A comparison of ASCAT and modelled soil moisture over South Africa, using TOPKAPI in land surface mode

47Citations
Citations of this article
80Readers
Mendeley users who have this article in their library.

Abstract

In this paper we compare two independent soil moisture estimates over South Africa. The first estimate is a Soil Saturation Index (SSI) provided by automated real-time computations of the TOPKAPI hydrological model, adapted to run as a collection of independent 1 km cells with centres on a grid with a spatial resolution of 0.125°, at 3 h intervals. The second set of estimates is the remotely sensed ASCAT Surface Soil Moisture product, temporally filtered to yield a Soil Wetness Index (SWI). For the TOPKAPI cells, the rainfall forcing used is the TRMM 3B42RT product, while the evapotranspiration forcing is based on a modification of the FAO56 reference crop evapotranspiration (ET0). ET0 is computed using forecast fields of meteorological variables from the Unified Model (UM) runs done by the South African Weather Service (SAWS); the UM forecast fields were used, because reanalysis is not done by SAWS. To validate these ET0 estimates we compare them with those computed using observed meteorological data at a network of weather stations; they were found to be unbiased with acceptable scatter. Using the rainfall and evapotranspiration forcing data, the percentage saturation of the TOPKAPI soil store is computed as a Soil Saturation Index (SSI), for each of 6984 unconnected uncalibrated TOPKAPI cells at 3 h time-steps. These SSI estimates are then compared with the SWI estimates obtained from ASCAT. The comparisons indicate a good correspondence in the dynamic behaviour of SWI and SSI for a significant proportion of South Africa. © Author(s) 2010.

Figures

  • Fig. 1. Plot showing the locations of the South African Weather Services (SAWS) current automatic weather stations. Other meteorological stations exist, but are either manually read, or are operated by different organizations. The coverage is sparse, with only 164 stations in 1.2 million km2.
  • Fig. 2. Example of the LSA-SAF DSSF product for Southern Africa. The data are available at half hour intervals, via the EUMETCast system.
  • Fig. 3. Comparison of observed solar radiation with observations in KwaZulu-Natal, South Africa made by the CSIR (C. Everson, personal communication, 2008). The blue points are the half-hourly DSSF estimates from the LSA-SAF product and the red crosses are the data observed at the ground, measured at 12 min intervals.
  • Fig. 4. An hourly estimate of ET0 computed from NWP forecast data and LSA-SAF radiation estimates.
  • Fig. 5. Daily total of ET0 computed by summing hourly estimates based on NWP forecast data and LSA-SAF radiation estimates, as in Fig. 4.
  • Fig. 6. A typical regression between hourly totals of ET0 computed from forecast model data and observed meteorological parameters at 164 automatic weather stations (Fig. 1).
  • Fig. 7. A schematic representation of a typical TOPKAPI cell and the associated water transfers. Greyed out portions are not used in the pseudo-LSM mode. Adapted from Vischel et al. (2008b).
  • Fig. 8. A flow chart, showing the sources of data used to compute SSI with the TOPKAPI model. The dynamic forcing data comprises rainfall (bottom left panel) and the data required to compute ET0 (top left panel). The static data are used to derive local slope (DEM) and soil properties. The overland roughness no and soil condictivity Ks control the flow of water in the cell, while the the residual and saturated soil moisture contents (θr ,θs ) are combined with the soil depth to compute available water storage capacity.

References Powered by Scopus

Get full text
1666Citations
891Readers
1450Citations
537Readers
Get full text

Cited by Powered by Scopus

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Cite

CITATION STYLE

APA

Sinclair, S., & Pegram, G. G. S. (2010). A comparison of ASCAT and modelled soil moisture over South Africa, using TOPKAPI in land surface mode. Hydrology and Earth System Sciences, 14(4), 613–626. https://doi.org/10.5194/hess-14-613-2010

Readers over time

‘11‘12‘13‘14‘15‘16‘17‘18‘19‘20‘21‘22‘23‘24‘25036912

Readers' Seniority

Tooltip

PhD / Post grad / Masters / Doc 36

60%

Researcher 16

27%

Professor / Associate Prof. 5

8%

Lecturer / Post doc 3

5%

Readers' Discipline

Tooltip

Earth and Planetary Sciences 22

46%

Engineering 14

29%

Environmental Science 10

21%

Chemistry 2

4%

Save time finding and organizing research with Mendeley

Sign up for free
0