Geostatistical upscaling of rain gauge data to support uncertainty analysis of lumped urban hydrological models
In this study we develop a method to estimate the spatially averaged rainfall intensity together with associated level of uncertainty using geostatistical upscaling. Rainfall data collected from a cluster of eight paired rain gauges in a 400 m × 200 m urban catchment are used in combination with s...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2017-02-01
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| Series: | Hydrology and Earth System Sciences |
| Online Access: | http://www.hydrol-earth-syst-sci.net/21/1077/2017/hess-21-1077-2017.pdf |
| Summary: | In this study we develop a method to estimate the spatially averaged rainfall
intensity together with associated level of uncertainty using geostatistical
upscaling. Rainfall data collected from a cluster of eight paired rain gauges
in a 400 m × 200 m urban catchment are used in combination with
spatial stochastic simulation to obtain optimal predictions of the spatially
averaged rainfall intensity at any point in time within the urban catchment.
The uncertainty in the prediction of catchment average rainfall intensity is
obtained for multiple combinations of intensity ranges and temporal averaging
intervals. The two main challenges addressed in this study are scarcity of
rainfall measurement locations and non-normality of rainfall data, both of
which need to be considered when adopting a geostatistical approach. Scarcity
of measurement points is dealt with by pooling sample variograms of repeated
rainfall measurements with similar characteristics. Normality of rainfall
data is achieved through the use of normal score transformation.
Geostatistical models in the form of variograms are derived for transformed
rainfall intensity. Next spatial stochastic simulation which is robust to
nonlinear data transformation is applied to produce
realisations of rainfall fields. These
realisations in transformed space are first back-transformed and next
spatially aggregated to derive a random sample of the spatially averaged
rainfall intensity. Results show that the prediction uncertainty comes mainly
from two sources: spatial variability of rainfall and measurement error. At
smaller temporal averaging intervals both these effects are high, resulting
in a relatively high uncertainty in prediction. With longer temporal
averaging intervals the uncertainty becomes lower due to stronger spatial
correlation of rainfall data and relatively smaller measurement error.
Results also show that the measurement error increases with decreasing
rainfall intensity resulting in a higher uncertainty at lower intensities.
Results from this study can be used for uncertainty analyses of hydrologic
and hydrodynamic modelling of similar-sized urban catchments as it provides
information on uncertainty associated with rainfall estimation, which is
arguably the most important input in these models. This will help to better
interpret model results and avoid false calibration and force-fitting of
model parameters. |
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| ISSN: | 1027-5606 1607-7938 |