Automatic Regionalization of Model Parameters for Hydrological Models.

Parameter estimation is one of the most challenging tasks in large-scale distributed modeling, because of the high dimensionality of the parameter space. Relating model parameters to catchment/landscape characteristics reduces the number of parameters, enhances physical realism, and allows the transfer of hydrological model parameters in time and space. This study presents the first large-scale application of automatic parameter transfer function (TF) estimation for a complex hydrological model.

Learning from mistakes-Assessing the performance and uncertainty in process-based models.

Typical applications of process- or physically-based models aim to gain a better process understanding or provide the basis for a decision-making process. To adequately represent the physical system, models should include all essential processes. However, model errors can still occur.

Reply to STOTEN 802 (2022) 149713: The fallacy in the use of the "best-fit" solution in hydrologic modeling.

In this reply we respond to the short discussion contribution by Abbaspour (2022) in which a fallacy in the use of "best-fit" model solutions to be employed in hydrologic modeling studies is illustrated. Abbaspour (2022) advised to perform stochastic model calibration and proposed to employ the R- and P-Factor statistics for a model evaluation together with suggested thresholds for a model to be acceptable. In a minimal working example we followed the proposed stochastic approach for model evaluation and show that the proposed R- and P-Factor metrics and their thresholds accept implausible model ensemble simulations which would have been rejected in an individual assessment with the NSE metric.

The coupled socio-ecohydrological evolution of river systems: Towards an integrative perspective of river systems in the 21st century.

River systems have undergone a massive transformation since the Anthropocene. The natural properties of river systems have been drastically altered and reshaped, limiting the use of management frameworks, their scientific knowledge base and their ability to provide adequate solutions for current problems and those of the future, such as climate change, biodiversity crisis and increased demands for water resources. To address these challenges, a socioecologically driven research agenda for river systems that complements current approaches is needed and proposed.

Storm event impacts on in-stream nitrate concentration and discharge dynamics: A comparison of high resolution in-situ measured data with model simulations.

The relationship between nitrogen and discharge (N-Q) in a stream can be captured with high frequency nitrate nitrogen (NO-N) samplers. In Austria, the Raab catchment (998 km) has high frequency NO-N data measured with a spectrometer probe. This study evaluated if the widely-used and typically calibrated eco-hydrological model Soil and Water Assessment Tool (SWAT) can reproduce the hysteresis loop direction and the dilution or accretion effects of NO-N dynamics during storm events in this agricultural catchment.

Towards Improved Field Application of Using Distributed Temperature Sensing for Soil Moisture Estimation: A Laboratory Experiment.

The "dual probe heat pulse" (DPHP) method using actively heated fiber optic (AHFO) cables combined with distributed temperate sensing (DTS) technology has been developed for monitoring thermal properties and soil water content at the field scale. Field scale application, however, requires the use of robust and thicker fiber optic cables, corroborating the assumption of an infinite thin heat source in the evaluation process. We therefore included a semi-analytical solution of the heat transport equation into the evaluation procedure in order to consider the finite thermal properties of the heating cable without a calibration procedure to estimate effective thermal properties of the soil.

Estimation of evapotranspiration of temperate grassland based on high-resolution thermal and visible range imagery from unmanned aerial systems.

Spatially distributed high-resolution data of land surface temperature (LST) and evapotranspiration (ET) are important information for crop water management and other applications in the agricultural sector. While satellite data can provide LST high-resolution data of 100 m, the current development of unmanned aerial systems (UAS) and affordable low-weight thermal cameras allows LST and subsequent ET to be derived at resolutions down to centimetre scale. In this study, UAS-based images in the thermal infrared (TIR) and visible spectral range were collected over a managed temperate grassland in July 2016 at the Terrestrial Environmental Observatories Networks TERENO preAlpine observatory site at Fendt, Germany.

Feasibility of Locating Leakages in Sewage Pressure Pipes Using the Distributed Temperature Sensing Technology.

The cost effective maintenance of underwater pressure pipes for sewage disposal in Austria requires the detection and localization of leakages. Extrusion of wastewater in lakes can heavily influence the water and bathing quality of surrounding waters. The Distributed Temperature Sensing (DTS) technology is a widely used technique for oil and gas pipeline leakage detection.

Estimating spatially distributed turbulent heat fluxes from high-resolution thermal imagery acquired with a UAV system.

In this study, high-resolution thermal imagery acquired with a small unmanned aerial vehicle (UAV) is used to map evapotranspiration (ET) at a grassland site in Luxembourg. The land surface temperature (LST) information from the thermal imagery is the key input to a one-source and two-source energy balance model. While the one-source model treats the surface as a single uniform layer, the two-source model partitions the surface temperature and fluxes into soil and vegetation components.

Monitoring and assessing of landscape heterogeneity at different scales.

In numerous studies, spatial and spectral aggregations of pixel information using average values from imaging spectrometer data are suggested to derive spectral indices and the subsequent vegetation parameters that are derived from these. Currently, there are very few empirical studies that use hyperspectral data, to support the hypothesis for deriving land surface variables from different spectral and spatial scales. In the study at hand, for the first time ever, investigations were carried out on fundamental scaling issues using specific experimental test flights with a hyperspectral sensor to investigate how vegetation patterns change as an effect of (1) different spatial resolutions, (2) different spectral resolutions, (3) different spatial and spectral resolutions as well as (4) different spatial and spectral resolutions of originally recorded hyperspectral image data compared to spatial and spectral up- and downscaled image data.