Assessing the predictive capability of randomized tree-based ensembles in streamflow modelling

Abstract. Combining randomization methods with ensemble prediction is emerging as an effective option to balance accuracy and computational efficiency in data-driven modelling. In this paper, we investigate the prediction capability of extremely randomized trees (Extra-Trees), in terms of accuracy, explanation ability and computational efficiency, in a streamflow modelling exercise. Extra-Trees are a totally randomized tree-based ensemble method that (i) alleviates the poor generalisation property and tendency to overfitting of traditional standalone decision trees (e.g. CART); (ii) is computationally efficient; and, (iii) allows to infer the relative importance of the input variables, which might help in the ex-post physical interpretation of the model. The Extra-Trees potential is analysed on two real-world case studies – Marina catchment (Singapore) and Canning River (Western Australia) – representing two different morphoclimatic contexts. The evaluation is performed against other tree-based methods (CART and M5) and parametric data-driven approaches (ANNs and multiple linear regression). Results show that Extra-Trees perform comparatively well to the best of the benchmarks (i.e. M5) in both the watersheds, while outperforming the other approaches in terms of computational requirement when adopted on large datasets. In addition, the ranking of the input variable provided can be given a physically meaningful interpretation

Calibrating macroscale hydrological models in poorly gauged and heavily regulated basins

The calibration of macroscale hydrological models is often challenged by the lack of adequate observations of river discharge and infrastructure operations. This modeling backdrop creates a number of potential pitfalls for model calibration, potentially affecting the reliability of hydrological models. Here, we introduce a novel numerical framework conceived to explore and overcome these pitfalls. Our framework consists of VIC-Res (a macroscale model setup for the Upper Mekong Basin), which is a novel variant of the Variable Infiltration Capacity (VIC) model that includes a module for representing reservoir operations, and a hydraulic model used to infer discharge time series from satellite data. Using these two models and global sensitivity analysis, we show the existence of a strong relationship between the parameterization of the hydraulic model and the performance of VIC-Res – a codependence that emerges for a variety of performance metrics that we considered. Using the results provided by the sensitivity analysis, we propose an approach for breaking this codependence and informing the hydrological model calibration, which we finally carry out with the aid of a multi-objective optimization algorithm. The approach used in this study could integrate multiple remotely sensed observations and is transferable to other poorly gauged and heavily regulated river basins.</p

Why the 2022 Po River drought is the worst in the past two centuries

The causes of recent hydrological droughts and their future evolution under a changing climate are still poorly understood. Banking on a 216-year river flow time series at the Po River outlet, we show that the 2022 hydrological drought is the worst event (30% lower than the second worst, with a six-century return period), part of an increasing trend in severe drought occurrence. The decline in summer river flows (−4.14 cubic meters per second per year), which is more relevant than the precipitation decline, is attributed to a combination of changes in the precipitation regime, resulting in a decline of snow fraction (−0.6% per year) and snowmelt (−0.18 millimeters per day per year), and to increasing evaporation rate (+0.013 cubic kilometers per year) and irrigated areas (100% increment from 1900). Our study presents a compelling case where the hydrological impact of climate change is exacerbated by local changes in hydrologic seasonality and water use

Cascading Failures in Interconnected Power-to-Water Networks

The manageability and resilience of critical infrastructures, such as power and water networks, is challenged by their increasing interdependence and interconnectivity. Power networks often experience cascading failures, i.e. blackouts, that have unprecedented economic and social impact. Al- though knowledge exists about how to control such complex non-linear phenomena within a single power network, little is known about how such failures can spread and coevolve in the water network when failing power components energize the water distribution infrastructure, i.e. pumps and valves. This paper studies such a scenario and specifically the impact of power cascading failures on shortages of water supply. A realistic exemplary of an interconnected power-to-water network is experimentally evaluated using a modular simulation approach. Power and waterflow dynamics are simulated separately by taking into account different maximum powerlines capacities and water demand requirements. Results showcase the strong dependency of urban water sup- ply systems on the reliability of power networks, with severe shortages of water supply being caused by failures originating indistant power lines, especially for heavily loaded power networks

A dimensionality reduction approach for many-objective Markov Decision Processes: Application to a water reservoir operation problem

The operation of complex environmental systems usually accounts for multiple, conflicting objectives, whose presence imposes to explicitly consider the preference structure of the parties involved. Multiobjective Markov Decision Processes are a useful mathematical framework for the resolution of such sequential, decision-making problems. However, the computational requirements of the available optimization techniques limit their application to problems involving few objectives. In real-world applications it is therefore common practice to select few, representative objectives with respect to which the problem is solved. This paper proposes a dimensionality reduction approach, based on the Non-negative Principal Component Analysis (NPCA), to aggregate the original objectives into a reduced number of principal components, with respect to which the optimization problem is solved. The approach is evaluated on the daily operation of a multi-purpose water reservoir (Tono Dam, Japan) with 10 operating objectives, and compared against a 5-objectives formulation of the same problem. Results show that the NPCA-based approach provides a better representation of the Pareto front, especially in terms of consistency and solution diversity

An evaluation framework for input variable selection algorithms for environmental data-driven models

Abstract not availableStefano Galelli, Greer B. Humphrey, Holger R. Maier, Andrea Castelletti, Graeme C. Dandy, Matthew S. Gibb

Predicting the Likely Thermal Impact of Current and Future Dams Around the World

Selective water release from the deeper pools of reservoirs alters the temperature of downstream rivers. Thermal destabilization of downstream rivers can be detrimental to the riverine ecosystem by disturbing the growth stages of various aquatic species. To predict this impact of planned hydropower dams worldwide, we present a framework called “FUture Temperatures Using River hISTory (FUTURIST).” The framework used historical records of in-situ river temperatures for existing dams in the U.S. and remote sensing observations for those in other regions to train an artificial neural network (ANN) model that predicts temperature change between upstream and downstream rivers. Validation of FUTURIST-modeled impacts for dams worldwide showed promising results with a root mean squared error of 2.5°C (0.9°C) and categorical accuracy of 63% (88%) during the summer (winter) season. The trained ANN model afforded prediction of the likely thermal impacts of 216 planned dams. Results suggest that during the summer season, 73% of future dams will potentially cool downstream rivers by up to 6.6°C. Winter season operations were predicted to consistently warm downstream rivers by temperatures of up to 2°C. Reservoirs that experience strong stratification have the most potential to impact downstream pre-dam thermal regimes. For copious existing or planned dams worldwide that are yet to be mapped of their thermal impacts, FUTURIST provides an efficient path forward to carry out a global thermal assessment and design sustainable hydropower expansion plans so that the upcoming dams can be operated in a more eco-sensitive manner than the existing ones