ALBERT

Description: This paper analyses the effect of spatial input data resolution on the simulated water balances and flow components using the multi-scale hydrological model TOPLATS. A data set of 25m resolution of the central German Dill catchment (693 km2) is used for investigation. After an aggregation of digital elevation model, soil map and land use classification to 50 m, 75 m, 100 m, 150 m, 200 m, 300 m, 500 m, 1000 m and 2000 m, water balances and water flow components are calculated for the entire Dill catchment as well as for 3 subcatchments without any recalibration. The study shows that model performance measures and simulated water balances almost remain constant for most of the aggregation steps for all investigated catchments. Slight differences in the simulated water balances and statistical quality measures occur for single catchments at the resolution of 50 m to 500 m (e.g. 0–3% for annual stream flow), significant differences at the resolution of 1000 m and 2000 m (e.g. 2–12% for annual stream flow). These differences can be explained by the fact that the statistics of certain input data (land use data in particular as well as soil physical characteristics) changes significantly at these spatial resolutions. The impact of smoothing the relief by aggregation occurs continuously but is barely reflected by the simulation results. To study the effect of aggregation of land use data in detail, in addition to current land use the effect of aggregation on the water balance calculations based on three different land use scenarios is investigated. Land use scenarios were available aiming on economic optimisation of agricultural and forestry practices at different field sizes (0.5 ha, 1.5 ha and 5.0 ha). The changes in water balance terms, induced by aggregation of the land use scenarios, are comparable with respect to catchment water balances compared to the current land use. A correlation analysis between statistics of input data and simulated annual water fluxes only in some cases reveals systematically high correlation coefficients for all investigated catchments and data sets (e.g. actual evapotranspiration is correlated to land use, surface runoff generation is correlated to soil properties). Predominantly the correlation between catchment properties (e.g. topographic index, transmissivity, land use) and simulated water flows varies from catchment to catchment. Catchment specific properties determine correlations between properties and fluxes, but do not influence the effect of data aggregation. This study indicates that an aggregation of input data for the calculation of regional water balances using TOPLATS type models leads to significant errors from a resolution exceeding 500 m. Correlating statistics of input data and simulation results indicates that a meaningful aggregation of data should in the first instance aim on preserving the areal fractions of land use classes.

Description: Ten conceptually different models in predicting discharge from the artificial Chicken Creek catchment in North-East Germany were used for this study. Soil texture and topography data were given to the modellers, but discharge data was withheld. We compare the predictions with the measurements from the 6 ha catchment and discuss the conceptualization and parameterization of the models. The predictions vary in a wide range, e.g. with the predicted actual evapotranspiration ranging from 88 to 579 mm/y and the discharge from 19 to 346 mm/y. The predicted components of the hydrological cycle deviated systematically from the observations, which were not known to the modellers. Discharge was mainly predicted as subsurface discharge with little direct runoff. In reality, surface runoff was a major flow component despite the fairly coarse soil texture. The actual evapotranspiration (AET) and the ratio between actual and potential ET was systematically overestimated by nine of the ten models. None of the model simulations came even close to the observed water balance for the entire 3-year study period. The comparison indicates that the personal judgement of the modellers was a major source of the differences between the model results. The most important parameters to be presumed were the soil parameters and the initial soil-water content while plant parameterization had, in this particular case of sparse vegetation, only a minor influence on the results.

Description: In practice, the catchment hydrologist is often confronted with the task of predicting discharge without having the needed records for calibration. Here, we report the discharge predictions of 10 modellers – using the model of their choice – for the man-made Chicken Creek catchment (6 ha, northeast Germany, Gerwin et al., 2009b) and we analyse how well they improved their prediction in three steps based on adding information prior to each following step. The modellers predicted the catchment's hydrological response in its initial phase without having access to the observed records. They used conceptually different physically based models and their modelling experience differed largely. Hence, they encountered two problems: (i) to simulate discharge for an ungauged catchment and (ii) using models that were developed for catchments, which are not in a state of landscape transformation. The prediction exercise was organized in three steps: (1) for the first prediction the modellers received a basic data set describing the catchment to a degree somewhat more complete than usually available for a priori predictions of ungauged catchments; they did not obtain information on stream flow, soil moisture, nor groundwater response and had therefore to guess the initial conditions; (2) before the second prediction they inspected the catchment on-site and discussed their first prediction attempt; (3) for their third prediction they were offered additional data by charging them pro forma with the costs for obtaining this additional information. Holländer et al. (2009) discussed the range of predictions obtained in step (1). Here, we detail the modeller's assumptions and decisions in accounting for the various processes. We document the prediction progress as well as the learning process resulting from the availability of added information. For the second and third steps, the progress in prediction quality is evaluated in relation to individual modelling experience and costs of added information. In this qualitative analysis of a statistically small number of predictions we learned (i) that soft information such as the modeller's system understanding is as important as the model itself (hard information), (ii) that the sequence of modelling steps matters (field visit, interactions between differently experienced experts, choice of model, selection of available data, and methods for parameter guessing), and (iii) that added process understanding can be as efficient as adding data for improving parameters needed to satisfy model requirements.

Description: We used ten conceptually different models to predict discharge from the artificial Chicken Creek catchment in North-East Germany. Soil textural and topography data were given to the modellers, but discharge data were withheld. We compare the predictions with the measurements from the 6 ha catchment and discuss the conceptualization and parameterization of the models. The predictions vary in a wide range, e.g. the predicted actual evapotranspiration ranged from 88 to 579 mm/y and the discharge from 19 to 346 mm/y. All model simulations revealed systematic deviations between observations of major components of the hydrological cycle (not known to the modellers) and the simulation results. Discharge was predicted mainly as subsurface discharge with little direct runoff. In reality, surface runoff was a major flow component despite the fairly coarse soil texture. The actual evapotranspiration (AET) was systematically overestimated by nine of ten models as was the ratio between actual and potential ET. Overall, none of the model simulations came close to the correct water balance during the entire 3-year study period. The comparison indicated that the personal judgement of the modellers was a major source of the differences between the model results. The most important parameters to be guessed were the soil parameters and the initial soil water content while plant parameterization had in this particular case of a sparse vegetation only a minor influence on the results.

Description: This paper analyses the effect of spatial input data resolution on the simulated water balances and flow components using the multi-scale hydrological model TOPLATS. A data set of 25m resolution of the central German Dill catchment (693 km2 is used for investigation. After an aggregation of digital elevation model, soil map and land use classification to 50 m, 75 m, 100 m, 150 m, 200 m, 300 m, 500 m, 1000 m and 2000 m, water balances and water flow components are calculated for the entire Dill catchment as well as for 3 subcatchments without any recalibration. The study shows that both model performance measures as well as simulated water balances almost remain constant for most of the aggregation steps for all investigated catchments. Slight differences occur for single catchments at the resolution of 50–500 m (e.g. 0–3% for annual stream flow), significant differences at the resolution of 1000 m and 2000 m (e.g. 2–12% for annual stream flow). These differences can be explained by the fact that the statistics of certain input data (land use data in particular as well as soil physical characteristics) changes significantly at these spatial resolutions, too. The impact of smoothing the relief by aggregation occurs continuously but is not reflected by the simulation results. To study the effect of aggregation of land use data in detail, three different land use scenarios are aggregated which were generated aiming on economic optimisation at different field sizes (0.5 ha, 1.5 ha and 5.0 ha). The changes induced by aggregation of these land use scenarios are comparable with respect to catchment water balances compared to the current land use. A correlation analysis only in some cases reveals high correlation between changes in both input data and in simulation results for all catchments and land use scenarios combinations (e.g. evapotranspiration is correlated to land use, runoff generation is correlated to soil properties). Predominantly the correlation between catchment properties (e.g. topographic index, transmissivity, land use) and simulated water flows varies from catchment to catchment. This study indicates that an aggregation of input data for the calculation of regional water balances using TOPLATS type models leads to significant errors from a resolution exceeding 500 m. A meaningful aggregation of data should in the first instance aim on preserving the areal fractions of land use classes.

Description: The purpose of this paper is to stimulate a re-thinking of how we, the catchment hydrologists, could become reliable forecasters. A group of catchment modellers predicted the hydrological response of a man-made 6 ha catchment in its initial phase (Chicken Creek) without having access to the observed records. They used conceptually different model families. Their modelling experience differed largely. The prediction exercise was organized in three steps: (1) for the 1st prediction modellers received a basic data set describing the internal structure of the catchment (somewhat more complete than usually available to a priori predictions in ungauged catchments). They did not obtain time series of stream flow, soil moisture or groundwater response. (2) Before the 2nd improved prediction they inspected the catchment on-site and attended a workshop where the modellers presented and discussed their first attempts. (3) For their improved 3rd prediction they were offered additional data by charging them pro forma with the costs for obtaining this additional information. Holländer et al. (2009) discussed the range of predictions obtained in step 1. Here, we detail the modeller's decisions in accounting for the various processes based on what they learned during the field visit (step 2) and add the final outcome of step 3 when the modellers made use of additional data. We document the prediction progress as well as the learning process resulting from the availability of added information. For the 2nd and 3rd step, the progress in prediction quality could be evaluated in relation to individual modelling experience and costs of added information. We learned (i) that soft information such as the modeller's system understanding is as important as the model itself (hard information), (ii) that the sequence of modelling steps matters (field visit, interactions between differently experienced experts, choice of model, selection of available data, and methods for parameter guessing), and (iii) that added process understanding can be as efficient as adding data for improving parameters needed to satisfy model requirements.

Description: In this study, the impact of climate change scenarios on the hydrological regimes of five different regions in Germany is investigated. These regions (Northwest Germany, Northeast Germany and East German basins, upper and lower Rhine, pre-Alps) differ with respect to present climate and projected climate change. The physically based SVAT-model SIMULAT is applied to theoretical soil columns based on combinations of land use, soil texture and groundwater depth to quantify climate change effects on the hydrological regime. Observed climate, measured at climate stations of the German Weather Service (1991–2007), is used for comparison with climate projections (2071–2100) generated by the regional scale climate model WETTREG. While all climate scenarios implicate an increase in precipitation in winter, a decrease in precipitation in summer and an increase in temperature, the simulated impacts on the hydrological regime are regionally different. In the Rhine region and in Northwest Germany, an increase in the annual runoff and groundwater recharge is simulated despite the increase in temperature and potential evapotranspiration. In the Eastern part of Germany and the pre-Alps, annual runoff and groundwater recharge will decrease. Due to dry conditions in summer, the soil moisture deficit will increase (in Northeast Germany and the East German basins in particular) or remain constant (Rhine region). In all regions the seasonal variability in runoff and soil moisture status will increase. Despite regional warming actual evapotranspiration will decrease in most regions except in areas with shallow groundwater tables and the lower Rhine. Although the study is limited by the fact that only one climate model was used to drive one hydrologic model, the study shows that the hydrological regime will be affected by climate change. The direction of the expected changes seems to be obvious as well as the necessity of the adaptation of future water management strategies.

Description: Many model applications suffer from the fact that although it is well known that model application implies different sources of uncertainty there is no objective criterion to decide whether a model is suitable for a particular application or not. This paper introduces a comparative index between the uncertainty of a model and the change effects of scenario calculations which enables the modeller to objectively decide about suitability of a model to be applied in scenario analysis studies. The index is called "signal-to-noise-ratio", and it is applied for an exemplary scenario study which was performed within the GLOWA-IMPETUS project in Benin. The conceptual UHP model was applied on the upper Ouémé basin. Although model calibration and validation were successful, uncertainties on model parameters and input data could be identified. Applying the "signal-to-noise-ratio" on regional scale subcatchments of the upper Ouémé comparing water availability indicators for uncertainty studies and scenario analyses the UHP model turned out to be suitable to predict long-term water balances under the present poor data availability and changing environmental conditions in subhumid West Africa.

Description: The hydrological catchment model WaSiM-ETH (Water Balance Simulation Model) is a spatially distributed, process- and grid-based hydrological catchment model which was primarily developed to simulate the water balance of mountainous catchments. In this study, the ability of WaSiM-ETH was tested to describe the hydrological processes of lowland catchments. In addition, the resulting model performance was related to subcatchment characteristics and the model's sensitivity to possible future land use change. The prediction of the hydrological effects of land use change is a major challenge in contemporary hydrological model applications. The study revealed that WaSiM-ETH is a suitable tool for the simulation of the hydrological behaviour of lowland catchments. However, for a few subcatchments model validation failed. Analysing the correlation between model performance and physiographic catchment characteristics revealed that WaSiM-ETH performs better in sloped catchments compared to plane ones. Modelling results were also better in heterogeneous catchments with respect to soils and vegetation compared to homogenous ones. However, the hydrological reaction to land use change scenarios was similar in all investigated catchments.

Description: Regional scale hydrological simulations are mostly based on the use of standard data sets such as soil maps which are based on soil texture classification schemes. This paper analyses the suitability of the German soil texture classification for the application of a physically based soil-vegetation-atmosphere-transfer scheme. Theoretical soil columns are defined to be able to represent the entire soil texture triangle by a 1% grid of the three particle size classes: sand, clay and silt. These theoretical soil columns are characterized by a homogenous soil texture and consist of two layers of increasing bulk density and decreasing content of organic matter with depth. Soil hydraulic parameterisation is derived by applying a pedotransfer function. Continuous water balance calculations are carried out for a ten year period for all grid cells of the 1% grid. The results of the water balance calculations are compared to the simulation results of the centre of gravity of the respective soil texture class. Texture class specific mean deviations and root mean squared deviations are calculated from the differences between the 1% pixels and texture class representatives. The results reveal that the loam and silt texture classes show only small deviations from the centres of gravity. For a few sand texture classes and most of the clay texture classes deviations are considerably large. Assuming an equal distributed probability of occurrence of all realisations within a soil texture class, an uncertainty of more than 100 mm/a with respect to runoff and actual evapotranspiration is detected for four clay texture classes, two sand texture classes and one silt texture class. These results are confirmed by a sensitivity analysis investigating the model response for a grid cell compared to the neighboured grid cells. High sensitivities mainly appear for sandy and clayey soils while the sensitivity of the model for loam and silt soils is smaller. Resuming it can be stated that most of the texture classes of the German texture classification scheme are suitable for the application of a physically based model, on regional scale in particular. Clay texture classes can be expected to cause high simulation uncertainties.