Rainfall Runoff Modelling - Science topic

Differents authors, such as Torma et al., 2015 recommended used fir reggriding of precipitation inverse of distance weithing (idw). If you use Climate Data Operator (CDO) you can use its command:

cdo remapdis,gridfile infile.nc outfile.nc

Please, you read CDO User Manual.

Best regards,

Axel

Hi Md. Tareq Aziz I would recommend the Kirpich formula, which relates length and slope, in addition to the De Rowe formula, which relates the length of the stream section to the difference in heights between one section and another.

Regards,

Of the many possible options for such problems related to shift and change detection, another potential choice is a Bayesian changepoint method available in R, Matlab, and Python (https://github.com/zhaokg/Rbeast). It has been used for problems in dozens of fields. Maybe it helps with problems like yours, but it may be not.

When dealing with modeling processes, everything can happen since you cannot predict the performance of a model before you test it on a data set. But you can confidently say that since the models are trained to fit the calibration data it is natural that the calibration efficiency (precision) is higher than validation.

Furthermore, I don't think you would find such a paper. I mean, to be honest, the subject it is not important to try to find a way to analytically prove whether the validation precision of a modeling process will tend to be lower. What will be the gain of such a result? Take also into account that it is not even certain that such proof even exists.

Moreover, to make a robust statistical claim we need to analyze a very huge set of models with a very much more enormous set of data. It is virtually impossible to study every model using every kind of data for every purpose possible.

I think if you stated more precisely, what you exactly you are after you definitely can find your answer here.

Cheers!

The Manning's n approach has been around for quite a while and may suit your needs quite well. Having said that, simply bear in mind that the approach assumes that all flow is 1-dimensional. In other words, this numeric model assumes that the water in the channel essentially flows in a straight line. Perhaps you are familiar with the old phase 'all models are wrong, some are useful'. Point being, just remember that the output you get is an approximation. In the off chance you have some measured discharge values in your study area, this can help quite a bit to calibrate the roughness values you end up using.

The HEC-RAS 1-D model relies on the Manning's equation for its calculations. If you crank up the roughness values, this will result in slower velocities and higher water surface elevations. Conversely, if you lower the roughness values, this results in higher velocities and lower depths.

As for the value of Manning's n that you use, I'll admit that these references might not be the most appropriate or useful for you particular application. Having said that, it doesn't mean that you can't learn something from these references either. So yes, they are 'oldies' but they are also 'goodies' and they can help to you start to better understand some appropriate Manning's n values.

Limerinos, 1970. Determinination of the Manning Coefficient From Measured Bed Roughness in Natural Channels

Arcement & Schnieder, 1989. Guide for Selecting Manning's Roughness Coefficients for Natural Channels and Flood Plains

Verified Roughness Characteristics of Natural Channels

Hopefully these references prove helpful. Best of luck with your project.

Hi,

It's possible too use analysis by cross-correlation between rainfall and hydrograph. You obtain the lag between rainfall and flood.

Dear Harsh Upadhyay,

Interesting question. In addition to the very useful comments and replies from colleagues, I would like to add some observations and comments. The calibration and validation strategy depends on different factors, such as the purpose of the modelling and possible final application, the availability of data and the hydroclimatological conditions in the studied catchment(s).

The modelling purpose can be very general (e.g. simulation of hydrological regime and behaviour of a catchment), specific (e.g. low flows) or supporting water systems and infrastructure design (extreme, non-observed conditions) and future impacts (e.g. due to climate change). Calibration and validation strategies for general purposes can be more straightforward by using the split-sample test and dividing the time series in two parts, indeed generally somewhat more data for calibration (e.g. 2/3) than for validation. Specific purposes and support for design and impact assessment require more tailor-made approaches showing the robustness of the model. For instance, for flood design purposes one would like to show that the calibrated model can also be used for more extreme conditions than the calibration conditions. Hence, one might select the calibration and validation periods in such a way that the validation period includes more extreme events than the calibration period. In this and similar cases a so-called differential split-sample test is therefore more appropriate. Similar reasoning applies for climate change impacts studies, where you might want to show that your model can also be used for changed climatic conditions. This can be (partly) achieved by selecting a validation period with (hydro)climatological conditions more similar to the expected changed climatic conditions than the calibration period.

Data avaliability obviously determines what is possible in the calibration and validation. When for instance streamflow data are not sufficiently available, you might look for alternative data regarding other output variables such as actual evaporation (e.g. based on satellite information) or state variables (e.g. soil moisture, snow and groundwater). Different calibration strategies for multiple variables (and objectives) are available in the literature, see for instance .

Recommended splits for calibration and validation periods (e.g. 50-50 or 70-30) will also depend on the hydroclimatological conditions in the study catchment (e.g. the occurrence of dry and wet periods) and the hydrological model used. The systematic study mentioned by Mehmet Cüneyd Demirel provides a nice example of the detemination of the optimal data length, but results obviously depend on the hydroclimatological conditions, the catchment characteristics and the model used.

Good luck,

Martijn

Harsch, you need to give some more detail about your model setup in order for anybody to be able to say anything about what may be the cause of this.

Thank you everyone for replying. I had an inkling that the equation referred to the Manning's equation. That is why the thesis author using a K value of 5 by jyst looking at the channel bed surface made no sense to me. To be honest, tgat thesis smells fishy to me now.

Define a reservoir, then put a pump on that and play with the rule base of the pump such that it always pumps unless (some condition you like)

Hope you get some idea from the following paper

If your discharges are affected by a reservoir operation you should make them natural flow by using reservoir operation records.

In general, for this area of land, you must first try to drill at least 10 boreholes. This is so that you can have parametric soundings to calibrate and validate electrical soundings.

Now, and given that the number of drilling is very limited, it seems judicious to me to carry out a grid of 500 m and to carry out a sounding (SEV) with AB/2 of 1000 m in each point of the grid.

The solicited depth of the SEV depends on the increase in the depth of the aquifers (according to geological data). It is therefore necessary to increase the length of electrical soundings in the areas of increase in P and to add SEVs with AB/2 between 3000 and 5000 m (for example).

The establishment of the geo-electrical profiles can be distributed, according to the configuration of the ground, in the following way:

Total number of SEV Number of profile Number of SEV/profile

Zone 1 24 04 06

Zone 2 18 03 06

Zone 3 20 05 04

Best regards

Oh I see, thanks for explaining!

It seems to me that you already provided one possible answer to your question: Your model should include noisiness of data.

Are you somehow stuck with a model that cannot adequately handle noisy data?

If so then you will have to fudge a bit and check if your fudgy approach will cause unwanted error/bias in your calibration. E.g.: Do you expect problems if you simply add the noise you had in mind and turn the negative values to 0's before starting the calibration?

Hello again Mr. Nazar. I recommend you to use HMS to calculate longest flow path. I used GRASS GIS R.LFP toolset in the past but with version of HEC-HMS 4.6 the computation is much simplier here. But it require to set up digital elevation model in HMS.

IntErO model for calculation of soil erosion intensity and runoff

Dear colleague/s,

(to Ibrahim, but open to some other interested colleagues)

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):

IntErO model

www.geasci.org/IntErO.

- Free download of the software;

- Simple installation;

- Examples of applications available on the same web page (but also on RG and on www.geasci.org/Publications);

- I, as an author, am available to demonstrate how it is working.

Finally, in case you decide to compare the results received with using the IntErO with some other model/s and or measurements, I will try to arrange to finance the publication of the paper having in a team of authors two of us from my team,

Sincerely,

Velibor Spalevic

Mob/Viber/WhatsApp: +38267201222

E-mail: velibor.spalevic@gmail.com

Check on this paper.

This is a good question.

That is a good question.

That is a good question.

That is a good question.

That is a good question.

Which version of the HEC-HMS do you use? Naqash Taroob

In the newer versions you can import GeoTIFF files, maybe this way helps you

Hi!

Has it ever happened to you that TRIGRS gives the maps all the same even changing the input values ​​in the model? Gridmatch doesn't show any errors and TRIGRS runs without a problem but then my Safety Factor maps are all the same regardless of whether I change values of precipitation, time, etc. Can anyone help me with this problem?

Thanks!

Hi, Najmus!

I suppose you have water level and cross - section data because you have a canal or, more likely, a river reach. And I suppose you have a maximum level into that stream. So you are trying to calibrate for the peaks of a storm.

If you are going to use the SCS method, you will only need to vary the Curve Number to get the maximum flow. So you will get the CN of your basin, but acccording to the SCS transformation.

If you have the CN (because in your country that value has been set by an official ocument) the CN is given; so you will have to use the Clark transformation method, and you will just be able to calibrate the storage coefficient.

If the basin is big and complicated, you will also have transport reachs, and you will have to estimate transport parameters. I advise you to use Muskingum - Cunge routing.

I also hope this helps a little bit!

José Luis

Interested

If referring directly or indirectly to the USLE R factor, sometimes called the rainfall runoff factor, it is developed from the frequency and intensity of rainfall events, on average, for specific areas. The US has a map developed for this. The rainfall intensity is one of the driving energy factors or forces that is accumulated into an energy R factor from long term rainfall records. Rainfall rates exceeding 3 inches per hour are apparently the maximum rainfall intensity applied as either the amount sufficient to dislodge soil particles, or perhaps that rate on agricultural soils often leads to puddling, which absorbs any added energy from more intense events. Soil loss estimates through USLE and associated equations do not include any erosion associated with channelized flow, it is basically what one might call sheet erosion and sheet flow. The USLE estimates the dislodgement, entrainment of soil particles in this surface flow and movement and deposited to another area or delivered to a channel network. Since rainfall is the driving force for particle dislodgement, it seems that soil loss models would have to consider local or regional rainfall information. Wind blown soil is another force consideration that is not evaluated in rainfall or runoff with USLE soil loss estimate.

Have a look at this related work :

Linear Regression (Decomentation):

Very late answer :)

I believe there's a temporal mismatch between soil moisture and precip data.

Two suggestions:

1) you can send us the SM and P data to have a quick check

2) please note that an updated version of SM2RAIN algorithm is available in GITHUB: https://github.com/IRPIhydrology/sm2rain

I am at disposal for any question

Generally, for a specific date, products from remote precipitation sensing will result in a matrix, which is consistent with the described. To ensure that it is this result that you are seeking, it is interesting to use a visualization tool for this data, the use of ncview or Panoply can help in this regard. If you use linux you can install and use ncdump, the understanding of the data is broader.

Hope this helps.

Try 0.2 or 0.3, more decimals don't accept me either.

Sure Isai Bassene!

Feel free to ask if you have any doubt.

Yes USGS based softwares eg MODFLOW, Modelmuse, mt3dms, modpath. check their website for more info. All softwares are free and open source

You actually do not need a soil map to set a HEC-HMS model or to calibrate it. It is a conceptual model, so lot of things are approximated. Of course you can use soil and land use to get the Curve Number and other related parameters in GeoHMS but during calibration they are likely to change also. So, a better idea is, do a literature search, start with reasonable values of soil related parameters and do the calibration and validation for streamflow simulation.

Hi,

SWAT+ fits for your goal. The following paper explain step by step simulation process:

Hello Amit,

You may use the ArcHydro toolbox version in the QGIS for estimating the catchment runoff. Also, you can go step by step, by delineating the catchment outlet, then the FDR, Flow accumulation, and drainage area for the given catchment. Also, you can use the SAGA toolbox for the same. The following links may also be very useful for you:

https://docs.qgis.org/2.8/en/docs/training_manual/processing/hydro.html

http://www.saga-gis.org/en/index.html

Cheers

This data has been of interest to me as well.

I think you should not select any method for initial loss or data which you inputted for initial loss for these basins which are shows in error.

you should select the method for initial loss and add appropriate data for selected methods and run again.

Dear Mourad,

I do not think that this material exists in one of the research laboratories in Algeria. Maybe you have to look at the meteorological office. It may be equipped with such a device.

Regards

Coefficient of Determination and Nash-Sutcliffe Efficiency are based on least squares so give greater weight to the peaks. A high CoD can indicate the peaks fit well but not the recessions. You can get a better idea of the fit of the recessions by taking logs. There are other measures you can use if you trawl through the literature however I used a combination of NSE and logNSE to indicate the fit of my models when working on my PhD. However what value to pick is a very good question - the answer to that is dependent on so many factors and is often a case of as good as you can get given the uncertainties involved. Good luck. It is probably good practice to determine your limits of acceptability before you start to model then throw out any models that don't comply - again trawl the literature particularly the work of Keith Beven.

here the Matlab code with an example of data for making the figure

Use Microsoft Excel, which has a wide range of functiona

Hi Oswaldo,

In addition to and Jose Pablo Ortiz Partida ; why would you like to estimate the effect of changes (only increases?) in rainfall on the runoff? Because of the expected effect of climate change on rainfall patterns? To determine the sensitivity of the runoff to rainfall changes?

In the first case it would be good to use outputs of different Global Climate Models (GCMs) for different Representative Concentration Pathways (RCPs) to assess how for instance rainfall and temperature will change in the area of interest. You then will need a downscaling method to obtain rainfall and temperature (and other meteorological variables) at the catchment scale (instead of the coarse GCM scale). Furthermore, bias correction might be needed.

In the second case, you can evaluate the changes in runoff or streamflow due to different rainfall changes and assess the sensitivity, for instance an increase in rainfall of 20% might lead to an increase in streamflow of 20% as well, but also might be amplified to an increase in streamflow of e.g. 30%.

Good luck,

Martijn

Hello Sir, I am also facing this problem. can You please suggest me the solution.

The priority would be for the surface layers. If low hydraulic conductivity layers are expected in the subsurface layer, the importance of characterizing these strata would be increased.

With budget constraints, it might be a valid idea to prioritize field infiltration testing to calibrate empirical models, in addition to soil density and macroporosity.

An additional note: in certain oxisols, texture is not a determinant of infiltration capacity, but rather soil structure (granular), which results in high macroporosity. In these cases, even for clay soils but with granular texture, the infiltration capacity is high.

if you search in the literature you will find many examples of lumped models based on stepwise multiple regression that include rainfall , landuse, evapotranspiration etc. I published a paper (sometime ago) Rainfall-Runoff Relationship and Water Budget for Zarqa River Basin I will add the full text to my profile ... hope that help

Go to "Geo-processing--> geo-processing options --> unclick background processing. "

Then re-compute river slope

This should work fin

@Vasker Sharma, you can use the autocorrelation fuction syntax in R,that is acf (), assuming you have a monthly data series of a data frame called Rubi, to check for auto correlation at lag=1 in the January series of Rubi in R you type as follows;

acf (Rubi$January,lag.max=1)$acf pressing your enter key will display the autocorrelation plot at lag=1,you will clearly see the limit line marked in blue,if your output crosses the limit line in either the positive or negative side of the divide then you can say that the January series of the data frame called Rubi is statistically significant at lag=1.Modified Mann-Kendall (MMK) is then recommended,in such case i will recommend the Bias Corrected Pre-whitening (BCPW).

Best of luck

What model you are using with Fuzzy Logic and what are the inputs and outputs of model.

Your question is not clear. If you clarify then I will be able to understand and attempt to suggest if it is possible.

for rainfall, try:http://iridl.ldeo.columbia.edu/

I think most kinds of ANNs could well simulate the river discharge with the previous observed streamflow and current rainfall data. But those works highlight the predicted discharge only. Hence, if for something new, your point should focus on if the LSTM can reasonably reflect the mechanistic processes. Specifically, the soil moisture has the memory of previous wetness in a watershed, which may be suitable for the function of short-term memory in LSTM. It means soil moisture productions made from remote sensing or land surface modeling may be useful for your research project. Moreover，some of my work published in 2013 and 2014 could be helpful, as well.

Dear Abdul Rahman Mahmud

A paper is attached. It can be useful for your question.

Sorry, I could not tell from question that you were licensed engineer. Your question direction seems to be using the road system and access potential to develop water storage. Water storage can have many benefits, but also effects and liabilities. Installing ponds, dikes, dams, channel modifications are various forms of hydrological modifications. If volume of water storage is important, identifying preferential dam locations may be important. These modifications can affect various aquatic habitat and channel functions, water quality and sediment transport; benefits possible for water uses as irrigation, fire control, perhaps recreation, fishing, etc. As with any project with intent to modify hydrology, it is best that decision maker be presented with full assessment of effects to make informed decision and as well identify areas of mitigation or design concerns. The larger the drainage size (named stream, watershed, or basin scale), the more complex and detail may be needed. If rural areas are involved and downstream, failure of structures are unlikely to have same effects as if failure would flood downstream communities or urban areas. I would make sure I have reviewed drainage land use, geology, soils, rainfall and streamflow records. Below moderate to large dams, culverts and other crossings are unlikely to be sufficient unless specifically designed for bipass or overflow.

I would consult with fisheries biologists, botanists, etc. I would make sure equipment used is washed as needed to avoid transfer of invasive species. I would make sure ponds have overflow channel or armored to allow overflow. If roads are used to develop permanent ponds or lakes with control to manage flow, I would design road much like a dam, with clay core, compaction, etc., design armored overflow. It is not unusual for many US areas even with great hydrology data, for the design storm uncertainty to be plus or minus 40%. I am unfamiliar with storm severity data for Africa, but uncertainty can be a significant factor. For many large stream systems, providing for aquatic organism passage is an important concern, so if you are installing culverts, they need to be designed for outlet control, submerged. I am used to the team approach to projects, but consulting with a variety of expertise, specialists, landowners in vicinity can provide both benefits and perhaps frustrations, depending on your opinion, and the direction of decision maker or developer.

We use various methods to allow for dependent species and instream flows, depending on the circumstance. In most instanced we would recommend bankfull flow releases every year or two, to maintain channel capacity, and some use the 7Q10 flow as the minimum flow released from control structures to maintain perennial flow, in addition to any downstream needs with water rights.

There are guides on internet if needed to address not only road construction, design, but also size of sediment basin, plunge or scour pool, pond, dike, dam, culvert sizing nomographs, erosion control, etc. Some of your training and texts probably covered topics, but pulling it all together into project form to fit specific circumstance adds complexity. I have learned, especially in relative flat or inaccessible terrain, the value of LiDAR adds substantial topographic and channel detail, improves identification of hydrologic boundaries, georeferencing streams and estimating stream types and morphology. DEM Contour intervals of less than 1 meter may be available for improvement of draft design before field survey Confirmation.

A few ideas. The size of basin with respect to drainage area and expected storm flow. The residence time for suspended solids to settle before discharge. The length or path distance that sediment would have to move within the sediment basin before being discharged. The type of sediments - storing clays is especially difficult to settle without sufficient storage time. Maintenance of sediment basin - will it be maintained on regular schedule, or just built and left. Sediment sources and activities in watershed. If capacity is exceeded, would the sediment basin fail or have emergency overflow? How long is sediment basin supposed to operate, short term associated with an activity and mitigation, or long term due to recurrent sediment issues and need for low sediment to support downstream uses, water treatment, etc. Consider type of upstream contributing streams - gullies and steep gradients deliver sediments efficiently, while upstream braided streams are result of high sediment load, and they also tend to be accumulating sediment (aggrading). What is potential for contributing upstream activities such as channelization, road development and land clearing, urbanization, etc. If not familiar with Roehl (about 1962), he developed sediment delivery ratio from drainage size using ponds and dams. His results suggest that the larger the contributing drainage area and associated reservoir, the greater efficiency at removing sediments, and one might also assume that larger watersheds often have ability to store some sediment on their floodplains.

Dear Jean,

Thanks for your explanation.

Hi Saber,

Depending on the level of detail needed and the amount of complexity you are willing to employ, there are a number of possibilities. My experience is mostly from working in cold / temperate landscapes, so I am not exactly sure how relevant these ideas will be to your situation. Here are the possibilities for simulating water (runoff) temperature on a given day (T_w(t)), from most simple to more complex:

(i) The average air temperature (T_air(t)) or zero degrees, whichever is greater.

T_w (t) = max(T_air(t), 0)

(ii) Lagged air temperature

T_w(t) = (1/k)(T_air(t)) + ((k-1)/k)(T_w(t-1))

The "Nordic" HBV model I have used in the past uses a default lag factor (k) value of 30

(iii) A process-based soil temperature model, I have used Rankinen et al. (2004) and been satisfied with the results

(iv) Any of the more complex, process based river temperature models (see, e.g. Webb et al. 2008)

Rankinen, K., Karvonen, T. and Butterfield, D., 2004. A simple model for predicting soil temperature in snow-covered and seasonally frozen soil: model description and testing. Hydrology and Earth System Sciences Discussions, 8(4), pp.706-716.

Webb, B.W., Hannah, D.M., Moore, R.D., Brown, L.E. and Nobilis, F., 2008. Recent advances in stream and river temperature research. Hydrological Processes: An International Journal, 22(7), pp.902-918.

Hi Roland,

Do you mean that the SWAT_CUP copys the lup.dat but It does not copy the associated files (HRUs fraction files)?

If this is the case, might be you could try to copy the land use change fraction files to the working folder of SWAT_CUP for it.

About the .hru changing constantly but the HRU fraction does not change. I think this is because every time the parameters was change, all of the file will be rewrite by SWAT_CUP. SWAT_CUP does not need to change the HRU_FR in .hru file because SWAT will change it according to the information in the lup.dat file.

I also create an R script to generate input files (TxtInOut) for SWAT (new HRUs emerings due to land use change will also be consider and you can also interpolated you HRU fraction at any time). If you interest in using this script, please let me know.

You could also look at the papers by Lamb and Beven 1997 and Beven et al 2011 on generating a master recession curve

The concept of 'effective rainfall ' is irrelevant for most environments. What is effective rainfall in an urban area? 'Effective rainfall' is the amount of rain which is 'used' to produce a crop or fill a reservoir. It is meaningless when applied to an area much bigger than a paddock - and even then if the terrain is variable in slope or soil, or the farming system is different, the calculation becomes very difficult very quickly.

Dear Andrea

Take a look at these files and link. Hope it might be useful.

Hi Daniel,

Water in the root zone may be measured by sampling and oven-drying the soil before and after every shower of rain. The increase in soil moisture, plus evapotranspiration loss (ETa) from the time the rain starts until the soil is sampled, is the amount of effective rainfall. After heavy rainfall evapotranspiration can be assumed to be at the potential rate during the short period from cessation of rainfall until the sampling time. This can be taken as 0.4 to 0.8 times the evaporation value of the Class A Pan

The method takes into account the soil and the crop characteristics. The determination is simple and accurate but it may involve errors due to soil variation; the sampling errors may range from 5 to 40 percent. The method is also laborious and time consuming. The use of neutron probes reduces the drudgery of periodic soil sampling, but these are costly methods for routine purposes and also subject to sampling errors

Best Regards.

I have not tried to do this. Most places have approximate tide elevations by date and hour calculated by moon influence. Wind speed and direction can affect tides. You should probably determine the exact elevations when tidal influence begins, and how much time after especially high tides like new or full moon does it take to drain the brackish water and return to freshwater. Since freshwater is less dense, obtaining salinity values is unlikely to help due to poor mixing. During non-storm periods, you can probably recover flow data by smoothing the recession curve before tidal influence with recession curve when tidal influence is gone.

If tidal influence is regular, such as twice a day, you might be able to use the past daily tide tables and using periods of low freshwater flow, determine the influence of the tides in reversing flow direction, flooding weir or upstream sections or floodplain with tidal water, and then the time it takes when the tide drops to return to baseflow. You might need to calibrate flows over a weir that has backwater, rather than a free drop in elevation. The flow velocities through culverts are different between inlet control, and outlet control, partially to fully submerged.

If your long term goal were only measuring freshwater response at these sites, an efficiently placed dike downstream at high tide elevation may be used to control salt water entry. A water control structure sometimes called a trunk, can limit salt water entry, but allow for freshwater passage at lower tide. Another option could be to raise the weir outlet so salt water influence is avoided or less frequent. Some of the advantages of a weir are lost if the v notch control is affected by backwater. As the backwater elevation increases, the effective cross section of the notch is reduced from free fall by gravity to push back by water, velocity slows.

Periods of storm hydrographs affected by high tides would be most difficult to recover. Storm hydrographs not affected by tides will be the best data. Flow data affected by tides will likely be of lesser quality. Placement of a transducer or other water level indicator upstream of tidal influence and downstream of the weir and/or gauged cross section would help differentiate better the timing of salt water influence and help improve quality of recovered data.

I do not know of any model that can help, but would not be surprised if there was one that could be calibrated or modified to assist. If you have stream gauging equipment or an agency such as US Geology Survey, I think a respectable level of calibration and recovery of flow data would be possible.

The discharge data thats measured hourly in m3/s can be averaged over period of 24hrs ( average of 24 values) to report mean daily discharge in m3/s.

The "lfstat" package on R looks quite good too, and provides scientific references. But the WHAT website is probably the easiest.

Hi Omar,

I would suggest looking at GPM data in addition to TRMM, GPM being a much newer product with a higher spatiotemporal resolution. NASA's Giovanni web portal (https://giovanni.gsfc.nasa.gov/giovanni/) is a great resource to download information from both missions, since you can do it in gridded format or with post-processing that allow things like obtaining area-averaged time series.

Also, since you are interested in flash floods, aren't daily estimates too coarse for that purpose? TRMM has an hourly resolution product and GPM has one with a resolution of 30 minutes (also available from Giovanni).

Dear Mrugaxi ,

I suggest you to see link and attached file in topic:

This is a data issue for the soil. The soil lookup and the soil database are not properly linked.

If you have other nearby weather stations with full time series around the station where you have gaps, you can calculate the gaps by linear regression with the closest station or multi-regresion with two or more. This is a tradicional method. Other more complex but currently used methods are Kriging described in geostatics. Rainfall is not a continue parameter like temperature and is just probably the estimation.

Dear Praveen,

In addition to the good points of Minjiao several other issues related to event-based and continuous rainfall-runoff (RR) modelling.

Event-based RR modelling is often used for short-term purposes such as flood forecasting and flow forecasting in general. Continuous RR modelling is useful for long-term purposes such as the estimation of design discharges, impacts of climate and land use change and impacts of water management strategies.

In event-based RR modelling, the model is run for a number of selected events (e.g. flood events, typically days to weeks) and in continuous RR modelling the model is run for a long time period depending on the data availability and purpose of the study (typically several years to several decades).

Indeed, the estimation of initial state variables is very important for event-based RR modelling and for instance satellite-based information on soil moisture can be very useful. In continuous RR modelling, often a spin-up (warm-up) period is used before the actual calibration is carried out to enable the state variables to adjust to the current hydrological conditions.

Regards,

Martijn

Dear Kasih,

I have looked at your data and it is a bit confusing. However, it seems to me that measured data are the times of bucket tips so I have similar opinion as Davi. It is clear that the values in the column ULL00317_2 are already processed data. For your purpose, I would better use the measured data. This means that to get the daily totals you need only to sum all the pulses in respective days. This is what you did in the column ULL00317_3. I believe that these values are your daily precipitation depths.

I have also the comment about your doubts about the calculation. The values of intensities are always average over some period. We are unable to measure instantaneous intensity but we are of course able to shorten the period of averaging. Hence, you will get the precipitation depth by multiplying the intensity by the duration of measurement period if the intensity is the average intensity corresponding to the period in focus.

Hope, this helps.

Dear Vikram,

In HEC-HMS five methods are included to represent recession/ baseflow as contribution to streamflow. These methods vary from the linear reservoir method to the non-linear Boussinesq method. Depending on the method you are using one or more parameters can be calibrated. For instance for the exponential recession method that will be one parameter k: the exponential decay constant.

Regards,

Martijn

Dear Vineet:

Depending of the basin and model characteristics, the DEM differences over the river should not alter significantly the runoff values at the basin outlet.

Anyway, you can read the answers from:

There has been a lot of work done on this, particularly since 2003 when the PUB decade started. As Mark says, the simplest way to proceed is the area ratio method. This assumes that the conditions in the downstream portion of the ungauged catchment are the same as those in the upstream portion. The next level up is to use a lumped rainfall runoff model to predict the flows. The issue is here that you need to get values for the model parameters. There are a number of approaches to do this.

The naive approach is to take the parameters from the upstream catchment, and use these for the ungauged catchment. The only difference affecting the modelled flow is then the rainfall and potential evaporation, plus the catchment area.

If you have access to other gauges in the region, then you could use a more general regionalisation approach. Options here are:

Another approach is to use a distributed rainfall-streamflow model, and try to use spatially distributed data to drive the model. You could at least partially calibrate the model using the upstream gauge. The issue here is the time required to collect the necessary data, and set up the model. There is also the issue of the impact of uncertainty in the data used to drive the model, and how this impacts on the confidence of the model predictions. Generally speaking, more complex models have the potential to more accurately represent the processes and the overall catchment behaviour, at the cost of a reduction in precision. Not that precision should not be determined by looking at the scatter in the model residuals. It should be determined from consideration of the uncertainty in the model residuals - a different thing entirely.

All depends on what data you have available.

Hi Zulfaqar,

SIMHYD is designed to use daily rainfall, PET and runoff data. You can not use monthly data in SIMHYD. Thanks

You can use either time series analysis or Artificial neural networks with wavelet and SVM techniques