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+ The persistence of errors: How evaluating models over data partitions relates to a global evaluation +

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Oral presentation for EGU General Assembly 2023. This is one about a certain phenomena that appears when we evaluate a model over subsets of the data. Eventually the plan is to make a technical note out of it.

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+ Deficiencies in Hydrological Modelling Practices +

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- + - Neural Hydrology - Using Neural Networks in Hydrology @@ -46,9 +45,9 @@ - + - + @@ -221,6 +220,50 @@

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+ The persistence of errors: How evaluating models over data partitions relates to a global evaluation +

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Oral presentation for EGU General Assembly 2023. This is one about a certain phenomena that appears when we evaluate a model over subsets of the data. Eventually the plan is to make a technical note out of it.

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- Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets -

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In this manuscript we show for the first time how to train a single LSTM-based neural network as general hydrology model for hundreds of basins. Furthermore, we proposed the Entity-Aware LSTM (EA-LSTM) in which static features are used explicitly to subset the model for a specific entity (here a catchment).

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- Using large data sets towards generating a catchment aware hydrological model for global applications -

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- + - Python Library: neuralHydrology @@ -45,9 +44,9 @@ - + - + @@ -320,7 +319,7 @@

- + - Neural Hydrology - Using Neural Networks in Hydrology @@ -46,9 +45,9 @@ - + - + @@ -216,6 +215,99 @@

Posts

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+ 02 August + + + + + + / + + paper + + + / + + code + + +
+
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+ Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets +

+
+
+
+

In this manuscript we show for the first time how to train a single LSTM-based neural network as general hydrology model for hundreds of basins. Furthermore, we proposed the Entity-Aware LSTM (EA-LSTM) in which static features are used explicitly to subset the model for a specific entity (here a catchment).

+ +
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+ + + + + + + + +
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+ 10 April + + + + + + / + + conference + + +
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+ Using large data sets towards generating a catchment aware hydrological model for global applications +

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- + - CAMELS US: Hourly USGS discharge observations and NLDAS forcings @@ -45,9 +44,9 @@ - + - + @@ -335,7 +334,7 @@

- + - LamaH-CE: LArge-SaMple DAta for Hydrology and Environmental Sciences for Central Europe @@ -45,9 +44,9 @@ - + - + @@ -350,7 +349,7 @@

- + - Post processing the U.S. National Water Model with a Long Short-Term Memory network @@ -45,9 +44,9 @@ - + - + @@ -252,8 +251,7 @@

Post processing the U.S. National Water Model with a Lo

Abstract

We build three long short-term memory (LSTM) daily streamflow prediction models (deep learning networks) for 531 basins across the contiguous United States (CONUS), and compare their performance: (1) a LSTM post-processor trained on the United States National Water Model (NWM) outputs (LSTM_PP), (2) a LSTM post-processor trained on the NWM outputs and atmospheric forcings (LSTM_PPA), and (3) a LSTM model trained only on atmospheric forcing (LSTM_A). We trained the LSTMs for the period 2004–2014 and evaluated on 1994–2002, and compared several performance metrics to the NWM reanalysis. Overall performance of the three LSTMs is similar, with median NSE scores of 0.73 (LSTM_PP), 0.75 (LSTM_PPA), and 0.74 (LSTM_A), and all three LSTMs outperform the NWM validation scores of 0.62. Additionally, LSTM_A outperforms LSTM_PP and LSTM_PPA in ungauged basins. While LSTM as a post-processor improves NWM predictions substantially, we achieved comparable performance with the LSTM trained without the NWM outputs (LSTM_A). Finally, we performed a sensitivity analysis to diagnose the land surface component of the NWM as the source of mass bias error and the channel router as a source of simulation timing error. This indicates that the NWM channel routing scheme should be considered a priority for NWM improvement.

Paper

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Frame, J.M., Kratzert, F., Raney, A., Rahman, M., Salas, F.R., and Nearing, G.S.. 2021. “ Post-Post-Processing the National Water Model with Long Short-Term Memory Networks for Streamflow Predictions and Model Diagnostics.” Journal of the American Water Resources Association 57( 6): 885– 905. https://doi.org/10.1111/1752-1688.12964.

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(submitted to WRR)

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Frame, J.M., Kratzert, F., Raney, A., Rahman, M., Salas, F.R., and Nearing, G.S.. 2021. “ Post-Post-Processing the National Water Model with Long Short-Term Memory Networks for Streamflow Predictions and Model Diagnostics.” Journal of the American Water Resources Association 57( 6): 885– 905. https://doi.org/10.1111/1752-1688.12964.

Code

Code for reproducing the results can be found in this GitHub repository.

Citation

@@ -342,7 +340,7 @@

- + - Deep learning rainfall-runoff predictions of extreme events @@ -45,9 +44,9 @@ - + - + @@ -221,8 +220,8 @@ 18 August / - + @@ -252,7 +251,7 @@

Deep learning rainfall-runoff predictions of extreme ev

Abstract

The most accurate rainfall-runoff predictions are currently based on deep learning. There is a concern among hydrologists that data-driven models based on deep learning may not be reliable in extrapolation or for predicting extreme events. This study tests that hypothesis using Long Short-Term Memory networks (LSTMs) and an LSTM variant that is architecturally constrained to conserve mass. The LSTM (and the mass-conserving LSTM variant) remained relatively accurate in predicting extreme (high return-period) events compared to both a conceptual model (the Sacramento Model) and a process-based model (US National Water Model), even when extreme events were not included in the training period. Adding mass balance constraints to the data-driven model (LSTM) reduced model skill during extreme events.

Paper

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Frame, J., Kratzert, F., Klotz, D., Gauch, M., Shelev, G., Gilon, O., Qualls, L. M., Gupta, H. V., and Nearing, G. S.: Deep learning rainfall-runoff predictions of extreme events, Hydrol. Earth Syst. Sci. Discuss. [preprint], https://doi.org/10.5194/hess-2021-423, in review, 2021.

+

Frame, J. M., Kratzert, F., Klotz, D., Gauch, M., Shalev, G., Gilon, O., Qualls, L. M., Gupta, H. V., and Nearing, G. S.: Deep learning rainfall–runoff predictions of extreme events, Hydrol. Earth Syst. Sci., 26, 3377–3392, https://doi.org/10.5194/hess-26-3377-2022, 2022.

Code

All experiments were made with the NeuralHydrology Python library. The exact snapshot for reproducing the results can be found in this GitHub repository.

Citation

@@ -340,7 +339,7 @@

- + - On Strictly Enforced Mass Conservation Constraints for Modeling the Rainfall-Runoff Process @@ -45,9 +44,9 @@ - + - + @@ -339,7 +338,7 @@

- + - Machine Learning for Streamflow Prediction: Current Status and Future Prospects @@ -45,9 +44,9 @@ - + - + @@ -333,7 +332,7 @@

- + - Streamflow Prediction with Limited Spatially-Distributed Input Data @@ -45,9 +44,9 @@ - + - + @@ -330,7 +329,7 @@

- + - LSTM-Based Rainfall–Runoff Modeling at Arbitrary Time Scales @@ -45,9 +44,9 @@ - + - + @@ -342,7 +341,7 @@

- + - The Proper Care and Feeding of CAMELS: How Limited Training Data Affects Streamflow Prediction @@ -45,9 +44,9 @@ - + - + @@ -342,7 +341,7 @@

- + - A Data Scientist's Guide to Streamflow Prediction @@ -45,9 +44,9 @@ - + - + @@ -337,7 +336,7 @@

- + - Rainfall-Runoff Prediction at Multiple Timescales with a Single Long Short-Term Memory Network @@ -45,9 +44,9 @@ - + - + @@ -342,7 +341,7 @@

- + - A Machine Learner's Guide to Streamflow Prediction @@ -45,9 +44,9 @@ - + - + @@ -338,7 +337,7 @@

- + - Multi-Timescale LSTM for Rainfall–Runoff Forecasting @@ -45,9 +44,9 @@ - + - + @@ -341,7 +340,7 @@

- + - Rate my Hydrograph: Evaluating the Conformity of Expert Judgment and Quantitative Metrics @@ -45,9 +44,9 @@ - + - + @@ -333,7 +332,7 @@

- + - In Defense of Metrics: Metrics Sufficiently Encode Typical Human Preferences Regarding Hydrological Model Performance @@ -45,9 +44,9 @@ - + - + @@ -337,7 +336,7 @@

- + - MC-LSTM: Mass-Conserving LSTM @@ -45,9 +44,9 @@ - + - + @@ -345,7 +344,7 @@

- + - Towards the quantification of uncertainty for deep learning based rainfall-runoff models @@ -45,9 +44,9 @@ - + - + @@ -332,7 +331,7 @@

- + - Examining the uncertainty estimation properties of LSTM based rainfall-runoff models @@ -45,9 +44,9 @@ - + - + @@ -341,7 +340,7 @@

- + - Learning from mistakes: Online updating for deep learning models @@ -45,9 +44,9 @@ - + - + @@ -333,7 +332,7 @@

- + - Uncertainty Estimation with Deep Learning for Rainfall-Runoff Modelling @@ -45,9 +44,9 @@ - + - + @@ -252,7 +251,7 @@

Uncertainty Estimation with Deep Learning for Rainfall-

Abstract

Deep Learning is becoming an increasingly important way to produce accurate hydrological predictions across a wide range of spatial and temporal scales. Uncertainty estimations are critical for actionable hydrological forecasting, and while standardized community benchmarks are becoming an increasingly important part of hydrological model development and research, similar tools for benchmarking uncertainty estimation are lacking. This contributions demonstrates that accurate uncertainty predictions can be obtained with Deep Learning. We establish an uncertainty estimation benchmarking procedure and present four Deep Learning baselines. Three baselines are based on Mixture Density Networks and one is based on Monte Carlo dropout. The results indicate that these approaches constitute strong baselines, especially the former ones. Additionaly, we provide a post-hoc model analysis to put forward some qualitative understanding of the resulting models. This analysis extends the notion of performance and show that learn nuanced behaviors in different situations.

Paper

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Klotz, D., Kratzert, F., Gauch, M., Keefe Sampson, A., Brandstetter, J., Klambauer, G., Hochreiter, S., and Nearing, G.: Uncertainty Estimation with Deep Learning for Rainfall–Runoff Modelling, Hydrol. Earth Syst. Sci. Discuss. [preprint], https://doi.org/10.5194/hess-2021-154, in review, 2021.

+

Klotz, D., Kratzert, F., Gauch, M., Keefe Sampson, A., Brandstetter, J., Klambauer, G., Hochreiter, S., and Nearing, G.: Uncertainty estimation with deep learning for rainfall–runoff modeling, Hydrol. Earth Syst. Sci., 26, 1673–1693, https://doi.org/10.5194/hess-26-1673-2022, 2022.

Code

The results of this paper were produced with the NeuralHydrology Python package.

Citation

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- + - Uncertainty estimation with LSTM based rainfall-runoff models @@ -45,9 +44,9 @@ - + - + @@ -341,7 +340,7 @@

- + + + Deficiencies in Hydrological Modelling Practices + + + + + + + + + + + + + + + + + + + + + + + + + +
+ Skip to content + + +
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+ 07 May / + + + + + + + + + / + + conference + + + + +
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Deficiencies in Hydrological Modelling Practices

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Oral presentation for EGU General Assembly 2022. This is where my model evaluation journey started.

+

Abstract

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The goal of this contribution is to demonstrate deficiencies that we observe in hydrological modelling studies. Our hope is that awareness of potential mistakes, errors, and habits will support accurate communication and analysis — and consequently lead to better modelling practises in our community.

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By deficiencies, we broadly mean wrong assumptions, false conclusions, and artificial limitations that impair our modelling efforts. To give some explicit examples:

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  • Model calibration: Often, only two data splits are used: one for model calibration and one for model validation. To provide a robust estimate of model quality on unseen data, one should, however, involve a three-way split: a calibration set used for parameter adaptation, a validation set used for hyperparameter tuning and intermediate model evaluations, and a test set used only once to derive the final, independent model accuracy.
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  • Artificial restrictions: Studies often restrict modelling setups to specific settings (e.g., model classes, input data, or objective functions) for comparative reasons. In general, one should use the best available data, inputs, and objective functions for each model, irrespective of the diagnostic metric used for evaluation and irrespective of what other models are (able to) use.
    • +
  • (Missing) Model rejection: Although benchmarking efforts are not an entirely new concept in our community, we do observe that the results of model comparisons are seemingly without consequences. Models that repeatedly underperform on a specific task continue to be used for the same task they were just proven not to be good for. At some point, these models should be rejected and we as a community should move forward to improve the other models or develop new models.
    • +
  • Interpretation of intermediate states: Many hydrologic models attempt to represent a variety of internal physical states that are not calibrated (e.g., soil moisture). Unfortunately, these states are often mistaken for true measurements and used as ground truth in downstream studies. We believe that (unless the quality of these states was evaluated successfully), using intermediate model outputs is of high risk, as it may distort subsequent analyses.
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  • Noise: Albeit it is commonly accepted that hydrological input variables are subject to large uncertainties and imprecisions, the influence of input perturbations is often not explicitly accounted for in models.
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  • Model complexity: We aim to model one of the most complex systems that exists, our nature. In practice, we will only be able to obtain a simplified representation of the system. However, we should not reduce complexity for the wrong reasons. While there is a tradeoff between simplicity and complexity, we should not tend towards the most simple models, such as two- or three-bucket models.
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Our belief is that modelling should be a community-wide effort, involving benchmarking, probing, model building, and examination. Being aware of deficiencies will hopefully bring forth a culture that adheres to best practises, rigorous testing, and probing for errors — ultimately benefiting us all by leading to more performant and reliable models.

+ +

Abstract

+

Citation

+
@inproceedings{klotz2022deficiencies,
+  title={Deficiencies in Hydrological Modelling Practices},
+  author={Klotz, Daniel and Gauch, Martin and Nearing, Grey and Hochreiter, Sepp and Kratzert, Frederik},
+  booktitle={EGU General Assembly 2023},
+  venue={online},
+  pages={EGU22--12403},
+  year={2022}
+}
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Using Neural Networks in Hydrology

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© 2018 Göran Svensson

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The persistence of errors: How evaluating models over data partitions relates to a global evaluation

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Oral presentation for EGU General Assembly 2023. This is one about a certain phenomena that appears when we evaluate a model over subsets of the data. Eventually the plan is to make a technical note out of it.

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Abstract

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Skillful today, inept tomorrow. Today’s hydrological models have pronounced and complex error dynamics (e.g., small, highly correlated errors for low flows and large, random errors for high flows). Modellers generally accept that simple, variance based evaluation criteria — like the Nash-Sutcliffe Efficiency (NSE) — are not fully able to capture these intricacies. The (implied) consequences of this are however seldom discussed.

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This contribution examines how evaluating the model over two data partitions (above and below a chosen threshold) relates to a global model evaluation of both partitions combined (i.e., the usual way of computing the NSE). For our experiments we manipulate dummy simulations with gradient descent to approximate specific NSE values for each partition individually. Specifically, we set the NSE for runoff values that fall below the threshold, and vary the NSE of the simulations above the threshold as well as the threshold itself. This enables us to study how the global NSE relates to the partition NSEs and the threshold. Intuitively, one would wish that the global NSE somehow reflects the performance on the partitions in a comprehensible manner. We do however show that this relation is not trivial.

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Our results also show that subdividing the data and evaluating over the resulting partitions yields different information regarding model deficiencies than an overall evaluation. The downside is that we have less data to estimate the NSE. In the future we can use this for model selection and diagnostic purposes.

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Abstract

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Citation

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@inproceedings{klotz2023persistence,
+  title={The persistence of errors: 
+  How evaluating models over data partitions relates to a global evaluation},
+  author={Klotz, Daniel and Gauch, Martin and Nearing, Grey and Hochreiter, Sepp and Kratzert, Frederik},
+  year={2023},
+  booktitle={EGU General Assembly 2023},
+  venue={online},
+  year={2021}
+}
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+ + Neural Hydrology + +

+ + +

Using Neural Networks in Hydrology

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+ +

© 2018 Göran Svensson

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+ + + + + + + + diff --git a/post/research/kratzert2018agu/index.html b/post/research/kratzert2018agu/index.html index c323c44..b1ba0af 100644 --- a/post/research/kratzert2018agu/index.html +++ b/post/research/kratzert2018agu/index.html @@ -25,12 +25,11 @@ "dateModified" : "2018-12-10 20:23:59 \u002b0530 \u002b0530", "url" : "\/post\/research\/kratzert2018agu\/", "wordCount" : "382", - "image" : "/img/kratzert2018agu/hyd_model.png"", + "image" : "\/img\/kratzert2018agu\/hyd_model.png", "keywords" : [ "Blog" ] } - Do internals of neural networks make sense in the context of hydrology? @@ -45,9 +44,9 @@ - + - + @@ -344,7 +343,7 @@

- + - A glimpse into the Unobserved: Runoff simulation for ungauged catchments with LSTMs @@ -45,9 +44,9 @@ - + - + @@ -329,7 +328,7 @@

- + - Rainfall–runoff modelling using Long Short-Term Memory (LSTM) networks @@ -45,9 +44,9 @@ - + - + @@ -331,7 +330,7 @@

- + - Large-Scale Rainfall-Runoff Modeling using the Long Short-Term Memory Network @@ -45,9 +44,9 @@ - + - + @@ -331,7 +330,7 @@

- + - Long Short-Term Memory (LSTM) networks for rainfall-runoff modeling @@ -45,9 +44,9 @@ - + - + @@ -317,7 +316,7 @@

- + - Using large data sets towards generating a catchment aware hydrological model for global applications @@ -45,9 +44,9 @@ - + - + @@ -330,7 +329,7 @@

- + - NeuralHydrology-Interpreting LSTMs in Hydrology @@ -45,9 +44,9 @@ - + - + @@ -329,7 +328,7 @@

- + - Using LSTMs for climate change assessment studies on droughts and floods @@ -45,9 +44,9 @@ - + - + @@ -330,7 +329,7 @@

- + - Toward Improved Predictions in Ungauged Basins: Exploiting the Power of Machine Learning @@ -45,9 +44,9 @@ - + - + @@ -342,7 +341,7 @@

- + - Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets @@ -45,9 +44,9 @@ - + - + @@ -342,7 +341,7 @@

- + - The performance of LSTM models from basin to continental scales @@ -45,9 +44,9 @@ - + - + @@ -336,7 +335,7 @@

- + - Towards deep learning based flood forecasting for ungauged basins @@ -45,9 +44,9 @@ - + - + @@ -333,7 +332,7 @@

- + - A note on leveraging synergy in multiple meteorological datasets with deep learning for rainfall-runoff modeling @@ -45,9 +44,9 @@ - + - + @@ -341,7 +340,7 @@

- + - Large-scale river network modeling using Graph Neural Networks @@ -45,9 +44,9 @@ - + - + @@ -334,7 +333,7 @@

- + - Caravan - A global community dataset for large-sample hydrology @@ -45,9 +44,9 @@ - + - + @@ -349,7 +348,7 @@

- + - NeuralHydrology — A Python library for Deep Learning research in hydrology @@ -45,9 +44,9 @@ - + - + @@ -356,7 +355,7 @@

- + - Hydrological Concept Formation inside Long Short-Term Memory (LSTM) networks @@ -45,9 +44,9 @@ - + - + @@ -341,7 +340,7 @@

- + - The Great Lakes Runoff Intercomparison Project Phase 4: the Great Lakes (GRIP-GL) @@ -45,9 +44,9 @@ - + - + @@ -352,7 +351,7 @@

- + - HydroNets: Leveraging River Network Structure and Deep Neural Networks for Hydrologic Modeling @@ -45,9 +44,9 @@ - + - + @@ -332,7 +331,7 @@

- + - HydroNets: Leveraging River Structure for Hydrologic Modeling @@ -45,9 +44,9 @@ - + - + @@ -331,7 +330,7 @@

- + - A Deep Learning Architecture for Conservative Dynamical Systems: Application to Rainfall-Runoff Modeling @@ -45,9 +44,9 @@ - + - + @@ -339,7 +338,7 @@

- + - What Role Does Hydrological Science Play in the Age of Machine Learning? @@ -45,9 +44,9 @@ - + - + @@ -331,7 +330,7 @@

- + - Technical Note: Data assimilation and autoregression for using near-real-time streamflow observations in long short-term memory networks @@ -45,9 +44,9 @@ - + - + @@ -340,7 +339,7 @@

- + - Forward vs. Inverse Methods for Using Near-Real-Time Streamflow Observation Data in Long Short-Term Memory Networks @@ -45,9 +44,9 @@ - + - + @@ -332,7 +331,7 @@

- + - Flood forecasting with machine learning models in an operational framework @@ -45,9 +44,9 @@ - + - + @@ -332,7 +331,7 @@

- +