Mh sw optimisation of g&d stations network of maharashtra

Optimization of Streamgauge and Raingauge Network for Upper Bhima Basin
CWPRS Report No: 4797 December 2010 1
OPTIMISATION OF STREAMGAUGE AND RAINGAUGE NETWORK FOR UPPER
BHIMA BASIN
________________________________________________________________________________
Report No.: 4797 Month: December 2010
________________________________________________________________________________
1. Introduction
1.1 General
Hydrological and related meteorological data are collected through a network of specialized
instruments to provide information on the quality and quantity of water moving through
catchments and along rivers of a country. Water data, in its entire gamut, collected through
the network, cater to the hydrological information needs of the region under purview; and
constitute the Hydrological Information System (HIS) for the area. Ideally, the water data
emanating from the network should enable accurate estimation of the hydrological regime of
the region. HIS essentially provides the data required for planning, design and management
of water resources of the regions; including operation and management of flood protection
measures in inundation prone areas.
Hydrological information system for a typical region comprises sub-systems for data
collection & storage, data communication & transmission, data transformation for producing
information and information-communication. Water data are collected, processed and stored
by agencies such as the Central Water Commission (CWC), India Meteorological
Department (IMD), State Irrigation Departments/ Water Resources Departments (WRDs),
etc. Basically, the hydrological networks operating in different river basins of the country,
and maintained by one or more of the agencies entrusted with the task, provide the data
forming the core of HIS. The functions of hydrological services or equivalent agencies inter
alia include: establishment and supervision of network; collection, processing and
publication of basic data; preparation of reports on water resources; research &
development; analysis/ design studies; and training.
A national hydrological network will provide data that will be used for many types of
decisions. Often, it is difficult to anticipate the uses to which water data will be put to use.
Network design is a complex facet of hydrological engineering. What constitutes a
hydrological network itself is open to debate, with many aspects such as hydrological
phenomena/ processes under consideration, geographical scope, stage of water resources
development in an area, and intended use of data coming into inter-play. In practice,
hydrological network design is an evolutionary process, wherein a minimum network is
established early in the development of a geographical area, and the network is reviewed
and upgraded periodically until an optimum network is attained.
Different types of hydrological and meteorological data that need to be a part of HIS for a
region, depending on its geographical scope. Precipitation, gauge-discharge data for rivers/

lakes/ reservoirs, evaporation, sediment transport, water quality, water temperature, soil
moisture, ground water, etc. are some of the major types of data that are relevant in this
respect. Data on discharge and water level are basic to the solution of most design and
operation problems. Information on precipitation is indispensable to water resources
development and management. Establishment of networks on an integrated basis is very
important, especially as regards streamflow and precipitation networks. In some cases, both
precipitation and streamflow networks are operated by the same agency, though often in
practice, such networks are managed independently. Obviously, good cooperation is
required for operating and developing networks. For the purpose of this study, hydrometric
network for streamflow related measurements, and raingauge networks for precipitation
related data, is primarily considered.
1.2 Hydrology Project
Hydrology Project (HP) is currently being implemented by the Government of India (GoI),
with external support from the World Bank. The primary objective of the project is
improvement of the country’s institutional and technical capabilities to measure, collate,
analyze and disseminate quality hydrometeorological data concerning all aspects of surface
water and ground water resources.
Hydrology Project I (HP I), the first phase of the project, was implemented during 1996-2003.
In all, five central agencies and nine states participated in the project. The central
Implementing Agencies (IAs) were: Central water Commission (CWC), Central Water and
Power Research Station (CWPRS), Central Ground Water Board (CGWB), India
Meteorological Department (IMD) and National Institute of Hydrology (NIH); and the State
IAs: Andhra Pradesh, Chattisgarh, Gujarat, Karnataka, Kerala, Madhya Pradesh,
Maharashtra, Orissa and Tamil Nadu.
Within the overall framework of HP, CWPRS performed the role of a facilitator in R&D,
training, activities involving special studies and technical support. Specific tasks undertaken
by CWPRS included: activities related to institutional strengthening such as upgradation of
Current Meter Rating Trolley (CMRT) and setting up of Hydrometric Instrumentation
Services Facility (HISF); R&D studies and training. Three R&D studies, namely a) Reservoir
Sedimentation Survey of Gangapur reservoir, b) Field Investigations and Development of
Mathematical Model for Predicting Water Quality in the Panshet and Ujjani Reservoir
Systems’ and c) Estimation of Irrigation Return Flows in the Kukadi Canal Command Area
(in association with Maharashtra) were conducted earlier under HP I.
Second phase of the project, namely Hydrology Project II (HP II) is currently under
implementation as a six-year project. The project commenced in June 2006; and is
scheduled to continue till June 2012. The IAs of HP II included, in addition to the IAs
involved in HP I, the central agencies of Central Pollution Control Board (CPCB) and
Bhakra-Beas Management Board (BBMB); and the states of Goa, Himachal Pradesh,
Pudhucherry and Punjab.

The primary objective of HP II is to extend and promote the sustained and effective use of
the Hydrological Information System (HIS) developed under HP I, by all potential users who
are concerned with water resources planning and management. This is to be achieved by: i)
strengthening the capacity of hydrology departments to develop and sustain the use of the
HIS for hydrological designs and decision tools; ii) improving the capabilities of implementing
agencies at state/ central level in using HIS for efficient water resources planning and
management to meet the country’s poverty reduction objectives; iii) establishing and
enhancing user-friendly, demand-responsive and easily-accessible HIS; and iv) improving
access to the HIS by public agencies, civil society organizations and the private sector
through supporting outreach services. Towards this end, HP II essentially consists of three
main components: i) institutional strengthening, ii) horizontal expansion, and iii). vertical
extension, CWPRS activities are restricted to the two categories afore-mentioned namely
institutional strengthening and vertical extension.
The vertical extension component involves a vertical shift from collection and processing HIS
data towards the use of such data in the development of decision support system (DSS) for
integrated planning and management of water resources in river basins/ sub-basins and
including such activities as early flood warning, drought measurement, conjunctive use of
surface and ground water and integrated operation of reservoirs. The specific activities
planned under vertical extension include: development of hydrological design, decision
support systems and purpose driven studies. Purpose driven studies under HP II are
oriented to address issues that are relevant to the implementing agencies. Studies are
expected to address surface/ ground water issues that are relevant to implementing
agencies. Within the said provision under HP II for undertaking PDS, the present study of
`Optimization of streamgauge network for Upper Bhima basin’ has been taken up. The
ensuing Section 1.3 below details the rational for taking up the purpose driven study (PDS)
as also scope of the proposed study.
1.3 Purpose Driven Study
A number of river basins constitute the geographical area of any state. Major rivers flowing
wholly/ partly through Maharashtra include Krishna, Godavari, Tapi, Narmada, Mahanadi;
and other west-flowing rivers originating from the Western Ghats. Amongst the major rivers,
Narmada and Tapi flow to the west, and Godavari and Krishna to east. As is the practice
elsewhere in the country, the hydrologic and meteorological stations in operation in different
river basins in the state are controlled by the CWC; Water Resources Department (WRD),
Government of Maharashtra (GoM); IMD and other agencies. WRD, GoM, is the primary
agency entrusted with collection of hydrometeorological data relating to Maharashtra. The
HIS for the state, in turn, is used to assess the quantum of water available in each basin/
sub-basin to facilitate optimum use of the water resources. The existing hydrometeorological
network of Maharashtra includes 264 Gauge-Discharge (GD) stations, 641 Ordinary Rain
Gauge (ORG) stations, 340 Self Recording Rain Gauge (SRRG) stations and 153 Full
Climatic Stations (FCS). This network was developed over a period of time to meet the
emerging needs from time-to-time for the basin.

GoM decided to review the hydrometric network in Maharashtra, in relation to such factors
as overall objectives, minimum/ ideal/ optimal network, available budget and overall benefits
of the water data. The review is oriented towards getting the network tuned to the present-
day data needs essentially by proposing new stations wherever necessary, and deleting
stations where not needed. Towards this, a purpose driven study (PDS) for optimization of
streamgauge network was proposed by GoM jointly with CWPRS under existing provisions
of Hydrology Project II (HP II). During discussions it was decided to conduct the detailed
study for a pilot basin, namely Upper Bhima up to Ujjani reservoir. The study was approved
by the Hydrological Information System Management Group (Technical) [HISMG-T]; and
concurred by World Bank [vide MoWR letter No. 12/ 94/ 2005-B&B/ Vol. 5/ 1821-49 dated
20/5/2009]. Scope of the present study includes checking adequacy of the existing GD and
raingauge network in the Upper Bhima basin, and detailed investigation on the preliminary
review carried out by GoM.
This report examines the methodology used in optimization of GD and associated raingauge
networks under consideration; and arriving at the optimum networks for different regions,
without compromising accuracy of the water data. While conducting the review, it is fully
recognized that further changes in the network will definitely be needed in future.
1.4 Objectives
A monitoring network is based upon two considerations, namely the monitoring objectives
and the physical characteristics of the system to be monitored. The identification of the
monitoring objectives is the first step in the design and optimization of monitoring systems. A
combination of analytical and practical approaches is adopted for optimizing the Upper
Bhima GD network of WRD, Maharashtra. A Generalized Least Squares (GLS) approach is
used to establish the empirical relationships between streamflow statistics of interest with
basin characteristics such as Catchment Area (CA) and meteorological variables such as
Mean Annual Precipitation (MAP), and to rank GD stations according to their influence on
streamflow statistics under consideration.
Monte Carlo studies by Stedinger & Tasker (1985, 1986) and Tasker & Stedinger (1987)
document the GLS procedure to develop empirical relationships between streamflow
statistics and basin characteristics. The GLS algorithm takes into account for differences in
record length, variations of flows at different sites, and cross-correlation among concurrent
streamflows. Spatial hydrologic regression is performed under GLS framework in the
present study to review the network.
Adequate number of rain gauges should be available, upstream of every streamgauging
station, to estimate the areal rainfall with a specified accuracy. For a study of the nature, the
raingauge network has to be considered in conjunction with surface water and groundwater
networks. The former should have sufficient spatial coverage such that all GD stations in the
hydrometric network are fully covered. This means that dependent on the objectives,
rainfall-runoff computations can be made and/ or water balance quantification done. For the
purpose of this study, the objective of the raingauge network in Upper Bhima basin is taken

to be reliable estimation of areal rainfall for the region, commensurate with the
streamgauging network.
Typically, rains in the Upper Bhima basin are almost entirely concentrated in the months
June to September. The estimation error in the average monthly and annual rainfall, after
computing spatial correlation structure of rainfall in the catchment, has been used as a
measure of effectiveness for raingauge network optimization.
Sections 2 and 3 of this report deal with the study area and the data availability for the study
respectively; and Section 4 the methodology adopted for hydrologic network design.
Sections 5-8 deal with review of streamgauge network and its optimization. Section 9
elaborates raingauge network optimization; and in Section 10 results of the study are
summarized.

2. Study Area
Bhima is a major tributary of Krishna river; and one of the two majors rivers of Maharashtra,
with the other being Godavari. Bhima originates at Bhimashankar in the Sahyadri Ghats at
the elevation of MSL 700m. The banks of Bhima are densely populated, and form fertile
agricultural area. The river is prone to frequent flooding due to heavy rainfall during the
monsoon season. Bhima flows southeast for a long journey of 725 km, before joining
Krishna River at Krishna, Raichur district, Karnataka. The total catchment area of Bhima is
48,631 km²; comprising 219 sub-watersheds.
For the present study, Upper Bhima basin up to Ujjani reservoir is considered. The Upper
Bhima basin is located in the western part of Maharashtra between 170
53' N to 190
24' N
latitude and 730
20' E to 750
18' E longitude. The basin covers a geographical area of 14,712
km2
; comprising 68 sub-basins. Of the total geographical area under study, 25 % is hilly
and/or highly dissected, 55 % plateau and the remaining plain area. Figure 2.1 gives a
location map of Upper Bhima basin up to Ujjani.
Figure 2.1: Location of Upper Bhima basin up to Ujjani
About 25 % of the Upper Bhima basin, lying in the western zone, falls in good rainfall region.
Remaining 75 % is rainfall deficit region; having annual rainfall less than 700 mm. In the
Parner-Shirur region, rainfall is normally less than 600 mm. Of the total rainfall, 85 % comes
from South-West monsoon during June-September, 11 % from North-East monsoon during
September-December, and about 4 % after December. About 89 % of the basin is classified
as drought-prone.

During the journey of Upper Bhima, many smaller rivers join it. The main tributaries of the
river are: Ghod and Mula-Mutha rivers. Mula rises in Mulshi taluka, and Mutha in Velhe.
River Ghod and its tributaries Kukadi and Meena also rise in the Sahyadri Ghats. The
places of origins of the tributaries of Bhima fall in comparatively heavy rainfall region. Length
of Bhima up to Ujjani is 275 km. The basin is spread over the three districts of Pune,
Solapur and Ahmednagar in Maharashtra; and covers 13 talukas. Table 2.1 gives details
relating to Bhima and its tributaries up to Ujjani reservoir.
Table 2.1: Details of Main River and Tributaries; Upper Bhima basin up to Ujjani reservoir
Origin Confluence
No River/
Tributary
Streams
Length
(km) Place
Altitude
(m)
with
Altitude
(m)
1 Bhima 275 Bhimashankar 700
Krishna in Raichur district,
Karnataka
343
2 Indrayani 83 Aapti 900 Bhima ---
3 Kundalika --- --- --- Indrayani ---
4 Bhama --- --- --- Bhima ---
5
Bhima
Wel 60 --- --- Bhima ---
6 Pawana 55 Mula 900 Mula near Dapodi 439
7 Mula 50 Mazgaon --- 522
8
Mula-
Mutha
Mutha 64 Davjhar 900
Mutha meet with Mula
near Khadki.
Mula-Mutha joins Bhima
near Pargaon 564
9 Ghod 170 Gawadewadi 1,000 Bhima near Daund 498
10 Meena 53 Amboli --- Ghod ---
11 Kukadi 85 Ghatghar --- Ghod near Shirur 562
12 Pushpavat
i
35 Khireshwar --- Kukadi ---
13 Arr --- --- --- Kukadi ---
14 Hanga --- --- --- Ghod ---
15
Ghod
Palsi --- --- --- Ghod ---
The region, covering Upper Bhima basin, is highly industrialised and urbanised; resulting in
substantial water quality problems in the region including Ujjani reservoir. Average annual
water yield of the Upper Bhima basin up to Ujjani is 7,594 Mm3
. There are 14 major and
medium dams in the Upper Bhima basin. Major dams in the basin include: Pavana, Ghod,
Mulshi (Tata), Khadakwasla, Chaskaman and Ujjani. The municipal corporations of Pune
and Pimpri-Chinchwad, forming the Pune Metropolitan Region, constitute a part of this
basin. Provisional population of the basin, as per 2001 census, is 73.72 lakh.
It is estimated that annually about 221 Mm3
water is utilized for domestic and 77 Mm3
for
industrial uses. The present requirement of water for non-irrigation use is projected to be
298 Mm3
; which is expected to increase to 844 Mm3
by 2030.
Hydrological challenges of the Upper Bhima basin are identified to be: drought
management, flood management in Pandharpur (situated downstream of Ujjani dam) and
Pune cities (due to releases from Panshet, Warasgaon, Temghar and Khadakwasala
reservoirs), high evaporation from Ujjani reservoir; and river water pollution primarily
resulting from industrial effluents and domestic waste.

3. Data Availability
Figure 3.1 gives the map of Upper Bhima basin. The existing hydrometeorological network
in the Upper Bhima basin, as of now includes 14 GD stations, 44 raingauge stations with 44
having ORG & 8 with SRRG facility; and 5 FCS. Daily streamflow data for 14 GD stations in
the basin, namely Aamdabad, Askheda, Budhawadi, Chaskaman, Dattawadi, Kashti,
Khamgaon, Nighoje, Pargaon, Paud, Pimpale-Gurav, Rakshewadi, Shirur and Wegre -
having different record lengths for each station with a minimum 11 years for Pimpale-Gurav
and a maximum 35 years for Chaskaman - are available. For optimization studies for
raingauge network, historical rainfall data for 44 raingauge stations were used.
Physiographic characteristics of the catchment/ sub-basins, used in the study, included
drainage area, latitude and longitude and the meteorological variable - mean annual
precipitation.
Figure 3.1: Existing GD and raingauge stations in Upper Bhima basin
3.1 Validation of Hydrometeorological data:
The procedure adopted by State Data Processing Center of WRD, Maharashtra for
validation of the hydrometeorological data, which is used in the current PDS, is summarized
below.

Two software were used for validation of hydrometeorological data namely SWDES (Surface
Water Data Entry Software) and HYMOS (Hydrological Modeling System). SWDES is used
for data entry and primary validation and HYMOS is used for secondary and hydrological
validation.
3.1.1 Data Entry and Primary Validation:
Primary validation of rainfall data is carried out at the Sub-divisional level using primary
module of dedicated data processing software SWDES and is concerned with data
comparisons at a single station:
a. For a single data series, between individual observations and pre-set physical limits
based on historical data. (Maximum, minimum, upper warning and lower warning limits)
b. Between two measurements of a variable at a single station, e.g. daily rainfall from a
daily rain gauge (standard rain gauge) and an accumulated total from autographic rain
gauge.
c. Multiple plot of rain gauge stations shows trend of the rainfall for selected stations
having same topography.
d. Similarly trend of water level can be observed by plotting water levels of the river
gauging stations, which are on the same river
3.1.2 Secondary validation:
Secondary validation is carried out at Division level. However since comparison with
neighbouring stations is limited by Divisional boundaries, the validation of some stations
near the Divisional boundaries is carried out at the State Data Processing Center, Nashik.
a. Secondary validation of rainfall data:
Following tests are carries out for secondary validation of rainfall data:
Spatial correlation test
Screening of data series
Scrutiny by multiple time series graphs
Scrutiny by tabulations of daily rainfall series of multiple stations
Spatial homogeneity test of rainfall
Checking for systematic shifts using double mass analysis
b. Secondary validation of river gauging data:
For secondary validation of water level data, transformation from water level to discharge
through the use of stage discharge relationships is done. Validation of this stage discharge
curve called as rating curve is done by comparing rating curve of current year with the rating
curves of previous years.
3.1.3 Hydrological Validation:

Hydrological validation is carried out at State Data Processing Center, Nashik. After
secondary validation of the rainfall, climatic, water level and discharge data, hydrological
validation is carried out on the same data. In hydrological validation the comparison of two
different parameters such as rainfall and resulting runoff of a basin is done. Also isolines for
rainfall and climatic parameters on ten-daily, fortnightly, monthly and yearly intervals are
drawn to check the pattern in different months of the year or compared the pattern of current
year with that of previous years.
3.1.4 Inter-agency validation
Inter-agency validation of meteorological data is carried out with Indian Meteorological
Department (IMD) data and hydrological data with Central Water Commission (CWC) data.

4. Network Design
4.1 Hydrological Information System
A watershed can be viewed as a landmass wherein the water incident upon it flows overland
to a common outlet. Hydrological Information System (HIS) for a region comprises sub-
systems for water data collection and storage, data communication/ transmission, data
transformation for producing information and information-communication. HIS provides
reliable data for planning, design and management of water resources and for related
research activities; and supports informed decision making. In India, primary data for HIS
are collected, processed and stored by agencies such as CWC, IMD, State Irrigation
Departments/ WRDs, etc. Basically, the hydrological networks operating in different river
basins of the country, and maintained by one or more of the agencies entrusted with the
task, provide the data forming the core of HIS.
HIS for a region, possibly covering a number of river systems/ subsystems, caters to the
varied needs of water resources planning and management such as: assessment of
regional/ national surface water resources; investigation of environmental, economic and
social impacts of current and planned management practices on water resources and
analysis and forecasting of extreme events of floods and droughts. Most problems arising
from activities relating to planning and management of water resources are solved, and
decisions made using the available information; namely facts, coupled with analysis and
judgment. In this problem-solving process, if the relevancy of the information is higher,
higher the quality of decision; and lower the uncertainty and element of risk.
A national hydrological network will provide water data to the HIS, which will be used for
many types of decisions. Often, it is difficult to anticipate the uses to which water data will be
put to use. Network design is a complex facet of hydrological engineering. What constitutes
a hydrological network itself is open to debate, with many aspects such as hydrological
phenomena/ processes under consideration, geographical scope, stage of water resources
development in an area, and intended use of data coming into inter-play. In practice,
hydrological network design is an evolutionary process, wherein a minimum network is
established early in the development of a geographical area, and the network is reviewed
and upgraded periodically until an optimum network is attained.
Different types of hydrological and meteorological data need to be part of HIS for a region,
depending on its geographical scope. Precipitation, gauge-discharge data for rivers/ lakes/
reservoirs, evaporation, sediment transport, water quality, water temperature, soil moisture,
ground water, etc. are some of the major types of data that are relevant in this respect. Data
on discharge and water level are basic to the solution of most design and operation
problems. Information on precipitation is indispensable to water resources development and
management. Establishment of networks on an integrated basis is very important, especially
as regards streamflow and precipitation networks. In some cases, both precipitation and
streamflow networks are operated by the same agency, though often in practice, such
networks are managed independently. Obviously, good cooperation is required for operating
and developing networks. For the purpose of this study, hydrometric network for streamflow

related measurements and rain gauge networks for precipitation related data are primarily
considered.
HIS output is having a wide variety of users, both in the public services domain and private
sector. Users fall under the two broad clusters of: i) large scale and repeat users, and ii)
occasional or one-time users. Large scale and repeat users of HIS generally belong to
various policy/ operational level government departments, financial institutions, command
area development authorities, irrigation/ water resources departments, Non-Government
Organisations (NGOs), etc. Occasional users are of two types: i) those who need to find and
use water in a micro-geographical area for their own use, and ii) those who need to find and
use water for commercial or community activities.
4.2 Hydrological Measurements
With increase in world human population and living standards, demand for water is rising
faster today than at any time in the history of this planet. This needs to be seen in the
context of diminishing water resources to support life in rivers, lakes, wetlands and similar
habitats. Personnel with responsibilities for water resources need to be better equipped to
deal with the issue. A hydrological network, the sum total of all the fixed hydrological
instruments and stations providing hydrometric measurements in a basin or region, makes
available the data used for assessing water resources for a variety of other purposes. The
purpose can be many, say planning and management of natural resources, flood
management, water quality control and environmental monitoring.
There are many different kinds of hydrological and meteorological data that needs to be a
part of the HIS for a region, depending on its geographical scope. Precipitation, gauge-
discharge data for rivers/ lakes/ reservoirs, evaporation, sediment transport, water quality,
water temperature, ice cover on rivers/ lakes/ reservoirs, soil moisture, ground water, etc.
are some of the major types of data that are relevant in this respect. Discharge and water
level data are basic to the solution of most design and operation problems. Information on
precipitation is indispensable to water resources development and management.
Establishment of various networks on an integrated basis is very important, especially as
regards streamflow and precipitation networks. In some cases, both the networks are
operated by the same agency. But often, each of these networks is managed independently.
Obviously, good cooperation is required for operating and developing the networks. For the
purpose of this study, hydrometric network for streamflow related measurements and rain
gauge networks for precipitation related data are primarily considered.
Hydrometric measurements are required to measure the variables of the hydrological cycle.
Most of the instruments for such measurements need to be maintained in continuous
operation, and many are exposed to the weather in harsh environment. These requirements
impose a need for high standards of design and manufacture for hydrometric instruments.
The main elements of the hydrologic cycle for which hydrometric instruments are required
include precipitation, gauge (for open channels), flow velocity, groundwater characteristics,
evaporation and soil moisture.

Countrywide networks have usually developed over a period of time in order to meet the
emerging national/ international needs from time to time. Examples of stages of evolvement
of a national network can be: flood prevention, development of hydropower, control of water
pollution, water resources management, contribution towards global environmental
monitoring, etc. The above scenario is in contrast to the networks set up by scientific design,
which have been employed particularly in the establishment of representative and
experimental basins and in specific projects and studies. Such networks are normally single-
purpose in their objective. They have often been designed to provide spatially distributed
random samples of the hydrological variable concerned. Alternatively, they produce
systematic samples, or stratified random samples or samples of other types across the
basin or region. In short, for hydrological design and water resources assessment purposes
proper estimates of river flow and river stages are required. Their measurement is the
domain of hydrometry.
The majority of streamflow measurement techniques are based on velocity area method.
Though the use of float measurements is sometimes inescapable, current meter gauging is
the most widely favoured velocity-area method technique. A recommended set of guidelines
for streamflow measurement techniques are available in the Design Manual on Hydrometry,
Vol. 4, prepared under Phase I of the Hydrology Project. The types of streamflow
measurement techniques for which details are available include: current meter gauging
sites, float measurement, discharge monitoring by Acoustic Doppler Current Profiler
(ADCP), slope-area method, selection of natural control (rated section) station site, and
selection of artificial control sites.
4.3 Network Design - General Requirements
A hydrometric network, essentially forming a subsystem of the full-fledged hydrologic
network, is a collection of stream gauging stations in a river basin, wherein essential data
such as river stage, discharge, sediment characteristics, etc. are measured as per design of
the specific station. Ideally, the water data emanating from a hydrometric network should
enable accurate estimation of the hydrological regime of the region. The network provides
water data needed for planning, design and management of the natural resources of the
region. In flood prone areas, the hydrologic network inter alia provides data for planning,
design, operation and management of flood protection measures.
A national hydrological network will provide data that will be used for many types of
decisions. Often, it is difficult to anticipate the uses to which water data will be put to use.
What constitutes a hydrological network itself is open to debate, with many aspects such as
hydrological phenomena/ processes under consideration, geographical scope, stage of
water resources development in an area, and intended use of data coming into inter-play. In
practice, hydrological network design is an evolutionary process wherein a minimum
network is established early in the development of a geographical area, and the network is
reviewed, and upgraded periodically until an optimum network is attained.

There are many different kinds of hydrological and meteorological data that needs to be a
part of the HIS for a region, depending on its geographical scope. Precipitation, gauge-
discharge data for rivers/ lakes/ reservoirs, evaporation, sediment transport, water quality,
water temperature, ice cover on rivers/ lakes/ reservoirs, soil moisture, ground water, etc.
are some of the major types of data that are relevant in this respect. Discharge and water
level data are basic to the solution of most design and operation problems. Information on
precipitation is indispensable to water resources development and management.
Establishment of various networks on an integrated basis is very important, especially as
regards streamflow and precipitation networks. In some cases, both the networks are
operated by the same agency. But often, each of these networks is managed independently.
Obviously, good cooperation is required for operating and developing the networks. For the
purpose of this study, however, hydrometric network for streamflow related measurements
and rain gauge networks for precipitation related data are primarily considered.
4.4 Hydrometric Network Design
4.4.1 General Considerations
Aspects involved in hydrometric network design for a region include inter alia the following
basic components.
i) Classification of stations
ii) Minimum networks
iii) Networks for large river basins
iv) Networks for small river basins
v) Networks for deltas and coastal flood plains
vi) Representative basins
vii) Sustainability
viii)Duplication avoidance, and
ix) Periodic re-evaluation
i) Classification of stations
All stations in the network need to be classified according to type of use of the station; which
may range from stations for: management and other decisions, regional and long-term
analysis of water resources, design and planning purposes, etc. A network can be national,
regional, representative or experimental. Measurements at individual stations might be
carried out during one year up to several years.
Primary stations are maintained as key/ principal/ benchmark stations; with measurements
continued for a long period of time to generate representative flow series of the river system,
and provide general coverage of a region.
Secondary stations are essentially short duration stations, intended to be operated only for
such a length of period that is sufficient to establish the flow characteristics of the river or
stream, relative to those of a basin gauged by the primary station.

Special purpose stations are usually required for the planning and design of projects or
special investigations; and are discontinued when their purpose is served. The purpose
could vary from design, management and operation of a project to monitoring and fulfillment
of legal agreements between states in respect of interstate basins. Primary and secondary
stations may also at times serve as special purpose stations.
ii) Minimum networks
A minimum network should include at least one primary streamflow station in each of the
climatologic and/ or physiographic areas in a state. A river that flows through more than one
state should invariably be gauged at the state boundary. At least one primary gauging
station should be established in those basins having potential for future development. A
minimum network should also include special stations, as required.
Where a project is of particular socio-economic importance to a state/ region, it is essential
that a gauging station is established for planning, design and operational purposes. At
times, special stations are required to fulfill a legal requirement, say quantification of the
compensation releases or abstraction controls. Benefit-cost ratios for special stations are
usually the highest, and can often help support the remainder of the hydrometric network.
iii) Networks for large river basins
A primary station might be planned at a point on the main river where the mean discharge
attains its maximum value. For rivers flowing across the plains, this site is usually at the
downstream region of the river; but immediately upstream of the point where the river
normally divides itself into branches before joining the sea or a lake or crosses a State
boundary. In the case of mountainous rivers, it is the point where water leaves the
mountainous reach and enters the plain land. Subsequent stations are established at sites
where significant changes in the volume of flow are noticed, namely below the confluence of
a major tributary or at the outflow point of a lake etc. If a suitable location is not available
below a confluence, the sites can be located above the confluence, preferably on the
tributary.
While establishing sites, care should be taken to ensure that no other small stream joins the
main river so as to avoid erroneous assessment of the contribution of the tributary to the
main river. In the case of a large river originating in mountains, though the major contribution
is from the upper regions of the basin, several stations may need to be located in the
downstream stretch of the river. Such stations are intended to provide an inventory of water
loss from the channel by way of evaporation, infiltration; and by way of utilization for
irrigation, power generation, industrial and other domestic needs.
The distance between two stations on the same river may vary from 30 km to several
hundred kilometers; depending on the volume of flow. The drainage areas, computed from
the origin up to consecutive observation sites on a large river, should preferably differ by
more than 10 percent, such that the difference in quantities of flow is significant. The
uncertainties in discharge values, especially for high flows, are unlikely to be less than ±10
percent. However, every reasonable attempt should be made to minimize such

uncertainties. When tributary inflows are to be known, it is generally better to gauge the
river directly, rather than deriving the flow from the difference of a downstream and an
upstream station along the main. A more accurate discharge record for a main stream is
obtained from monitoring the feeder-rivers, than by a station on the main stream alone,
though at an additional cost.
iv) Network for small river basin
There are a large number of independent rivers, which flow directly into the sea, as is the
case of west flowing rivers of Maharashtra. In such cases, the first hydrological observation
station might be established on a stream that is typical of the region. Further stations can be
added to the network so as to widely cover the area. Streams in a particular area having
meager yields should not be avoided from inclusion in the network. Absence of a station on
a low flow stream can lead to wrong conclusions on the water potential of the area as a
whole, evaluated on the basis of the flow in the high flow streams. Care needs to be
exercised in designing the network so as to ensure that all distinct hydrologic areas are
adequately covered. However, in practice, it may not be possible to operate and maintain
gauging stations on all the smaller watercourses. Hence, representative basins may need to
be selected, and the data from those used to develop techniques for estimating flows for
similar un-gauged sites.
v) Network for deltas and coastal flood plains
Deltaic areas where gradients are usually low and channels bifurcate are often important as
water use is productive. Such areas need monitoring. This is particularly important since
deltas are dynamic systems, which often continually change. However, the type of network
required may differ from more conventional river basins. On account of the low gradients, it
is often not possible to locate stations with stable stage-discharge relationships; and
variable backwater effects can occur due to tidal influences and/ or changes in aquatic
vegetation growth. Stage readings should be made at all principal off-takes/ bifurcations/
nodes in the system; which can be supplemented by current meter gauging wherever
required. At some sites, consideration may need to be given to installing a slope-area
method station.
vi) Representative Basin
When gauging stations are included in a network to obtain representative data from a
particular physiographic zone, it is better if the chosen basins are those with water resource
relatively underutilized wherein the basins can be considered to be close to their natural
state.
vii) Sustainability
As regards hydrometric networks, sustainability is of paramount importance. It is a relatively
straightforward task to design a dense network of streamflow stations. However, the
implementation and operation of a network is lot more difficult. Experience shows that there
is a tendency to adopt an idealistic approach and attempt to have as many stations as

possible. There are many examples of networks throughout the world that are no longer
functioning well due to issues that can be attributed to financial support, skilled manpower
and logistic support such as vehicles. It is far better to operate and maintain 10 gauging
stations well, than to operate and maintain double the stations badly. Higher quality data
from fewer stations is preferable to lower quality of data from a greater number of stations.
viii) Duplication avoidance
Since more than one organization such as State WRDs, CWC, local bodies, etc. is generally
responsible for establishment of gauging stations, it is essential that the activities are
coordinated such that they complement each other, with duplication of efforts avoided.
ix) Periodic Re-evaluation
Gauging station networks require periodic re-evaluation. Developments, which take place in
the basin such as construction of new irrigation/ hydroelectric projects and industrialization
of the area, can warrant addition/ closure/ re-location of stations. For example, a river reach
can become increasingly polluted due to discharge of effluents from a newly set up industry.
Hence, a need may arise to establish station(s) to assist with water quality monitoring and
pollution assessments.
Since hydrometric network normally exist for any region, the network design process tends
to be a matter of evaluation, reviewing and updating of an existing network. The historic
evolution of a large many hydrometric networks tends to be of reactive in nature; rather than
strategically planned. Often gauging stations continue to be operated, with the original
objectives remaining unclear. Hence, it is necessary to regularly undertake a detailed review
of the existing networks to achieve the following.
Define and/ or re-define the purpose of each gauging station
Identify gaps in the existing network
Identify stations which are no longer required
Establish a framework for the continual evaluation and updating of the network
There is a tendency for hydrologists and water resources planners to be reluctant to
discontinue gauging stations, even though the stations might have fulfilled their intended
objectives. In design and evaluation of networks, it is essential that a hard-nosed approach
is adopted, and stations that are no longer providing significant benefits discontinued.
4.4.2 Main Steps in Network Design
Keeping in view the above-mentioned general principles, the main steps in the network
design process can be summarized as follows:
i) Review mandates, roles and aims of the organizations involved in the operation
of HIS in the particular area and evaluate communication links.
ii) Collect maps and other background information

iii) Define the purposes of the network; who are the data users, and what will the
data be used for? Define the objectives of the network; what data are required,
and with what frequency?
iv) Evaluate the existing network; How well does the existing network meet the
overall objectives?
v) Review existing data to identify gaps, ascertain catchment behaviour and
variability
vi) Identify gaps and over-design in the existing network; Propose new stations and
delete existing stations wherever necessary
vii) Prioritize gauging stations
viii)Estimate average capital and recurrent costs of installing and maintaining
different categories of hydrometric stations. Estimate overall cost of operating
and maintaining the network.
ix) Review revised network in relation to overall objectives, ideal network, available
budget and the overall benefits of the data. Investigate sustainability of the
proposed network
x) Prepare a phased implementation plan; which needs to be prioritized, realistic
and achievable
xi) Decide on approximate locations of sites, and commence site surveys. If site is
not feasible, review the location and see if another strategy can be adopted, say
gauge a tributary to estimate total flow at required spot, rather than trying to
measure total flow in the main stem river
xii) Establish framework for regular periodic network reviews. As hydrometric
network design is a dynamic process, networks have to be continually reviewed
and updated such that they react to new priorities, changes in policies and fiscal
changes. Regular formalized network reviews are recommended to take place
after three years, or at a shorter interval, if new data needs to be developed.
4.4.3 Design Considerations
i) Designers and planners of water resources projects increasingly utilize the statistical
characteristics of streamflow rather than flow at specific times. The probability that
the historical sequence of flow observed at a given site will occur again is remote,
and the prediction of future flows needed for design and planning must consider all
probable flow sequences. The information on long streamflow records enables
prediction of future streamflow, not in terms of specific events, but in terms of
probability of occurrence over a span of years. It is not feasible to collect a long
continuous record at every site where it will be needed. A number of such stations
are required to provide information which can be transferred to un-gauged sites or to
sites where a small amount of streamflow data is available.
ii) Network should have at least one primary streamflow station in each climatologic
and/ or physiographic area in a state
iii) A river or stream which flows through more than one state needs to be gauged at the
state boundary

iv) A primary station might be planned at a point on the main river where the mean
discharge attains its maximum value. For rivers flowing across the plains, this site is
usually in the downstream part of the river, immediately upstream of the point where
the river normally divides itself into branches before joining the sea or a lake or
crosses a State boundary. In the case of mountainous rivers, it is the point where
water leaves the mountainous reach and enters the plain land. Subsequent stations
are established at sites where significant changes in the volume of flow are noticed,
say below the confluence of a major tributary or at the outflow point of a lake, etc.
v) Several stations may need to be located at downstream stretch of a river. Such
stations are intended to provide inventory of water loss from the channel by way of
evaporation, infiltration and by way of utilisation for irrigation, power generation,
industrial and domestic needs.
vi) The distance between two stations on the same river may vary from 30 km to several
hundred kilometers, depending on the volume of flow. The drainage areas computed
from origin up to consecutive observation sites on a large river should preferably
differ by more than 10 percent so that the difference in quantities of flow is
significant.
vii) A different approach is to be adopted in dealing with small independent rivers that
flow directly into the sea, as in the case of west flowing rivers of Kerala and
Maharashtra and some east flowing rivers of Tamil Nadu.
viii)In such cases, the first hydrological observation station might be established on a
stream that is typical of the region. Further stations could be added to the network so
as to widely cover the area. For example, it may not be possible to operate and
maintain gauging stations on all smaller watercourses in the Western Ghats. Hence,
representative basins have to be selected and the data from those are used to
develop techniques for estimating flows for similar un-gauged sites.
ix) For trans-boundary water balance studies, it is indispensable to have for each
international river a gauge at the entrance and/ or the outlet of the country
x) Confluence between a major and a minor tributary: It is useful to have a gauge in
order to appreciate the discharge variation for the main river, downstream of the
confluence
xi) Along a river, installation of a gauge should consider the other stations available on
the river. If the difference between the flows at two stations is inferior to the margin of
error of flow measurement, it is useless to intercalate a supplementary station
4.5 Network Density
The World Meteorological Organization (WMO) has issued guidelines on the density of
minimum hydrometric network, and is given in Table 4.1 (a) and (b).
It is not possible to provide specific, general guidelines on an appropriate network density.
WMO recommendations are general guidelines, which if adopted literally for some of India’s
larger river basins could result in an excessively dense network. Even though the WMO
guidelines might be used as rough rule of thumb as part of an initial network appraisal, their
use in the final design of the network can possibly be avoided. Network density must

ultimately be based on the network objectives, the temporal and spatial variability of river
stages and flow and on the availability of finance, manpower and other resources.
Table 4.1 (a): Minimum density of hydrometric network (WMO)
[Area in km2
per station]
No Type of region Range of norms for
minimum network
Range of provisional norms
tolerated in difficult1
conditions
I
Flat regions of
temperate and tropical
zones
1,000 - 2,000 3,000 - 10,000
II
Mountainous regions of
temperate and tropical
regions
300 - 1,0002
1,000 - 5,0003
III Arid zones 5,000 - 20,0004
------------------
Source: Design Manual, Hydrological Information System, Hydrometry, Hydrometeorology Vol. 1, Hydrology
Project Technical Assistance, Government of India & Government of the Netherlands, 2001
Table 4.1 (b): Recommended minimum densities of streamflow stations
[Area in km2
per station]
Physiographic unit Minimum density per station
Coastal 2,750
Mountainous 1,000
Interior plains 1,875
Hilly/ undulating 1,875
Small islands 300
Polar/arid 20,000
Source: WMO No.168, Guide to Hydrological practices, Fifth edition, 1994
4.6 Optimization of Network
4.6.1 Criteria for network optimization
Identification of a set of criteria for hydrological network adequacy assessment is a complex
task. The criteria to be applied should depend on the network type but also on the climatic
conditions and on the territory characteristics and vulnerability. Figure 4.1 gives a schematic
showing different methods/ criteria used for network optimization. As depicted therein,
streamgauge network optimization can be broadly tackled through knowledge, empirical
criteria and analytical methods.
Knowledge:
The characteristics of the existing network, along with the territory and climate properties,
have to be considered to address the problem of optimization of streamgauge network. The
1
Last figure in the range should be tolerated only for exceptionally difficult conditions
2
Under very difficult conditions, this may be extended up to 10,000 km2
3
Under very difficult conditions, this may be extended up to 10,000 km2
4
Great deserts not included

knowledge of territory - say, orography, geo-morphological properties, urban and rural
locations, etc. - is fundamental to the process. For instance, the local orography governs
spatial distribution of precipitation over complex terrain. Moreover, information on historical
flooding events could be a basic tool to optimize location of stations for flood mitigation
purposes. Appropriate spatial distribution of the measurement stations should also consider
location of urban and industrial areas adjacent to rivers and flood plains, where a continuous
water level monitoring should be carried out. Knowledge of climate characteristics is also
useful since the precipitation type influences the spatial resolution of the rain gauge network.
Clearly, optimization criteria based on knowledge statement have to be integrated with a
more in-depth analysis based on empirical approaches, or more sophisticated statistics and/
or geostatistical methodologies.
Empirical rules:
The problem of streamgauge location can be mainly addressed through empirical
considerations. Obviously a stream gauge has to be located at accessible sites, and should
monitor water level at appropriate sites upstream of historical flooding prone regions;
particularly when urbanized areas are involved. For man-made reservoirs, stream gauging
upstream and downstream of the structure for monitoring inflows and outflows from the
facility.
Figure 4.1: Overview of the Criteria for Streamgauge Network Optimization
Streamgauge Network Optimization for Water
Resources Assessment and Planning
Knowledge Analytical criteria Direct/ empirical Criteria
Existing streamgauge
Network
Territory characteristics
• Orography
• Geomorphological
characteristics
• Historical flooding
events
• Urban/ rural area
location
Climate Characteristics
• Precipitation type
Statistical approach
Geostatistical methods
Coverage models
• Each main tributary
be gauged
• Reservoirs
• Location of urban
areas,
industrialization
•

Analytical Methods:
Streamgauge network assessment and optimization can be based also on statistical
approaches using clustering technique to identify groups of similar gauging stations, and on
entropy-based methods; which allow quantify the relative information content. Geostatistical
methods (Moss, 1982; Tasker, 1986), most commonly based on the standard error in
estimating regional discharge at un-gauged sites, or coverage models that deal with the
network design as a facility location problem, can also be employed (Barbetta et al, 2009).
Methodology of spatial hydrologic regression under generalized least squares framework,
adopted in optimizing Upper Bhima streamflow network is introduced in section 4.6.2 and
detailed in section 6.
4.6.2 Statistical Approach
Identification of monitoring objectives is the first step in the design and optimisation of
monitoring systems. The second variable to be considered is the dynamics of river flow and
stages in time and space. This requires a critical analysis of historical data. To enable
optimal design of the monitoring system, a measure that quantifies the effectiveness level is
required. This measure depends on the monitoring objectives, and can be related to an
admissible error in say the mean flow during a certain period, monthly flow values for water
balances, extreme flows and/ or river stages, etc. This error is a function of the sampling
locations, sampling frequency and sampling accuracy, i.e. where, when and with what river/
reservoir are stages and flows to be measured.
Network design approaches have traditionally been relied largely on statistical methods, with
the most commonly used method based on the standard error in estimating regional
discharge at ungauged sites. During the 1970s and 1980s, USGS developed and applied
statistical regression techniques to locate gauges (Moss, 1982; Stedinger & Tasker 1985 &
1986). A regional optimization model for a hydrological region can be developed using
regression. Dependent variable is often taken to be annual average flow, annual maximum
flow, 50-year (yr) flood or 100-yr flood, 7-day 10-yr low flow, etc. Basin characteristics such
as catchment area, length of the major stream, elevation, population, annual average
rainfall, total annual monsoon discharge, location-parameter of the station, length of data or
forest cover percentage, etc. can be used as explanatory variable in multivariate regression
equation. A planning horizon of say, 5-yr, 10-yr or 25-yr can be considered.
In each region, the analysis begins with all candidate stations included, and then stepped
backwards, eliminating the least informative station at each step. There are both strengths
and limitations of the statistical approach to network design. The method is rigorous and
reproducible, yielding quantitative results about the degree of uncertainty of particular
quantiles for a given gauge network. The gauge sites can thereby be arranged in an
unambiguous rank ordering from highest to lowest information content. Although statistical
methods can quantify trade-offs between information and cost, these trade-offs (and the
value of any particular gauge network) change with different design objectives. For example,
the optimal network to support regional estimation of annual average flow and the 50-year

flood may differ substantially from the optimal network supporting regionalized estimation of
7-day 10-yr low flow.
Thus, statistical methods for stream network design should be used to justify incremental
decisions to add or eliminate individual gauges within a local gauge network serving narrow,
well-defined goals (such as hydrologic regionalization). However, one important limitation of
statistical methods is the decoupling of performance metrics used to evaluate network
performance from the possibly unrelated purposes for which the gauges were installed in
the first place. For example, a gauge may serve a critical purpose for water management or
flood forecasting even if it is not one of the gauges most useful for estimating regional
hydrologic information at ungauged sites. Although statistical procedures offer numerical
precision for network design, supporting regional hydrologic estimation, these approaches
do not support the many other goals and uses of site-specific streamflow data.
To decide on the number of sites to be sampled, accuracy goals need to be set, which in
turn need streams to be classified as principal streams and minor streams. More costly
developments on large streams justify a higher accuracy goal for principal streams than for
minor streams. The proposed goal for principal streams is an accuracy equivalent to that
obtained from 25-yr record. For remaining streams, accuracy equivalent to that obtained
from 10-yr of record is proposed as the goal.
Besides the regional regression analysis, Slade et al. (2001) analyzed the correlation among
paired stations upstream and downstream of one another on the same river. They found the
expected strong correlations in flows for upstream and downstream stations on the same
river, especially for the annual average flow. As a result, stations for a core network can be
selected which were not highly correlated with other selected stations.
4.6.3 Periodic review using survey techniques/ multi-criteria analysis:
Tools like survey techniques/ multi-criteria analysis can be used in the periodic review and
optimisation of hydrometric stations network. This will enable judging density of network and
comparing it with WMO norms. It will also help in analyzing the network according to
population density. The existing problems with GD stations such as construction of
structures upstream or downstream, site(s) affected by backwater effect, sufficient discharge
data being collected for the stable channel and only gauge need to be measured, need for
upgradation of the methods of measurements, station becoming obsolete, new data
requirement for design/ planning purpose, etc. can be answered using tools like survey
techniques and/ or multi-criteria analysis.
4.6.4 Coverage sub-watershed model approach:
In contrast, coverage models are based on articulating a goal, defining a measure of
success (“metric”) or procedure that identifies locations supporting that goal, and applying
this procedure using geographic information system (GIS) analysis to yield a set of potential
sites (e.g., for gauges). The design of a streamgauge network has much in common with a
rich family of facility location problems. These include the siting of facilities for fire protection,
ambulances and hospitals, vehicle emission test stations, hazardous facilities, oil-spill
response centers, and “hubs” for air passengers and cargo transport.

As a consequence of defining a coverage model, sampling at discrete locations subdivides a
spatial domain into sub-regions; each sub-region is explicitly associated with its respective
measurement point. When streamgauges are located in a stream network, the watershed
draining to that streamgauge can analogously be delineated; a unique subarea associated
with each gauge defines the land area whose drainage flows past that gauge before it
reaches any other gauge (Figure 4.2a). This sub-watershed is the coverage area associated
with that streamgauge. Any set of points on a stream network can be used to subdivide a
watershed into sub-watersheds. In contrast to network designs used to monitor continuous
surfaces, fluxes, or fields (e.g., air quality, solar radiation, contaminated groundwater),
streamgauge locations are confined to the stream network (Figure 4.2a), suggesting
analogues with facility location in transportation and communication networks. For example,
facilities may be optimally sited in a transportation network to intercept traffic flows for
vehicle safety inspections or to detect the transportation of hazardous substances. The flow
interception location problem engenders subtle trade-offs between maximizing capture by
locating facilities at the “outlet” of directed networks through which all traffic must flow and
“protecting” the network which favors siting more facilities in the “upstream” reaches of the
network for early detection.
Figure 4.2a: Spatial subdivision of a region
using sub-watersheds of streamgauges
Figure 4.2b:Spatial subdivision of a region
using Thiessen polygons.
Rainfall varies continuously over space, but it can be directly measured only at discrete
points. This is typically the case for computing mean areal rainfall from point measurements
at raingauges, in which Thiessen polygons drawn around the raingauge locations are used
to estimate watershed average rainfall using an areally weighted average of the raingauge
values (Figure 4.2b).

5. Review of Streamgauge Network
5.1 Existing Network:
The existing hydrometeorological network for the Upper Bhima basin was finalised by the
WRD, GoM, in consultation with the IMD and CWC. Table 5.1 gives the details of the 14 GD
stations in the basin.
Table 5.1: GD Stations in Upper Bhima Basin
No. Station
Year of
establishment
District Tributary
Catchment
Area (km
2
)
1 Aamdabad 1996 Pune Ghod 1522.528
2 Askheda 1983 Pune Bhama 239.470
3 Budhawadi 1981 Pune Kundalika 151.920
4 Chaskaman 1973 Pune Bhima 389.050
5 Dattawadi 1982 Pune Mutha 741.290
6 Kashti 1984 Ahmednagar Ghod 4392.000
7 Khamgaon 1985 Pune Mula-Mutha 2832.970
8 Nighoje 1991 Pune Indrayani 832.300
9 Pargaon 1982 Pune Bhima 6251.000
10 Paud 1984 Pune Mula 473.640
11
Pimple
Gurav
1997 Pune Pawana 506.700
12 Rakshewadi 1984 Pune Bhima 3279.844
13 Shirur 1984 Pune Ghod 3204.180
14 Wegre 1994 Pune Mutha 91.150
5.2 Objective of Streamgauge Network:
The prime objective of the hydrometric network in Upper Bhima basin maintained by WRD,
Maharashtra is adjudged to be the collection of hydrometeorological data, which will be used
to assess the quantum of water available in each basin/sub basin for water resources
development and management, and simultaneously for flood management purpose.
5.3 Classification of Stations:
The stations in the GD network of Upper Bhima basin is classified into the following three
categories.

I. Primary Stations (maintained as benchmark stations with measurements continued
for a longer period of time to generate representative flow series of the river system,
and provide general coverage of a region):
Pargaon and Chaskaman
II. Secondary Stations (essentially short duration stations, intended to be operated
only for such a length of period that is sufficient to establish the flow characteristics
of the stream, relative to those of a basin gauged by the primary station):
Aamdabad, Askheda, Budhawadi, Dattawadi, Kashti, Khamgaon, Nighoje, Paud,
Pimpale Gurav, Rakshewadi, Shirur and Wegre
III. Special purpose Stations (for planning and design of projects or special
investigations monitoring and fulfillment of legal agreements between states in
respect of interstate basins, special studies or research; discontinued when their
purpose is served):
None identified in the Upper Bhima basin
According to WMO, minimum density of stream gauge network for mountainous region is
recommended as 1,000 km2
/ station and for interior plains or hilly/ undulating regions, it is
1,875 km2
/ station (in terms of ranges, for flat region, the range is 1,000-2,000 km2
/ station
and for Mountainous regions, it is 300-1,000 km2
/station). Maharashtra is considered as
semi-hilly area. Upper Bhima basin consists of 14 GD stations for geographical area 14,712
km2
. Thus, the network density for existing network works out approximately to 1,050 km2
/
station; which agrees with the minimum density norms provided by WMO.
5.4 Summary of Preliminary Review:
A preliminary review of existing GD network in the basin was performed by WRD by
surveying all the 14 GD stations under the network. The procedure adopted by GoM for
preliminary review inter alia included: review of existing GD network to see whether the
station is affected by backwater on account of Kolhapur Type (KT) weirs, other hydraulic
structures or unsteady flow conditions; whether sufficient discharge data has been collected
for the stable channel and only gauge can be measured; need for upgradation of the
methods of measurements, station becoming obsolete; and identifying new locations on
basins/ sub-basins where gauging needs to be done (for reasons to be explicitly
established). Detailed proposals are also being made for closing/ establishing new stations,
preparing maps showing location-details of the network, etc. This has helped in judging the
density of network and to compare it with WMO norms. Findings of the review conducted
have been analyzed in the following paragraphs. Table-5.2 gives the summary of survey
findings by WRD, GoM. In the table, reasons for establishment of the particular GD station
are included, as also recommendations on the basis of the review.

Table 5.2: Results of preliminary review of GD stations in the Upper Bhima
No GD Station Purpose/ objectives
Method of
measurement
Priority1
(A/B/C)
Decision2
(N/R/D)
Data
length
(yr)
Sites affected by
Hydraulic structures
Remarks
1 Aamdabad
Measure gauge and
discharge
Current
meter
A R 12
2 Askheda
Measure gauge and
discharge & rainfall
Cableway A R 25
Discharge measured
during rainy season only;
Bhama-Askheda dam
located 5 km u/s
Proposed for
closure,
Bhama-
Askheda dam
will act as GD
3 Budhawadi
Measure gauge and
Current
meter
A R 27 NA
4 Chaskaman
Measure gauge and
discharge and
climatic parameters
Cableway A R 35
Discharge measured
Chaskaman dam 10 km
u/s
Proposed for
closure,
ChaskmanDam
will act as GD
5 Dattawadi
Measure discharge
of Mutha river
Bridge C D 26
Discharge is measured
Khadakwasla dam
located 25km u/s
Proposed for
closure,
Khadakwasla
Dam will act as
GD
6 Kashti
Measure gauge and
discharge and
climatic parameters
Current
meter
A R 21 Ghod dam 25 km u/s
7 Khamgaon
Measure gauge and
Current
meter
A R 23 NA
8 Nighoje
Measure gauge and
Current
meter
A R 17 NA
9 Pargaon
Measure gauge and
discharge and
climatic parameters
Current
meter
A R 26 NA
10 Paud
Measure gauge and
Current
meter
A R 24 NA
11 PimpleGurav NA
Current
meter
C D 11 NA
12 Rakshewadi
Bhima discharge
measurement
before confluence
with Mula-Mutha
river
Cableway C D 20
Velocity affected due to
backwater of Bhima;
Mula-Mutha confluence
0.5 km d/s of site;
Pargaon KT weir located
4.92 km d/s; Discharge is
measured during rainy
season only; Nearby
station on the same
stream, Pargaon 5 km
d/s
Proposed for
closure, and
Gauges and
discharges will
be measured
at Pargaon GD
13 Shirur
Discharge
measurement of
Ghod river before
Ghod dam
Cableway C D 17
Discharge measured
Affected by backwater
effect of KT weir located
d/s; Dimbhe dam 105 km
u/s and Ghod dam 25 km
d/s; Ghod dam 25 km
d/s and Kashti station 50
km d/s
Proposed for
closure, AWS
is proposed
under ongoing
RTDAS project
14 Wegre
Discharge
measurement of
Mutha river
Cableway C D 13
Discharge is measured
Temghar dam 9 km u/s;
Discharge can be
measured at Temghar
dam site.
Proposed for
closure,
Temghar dam
will act as GD
Note: 1. Priorities: A – High, B – Medium, C – Low
2. Decision: N – Establish new station, R – retain existing station, D – discontinue station
On the basis of the preliminary review, GoM has proposed closure of six GD stations due to
backwater effect arising from construction of structures downstream or flow affected due to

construction of structures upstream of the existing site (Table 5.2). In case, this is
considered, there will remain only eight GD stations in the basin and the network density will
reduce to 1,839 km2
/ station. Out of these above mentioned six GD sites, streamflow has
affected due to construction of structures upstream for four sites, namely, Askheda,
Chaskaman, Dattawadi and Wegre. For these four sites, the required discharge data can be
continued to be measured at spillways of the upstream dams, namely, Bhama-Askheda,
Chaskaman, Khadakwasala and Temghar respectively. Due to the budget constraint for
future maintenance, two GD stations out of existing fourteen may be required to be closed,
for which case data collection at 12 GD stations will be continued and the network density
will reduce to 1,226 km2
/ station.
By looking at the hydrological problems in the basin such as flooding and importance of the
basin for the whole region, it is not considered advisable to close all the six GD stations
proposed for closure, unless alternative arrangements are made for collection of
hydrometeorological data relating to these stations.
Common improvements required in the infrastructure, as reported in review, included the
following.
i) All GD sites should be converted to a common benchmark, say GTS.
ii) Use of current meter on GD sites wherever feasible.
iii) Adequate staff to be posted at each GD site.
iv) Shifting of GD sites affected by backwater effect; else provision of auxiliary site
downstream of the present GD site.
v) When rating curves are consistent for a GD station, stages can be recorded; and
discharge worked out using stage-discharge curve.
vi) Installation of DWLR/ AWLR on the sites which represents the sub-basin.
vii) Stages should be recorded at least for five years after closure of the site.
viii) Provision of raingauge station proposed in the catchment having GD site, but with no
raingauge station at present.
ix) Establishment of new GD sites as per need.

6. Spatial Hydrologic Regression for Regional Information
6.1 Spatial Hydrologic Regression in GLS framework
One of the important uses of GD-station network is the estimation of flood quantities and
other parameters such as annual flood peaks, monthly and annual/ seasonal flow volumes,
low-flow d-day averages at ungauged locations, etc. Such statistics can be estimated by
employing spatial/ regional models by use of physiographic characteristics of a catchment
such as catchment area, main channel slope, land use and land cover statistics and
meteorological variables such as mean annual precipitation.
Ordinary least squares (OLS) procedure, often used to calibrate the fit of empirical
hydrologic models is not the most efficient or statistically appropriate estimation procedure.
OLS ignores the actual length of the gauged records employed in the parameter estimation
step, the differences in the variations of flows at different sites, and possible cross-
correlation among concurrent streamflows at the various gauged sites. Inclusion of short-
record sites often decreases the precision of estimated model parameters while using OLS
(Moss & Karlinger, 1974).
Monte Carlo studies by Stedinger & Tasker (1985, 1986) and Tasker & Stedinger (1987)
have documented the value of generalized least squares (GLS) procedure to estimate
empirical relationships between streamflow statistics and basin physiographic
characteristics. The GLS algorithm takes into account for differences in record length, the
variations of flows at different sites, and cross-correlation among concurrent streamflows.
Model description and assumptions:
Let the hydrological region under consideration have ‘n’ GD (stream gauging) stations. We
estimate, at each gauging site, a streamflow characteristic, say ‘monsoon average daily flow’
or ‘50 year flood’. Let us assume that the streamflow statistics of interest, after suitable
transformation of the response and the explanatory variables can be written in a linear
multivariate regression model as follows.
Yi = β0 + β1X1i + β2X 2 i+ εi (two explanatory variables ‘catchment
area’ and ‘mean monsoon precipitation’)
In matrix notation, εβ += XY
where ‘Y’ is an n x 1 vector of streamflow statistics at n sites, X is an n x (k+1) matrix of k
basin characteristics augmented by a column of 1’s. β is a (k+1) x 1 vector of regression
parameters and ε is an n x 1 vector of random errors. Error term ε has two
components, ηγε += , where γ is model error and η is sampling error. βX is the true but
unknown value of streamflow statistics. The dependent variable ‘Y’ is a flow characteristic,
such as logarithm of ‘monsoon average daily flow’ or ‘50 year flood’. The natural logarithm
of basin characteristics may also be taken. It is assumed that
Λ=== )()(;0)( '
εεεε EVarE i.e. errors have zero means. The unknown variance-

covariance matrix can be estimated using the relation
∧∧∧
Σ+=Λ I2
γ where
∧
2
γ is an estimate
of the model error variance due to an imperfect model and
∧
Σ , an n x n matrix of sampling
covariance with elements







≠
=
=Σ
jifor
nn
m
jifor
n
ji
jiij
ij
i
i
ij
,
,
2
σσ
ρ
σ
∧
σ is an estimate of the standard deviation of the observed transformed flows at site i, in is
the record length at site i, ijm is the concurrent record length of sites i and j, and ijρ is an
estimate of correlation of flows between sites i and j.
A more appropriate estimate of parameter vector β is the GLS estimator given by
YXXX 1'11'
)( −−−
∧
ΛΛ=β
In the Estimated Generalized Least Squares (EGLS) model, β and 2
γ are determined by a
numerical search method so that
knXYXY −−=−Λ−
∧∧
−
∧
)1()()( 1'
ββ
The variance–covariance matrix of
∧
β is
11'
)()( −−
∧
Λ= XXVar β
Residuals are calculated using
∧∧
−=−= βXYYYe
A measure of the capacity of the regression model to explain streamflow statistics through
physiographic basin characteristics is R2
; which is also called as coefficient of determination
and is calculated using the following formula
2'
'
2 )()(
1
ynYY
XYXY
R
−
−−
−=
∧∧
ββ

This is actually the multiple correlation coefficient. For comparing two subsets of regressors,
R2
and R2
adj statistics have been used. The advantage of R2
adj is that it does not
automatically increase as new regressors are inserted into the model. R2
adj is calculated
using the following relation
)1(
1
1
1 22
R
kn
n
Radj −





−−
−
−=
The standard error of estimate or sample standard deviation of regression is computed as
the expected value of the squares of the observed values of Y from the expected
(estimated) values
∧
Y using the following formula
1
)()(
)(
'
2
−−
−−
=−=
∧∧
∧
kn
XYXY
YYESE
ββ
Influence/ Diagnostics statistics:
The leverage of site i is the ith
diagonal element of matrix 1'11'
)( −−−
ΛΛ= XXXXH .
The leverage statistics identifies points that are potentially influential due to their location in
the regression variable space. 1
1
+=∑=
kh
n
i
ii , on an average hii will have value
n
k 1+
, so that
observations with values of hii in excess of
n
k 1
2
+
can be considered as high leverage
observations.
In deciding how to extend an existing streamflow data collection network by adding new
stations, generally, the best new stations to include in a network are those new stations that
have leverage at least as large as
n
k 1+
.
One of the most important influence statistics in regression is Cook’s D. This statistic is a
natural measure of how the fit of the model at site i is changed by deletion of the observation
at site i. The generalized version of Cook’s D is
2'
2'
))(1( iiii
iii
i
hk
h
D
−+
=
∧
λ
ε
where '
iih are the diagonal elements of matrix Λ= HH '
and iiλ are the ith
diagonal elements
of matrix Λ . If Di is large, say in excess of
n
4
, then the site i observation has more
influence on model fit and can be singled out for close examination for possible data errors.
The precision with which the regression estimate at the site approximates the true
streamflow statistic at a site can be described by the sampling mean squared error (MSE).

xXXxxMSESampling 11''
)()( −−
Λ=
The regional information contained in the regression model for the site is proportional to the
reciprocal of sampling MSE.
Because of the cost and limited availability of resources for regional data collection, it is
important to develop efficient data collection plans. The GLS technique provides a means
by which a data collection network can be evaluated and future gauging strategies and
plans ranked in terms of their efficiency in collecting regional statistical information. To
objectively evaluate the merits of each of the GD stations operating within the network, GLS
method is useful.
6.2 Algorithm
Steps for carrying out spatial hydrologic regression of streamflow statistics of interest, say,
monsoon average daily flow on basin characteristics, say, catchment area & mean monsoon
precipitation, using GLS procedure as discussed above, are summarized below.
Steps:
i. Calculate monsoon average daily flow series for each station from historical records
a. Consider historical daily streamflow series for each GD station in the network
for monsoon period June-October
b. Perform initial validation checks; treat missing values, outliers, etc
c. Calculate basic annual series for each station for deriving station’s streamflow
statistics
d. Calculate single value of streamflow statistics for all existing GD station
ii. Compile catchment area & mean monsoon precipitation for all stations
iii. Transform the series using natural logarithm to take care of positively skewed nature
of probability distribution of streamflow statistics
iv. Calculate data lengths (in years) and standard deviation for each GD station for
series in finalized in step-i
v. Calculate concurrent record length matrix (14X14) for network’s stations series of
step-i
vi. Calculate cross-correlation matrix (14X14) for series in step-i
vii. Construct matrices Y (dependent variable) and X (regressors or independent
variables).
viii. In GLS spatial hydrologic regression set-up, estimate regression coefficients β,
variance-covariance matrix, model error using numerical search technique.
Computer program will take care of computations involved in iterations.

ix. Calculate R2
, predicted values of monsoon average daily flow and standard error of
estimate.
x. Calculate leverage statistics, Generalized Cook's D statistic, sampling MSE which
will help in ranking GD stations according to their influence for computing monsoon
average daily flow at ungauged locations.
xi. Use of fitted model for prediction
xii. Make an estimate of the costs involved in development and operation of the network.
6.3 Data Analysis and Results
Homogeneity of the region:
Substantial database is a pre-requisite for carrying out network optimization studies. Upper
Bhima basin is a geographically contiguous region, and assumed to be statistically/
hydrologically homogeneous. The rational behind this assumption is that geographically
adjacent catchments could have similarities in hydrological response since; in general,
climate and watershed conditions vary gradually in space. Regional homogeneity of the
region is also checked using simple regression approach wherein the overall goodness of fit
of the regression was seen. A plot of the data-based estimates of monsoon average daily
flow against those derived from the regression shows no unusual outliers.
Data Collection Status:
The Upper Bhima GD network consists of 14 stations, established during 1973-1997. Daily
discharge data are being collected at each of the 14 GD stations. Historical data lengths of
different stations in this network vary between 12 years to 35 years. In majority of the cases,
streamflow is recorded during monsoon season; with the non-monsoon flows being
recorded nil. Moreover, it is noticed that some stations report nil flow for the entire year. For
example, in the case of Dattawadi station, for 1985, 1989, 2001 and 2003, daily discharges
have been reported as nil for the entire part. Table 6.1 shows the detailed annual data
collection status of daily streamflow for Upper Bhima basin.

Table 6.1: Annual Data Collection Status of Daily Streamflow in Upper Bhima GD Network
1 2 3 4 5 6 7 8 9 10 11 12 13 14
Yr
Aamdabad
Askheda
Budhawadi
Chaskman
Dattawadi
Kashti
Khamgaon
Nighoje
Pargaon
Paud
PimpaleGurav
Rakshewadi
Shirur
Wegre
1973 70
1974 0
1975 12
1976 118
1977 126
1978 133
1979 127
1980 130
1981 111 117
1982 129 117 101 92
1983 105 123 125 93 210
1984 94 138 112 30 43 301 111 93 102
1985 70 123 128 0 50 153 161 130 66 87
1986 72 153 108 153 61 153 362 123 105 113
1987 68 83 87 153 28 153 210 112 71 110
1988 95 111 100 61 73 153 366 76 93 56
1989 103 115 121 0 76 153 365 35 90 40
1990 135 130 107 152 83 153 279 97 125 115
1991 97 112 104 153 98 153 127 214 131 126 118
1992 66 101 66 92 51 183 101 274 75 74 40
1993 116 69 95 135 58 183 145 331 108 126 84
1994 110 119 92 106 67 153 116 230 92 83 71 101
1995 27 57 93 10 15 105 96 104 47 87 17 87
1996 96 40 74 96 32 73 153 122 148 52 107 88 89
1997 68 40 62 15 54 18 153 153 115 68 64 88 48 50
1998 95 0 115 83 19 84 183 86 197 116 118 110 74 88
1999 93 127 135 59 30 66 168 132 158 105 140 92 42 128
2000 34 66 75 58 2 14 147 101 140 65 135 62 6 86
2001 40 92 39 46 0 12 164 135 140 92 101 95 102
2002 9 66 77 81 1 8 147 128 0 91 54 95 102
2003 0 89 105 46 0 0 130 110 105 72 87 84 134
2004 82 54 82 56 15 39 139 133 127 123 47 129
2005 122 90 152 89 153 33 150 123 153 153 153
2006 109 83 131 88 123 122 124 125 149 124 124
2007 107 82 87 73 11 111 121 124 38 86

Streamflow statistics of interest:
Three streamflow characteristics, namely `Mean of Monsoon Average Daily Flow (µMADQ)’,
‘Mean of Annual Maximum Flow (µAMQ)’ and ’50 Year Flood (Q50)’ have been considered in
the analysis for regional information. GD station-wise series of µMADQ has been derived as
per the procedure described in Sec 6.2. ’50-Year Flood (Q50)’ at 14 GD sites were obtained
by fitting Pearson Type-III probability distribution to the annual maximum series of respective
GD station. The probability distribution function of Pearson Type-III probability distribution is
given below:
( )
( )





 −
−
−





 −
Γ
= β
αγ
β
α
γβ
x
e
x
xf
1
1
; where -∞< x < ∞, γ>0, -∞ < β < ∞
where α, β and γ are location, scale and shape and position parameters of the distribution.
Floods corresponding 50-year return period (Q50) were estimated using computer
programme.
The GD station-wise series for µAMQ has been derived in the similar fashion as for µMADQ.
The spatial regression model which will be developed for µMADQ will give an idea about water
availability at any ungauged locations on stream in the basin. The other two streamflow
statistics µAMQ and Q50 will give information on peak flows in the basin. Table 6.2 gives the
computed values of these three streamflow statistics based on available historical data.
Table 6.2: Streamflow statistics of Upper Bhima basin
No GD Station
Data
length
Mean of Annual
Average Flow
µAAQ (m
3
/ s)
Mean of Monsoon
Average Daily Flow
µMADQ (m
3
/ s)
Mean of Annual
Maximum Flow
µAMQ (m
3
/ s)
50 Year
Flood
Q50 (m
3
/ s)
1 Aamdabad 12 13.588 32.301 405.805 2108.88
32 Askheda 25 6.041 14.411 291.177 1042.09
83 Budhawadi 27 5.387 12.851 142.442 353.472
4 Chaskaman 35 9.787 23.348 405.341 1475.46
05 Dattawadi 26 14.675 35.010 552.756 2215.21
76 Kashti 21 17.188 40.717 759.044 3639.07
97 Khamgaon 23 51.980 123.153 1407.431 5729.63
18 Nighoje 17 22.741 54.221 632.099 2213.19
09 Pargaon 26 102.625 244.437 2591.806 9485.63
810 Paud 24 8.675 20.694 256.648 1187.30
211 PimpleGurav 11 12.260 29.247 417.568 2149.04
912 Rakshewadi 20 41.439 98.858 1408.020 5516.11
313 Shirur 17 23.909 57.039 732.217 3388.66
814 Wegre 13 6.554 15.634 152.894 429.043
Basin physiographic characteristics:
The physiographic characteristics of the Upper Bhima basin such as catchment area (CA)
and meteorological variable – mean monsoon precipitation (MMP) or mean annual

precipitation (MAP), used as independent variables in the regression model are shown in
Table-6.3.
Table 6.3: Catchment Area & Mean Monsoon/ Annual Precipitation at GD Network of Upper Bhima basin
Sr
No
GD Station
Year of
Establishment
Taluka Tributary
Catchment
Area
(km
2
)
Mean Monsoon
Precipitation
(mm)
Mean Annual
Precipitation
(mm)
1 Aamdabad 1996 Shirur Ghod 1522.5 1012.8 1061.7
2 Askheda 1983 Khed Bhama 239.4 1703.4 1745.8
3 Budhawadi 1981 Maval Kundalika 151.9 2907.4 2954.9
4 Chaskaman 1973 Khed Bhima 389.0 1581.2 1633.8
5 Dattawadi 1982 Haveli Mutha 741.2 2056.3 2105.6
6 Kashti 1984 Shrigonda Ghod 4392.0 700.0 747.4
7 Khamgaon 1985 Daund Mula-Mutha 2832.9 1713.1 1756.6
8 Nighoje 1991 Khed Indrayani 832.3 2271.6 2319.8
9 Pargaon 1982 Daund Bhima 6251.0 1376.0 1420.5
10 Paud 1984 Mulshi Mula 473.6 2731.2 2749.6
11 PimpleGurav 1997 Haveli Pawana 506.7 2127.8 2179.6
12 Rakshewadi 1984 Shirur Bhima 3279.8 1167.9 1215.4
13 Shirur 1984 Shirur Ghod 3204.1 798.6 845.5
14 Wegre 1994 Mulshi Mutha 91.1 1898.3 1923.8
For performing the spatial hydrologic regression of streamflow statistics on selected
physiographic/ meteorological basin characteristics using GLS procedure, computer-
oriented models in FORTRAN have been developed. The computer program and output of
GLS Regression of ‘Monsoon Average Daily Flow’ on Catchment Area and Mean Monsoon
Precipitation is enclosed in Appendixes 1 to 3.
Spatial GLS Regression results:
The summary of regression results is presented in Table 6.4. Regression standard errors of
estimate are less when the two basin characteristics catchment area and mean monsoon/ or
annual precipitation are used. The coefficients of determination (R2
) for three regression
models for µMADQ, µAMQ and Q50 on two basin characteristics are computed as 78.2 %, 84.7
% and 89.6 % respectively. When the information on MMP/ MAP is not available, then
streamflow statistics at any location on stream in the basin can be estimated using
catchment area only, but with somewhat less accuracy.

Table 6.4: Spatial GLS Regression results for Upper Bhima Basin
Standard Error
Sr No Fitted model
R
2
(%)
R
2
adj
(%) ( log units) (original units)
1 µAAQ=0.293 CA
0.617
70.2 45.1 0.490 40.1
2 µAAQ=0.012 CA
0.679
MMP
0.367
78.2 54.0 0.438 34.9
3 µAMQ=7.933 CA
0.626
81.4 63.5 0.374 364.2
4 µAMQ=0.974 CA
0.670
MAP
0.240
84.7 66.6 0.355 312.3
5 Q50=14.339 CA
0.730
87.9 75.4 0.339 1147.2
6 Q50=2.008 CA
0.771
MAP
0.226
89.6 76.7 0.328 896.8
Abbreviations: Mean of Annual Average Flow (m
3
/Sec) - µMADQ ; Mean of Annual Maximum Flow - µAMQ;
50 Year Peak Flow – Q50; Catchment Area (km
2
) – CA; Mean Monsoon Precipitation (mm) – MMP;
Mean Annual Precipitation (mm) – MAP
Table 6.5 gives the Observed & Estimated values of Mean of Monsoon Average Daily Flow
using only one regressor CA and two regressors CA and MMP, after carrying out the spatial
GLS regression for Upper Bhima basin GD network.
Table 6.5: Observed & Estimated Mean of Monsoon Average Daily Flow (m3
/ sec) using
GLS Spatial Regression for Upper Bhima Basin
EstimatedSr
No
GD Station Observed
µMADQ µMADQ=0.7 CA
0.617
µMADQ=0.043 CA
0.672
MMP
0.322
1 Aamdabad 32.301 64.336 54.683
2 Askheda 14.411 20.550 18.633
3 Budhawadi 12.851 15.521 16.302
4 Chaskaman 23.348 27.735 25.228
5 Dattawadi 35.010 41.263 42.339
6 Kashti 40.717 123.720 99.043
7 Khamgaon 123.153 94.368 98.354
8 Nighoje 54.221 44.324 47.257
9 Pargaon 244.437 153.725 156.019
10 Paud 20.694 31.299 34.315
11 PimpleGurav 29.247 32.640 33.153
12 Rakshewadi 98.858 103.326 95.974
13 Shirur 57.039 101.807 83.559
14 Wegre 15.634 11.331 10.086

A plot of observed mean of monsoon average daily flow (in log transformation) against those
derived from the spatial GLS regression in Figures 6.1 (a) & 6.1 (b) below shows no unusal
outliers.
Figure 6.1: Observed versus predicted mean of annual average flow in log units (µAAQ in m3
/ s)
Tables 6.6 and 6.7 details the corresponding observed and estimated values of ‘Mean of
Annual Maximum Flow’ and ’50 Year Flood’ using only one regressor CA and two
regressors CA and MAP, using GLS Spatial Regression for Upper Bhima basin GD network.

Table 6.6: Observed & Estimated Mean of Annual Maximum Flow (m3
/ s) using GLS Spatial
Regression for Upper Bhima Basin
Estimated
No GD Station Observed
µAMQ µAMQ=7.933 CA
0.626
µAMQ=0.974 CA
0.670
MAP
0.240
1 Aamdabad 405.805 781.246 704.713
2 Askheda 291.177 245.231 229.800
3 Budhawadi 142.442 184.421 192.197
4 Chaskaman 405.341 332.483 313.278
5 Dattawadi 552.756 497.671 512.561
6 Kashti 759.044 1517.455 1318.118
7 Khamgaon 1407.431 1152.674 1205.573
8 Nighoje 632.099 535.173 567.046
9 Pargaon 2591.806 1891.755 1946.815
10 Paud 256.648 375.910 404.742
11 PimpleGurav 417.568 392.266 400.671
12 Rakshewadi 1408.020 1263.840 1217.920
13 Shirur 732.217 1244.974 1098.488
14 Wegre 152.894 133.995 123.194
Table 6.7: Observed & Estimated Fifty Year Flood (m3
/ s) using Spatial GLS Regression for
Upper Bhima Basin
Estimated
No GD Station Observed
Q50 Q50=14.339 CA
0.730
Q50=2.008 CA
0.771
MAP
0.226
1 Aamdabad 2108.883 3021.716 2746.191
2 Askheda 1042.098 782.646 738.108
3 Budhawadi 353.472 561.398 585.274
4 Chaskaman 1475.460 1116.060 1057.666
5 Dattawadi 2215.217 1786.154 1839.686
6 Kashti 3639.079 6552.618 5743.534
7 Khamgaon 5729.631 4755.466 4965.106
8 Nighoje 2213.190 1944.053 2056.293
9 Pargaon 9485.638 8473.085 8708.632
10 Paud 1187.302 1287.799 1383.359
11 PimpleGurav 2149.049 1353.352 1383.442
12 Rakshewadi 5516.113 5294.321 5117.596
13 Shirur 3388.668 5202.372 4628.601
14 Wegre 429.043 386.840 358.572

The plots of observed and predicted values of ‘Monsoon Average Daily Flow’ and ‘50-Year
Flood’ after the application of GLS spatial regression using only one regressor-catchment
area for Upper Bhima basin are shown in Figure 6.2. With the help of existing GD network of
the basin, this process will assist in estimating water availability at any ungauged locations/
sub-basins of Upper Bhima in terms of average monsoon flow. The only information needed
for the purpose is catchment area of that sub-basin. Accuracy of the prediction can be
improved by incorporating more and more informative basin characteristics into the spatial
regression model. Similarly, for any ungauged location in the basin, the 50-year flood, or
flood magnitude of desired return period can be estimated based on information on some
basin physiographic characteristics, as demonstrated in Figure 6.2.
Figure 6.2: Observed & Estimated streamflow statistics using GLS Spatial Regression for
Upper Bhima

GLS spatial hydrologic regression has been used to correlate streamflow statistics of
interest with basin characteristics by using existing network of hydrologic stations of a basin.
Due to cost constraints for future maintenance of network, data collection agencies may
think of reducing the size of the network. In such situations, the stations in a network which
adds less information on streamflow statistics can be separated out for termination. The
diagnostic statistics discussed in Sec. 6.1, can be used for ranking the stations in terms of
their influence on streamflow statistics of interest. The determination of final optimal network
can be done by considering the outcomes of analytical methods and empirical rules. This
has been worked out for Upper Bhima basin network in the next section.

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