Acquisition and Processing of Water Resources Data

S.K. Jain    National Institute of Hydrology, Roorkee 247 667, Uttaranchai, India

V.P. Singh    Department of Civil and Environmental Engineering, Louisiana State University, Baton Rouge, LA 70803-6405, USA

The objectives of this chapter are:

• to explain various categories of water resources data;

• to discuss techniques of acquisition, validation, and processing of precipitation and discharge data;

• to discuss meteorological, water quality, and other data used in water resources planning and management; and

• to explain important features of a water resources information system.

Data are the foundations on which any analysis rests. The practice of hydrological measurements is very old. Kautilya initiated systematic precipitation measurements in India in the fourth century BC. Streamflow was probably first monitored by Hero of Alexandria in the first century AD. With development in water sciences, there have been simultaneous developments in equipment and techniques of data collection. A number of international / national standards have been prepared to ensure systematic measurements of water resources and it is necessary that the observatories should conform to these standards. The Committee on Opportunities in the Hydrological Sciences (1991) has appropriately summarized the necessity of good water resources data: “Modeling and data collection are not independent processes. Ideally, each drives and directs the other. Better models illuminate the type and quantity of data that are required to test the hypotheses. Better data, in turn, permit the development of better and more complete models and new hypotheses.”

The data needed for water resources are: hydrometeorologic, geomorphologic, agricultural, pedologic, geologic, and hydrologic. Hydrometeorologic data include rainfall, snowfall, temperature, radiation, humidity, vapor pressure, sunshine hours, wind velocity, and pan evaporation. Agricultural data include vegetative cover, land use, treatment, and fertilizer application. Pedologic data include soil type, texture and structure; soil condition; soil particle size; porosity; moisture content and capillary pressure; steady-state infiltration saturated hydraulic conductivity, and antecedent moisture content. Geologic data include data on stratigraphy, lithology, and structural controls. More specifically, data on the type, depth and areal extent of aquifers are needed. Depending on the nature of aquifers these data requirements vary. For confined aquifers, hydraulic conductivity, transmissivity storativity, compressibility, and porosity are needed. For unconfined aquifers, data on specific yield, specific storage, hydraulic conductivity, porosity, water table, and recharge are needed. Each data set is examined with respect to homogeneity, completeness, and accuracy. Geomorphologic data include topographic maps showing elevation contours, river networks, drainage areas, slopes and slope lengths, and watershed area. Hydrologic data include flow depth, streamflow discharge, base flow, interflow, stream-aquifer mteraction, potential, water table, and drawdowns. Thus, for a water resources study, one needs data of a number of variables in the vertical as well as horizontal planes.

The activities of a hydrological service are shown in Fig. 2.1. The term hydrological data processing is a widely used but loosely defined term that includes a range of activities varying from simple analysis to complete modeling. The processing of hydrological data is a multi-step process that begins with a preliminary checking of raw data in the field and successively higher levels of validation before it is accepted as fully validated data for further use. The passage of data from field to data storage is also not a one-way process and includes several feedbacks. Sometimes, channels for feedback from data users are also maintained. Actually, processing and validation of hydrological data are not a purely statistical exercise – these require an understanding of field practices, the principles of observation, and the physics of the hydrological variable being measured.

Data processing also includes aggregation of data observed at a certain time interval to a different interval, e.g., hourly to daily and daily to monthly. Occasionally disaggregation, i.e., conversion from a long (say daily) to short (say, hourly) time step is also carried out. The computation of areal averages, for example, catchment rainfall, is also required for validation. This also provides a convenient means for summarizing large volumes of data.

Typical stages in water resources data processing are:

• Scrutiny of raw data;

• data entry to computer, validation, and correction; and

• data archival and dissemination.

In view of the central role of water resources data in planning, design, and management of water resources projects, this chapter is devoted to basic concepts of data acquisition, processing, storage, and dissemination. Some of the latest techniques of data acquisition are also discussed.

2.1 TYPES OF WATER RESOURCES DATA

There are several ways to classify water resources data. The most common way is to classify the data into three categories: time-oriented data, space-oriented data, and relation-oriented data.

The time-oriented or time-series data consist of hydrometeorological, water quantity, and water quality data that are periodically measured at a station. The time interval between observations can be constant or varying. The examples of such types of data are rainfall, river stage, and sediment concentration. Some surface water data are equally spaced in time. The space oriented data comprise topographic maps of catchments, river networks, soil maps, etc. Traditionally, such data are stored in the form of paper maps and manually analyzed. The recent trend is to use a Geographical Information System (GIS) to input, store and analyze such data. As described in detail in Section 3.3 in Chapter 3, different types of information, such as topographical and land use of an area, are stored in a GIS in different layers of a map which can be overlaid and analyzed. The relation-oriented data consist of mathematical relationships between two or more variables. Typical examples are river rating curves or a spillway rating table.

The water resources data can also be classified as time vaiying or time non-varying data. The time non-varying or static data includes most space-oriented data which do not change with time, for example catchment topographic map, soil map, etc. Some features, such as river network and land use in a catchment, might gradually change with time and can be considered as semi-static. A brief description of each type of data is presented in what follows.

2.1.1 Time-Oriented Data

The values of most hydrometeorological and water quality variables change in time and such variables are classified as time-oriented data. These data can be further classified as meteorological data, hydrological data, and water quality data.

The time-series data include all the measurements which have an observation time associated with them and most water resources data have this property. The variable could be an instantaneous value, e.g., water level in a river; an accumulated value, e.g., daily rainfall; an averaged value, e.g., mean daily discharge; or a statistic over a specified time period, e.g., annual maximum flow. The distinction between instantaneous and accumulative values is important when the data are further processed.

Depending on the frequency of observations, the time-series data can also be classified as:

• Equidistant time-series are measurements which are made at regular intervals of time (hourly, daily); the values may be instantaneous, accumulated or averaged.

• Cyclic time-series are the measurements which are made at irregular intervals of time but the irregular time sequence is repeated regularly, for example, the observation of river stage daily at 08:30 and 17:30 hrs.

• Non-equidistant time-series are the measurements which are made when some specified event takes place. For example, in a tipping bucket raingage, each tip of the bucket is recorded after a certain depth of rain has fallen.

The two most important data for hydrological analysis are precipitation and streamflow. The measurement and processing of these two will be discussed in greater detail. The time-series of evaporation data forms another important input in hydrological studies. The temperature of air, soil and water bodies is important as many processes depend on it. Other important meteorological variables include humidity, wind speed and direction, and sunshine duration.

2.1.2 Space-Oriented Data

The space-oriented data comprise of catchment data (physical and morphological characteristics), river data (cross-sections, profile, bed characteristics), and lake or reservoir data (elevation-area-storage capacity). These have been further discussed in Section...