English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Root zone soil moisture plays an important role in water storage in hydrological processes. The recently launched Soil Moisture Active Passive (SMAP) mission has produced a high-resolution assimilation product of global root zone soil moisture that can be applied to improve the performance of hydrological models. In this study, we compare three calibration approaches in the Beimiaoji watershed. The first approach is single-objective calibration, in which only observed streamflow is used as a benchmark for comparison with the other approaches. The second and third approaches use multi-objective calibration based on SMAP root zone soil moisture and observed streamflow. The difference between the second and third approaches is the metric used to characterize the root zone soil moisture. The second approach applies the mean, which was commonly used in previous studies, whereas the third approach applies the hydrologic complexity μ, a dimensionless metric based on information entropy theory. These approaches are implemented to calibrate the distributed hydrological model MIKE SHE. Results show that the root zone soil moisture simulation is clearly improved, whereas streamflow simulation suffers from a slightly negative impact with multi-objective calibration. The hydrologic complexity μ performs better than the mean in capturing the features of root zone soil moisture.

Multi-objective calibration of MIKE SHE with SMAP soil moisture datasets