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Grey Nearing

I am a Research Scientist on the Google Flood Forecasting team, and work on water-related topics.

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    Preview abstract 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. View details
    Preview abstract Google has developed an AI-based river and inundation forecasting system, and is partnering with governments and water agencies around the world to provide real-time flood alerts directly to individuals, communities, and NGOs through existing Google information channels like Maps, Search, and Android Alerts. This talk will cover the background, development, and impact of this effort. View details
    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, vol. 59, e2022WR033918 (2023)
    Preview abstract 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. View details
    AI Increases Global Access to Reliable Flood Forecasts
    Asher Metzger
    Dana Weitzner
    Frederik Kratzert
    Guy Shalev
    Martin Gauch
    Sella Nevo
    Shlomo Shenzis
    Tadele Yednkachw Tekalign
    Vusumuzi Dube
    arXiv (2023)
    Preview abstract 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. View details
    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, vol. 10 (2023), pp. 61
    Preview abstract 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. View details
    Preview abstract 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. View details
    NeuralHydrology --- A Python library for Deep Learning research in hydrology
    Frederik Kratzert
    Martin Gauch
    Daniel Klotz
    Journal of Open Source Software, vol. 7(71) (2022), pp. 4050
    Preview abstract Summary: This manuscript is intended to be submitted to the Journal of Open Source Software for the Python library NeuralHydrology https://github.com/neuralhydrology/neuralhydrology I created this library during my PhD at the JKU in Linz and it was open sourced in 2019 and is currently maintained by myself, two former colleagues from the JKU and Grey Nearing (@gsnearing). The purpose of this library is to make machine learning more accessible to hydrologists, who have usually a) little training in programming and b) no machine learning classes/experience. The NeuralHydrology library is designed to make state of the art models for e.g. rainfall-runoff modeling easily accessible (training and evaluation can be configured from a YAML config file, no coding required) but also easily extendable (e.g. new datasets, models, loss functions etc.) for a more research oriented use case. The library is fully documented and has a number of tutorials. We used this library in the past years for all of our publications and research. Since its publication, it is also being used by several other groups in their day-to-day research and in their journal publications. The JOSS publication is meant to make this library more easy to reference (as requested by users of this library). JOSS publications are usually a 1-2 page description and during the review period the focus is more on the code/documentation etc. itself than on the written paper. Note on the document: JOSS paper's are submitted as Markdown + Bibtex and then rendered into a PDF. I can't render the Markdown offline and since I should not submit the document somewhere online before approval, I can only share the Markdown file. View details
    Global Flood Forecasting at a Fine Catchment Resolution using Machine Learning
    Asher Metzger
    Dana Weitzner
    Frederik Kratzert
    Guy Shalev
    Sella Nevo
    Shlomo Shenzis
    Tadele Yednkachw Tekalign
    (2022)
    Preview abstract Machine learning has been shown to be a promising tool for hydrological modeling. We have used this technology to develop an operational real-time global streamflow prediction model. The model architecture is based primarily on an LSTM (Long Short Term Memory), which is a form of RNN (Recurrent Neural Network) that includes a state vector similar to dynamical systems models. Our model has been shown to outperform physical and conceptual hydrologic models across time and spatial scales. The main advantage of this ML approach is that models can be trained (calibrated) over many diverse catchments simultaneously rather than being calibrated separately per catchment. This advantage is especially important when modeling on a global scale where the model is trained on a very large number of catchments that have diverse climatology and geographical settings. Consequently, the model learns different rainfall-runoff dynamics of rivers across these settings and is able to predict accordingly. Once the model is trained (a very short process in comparison to calibrating traditional global models), it can be applied almost anywhere where basin attributes are available, in particular, at ungauged locations. We use globally available, near-real time datasets for training and inference, which allows running the model operationally. Global datasets used: HydroSHEDS database for global catchments delineation and static attributes. Meteorological forcing data from: ECMWF weather data, including the ERA5-Land reanalysis and the IFS HRES real-time forecasts and re-forecasts. NOAA’s IMERG (early) global precipitation estimates. CPC Global Unified Gauge-Based Analysis of Daily Precipitation. Stream flow global datasets such as GRDC and Caravan for streamflow discharge labels. View details
    Flood forecasting with machine learning models in an operational framework
    Asher Metzger
    Chen Barshai
    Dana Weitzner
    Frederik Kratzert
    Gregory Begelman
    Guy Shalev
    Hila Noga
    Moriah Royz
    Niv Giladi
    Ronnie Maor
    Sella Nevo
    Yotam Gigi
    HESS (2022)
    Preview abstract Google’s operational flood forecasting system was developed to provide accurate real-time flood warnings to agencies and the public, with a focus on riverine floods in large, gauged rivers. It became operational in 2018 and has since expanded geographically. This forecasting system consists of four subsystems: data validation, stage forecasting, inundation modeling, and alert distribution. Machine learning is used for two of the subsystems. Stage forecasting is modeled with the Long Short-Term Memory (LSTM) networks and the Linear models. Flood inundation is computed with the Thresholding and the Manifold models, where the former computes inundation extent and the latter computes both inundation extent and depth. The Manifold model, presented here for the first time, provides a machine-learning alternative to hydraulic modeling of flood inundation. When evaluated on historical data, all models achieve sufficiently high-performance metrics for operational use. The LSTM showed higher skills than the Linear model, while the Thresholding and Manifold models achieved similar performance metrics for modeling inundation extent. During the 2021 monsoon season, the flood warning system was operational in India and Bangladesh, covering flood-prone regions around rivers with a total area of 287,000 km2, home to more than 350M people. More than 100M flood alerts were sent to affected populations, to relevant authorities, and to emergency organizations. Current and future work on the system includes extending coverage to additional flood-prone locations, as well as improving modeling capabilities and accuracy. View details
    A Deep Learning Architecture for Conservative Dynamical Systems: Application to Rainfall-Runoff Modeling
    Frederik Kratzert
    Daniel Klotz
    Pieter-Jan Hoedt
    Günter Klambauer
    Sepp Hochreiter
    Hoshin Gupta
    Sella Nevo
    NeurIPS AI for Earth Sciences workshop (2020)
    Preview abstract The most accurate and generalizable rainfall-runoff models produced by the hydrological sciences community to-date are based on deep learning, and in particular, on Long Short Term Memory networks (LSTMs). Although LSTMs have an explicit state space and gates that mimic input-state-output relationships, these models are not based on physical principles. We propose a deep learning architecture that is based on the LSTM and obeys conservation principles. The model is benchmarked on the mass-conservation problem of simulating streamflow View details
    Preview abstract Floods are among the most common and deadly natural disasters in the world, and flood warning systems have been shown to be effective in reducing harm. Yet the majority of the world's vulnerable population does not have access to reliable and actionable warning systems, due to core challenges in scalability, computational costs, and data availability. In this paper we present two components of flood forecasting systems which were developed over the past year, providing access to these critical systems to 75 million people who didn't have this access before. View details
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