FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2897080377> ?p ?o ?g. }

• W2897080377 endingPage "683" @default.
• W2897080377 startingPage "668" @default.
• W2897080377 abstract "A hydrologic model, calibrated using only streamflow data, can produce acceptable streamflow simulation at the watershed outlet yet unrealistic representations of water balance across the landscape. Recent studies have demonstrated the potential of multi-objective calibration using remotely sensed evapotranspiration (ET) and gaged streamflow data to spatially improve the water balance. However, methodological clarity on how to best integrate ET data and model parameters in multi-objective model calibration to improve simulations is lacking. To address these limitations, we assessed how a spatially explicit, distributed calibration approach that uses (1) remotely sensed ET data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and (2) frequently overlooked biophysical parameters can improve the overall predictability of two key components of the water balance: streamflow and ET at different locations throughout the watershed. We used the Soil and Water Assessment Tool (SWAT), previously modified to represent hydrologic transport and filling-spilling of landscape depressions, in a large watershed of the Prairie Pothole Region, United States. We employed a novel stepwise series of calibration experiments to isolate the effects (on streamflow and simulated ET) of integrating biophysical parameters and spatially explicit remotely sensed ET data into model calibration. Results suggest that the inclusion of biophysical parameters involving vegetation dynamics and energy utilization mechanisms tend to increase model accuracy. Furthermore, we found that using a lumped, versus a spatially explicit, approach for integrating ET into model calibration produces a sub-optimal model state with no potential improvement in model performance across large spatial scales. However, when we utilized the same MODIS ET datasets but calibrated each sub-basin in the spatially explicit approach, water yield prediction uncertainty decreased, including a distinct improvement in the temporal and spatial accuracy of simulated ET and streamflow. This further resulted in a more realistic simulation of vegetation growth when compared to MODIS Leaf-Area Index data. These findings afford critical insights into the efficient integration of remotely sensed big data into hydrologic modeling and associated watershed management decisions. Our approach can be generalized and potentially replicated using other hydrologic models and remotely sensed data resources - and in different geophysical settings of the globe." @default.
• W2897080377 created "2018-10-26" @default.
• W2897080377 creator A5022509970 @default.
• W2897080377 creator A5050262852 @default.
• W2897080377 creator A5060338356 @default.
• W2897080377 creator A5065342304 @default.
• W2897080377 date "2018-12-01" @default.
• W2897080377 modified "2023-10-14" @default.
• W2897080377 title "Hydrologic model predictability improves with spatially explicit calibration using remotely sensed evapotranspiration and biophysical parameters" @default.
• W2897080377 cites W1015681294 @default.
• W2897080377 cites W1152504716 @default.
• W2897080377 cites W1512032832 @default.
• W2897080377 cites W1513383932 @default.
• W2897080377 cites W1570698328 @default.
• W2897080377 cites W1971651443 @default.
• W2897080377 cites W1975768883 @default.
• W2897080377 cites W1976014897 @default.
• W2897080377 cites W1987557370 @default.
• W2897080377 cites W1990471334 @default.
• W2897080377 cites W1991273147 @default.
• W2897080377 cites W1991921673 @default.
• W2897080377 cites W2000979050 @default.
• W2897080377 cites W2001747391 @default.
• W2897080377 cites W2011287653 @default.
• W2897080377 cites W2017922720 @default.
• W2897080377 cites W2018178054 @default.
• W2897080377 cites W2021708609 @default.
• W2897080377 cites W2025544738 @default.
• W2897080377 cites W2025750461 @default.
• W2897080377 cites W2027479280 @default.
• W2897080377 cites W2032353008 @default.
• W2897080377 cites W2041307865 @default.
• W2897080377 cites W2049760535 @default.
• W2897080377 cites W2054556431 @default.
• W2897080377 cites W2059646894 @default.
• W2897080377 cites W2068410395 @default.
• W2897080377 cites W2070556382 @default.
• W2897080377 cites W2073194947 @default.
• W2897080377 cites W2078416067 @default.
• W2897080377 cites W2088091457 @default.
• W2897080377 cites W2088837030 @default.
• W2897080377 cites W2090915513 @default.
• W2897080377 cites W2091232816 @default.
• W2897080377 cites W2094648226 @default.
• W2897080377 cites W2112828507 @default.
• W2897080377 cites W2115049406 @default.
• W2897080377 cites W2117255900 @default.
• W2897080377 cites W2126349297 @default.
• W2897080377 cites W2126658193 @default.
• W2897080377 cites W2126817991 @default.
• W2897080377 cites W2128003492 @default.
• W2897080377 cites W2135801689 @default.
• W2897080377 cites W2138763184 @default.
• W2897080377 cites W2142000700 @default.
• W2897080377 cites W2144378718 @default.
• W2897080377 cites W2146577723 @default.
• W2897080377 cites W2147571330 @default.
• W2897080377 cites W2151647062 @default.
• W2897080377 cites W2159225725 @default.
• W2897080377 cites W2163244228 @default.
• W2897080377 cites W2164244878 @default.
• W2897080377 cites W2165924262 @default.
• W2897080377 cites W2178254792 @default.
• W2897080377 cites W2208198416 @default.
• W2897080377 cites W2239261344 @default.
• W2897080377 cites W2260078223 @default.
• W2897080377 cites W2276342788 @default.
• W2897080377 cites W2280203836 @default.
• W2897080377 cites W2281193934 @default.
• W2897080377 cites W2303468042 @default.
• W2897080377 cites W2328399833 @default.
• W2897080377 cites W2345175558 @default.
• W2897080377 cites W2398837725 @default.
• W2897080377 cites W2409334180 @default.
• W2897080377 cites W2470024313 @default.
• W2897080377 cites W2483355106 @default.
• W2897080377 cites W2513513357 @default.
• W2897080377 cites W2529833465 @default.
• W2897080377 cites W2550577527 @default.
• W2897080377 cites W2574032337 @default.
• W2897080377 cites W2595102880 @default.
• W2897080377 cites W2595433512 @default.
• W2897080377 cites W2604358836 @default.
• W2897080377 cites W2738929243 @default.
• W2897080377 cites W2756380816 @default.
• W2897080377 cites W2765495150 @default.
• W2897080377 cites W2766003150 @default.
• W2897080377 cites W2768085084 @default.
• W2897080377 cites W2774182742 @default.
• W2897080377 cites W2784104309 @default.
• W2897080377 cites W2790072303 @default.
• W2897080377 cites W2792778139 @default.
• W2897080377 cites W2806492187 @default.
• W2897080377 doi "https://doi.org/10.1016/j.jhydrol.2018.10.024" @default.
• W2897080377 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6687302" @default.
• W2897080377 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/31395990" @default.
• W2897080377 hasPublicationYear "2018" @default.
• W2897080377 type Work @default.

• next