NeuralHydrology -- Interpreting LSTMs in Hydrology

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 inf

Rainfall–runoff modelling using Long Short-Term Memory (LSTM) networks

Rainfall–runoff modelling is one of the key challenges in the field of hydrology. Various approaches exist, ranging from physically based over conceptual to fully data-driven models. In this paper, we propose a novel data-driven approach, using the Long Short-Term Memory (LSTM) network, a special type of recurrent neural network. The advantage of the LSTM is its ability to learn long-term dependencies between the provided input and output of the network, which are essential for modelling storage effects in e.g. catchments with snow influence. We use 241 catchments of the freely available CAMELS data set to test our approach and also compare the results to the well-known Sacramento Soil Moisture Accounting Model (SAC-SMA) coupled with the Snow-17 snow routine. We also show the potential of the LSTM as a regional hydrological model in which one model predicts the discharge for a variety of catchments. In our last experiment, we show the possibility to transfer process understanding, learned at regional scale, to individual catchments and thereby increasing model performance when compared to a LSTM trained only on the data of single catchments. Using this approach, we were able to achieve better model performance as the SAC-SMA&thinsp;+&thinsp;Snow-17, which underlines the potential of the LSTM for hydrological modelling applications.</p

Twenty-three unsolved problems in hydrology (UPH) – a community perspective

This paper is the outcome of a community initiative to identify major unsolved scientific problems in hydrology motivated by a need for stronger harmonisation of research efforts. The procedure involved a public consultation through on-line media, followed by two workshops through which a large number of potential science questions were collated, prioritised, and synthesised. In spite of the diversity of the participants (230 scientists in total), the process revealed much about community priorities and the state of our science: a preference for continuity in research questions rather than radical departures or redirections from past and current work. Questions remain focussed on process-based understanding of hydrological variability and causality at all space and time scales. Increased attention to environmental change drives a new emphasis on understanding how change propagates across interfaces within the hydrological system and across disciplinary boundaries. In particular, the expansion of the human footprint raises a new set of questions related to human interactions with nature and water cycle feedbacks in the context of complex water management problems. We hope that this reflection and synthesis of the 23 unsolved problems in hydrology will help guide research efforts for some years to come

From runoff to rainfall: inverse rainfall–runoff modelling in a high temporal resolution

Rainfall exhibits a large spatio-temporal variability, especially in complex alpine terrain. Additionally, the density of the monitoring network in mountainous regions is low and measurements are subjected to major errors, which lead to significant uncertainties in areal rainfall estimates. In contrast, the most reliable hydrological information available refers to runoff, which in the presented work is used as input for an inverted HBV-type rainfall–runoff model that is embedded in a root finding algorithm. For every time step a rainfall value is determined, which results in a simulated runoff value closely matching the observed runoff. The inverse model is applied and tested to the Schliefau and Krems catchments, situated in the northern Austrian Alpine foothills. The correlations between inferred rainfall and station observations in the proximity of the catchments are of similar magnitude compared to the correlations between station observations and independent INCA (Integrated Nowcasting through Comprehensive Analysis) rainfall analyses provided by the Austrian Central Institute for Meteorology and Geodynamics (ZAMG). The cumulative precipitation sums also show similar dynamics. The application of the inverse model is a promising approach to obtain additional information on mean areal rainfall. This additional information is not solely limited to the simulated hourly data but also includes the aggregated daily rainfall rates, which show a significantly higher correlation to the observed values. Potential applications of the inverse model include gaining additional information on catchment rainfall for interpolation purposes, flood forecasting or the estimation of snowmelt contribution. The application is limited to (smaller) catchments, which can be represented with a lumped model setup, and to the estimation of liquid rainfall