TRMM for Flood Forecasting

Tropical Rainfall Measurement Mission (TRMM) is a satellite that observes precipitation distribution from 403 km above the earth with the orbital inclination at 35 degrees. TRMM has five instruments, which are Precipitation Radar (PR), TRMM Microwave Imager (TMI), Visible Infrared Scanner (VIRS), Clouds and the Earth's Radiant Energy System (CERES), and Lighting Imaging Sensor (LIS). Among the three primary instruments on TRMM, the most innovative is PR. Other instruments similar to the TMI and the VIRS have operated in space before [4]. TMI is similar to Special Sensor Microwave/Imager (SSM/I) [Simpson et al., 1996 on 2], while infrared sensors on geostationary platforms for tracking cloud movement has been introduced in 1970’s [3].

These five instruments are working independently in sensing specific mechanism in hydrological cycle. However, the data resulted by each instrument are possible to be combined for giving a comprehensive measurement of an event. As can be seen at its official website http://trmm.gsfc.nasa.gov/publications_dir/potential_flood_hydro.html, TRMM can also estimate flood and landslide by analysis using merged satellite rainfall. The rainfall-runoff simulation approach that uses that model is the Natural Resources Conservation Service (NRCS) runoff curve number (CN) method and using an antecedent precipitation index (API) as a proxy of antecedent moisture conditions [2]. For certain detail needs, data from TRMM are also uses for flood early warning. As stated in [3], for operational of flood early warning system, the three most important components are: 1) a rainfall measuring system (the major input to the hydrological model); 2) a soil moisture updating system (for initializing the hydrological model); and 3) a surface discharge measuring system (for calibration and constraining predictions of the hydrological model); while TRMM is considered to support component no 1).

Some study has conducted by utilizing TRMM product for forecasting a flood event. As been performed in [6], 3B42 version 6 was used for flood simulation in Huong River, Vietnam. The algorithm of this TRMM product is to produce merged high quality (HQ)/infrared (IR) precipitation and root-mean-square (RMS) precipitation-error estimates. These gridded estimates are on a 3-hour temporal resolution and a 0.25-degree spatial resolution in from 50 degrees south to 50 degrees north latitude [4]. As the result, streamflow simulation by using TRMM data was found to be in good agreement with the ground-based measurements during the simulated flood peaks of 2004 and 2006 in Huong River, though recommend to be validated with more events [6].

The performance of TRMM data also been examined in Bangladesh, that suffers from flooding in most of the year because of highly intensive rainfall within and outside of the country. The performance of satellite rainfall is an important issue for hydro-meteorological application in Bangladesh [1]. The analysis was using TRMM products PR-2A25 data and 2A23. As evaluated to the rain-gauge observation, and compare to the performance in United States’ area, the TRMM data in Bangladesh has relatively higher rain detection errors for the Passive Microwave retrievals and so for microwave rainfall detection. The main reason could be that summer rain for this region comes mainly from extensive mid-level stratiform clouds, and the retrieval algorithm is most suited for deep convective clouds [1].

As many extreme flood and weather events has been successfully measured, in general, TRMM is a very helpful tool for forecasting disaster and prepare for it. For a future optimum utilization and improvement, more examination and analysis should be continued. However, the availability cost for analyzing flood forecasting by using TRMM data is need to be reasonable, whether in combination or not with soil moisture and surface discharge. Since many flood events happen in tropical developing countries that considered having lack resources for analysis conducting.

Reference:

1.

Alamgir, S., Bernier, M., Racine, M. (2004), Performance of TRMM Satellite Data over the Rain-Gauge Observations in Bangladesh, Remote Sensing for Agriculture, Ecosystems, and Hydrology VI, Proceedings of SPIE Vol. 5568 (SPIE, Bellingham, WA, 2004) · 0277-786X/04, doi: 10.1117/12.565815

2.

Hong, Y., R. F. Adler, F. Hossain, S. Curtis, and G. J. Huffman (2007), A first approach to global runoff simulation using satellite rainfall estimation, Water Resour. Res., 43, W08502, doi: 10.1029/2006WR005739

3.

Hossain, F. (2006), Towards Formulation of a Space-borne System for Early Warning of Floods: Can Cost-Effectiveness Outweigh Prediction Uncertainty?, Natural Hazards 37: 263–276, DOI 10.1007/s11069-005-4645-0

4.

http://trmm.gsfc.nasa.gov/

5.

http://www.eorc.jaxa.jp/TRMM/index_e.htm

6.

Valeriano, O. C. S. , Koike, T. , Yang, D. , Nyunt, C. T., Van Khanh, D., and Chau, N. L. (2009), Flood simulation using different sources of rainfall in the Huong River, Vietnam, Hydrological Sciences Journal, 54: 5, 909 — 917, DOI: 10.1623/hysj.54.5.909