Differentiable modelling to unify machine learning and physical models for geosciences

Nature Reviews Earth & Environment, Journal Year: 2023, Volume and Issue: 4(8), P. 552 - 567

Published: July 11, 2023

Language: Английский

---

Hybrid forecasting: blending climate predictions with AI models

Hydrology and earth system sciences, Journal Year: 2023, Volume and Issue: 27(9), P. 1865 - 1889

Published: May 15, 2023

Abstract. Hybrid hydroclimatic forecasting systems employ data-driven (statistical or machine learning) methods to harness and integrate a broad variety of predictions from dynamical, physics-based models – such as numerical weather prediction, climate, land, hydrology, Earth system into final prediction product. They are recognized promising way enhancing the skill meteorological variables events, including rainfall, temperature, streamflow, floods, droughts, tropical cyclones, atmospheric rivers. now receiving growing attention due advances in climate at subseasonal decadal scales, better appreciation strengths AI, expanding access computational resources methods. Such attractive because they may avoid need run computationally expensive offline land model, can minimize effect biases that exist within dynamical outputs, benefit learning, learn large datasets, while combining different sources predictability with varying time horizons. Here we review recent developments hybrid outline key challenges opportunities for further research. These include obtaining physically explainable results, assimilating human influences novel data sources, integrating new ensemble techniques improve predictive skill, creating seamless schemes merge short long lead times, incorporating initial surface ocean/ice conditions, acknowledging spatial variability landscape forcing, increasing operational uptake schemes.

Language: Английский

---

Improving daily streamflow simulations for data-scarce watersheds using the coupled SWAT-LSTM approach

Journal of Hydrology, Journal Year: 2023, Volume and Issue: 622, P. 129734 - 129734

Published: May 30, 2023

Language: Английский

---

Hybridized artificial intelligence models with nature-inspired algorithms for river flow modeling: A comprehensive review, assessment, and possible future research directions

Engineering Applications of Artificial Intelligence, Journal Year: 2023, Volume and Issue: 129, P. 107559 - 107559

Published: Dec. 3, 2023

Language: Английский

---

The suitability of differentiable, physics-informed machine learning hydrologic models for ungauged regions and climate change impact assessment

Hydrology and earth system sciences, Journal Year: 2023, Volume and Issue: 27(12), P. 2357 - 2373

Published: June 30, 2023

Abstract. As a genre of physics-informed machine learning, differentiable process-based hydrologic models (abbreviated as δ or delta models) with regionalized deep-network-based parameterization pipelines were recently shown to provide daily streamflow prediction performance closely approaching that state-of-the-art long short-term memory (LSTM) deep networks. Meanwhile, full suite diagnostic physical variables and guaranteed mass conservation. Here, we ran experiments test (1) their ability extrapolate regions far from gauges (2) make credible predictions long-term (decadal-scale) change trends. We evaluated the based on hydrograph metrics (Nash–Sutcliffe model efficiency coefficient, etc.) predicted decadal For in ungauged basins (PUB; randomly sampled representing spatial interpolation), either approached surpassed LSTM metrics, depending meteorological forcing data used. They presented comparable trend for annual mean flow high but worse trends low flow. (PUR; regional holdout extrapolation highly data-sparse scenario), advantages became prominent. In addition, an untrained variable, evapotranspiration, retained good seasonality even extrapolated cases. The models' pipeline produced parameter fields maintain remarkably stable patterns data-scarce scenarios, which explains robustness. Combined interpretability assimilate multi-source observations, are strong candidates global-scale simulations climate impact assessment.

Language: Английский

---

Improving River Routing Using a Differentiable Muskingum‐Cunge Model and Physics‐Informed Machine Learning

Water Resources Research, Journal Year: 2024, Volume and Issue: 60(1)

Published: Jan. 1, 2024

Abstract Recently, rainfall‐runoff simulations in small headwater basins have been improved by methodological advances such as deep neural networks (NNs) and hybrid physics‐NN models—particularly, a genre called differentiable modeling that intermingles NNs with physics to learn relationships between variables. However, hydrologic routing simulations, necessary for simulating floods stem rivers downstream of large heterogeneous basins, had not yet benefited from these it was unclear if the process could be via coupled NNs. We present novel method ( δ MC‐Juniata‐hydroDL2) mimics classical Muskingum‐Cunge model over river network but embeds an NN infer parameterizations Manning's roughness n ) channel geometries raw reach‐scale attributes like catchment areas sinuosity. The trained solely on hydrographs. Synthetic experiments show while geometry parameter unidentifiable, can identified moderate precision. With real‐world data, produced more accurate long‐term results both training gage untrained inner gages larger subbasins (>2,000 km 2 than either machine learning assuming homogeneity, or simply using sum runoff subbasins. parameterization short periods gave high performance other periods, despite significant errors inputs. learned pattern consistent literature expectations, demonstrating framework's potential knowledge discovery, absolute values vary depending periods. traditional models improve national‐scale flood simulations.

Language: Английский

---

Alternate pathway for regional flood frequency analysis in data-sparse region

Journal of Hydrology, Journal Year: 2024, Volume and Issue: 629, P. 130635 - 130635

Published: Jan. 17, 2024

Language: Английский

---

Distributed Hydrological Modeling With Physics‐Encoded Deep Learning: A General Framework and Its Application in the Amazon

Water Resources Research, Journal Year: 2024, Volume and Issue: 60(4)

Published: April 1, 2024

Abstract While deep learning (DL) models exhibit superior simulation accuracy over traditional distributed hydrological (DHMs), their main limitations lie in opacity and the absence of underlying physical mechanisms. The pursuit synergies between DL DHMs is an engaging research domain, yet a definitive roadmap remains elusive. In this study, novel framework that seamlessly integrates process‐based model encoded as neural network (NN), additional NN for mapping spatially physically meaningful parameters from watershed attributes, NN‐based replacement representing inadequately understood processes developed. Multi‐source observations are used training data, fully differentiable, enabling fast parameter tuning by backpropagation. A hybrid Amazon Basin (∼6 × 10 6 km 2 ) was established based on framework, HydroPy, global‐scale DHM, its backbone. Trained simultaneously with streamflow Gravity Recovery Climate Experiment satellite yielded median Nash‐Sutcliffe efficiencies 0.83 0.77 dynamic simulations total water storage, respectively, 41% 35% higher than those original HydroPy model. Replacing Penman‒Monteith formulation produces more plausible potential evapotranspiration (PET) estimates, unravels spatial pattern PET giant basin. parameterization interpreted to identify factors controlling variability key parameters. Overall, study lays out feasible technical modeling big data era.

Language: Английский

---

On the need for physical constraints in deep learning rainfall–runoff projections under climate change: a sensitivity analysis to warming and shifts in potential evapotranspiration

Hydrology and earth system sciences, Journal Year: 2024, Volume and Issue: 28(3), P. 479 - 503

Published: Feb. 7, 2024

Abstract. Deep learning (DL) rainfall–runoff models outperform conceptual, process-based in a range of applications. However, it remains unclear whether DL can produce physically plausible projections streamflow under climate change. We investigate this question through sensitivity analysis modeled responses to increases temperature and potential evapotranspiration (PET), with other meteorological variables left unchanged. Previous research has shown that temperature-based PET methods overestimate evaporative water loss warming compared energy budget-based methods. therefore assume reliable should exhibit less when forced smaller, energy-budget-based PET. conduct assessment using three models, trained tested across 212 watersheds the Great Lakes basin. The include Long Short-Term Memory network (LSTM), mass-conserving LSTM (MC-LSTM), novel variant MC-LSTM also respects relationship between (MC-LSTM-PET). After validating against historical actual evapotranspiration, we force all scenarios warming, precipitation, both (Hamon) (Priestley–Taylor) PET, compare their long-term mean daily flow, low flows, high seasonal timing. explore similar national fit 531 United States assess how inclusion larger more diverse set basins influences signals hydrological response warming. main results study are as follows: substantially estimation. MC-LSTM-PET matches best outperforms estimating evapotranspiration. All show downward shift flows but median shifts considerably (−17 % −25 %) than (−6 −9 %). model exhibits differences different forcings. Conversely, unrealistically large losses Priestley–Taylor (−20 %), while is relatively insensitive method. smaller changes timing estimates within estimated by models. Like LSTM, shows (−25 stable many inputs changed better aligns for flows. Ultimately, suggest physical considerations regarding architecture input may be necessary promote realism deep-learning-based

Language: Английский

---

Reconstructing Long-Term Daily Streamflow Data at the Discontinuous Monitoring Station in the Ungauged Transboundary Basin Using Machine Learning

Water Resources Management, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 17, 2025

Language: Английский

---