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This paper has different aspects related to hydrological modeling studies: (1) successfully applied the SWAT model setup for watershed (2) calibrations and validation have been carried out for the ungaged catchment using the observed flow data of the adjacent gaged catchment.
While calculated energies remained sensitive to the model setup (for details see Supplementary Information), these data support the hypothesis that a single zinc ion may be sufficient to support at least the first stage of the MCR-1 catalysed reaction, i.e. PEA transfer onto Thr285.
This finding implies that the simplified parameterization of the SSEBop model did not significantly affect the accuracy of the ET estimate while increasing the ease of model setup for operational applications.
In this paper (Part II), a general overview is first provided on the numerical parameter study, especially on the model setup for the extraction of data related to the wave-induced pore pressures in the sand core beneath PBA-revetments.
An integrated model setup for far-field pattern distribution of a 2 × 2 array-optical system is described, which simulates the combining results under the influence of external vibration in PID control mode.
According to the exergetic model setup for the heat pump system based on the second law of thermodynamics, the influences of the intermediate pressure on the performances are comprehensively analyzed using experimental data of the heat pump prototype.
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The model setups are presented in table 3. Table 3 Model setups for the variability analysis Model Meteorological input Land cover classification Short Name BETHY/DLR ZAMG CORINE 2000 BETHY(1) ZAMG reallocated CORINE 2000 BETHY(2) ECMWF CORINE 2000 BETHY(3) ECMWF GLC2000 BETHY(4) EPIC ZAMG - EPIC(1) ZAMG reallocated - EPIC(2) ECMWF - EPIC 3).
The model software (hydrologic simulation code FEHM and parameter estimation software PEST) and model setup allows for efficient calibration of multiple conceptual models.
The accuracy of the maps depends strongly on the quality and relevance of satellite input layers, the model setup suitable for tropical forest structure prediction, as well as the design and selection of the training samples.
Using measured meteorological data from ZAMG results in an overestimation of NPP of about 11% (Figure 2A), which is combined with the highest variability within the sub-regions and years for all four model setups of BETHY/DLR.
Using measured meteorological data from ZAMG results in an overestimation of NPP of about 11%, which is combined with the highest variability within the sub-regions and years for all four model setups of BETHY/DLR.
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