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Scenario-neutral response surfaces illustrate the sensitivity of a simulated natural system, represented by a specific impact variable, to systematic perturbations of climatic parameters. This type of approach has recently been developed as an alternative to top-down approaches for the assessment of climate change impacts. A major limitation of this approach is the underrepresentation of changes in the temporal structure of the climate input data (i.e., the seasonal and day-to-day variability) since this is not altered by the perturbation. This paper presents a framework that aims to examine this limitation by perturbing both observed and projected climate data time series for a future period, which both serve as input into a hydrological model (the HBV model). The resulting multiple response surfaces are compared at a common domain, the standardized runoff response surface (SRRS). We apply this approach in a case study catchment in Norway to (i) analyze possible changes in mean and extreme runoff and (ii) quantify the influence of changes in the temporal structure represented by 17 different climate input sets using linear mixed-effect models. Results suggest that climate change induced increases in mean and peak flow runoff and only small changes in low flow. They further suggest that the effect of the different temporal structures of the climate input data considerably affects low flows and floods (at least 21% influence), while it is negligible for mean runoff.
Modelers can improve a model by addressing the causes for the model errors (data errors and structural errors). This leads to implementing model enhancements (MEs), for example, meteorological data based on more monitoring stations, improved calibration data, and/or modifications in process formulations. However, deciding on which MEs to implement remains a matter of expert knowledge. After implementing multiple MEs, any improvement in model performance is not easily attributed, especially when considering different objectives or aspects of this improvement (e.g., better dynamics vs. reduced bias). We present an approach for comparing the effect of multiple MEs based on real observations and considering multiple objectives (MMEMO). A stepwise selection approach and structured plots help to address the multidimensionality of the problem. Tailored analyses allow a differentiated view on the effect of MEs and their interactions. MMEMO is applied to a case study employing the mesoscale hydro-sedimentological model WASA-SED for the Mediterranean-mountainous Isabena catchment, northeast Spain. The investigated seven MEs show diverse effects: some MEs (e.g., rainfall data) cause improvements for most objectives, while other MEs (e.g., land use data) only affect a few objectives or even decrease model performance. Interaction of MEs was observed for roughly half of the MEs, confirming the need to address them in the analysis. Calibration and increasing the temporal resolution showed by far stronger impact than any of the other MEs. The proposed framework can be adopted in other studies to analyze the effect of MEs and, thus, facilitate the identification and implementation of the most promising MEs for comparable cases.
A comprehensive hydro-sedimentological dataset for the Isabena catchment, northeastern (NE) Spain, for the period 2010-2018 is presented to analyse water and sediment fluxes in a Mediterranean mesoscale catchment. The dataset includes rainfall data from 12 rain gauges distributed within the study area complemented by meteorological data of 12 official meteo-stations. It comprises discharge data derived from water stage measurements as well as suspended sediment concentrations (SSCs) at six gauging stations of the River Isabena and its sub-catchments. Soil spectroscopic data from 351 suspended sediment samples and 152 soil samples were collected to characterize sediment source regions and sediment properties via fingerprinting analyses. The Isabena catchment (445 km(2)) is located in the southern central Pyrenees ranging from 450 m to 2720 m a.s.l.; together with a pronounced topography, this leads to distinct temperature and precipitation gradients. The River Isabena shows marked discharge variations and high sediment yields causing severe siltation problems in the downstream Barasona Reservoir. The main sediment source is badland areas located on Eocene marls that are well connected to the river network. The dataset features a comprehensive set of variables in a high spatial and temporal resolution suitable for the advanced process understanding of water and sediment fluxes, their origin and connectivity and sediment budgeting and for the evaluation and further development of hydro-sedimentological models in Mediterranean mesoscale mountainous catchments.
Two lines of research are combined in this study: first, the development of tools for the temporal disaggregation of precipitation, and second, some newer results on the exponential scaling of heavy short-term precipitation with temperature, roughly following the Clausius-Clapeyron (CC) relation. Having no extra temperature dependence, the traditional disaggregation schemes are shown to lack the crucial CC-type temperature dependence. The authors introduce a proof-of-concept adjustment of an existing disaggregation tool, the multiplicative cascade model of Olsson, and show that, in principal, it is possible to include temperature dependence in the disaggregation step, resulting in a fairly realistic temperature dependence of the CC type. They conclude by outlining the main calibration steps necessary to develop a full-fledged CC disaggregation scheme and discuss possible applications.
Most hydrological studies rely on a model calibrated using discharge alone. However, judging the model reliability based on such calibration is problematic, as it does not guarantee the correct representation of internal hydrological processes. This study aims (a) to develop a comprehensive multi-objective calibration framework using remote sensing vegetation data and hydrological signatures (flow duration curve - FDC, and baseflow index) in addition to discharge, and (b) to apply this framework for calibration of the Soil and Water Assessment Tool (SWAT) in a typical Andean catchment. Overall, our calibration approach outperformed traditional discharge-based and FDC signature-based calibration strategies in terms of vegetation, streamflow, and flow partitioning simulation. New hydrological insights for the region are the following: baseflow is the main component of the streamflow sustaining the long dry-season flow, and pasture areas offer higher water yield and baseflow than other land-cover types. The proposed approach could be used in other data-scarce regions with complex topography.
A problem encountered by many distributed hydrological modelling studies is high simulation errors at interior gauges when the model is only globally calibrated at the outlet. We simulated river runoff in the Elbe River basin in central Europe (148 268 km(2)) with the semi-distributed eco-hydrological model SWIM (Soil and Water Integrated Model). While global parameter optimisation led to Nash-Sutcliffe efficiencies of 0.9 at the main outlet gauge, comparisons with measured runoff series at interior points revealed large deviations. Therefore, we compared three different strategies for deriving sub-basin evapotranspiration: (1) modelled by SWIM without any spatial calibration, (2) derived from remotely sensed surface temperatures, and (3) calculated from long-term precipitation and discharge data. The results show certain consistencies between the modelled and the remote sensing based evapotranspiration rates, but there seems to be no correlation between remote sensing and water balance based estimations. Subsequent analyses for single sub-basins identify amongst others input weather data and systematic error amplification in inter-gauge discharge calculations as sources of uncertainty. The results encourage careful utilisation of different data sources for enhancements in distributed hydrological modelling.
Hydrological response to earthquakes has long been observed, yet the mechanisms responsible still remain unclear and likely vary in space and time. This study explores the base flow response in small upland catchments of the Coastal Range of south-central Chile after the M-W 8.8 Maule earthquake of 27 February 2010. An initial decline in streamflow followed by an increase of up to 400% of the discharge measured immediately before the earthquake occurred, and diurnal streamflow oscillations intensified after the earthquake. Neither response time, nor time to maximum streamflow discharge showed any relationship with catchment topography or size, suggesting non-uniform release of water across the catchments. The fast response, unaffected stream water temperatures and a simple diffusion model point to the sandy saprolite as the source of the excess water. Base flow recession analysis reveals no evidence for substantial enhancement of lateral hydraulic conductivity in the saprolite after the earthquake. Seismic energy density reached similar to 170 J/m(3) for the main shock and similar to 0.9 J/m(3) for the aftershock, exceeding the threshold for liquefaction by undrained consolidation only during the main shock. Although increased hydraulic gradient due to ground acceleration-triggered, undrained consolidation is consistent with empirical magnitude-distance relationships for liquefaction, the lack of independent evidence for liquefaction means that enhanced vertical permeability (probably in combination with co-seismic near-surface dilatancy) cannot be excluded as a potential mechanism. Undrained consolidation may have released additional water from the saturated saprolite into the overlying soil, temporarily reducing water transfer to the creeks but enlarging the cross-section of the saturated zone, which in turn enhanced streamflow after establishment of a new hydraulic equilibrium. The enlarged saturated zone facilitated water uptake by roots and intensified evapotranspiration.
Knowledge of sediment sources is a prerequisite for sustainable management practices and may furthermore improve our understanding of water and sediment fluxes. Investigations have shown that a number of characteristic soil properties can be used as "fingerprints" to trace back the sources of river sediments. Spectral properties have recently been successfully used as such characteristics in fingerprinting studies. Despite being less labour-intensive than geochemical analyses, for example, spectroscopy allows measurements of small amounts of sediment material (> 60 mg), thus enabling inexpensive analyses even of intra-event variability. The focus of this study is on the examination of spectral properties of fluvial sediment samples to detect changes in source contributions, both between and within individual flood events.
Sediment samples from the following three different origins were collected in the Isabena catchment (445 km(2)) in the central Spanish Pyrenees: (1) soil samples from the main potential source areas, (2) stored fine sediment from the channel bed once each season in 2011 and (3) suspended sediment samples during four flood events in autumn 2011 and spring 2012 at the catchment outlet as well as at several subcatchment outlets. All samples were dried and measured for spectral properties in the laboratory using an ASD spectroradiometer. Colour parameters and physically based features (e.g. organic carbon, iron oxide and clay content) were calculated from the spectra. Principal component analyses (PCA) were applied to all three types of samples to determine natural clustering of samples, and a mixing model was applied to determine source contributions.
We found that fine sediment stored in the river bed seems to be mainly influenced by grain size and seasonal variability, while sampling location-and thus the effect of individual tributaries or subcatchments-seem to be of minor importance. Suspended sediment sources were found to vary between, as well as within, flood events; although badlands were always the major source. Forests and grasslands contributed little (< 10 %), and other sources (not further determinable) contributed up to 40 %. The analyses further suggested that sediment sources differ among the subcatchments and that subcatchments comprising relatively large proportions of badlands contributed most to the four flood events analyzed.
Spectral fingerprints provide a rapid and cost-efficient alternative to conventional fingerprint properties. However, a combination of spectral and conventional fingerprint properties could potentially permit discrimination of a larger number of source types.
Soil moisture estimation under low vegetation cover using a multi-angular polarimetric decomposition
(2013)
The estimation of volumetric soil moisture under low agricultural vegetation from fully polarimetric synthetic aperture radar (SAR) data at L-band using a multi-angular polarimetric decomposition is investigated. Radar polarimetry provides the framework to decompose the backscattered signal into different canonical scattering mechanisms referring to scattering contributions from the underlying soil and the vegetation cover. Multiangular observation diversity further increases the information space for soil moisture inversion enabling higher inversion rates and a stable inversion performance. The developed approach was applied on the multi-angular L-band data set acquired by German Aerospace Center's ESAR sensor as part of the OPAQUE campaign in 2008. The obtained results are compared against ground measurements collected by the OPAQUE team over a variety of vegetated agricultural fields. The validation of the estimated against ground measured soil moisture results in an root mean square error level of 6-8 vol.% including all test fields with a variety of crop types.
Shaking water out of soil
(2015)
Moderate to large earthquakes can increase the amount of water flowing in streams. Previous interpretations and models assume that the extra water originates in the saturated zone. Here we show that earthquakes may also release water from the unsaturated zone when the seismic energy is sufficient to overcome the threshold of soil water retention. Soil water may then be released into aquifers, increasing streamflow. After the M8.8 Maule, Chile, earthquake, the discharge in some headwater catchments of the Chilean coastal range increased, and the amount of extra water in the discharge was similar to the total amount of water available for release from the unsaturated zone. Assuming rapid recharge of this water to the water table, a groundwater flow model that accounts for evapotranspiration and water released from soils can reproduce the increase in discharge as well as the enhanced diurnal discharge variations observed after the earthquake. Thus the unsaturated zone may play a previously unappreciated, and potentially significant, role in shallow hydrological responses to earthquakes.
Deforestation is a prominent anthropogenic cause of erosive overland flow and slope instability, boosting rates of soil erosion and concomitant sediment flux. Conventional methods of gauging or estimating post-logging sediment flux often focus on annual timescales but overlook potentially important process response on shorter intervals immediately following timber harvest. We resolve such dynamics with non-parametric quantile regression forests (QRF) based on high-frequency (3 min) discharge measurements and sediment concentration data sampled every 30-60 min in similar-sized (similar to 0.1 km(2)) forested Chilean catchments that were logged during either the rainy or the dry season. The method of QRF builds on the random forest algorithm, and combines quantile regression with repeated random sub-sampling of both cases and predictors. The algorithm belongs to the family of decision-tree classifiers, which allow quantifying relevant predictors in high-dimensional parameter space. We find that, where no logging occurred, similar to 80% of the total sediment load was transported during extremely variable runoff events during only 5% of the monitoring period. In particular, dry-season logging dampened the relative role of these rare, extreme sediment-transport events by increasing load efficiency during more efficient moderate events. We show that QRFs outperform traditional sediment rating curves (SRCs) in terms of accurately simulating short-term dynamics of sediment flux, and conclude that QRF may reliably support forest management recommendations by providing robust simulations of post-logging response of water and sediment fluxes at high temporal resolution.
The semiarid northeast of Brazil is one of the most densely populated dryland regions in the world and recurrently affected by severe droughts. Thus, reliable seasonal forecasts of streamflow and reservoir storage are of high value for water managers. Such forecasts can be generated by applying either hydrological models representing underlying processes or statistical relationships exploiting correlations among meteorological and hydrological variables. This work evaluates and compares the performances of seasonal reservoir storage forecasts derived by a process-based hydrological model and a statistical approach.
Driven by observations, both models achieve similar simulation accuracies. In a hindcast experiment, however, the accuracy of estimating regional reservoir storages was considerably lower using the process-based hydrological model, whereas the resolution and reliability of drought event predictions were similar by both approaches. Further investigations regarding the deficiencies of the process-based model revealed a significant influence of antecedent wetness conditions and a higher sensitivity of model prediction performance to rainfall forecast quality.
Within the scope of this study, the statistical model proved to be the more straightforward approach for predictions of reservoir level and drought events at regionally and monthly aggregated scales. However, for forecasts at finer scales of space and time or for the investigation of underlying processes, the costly initialisation and application of a process-based model can be worthwhile. Furthermore, the application of innovative data products, such as remote sensing data, and operational model correction methods, like data assimilation, may allow for an enhanced exploitation of the advanced capabilities of process-based hydrological models.
Ecohydrology analyses the interactions of biotic and abiotic aspects of our ecosystems and landscapes. It is a highly diverse discipline in terms of its thematic and methodical research foci. This article gives an overview of current German ecohydrological research approaches within plant-animal-soil-systems, meso-scale catchments and their river networks, lake systems, coastal areas and tidal rivers. It discusses their relevant spatial and temporal process scales and different types of interactions and feedback dynamics between hydrological and biotic processes and patterns. The following topics are considered key challenges: innovative analysis of the interdisciplinary scale continuum, development of dynamically coupled model systems, integrated monitoring of coupled processes at the interface and transition from basic to applied ecohydrological science to develop sustainable water and land resource management strategies under regional and global change.
Timber harvesting by clear cutting is known to impose environmental impacts, including severe disturbance of the soil hydraulic properties which intensify the frequency and magnitude of surface runoff and soil erosion. However, it remains unanswered if harvest areas act as sources or sinks for runoff and soil erosion and whether such behavior operates in a steady state or evolves through time. For this purpose, 92 small-scale rainfall simulations of different intensities were carried out under pine plantation conditions and on two clear-cut harvest areas of different age. Nonparametrical Random Forest statistical models were set up to quantify the impact of environmental variables on the hydrological and erosion response. Regardless of the applied rainfall intensity, runoff always initiated first and yielded most under plantation cover. Counter to expectations, infiltration rates increased after logging activities. Once a threshold rainfall intensity of 20mm/h was exceeded, the younger harvest area started to act as a source for both runoff and erosion after connectivity was established, whereas it remained a sink under lower applied rainfall intensities. The results suggest that the impact of microtopography on surface runoff connectivity and water-repellent properties of the topsoil act as first-order controls for the hydrological and erosion processes in such environments. Fast rainfall-runoff response, sediment-discharge-hystereses, and enhanced postlogging groundwater recharge at catchment scale support our interpretation. At the end, we show the need to account for nonstationary hydrological and erosional behavior of harvest areas, a fact previously unappreciated in predictive models.
A general increase in precipitation has been observed in Germany in the last century, and potential changes in flood generation and intensity are now at the focus of interest. The aim of the paper is twofold: a) to project the future flood conditions in Germany accounting for various river regimes (from pluvial to nival-pluvial regimes) and under different climate scenarios (the high, A2, low, B1, and medium, A1B, emission scenarios) and b) to investigate sources of uncertainty generated by climate input data and regional climate models. Data of two dynamical Regional Climate Models (RCMs), REMO (REgional Model) and CCLM (Cosmo-Climate Local Model), and one statistical-empirical RCM, Wettreg (Wetterlagenbasierte Regionalisierungsmethode: weather-type based regionalization method), were applied to drive the eco-hydrological model SWIM (Soil and Water Integrated Model), which was previously validated for 15 gauges in Germany. At most of the gauges, the 95 and 99 percentiles of the simulated discharge using SWIM with observed climate data had a good agreement with the observed discharge for 1961-2000 (deviation within +/- 10 %). However, the simulated discharge had a bias when using RCM climate as input for the same period. Generalized Extreme Value (GEV) distributions were fitted to the annual maximum series of river runoff for each realization for the control and scenario periods, and the changes in flood generation over the whole simulation time were analyzed. The 50-year flood values estimated for two scenario periods (2021-2060, 2061-2100) were compared to the ones derived from the control period using the same climate models. The results driven by the statistical-empirical model show a declining trend in the flood level for most rivers, and under all climate scenarios. The simulations driven by dynamical models give various change directions depending on region, scenario and time period. The uncertainty in estimating high flows and, in particular, extreme floods remains high, due to differences in regional climate models, emission scenarios and multi-realizations generated by RCMs.
To support scientifically sound water management in dryland environments a modelling system has been developed for the quantitative assessment of water and sediment fluxes in catchments, transport in the river system, and retention in reservoirs. The spatial scale of interest is the mesoscale because this is the scale most relevant for management of water and land resources.
This modelling system comprises process-oriented hydrological components tailored for dryland characteristics coupled with components comprising hillslope erosion, sediment transport and reservoir deposition processes. The spatial discretization is hierarchically designed according to a multi-scale concept to account for particular relevant process scales. The non-linear and partly intermittent run-off generation and sediment dynamics are dealt with by accounting for connectivity phenomena at the intersections of landscape compartments. The modelling system has been developed by means of data from nested research catchments in NE-Spain and in NE-Brazil.
In the semi-arid NE of Brazil sediment retention along the topography is the main process for sediment retention at all scales, i.e. the sediment delivery is transport limited. This kind of deposition retains roughly 50 to 60 % of eroded sediment, maintaining a similar deposition proportion in all spatial scales investigated. On the other hand, the sediment retained in reservoirs is clearly related to the scale, increasing with catchment area. With increasing area, there are more reservoirs, increasing the possibility of deposition. Furthermore, the area increase also promotes an increase in flow volume, favouring the construction of larger reservoirs, which generally overflow less frequently and retain higher sediment fractions. The second example comprises a highly dynamic Mediterranean catchment in NE-Spain with nested sub-catchments and reveals the full dynamics of hydrological, erosion and deposition features. The run-off modelling performed well with only some overestimation during low-flow periods due to the neglect of water losses along the river. The simulated peaks in sediment flux are reproduced well, while low-flow sediment transport is less well captured, due to the disregard of sediment remobilization in the riverbed during low flow.
This combined observation and modelling study deepened the understanding of hydro-sedimentological systems characterized by flashy run-off generation and by erosion and sediment transport pulses through the different landscape compartments. The connectivity between the different landscape compartments plays a very relevant role, regarding both the total mass of water and sediment transport and the transport time through the catchment.
Subsurface stormflow is thought to occur mainly in humid environments with steep terrains. However, in semi-arid areas, preferential flow through macropores can also result in a significant contribution of subsurface stormflow to catchment runoff for varying catchment conditions. Most hydrological models neglect this important subsurface preferential flow. Here, we use the process-oriented hydrological model Hillflow-3D, which includes a macropore flow approach, to simulate rainfall-runoff in the semi-arid Parapunos catchment in Spain, where macropore flow was observed in previous research. The model was extended for this study to account for sorptivity under very dry soil conditions. The results of the model simulations with and without macropore flow are compared. Both model versions give reasonable results for average rainfall situations, although the approach with the macropore concept provides slightly better results. The model results for scenarios of extreme rainfall events (>13.3mm30min(-1)) however show large differences between the versions with and without macropores. These model results compared with measured rainfall-runoff data show that the model with the macropore concept is better. Our conclusion is that preferential flow is important in controlling surface runoff in case of specific, high intensity rainfall events. Therefore, preferential flow processes must be included in hydrological models where we know that preferential flow occurs. Hydrological process models with a less detailed process description may fit observed average events reasonably well but can result in erroneous predictions for more extreme events. Copyright (c) 2013 John Wiley & Sons, Ltd.
Hydrological models are commonly used to perform real-time runoff forecasting for flood warning. Their application requires catchment characteristics and precipitation series that are not always available. An alternative approach is nonparametric modelling based only on runoff series. However, the following questions arise: Can nonparametric models show reliable forecasting? Can they perform as reliably as hydrological models? We performed probabilistic forecasting one, two and three hours ahead for a runoff series, with the aim of ascribing a probability density function to predicted discharge using time series analysis based on stochastic dynamics theory. The derived dynamic terms were compared to a hydrological model, LARSIM. Our procedure was able to forecast within 95% confidence interval 1-, 2- and 3-h ahead discharge probability functions with about 1.40 m(3)/s of range and relative errors (%) in the range [-30; 30]. The LARSIM model and the best nonparametric approaches gave similar results, but the range of relative errors was larger for the nonparametric approaches.
Flood generation in mountainous headwater catchments is governed by rainfall intensities, by the spatial distribution of rainfall and by the state of the catchment prior to the rainfall, e. g. by the spatial pattern of the soil moisture, groundwater conditions and possibly snow. The work presented here explores the limits and potentials of measuring soil moisture with different methods and in different scales and their potential use for flood simulation. These measurements were obtained in 2007 and 2008 within a comprehensive multi-scale experiment in the Weisseritz headwater catchment in the Ore-Mountains, Germany. The following technologies have been applied jointly thermogravimetric method, frequency domain reflectometry (FDR) sensors, spatial time domain reflectometry (STDR) cluster, ground-penetrating radar (GPR), airborne polarimetric synthetic aperture radar (polarimetric SAR) and advanced synthetic aperture radar (ASAR) based on the satellite Envisat. We present exemplary soil measurement results, with spatial scales ranging from point scale, via hillslope and field scale, to the catchment scale. Only the spatial TDR cluster was able to record continuous data. The other methods are limited to the date of over-flights (airplane and satellite) or measurement campaigns on the ground. For possible use in flood simulation, the observation of soil moisture at multiple scales has to be combined with suitable hydrological modelling, using the hydrological model WaSiM-ETH. Therefore, several simulation experiments have been conducted in order to test both the usability of the recorded soil moisture data and the suitability of a distributed hydrological model to make use of this information. The measurement results show that airborne-based and satellite-based systems in particular provide information on the near-surface spatial distribution. However, there are still a variety of limitations, such as the need for parallel ground measurements (Envisat ASAR), uncertainties in polarimetric decomposition techniques (polarimetric SAR), very limited information from remote sensing methods about vegetated surfaces and the non-availability of continuous measurements. The model experiments showed the importance of soil moisture as an initial condition for physically based flood modelling. However, the observed moisture data reflect the surface or near-surface soil moisture only. Hence, only saturated overland flow might be related to these data. Other flood generation processes influenced by catchment wetness in the subsurface such as subsurface storm flow or quick groundwater drainage cannot be assessed by these data. One has to acknowledge that, in spite of innovative measuring techniques on all spatial scales, soil moisture data for entire vegetated catchments are still today not operationally available. Therefore, observations of soil moisture should primarily be used to improve the quality of continuous, distributed hydrological catchment models that simulate the spatial distribution of moisture internally. Thus, when and where soil moisture data are available, they should be compared with their simulated equivalents in order to improve the parameter estimates and possibly the structure of the hydrological model.
This study presents an application of an innovative sampling strategy to assess soil moisture dynamics in a headwater of the Weisseritz in the German eastern Ore Mountains. A grassland site and a forested site were instrumented with two Spatial TDR clusters (STDR) that consist of 39 and 32 coated TDR probes of 60 cm length. Distributed time series of vertically averaged soil moisture data from both sites/ensembles were analyzed by statistical and geostatistical methods. Spatial variability and the spatial mean at the forested site were larger than at the grassland site. Furthermore, clustering of TDR probes in combination with long-term monitoring allowed identification of average spatial covariance structures at the small field scale for different wetness states. The correlation length of soil water content as well as the sill to nugget ratio at the grassland site increased with increasing average wetness and but, in contrast, were constant at the forested site. As soil properties at both the forested and grassland sites are extremely variable, this suggests that the correlation structure at the forested site is dominated by the pattern of throughfall and interception. We also found a very strong correlation between antecedent soil moisture at the forested site and runoff coefficients of rainfall-runoff events observed at gauge Rehefeld. Antecedent soil moisture at the forest site explains 92% of the variability in the runoff coefficients. By combining these results with a recession analysis we derived a first conceptual model of the dominant runoff mechanisms operating in this catchment. Finally, we employed a physically based hydrological model to shed light on the controls of soil- and plant morphological parameters on soil average soil moisture at the forested site and the grassland site, respectively. A homogeneous soil setup allowed, after fine tuning of plant morphological parameters, most of the time unbiased predictions of the observed average soil conditions observed at both field sites. We conclude that the proposed sampling strategy of clustering TDR probes is suitable to assess unbiased average soil moisture dynamics in critical functional units, in this case the forested site, which is a much better predictor for event scale runoff formation than pre-event discharge. Long term monitoring of such critical landscape elements could maybe yield valuable information for flood warning in headwaters. We thus think that STDR provides a good intersect of the advantages of permanent sampling and spatially highly resolved soil moisture sampling using mobile rods.