Hydrology¶
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Learning Surrogate Rainfall-driven Inundation Models with Few Data¶
Flood hazard assessment demands fast and accurate predictions. Hydrodynamic models are detailed but computationally intensive, making them impractical for quantifying uncertainty or identifying...Flood hazard assessment demands fast and accurate predictions. Hydrodynamic models are detailed but computationally intensive, making them impractical for quantifying uncertainty or identifying extremes. In contrast, machine learning surrogates can be rapid, but training on scarce simulated or observed extreme data can also be ineffective. This work demonstrates the development of an effective surrogate model for flood hazard prediction by initializing deep learning (ResNet-18) with ensemble-approximated Conditional Gaussian Processes (EnsCGP) and finalizing it with a bias correction. The proposed methodology couples EnsCGP with a ResNet-18 architecture to estimate flood depth and uses ensemble optimal estimation for bias correction. The surrogate model was trained and evaluated using rainfall data from Daymet and hydrodynamic simulations from LISFLOOD-FP, spanning the period from 1981 to 2019. The training involved using data up to a certain year and testing on the subsequent year, iteratively progressing through the dataset. This process required approximately 100 training iterations and extensive data. Inundation depths are estimated rapidly at runtime (approximately 0.006 seconds per event). Results over multiple years in the current climate over Chicago demonstrate an average R-squared greater than 0.96, with median relative errors in flood depth estimates of about 1 percent. more
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Unravelling compound risks of hydrological extremes in a changing climate: Typology, methods and futures¶
We have witnessed and experienced increasing compound extreme events resulting from simultaneous or sequential occurrence of multiple events in a changing climate. In addition to a growing demand for...We have witnessed and experienced increasing compound extreme events resulting from simultaneous or sequential occurrence of multiple events in a changing climate. In addition to a growing demand for a clearer explanation of compound risks from a hydrological perspective, there has been a lack of attention paid to socioeconomic factors driving and impacted by these risks. Through a critical review and co-production approaches, we identified four types of compound hydrological events based on autocorrelated, multivariate, and spatiotemporal patterns. A framework to quantify compound risks based on conditional probability is offered, including an argument on the potential use of generative Artificial Intelligence (AI) algorithms for identifying emerging trends and patterns for climate change. Insights for practices are discussed, highlighting the implications for disaster risk reduction and knowledge co-production. Our argument centres on the importance of meaningfully considering the socioeconomic contexts in which compound risks may have impacts, and the need for interdisciplinary collaboration to effectively translate climate science to climate actions. more
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High-Resolution Flood Probability Mapping Using Generative Machine Learning with Large-Scale Synthetic Precipitation and Inundation Data¶
High-resolution flood probability maps are instrumental for assessing flood risk but are often limited by the availability of historical data. Additionally, producing simulated data needed for...High-resolution flood probability maps are instrumental for assessing flood risk but are often limited by the availability of historical data. Additionally, producing simulated data needed for creating probabilistic flood maps using physics-based models involves significant computation and time effort, which inhibit its feasibility. To address this gap, this study introduces Precipitation-Flood Depth Generative Pipeline, a novel methodology that leverages generative machine learning to generate large-scale synthetic inundation data to produce probabilistic flood maps. With a focus on Harris County, Texas, Precipitation-Flood Depth Generative Pipeline begins with training a cell-wise depth estimator using a number of precipitation-flood events model with a physics-based model. This cell-wise depth estimator, which emphasizes precipitation-based features, outperforms universal models. Subsequently, the Conditional Generative Adversarial Network (CTGAN) is used to conditionally generate synthetic precipitation point cloud, which are filtered using strategic thresholds to align with realistic precipitation patterns. Hence, a precipitation feature pool is constructed for each cell, enabling strategic sampling and the generation of synthetic precipitation events. After generating 10,000 synthetic events, flood probability maps are created for various inundation depths. Validation using similarity and correlation metrics confirms the accuracy of the synthetic depth distributions. The Precipitation-Flood Depth Generative Pipeline provides a scalable solution to generate synthetic flood depth data needed for high-resolution flood probability maps, which can enhance flood mitigation planning. more
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Time Distributed Deep Learning Models for Purely Exogenous Forecasting: Application to Water Table Depth Prediction using Weather Image Time Series¶
Groundwater resources are one of the most relevant elements in the water cycle, therefore developing models to accurately predict them is a pivotal task in the sustainable resource management...Groundwater resources are one of the most relevant elements in the water cycle, therefore developing models to accurately predict them is a pivotal task in the sustainable resource management framework. Deep Learning (DL) models have been revealed to be very effective in hydrology, especially by feeding spatially distributed data (e.g. raster data). In many regions, hydrological measurements are difficult to obtain regularly or periodically in time, and in some cases, the last available data are not up to date. Reversely, weather data, which significantly impacts water resources, are usually more available and with higher quality. More specifically, we have proposed two different DL models to predict the water table depth in the Grana-Maira catchment (Piemonte, IT) using only exogenous weather image time series. To deal with the image time series, both models are made of a first Time Distributed Convolutional Neural Network (TDC) which encodes the image available at each time step into a vectorial representation. The first model, TDC-LSTM uses then a Sequential Module based on an LSTM layer to learn temporal relations and output the predictions. The second model, TDC-UnPWaveNet uses instead a new version of the WaveNet architecture, adapted here to output a sequence shorter and completely shifted in the future with respect to the input one. To this aim, and to deal with the different sequence lengths in the UnPWaveNet, we have designed a new Channel Distributed layer, that acts like a Time Distributed one but on the channel dimension, i.e. applying the same set of operations to each channel of the input. TDC-LSTM and TDC-UnPWaveNet have shown both remarkable results. However, the two models have focused on different learnable information: TDC-LSTM has focused more on lowering the bias, while TDC-UnPWaveNet has focused more on the temporal dynamics, maximizing correlation, and KGE. more
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CAS-Canglong: A skillful 3D Transformer model for sub-seasonal to seasonal global sea surface temperature prediction¶
Accurate prediction of global sea surface temperature at sub-seasonal to seasonal (S2S) timescale is critical for drought and flood forecasting, as well as for improving disaster preparedness in...Accurate prediction of global sea surface temperature at sub-seasonal to seasonal (S2S) timescale is critical for drought and flood forecasting, as well as for improving disaster preparedness in human society. Government departments or academic studies normally use physics-based numerical models to predict S2S sea surface temperature and corresponding climate indices, such as El Niño-Southern Oscillation. However, these models are hampered by computational inefficiencies, limited retention of ocean-atmosphere initial conditions, and significant uncertainty and biases. Here, we introduce a novel three-dimensional deep learning neural network to model the nonlinear and complex coupled atmosphere-ocean weather systems. This model incorporates climatic and temporal features and employs a self-attention mechanism to enhance the prediction of global S2S sea surface temperature pattern. Compared to the physics-based models, it shows significant computational efficiency and predictive capability, improving one to three months sea surface temperature predictive skill by 13.7% to 77.1% in seven ocean regions with dominant influence on S2S variability over land. This achievement underscores the significant potential of deep learning for largely improving forecasting skills at the S2S scale over land. more
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Evapotranspiration trends over the last 300 years reconstructed from historical weather station observations via machine learning¶
Estimating historical evapotranspiration (ET) is essential for understanding the effects of climate change and human activities on the water cycle. This study used historical weather station data to...Estimating historical evapotranspiration (ET) is essential for understanding the effects of climate change and human activities on the water cycle. This study used historical weather station data to reconstruct ET trends over the past 300 years with machine learning. A Random Forest model, trained on FLUXNET2015 flux stations' monthly data using precipitation, temperature, aridity index, and rooting depth as predictors, achieved an R2 of 0.66 and a KGE of 0.76 through 10-fold cross-validation. Applied to 5267 weather stations, the model produced monthly ET data showing a general increase in global ET from 1700 to the present, with a notable acceleration after 1900 due to warming. Regional differences were observed, with higher ET increases in mid-to-high latitudes of the Northern Hemisphere and decreases in some mid-to-low latitudes and the Southern Hemisphere. In drylands, ET and temperature were weakly correlated, while in humid areas, the correlation was much higher. The correlation between ET and precipitation has remained stable over the centuries. This study extends the ET data time span, providing valuable insights into long-term historical ET trends and their drivers, aiding in reassessing the impact of historical climate change and human activities on the water cycle and supporting future climate adaptation strategies. more
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Ensemble quantile-based deep learning framework for streamflow and flood prediction in Australian catchments¶
In recent years, climate extremes such as floods have created significant environmental and economic hazards for Australia. Deep learning methods have been promising for predicting extreme climate...In recent years, climate extremes such as floods have created significant environmental and economic hazards for Australia. Deep learning methods have been promising for predicting extreme climate events; however, large flooding events present a critical challenge due to factors such as model calibration and missing data. We present an ensemble quantile-based deep learning framework that addresses large-scale streamflow forecasts using quantile regression for uncertainty projections in prediction. We evaluate selected univariate and multivariate deep learning models and catchment strategies. Furthermore, we implement a multistep time-series prediction model using the CAMELS dataset for selected catchments across Australia. The ensemble model employs a set of quantile deep learning models for streamflow determined by historical streamflow data. We utilise the streamflow prediction and obtain flood probability using flood frequency analysis and compare it with historical flooding events for selected catchments. Our results demonstrate notable efficacy and uncertainties in streamflow forecasts with varied catchment properties. Our flood probability estimates show good accuracy in capturing the historical floods from the selected catchments. This underscores the potential for our deep learning framework to revolutionise flood forecasting across diverse regions and be implemented as an early warning system. more