Infrastructure Engineering - Research Publications
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ItemNo Preview AvailableA 2D hydrodynamic model-based method for efficient flood inundation modelling(IWA PUBLISHING, 2022-09)Abstract Efficient and accurate flood inundation predictions can provide useful information for flood risk mitigation and water resource management. In this paper, we propose a new modelling method, LoHy + , which can be applied to efficiently simulate the spatiotemporal evolution of flood inundation with reasonable accuracy. The method integrates a low-fidelity two-dimensional (2D) hydrodynamic model and a mapping module to estimate water depth in a catchment during floods. The performance of the proposed modelling method was evaluated using a real-world catchment of approximate 2,000 km2, in the Southern Murray–Darling Basin, Australia. The results show that there is a good agreement between flood inundation obtained from the proposed method and that simulated using a high-fidelity 2D hydrodynamic model. The proposed method is much more efficient than the high-fidelity 2D hydrodynamic model, which makes it an alternative method for applications requiring many model runs or long simulation durations. Also, the LoHy+ model has the potential to be applied in flood inundation forecast, flood risk mitigation design water resource management, etc.
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ItemNo Preview AvailableDeep Learning-Based Rapid Flood Inundation Modeling for Flat Floodplains With Complex Flow Paths(Wiley, 2022-12-01)Flood inundation emulation models based on deep neural networks have been developed to overcome the computational burden of two-dimensional (2D) hydrodynamic models. Challenges remain for flat and complex floodplains where many anabranches form during flood events. In this study, we propose a new approach to simulate the temporal and spatial variation of flood inundation for a floodplain with complex flow paths. A U-Net-based spatial reduction and reconstruction method (USRR) is used to find representative locations on the floodplain with complex flow paths. The water depths at these locations are simulated using one-dimensional convolutional neural network (1D-CNN) models, which are well-suited to handling multivariate timeseries inputs. The flood surface is then reconstructed using the USRR method and the simulated flood depths at the representative locations. The combined 1D-CNN and USRR method is compared with a previously developed approach based on the long short-term memory recurrent neural network (LSTM) models and a 2D linear interpolation-based SRR method. Compared to the LSTM model, the 1D-CNN model is not only more accurate, but also takes less time to develop. Although both surface reconstruction methods take <1 s to produce an inundation map for a specific point in time, the USRR method is more accurate than the SRR method, leading to an increase of 5.6% in the proportion of correctly detected inundation area. The combination of 1D-CNN and USRR can detect over 95% of the inundated area simulated using a 2D hydrodynamic model but is 98 times faster.
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ItemNo Preview AvailableA hybrid framework for short-term irrigation demand forecasting(ELSEVIER, 2022-11-01)
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ItemUpskilling Low-Fidelity Hydrodynamic Models of Flood Inundation Through Spatial Analysis and Gaussian Process Learning(AMER GEOPHYSICAL UNION, 2022-08)Abstract Accurate flood inundation modeling using a complex high‐resolution hydrodynamic (high‐fidelity) model can be very computationally demanding. To address this issue, efficient approximation methods (surrogate models) have been developed. Despite recent developments, there remain significant challenges in using surrogate methods for modeling the dynamical behavior of flood inundation in an efficient manner. Most methods focus on estimating the maximum flood extent due to the high spatial‐temporal dimensionality of the data. This study presents a hybrid surrogate model, consisting of a low‐resolution hydrodynamic (low‐fidelity) and a Sparse Gaussian Process (Sparse GP) model, to capture the dynamic evolution of the flood extent. The low‐fidelity model is computationally efficient but has reduced accuracy compared to a high‐fidelity model. To account for the reduced accuracy, a Sparse GP model is used to correct the low‐fidelity modeling results. To address the challenges posed by the high dimensionality of the data from the low‐ and high‐fidelity models, Empirical Orthogonal Functions analysis is applied to reduce the spatial‐temporal data into a few key features. This enables training of the Sparse GP model to predict high‐fidelity flood data from low‐fidelity flood data, so that the hybrid surrogate model can accurately simulate the dynamic flood extent without using a high‐fidelity model. The hybrid surrogate model is validated on the flat and complex Chowilla floodplain in Australia. The hybrid model was found to improve the results significantly compared to just using the low‐fidelity model and incurred only 39% of the computational cost of a high‐fidelity model.
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ItemNo Preview AvailableImpacts of urbanization on precipitation patterns in the greater Beijing-Tianjin-Hebei metropolitan region in northern China(IOP Publishing Ltd, 2021-01)Abstract We present a statistical method to quantify the contribution of urbanization to precipitation changes during 1958–2017 across the greater Beijing–Tianjin–Hebei metropolitan region in northern China. We find distinct trends in precipitation in the past six decades: decreasing in annual and summer while increasing in other seasons. The spatial patterns of precipitation show discernible terrain-induced characteristics with high values in the buffer zones of plain and mountain areas and low values in the northwestern mountainous regions. Our results indicate that although urbanization has limited impacts on the trends and spatial patterns of precipitation, it has a positive contribution to the changes in precipitation for about 80% of the comparisons conducted, especially in autumn (100%), with the negative contribution being dominant in summer (66.67%). In addition, these results are sensitive to the classifications of urban and rural stations, suggesting that how to classify urban/rural areas is a crucial step to estimate the potential contribution of urbanization to precipitation changes. These findings also support that urbanization can diversify and enhance the variations in precipitation, with urban areas becoming a secondary center along with more increasing or less decreasing trends in precipitation.
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ItemReconstructing climate trends adds skills to seasonal reference crop evapotranspiration forecasting(COPERNICUS GESELLSCHAFT MBH, 2022-02-18)Abstract. Evapotranspiration plays an important role in the terrestrial water cycle. Reference crop evapotranspiration (ETo) has been widely used to estimate water transfer from vegetation surface to the atmosphere. Seasonal ETo forecasting provides valuable information for effective water resource management and planning. Climate forecasts from general circulation models (GCMs) have been increasingly used to produce seasonal ETo forecasts. Statistical calibration plays a critical role in correcting bias and dispersion errors in GCM-based ETo forecasts. However, time-dependent errors resulting from GCM misrepresentations of climate trends have not been explicitly corrected in ETo forecast calibrations. We hypothesize that reconstructing climate trends through statistical calibration will add extra skills to seasonal ETo forecasts. To test this hypothesis, we calibrate raw seasonal ETo forecasts constructed with climate forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) SEAS5 model across Australia, using the recently developed Bayesian joint probability trend-aware (BJP-ti) model. Raw ETo forecasts demonstrate significant inconsistencies with observations in both magnitudes and spatial patterns of temporal trends, particularly at long lead times. The BJP-ti model effectively corrects misrepresented trends and reconstructs the observed trends in calibrated forecasts. Improving trends through statistical calibration increases the correlation coefficient between calibrated forecasts and observations (r) by up to 0.25 and improves the continuous ranked probability score (CRPS) skill score by up to 15 (%) in regions where climate trends are misrepresented by raw forecasts. Skillful ETo forecasts produced in this study could be used for streamflow forecasting, modeling of soil moisture dynamics, and irrigation water management. This investigation confirms the necessity of reconstructing climate trends in GCM-based seasonal ETo forecasting and provides an effective tool for addressing this need. We anticipate that future GCM-based seasonal ETo forecasting will benefit from correcting time-dependent errors through trend reconstruction.
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ItemPython program for spatial reduction and reconstruction method in flood inundation modelling(ELSEVIER, 2021)Fast and accurate modelling of flood inundation has gained increasing attention in recent years. One approach gaining popularity recently is the development of emulation models using data driven methods, such as artificial neural networks. These emulation models are often developed to model flood depth for each grid cell in the modelling domain in order to maintain accurate spatial representation of the flood inundation surface. This leads to redundancy in modelling, as well as difficulties in achieving good model performance across floodplains where there are limited data available. In this paper, a spatial reduction and reconstruction (SRR) method is developed to (1) identify representative locations within the model domain where water levels can be used to represent flood inundation surface using deep learning models; and (2) reconstruct the flood inundation surface based on water levels simulated at these representative locations. The SRR method is part of the SRR-Deep-Learning framework for flood inundation modelling and therefore, it needs to be used together with data driven models. The SRR method is programmed using the Python programming language and is freely available from https://github.com/yuerongz/SRR-method.•The SRR method identifies locations which are representative of flood inundation behavior in surrounding areas.•The representative locations selected following the SRR method have sufficient flood data for developing emulation models.•Flood inundation surfaces can be reconstructed using the SRR method with a detection rate of above 99%.
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ItemWhich precipitation forecasts to use? Deterministic versus coarser-resolution ensemble NWP models(WILEY, 2021-01)Abstract Deterministic numerical weather prediction (NWP) models and ensemble NWP models are routinely run worldwide to assist weather forecasting. Deterministic forecasts are capable of capturing more detailed spatial features, while ensemble forecasts, often with a coarser resolution, have the ability to predict uncertainty in future conditions. A comparative understanding of the performance of these two types of forecasts is valuable for both users of NWP products and model developers. Past published comparisons tended to be limited in scope, for example, for only specific locations and weather events, and involving only raw forecasts. In this study, we conduct a comprehensive comparison of the performance of a deterministic model and an ensemble model of the Australian Bureau of Meteorology in forecasting daily precipitation across Australia over a period of 3 years. The deterministic model has a horizontal grid spacing of approximately 25 km, and the ensemble model 60 km. Despite the coarser resolution, the ensemble forecasts are found to be superior by a number of measures, including correlation, accuracy and reliability. This finding holds true for both raw forecasts from the NWP models and forecasts post‐processed using the recently developed seasonally coherent calibration (SCC) model. Post‐processing is shown to greatly improve the forecasts from both models; however, the improvement is greater for the deterministic model, narrowing the performance gap between the two models. This study adds strong evidence to the general notion that coarser‐resolution ensemble NWP forecasts perform better than deterministic forecasts.