Michael Vohland

The land-use-specific calibration of evapotranspiration parameters in hydrologic modeling is challenging due to the lack of appropriate reference data. We present a MODIS-based calibration approach of vegetation-related evaporation parameters for two mesoscale catchments in western Germany with the physically based distributed hydrological model WaSiM-ETH. Time series of land-use-specific actual evapotranspiration (ETa) patterns were generated from MOD16A2 evapotranspiration and CORINE land-cover data from homogeneous image pixels for the major land-cover types in the region. Manual calibration was then carried out for 1D single-cell models, each representing a specific land-use type based on aggregated 11-year mean ETa values using SKout and PBIAS as objective functions (SKout > 0.8, |PBIAS| < 5%). The spatio-temporal evaluation on the catchment scale was conducted by comparing the simulated ETa pattern to six daily ETa grids derived from LANDSAT data. The results show a clear overall improvement in the SPAEF (spatial efficiency metric) for most land-use types, with some deficiencies for two scenes in spring and late summer due to phenological variation and a particularly dry hydrological system state, respectively. The presented method demonstrates a significant improvement in the simulation of ETa regarding both time and spatial scale.

Use of X‐ray fluorescence (XRF) spectrometry for estimation of soil texture, pH, and cation exchange capacity (CEC) is desirable given the time‐consuming nature of traditional methods. Recent studies have shown promising results; however, further investigation is required to determine the effects of sample preparation and data evaluation techniques on accuracy. Our objective was to compare (I) a simple but well‐founded approach, combining measurement of powder samples and modeling with elemental contents as predictors in stepwise multiple linear regressions (MLR), with alternative approaches including (II) use of partial least squares regression (PLSR), (III) sample preparation as a pressed pellet, and (IV) spectral intensities as predictors (20 kV, 40 kV, and concatenated 20 + 40 kV). A total of 395 loess soils from three arable fields were used with a fivefold random training‐testing approach and a hold‐one‐site‐out training‐testing approach. With random partitioning, clay, silt, and sand accuracy with approach I was excellent (ratio of performance to interquartile distance in validation (RPIQv) = 8.5–12.9), while pH and CEC estimations were satisfactory to excellent (RPIQv = 2.0–2.5 and 2.2–3.3, respectively). Differences between MLR and PLSR were negligible. Increases in accuracy with pellet samples were 1%–13% of RPIQv for 20 kV intensities, but effects were inconsistent for other predictors. The optimal predictor varied by property, and differences ranged from 3% to 13% of RPIQv. Improvements to accuracy from Approach I to the best alternative were largest for texture (10%–15%) but may be superfluous given the excellent accuracy across all approaches. Although the leave‐one‐site‐out training resulted in variable performance, inclusion of soils from the target site in training assured reliable accuracy.