Create a classifier
var classifier = ee.Classifier.smileRandomForest().setOutputModel('REGRESSION').train({
features:training_data,
classProperty: 'what_I_want_to_predict',
inputProperties: ['predictor_variables']
});
var classified = predictor_variables_data.classify(classifier);
My question is:
1. How do I include temporal and static data as predictor variables (training data)? How do I sample it?
2. How does one apply the RDR model across monthly images over 2 years for example? Do you run the model 24 respective times?
Regards,
Cindy
For those following this question - the solution was:
1 joining time series data
2. concatenating the stationary data.
// JOIN - Combine/join image collections for training
var joinA = GW_anomaly2.select('GW_anomaly');
var joinB = CHIRPS_resampleD.select('precipitation');
var joinC = MODIS_resampleD.select('ET','PET');
var filter2 = ee.Filter.equals({
leftField: 'system:time_start',
rightField: 'system:time_start'
});
var simpleJoinB = ee.Join.inner();
var innerJoinB = ee.ImageCollection(simpleJoinB.apply(joinA, joinB, filter2));
var joinedB = innerJoinB.map(function(feature) {
return ee.Image.cat(feature.get('primary'),feature.get('secondary'));
});
var simpleJoinC = ee.Join.inner();
var innerJoinC = ee.ImageCollection(simpleJoinC.apply(joinedB, joinC, filter2));
var joinedC = innerJoinC.map(function(feature) {
return ee.Image.cat(feature.get('primary'),feature.get('secondary'));
});
// Concatenate (combine into single collection with multiple bands) the given long term mean (single band)
var concatenate2 = ee.Image.cat([DEM_resampleD]);
}
//Map function over entire time-series collection
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I am looking for an equation similar to Priestley-Taylor (1972) but with the fewest parameters.
Thank you @ Mohsen Hoshan sir
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The papers using machine-learning particularly deep-learning models in hydrological prediction (runoff, soil moisture, evapotranspiration, etc.) increase dramatically in recent years. In my viewpoint, these data-driven methods require substantial data to derive solid predictions. I am not sure what is the advantage of these models over the process-based models in predicting hydrological processes.
Hi All and Prof. Gao,
I came across this discussion. I think this is really an interesting topic and a good question. First of all, I'd like to say that your point "these data-driven approaches require substantial data" is correct. To answer this question, I think we need to understand the background of the emergence and widespread application of AI and "big data" techniques, and we should think about why we need machine learning or other "big data" techniques in hydrology/earth system science? What are the pitfalls of our current process-based models at present? Can machine learning methods fill these gaps or improve the prediction?
In recent decades, we have vast amounts of spatiotemporal data from in-situ observations, remote sensing, reanalysis data, and model outputs. In my opinion, data-driven methods can help us gain innovative knowledge from these data, and their greatest advantage is that they do not need to rely on parametric assumptions, and thus can dynamically capture the effects of non-stationary surface processes. For the advantages of deep learning in Earth system science, I recommend reading the review paper "Deep learning and process understanding for data-driven Earth system science" by Reichstein et al. I think this paper discusses it very well.
Frankly, there are some challenges with distributed hydrological models, especially when tuning parameters. In the parameter calibration process, why do different parameters get the same effect? When we use the historical data to set the parameters, will the value of the parameters change in future?
In the future, we hope to be able to see the coupling between these approaches so that we can express the real world more realistically. Hope this information is helpful to you. Good luck!
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I want to get the source and how to interpret the data
Hello,
Please find the attached paper. It discusses designing a new PET product based on ERA5-land product over the period 1981-2020 with hourly and daily time scales in 0.1 degree horizontal resolution. Hope it can be useful for you.
Best Regards
Samy
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Hi,
The aim of the study is to calibrate hydrological model (SWAT model in this case) using remotely sensed actual evapotranspiration (ET) at a daily basis. Consequently, autocalibration requires daily remotely sensed ET. Unfortunately, the MOD16 estimates of ET are at a level of total 8-day, and cannot be directly used in autocalibration.
In this paper, for a similar problem, authors used NLDAS estimates of daily actual ET to disaggregate MODIS 8-day ET using a temporal scaling factor. According to the authors this method can be valid since the NLDAS estimates capture the daily variation of ET well. https://www.sciencedirect.com/science/article/pii/S0022169418307856
In my region, such data are not available freely. I have only MODIS 8-day ET, and daily observed meteorological data (precipitation and temperature, wind speed, humidity, solar radiation). I don't know if MODIS 8-day ET can be disaggregated to daily using other information such as precipitation and temperature?
Thank you very much
Smaranika Mahapatra and Timothy O Randhir have provided I believe the best options for you. You start doing it, once any issues pertain to uncertainties arise we can discuss it further here.
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I want to estimate temperature-based Reference/Potential Evapotranspiration for humid conditions.
Four temperature-based method:
Jansen and Haise (1963)
McGuiness Bordne (1972)
Hargreaves model (Hargreaves and samani, 1985)
Oudin et al (2015).
Above these method, which one is best for humid conditions?
Hello
I suggest comparing models such as Hargreaves method--Blaney-Criddle-Linacre and various temperature-based methods with the FAO Penman-Monteith method, and finally selecting the best model and using this method for future research. For my study area, the Blaney-Criddle method was very accurate.
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I want to calculate Potential evapo-transpiration with less data requirement so which method can be used for this which will give accurate and appropriate results.
Shruti Bansode i can add the answer like For example, air temperature, relative humidity and wind velocity are all taken into account in the penman monteith method, which is more accurate than the Thornthwaite method. The best method for estimating potential evapotranspiration is FAO 56 Penmann-Monteith.
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I'm looking for a software or model to calculate evapotranspiration. The type of the output should be excel spreadsheet or database . Is there any tool to calculate evapotranspiration in ArcGIS ? Is there any method to convert the output of cropwat or ETo calculator into excel spreadsheet or database?
Noha Abdelwarth The FAO's Land and Water Division developed the ETo calculator. FAO-standard reference evapotranspiration (ETo) is one of its primary functions. The ETo calculator uses the Penman-Monteith equation to estimate ETo from meteorological data.
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Crop coefficients can be greater than 1.00.
Ex: Corn 1.15
Then ETa=Kc *ETo, it seems to be ETa greater than ETo.
But ETo is the evaporative power of the amosphere. is it possible to go over that value?
Yahampath Marambe yes i do like to add the statement that It isn't required. Your analogy may hold true if reference ET is equal to potential evapotranspiration. The actual water consumption of a crop is represented by its evapotranspiration (ETa). Because the field is not under standard conditions, ETa is usually equal to or less than ETc.
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How to caculate monthly evapotranspiration without radiation data?
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I have LULC and ET data for the past three decades and have predicated LULC for future time period (e.g., 2025-2055). Now, I want to predict ET using land use land cover changes.
I think, evapotranspiration (ET) is directly related to water body area, temperature, and wind speed. LULC can directly help to caculate water body area. LULC is one component, ET is one vector of many components. Thus, predicting ET with LULC is one issue of weak or partial correlation in statistics. You need to enhance the cost function of machine learning optimization or the set of statistics estimation criteria.
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I am trying to determine the actual evapotranspiration using soil water balance method. But cannot determine deep percolation. Could you please let me know if there's any method, formulas to estimate the DP. Thank you.
Hello Dear, please check these articles:
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Is there an equation to calculate soil moisture excess by using daily precipitation and evapotranspiration data, and soil water content at field capacity, wilting point and saturation?
Hello Dear, these articles may be useful:
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Currently i am getting NSE= -12.371 and PBIAS= 205.91%. Trying to get them into acceptable performance measures. Kindly suggest some solutions.
Thanks.
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A lysimeter decreased in weight by 120 kg over a period when irrigation and rain was 30 mm. What was the evapotranspiration (in mm) if the area of the lysimeter was 1 m2 an the height 0.8 m ?
In this case ET will 150 {120 +30 (irrigation and rainwater)} mm only. increase or decrease in weight in lysimeter by one kg will be equal to one mm in depth.
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My final goal is to find a multiple linear regression to predict actual evapotranspiration, ET (response variable) using remotely sensed land surface temperature and ground-observed air temperature (explanatory variable). All are raster (.tif) format. They are 5-year dataset with the same extent and temporal (16days) and spatial (500m by 500m) resolution.
I was trying to find some ways for spatial regression, raster regression, spatial statistics, etc. but I fail to one that can be applicable to my case. Most ways that I found didn't handle the time series as two explanatory variable at the same time.
Has there been anyone who tried or applied this type of approaches?
You can develop your own code using Python or R.
Many packages are available for multilinear regression such as "SCI-KIT Learn".
This packages efficiently work for regression of time-series data also.
I would suggest you to use Python and do the following steps:
1. Read raster file in Python using Rasterio package
2. Convert it into Numpy array by keeping dimension as your time steps
3. Fit the regression model in Numpy Array data
I hope this would solve your problem.
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In each iteration, I get the same value of H for different values of aerodynamic resistance and the temperature difference between two heights. Do the results make sense? Is it necessary to adjust the air density value for each step?
That is a good question.
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I am trying to get information about potential evapotranspiration for the south of Ecuador, however, until the moment all the sources visited are monthly, or daily but with poor resolution.
Hi dear Gonzalo.
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I wish to shedule irrigation for a crop cultivar of which Kc value is unknown. Can't I use ETo directly without considering Kc since Kc is unknown?
If no local crop coefficient (Kc) is available, the reference evaporation (ETo) can be used to determine the water requirement of the target crop.
Please see and enter this link to download my different papers (field, fruits Vegetables crops) using reference evaporation (ETo) to determine of water requirements and irrigation scheduling whit modern irrigation systems in Egypt
https./www.researchgate.net profile/Ahmed Taha 23
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I need a website
You can get evapotranspiration data (csv or excel files) from website of the weather station of meteorological department of each country.
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1. I was just curious to know the difference between the reference evapotranspiration (RET) and the potential evapotranspiration (PET). Are they same? If not, then what is the basic difference and what is the relationship between them? I mean, if I have PET, then can I calculate the other one or vice-versa? If yes, then how?
2. I have some automated weather station (AWS) data which contains the sunshine hours (daily). How can I estimate the solar radiation from that data? Is it possible? Also, is there any readily global data available for solar radiation/humidity/wind speed etc data for calculating the ET?
Just like PET and ETo, there are several concepts, which may confuse you. For example, water use efficiency and water productivity; crop factor and crop coefficient; water foot print and virtual water; diffusion pressure deficit(DPD) and cell water potential; and so on. I have tried to discuss these kinds of issues in my latest book "Irrigation and Water Management" Published by Ane Books, New Delhi (2021)
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I am working on a small mountainous forested catchment. any advice for the simplest distributed hydrological model able to account for subsurface flow and deep infiltration with accurate modeling of flow in gullies and rivers and strongly simplified evapotranspiration ?
Try the Water Erosion Prediction. (WEPP) model. We have been working for more than 20 years enhancing the hydrology in it for steep forested watersheds. The water file it generates has daily ET, surface runoff, shallow lateral flow, and deep seepage. Post processing the deep seepage can give the daily base flow. Base flow estimation is done internally for US, European and Australian watersheds on WEPP CLOUD but requires more effort with QGIS and QWEPP elsewhere in the world. Let me know if you have questions.
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I need some reanalysis datasets to verify the outputs from a land surface model. The desired parameters are surface latent as well as sensible heat fluxes, evapotranspiration, leaf area index, Gross Primary Production, NEP, Ecosystem respiration, etc.
Yeah, I've tried those. I also have Fluxcom, Fluxnet datasets. I'm looking beyond these.
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For instance if a 250m x 250m pixel has 20 mm ET value, it is how much mm for one hectare, also how much it would be in m3/ha?
Thanks
he or ha?
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Simple water balance equation, P = E + Q, where change in storage can be considered negligible. Apart from having sufficiently long time series of observation of hydrologic variables like streamflow (Q), meteorolgical variables like (P = precipitation, E = evapotranspiration) for a particular time series, what are the other criteria that must be needed to be satisfied to use this simple water balance equation for study of impact of climate change on water resources?
and consider the variations in the hydrogeologic environment
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I have been using the SWAT model, and simulation has been done several times by changing the evapotranspiration calculation method and the rate of hydrological parameters like Soil Evaporation Compensation (ESCO), Groundwater Re-evaporation (GW_REEVAP), Curve Number (CN2), Available water capacity of the soil layer (Sol_Awc), and Slope range of the area.
So, the annual precipitation is 318 mm, the annual surface runoff is 602 mm, but the annual evapotranspiration is based on the Hargraves method (1450 mm) and based on thornthwaite method ( 840 mm). so why runoff and ET are more than precipitation? it can be acceptable in terms of the water balance?
Any solution or recommendation to encounter it, please?
I don’t know if I could be of help. From what I gather, everything is generated by models. Typically at a minimum, rainfall and runoff are measured, and then the unknowns are estimated with models perhaps. Also, if you try to do the water balance by calendar year, one year may be very wet such as in January or December, and the other month or year may be dry, resulting in some estimation error. The USGS uses the water year approach, with September 30 and October 1 the end and beginning of a water year. But you might in SE USA imagine a hurricane toward end of a September, and much of the flow in a large river showing up in October, for example. Truthfully, your situation is quite unusual, suggesting some errors in assumptions or models. But if your project is in karst terrain, it was shown by using dye studies developed by hydrologist Tom Aley of Missouri, that there were instances of interbasin transfer of water underground. And one might imagine that a watershed with a large karst spring might have more runoff than rainfall. When I worked on the Big Turnaround and other associated wildfires in FL and GA, USA, air became reacquainted with the Suwannee River, which has instances of discharge losses of streamflow due to faults or karst terrain loses underground. Another potential source of error is the rainfall measurement, unless one has a grid of rain gauges. Measuring stream flow professionally even has some error, perhaps plus or minus 20 percent unless very carefully gauged. With the anomalies suggested in your data, I would agree there is something difficult to accept without clarification and perhaps more detailed study, including geology.
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ERA5 reanalysis data has four distinct variable domains such as atmosphere (surface), atmosphere (upper air), land (biosphere), and land (hydrology). However, I'm confused about which variable domain's data should I use to calculate the reference evapotranspiration?
@Eliton Sancler Gomes Sales Thanks
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I have to validate the actual ET obtained from model output by using the actual ET value derived from the pan evaporation value. Is there a method to calculate the daily AET from pan evaporation?
Please note that the relationship between reference crop evapotranspiration (ETo) and pan evaporation (Ep) is well known and you can estimate ETo using a proper pan-crop coefficient.
ETo = Kpc x Ep.
Where, ETo- reference evapotranspiration (mm/day); Kpc- pan-crop coefficient, and Ep- pan evaporation (mm/day).
If you want to calculate actual crop evapotranspiration (ETc), you have to multiply ETo with another coefficient, Kc (crop coefficient).
ETc = Kpc X Ep X Kc
Analysis of data on ETc indicates a high degree of correlation between pan evaporation values and crop evapotranspiration. This indicates the possibility of using pan evaporation values directly to estimate crop evapotranspiration (ETc) employing a coefficient called crop factor (Cf). The crop factor accounts for both Kpc and Kc. Therefore, if you can estimate the values of “crop factors” locally, you are freed of the burden of looking for pan coefficients and crop coefficients. The crop factor (Cf) varies with crop type and crop growth stages. This method is also known as Hargreaves Class A Pan evaporation method (Hargreaves, 1968). The relationship between crop evapotranspiration (ETc) and pan evaporation (Ep) is usually expressed as:
ETc= Ep X Cf
Where, ETc - crop evapotranspiration (mm/day); Cf - crop factor for the period; and Ep- pan evaporation (mm/day).
Estimation of daily ETc relies on daily readings of pan evaporation from a suitably placed evaporation pan together with an appropriate set of crop factors. Approximate value of crop factors were given by Hargreaves(1968). However, for accuracy, you have to find local Cf values. For using this method, the user has to enter daily readings of pan evaporation to calculate the crop water use for the day.
Reference: Hargreaves, G.H. 1968. Consumptive use derived from evaporation pan data. J. Irrig. Drain. Div. (American Society of Civil Engineers) 94(1): 97-104.
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What is the difference between the AET and the PET ?
Please note that the concept of potential evapotranspiration" (PET) is no longer used in irrigation, and it has been replaced by reference crop evapotranspiration” (ETo) because of certain reasons. I will explain these two concepts first, and then come to actual evapotranspiration.
The potential evapotranspiration concept was first introduced in the late 1940s. It was defined as “the amount of evapotranspiration in a given time by a large vegetation of short green crop, completely shading the ground, of uniform height and with adequate moisture at all the times in the soil”. The major objective in bringing this “potential” evapotranspiration concept was to remove the effects specific to crops in the evapotranspiration process.
The notion of PET was well accepted by the scientific fraternity, and it became the focal idea in estimating crop water use for a long time. After some time, however, certain limitations cropped up. In the definition of PET, the ET rate was not related to a specific crop. The main confusion with PET was that there were many types of crops that fitted into the description of a ‘short green crop’, and one might be confused as to which short crop should be selected! Having considered the ambiguities and limitations of PET, by the early 1980s, the concept of “reference crop evapotranspiration” (ETo) was introduced.
The major objective of introducing ETo was to assess the evapotranspiration demand of the atmosphere independently without considering the effects of crops, cultivar differences, crop development, and management practices.
In reference evapotranspiration, the reference surface closely resembles an extensive surface of green grass of uniform height, actively growing, completely shading the ground, and with adequate water. The FAO Expert Consultation on Revision of FAO Methodologies for Crop Water Requirements (1990) after specifying the reference surface explicitly, defines reference evapotranspiration as : “the rate of evapotranspiration from a hypothetical reference crop with an assumed crop height of 0.12m, a fixed surface resistance of 70 s/m and an albedo of 0.23, closely resembling the evapotranspiration from an extensive surface of green grass of uniform height, actively growing, well-watered, and completely shading the ground.”
"Actual crop evapotranspiration" (ETa) or simply "crop evapotranspiration" (ETc) refers to the evapotranspiration by a particular crop in a given period under the prevailing soil, water, and atmospheric conditions. Actual crop evapotranspiration can be defined “as the evapotranspiration from disease-free, well-fertilized crops grown in large fields under optimum soil water conditions, which achieve full production under the given climatic conditions”.
ETa can be determined by direct methods such as lysimeters or estimated from climatic data by integrating the effect of crop characteristics into ETo. The ratio of crop ET to reference ET (ETa/ETo), called crop coefficient (Kc), is used to relate them as given in the following relationship..
ETa = ETo x Kc
Where, ETa = Actual crop evapotranspiration (mm/day); ETo = Reference crop evapotranspiration (mm/day); and Kc = Crop coefficient
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I need to calculate actual evapotranspiration. kindly suggest me if there is any alternative method of evapotranspiration calculation.
You need to divide the Le, which should be in an energy unit such as W/m-2 by the latent heat of evaporation (i.e. the amount of energy required to evaporate 1g or 1ml of water) which is 2257 J/g . For example, if you have a total LE of 500 W/m-2 for one hour this would be 1800 000 J of energy (with watts equal to jouls per second). Enough to evaporate 798 g of water per m-2 (1800000/2257). This is equal to 0.798 mm of evaporation (1 kg H20 per m-2 = 1 mm). Good luck with the conversion!
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I would like to examine seasonal actual evapotranspiration as a function of potential evapotranspiration and soil moisture. I have some water balance information: precipitation, streamflow, spring flow, and volumetric water content but not groundwater recharge. I also have sufficient meteorological information to estimate potential ET. This is in a heterogeneous landscape and evapotranspiration is water-limited throughout most of the growing season. I have information regarding the bulk density, field capacity, and texture of some soils in the area. Are there any established methods that examine this relationship? Any suggestion regarding existing research into this topic, particularly for arid and semi-arid landscapes, is appreciated. Thank you!
The last addition was incomplete. The Richards equation does not fully describe the measured phenomena so conceptual representations are equally valid in my experience. The real physics must include air and vapour flow.
I should also have given you my last "accepted" paper :-)
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Is the Penman-Monteith equation applicable when the wind speed is very low (close to zero)? Thank you
Hi Dong, In a greenhouse having nearly zero wind speed, movement of air by convection takes over. Leaves are warmed by photons. The air in contact with the leaves warms and then expands and becomes more bouyant than the air above it. It therefore "floats" upward, vertically along with water vapor and is replaced by cooler air from above it. That process sustains some evapotranspiration even with zero wind speed. ET is all about energy. The energy inside a greenhouse that comes from solar radiation must be consumed by some other process that is represented by G, H or ET. FAO56 and ASCE literature therefore recommend that a lower limit of 0.5 m/s is used for the wind term in the PM equation to approximate the bouyant, convective transport and mixing of air under very low wind conditions. Rick Allen
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I need at least two hydrological model suitable for use over west Africa and takes precipitation and potential evapotranspiration as input dataset
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When I need to determine an alternative equation for estimating reference evapotranspiration for local with missing data, using meteorological data, from official weather stations, this means that I assume that the humidity is missing and I ignore it and estimate it according to the equations recommended by (FAO Irrigation and Drainage Paper No. 56 Chapter 3) and then determine suitable an alternative equation for estimating reference evapotranspiration, and then also determine the evapotranspiration from the standard equation (FAO Penman-Monteith equation 56) to performance evaluation with an alternative equation.
I also assume for temperature is missing and I ignore it and estimate it according to the equations and determine an alternative equation for estimating reference evapotranspiration, and so on for all climatological parameters.
Or what is the basic rule in determining alternative equations to calculate the reference evapotranspiration in places that are missing data, using meteorological data?
Because all those who define alternative equations for estimating reference evapotranspiration rely on data from agro-meteorological stations.
The FAO recommends hargreaves samani equation (HG) is an alternative method to compute PET (ETo) in case the penman monteith equation can't be used for any reason
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I am looking for an equation or any method basis on digital parameters to calculate evpotranspiration . I want to design this in hdl code and implement on fpga.
hi,
u can check these references.
@article{SALAMA2015418,
title = "Simple equation for estimating actual evapotranspiration using heat units for wheat in arid regions",
journal = "Journal of Radiation Research and Applied Sciences",
volume = "8",
number = "3",
pages = "418 - 427",
year = "2015",
issn = "1687-8507",
author = "M.A. Salama and Kh.M. Yousef and A.Z. Mostafa",
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I am measuring the crop water use (ET) by using pots and lettuce crop as plant material, all the data of daily weighted pots are ready but I want to find the surface area of each pot. Can please help me to find the surface area of a pot with the above-mentioned diameters and height to compute daily evapotranspiration.
For evaporation you take the top most area. So the area contributing to evaporation is 3.14 x 0.1725 x 0.1725 = 0.093 m2 or 930 cm2
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Exist studies which compare evapotranspiration in the water-stressed condition and in condition with unlimited water availability? For example, is there a significant difference between evapotranspiration from a forest in a floodplain or around a water reservoir and evapotranspiration from a similar forest far from water resources.
Yes there is a difference in evaporatranspiration. Evapotranspiration will be high in areas where water is not limited, that is where there are more water resources.
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I put the Algorithm Sebal in Python language, can be opened in .txt, to estimate evapotranspiration in TM - Landsat 5 images, which works on Grass's G.dal platform, which is an extension of Qgis. Soon, I will make available those of Landsat 8.
Energy exchanges at the soil-plant-atmosphere interface, through the components of the radiation balance (Rn) and the heat fluxes in the soil (G), sensitive (H) and latent (LE), are essential for climate modeling and hydrological, which in turn affect the entire biosphere. Thus, having as objectives: (1) to estimate and compare the behavior of the energy balance components, using the SEBAL algorithm - Surface Energy Balance Algorithm for Land, in different types of land use and cover and (2) validation.
Please, when using, quote from: Lima, T.A.S .; (2020) Hydrogeological characterization and water use of a sector of sands, sandstones and gravel on the coast of Baixo Alentejo. 203 pp Master's Dissertation in the Geomatics course, University of Algarve.
Access:
Thank you very much, it's a great job. When you have the same algorithm in R let me know.
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What will be the best method to calculate evapotranspiration using NACORDEX data (Relative Humidity, Shortwave Radiation, Wind, Temp(max,min), Precp)?
There are some explanation in the previous post.
I believe that NACORDEX has many more variables, and we can use them in a more scientific way to calculate Evapotranspiration. Your thoughts are welcome. Thanks
Penman-Monteith (PM) approach
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The given Penman-Monteith method is an energy balance method. The key equation shows Rn - G - λET - H = 0 . Whereas there is no method given to calculate H, and I could not find its explanation.
The energy balance equation is equal to:
rn= H+LE+H
Also the value H is equal to:
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Dear All,
I want to plot a Scatterplot of Evapotranspiration rasters to compare two ET products, just like the figure attached, which software should I use. thank you in advance
Regards,
Use gdal, numpy, and matplotlib in Python.
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I am measuring the crop water use (ET) by using pots and lettuce crop as plant material, all the data of daily weighted pots are ready but I want to find the surface area of each pot. Can you please help me to find the surface area of a pot with the above-mentioned diameters and height to compute daily evapotranspiration?
I guess your pot has more or less the shape of a conical frustum (i.e. a cone with a cut-off tip). The surface area of a conical frustum without the bottom and top areas is A = pi * (r1 + r2) * s = pi * (r1 + r2) * sqrt((r1 - r2)^2 + h^2), where r1 and r2 are the bigger and smaller radius of the frustum, respectively, s is the slant height (length of the side) and h is the height of the frustum.
You likely want to include the area of the bottom, but not of the opening at the top (because the pot is open there), so you have to add pi * r2^2 and end up with pi * ((r1 + r2) * sqrt((r1 - r2)^2 + h^2) + r2^2) = 3.141593 * ((34.5 / 2 + 32 / 2) * sqrt((34.5 / 2 - 32 / 2)^2 + 32^2 ) + 32^2 / 4) cm^2 = 4149.5 cm^2
Here are the equations for surface and volume of a conical frustum: https://mathworld.wolfram.com/ConicalFrustum.html
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How can we calculate the Bowen ratio ?
Lecture 14 - Science of Climate
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increasing transpiration and evaporation with raising temperature cause increasing in actual evapotranspiration ETa and reduction of yield and Biomass
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Does anyone know how to perform a sensitivity analysis for crop evapotranspiration (
FAO Penman-Monteith equation) using daily meteorological data?
2011 Sensitivity of evapotranspiration to solar radiation, vapour pressure deficit and other meteorological parameters. Search on google.
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Who can help me to understand the SEBAL Model for Evapotranspiration estimation for Sentinel Data (S2 and S3)
You are welcome.
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I am working on two projects that both require me to estimate Evapotraspiration in my areas of interest. The first project is to calculate ET in rice fields (in a controlled field and in a local or subsistence farm set-up). The second project is looking at calculating ET in a natural woodland environment that offers natural resources ecosystem services within community forests.
in both cases, I have limited climate data to estimate ET and I am therefore relying on freely avail satellite data to extract these information.
Dear Ngula,
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I am conducting a study to comapare different ET products. So far I have downloaded Gleam, landflux Eval, Terra Climate, Modis Mod16 products. I need the name of atleast 4 more products
Have a look at these articles for satellite-based ET estimates and CGIAR‐CSI datasets and also the methodology to standardize these products.
Article Evaluation of Variable-Infiltration Capacity Model and MODIS...
Article Hydrological Response to Agricultural Land Use Heterogeneity...
Article Modelling the dynamics of evapotranspiration using Variable ...
Article The Grass is not Always Greener on the Other Side: Seasonal ...
Article Multi-Model Approach to Assess the Dynamics of Hydrologic Co...
Article An Approach for Estimation of Evapotranspiration by Standard...
I hope you may find it useful
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