Grey Nearing
I am a Research Scientist on the Google Flood Forecasting team, and work on water-related topics.
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Floods are one of the most common natural disasters, with a disproportionate impact in developing countries that often lack dense streamflow gauge networks. Accurate and timely warnings are critical for mitigating flood risks, but hydrological simulation models typically must be calibrated to long data records in each watershed. Here we show that AI-based forecasting achieves reliability in predicting extreme riverine events in ungauged watersheds at up to a 5-day lead time that is similar to or better than the reliability of nowcasts (0-day lead time) from a current state of the art global modeling system (the Copernicus Emergency Management Service Global Flood Awareness System). Additionally, we achieve accuracies over 5-year return period events that are similar to or better than current accuracies over 1-year return period events. This means that AI can provide flood warnings earlier and over larger and more impactful events in ungauged basins. The model developed in this paper 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.
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A data-centric perspective on the information needed for hydrological uncertainty predictions
Andreas Auer
Martin Gauch
Frederik Kratzert
Sepp Hochreiter
Daniel Klotz
Hydrology and Earth System Sciences (2024)
Preview abstract
Uncertainty estimates are fundamental to assess the reliability of predictive models in hydrology. We use the framework of conformal prediction to investigate the impact of temporal and spatial information on uncertainty estimates within hydrological predictions. Integrating recent information significantly enhances overall uncertainty predictions, even with substantial gaps between updates. While local information yields good results on average, it proves to be insufficient for peak-flow predictions. Incorporating global information improves the accuracy of peak-flow bounds, corroborating findings from related studies. Overall, the study underscores the importance of continuous data updates and the integration of global information for robust and efficient uncertainty estimation.
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An artificial neural network to estimate the foliar and ground cover input variables of the Rangeland Hydrology and Erosion Model
Mahmoud Saeedimoghaddam
David Goodrich
Mariano Hernandez
David Phillip Guertin
Loretta J. Metz
Guillermo Ponce-Campos
Haiyan Wei
Shea Burns
Sarah E. McCord
Mark A. Nearing
C. Jason Williams
Carrie-Ann Houdeshell
Mashrekur Rahman
Menberu B. Meles
Steve Barker
Journal of Hydrology (2024)
Preview abstract
Models like the Rangeland Hydrology and Erosion Model (RHEM) are useful for estimating soil erosion, however, they rely on input parameters that are sometimes difficult or expensive to measure. Specifically, RHEM requires information about foliar and ground cover fractions that generally must be measured in situ, which makes it difficult to use models like RHEM to produce erosion or soil risk maps for areas exceeding the size of a hillslope such as a large watershed. We previously developed a deep learning emulator of RHEM that has low computational expense and can, in principle, be run over large areas (e.g., over the continental US). In this paper, we develop a deep learning model to estimate the RHEM ground cover inputs from remote sensing time series, reducing the need for extensive field surveys to produce erosion maps. We achieve a prediction accuracy on hillslope runoff of r2=0.9, and on soil loss and sediment yield of r2 = 0.4 at 66,643 field locations within the US. We demonstrate how this approach can be used for mapping by developing runoff, soil loss, and sediment yield maps over a 1356 km2 region of interest in Nebraska.
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Technical Note: The divide and measure nonconformity – how metrics can mislead when we evaluate on different data partitions
Daniel Klotz
Martin Gauch
Frederik Kratzert
Jakob Zscheischler
Hydrology and Earth System Sciences (2024)
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The evaluation of model performance is an essential part of hydrological modeling. However, leveraging the full information that performance criteria provide requires a deep understanding of their properties. This Technical Note focuses on a rather counterintuitive aspect of the perhaps most widely used hydrological metric, the Nash–Sutcliffe efficiency (NSE). Specifically, we demonstrate that the overall NSE of a dataset is not bounded by the NSEs of all its partitions. We term this phenomenon the “divide and measure nonconformity”. It follows naturally from the definition of the NSE, yet because modelers often subdivide datasets in a non-random way, the resulting behavior can have unintended consequences in practice. In this note we therefore discuss the implications of the divide and measure nonconformity, examine its empirical and theoretical properties, and provide recommendations for modelers to avoid drawing misleading conclusions.
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On the predictability of turbulent fluxes from land: PLUMBER2 MIP experimental description and preliminary results
Gab Abramowitz
Anna Ukkola
Sanaa Hobeichi
Jon Cranko Page
Mathew Lipson
Martin De Kauwe
Sam Green
Claire Brenner
Jonathan Frame
Martyn Clark
Martin Best
Peter Anthoni
Gabriele Arduini
Souhail Boussetta
Silvia Caldararu
Kyeungwoo Cho
Matthias Cuntz
David Fairbairn
Craig Ferguson
Hyungjun Kim
Yeonjoo Kim
Jürgen Knauer
David Lawrence
Xiangzhong Luo
Sergey Malyshev
Tomoko Nitta
Jerome Ogee
Keith Oleson
Catherine Ottlé
Phillipe Peylin
Patricia de Rosnay
Heather Rumbold
Bob Su
Nicolas Vuichard
Anthony Walker
Xiaoni Wang-Faivre
Yunfei Wang
Yijian Zeng
Hydrology and Earth Systems Sciences Discussions (2024)
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Accurate representation of the turbulent exchange of carbon, water, and heat between the land surface and the atmosphere is critical for modelling global energy, water, and carbon cycles, both in future climate projections and weather forecasts. We describe a Model Intercomparison Project (MIP) that compares the surface turbulent heat flux predictions of around 20 different land models provided with in-situ meteorological forcing, evaluated with measured surface fluxes using quality-controlled data from 170 eddy-covariance based flux tower sites.
Several out-of-sample empirical model predictions of site fluxes are used as benchmarks to quantify the degree to which land model performance could improve across a broad range of metrics. The performance discrepancy between empirical and physically-based model predictions also provides a potential pathway to understand sources of model error. Sites with unusual behaviour, complicated processes, poor data quality or uncommon flux magnitude will be more difficult to predict for both mechanistic and empirical models.
Results suggest that latent heat flux and net ecosystem exchange of CO2 are better predicted by land models than sensible heat flux, which at least conceptually would appear to have fewer physical processes controlling it. Land models that are implemented in Earth System Models also appear to perform notably better than stand alone ecosystem (including demographic) models, at least in terms of the fluxes examined here.
Flux tower data quality is also explored as an uncertainty source, with the difference between energy-balance corrected versus raw fluxes examined, as well as filtering for low wind speed periods. Land model performance does not appear to improve with energy-balance corrected data, and indeed some results raised questions about whether the correction process itself was appropriate. In both cases results were broadly consistent, with simple out-of-sample empirical models, including linear regression, comfortably outperforming mechanistic land models. The PLUMBER2 approach, and its openly-available data, enable precise isolation of the locations and conditions in which model developers can know that a given land model can improve, allowing information pathways and discrete parametrisations in models to be identified and targeted for model development.
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Machine learning (ML) has played an increasing role in the hydrological sciences. In particular, Long Short-Term Memory (LSTM) networks are popular for rainfall–runoff modeling. A large majority of studies that use this type of model do not follow best practices, and there is one mistake in particular that is common: training deep learning models on small, homogeneous data sets, typically data from only a single hydrological basin. In this position paper, we show that LSTM rainfall–runoff models are best when trained with data from a large number of basins.
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Caravan - A global community dataset for large-sample hydrology
Frederik Kratzert
Nans Addor
Tyler Erickson
Martin Gauch
Lukas Gudmundsson
Daniel Klotz
Sella Nevo
Guy Shalev
Scientific Data, 10 (2023), pp. 61
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High-quality datasets are essential to support hydrological science and modeling. Several CAMELS (Catchment Attributes and Meteorology for Large-sample Studies) datasets exist for specific countries or regions, however these datasets lack standardization, which makes global studies difficult. This paper introduces a dataset called Caravan (a series of CAMELS) that standardizes and aggregates seven existing large-sample hydrology datasets. Caravan includes meteorological forcing data, streamflow data, and static catchment attributes (e.g., geophysical, sociological, climatological) for 6830 catchments. Most importantly, Caravan is both a dataset and open-source software that allows members of the hydrology community to extend the dataset to new locations by extracting forcing data and catchment attributes in the cloud. Our vision is for Caravan to democratize the creation and use of globally-standardized large-sample hydrology datasets. Caravan is a truly global open-source community resource.
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In Defense of Metrics: Metrics Sufficiently Encode Typical Human Preferences Regarding Hydrological Model Performance
Martin Gauch
Frederik Kratzert
Hoshin Gupta
Juliane Mai
Bryan A. Tolson
Sepp Hochreiter
Daniel Klotz
Water Resources Research, 59, e2022WR033918 (2023)
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Building accurate rainfall–runoff models is an integral part of hydrological science and practice. The variety of modeling goals and applications have led to a large suite of evaluation metrics for these models. Yet, hydrologists still put considerable trust into visual judgment, although it is unclear whether such judgment agrees or disagrees with existing quantitative metrics. In this study, we tasked 622 experts to compare and judge more than 14,000 pairs of hydrographs from 13 different models. Our results show that expert opinion broadly agrees with quantitative metrics and results in a clear preference for a Machine Learning model over traditional hydrological models. The expert opinions are, however, subject to significant amounts of inconsistency. Nevertheless, where experts agree, we can predict their opinion purely from quantitative metrics, which indicates that the metrics sufficiently encode human preferences in a small set of numbers. While there remains room for improvement of quantitative metrics, we suggest that the hydrologic community should reinforce their benchmarking efforts and put more trust in these metrics.
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We develop a deep learning based convolutional-regression model that estimates the volumetric soil moisture content in the top ~5 cm. Input predictors include Sentinel-1 (active radar), Sentinel-2 (optical imagery), and SMAP (passive radar) as well as geophysical variables from SoilGrids and modelled soil moisture fields from GLDAS. The model was trained and evaluated on data from ~1300 in-situ sensors globally over the period 2015 - 2021 and obtained an average per-sensor correlation of 0.72 and ubRMSE of 0.0546. These results are benchmarked against 13 other soil moisture estimates at different locations, and an ablation study was used to identify important predictors.
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On strictly enforced mass conservation constraints for modelling the Rainfall-Runoff process
Jonathan Frame
Frederik Kratzert
Hoshin Gupta
Paul Ullrich
Hydrological Processes (2023)
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It has been proposed that conservation laws might not be beneficial for accurate hydrological modelling due to errors in input (precipitation) and target (streamflow) data (particularly at the event time scale), and this might explain why deep learning models (which are not based on enforcing closure) can out-perform catchment-scale conceptual and process-based models at predicting streamflow. We test this hypothesis with two forcing datasets that disagree in total, long-term precipitation. We analyse the roll of strictly enforced mass conservation for matching a long-term mass balance between precipitation input and streamflow output using physics-informed (mass conserving) machine learning and find that: (1) enforcing closure in the rainfall-runoff mass balance does appear to harm the overall skill of hydrological models; (2) deep learning models learn to account for spatiotemporally variable biases in data (3) however this ‘closure’ effect accounts for only a small fraction of the difference in predictive skill between deep learning and conceptual models.
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