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Soil moisture plays a key role in runoff generation processes, and the assimilation of soil moisture observations into rainfall runoff models is regarded as a way to improve their prediction accuracy.
Three approaches of Ensemble Kalman Filter (EnKF) with dual state-parameter estimation are applied: (1) streamflow assimilation, (2) soil moistue assimilation, and (3) combined assimilation of soil moisture and streamflow.
Given that the initial mineral N content of the soil is very low (30 mg kg soil−1; Table 1), the high C/N ratio imposed on the soils due to the combined application of biochar and manure resulted in microbial assimilation of soil mineral N for cell biosynthesis, leading to the observed negative NNM or N immobilization.
Quantification of moisture supply in the root zone remains a gray area in research community, this challenge can be partly overcome by incorporating assimilation of soil moisture and leaf area index into crop modeling framework for agricultural drought quantification, as it performs better in simulating crop yield.
Since plants require large quantities of N, greater than those of any other primary nutrient, plant assimilation of soil N often exceeds the amount being replenished (Epstein and Bloom 2005).
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The combined assimilation of surface soil moisture and streamflow outperforms the assimilation with only surface soil moisture or streamflow especially in the estimate of full profile soil moisture.
Results show that daily assimilation of surface soil moisture for each HRU improves model predictions especially reducing errors in surface and profile soil moisture estimation.
Assimilation of either soil moisture or streamflow has been well demonstrated to improve flood forecasting.
A framework was developed for the continental assimilation of satellite soil moisture (SM) into an operational water balance modelling system.
Assimilation of satellite soil moisture and streamflow data into a distributed hydrologic model has received increasing attention over the past few years.
In this work we evaluate the assimilation of satellite soil moisture into the probability distributed model (PDM) for the purpose of reducing flood prediction uncertainty in an operational context.
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