An assessment of the use of remote sensing to estimate catchment rainfall for use in hydrological modelling and design flood estimation.
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Date
2022
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Abstract
The accurate estimation of catchment rainfall is crucial, especially in hydrological modelling
and flood hydrology which is used for the planning and design of hydrological infrastructures
such as dams and bridges. Traditionally, catchment rainfall is estimated by making use of
ground-based point rainfall measurements from rain gauges. The literature review conducted in
this study supports that there is evidence of a decrease in the number of operational groundbased
rainfall stations in South Africa which presents a challenge when estimating catchment
rainfall for use in hydrological modelling and design flood estimation. Thus, innovative ways
are required to estimate catchment rainfall and to improve the estimation of catchment design
rainfall.
This study investigated the use of remote sensing as an alternative way to estimate catchment
design rainfall. To do this, a pilot study was first used to develop and test the methodology
using a quaternary catchment that was selected based on the raingauge density. This was
followed by the application of a refined methodology in another quaternary catchment which
was used to verify the results that were obtained in the pilot study.
After a comprehensive review of the literature, the remote sensing product selected for this
study was the CHIRPS rainfall product. The methodology adopted first validated the remotely
sensed rainfall data using the observed rainfall data and the estimated remotely sensed rainfall
values were bias corrected using the observed rainfall data. The statistics that were used for
validating are MAE, MBE, RMSE and D. The method that was used for bias correction was
empirical quantile mapping Issues encountered, and as documented in the literature, include
the unavailability of long periods of observed quality rainfall data and the limited and uneven
spatial distribution of rainfall stations.
Catchment rainfalls were estimated using observed rainfall, and this was assumed as the best
estimate and was compared to the catchment rainfalls that were estimated using the biascorrected
remotely sensed rainfalls. The performance of CHIRPS rainfall was varied among the
approaches and the selected catchments.
Nevertheless, the results from this study still show the potential of the use of remotely sensed
rainfall to estimate catchment design rainfalls. At the daily timescale, satellite-derived and
observed rainfall were poorly correlated and variable among locations. However, monthly and
annual rainfall totals were in closer agreement with historical observations than the daily values. Despite the varied performance , the result of the study shows that CHIRPS rainfall product can
be used to estimate catchment rainfall for hydrological modelling and flood frequency analysis.
By acknowledging that the performance of remote sensing products is robust, it is of importance
to note that the performance of the results presented is strictly for the catchments and stations
selected for this project as well as the methods selected to validate and correct the bias in
remotely sensed rainfall. The recommendations from the study are that a similar study is
conducted in another region where there is even distribution of stations and a long record of
quality observed rainfall beyond the year 2000 and consideration of the methods to identify
outliers before making any meaningful estimations such as catchment rainfall from rainfall data.
Description
Masters Degree. University of KwaZulu-Natal, Pietermaritzburg.