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"Flood forecasting Mathematical models."
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Framework for dynamic modelling of urban floods at different topographical resolutions
Urban flood risks and their impacts are expected to increase as urban development in flood prone areas continues and rain intensity increases as a result of climate change while aging drainage infrastructures limit the drainage capacity in existing urban areas. The research presented in this thesis addresses the problem of capturing small-scale features in coarse resolution urban flood models with the aim of improving flood forecasts in geometrically complex urban environments.
Book
Urbanization exacerbated the rainfall and flooding caused by hurricane Harvey in Houston
Category 4 landfalling hurricane Harvey poured more than a metre of rainfall across the heavily populated Houston area, leading to unprecedented flooding and damage. Although studies have focused on the contribution of anthropogenic climate change to this extreme rainfall event
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, limited attention has been paid to the potential effects of urbanization on the hydrometeorology associated with hurricane Harvey. Here we find that urbanization exacerbated not only the flood response but also the storm total rainfall. Using the Weather Research and Forecast model—a numerical model for simulating weather and climate at regional scales—and statistical models, we quantify the contribution of urbanization to rainfall and flooding. Overall, we find that the probability of such extreme flood events across the studied basins increased on average by about 21 times in the period 25–30 August 2017 because of urbanization. The effect of urbanization on storm-induced extreme precipitation and flooding should be more explicitly included in global climate models, and this study highlights its importance when assessing the future risk of such extreme events in highly urbanized coastal areas.
Modelling the contribution of urbanization to the impacts associated with hurricane Harvey in August 2017 shows that urbanization worsens rainfall and flooding.
Journal Article
Skilful seasonal forecasts of streamflow over Europe?
This paper considers whether there is any added value in using seasonal climate forecasts instead of historical meteorological observations for forecasting streamflow on seasonal timescales over Europe. A Europe-wide analysis of the skill of the newly operational EFAS (European Flood Awareness System) seasonal streamflow forecasts (produced by forcing the Lisflood model with the ECMWF System 4 seasonal climate forecasts), benchmarked against the ensemble streamflow prediction (ESP) forecasting approach (produced by forcing the Lisflood model with historical meteorological observations), is undertaken. The results suggest that, on average, the System 4 seasonal climate forecasts improve the streamflow predictability over historical meteorological observations for the first month of lead time only (in terms of hindcast accuracy, sharpness and overall performance). However, the predictability varies in space and time and is greater in winter and autumn. Parts of Europe additionally exhibit a longer predictability, up to 7 months of lead time, for certain months within a season. In terms of hindcast reliability, the EFAS seasonal streamflow hindcasts are on average less skilful than the ESP for all lead times. The results also highlight the potential usefulness of the EFAS seasonal streamflow forecasts for decision-making (measured in terms of the hindcast discrimination for the lower and upper terciles of the simulated streamflow). Although the ESP is the most potentially useful forecasting approach in Europe, the EFAS seasonal streamflow forecasts appear more potentially useful than the ESP in some regions and for certain seasons, especially in winter for almost 40 % of Europe. Patterns in the EFAS seasonal streamflow hindcast skill are however not mirrored in the System 4 seasonal climate hindcasts, hinting at the need for a better understanding of the link between hydrological and meteorological variables on seasonal timescales, with the aim of improving climate-model-based seasonal streamflow forecasting.
Journal Article
Applying the flood vulnerability index as a knowledge base for flood risk assessment
Floods are one of the most common and widely distributed natural risks to life and property worldwide.?There is a need to identify the risk of flooding in flood prone areas to support decisions for flood management from high level planning proposals to detailed design. An important part of modern flood risk management is to assess vulnerability to floods. This assessment can be done only by using a parametric approach.?Worldwide there is a need to enhance our understanding of vulnerability and to also develop methodologies and tools to assess vulnerability.?One of the most important goals of assessing flood vulnerability is to create a readily understandable link between the theoretical concepts of flood vulnerability and the day-to-day decision-making process and to encapsulate this link in an easily accessible tool.?The present book portrays a holistic parametric approach to be used in flood vulnerability assessment and this way to facilitate the consideration of system impacts in water resources decision-making.?The approach was verified in practical applications on different spatial scales and comparison with deterministic approaches. The use of flood vulnerability approach can produce helpful understanding into vulnerability and capacities for using it in planning and implementing projects.
Book
A Path Toward Short-Term Probabilistic Flash Flood Prediction
There are ongoing efforts to move beyond the current paradigm of using deterministic products driven by observation-only data to make binary warning decisions. Recent works have focused on severe thunderstorm hazards, such as hail, lightning, and tornadoes. This study discusses one of the first steps toward having probabilistic information combined with convective-scale short-term precipitation forecasts available for the prediction and warning of flash flooding. Participants in the Hydrometeorology Testbed–MRMS Hydrology (HMT-Hydro) experiment evaluated several probabilistic-based hydrologic model output from the probabilistic Flooded Locations and Simulated Hydrographs (PRO-FLASH) system during experimental real-time warning operations. Evaluation of flash flood warning performance combined with product surveys highlighted how forecasters perceived biases within the probabilistic information and how the different probabilistic approaches influenced warnings that were verified versus those that were unverified. The incorporation of the Warn-on-Forecast System (WoFS) ensemble precipitation forecasts into the PRO-FLASH product generation provided an opportunity to evaluate the first coupling of subhourly convective-scale ensemble precipitation forecasts with probabilistic hydrologic modeling at the flash flood warning time scale through archived case simulations. The addition of WoFS precipitation forecasts resulted in an increase in warning lead time, including four events with ≥29 min of additional lead time but with increased probabilities of false alarms. Additional feedback from participants provided insights into the application of WoFS forecasts into warning decisions, including how flash flood expectations and confidence evolved for verified flash flood events and how forecast probabilistic products can positively influence the communications of the potential for flash flooding.
Journal Article
A Multiscale, Hydrometeorological Forecast Evaluation of National Water Model Forecasts of the May 2018 Ellicott City, Maryland, Flood
The NOAA National Water Model (NWM) became operational in August 2016, producing the first ever real-time, distributed, continuous set of hydrologic forecasts over the continental United States (CONUS). This project uses integrated hydrometeorological assessment methods to investigate the utility of theNWMto predict catastrophic flooding associated with an extreme rainfall event that occurred in Ellicott City, Maryland, on 27–28 May 2018. Short-range forecasts (0–18-h lead time) from the NWM version 1.2 are explored, focusing on the quantitative precipitation forecast (QPF) forcing from the High-Resolution Rapid Refresh (HRRR) model and the corresponding NWM streamflow forecast. A comprehensive assessment of multiscale hydrometeorological processes are considered using a combination of object-based, grid-based, and hydrologic point-based verification. Results highlight the benefits and risks of using a distributed hydrologic modeling tool such as the NWM to connect operational CONUS-scale atmospheric forcings to local impact predictions. For the Ellicott City event, reasonably skillful QPF in several HRRR model forecast cycles produced NWM streamflow forecasts in the small Ellicott City basin that were suggestive of flash flood potential. In larger surrounding basins, the NWM stream flow response was more complex, and errors were found to be governed by both hydrologic process representation, as well as forcing errors. The integrated, hydrometeorological multi-scale analysis method demonstrated here guides both research and ongoing model development efforts, along with providing user education and engagement to ultimately engender improved flash flood prediction.
Journal Article
Global prediction of extreme floods in ungauged watersheds
Floods are one of the most common natural disasters, with a disproportionate impact in developing countries that often lack dense streamflow gauge networks
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. Accurate and timely warnings are critical for mitigating flood risks
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, but hydrological simulation models typically must be calibrated to long data records in each watershed. Here we show that artificial intelligence-based forecasting achieves reliability in predicting extreme riverine events in ungauged watersheds at up to a five-day lead time that is similar to or better than the reliability of nowcasts (zero-day lead time) from a current state-of-the-art global modelling system (the Copernicus Emergency Management Service Global Flood Awareness System). In addition, we achieve accuracies over five-year return period events that are similar to or better than current accuracies over one-year return period events. This means that artificial intelligence can provide flood warnings earlier and over larger and more impactful events in ungauged basins. The model developed here was incorporated into an operational early warning system that produces publicly available (free and open) forecasts in real time in over 80 countries. This work highlights a need for increasing the availability of hydrological data to continue to improve global access to reliable flood warnings.
Artificial intelligence-based forecasting improves the reliability of predicting extreme flood events in ungauged watersheds, with predictions at five days lead time that are as good as current systems are for same-day predictions.
Journal Article
Runoff Forecasting using Convolutional Neural Networks and optimized Bi-directional Long Short-term Memory
Water resources matters considerably in maintaining the biological survival and sustainable socio-economic development of a region. Affected by a combination of factors such as geographic characteristics of the basin and climate change, runoff variability is non-linear and non-stationary. Runoff forecasting is one of the important engineering measures to prevent flood disasters. The improvement of its accuracy is also a difficult problem in the research of water resources management. To this end, an ensemble deep learning model was hereby developed to forecast daily river runoff. First, variational mode decomposition (VMD) was used to decompose the original daily runoff series data set into discrete internal model function (IMF) and distinguish signals with different frequencies. Then, for each IMF, a convolutional neural network (CNN) was introduced to extract the features of each IMF component. Subsequently, a bi-directional long short-term memory network (BiLSTM) based on an attention mechanism (AM) was used for prediction. A Bayesian optimization algorithm (BOA) was also introduced to optimize the hyperparameters of the BiLSTM, thereby further improving the estimation precision of the VMD-CNN-AM-BOA-BiLSTM model. The model was applied to the daily runoff data from January 1, 2010 to November 30, 2021 at the Wushan and Weijiabao Hydrological Stations in the Wei River Basin, and the RMSEs of 3.54 and 15.23 were obtained for the test set data at the two stations respectively, which were much better than those of EEMD-VMD-SVM and CNN-BiLSTM-AM models. Additionally, the hereby proposed model is proven to have better peak flood prediction capability and adaptability under different hydrological environments. Based on this sound performance, the model becomes an effective data-driven tool in hydrological forecasting practice, and can also provide some reference and practical application guidance for water resources management and flood warning.
Journal Article
AI-based techniques for multi-step streamflow forecasts: application for multi-objective reservoir operation optimization and performance assessment
Streamflow forecasts are traditionally effective in mitigating water scarcity and flood defense. This study developed an artificial intelligence (AI)-based management methodology that integrated multi-step streamflow forecasts and multi-objective reservoir operation optimization for water resource allocation. Following the methodology, we aimed to assess forecast quality and forecast-informed reservoir operation performance together due to the influence of inflow forecast uncertainty. Varying combinations of climate and hydrological variables were input into three AI-based models, namely a long short-term memory (LSTM), a gated recurrent unit (GRU), and a least-squares support vector machine (LSSVM), to forecast short-term streamflow. Based on three deterministic forecasts, the stochastic inflow scenarios were further developed using Bayesian model averaging (BMA) for quantifying uncertainty. The forecasting scheme was further coupled with a multi-reservoir optimization model, and the multi-objective programming was solved using the parameterized multi-objective robust decision-making (MORDM) approach. The AI-based management framework was applied and demonstrated over a multi-reservoir system (25 reservoirs) in the Zhoushan Islands, China. Three main conclusions were drawn from this study: (1) GRU and LSTM performed equally well on streamflow forecasts, and GRU might be the preferred method over LSTM, given that it had simpler structures and less modeling time; (2) higher forecast performance could lead to improved reservoir operation, while uncertain forecasts were more valuable than deterministic forecasts, regarding two performance metrics, i.e., water supply reliability and operating costs; (3) the relationship between the forecast horizon and reservoir operation was complex and depended on the operating configurations (forecast quality and uncertainty) and performance measures. This study reinforces the potential of an AI-based stochastic streamflow forecasting scheme to seek robust strategies under uncertainty.
Journal Article