Dear colleague,

I think that the following paper will help you in your study:

Article A Hybrid of Optical Remote Sensing and Hydrological Modeling...

https://crisp.nus.edu.sg/~acrs2001/pdf/075GANGO.pdf

https://www.tandfonline.com/doi/pdf/10.1080/02626669609491526?needAccess=true

Chapter Remote Sensing in Hydrology and Water Management

R. Hadria

Dear Sesethu Fikileni ,

You can also

TRMM data (Tropical Rainfall Measuring Mission)

CRU (Climate Research Unit)

NOAA (National Oceanic and Atmospheric Administration )

All these are free data sources

Hope my answer would help you

Regards

Miyuru

Well, why not using our global scale SM2RAIN-ASCAT dataset?

https://zenodo.org/record/2591215

I recommend DR. Miyuru Bandara Gunathilake

Hi, the Water Cycle Integrator of the eartH2Observe project have several gauge interpolation, gauge adjusted satellite and reanalysis precipitation and temperature datasets at http://www.wci.earth2observe.eu/

However, validation of precipitataion is important before application, not only by just comparing the data with observed time series (with raingauge data or hydrological modelling) but also comparing certain features of the time series is important.

Article Analysis of precipitation features estimated by reanalysis d...

If you use Precipitation from remote sensing, you can improve its performance using local raingauge. The merging algorithm descrived in this work have proved to be succesful in reducing the remote sensed field error. In the paper the parameters of the merging are analyzed against the raingauges density so that one defines their value with the features of the local raingauge network and, therefore, one will not need to perform a calibration procedure.

Conference Paper Analysis of the kernel bandwidth influence in the double smo...

Regards

I would rather use observed stations' data.

You could as well compare the performance of CHIRPS with your observed series over your study area.

A study conducted over West Africa in simulating discharge shows that

• Results show that satellite products which use MW, IR and RG data

generally produce good to very good simulations

• Best results were achieved by PERSIANN-CDR, CHIRPS and TRMM

3B42

• CMORPH and CFSR deliver the least robust simulations

*********Microwave Imager (MW)

********** Infrared Scanners (IR)

*********bias-corrected using rain gauge data (RG)

Please see the attached file for more products.

The performance of satellite data-sests varies spatially and sometimes even temporally. I suggest testing and validating the data-sets for the study area, and may be also do some bias correction to the best data-set chosen.

Hi, you may want to improve it with a merging method! Want to improve the performance of your hydrological model on a scarce data basin?!! We introduce you to our new article where a satellite-reanalysis-gauge merging algorithm is detailed. Article Improving Rainfall Fields in Data-Scarce Basins: Influence o...

@Nicolas thank you for response. I will definitely look into this article.