scholarly journals Low-flow hydrology of the Sulphur Fork Red River basin, Robertson County, north-central Tennessee

1979 ◽  
Keyword(s):  
River Basin ◽  
Red River ◽  
Low Flow ◽  
North Central ◽  
Red River Basin

scholarly journals Evaluation and Hydrologic Validation of Three Satellite-Based Precipitation Products in the Upper Catchment of the Red River Basin, China

2018 ◽  
Vol 10 (12) ◽  
pp. 1881 ◽  
Author(s):  
Yueyuan Zhang ◽  
Yungang Li ◽  
Xuan Ji ◽  
Xian Luo ◽  
Xue Li
Keyword(s):  
River Basin ◽  
Southwest China ◽  
Spatial Scales ◽  
Red River ◽  
Low Flow ◽  
Efficiency Coefficient ◽  
Monthly Streamflow ◽  
Red River Basin ◽  
Hydrological Applications ◽  
Trmm 3B42

Satellite-based precipitation products (SPPs) provide alternative precipitation estimates that are especially useful for sparsely gauged and ungauged basins. However, high climate variability and extreme topography pose a challenge. In such regions, rigorous validation is necessary when using SPPs for hydrological applications. We evaluated the accuracy of three recent SPPs over the upper catchment of the Red River Basin, which is a mountain gorge region of southwest China that experiences a subtropical monsoon climate. The SPPs included the Tropical Rainfall Measuring Mission (TRMM) 3B42 V7 product, the Climate Prediction Center (CPC) Morphing Algorithm (CMORPH), the Bias-corrected product (CMORPH_CRT), and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) Climate Data Record (PERSIANN_CDR) products. SPPs were compared with gauge rainfall from 1998 to 2010 at multiple temporal (daily, monthly) and spatial scales (grid, basin). The TRMM 3B42 product showed the best consistency with gauge observations, followed by CMORPH_CRT, and then PERSIANN_CDR. All three SPPs performed poorly when detecting the frequency of non-rain and light rain events (<1 mm); furthermore, they tended to overestimate moderate rainfall (1–25 mm) and underestimate heavy and hard rainfall (>25 mm). GR (Génie Rural) hydrological models were used to evaluate the utility of the three SPPs for daily and monthly streamflow simulation. Under Scenario I (gauge-calibrated parameters), CMORPH_CRT presented the best consistency with observed daily (Nash–Sutcliffe efficiency coefficient, or NSE = 0.73) and monthly (NSE = 0.82) streamflow. Under Scenario II (individual-calibrated parameters), SPP-driven simulations yielded satisfactory performances (NSE >0.63 for daily, NSE >0.79 for monthly); among them, TRMM 3B42 and CMORPH_CRT performed better than PERSIANN_CDR. SPP-forced simulations underestimated high flow (18.1–28.0%) and overestimated low flow (18.9–49.4%). TRMM 3B42 and CMORPH_CRT show potential for use in hydrological applications over poorly gauged and inaccessible transboundary river basins of Southwest China, particularly for monthly time intervals suitable for water resource management.

scholarly journals Snowpack water-equivalent estimates from satellite and aircraft remote-sensing measurements of the Red River basin, north-central U.S.A.

1998 ◽  
Vol 26 ◽  
pp. 119-124 ◽  
Author(s):  
E. G. Josberger ◽  
N. M. Mognard ◽  
B. Lind ◽  
R. Matthews ◽  
T. Carroll
Keyword(s):  
Artificial Intelligence ◽  
Remote Sensing ◽  
Linear Regression ◽  
River Basin ◽  
Satellite Observations ◽  
Passive Microwave ◽  
Red River ◽  
General Linear ◽  
North Central ◽  
Red River Basin

Most algorithms to extract dry snowpack water equivalent (SWE) from satellite passive-microwave observations are based on point measurements of SWE or extrapolation of point measurements to the 30 km footprint of the satellite observations. SWE observations on a scale comparable to the satellite observations can be obtained from airborne gamma-ray attenuation techniques from flight lines that are approximately 10 km long. During the winter of 1989, the NOAA National Operational Hydrologic Remote Sensing Center (NOHRSC) flew 92 of these night lines over a 200 × 250 km area of the Red River basin which is located in the north-central part of the United States of America. These observations provide a unique dataset of snow water-equivalent determinations on spatial scales similar to the satellite passive-microwave observations as acquired by the Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave Imager (SSM/I) F-8 satellite. Land-classification determinations from the Advanced Very High Resolution Radiometer (AVHRR) show that the eastern part of the region contains a coniferous forest of varying coverage, while the remainder is farmland or prairie. SSM/I data, including observations from a no-snow case in the preceding fall, the flight-line data and the AVHRR data were all co-registered to a common 20 km grid. The resulting dataset was analyzed using linear regression, artificial intelligence and general linear models. The results showed that the passive-microwave response was similar to the response predicted by Mie scattering theory. A comparison of the three techniques found that the artificial intelligence technique and the general linear model explained significantly more of the variance in the dataset, as evidenced byR2values of 0.97 compared to 0.88 for the linear multiple-regression analysis. Hence, a neural network approach which was continually trained on new datasets as they became available, could provide better estimates of snowpack water equivalent than algorithms based on linear-regression techniques.

scholarly journals Snowpack water-equivalent estimates from satellite and aircraft remote-sensing measurements of the Red River basin, north-central U.S.A.

1998 ◽  
Vol 26 ◽  
pp. 119-124 ◽  
Author(s):  
E. G. Josberger ◽  
N. M. Mognard ◽  
B. Lind ◽  
R. Matthews ◽  
T. Carroll
Keyword(s):  
Artificial Intelligence ◽  
Remote Sensing ◽  
Linear Regression ◽  
River Basin ◽  
Satellite Observations ◽  
Passive Microwave ◽  
Red River ◽  
General Linear ◽  
North Central ◽  
Red River Basin

Most algorithms to extract dry snowpack water equivalent (SWE) from satellite passive-microwave observations are based on point measurements of SWE or extrapolation of point measurements to the 30 km footprint of the satellite observations. SWE observations on a scale comparable to the satellite observations can be obtained from airborne gamma-ray attenuation techniques from flight lines that are approximately 10 km long. During the winter of 1989, the NOAA National Operational Hydrologic Remote Sensing Center (NOHRSC) flew 92 of these night lines over a 200 × 250 km area of the Red River basin which is located in the north-central part of the United States of America. These observations provide a unique dataset of snow water-equivalent determinations on spatial scales similar to the satellite passive-microwave observations as acquired by the Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave Imager (SSM/I) F-8 satellite. Land-classification determinations from the Advanced Very High Resolution Radiometer (AVHRR) show that the eastern part of the region contains a coniferous forest of varying coverage, while the remainder is farmland or prairie. SSM/I data, including observations from a no-snow case in the preceding fall, the flight-line data and the AVHRR data were all co-registered to a common 20 km grid. The resulting dataset was analyzed using linear regression, artificial intelligence and general linear models. The results showed that the passive-microwave response was similar to the response predicted by Mie scattering theory. A comparison of the three techniques found that the artificial intelligence technique and the general linear model explained significantly more of the variance in the dataset, as evidenced by R2 values of 0.97 compared to 0.88 for the linear multiple-regression analysis. Hence, a neural network approach which was continually trained on new datasets as they became available, could provide better estimates of snowpack water equivalent than algorithms based on linear-regression techniques.

Cultural Exchange in the Red River Basin

2008 ◽  
Vol 49 (3) ◽  
pp. 4-5
Author(s):  
Siobhán M Mattison
Keyword(s):  
River Basin ◽  
Cultural Exchange ◽  
Red River ◽  
Red River Basin

Drainage areas of streams in Arkansas--Red River basin

1978 ◽  
Author(s):  
Marion S. Hines ◽  
John J. Yanchosek
Keyword(s):  
River Basin ◽  
Red River ◽  
Red River Basin
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