414
I. Papadakis and G.A. Schultz
wet season (months: J, J, A, 0, N)
Q . =vp· +v·b·P· I +v·b 2 .p. 2
1
I
1with:
v=0.112
b=0.507
T=O
P j= precipitation in time interval i.
dry season (J, F, M, A, M, S, D):
Q . =vp· +v·b·P· I +vb 2 .p. 2 +vb 3 .p. 3
1
1
1with:
v=0.063
b=0.582
T=O
Pj= precipitation in time interval i.
(18.5)
(18.6)
Consequently, the rainfall based on satellite spectral information is transformed
into monthly runoff for the period when both satellite data and runoff values were
available, namely from 07.1983 until 04.1985. The resulting fit is shown in Fig.
18.7 and is found to be satisfactory. This means that the technique is suitable for
practical applications in data scarce tropical areas.
18.4 Further Applications
Not only design of water management systems but also river management (monitoring, forecasting and simulation of flows) requires adequate hydrometeorological data. Because of the frequent lack of such data, remote sensing information
may be used to obtain better temporal and spatial resolution of relevant data over
large river basins.
Based on remote sensing data, a technique was developed by Koren et al. (1995)
to monitor, forecast, and simulate (MFS) flows along the Nile river, where no
relevant data were available. The principle of this technique is shown in the flow
chart of Fig. 18.8.
The MFS System consists of three subsystems: the Primary Data User System
(PDUS) which provides the continuous input of Meteosat satellite data, the Nile
Forecasting System (NFS) which resides on workstation and the High Aswan Dam
(HAD) Decision Support system. The hydroclimate data base consists of observed hydrometeorological data and Meteosat raw imagery data. The Preprocessor Component of the NFS converts incoming raw data into precipitation estimates required by hydrologic models. Because very little observed precipitation
data in the Nile basin are available, satellite estimates become a major source of
rainfall data. A unique Hybrid Climatological Technique (Schaake and GreenNewby, 1993) was developed which uses Cold Cloud Duration (CCD) data for
different temperature thresholds. CCD means the number of hours anyone Pixel
I. Papadakis and G.A. Schultz
wet season (months: J, J, A, 0, N)
Q . =vp· +v·b·P· I +v·b 2 .p. 2
1
I
1with:
v=0.112
b=0.507
T=O
P j= precipitation in time interval i.
dry season (J, F, M, A, M, S, D):
Q . =vp· +v·b·P· I +vb 2 .p. 2 +vb 3 .p. 3
1
1
1with:
v=0.063
b=0.582
T=O
Pj= precipitation in time interval i.
(18.5)
(18.6)
Consequently, the rainfall based on satellite spectral information is transformed
into monthly runoff for the period when both satellite data and runoff values were
available, namely from 07.1983 until 04.1985. The resulting fit is shown in Fig.
18.7 and is found to be satisfactory. This means that the technique is suitable for
practical applications in data scarce tropical areas.
18.4 Further Applications
Not only design of water management systems but also river management (monitoring, forecasting and simulation of flows) requires adequate hydrometeorological data. Because of the frequent lack of such data, remote sensing information
may be used to obtain better temporal and spatial resolution of relevant data over
large river basins.
Based on remote sensing data, a technique was developed by Koren et al. (1995)
to monitor, forecast, and simulate (MFS) flows along the Nile river, where no
relevant data were available. The principle of this technique is shown in the flow
chart of Fig. 18.8.
The MFS System consists of three subsystems: the Primary Data User System
(PDUS) which provides the continuous input of Meteosat satellite data, the Nile
Forecasting System (NFS) which resides on workstation and the High Aswan Dam
(HAD) Decision Support system. The hydroclimate data base consists of observed hydrometeorological data and Meteosat raw imagery data. The Preprocessor Component of the NFS converts incoming raw data into precipitation estimates required by hydrologic models. Because very little observed precipitation
data in the Nile basin are available, satellite estimates become a major source of
rainfall data. A unique Hybrid Climatological Technique (Schaake and GreenNewby, 1993) was developed which uses Cold Cloud Duration (CCD) data for
different temperature thresholds. CCD means the number of hours anyone Pixel