Stormflow generation in two headwater catchments in eastern Amazonia, Brazil

2008 ◽  
Vol 22 (17) ◽  
pp. 3285-3293 ◽  
Author(s):  
A. J. Wickel ◽  
N. C. van de Giesen ◽  
T. D. d. A. Sá
Keyword(s):  
Headwater Catchments ◽  
Stormflow Generation

Progress Towards Understanding Stormflow Generation in Headwater Catchments

1998 ◽  
pp. 483-498 ◽  
Author(s):  
Roy C. Sidle ◽  
Yoshio Tsuboyama ◽  
Shoji Noguchi ◽  
Ikuhiro Hosoda ◽  
Motohisa Fujieda ◽  
...  
Keyword(s):  
Headwater Catchments ◽  
Stormflow Generation

EVALUATING THE NATURAL TRANSPORT PATHWAYS OF IODINE IN HEADWATER CATCHMENTS

2017 ◽  
Author(s):  
Kristina M. Gutchess ◽  
◽  
Shannon Garvin ◽  
Li Jin ◽  
Wanyi Lu ◽  
...  
Keyword(s):  
Transport Pathways ◽  
Headwater Catchments

scholarly journals Development of random forest model as decision support tool in water resources management of Ogun headwater catchments

2021 ◽  
Vol 11 (7) ◽  
Author(s):  
O. O. Aiyelokun ◽  
O. A. Agbede
Keyword(s):  
Random Forest ◽  
Water Resources ◽  
Potential Evapotranspiration ◽  
Decision Support Tool ◽  
Maximum Temperature ◽  
Computational Technique ◽  
Percentage Increase ◽  
Support Tool ◽  
Headwater Catchments ◽  
Minimal Depth

AbstractWater resources cannot be effectively managed unless potential evapotranspiration is determined with high accuracy at headwater catchments. The study presents the most suitable feature combinations for building a reliable potential evapotranspiration (PET) model in the headwater catchments of Ogun River Basin, Southwest Nigeria. Using rainfall (R), wind speed (U2), sunshine hour (S), relative humidity (Rh), minimum temperature (Tmin) and maximum temperature (Tmax) as input features, a Random Forest (RF) model was developed to predict PET. Although the model yielded satisfactory results, it was subjected to the minimal depth and percentage increase in mean square error (%IncMSE). This was done to reduce the input features and to increase model accuracy. Thereafter various combinations of important input features were examined in order to establish the best combinations required to yield optimum results. The study revealed that although Tmax (%IncMSE of 652.09, p value < 0.05) and Rh (%IncMSE of 254.36, p value < 0.05) were the most important predictors of PET, a more reliable RF model was achieved when S and U2 were combined with them. Consequently, this study presents RF with a combination of four parameters (Tmax, Rh, S and U2) as an excellent computational technique for the prediction of PET in headwater catchments.

Predicting critical source areas of sediment in headwater catchments

2013 ◽  
Vol 179 ◽  
pp. 41-52 ◽  
Author(s):  
Joshua Thompson ◽  
Rachel Cassidy ◽  
Donnacha G. Doody ◽  
Ray Flynn
Keyword(s):  
Critical Source Areas ◽  
Headwater Catchments ◽  
Source Areas

Potentials and constraints of different types of soil moisture observations for flood simulations in headwater catchments

2011 ◽  
Vol 60 (3) ◽  
pp. 879-914 ◽  
Author(s):  
Axel Bronstert ◽  
Benjamin Creutzfeldt ◽  
Thomas Graeff ◽  
Irena Hajnsek ◽  
Maik Heistermann ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Headwater Catchments ◽  
Different Types ◽  
Flood Simulations
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