Predicting soil moisture conditions across a heterogeneous boreal catchment using terrain indices Hydrology and Earth System Sciences DOI 10.5194/hess-26-4837-2022 19 December 2022 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. Read more
Bedrock depth influences spatial patterns of summer baseflow, temperature and flow disconnection for mountainous headwater streams Hydrology and Earth System Sciences DOI 10.5194/hess-26-3989-2022 4 November 2022 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. Read more
Technical note: Conservative storage of water vapour – practical in situ sampling of stable isotopes in tree stems Hydrology and Earth System Sciences DOI 10.5194/hess-26-3573-2022 12 October 2022 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. Read more
The Great Lakes Runoff Intercomparison Project Phase 4: the Great Lakes (GRIP-GL) Hydrology and Earth System Sciences DOI 10.5194/hess-26-3537-2022 10 October 2022 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. Read more
Morphological controls on surface runoff: an interpretation of steady-state energy patterns, maximum power states and dissipation regimes within a thermodynamic framework Hydrology and Earth System Sciences DOI 10.5194/hess-26-3125-2022 9 September 2022 In hydrology the formation of landform patterns is of special interest as changing forcings of the natural systems, such as climate or land use, will change these structures. In our study we developed a thermodynamic framework for surface runoff on hill slopes and highlight the differences of energy conversion patterns on two related spatial and temporal scales. The results indicate that surface runoff on hill slopes approaches a maximum power state. Read more
Agricultural intensification vs. climate change: what drives long-term changes in sediment load? Hydrology and Earth System Sciences DOI 10.5194/hess-26-3021-2022 7 September 2022 This study explored the quantitative contribution of agricultural intensification and climate change to the sediment load of a small agricultural watershed. Rather than a change in climatic conditions, changes in the land structure notably altered sediment concentrations under high-flow conditions, thereby contributing most to the increase in annual sediment loads. More consideration of land structure improvement is required when combating the transfer of soil from land to water. Read more
The influence of vegetation water dynamics on the ASCAT backscatter–incidence angle relationship in the Amazon Hydrology and Earth System Sciences DOI 10.5194/hess-26-2997-2022 2 September 2022 This study investigates spatial and temporal patterns in the incidence angle dependence of backscatter from the ASCAT C-band scatterometer and relates those to precipitation, humidity, and radiation data and GRACE equivalent water thickness in ecoregions in the Amazon. The results show that the ASCAT data record offers a unique perspective on vegetation water dynamics exhibiting sensitivity to moisture availability and demand and phenological change at interannual, seasonal, and diurnal scales. Read more
HESS Opinions: Chemical transport modeling in subsurface hydrological systems – space, time, and the “holy grail” of “upscaling” Hydrology and Earth System Sciences DOI 10.5194/hess-26-2161-2022 18 July 2022 Extensive efforts have focused on quantifying conservative chemical transport in geological formations. We assert that an explicit accounting of temporal information, under uncertainty, in addition to spatial information, is fundamental to an effective modelling formulation. We further assert that efforts to apply chemical transport equations at large length scales, based on measurements and model parameter values relevant to significantly smaller length scales, are an unattainable “holy grail”. Read more
Uncertainty estimation with deep learning for rainfall–runoff modeling Hydrology and Earth System Sciences DOI 10.5194/hess-26-1673-2022 24 June 2022 This contribution evaluates distributional run-off predictions from deep-learning-based approaches. We propose a benchmarking setup and establish four strong baselines. The results show that accurate, precise, and reliable uncertainty estimation can be achieved with deep learning. Read more
Towards hybrid modeling of the global hydrological cycle Hydrology and Earth System Sciences DOI 10.5194/hess-26-1579-2022 15 June 2022 We present a physics-aware machine learning model of the global hydrological cycle. As the model uses neural networks under the hood, the simulations of the water cycle are learned from data, and yet they are informed and constrained by physical knowledge. The simulated patterns lie within the range of existing hydrological models and are plausible. The hybrid modeling approach has the potential to tackle key environmental questions from a novel perspective. Read more