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192 results on '"Herrnegger, Mathew"'

4. NeuralHydrology -- Interpreting LSTMs in Hydrology

Author

Kratzert, Frederik, Herrnegger, Mathew, Klotz, Daniel, Hochreiter, Sepp, and Klambauer, Günter

Subjects
Computer Science - Machine Learning, Physics - Atmospheric and Oceanic Physics, Statistics - Machine Learning
Abstract

Despite the huge success of Long Short-Term Memory networks, their applications in environmental sciences are scarce. We argue that one reason is the difficulty to interpret the internals of trained networks. In this study, we look at the application of LSTMs for rainfall-runoff forecasting, one of the central tasks in the field of hydrology, in which the river discharge has to be predicted from meteorological observations. LSTMs are particularly well-suited for this problem since memory cells can represent dynamic reservoirs and storages, which are essential components in state-space modelling approaches of the hydrological system. On basis of two different catchments, one with snow influence and one without, we demonstrate how the trained model can be analyzed and interpreted. In the process, we show that the network internally learns to represent patterns that are consistent with our qualitative understanding of the hydrological system., Comment: Pre-print of published book chapter. See journal reference and DOI for more info

Published
2019
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33. Analysis of land-cover changes in the Transboundary Sio-Malaba-Malakisi River Basin of East Africa: Towards identifying potential land-use transition regimes.

Author

Chasia, Stanley, Herrnegger, Mathew, Juma, Benard, Kimuyu, Jacinta, Sitoki, Lewis, and Olang, Luke

Subjects
*WATERSHEDS, *IMAGE recognition (Computer vision), *DISTRIBUTION (Probability theory), *REMOTE-sensing images, *FOREST declines
Abstract

This study evaluated historical land-cover states in order to identify potential land-use transition regimes leading to land degradation. Landsat satellite datasets were used to characterize land-cover states for 1986–2017 period. The multinomial probability distribution was used to establish sample size for training and accuracy assessment. Using a hybrid image classification approach, individual satellite images were initially clustered using the ISODATA technique, and spectral classes later transformed posteriori into respective thematic classes. Maximum Likelihood Function was subsequently used to assign pixels into classes with highest probability. Approximately 12% of mixed forest declined, while cropland increased by 30% between 1995–2008. [ABSTRACT FROM AUTHOR]

Published
2023
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43. LamaH | Large-Sample Data for Hydrology and Environmental Sciences for Central Europe

Author

Klingler, Christoph, Schulz, Karsten, and Herrnegger, Mathew

Abstract

Very large and comprehensive datasets are increasingly used in the field of hydrology. Large-sample studies provide insights into the hydrological cycle that might not be available with small-scale studies. LamaH (Large-Sample Data for Hydrology) is a new dataset for large-sample studies and comparative hydrology in Central Europe. It covers the entire upper Danube to the state border Austria/Slovakia, as well as all other Austrian catchments including their foreign upstream areas. LamaH covers an area of 170 000 km2 in 9 different countries, ranging from lowland regions characterized by a continental climate to high alpine zones dominated by snow and ice. Consequently, a wide diversity of properties is present in the individual catchments. We represent this variability in 859 observed catchments with over 60 catchment attributes, covering topography, climatology, hydrology, land cover, vegetation, soil and geological properties. LamaH further contains a collection of runoff time series as well as meteorological time series. These time series are provided with daily and also hourly resolution. All meteorological and the majority of runoff time series cover a span of over 35 years, which enables long-term analyses, also with a high temporal resolution. The runoff time series are classified by over 20 different attributes including information about human impacts and indicators for data quality and completeness. The structure of LamaH is based on the well-known CAMELS datasets. In contrast, however, LamaH does not only consider headwater basins. Intermediate catchments are also covered, allowing, for the first time within a hydrological large sample dataset, to consider the hydrological network and river topology in applications. We discuss not only the data basis and the methodology of data preparation, but also focus on possible limitations and uncertainties. Potential applications of LamaH are also outlined, since it is intended to serve as a uniform basis for further research. LamaH is available at https://doi.org/10.5281/zenodo.4525244 (Klingler et al., 2021).

Published
2021

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