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Artificial tracers are often used for quantitative estimates of solute transport properties in rivers. However, single‐injection tracer tests give insights in transport characteristics limited to the ecohydrological conditions at the testing time. Series of time‐consuming and laborious tracer tests would be required to properly capture seasonal changes. The present study uses intrinsic diurnal fluctuations of electrical conductivity (EC) caused by discharge of treated wastewater as a tracer to evaluate solute transport processes along a 4.7‐km reach of the River Erpe, Germany. By reproducing the fluctuations recorded along the river using the solute transport model one‐dimensional transport with inflow and storage, this study investigated the long‐term dynamics in solute transport properties. Individual 48‐hr curves of EC were used in the steady state configuration of the model to gain 48‐hr‐integrated estimates of selected transport parameters. Using a sliding window approach in 1‐hr steps along the 2,270‐hr time series of EC the temporal variability of solute transport between April and June 2016 was assessed. To test the identifiability of parameters using the proposed method, sensitivity analyses and a breakthrough curve analysis of selected 48‐hr windows were implemented. With time advancing into the summer, a significant rising trend (Mann‐Kendall test p‐value < 0.05) of the cross sectional area of the channel was observed and attributed to the growth of macrophytes and a significant slightly decreasing trend for the storage rate was found. The presented method is of high value for river management, as promoting transient storage enhances biogeochemical cycling and benefits water quality. Key Points: Electrical conductivity fluctuations caused by discharge of treated wastewater into a river contain information about solute transportA solute transport model applied to recordings of solute fluctuations can be used to obtain long‐term time series of transport metricsDifferences in transport metrics in adjacent river sections were found; Mowing of macrophytes may increase the transient storage area [ABSTRACT FROM AUTHOR]

A new method is presented to efficiently estimate daily groundwater level time series at unmonitored sites by linking groundwater dynamics to local hydrogeological system controls. The proposed approach is based on the concept of comparative regional analysis, an approach widely used in surface water hydrology, but uncommon in hydrogeology. Using physiographic and climatic site descriptors, the method utilizes regression analysis to estimate cumulative frequency distributions of groundwater levels (groundwater head duration curves, HDC) at unmonitored locations. The HDC is then used to construct a groundwater hydrograph using time series from distance‐weighted neighboring monitored (donor) locations. For estimating times series at unmonitored sites, in essence, spatio‐temporal interpolation, stepwise multiple linear regression (MLR), extreme gradient boosting (XGB), and nearest neighbors are compared. The methods were applied to 10‐year daily groundwater level time series at 157 sites in unconfined alluvial aquifers in Southern Germany. Models of HDCs were physically plausible and showed that physiographic and climatic controls on groundwater level fluctuations are nonlinear and dynamic, varying in significance from "wet" to "dry" aquifer conditions. XGB yielded a significantly higher predictive skill than nearest neighbor and MLR. However, donor site selection is of key importance. The study presents a novel approach for regionalization and infilling of groundwater level time series that also aids conceptual understanding of controls on groundwater dynamics, both central tasks for water resources managers. Key Points: Daily groundwater levels at unmonitored sites are estimated through transfer of head duration curves based on the similarity of site characteristics at monitored sitesNonlinearity of controls on groundwater levels favors using of Machine Learning (e.g., regression trees) over multiple linear regression for predictionThe dynamic nature of controls on groundwater levels can be disentangled, which is central for studies of recharge seasonality, droughts, and floods [ABSTRACT FROM AUTHOR]

Groundwater abstraction and drainage are considered to be the main drivers for the salinization of low‐lying coastal groundwater systems, while the role of past boundary conditions is less clear. In this study, 3‐D paleo‐hydrogeological variable‐density groundwater flow and salt transport modeling ("paleo‐modeling") is applied to reconstruct the evolution of groundwater salinities during the Holocene, that is, the last 9,000 years, in Northwestern Germany. Novel aspects of this study include the consideration of highly resolved time‐variant boundary conditions in a 3‐D paleo‐modeling framework, for example, sea‐level rise, surface elevation and coastline changes, development of drainage networks and groundwater abstraction, as well as the quantification of isolated processes impacting salinization. Results show that salinization was a function of sea‐level rise from 9000 BP until 1300 CE. The creation of the dike line ∼1300 CE set the starting point for increasing anthropogenic control of the hydro(geo)logical system: changes in surface elevation and drainage of low‐lying marshes have become main drivers for salinization after 1600 CE when peat was artificially degenerated. Moreover, changes in the dike line caused by storm floods impacted the salinities. Model results for 2020 CE match well with present‐day salinity observations. Yet, salinization will continue in the future, as the hydro(geo)logical system has not reached an equilibrium. The presented paleo‐modeling framework can be viewed as a blueprint for similar low‐lying coastal groundwater systems, influenced by marine transgression and human development. Thereby, it enables the reconstruction of meaningful present‐day salinity distributions, serving as a vital basis for modeling future groundwater systems in a changing climate. Key Points: Salinization of a low‐lying coastal groundwater system during the Holocene, that is, 9000 BP until present‐day, was investigatedSea‐level rise and the evolution of paleogeography were major controls on groundwater salinization during most of the timeSurface elevation changes due to land cultivation, as well as the development of drainage networks, control salinization since ∼1600 CE [ABSTRACT FROM AUTHOR]

Large‐scale groundwater models are required to estimate groundwater availability and to inform water management strategies on the national scale. However, parameterization of large‐scale groundwater models covering areas of major river basins and more is challenging due to the lack of observational data and the mismatch between the scales of modeling and measurements. In this work, we propose to bridge the scale gap and derive regional hydraulic parameters by spectral analysis of groundwater level fluctuations. We hypothesize that specific locations in aquifers can reveal regional parameters of the hydraulic system. We first generate ensembles of synthetic but realistic aquifers which systematically differ in complexity. Applying Liang and Zhang's (2013), https://doi.org/10.1016/j.jhydrol.2012.11.044, semi‐analytical solution for the spectrum of hydraulic head time series, we identify for each ensemble member and at different locations representative aquifer parameters. Next, we extend our study to investigate the use of spectral analysis in more complex numerical models and in real settings. Our analyses indicate that the variance of inferred effective transmissivity and storativity values for stochastic aquifer ensembles is small for observation points which are far away from the Dirichlet boundary. Moreover, the head time series has to cover a period which is roughly 10 times as long as the characteristic time of the aquifer. In deterministic aquifer models we infer equivalent, regionally valid parameters. A sensitivity analysis further reveals that as long as the aquifer length and the position of the groundwater measurement location is roughly known, the parameters can be robustly estimated. Plain Language Summary: We build large‐scale (regional) computer models of the subsurface flow conditions in order to quantify the long‐term shift in groundwater storage and response on the national level under changing climatic conditions and increasing human water demands. These models must be fed with hydrogeological parameters obtained from subsurface observation wells, drilling logs, and hydraulic tests in conjunction with (hydro)geological and geostatistical methods. In some regions these wells are sparsely distributed and derived parameters are representative only for small areas. We hypothesize that groundwater level records can reveal regional aquifer information when analyzed in the spectral domain. In order to bridge that scale gap and because groundwater level time series are generally available, we propose to infer regional parameters by analyzing the frequency content (spectrum) of long groundwater level time series. The required parameters were determined using mathematical formulations of the theoretical spectrum for simplified settings. We tested the methodology in computer models with limited complexity and found that the groundwater level time series indeed contain regional information if the time of observation is sufficiently long. Lastly, we apply the spectral analysis to real groundwater data to test the capability of the method to infer regional aquifer parameters in real aquifers. Key Points: We successfully tested the spectral analysis of groundwater level fluctuations in numerical models and obtained regional aquifer parametersIn a sensitivity analysis of the spectral analysis using field data, the storativity and the response times could be robustly estimatedThe application of the suggested methodology to the field data from a catchment in central Germany produced plausible results [ABSTRACT FROM AUTHOR]

Improving nitrogen (N) status in European water bodies is a pressing issue. N levels depend not only on current but also past N inputs to the landscape, that have accumulated through time in legacy stores (e.g., soil, groundwater). Catchment‐scale N models, that are commonly used to investigate in‐stream N levels, rarely examine the magnitude and dynamics of legacy components. This study aims to gain a better understanding of the long‐term fate of the N inputs and its uncertainties, using a legacy‐driven N model (ELEMeNT) in Germany's largest national river basin (Weser; 38,450 km2) over the period 1960–2015. We estimate the nine model parameters based on a progressive constraining strategy, to assess the value of different observational data sets. We demonstrate that beyond in‐stream N loading, soil N content and in‐stream N concentration allow to reduce the equifinality in model parameterizations. We find that more than 50% of the N surplus denitrifies (1480–2210 kg ha−1) and the stream export amounts to around 18% (410–640 kg ha−1), leaving behind as much as around 230–780 kg ha−1 of N in the (soil) source zone and 10–105 kg ha−1 in the subsurface. A sensitivity analysis reveals the importance of different factors affecting the residual uncertainties in simulated N legacies, namely hydrologic travel time, denitrification rates, a coefficient characterizing the protection of organic N in source zone and N surplus input. Our study calls for proper consideration of uncertainties in N legacy characterization, and discusses possible avenues to further reduce the equifinality in water quality modeling. Plain Language Summary: Lowering nitrogen (N) amounts in European surface waters is a pressing issue. N levels largely result from fertilizer application in agricultural areas, and deposition of atmospheric N coming from fossil fuel combustion. These N inputs to the landscape can accumulate below the ground surface in so‐called legacy stores (including the soil and aquifer), from which they can be released progressively through time. Therefore, N levels depend not only on the recent N inputs, but also on their history. Our modeling study aims to improve our understanding of the long‐term fate of the N inputs and its uncertainties in Germany's largest national river basin (Weser) over the period 1960–2015. It suggests that more than 50% of the N inputs to land is lost to the atmosphere (denitrification, 1480–2210 kg ha−1) and the stream export amounts to around 18% (410–640 kg ha−1), leaving behind as much as around 16% (264–820 kg ha−1) in the landscape (legacy). However, the uncertainties in these estimates remain large, partly due to a lack of observational data on internal (legacy) components and uncertainties in N inputs. Overall, our study calls for proper consideration of uncertainties in N legacy characterization, and discusses possible avenues to further reduce them. Key Points: We use a parsimonious model to examine the long‐term (1960–2015) fate of nitrogen inputs to the landscape including the legacy buildupMore than 50% of the nitrogen surplus denitrifies and around 491 kg ha−1 accumulates in legacy stores in Germany's largest river basinHydrologic travel time and denitrification rates largely contribute to the residual uncertainty in the simulated nitrogen legacies [ABSTRACT FROM AUTHOR]

Debris floods most commonly occur in steep mountain channels and on their alluvial fans but can also occur on small gravel bed rivers with watershed areas up to several hundred square kilometers. This became obvious during July 2021 and November 2021 debris floods in northwestern Germany and southwestern British Columbia, Canada. We subdivide debris floods into three categories: those triggered by a supra‐critical bed shear stress ratio, form by dilution from debris flows, and resulting from outbreak floods. This trichotomy challenges traditional hazard assessments; debris floods classify as a fluvial process, yet their destructive mechanics are difficult to characterize. Key hazards interact spatially and temporally in debris floods: inundation, scour, sediment transport and deposition and bank erosion. We describe approaches to quantify hazards by systematically accounting for these processes and introduce a novel approach for hazard quantification and mapping in which flow velocity, depth, and presumed fluid density are combined with the annual event frequency for all event scenarios. The derivative "composite hazard maps" are equally valid for debris floods and debris flows. Isolines of bank erosion based on a probabilistic analysis of a physically based model are added to capture the potential of debris floods to abruptly widen their channels. Substantial challenges remain, specifically in the reliable prediction of sediment transport and progressive bank erosion. Our intention is to homogenize debris‐flood hazard assessments and especially mapping methodologies. This could allow for systematic integration with landuse policies assuring consistency among approaches executed by practitioners acting in this field. Plain Language Summary: Debris floods can be highly destructive. We provide a comprehensive methodology to assess debris‐flood hazards with the goal of developing a consistent approach, allowing for better input to quantitative risk assessments and the selection of appropriate mitigation measures. We suggest a method for debris‐flood mapping that combines the recurrence interval of various debris flood scenarios with their impact force and bank erosion to support a multi‐faceted hazard assessment. Key Points: Debris flood assessments are under‐researched and poorly constrained. This contribution attempts to rectify that situationBeyond debris flood inundation, this contribution outlines methods for sediment bulking, sediment transport, and bank erosionA new method for hazard mapping is proposed based on impact force and event frequency. This allows direct comparison of study sites [ABSTRACT FROM AUTHOR]

The dynamic relationships between water flux and storage, together with the associated water ages and speed of hydrological responses (as proxies for velocity and celerity respectively) are fundamental to understanding how catchments react to hydroclimate perturbations, such as floods and droughts. Using results from a calibrated, tracer‐aided ecohydrological model (EcH2O‐iso) we analyzed the dynamics of storage‐flux‐age‐response time (RT) interactions at scales that resolve the internal heterogeneity of these non‐stationary relationships. EcH2O‐iso has previously shown an adequate representation of ecohydrological flux partitioning and storage dynamics (celerity), and water ages (velocity) over 11‐year at Demnitzer Millcreek catchment (DMC, 66 km2), a drought‐sensitive, lowland catchment in Germany. The 11‐year period had marked hydroclimatic contrasts facilitating the evaluation of flux‐storage‐age‐RT dynamics under different wetness anomalies. Our results show that the spatio–temporal variability of soil moisture and ecohydrological partitioning dynamics reflect both land use (especially forest cover) and distinct soil units (i.e., brown earth vs. podzolic soils). Spatial differences in RTs of storage were driven by rapid soil evaporation and transpiration responses to rainfall, which revealed a divergence of transpiration ages from RTs. RTs of groundwater and streamflow were fast (days), but mediation by soil water storage dynamics caused marked separation from water ages (years‐decades) of deeper flow paths. Analysis of RTs and ages revealed a degradation of process representation with coarsening model spatial resolution. This study uses novel analysis of the spatio‐temporal interactions of flux‐storage‐age‐RT from a model to understand the sensitivity and resilience of catchment functionality to hydroclimatic perturbations. Key Points: Spatio‐temporal variability of soil storage and ecohydrological partitioning was modulated by vegetation characteristicsTranspiration, groundwater, and streamflow response times were distinct from water ages, with spatial differences driven by vegetation unitsLower model resolution reduced spatial variability and increased the difference of catchment response and water age of fluxes and storages [ABSTRACT FROM AUTHOR]

The reliability of the machine learning model prediction for a given input can be assessed by comparing it against the actual output. However, in hydrological studies, machine learning models are often adopted to predict future or unknown events, where the actual outputs are unavailable. The prediction accuracy of a model, which measures its average performance across an observed data set, may not be relevant for a specific input. This study presents a method based on metamorphic testing (MT), adopted from software engineering, to assess the prediction reliability where the actual outputs are unknown. In this method, the predictions for a group of related inputs are considered consistent only if the input and output follow certain relations that are deduced from the properties of the system being modeled. For instance, the predicted runoff volume should increase in a rainfall‐runoff model as the rainfall magnitude of an input increases. In this study, the MT‐based method was applied to assess the predictions made by various machine learning models that were trained to predict the magnitude of flood events in Germany. Surprisingly, the prediction accuracy of a model and its ability to provide consistent predictions were found to be uncorrelated. This study further investigated the factors influencing the assessment result of a given input, such as its similarity to observed data. Overall, this research shows that MT is an effective and simple method for detecting inconsistent model predictions and is recommended when a model is employed to making predictions under new conditions. Plain Language Summary: In hydrological studies, machine learning models are often built to learn the statistical correlations between random variables of interests. In these models, predictions are made based on learned statistical correlations, and the involved hydrological processes are not explicitly considered. Thus, it is difficult to examine the mechanisms that lead to the prediction made for a given input. Metamorphic testing (MT) is a technique to assess the correctness of the predicted output for a given input that does not require knowledge of the actual output. In MT, the relation among a group of related inputs and the corresponding outputs is examined, and the predictions are considered consistent if this relation satisfies certain criteria that are related to the properties of the system being modeled. This study applied MT to assess various machine learning models that predicted the magnitude of flood events in Germany. MT was found to be very effective in detecting inconsistent predictions. Many models failed to capture the positive correlation between precipitation magnitude and the magnitude of flood events, even for those with high prediction accuracies. The MT‐based method presented in this study is useful for assessing the reliability of model predictions, especially when the actual outcomes are unknown. Key Points: This study presents a metamorphic testing‐based method to assess the reliability of model predictions when the actual outcomes are unknownSpecific tests can be designed using hydrological knowledge to check if a model preserves certain properties of the system being modeledSome models with high prediction accuracies can often respond incorrectly when input changes and thereby produce inconsistent predictions [ABSTRACT FROM AUTHOR]