The dissertation thesis pursues two major research goals. First, the uncertainty and sensitivity in parameteris-ing and evaluatparameteris-ing empirical SDR models are assessed on the regional scale and beyond. For this purpose, SDR models in combination with the USLE are applied to predict annual to multi-year average sediment yields of river catchments. Second, the applicability of this modelling framework is evaluated in the European context and promising steps to improve the model performance and to overcome some restrictions are discussed. The motivation is to complement studies on validating European soil loss maps (van Rompaey et al. 2003b), analys-ing the relationships between soil loss and sediment yield in European catchments (Vanmaercke et al. 2012a), predicting the spatio-temporal variability of SDR and sediment yield in European regions (e.g., de Vente et al.
2007; Diodato and Grauso 2009), and assessing the uncertainty and sensitivity of soil erosion models and topo-graphic indices (e.g., Gündra et al. 1995; Wolock and McCabe 2000; Yong et al. 2009; Tetzlaff et al. 2013).
The goal is to provide new insights into i) the relevance of topographic uncertainties and the uncertainty in critical yields of total suspended solids for the application, calibration and validation of empirical erosion models, ii) the sensitivity of SDR models to the estimation of USLE factors and iii) the pan-European applicabil-ity of empirical SDR models. The emphasis is put on systematically examining a broad range of parameters used for the USLE and SDR models, the variety of existing input data and algorithms to estimate model param-eters, the catchment scale, and Europe-wide model applications. How the modellers’ choices affect the model quality, i.e. the explained variability of SDR and sediment yields, is of special relevance because quantitative differences can be diminished during model calibration.
The alternatives for the assessment are selected from three categories which are of general relevance: topog-raphy, soil loss, and sediment data. Unfortunately, the multitude of available DEM and algorithms does not allow consideration of all the possible relationships between sediment data and model outcomes. To take into account as many factors as possible, the assessment has to be restricted to a few alternatives for each one.
First, topography and topographic parameters are fundamental for soil erosion and environmental models in general. Different DEM are available even for large-scale applications and many approaches have been pro-posed in the past to derive simple and complex parameters from these DEM. Plenty of studies have already
been conducted on this topic. However, they mostly focused on a few topographic parameters as well as on the distribution and spatial pattern of raster values. A broad and systematic evaluation is still missing for semi-distributed erosion models, including the question which DEM is most suitable.
Second, each SDR and SDR model per definitionem depends on a soil loss map. As mentioned above, the USLE and its derivatives are the most common soil loss models for large-scale applications. However, the huge vari-ability of soil loss and USLE factors has led to different approximations of USLE factors from limited input data. This raises the questions how to derive the USLE factors and soil loss maps and how these decisions in-fluence model predictions.
Third, any SDR (model) also inherently depends on the sediment yield. In fact, any quantification of soil ero-sion at the catchment scale needs sediment data for calibration and validation. As mentioned above, estimat-ing the sediment export from river catchments is associated with uncertainty and error. In the case of USLE-based models, long-term average annual sediment yields are needed which implies that the monitoring strat-egy is usually not subject to the modellers’ decision. Even if we ignore the inherent uncertainty in a given set of actual measurements, the decision how to interpolate and extrapolate measured data strongly affects quan-titative estimates of annual yields, as was pointed out in the previous section. Still, the consequences of algo-rithm choice for the variability in space and time have remained unclear until now. Another common problem is that the monitoring covers (filterable) suspended solids instead of sediments. Relating soil losses to ob-served loads of suspended solids overestimates the contribution of soil erosion (neglecting the bed load which is rarely systematically measured) if non-erosive sources like phytoplankton and industrial discharges con-tribute significantly. The inverse disaggregation of total suspended-solids data is not straightforward because of the huge variability of soil erosion and sediment fluxes. The few examples in the literature suggest two separate types of approaches to derive calibration and evaluation data for erosion models to be compared:
statistical-lumped (e.g., Behrendt et al. 1999) and event-based approaches (e.g., Walling and Webb 1982). To analyse uncertainties and sensitivities in model evaluation and explore methodical limitations, the approach of Behrendt et al. (1999) was exemplarily compared to a new event-based approach based on the “functional streamflow disaggregation” (FSD) (Carl 2009).
Strictly speaking, neither the coarse input data nor the simplified modelling framework allow for proof that any erosion estimate is “better” than the alternative(s). The evaluation rather provides insights in how suita-ble the solution is for successfully establishing and applying SDR models and how strongly the choice affects the (explained) spatial and temporal variability of soil erosion at the catchment scale.
The European context is important for testing the applicability of erosion (SDR) models and working out re-gional limitations because the large range of environmental conditions and human activity correspond to highly variable sediment yields in Europe (Vanmaercke et al. 2011). However, such a large-scale and inter-regional assessment requires homogeneous input data for estimating the soil loss and SDR parameters. Unfor-tunately, the content and geometric resolutions of pan-European data are insufficient to directly calculate USLE factors. To accomplish the research goals, researching European sediment data and reviewing regional approximations of USLE factors have been important steps.
The following research questions and objectives are specifically addressed:
1. Are better resolved data helpful to improve the modelling of sediment delivery at the catchment scale?
This is of special relevance for topography because of the huge range of available DEM which differ in res-olution and content. In addition to the few studies on scale relationships, a wider range of DEM resolu-tions as well as simple and complex parameters related to soil loss and SDR estimaresolu-tions are included.
2. How do methods and input data influence calibrated models and the model evaluation? If the model eval-uation is sensitive to these choices which alternative has to be recommended?
Which approaches have been proposed in the past to estimate USLE factors from European data? Are al-ternatives relevant for the evaluation and applicability of SDR models? Do the numerous approaches to estimate topographic parameters from DEM or alternative estimations of erosion-related fractions of total suspended solids (for model calibration and validation) play a role at the catchment scale, i.e. are careful choices promising to improve the predicted soil erosion or has the model to be adjusted? Specifically, does the higher working resolution of the FSD approach allow disaggregating more reasonable annual critical yields than the statistical approaches?
3. Which parameters improve the prediction of the spatial and temporal pattern of sediment yields and sediment delivery ratios? Which application constraints exist for the empirical SDR model?
Due to the high variability of soil erosion, sediment yields of catchments with different environmental characteristics and long sampling periods are required to adequately evaluate model results. Are there general catchment and data properties which explain high residuals and depict model limitations?
4. How uncertain are model results because of alternative data and algorithms within the given empirical modelling framework and the given data base? Which choices contribute most to the model uncertainty?
This dissertation thesis is part of the evaluation and development of MONERIS (http://www.moneris.igb-berlin.de), a conceptual, semi-distributed model (Behrendt et al. 1999; Venohr et al. 2011). MONERIS is applied
to quantify and apportion nutrient fluxes in large river catchments primarily inside but also outside of Eu-rope. MONERIS and similar models recognize soil erosion as an important diffuse pathway of nutrients into surface waters and implement empirical relationship to estimate the variability of SDR. In MONERIS, the mod-elled SDR is combined with the long-term average soil loss estimated with the USLE and European data to obtain average sediment yields. These values are weighted with annual rainfall to get annual yields. MONERIS was calibrated with critical yields of total suspended solids. Although MONERIS is seldom directly addressed in the following chapters, the results are expected to be relevant for better understanding how predictions of sediment-bound nutrient fluxes in European river catchments can be improved with this (and similar) models.