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In this paper, a physically-based analytic model of streamflow dynamics is combined with a set of water balance models and a geomorphological recession flow model in order to estimate streamflow probability distributions based on catchment-scale climatic and morphologic features.
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Digital elevation models (DEMs) are widely used to define the flow direction in distributed hydrological models for simulation of streamflow.
Such properties exacerbate the difficulties in spatiotemporal modeling of streamflow data.
Time-varying vegetation cover characteristics were directly assimilated to the model from satellite observations, and model simulations of streamflow at the outlet of various catchments were compared against observations to assess the model's ability to capture basic patterns of space time seasonal variability within the basin.
Different conclusions have been drawn in literature as using reanalysis rainfall products to drive hydrological models for the simulation of streamflow, which warrant a need of further investigation before a generalised conclusion can be drawn.
We discussed performances of models in simulation and forecasting of streamflow regarding model types, input structures and catchment dynamics in detail.
Kalteh (2008) applied ANNs model for the estimation of streamflow and used Garson's algorithm for determining the relative significant of inputs, neural interpretation diagram, and randomization approach.
Moreover, the CEREF model can enlarge the effective intervals of streamflow forecasting compared to the EMD RBFNN model by introducing the water demand planned by the government department to improve long-term prediction accuracy.
By updating the coefficients of prediction periodically, the model efficiently captures the evolution of streamflow forecast.
We demonstrate an integration of monitoring data with a dynamic, metapopulation model developed to assess effects of streamflow alteration on fish occupancy in a southeastern US stream system.
This paper proposes a copula-based uncertainty evolution (CUE) model to describe the evolution of streamflow forecast uncertainty.
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