Hazards stem from different processes and agents; flood hazards in particular arise from the geomorphological and hydro-meteorological conception (Alca´ntara-Ayala, 2002).
While quantifying vulnerability relies on socio-economic data that are richly available, the availability and quality of hydro-meteorological data in most developing countries are an impediment to flood hazard severity estimation and water management in general. Hydro-meteorological data in these developing nations are characterised by gaps, short durations and dubious quality (Adeloye, 2011; Dastorani et al., 2010; Gyau-Boake and Schultz, 1994; Ilunga and Stephenson, 2005).
The above deficiencies of hydrological and meteorological data arise from several factors. For example, observational networks for both hydrological and meteorological data have been declining over the decades globally (Smakhtin and Wichelns, 2009) but the decline is more marked for SSA (Giles, 2005). In their review of the operational status of observational points installed under the SADC HYCOS (Southern Africa Development Community – Hydrological Cycle Observation System) project, Houghton-Carr and Fry (2006) found that of the 48 data collection platforms installed between 1998 - 2000, only 7 were working by 2006 due to broken sensors, vandalism, theft, electrical and transmission faults, a general lack of maintenance and unavailability of resources.
Recently, Phalira (2012) reviewed the capacity of weather stations in providing meteorological data in the Lake Chilwa basin in Malawi. They found that only 14 out of
the 20 stations investigated were operational and only 7 out of the 14 operational stations had standard equipment. There was also lack of trained personnel, limited equipment, inadequate or no funding resulting in failure to maintain or buy new equipment, failure to recruit and pay skilled labour and in some cases resulting in total closure of the station. Phalira further found that only secondary stations stored data electronically; primary stations stored data manually limiting retrieval and hence utilisation of data in the later and also leading to data loss. He observed that electronic databases were equally prone to loss due to computer viruses. Quality checks were also rarely carried out.
In view of the above, hydrological data constitute a major problem to water management studies in developing countries. In their analysis of rainfall and flow variability in SSA, Conway et al. (2009) had to combine data from international data bases and national bases. Even then, the number of gauging stations used in some basins in the analyses is very small. For example, only two stations are used for the whole Zambezi River basin: Victoria Falls on the Zambezi and Mohembo on the Okavango, raising concerns over the reliability of their results.
In her assessment of hydrological impacts of climate change and variability at sub basin scale in the Zambezi Basin, Tirivarombo (2012) is also confronted with ungauged basins and basins with sparse stations whose data is characterised by short durations and extensive gaps. Consequently, the work is significantly supported by global data sets:
the Climate Research Unit (CRU TS2.1) rainfall of the University of East Anglia and flow from the Global Runoff Data Center (GRDC) (The Global Runoff Data Center, 2003). While very important, global data sets may not always be available at a time resolution or spatial unit of analysis required.
Due to unavailability of instantaneous flow values in some countries, Mkhandi et al.
(2000) derived regional flood frequency distributions for 11 countries in Southern Africa (Angola, Botswana, Lesotho, Malawi, Mozambique, Namibia, South Africa, Swaziland, Tanzania, Zambia and Zimbabwe) based on annual maximum daily discharges. From 44 delineated regions, Mkhandi et al. (2000) found that 33 regions
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failed the homogeneity test and attributed this to a large variance from short samples of data in these regions.
The Lower Shire Valley in Malawi is no exception to hydrological data deficiencies.
Due to hydrological data issues among other factors, the existing flood warning system is manual, dependant on manual observation of gauge readings and rainfall in the Shire and Ruo catchments (Nilson et al., 2010). The system has been described as inefficient and unreliable sometimes leading to false, late or no alert at all (MoIWD, 2003; Nilson et al., 2010; Shela et al., 2008). There have been efforts towards automated real time flood warning system. An automated system installed in the 1990’s failed due to vandalism, lack of maintenance, and lack of local support (Nilson et al., 2010).
However, in the status report on flood warning and forecasting by the Ministry of Irrigation and Water Development (MoIWD, Undated), the report also points to hydrological data challenges that could not support the Hydrologiska Byråns Vattenbalansavdelning (HBV) conceptual model for forecasting.
2.4.4 Summary
It emerges from the foregone sections that patterns exhibited in flood risk studies in SSA are not atypical to the Lower Shire Valley. Previous work in the valley has been dominated by vulnerability assessments that have strived to understand causation, impacts, perceptions and coping strategies qualitatitively. It is also clear that availability and quality of the hydrological data is also a challenge to water resources assessments including flood risk assessments. Ultimately, several aspects on the flood risk of the rural subsistent people in the Lower Shire Valley and in SSA at large remain unknown.
In particular, from a contemporary disaster management perspective, it emerges that the degree of vulnerability, hazardousness and ultimately risk and the broader dimensions rather factors driving these components have not been brought empirically into a strategic picture for disaster management.