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Global biomass burning fuel consumption and emissions at 500m spatial resolution based on the Global Fire Emissions Database (GFED)

We present a global fire emission model based on the GFED model framework with a spatial resolution of 500 m. The higher resolution allowed for a more detailed representation of spatial heterogeneity in fuels and emissions. Specific modules were developed to model, for example, emissions from fire-related forest loss and belowground burning. Results from the 500 m model were compared to GFED4s, showing that global emissions were relatively similar but that spatial differences were substantial.

High-resolution drought simulations and comparison to soil moisture observations in Germany

In this paper, we deliver an evaluation of the second generation operational German drought monitor (www.ufz.de/duerremonitor) with a state-of-the-art compilation of observed soil moisture data from 40 locations and four different measurement methods in Germany. We show that the expressed stakeholder needs for higher resolution drought information at the one-kilometer scale can be met and that the agreement of simulated and observed soil moisture dynamics can be moderately improved.

Improving hydrologic models for predictions and process understanding using neural ODEs

Neural ODEs fuse physics-based models with deep learning: neural networks substitute terms in differential equations that represent the mechanistic structure of the system. The approach combines the flexibility of machine learning with physical constraints for inter- and extrapolation. We demonstrate that neural ODE models achieve state-of-the-art predictive performance while keeping full interpretability of model states and processes in hydrologic modelling over multiple catchments.

Predicting soil moisture conditions across a heterogeneous boreal catchment using terrain indices

Terrain indices constitute a good candidate for modelling the spatial variation of soil moisture conditions in many landscapes. In this study, we evaluate nine terrain indices on varying DEM resolution and user-defined thresholds with validation using an extensive field soil moisture class inventory. We demonstrate the importance of field validation for selecting the appropriate DEM resolution and user-defined thresholds and that failing to do so can result in ambiguous and incorrect results.

HORAYZON v1.2: an efficient and flexible ray-tracing algorithm to compute horizon and sky view factor

Terrain horizon and sky view factor are crucial quantities for many geoscientific applications; e.g. they are used to account for effects of terrain on surface radiation in climate and land surface models. Because typical terrain horizon algorithms are inefficient for high-resolution (< 30 m) elevation data, we developed a new algorithm based on a ray-tracing library. A comparison with two conventional methods revealed both its high performance and its accuracy for complex terrain.

Bedrock depth influences spatial patterns of summer baseflow, temperature and flow disconnection for mountainous headwater streams

The geologic structure of mountain watersheds may control how groundwater and streamwater exchange, influencing where streams dry. We measured bedrock depth at 191 locations along eight headwater streams paired with stream temperature records, baseflow separation and observations of channel dewatering. The data indicated a prevalence of shallow bedrock generally less than 3 m depth, and local variation in that depth can drive stream dewatering but also influence stream baseflow supply.

Root-mean-square error (RMSE) or mean absolute error (MAE): when to use them or not

The task of evaluating competing models is fundamental to science. Models are evaluated based on an objective function, the choice of which ultimately influences what scientists learn from their observations. The mean absolute error (MAE) and root-mean-squared error (RMSE) are two such functions. Both are widely used, yet there remains enduring confusion over their use. This article reviews the theoretical justification behind their usage, as well as alternatives for when they are not suitable.

The eWaterCycle platform for open and FAIR hydrological collaboration

With the eWaterCycle platform, we are providing the hydrological community with a platform to conduct their research that is fully compatible with the principles of both open science and FAIR science. The eWatercyle platform gives easy access to well-known hydrological models, big datasets and example experiments. Using eWaterCycle hydrologists can easily compare the results from different models, couple models and do more complex hydrological computational research.

Technical note: Conservative storage of water vapour – practical in situ sampling of stable isotopes in tree stems

We developed a method of sampling and storing water vapour for isotope analysis, allowing us to infer plant water uptake depth. Measurements can be made at high temporal and spatial resolution even in remote areas. We ensured that all necessary components are easily available, making this method cost efficient and simple to implement. We found our method to perform well in the lab and in the field, enabling it to become a tool for everyone aiming to resolve questions regarding the water cycle.

The Great Lakes Runoff Intercomparison Project Phase 4: the Great Lakes (GRIP-GL)

Model intercomparison studies are carried out to test various models and compare the quality of their outputs over the same domain. In this study, 13 diverse model setups using the same input data are evaluated over the Great Lakes region. Various model outputs – such as streamflow, evaporation, soil moisture, and amount of snow on the ground – are compared using standardized methods and metrics. The basin-wise model outputs and observations are made available through an interactive website.

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