Question

Briefly discuss the causes that are responsible for missing data and inconsistency in rainfall record.

Answer 1

Sol: In developing countries historical rainfall data play an important role in hydrological management systems. A large number of rain gauge stations are installed throughout the study area to record the rainfall data. Those rainfall data are essential in creating the rain maps for the region. However, in practice, rainfall records often contain missing data values due to malfunctioning of equipment and severe environmental conditions. Furthermore, in some cases, a large number ofstations could be down simultaneously, thus creating many inaccurate readings or missing data. Such imperfect rainfall data could affect the accuracy of the rain maps. Therefore, estimating missing rainfall data is an important task in hydrology.

Environmental data, particularly, rainfall data are highly vulnerable to be missed, which is due to several reasons, such as incorrect measurements, and relocation of stations. Rainfall data are also affected by the presence of outliers due to the temporal and spatial variability of rainfall measurements. These problems may harm the quality of rainfall data and subsequently, produce inaccuracy in the results of analysis.

Main Causes that are responsible for missing data and inconsistency in rainfall record.:

• The failure of the observer to make the necessary visit to the gage may result in missing data.
• Vandalism of recording gages is another problem that results in incomplete data records
• Instrument failure because of mechanical or electrical malfunctioning canresult in missing data.
• Shifting of the raingauge to a new location
• Neighbourhood of the station is undergoing significant changes
• Change in the ecosystem due to natural calamities such as forest fires, landslides etc
• Errors in observation from a certain date

Some precipitation stations may have short breaks in the records because of absence of the observer or because of instrumental failures. It is often necessary to estimate this missing record. In the procedure used by the U.S. Weather Bureau, the missing precipitation of a station is estimated from the observations of precipitation at some other stations as close to and as evenly spaced around the station with the missing record as possible. The station whose data is missing is called interpolation station and gauging stations whose data are used to calculate the missing station data are called index stations.

Estimation of Missing Precipitation Data

This situation will arise if data for rain gauges are missing (e.g. due to instrument failure). Data from surrounding gauges are used to estimate the missing data. Three approaches are used:

1. Simple Arithmetic Method

2. Normal Ratio Method

3. Inverse distance method

4. Linear programming method

Simple Arithmetic Method

In this method simultaneous rainfall records of three close-by stations are made use of. The stations should however be evenly spaced around the station with missing records. A simple arithmetic average of the rainfall of the three selected stations gives the estimate of the missing value. This method can be used to calculate monthly as well as annual missing rainfall values. This method should be used only when normal annual precipitation at each of the selected stations is within 10% of that station for which records are missing.

Normal Ratio Method

When the normal annual rainfall of any of the selected stations is more than 10% of that station with missing records simple average method cannot be used, then the method to be adopted consists of weighting the rainfall value by the ratios of the normal annual rainfall values.

Inverse distance method

The inverse distance method has been advocated to be the most accurate method as compare to other two methods discussed above.

Amount of rainfall to be estimated at a location is a function of;

1. rainfall measured at the surrounding index stations

2. distance to each index station from the ungauged location

Linear programming method

Linear programming (LP) method selects a base station and several surrounding index stations and determines optimal weighting factor by minimizing the deviation between observed and computed rainfall at a base station for a number of rainfall events.Thus it determines optimal weighting factors for the base station and associated index stations.