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How to deal w___ missing input data Open
Deep learning hydrologic models have made their way from research to applications. More and more national hydrometeorological agencies, hydro power operators, and engineering consulting companies are building Long Short-Term Memory (LSTM) …
SEA - Global Early Warning System for Severe Flood Events Open
Floods are one of the most common natural disasters, and the rate of flood-related disasters has more than doubled since the year 2000. Consequently, accurate and timely warnings are critical for mitigating flood risks, especially for majo…
Semi-Distributed Hydrological Modeling Based on Deep Learning at Scale Open
In recent years, deep learning models have gained traction in hydrology, particularly in streamflow modeling, which is a prerequisite for accurate riverine flood forecasts. However, current state-of-the-art streamflow models are generally …
Closing the Gap in Pluvial Flash Flood Prediction: A Generalizable AI Model for Global Flash Flood Forecasting Open
Flash floods, characterized by rapid onset time, often within six hours of a causative meteorological event, constitute a significant global hazard, causing fatalities comparable to riverine floods globally. Despite their impact, these eve…
Caravan - A global community dataset for large-sample hydrology Open
This is the accompanying dataset to the following paper https://www.nature.com/articles/s41597-023-01975-w Caravan is an open community dataset of meteorological forcing data, catchment attributes, and discharge daat for catchments around …
How to deal w___ missing input data Open
Deep learning hydrologic models have made their way from research to applications. More and more national hydrometeorological agencies, hydro power operators, and engineering consulting companies are building Long Short-Term Memory (LSTM) …
How to deal w___ missing input data Open
Deep learning hydrologic models have made their way from research to applications. More and more national hydrometeorological agencies, hydro power operators, and engineering consulting companies are building Long Short-Term Memory (LSTM) …
Towards Deep Learning River Network Models Open
Deep Learning models for streamflow prediction are now more than five years old (Kratzert et al., 2018, 2019), and lumped LSTMs, trained on as many basins and forcing products as we can get our hands on, continue to pose the state of the a…
Do earthquakes "know" how big they will be? a neural-net aided study Open
Earthquake occurrence is notoriously difficult to predict. While some aspects of their spatiotemporal statistics can be relatively well captured by point-process models, very little is known regarding the magnitude of future events, and it…
Global prediction of extreme floods in ungauged watersheds Open
Floods are one of the most common natural disasters, with a disproportionate impact in developing countries that often lack dense streamflow gauge networks 1 . Accurate and timely warnings are critical for mitigating flood risks 2 , but hy…
Living among Artiodactyls - Current status and future plans of the Caravan dataset Open
High-quality datasets are essential to support hydrological science and modeling. Several datasets exist for specific countries or regions (e.g. the various CAMELS datasets). However, these datasets lack standardization, which makes global…
AI Increases Global Access to Reliable Flood Forecasts Open
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 hydrolog…
Deep Learning for Spatially Distributed Rainfall–Runoff Modeling Open
Deep learning approaches have emerged as the state of the art for rainfall–runoff modeling. Yet—until now—the best-performing models have typically been used with inputs that are averaged across possibly large catchment areas, modeling eac…
Reproducing flash flood warnings with Machine Learning Open
Flash floods account for a large proportion of flood-based fatalities, and they are becoming more frequent due to climate change. A global flash flood warning system therefore has the potential to be life saving.Standard approaches to flas…
AI Increases Global Access to Reliable Flood Forecasts Open
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 hydrolog…
View article: In Defense of Metrics: Metrics Sufficiently Encode Typical Human Preferences Regarding Hydrological Model Performance
In Defense of Metrics: Metrics Sufficiently Encode Typical Human Preferences Regarding Hydrological Model Performance Open
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…
Caravan - A global community dataset for large-sample hydrology Open
This is the accompanying dataset to the following paper https://www.nature.com/articles/s41597-023-01975-w Caravan is an open community dataset of meteorological forcing data, catchment attributes, and discharge daat for catchments around …
View article: Peeking Inside Hydrologists' Minds: Comparing Human Judgment and Quantitative Metrics of Hydrographs
Peeking Inside Hydrologists' Minds: Comparing Human Judgment and Quantitative Metrics of Hydrographs Open
Everyone wants their hydrologic models to be as good as possible. But how do we know if a model is accurate or not? In the spirit of rigorous and reproducible science, the answer should be: we calculate metrics. Yet, as humans, we sometime…
From Hindcast to Forecast with Deep Learning Streamflow Models Open
Deep learning has become the de facto standard for streamflow simulation. While there are examples of deep learning based streamflow forecast models (e.g., 1-5), the majority of the development and research has been done with hindcast mode…
Earthquake Magnitude Prediction Using a Machine Learning Model Open
Standard approaches to earthquake forecasting - both statistics-based models, e.g. the epidemic type aftershock (ETAS), and physics-based models, e.g. models based on the Coulomb failure stress (CFS) criteria, estimate the probability of a…
Caravan - A global community dataset for large-sample hydrology Open
High-quality datasets are essential to support hydrological science and modeling. Several datasets exist for specific countries or regions (e.g. the various CAMELS datasets). However, these datasets lack standardization, which makes global…
Towards flood warnings everywhere - data-driven rainfall-runoff modeling at global scale Open
The goal of Google’s Flood Forecasting Initiative is to provide timely and actionable flood warnings to everyone, globally. Until recently, Google provided operational flood warnings only for specific partner countries, namely India, Bangl…
View article: Technical note: Data assimilation and autoregression for using near-real-time streamflow observations in long short-term memory networks
Technical note: Data assimilation and autoregression for using near-real-time streamflow observations in long short-term memory networks Open
Ingesting near-real-time observation data is a critical component of many operational hydrological forecasting systems. In this paper, we compare two strategies for ingesting near-real-time streamflow observations into long short-term memo…
View article: In Defense of Metrics: Metrics Sufficiently Encode Typical Human Preferences Regarding Hydrological Model Performance
In Defense of Metrics: Metrics Sufficiently Encode Typical Human Preferences Regarding Hydrological Model Performance Open
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…
View article: Flood forecasting with machine learning models in an operational framework
Flood forecasting with machine learning models in an operational framework Open
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 expand…
View article: Deep learning rainfall–runoff predictions of extreme events
Deep learning rainfall–runoff predictions of extreme events Open
The most accurate rainfall–runoff predictions are currently based on deep learning. There is a concern among hydrologists that the predictive accuracy of data-driven models based on deep learning may not be reliable in extrapolation or for…
Caravan - A global community dataset for large-sample hydrology Open
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 …
Caravan - A global community dataset for large-sample hydrology Open
THIS IS A PRE-RELEASE, WHILE THE CARAVAN IS UNDER REVISION. Check out the preprint at: https://eartharxiv.org/repository/view/3345/ Caravan is an open community dataset of meteorological forcing data, catchment attributes, and discharge da…
Caravan - A global community dataset for large-sample hydrology Open
THIS IS A PRE-RELEASE, WHILE THE CARAVAN IS UNDER REVISION. Check out the preprint at: https://eartharxiv.org/repository/view/3345/ (accepted for publication at Nature Scientific Data). Caravan is an open community dataset of meteorologica…