3. Physical characteristics and processes leading to water scarcity
3.5. Meteorological and hydrological data collection and handling
The meteorological and hydrological data in an area must be sufficient, reliable and accessible to be of maximum use and benefit for the community. The hydrological systems are not understood well enough for reliable theoretical models of local conditions to be developed. In order to carry out water resources planning and design it is vital that some local data be available. For maximum usefulness there needs to be a combination of field data measurements and storage and retrieval systems together with the application of advanced processing techniques. Data acquisition and their management should be planned in such a way as to suit the local conditions, the available staff and the financial capability.
This could be developed gradually and prioritized so as to solve the most immediate problems.
Quality control and well-developed operating systems, including field check observations, should form components of the primary data-processing system. These
primary data should be purpose oriented and follow a plan for meeting defined objectives.
Among others, one may distinguish objectives of different types: meteorological, in relation to the synoptic weather stations; hydrological and water resources planning and management, relative to hydrological and water quality monitoring; agricultural, concerning agrometeorological networks for provision of irrigation advice and pest and disease information; drought management, referring to drought watch systems.
Data processing includes checking for completeness, long-term consistency, stationarity of the measured variables and statistical analysis of the data (Haan, 1977;
Conover, 1980; Kottegoda, 1980; Helsel and Hirsch, 1992). The latter includes fitting of frequency distributions to the data and using the data in parametric models and multivariate time-series analysis. The validity of derived relationships should be tested on independent data. The degree of both detail and precision of the analysis should be consistent with the quality and sampling frequency of the available data and with the accuracy required by the application of the analysis. Using good data is always more important than using abundant data.
Time-series play a crucial role in water resources evaluations. Stochastic time series models are fitted to the corresponding input variables such as river flows, rainfall, evapotranspiration, and temperature. These models in turn can be used for the simulation of the operation of the system. Mean values and variances often give a good characterization of the phenomena under study; however, these values do not reflect the internal properties of the investigated time series and further statistical analysis is required.
The homogeneity of hydrological data is an important requirement for a valid statistical application. A detailed analysis of the data is the most effective method of evaluating data homogeneity. The methods of analysis are usually based on plotting different types of variables against time or data collected in other locations in the same environmental area, or by relating them to other variables to discover causes of a disturbance of homogeneity. Anthropogenic disturbances and changes of climate can only be detected using high quality data (e.g. Refsgaard et al., 1989).
The WMO Guide to Hydrological Practices (WMO, 1994) and numerous other textbooks and reports (e.g. UNESCO, 1982 and 1987) provide methods and techniques for processing and numerous tests for examining the normality and homogeneity of hydrological data. The integrity and quality of data cannot be over-emphasized and one needs to ensure their quality before applying them for any hydrologic analysis.
Of particular interest are the frequency, severity, duration and spatial extent of droughts that seriously worsen the water scarcity conditions. There are several methods of analysing hydrological and meteorological data for determining drought sequences. WMO (1994) adopted a statistical procedure for analysing low flow time series. Other approaches for analysing streamflow discharges are available (Cancelliere et al., 1995). Rainfall data is currently analysed at the local scale through the theory of runs (Guerrero-Salazar and Yevjevich, 1975), the Palmer drought index, PDI (Palmer, 1965), and the standard precipitation index, SPI (McKee et al., 1993). Regional droughts are often based on the theory of runs (Santos, 1983; Cancellieri and Rossi, 2002) but a modified SPI may also be successfully utilised (Paulo et al., 2002). Drought indices may be used for drought warning and lead time assessment (McKee et al., 1995; Lohani and Loganathan, 1997).
The value of data in water resources management, especially under water scarcity conditions is immense. Nonetheless, water resources data collection programs are very vulnerable to cuts in government expenditures since they are not seen to have an immediate impact upon the community. Any adverse effects arising from a curtailment of these programs may not become evident until many years in the future. Because costs of data acquisition and primary analysis are very high, some governments only provide data to potential users for a fee. Then, to save costs, users often restrict to a minimum, often below what may be reasonable, the amount of data they use, so degrading the quality of the outcomes of any studies and projects based on the data. On the other hand, the increasing demand on limited water resources and increasing environmental degradation due to human activities, has ensured that the demand for reliable water resources data is increasing. At the same time it is becoming evident that there is an increasing scrutiny of the reliability of data and that there are expectations from users for higher quality data.
The above clearly point to the need for a well thought out and technologically advanced information system for water resources management (Iacovides, 2001). Such a system would define the type and extent of data required and should be such as to provide all the information necessary for integrated water resources management and for coping with water scarcity. To this end it should include a database system, and use of models and geographic information systems to facilitate decision support systems in water resources management.