Module 2 Measurement and Processing of Hydrologic Data 2.1 Introduction 2.1.1 Methods of Collection of Hydrologic Data 2.2 Classification of Hydrologic Data 2.2.1 Time-Oriented Data 2.2.2 Space-Oriented Data 2.2.3 Relation-Oriented Data 2.3 Design of Hydrometeorological Data Networks 2.3.1 Classification of Observation Networks 2.4 Precipitation Networks 2.5 Stream Gauging Networks 2.5.1 Network Design Process 2.5.2 Criteria for Location of Stations 2.5.3 Evaluation and Adequacy of Networks 2.5.4 Site Selection Surveys 2.5.5 General site selection guidelines 2.5.6 Criteria for Water Level Gauging Sites 2.5.7 Criteria for Streamflow Measurement Sites 2.5.8 Criteria for Natural Control Sites 2.5.9 Criteria for Artificial Control Sites 2.5.10 Bureau of Indian Standards (BIS) criteria for selection of river gauging sites 2.5.11 World Meteorological Organisation (WMO) criteria for selection of site 2.5.12 International Standards for Hydrometry 2.6 Errors in Hydrological Observations 2.6.1 Sources of Errors 2.6.2 Secondary Errors of Measurement 2.7 Validation of Hydrologic Data 2.7.1 Levels of Validation 2.7.2 Primary Validation 2.7.3 Secondary Validation 2.7.4 Hydrological Validation 2.7.5 Validation of Climatic Data 2.7.6 Single series tests of homogeneity 2.7.7 Multiple stations validation 2.A Definitions of terms related to measurement errors 2.11 References Keywords: Hydrologic Data, Time-Oriented, Space-Oriented, Relation-Oriented, Networks, Precipitation, Stream Gauging, Design, Site Selection, Measurement Errors, Validation 2.1 Introduction 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. Equipment and techniques of hydrologic data collection have evolved with growth in technology and water sciences. For a water resources study, one needs data of a number of variables in the vertical as well as horizontal planes. The data needed for water resources development come from a vast swath of disciplines: hydro-meteorologic, geomorphologic, agricultural, pedologic, geologic, hydrologic, social, economic, ecological and environmental sciences, etc. Hydrometeorologic data include rainfall, snowfall, temperature, radiation, humidity, vapor pressure, sunshine hours, wind velocity, and pan evaporation. Agricultural data include crop cover, irrigation application, and fertilizer application. Pedologic data include soil type and texture; soil particle size; porosity; moisture content; steady-state infiltration, and saturated hydraulic conductivity. Geologic data include stratigraphy, lithology, and structural controls. Frequently, data on the type, depth and areal extent of aquifers are needed. Ecological and environmental data includes water quality variables, aquatic plants and animals and their habitats. 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, discharge, base flow, stream-aquifer interaction, depth to water table, and drawdowns. Fig. 2.1 shows the activities of a hydrological service. The term hydrological data processing is a widely used but loosely defined and includes a range of activities varying from simple analysis to complete modeling. Before this, of course, the data are observed and this step is known as origination and collection. Hydrological data processing is a multi-step process that begins with a preliminary checking of raw data in the field and successively higher levels of validation before the data are accepted as fully validated. Passage of data from field to storage is not a one-way process and contains feedbacks. Further, processing and validation of hydrological data is not a purely statistical exercise an understanding of field practices, the principles of observation, and the physics of the variable being measured are required. The activities in data processing life cycle are shown in Fig. 2.1. Data processing also includes aggregation of data observed at a given time interval (e.g. hourly) to a different interval (e.g., daily and daily to monthly) or disaggregation, i.e., conversion from a long to short (say daily to hourly) time step is also carried out. Typical stages in hydrological data processing are: Scrutiny of raw data; data entry to computer, validation, and correction; and data archival and dissemination. 2.1.1 Methods of Collection of Hydrologic Data
Hydrological observations are the scientific ways for collection of water related data at a specific location. There are many ways in which the hydrologic data can be collected. The major techniques are described below. Direct Measurement This is the most common way to measure hydrometeorological variables, such as precipitation and streamflow. A gauging site is established and is equipped with the devices that can measure the variable(s) of interest. In case of manual observations, an observer visits the site, measures the values of the concerned variables, and records or transmits them to the controlling office for processing and storage. On the other hand, at an automated hydrologic or weather station the seasons can measure a number of hydrometeorological variables and store/transmit the data to the controlling office without any human intervention. The equipment may be programmed to transmit the data at selected time interval or it can be interrogated as per the needs to get the data. With improvement in communication technology, it is possible to get the desired data from the stations widely spread over an area at a central place in real-time. Remote Sensing In this technique, the data about an object are obtained without coming in physical contact with the object. This technique is now very commonly used to provide spatial data of terrain features. Similarly, weather radars are being increasingly used for measurement of precipitation. OBSERVATION INPUT STORE FEEDBACK RETRIEVE UTILISE DATA PROCESSING OPERATIONS Observation and input, processing and storage, Retrieval and use, Feedback, Figure 2.1: An illustration of data processing life cycle activities
Chemical Tracers In this approach, some chemicals, known as tracers, are added to the process whose data are to be obtained. Tracers can also be used to determine the flow path of water or a pollutant. The nuclear or isotope techniques are being employed to trace the movement of water molecules in different parts of the hydrological cycle. Nuclear techniques are helpful to assess the rate of sediment deposition in a water body, identify the rainfall recharge and recharge areas of aquifers, study of seawater intrusion in coastal regions, measure seepage and leakage from surface water bodies, analyse surface water and ground water interaction, etc. 2.2 Classification of Hydrologic Data Hydrologic data can be classified in several ways. Most commonly, data are classified in three categories: time-oriented data, space-oriented data, and relation-oriented data. Hydrologic data can also be classified as time varying or time non-varying data. The time non-varying or static data includes most space-oriented data which do not change (or very-very slowly 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 next. Data acquisition Design of data collection network Data collection and transmission Validation, processing and storage Data processing and analysis Decision making Analysis of data Data for design and operation Public information Fig. 2.2 Activities of a hydrological service [adapted from WMO (1994)].
2.2.1 Time-Oriented Data Values of most hydrometeorological variables change with time and such variables are known as time-oriented 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. river water level); an accumulated value (e.g., daily rainfall); or an averaged value (e.g., mean daily discharge). The distinction between instantaneous and accumulative values is important when the data are processed. These data can be further classified as meteorological data, hydrological data, and water quality data. Depending on the frequency of observations, the time-series data can also be classified as: Equidistant time-series data which are the measurements made at regular intervals (hourly, daily); the reported values may be instantaneous, accumulated or averaged. Cyclic time-series data are the data measured at irregular intervals of time but the time sequence is repeated regularly. For example, the observation of at many places river stage is measured daily at 08:30 and 17:30 hrs. Values of non-equidistant data series are observed when some specified event takes place. For example, in a tipping bucket rain gauge, the bucket tips after a certain depth of rain has fallen and the value is recorded. 2.2.2 Space-Oriented Data Space-oriented data comprise of the information related to physical and morphological characteristics of catchments, rivers (cross-sections, profile, bed characteristics, networks), soil maps, lakes and reservoirs data (elevation-area variation), etc. Traditionally, such data are stored in the form of paper maps and manually analyzed. The modern trend is to use a Geographical Information System (GIS) to input, store and analyze such data. 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. 2.2.3 Relation-Oriented Data Such data comprise of information about mathematical relationships established between two or more variables. A mathematical relationship between two or more variables is established for many purposes, such as data validation, filling-in missing data, etc. The variables themselves may form a time-series but their relationship is of interest here. The relationship may be expressed in mathematical, tabular, or graphical form. The stage-discharge rating, spillway rating table and the calibration ratings of various instruments are typical example of relation-oriented data. More than one equation may be required to characterize the relationship which may change with time.