Sentence examples for hydrologic sensitivity from inspiring English sources

(3) Hydrologic Sensitivity to climate warming includes small changes in annual streamflow and actual evapotranspiration, significant changes in streamflow timing and increased frequency and magnitude in extreme flows.

The physically-based and spatially-distributed Precipitation-Runoff Modeling System (PRMS) model was used to force uniform climate warming (+1 °C to +4 °C) to investigate hydrologic sensitivity to temperature increase.

Modeling the effect of forest clear-cutting with a distributed hydrological model can be used to detect hydrologic changes as an alternative to paired-catchment studies, and also to estimate the hydrologic sensitivity of a catchment to assist in forest management decisions.

The presence of these humid (mound) and arid (complete disappearance) phenomena at one location within the same year, reveal the sensitivity of hydrologic systems in this sub-humid region to small variations in climate.

We use results from a climate-forced rainfall-runoff model to explicitly simulate intra-basin hydrologic dynamics and understand localized sensitivity to climate warming.

This study aims to evaluate the potential of transferring hydrologic model parameters in CLM through sensitivity analyses and classification across watersheds from the Model Parameter Estimation Experiment (MOPEX) in the United States.

Thus, Igdlugdlip Sermia may provide a test glacier for further investigation of the development and sensitivity of the subglacial hydrologic system.

Spatial sensitivity of drought indicators to hydrologic ratios suggests that an increase in hydrologic ratios may result in augmentation of magnitude of drought indicators in majority of the river basins.

Uncertain values of sensitive parameters were investigated through sensitivity analysis of flow sediment parameters in three hydrologic catchments.

Mosquitoes and mosquitoborne disease transmission are sensitive to hydrologic variability.

The basins are then classified according to their parameter sensitivity patterns (internal attributes), as well as their hydrologic indices/attributes (external hydrologic factors) separately, using Principal component analysis (PCA) and expectation maximization (EM) – based clustering approach.