Evaluation of a model for disaggregating point daily rainfall into hourly rainfields: The Albert catchment case study

Gyasi-Agyei, Y ORCiD: 0000-0002-2671-1180The Albert catchment (688 km2) is located in south east Queensland, Australia. There are no automatic weather stations (AWS) that record fine timescale rainfall located within the catchment, but there are 32 AWS located within a square region (150 km x 150 km) encompassing the catchment. Also, the catchment is under the Mt. Stapylton rainfall radar. For daily rain gauges, 11 are located within the catchment and 266 within the square region. These point datasets were used to generate hourly rainfields over the catchment at a grid resolution of 1 km2. The daily spatial rainfall model is based on Kriging interpolation with the parameters estimated using point daily rainfall data. Disaggregation of the spatial daily rainfields into hourly rainfields is achieved using the scaled hourly storm profile of the nearest AWS station. In order to test the model, the full hourly radar rainfields were used as input into a GIS rainfall-runoff model, and the generated discharges were considered as the 'TRUTH'. Then, the collocated point daily radar rainfall values were selected to run the spatial rainfall model to generate the daily rainfields. These daily rainfields were then disaggregated into hourly rainfields using the nearest collocated AWS station's radar scale hourly storm profile, which were used as input into the GIS rainfall-runoff model. Discharges at 10 sub-catchments' outlets were compared, assuming the same parameters, topographic, soil and initial conditions. Of the 3 days of different rainfall patterns over the 10 sub-catchments examined, 17% have NSE values between 0.84 and 0.90, the rest having values being greater than 0.9, indicating a perfect match of simulated radar rainfall modelled runoff to the observed radar rainfall data. With these results, the over 100 years of observed point daily rainfall and limited AWS data can, therefore, be confidently used to generate corresponding hourly rainfields for meaningful hydrological modelling

Realistic sampling of anisotropic correlogram parameters for conditional simulation of daily rainfields

This paper has established a link between the spatial structure of radar rainfall, which more robustly describes the spatial structure, and gauge rainfall for improved daily rainfield simulation conditioned on the limited gauged data for regions with or without radar records. A two-dimensional anisotropic exponential function that has parameters of major and minor axes lengths, and direction, is used to describe the correlogram (spatial structure) of daily rainfall in the Gaussian domain. The link is a copula-based joint distribution of the radar-derived correlogram parameters that uses the gauge-derived correlogram parameters and maximum daily temperature as covariates of the Box-Cox power exponential margins and Gumbel copula. While the gauge-derived, radar-derived and the copula-derived correlogram parameters reproduced the mean estimates similarly using leave-one-out cross-validation of ordinary kriging, the gauge-derived parameters yielded higher standard deviation (SD) of the Gaussian quantile which reflects uncertainty in over 90% of cases. However, the distribution of the SD generated by the radar-derived and the copula-derived parameters could not be distinguished. For the validation case, the percentage of cases of higher SD by the gauge-derived parameter sets decreased to 81.2% and 86.6% for the non-calibration and the calibration periods, respectively. It has been observed that 1% reduction in the Gaussian quantile SD can cause over 39% reduction in the SD of the median rainfall estimate, actual reduction being dependent on the distribution of rainfall of the day. Hence the main advantage of using the most correct radar correlogram parameters is to reduce the uncertainty associated with conditional simulations that rely on SD through kriging

Assessment of radar-based locally varying anisotropy on daily rainfall interpolation

Gyasi-Agyei, Y ORCiD: 0000-0002-2671-1180Spatial variability of rainfall has been recognised as an important factor controlling the hydrological response of catchments. However, gauged daily rainfall data are often available at scattered locations over the catchments. This paper looks into how to capitalise on the spatial structure of radar rainfall data for improving kriging interpolation of limited gauge data over catchments at the 1-km2 grid scale, using for the case study 117 gauged stations within the 128 km 128 km region of the Mt Stapylton weather radar field (near Brisbane, Australia). Correlograms were developed using a Fast Fourier Transform method on the Gaussianised radar and gauged data. It is observed that the correlograms vary from day to day and display significant anisotropy. For the radar data, locally varying anisotropy (LVA) was examined by developing the correlogram centred on each pixel and for different radial distances. Cross-validation was carried out using the empirical correlogram tables, as well as different fitting strategies of a two-dimensional exponential distribution for both the gauged and the radar data. The results indicate that the correlograms based on the radar data outperform the gauged ones, as judged by statistical measures including root mean square error, mean bias, mean absolute bias, mean standard deviation and mean inter-quartile range. While the radar data display significant LVA, it was observed that LVA did not significantly improve the estimates compared with the global anisotropy. This was also confirmed by conditional simulation of 120 rainfields using different options of correlogram development

Modelling the dependence and internal structure of storm events for continuous rainfall simulation

Gyasi-Agyei, Y ORCiD: 0000-0002-2671-1180Pair-copula construction methodology has been explored to model the dependence structure between net storm event depth (R), maximum wet periods’ depth (M), and the total wet periods’ duration (L), noting that the total storm event depth is RT = R + M. Random variable R was used instead of RT in order to avoid physical boundary effects due to the condition of RTPM. The flexibility of pair-copula construction allowed the examination of 11 bivariate copulas at the three bivariate stages of the three-dimensional (3D) copula. For 21 years of hourly rainfall data from Cook County, Illinois, USA, examined, three different copulas were found suitable for the bivariate stages. For the internal storm event structure, a Geometric distribution was used to model the net event duration, defined as the difference between the total duration (D) and L. A two-parameter Poisson model was adopted for modelling the distribution of the L wet periods within D, and the first-order autoregressive Lognormal model was applied for the distribution of RT over the L wet periods. Incorporation of an inter-event (I) sub-model completed the continuous rainfall simulation scheme. The strong seasonality in the marginal and dependence model parameters was captured using first harmonic Fourier series, thus, reducing the number of parameters. Polynomial functions were fitted to the internal storm event model parameters which did not exhibit seasonal variability. Four hundred simulation runs were carried out in order to verify the developed model. Kolmogorov–Smirnov (KS) tests found the hypothesis that the observed and simulated storm event quantiles come from the same distribution cannot be rejected at the 5% significance level in nearly all cases. Gross statistics (dry probability, mean, variance, skewness, autocorrelations, and the intensity–duration–frequency(IDF) curves) of the continuous rainfall time series at several aggregation levels were very well preserved by the developed model

A stochastic model for daily rainfall disaggregation into fine time scale for a large region

A robust model for disaggregation of daily rainfall data at a point within a large region to any fine timescale of choice is presented. Limited fine timescale data are required to calibrate only three parameters for the regional model, to establish monthly variation of simulation timescale lag-1 autocorrelations, and also to establish a scaling law between the simulation timescale and the 24-h aggregation levels. Site specific parameters are obtained using the 24-h statistics to disaggregate a long record of daily data by repetition and proportional adjusting techniques with capping. An Australia-wide data set has been used as a case study to illustrate the capability of the model. It has been demonstrated that the disaggregation model predicts very well the gross statistics (including extreme values) of rainfall time series down to 6-min timescale. The possibility of linking the disaggregation model to daily, or global circulation, models that can capture the inter-annual variability of the rainfall process for simulation beyond the number of years of record is being explored

The evolution of urban water metering and conservation in Australia

Gyasi-Agyei, Y ORCiD: 0000-0002-2671-1180; Koech, RK ORCiD: 0000-0002-0563-6687Water metering has traditionally been seen as a means of measuring the amount of water consumed primarily for billing purposes. However, with the recent entry into the market of smart water meters, metering is now increasingly considered as an integral aspect of integrated water management. The purpose of this paper was to undertake a review of urban water metering in Australia, and to understand the nexus between water metering and water conservation. The review has demonstrated that metering has contributed to water conservation. The use of smart meters, and their integration with other household appliances, is on the increase

A catchment-based approach to the mitigation of erosion problems in a railway cutting

Gyasi-Agyei, Y ORCiD: 0000-0002-2671-1180The strategies used to mitigate complex erosion problems in a railway cutting are presented in this paper. The field trials site consists of a 650 m long cutting located in the Boundary Hill coalmine railway balloon, approximately 40 km north-east of Biloela, in Central Queensland. A catchment-based approach was adopted in the mitigation of the erosion problems at the field trials site, viewing the cutting as an integral part of a drainage system that extends beyond the railway easement. Several earth banks and catch drains were constructed to divert runoff away from the railway cutting crest. A series of rock check dams were put in the catch drains to help control flow velocity, erosion, and trap sediment. All erosion pipe inlets and outlets were excavated and filled with basalt spoil material obtained from the nearby mine. The site was well graded to prevent ponding of runoff water on the disturbed areas. The batters are being stabilised with grasses and soil ameliorants. Although it is too early to fully assess the effectiveness of the strategies employed, the field trials are successful so far. It is believed that the research outcomes from the field trials have a high potential for transfer to other erosion problem sites