Benchmark, storm water SLB indicators, sources of data, storm water drainage status in Indian pilot cities

1. PLANNING WORKSHOP II
(Infrastructure Studies)
SUBMITTED TO:
Mr. Praman
Dept. of Urban and Regional Planning
SUBMITTED BY:
13011BA010
13011BA020
13011BA027

2. BENCHMARKING
Benchmarking is a process of
Measuring ULB’s/Water Board’s Performance and practices in key areas,
Comparing them with best practice
Subsequent translation of this best practice into use,
Leading to superior performance –Performance Improvement
A process through which practices are analysed to provide a standard measurement
('benchmark') of effective performance within an organisation.

3. Types of Benchmarking
Generally, there are two approaches to benchmarking: metric and process.
• Metric benchmarking is a quantitative comparative assessment using
standard performance indicators that enables utilities to track internal
performance over time, compare this performance against that of similar
utilities, and establish target levels of performance.
• Process benchmarking involves first identifying specific work procedures
to be improved through a step-by-step ‘process mapping’, and then
locating external examples of excellence for standard setting and possible
emulation.

4. Service Level Benchmarking has been developed and released by the MoUD. It seeks
to:
• identify a minimum set of standard performance parameters for the water and
sanitation sector that are commonly understood and used by all stakeholders across
the country;
• define a common minimum framework for monitoring and reporting on these
indicators; and
• Set out guidelines on how to operationalise this framework in a phased manner.

5. PRINCIPLES UNDERLYING THE BENCHMARKING PROCESS
The model of benchmarking process is famously referred to as the
“Deming cycle” and it includes a minimum of four phases “Plan –
Do-Action-Check”
Deming’s Benchmarking Cycle

6. BENCHMARKING AN APPROACH
Identify Problem
or Area for review
Map the Process
Identify Partners
& Data Sources
Collect Data
Identify
Good or
Best
Practise

7. SLB framework
Indicators
List of service level
indicators for each sector
Rationale
Why is this indicator
important for the service
Definition
Objective and
mathematical definition
Data Requirements
Data that is required to
calculate the indicator
Reliability of measurement
What is the reliability of
data should be targetted
Frequency of measurement
How often should the
indicator be measured
Level of detail
What is the level of
geographical detail to
which the data should be
available
Target for the indicator
What is the performance
level that should be
targetted?

8. STROM WATER DRAINAGE
• Extent of the network and effectiveness of the network are emphasized to assess
storm water drainage systems performance.
• As this service does not yield any direct revenues, financial sustainability is not
considered.
INDICATORS:
• Coverage of storm water drainage network
• Incidence of water logging/flooding

9. SLB FOR STORM WATER DRAINAGE
Sl.
No
Proposed Indicator Benchmark
1 Coverage of storm water drainage network 100 %
2 Incidence of water logging/flooding 0

10. 1.COVERAGE OF STORM WATER DRAINAGE NETWORK
Indicator Unit Definition
Coverage of storm water
drainage network
% Coverage is defined in terms of road length covered
by the storm water drainage network
Data Requirements
Data Requiring for
calculating the indicator
UNIT Remarks
(a)Total length of road
network in the ULB
Km Only consider roads that are more than 3.5 m wide
carriageway
(b)Total length of primary
secondary and tertiary
drains
km Only consider drains that are trained, made
of pucca construction and are covered.
Coverage of storm water
drainage networks
% Coverage = [(b/a)*100]

11. RATIONALE FOR THE INDICATOR
This indicator provides an estimation of the extent of coverage of the storm water drainage network in the
city. This value should be 100 percent.
Reliability of Measurement
Reliability scale Description of method
Lowest level of reliability (D) Not applicable.
Intermediate level (C) Estimated from city road maps, not updated in the past five years.
Intermediate level (B) Estimated from city road maps (that are detailed and to scale), which
have been updated in the past five years.
Highest/preferred level of reliability
(A)
Actual ground level surveys are carried out to measure drain and road
length. Surveys are carried out to verify that drains are of pucca
construction and covered.
Minimum frequency of measurement
of performance indicator
Smallest geographical jurisdiction for
of performance indicator measurement of performance
Measurement Annually Measurement Ward level

12. 2.INCIDENCE OF WATER LOGGING/FLOODING
Performance Indicator
Indicator Unit Definition
Data Requirements
Data required for calculating
the indicator
Unit Remarks
a. Identification of flood prone
points within the ULB limits. The
points may be named as A1, A2,
A3,….An
Number Flood prone points within the city should be
identified as locations that experience water
logging at key road intersections, or along a road
length of 50 m or more, or in a locality affecting
50 households or more.
b. Number of occasions of
flooding/water logging in a year
Number
per year
An occasion or incident of flooding/water logging
should be considered if it affects transportation
and normal life. Typically, stagnant water for more
than four hours of a depth more than six inches.
The aggregate number of
instances or occasions of water
logging/flooding reported
across the city in a year
Number
per year
Aggregate incidence = (b at A1) + (b at A2) +
….. (b at An)

13. Rationale for the Indicator
This indicator provides a picture of the extent to which water logging and flooding are reported in the ULB
within a year, which have impacted a significant number of persons as well as normal life and mobility. This
indicator provides an assessment of the impact or outcome of storm water drainage systems. The benchmark
value for this indicator should be zero.
Reliability of Measurement
Reliability scale Description of method
Lowest level of reliability (D) Not applicable.
Intermediate level (C) Not applicable.
Intermediate level (B) Based on reports/complaints filed by citizens.
Highest/preferred level of
reliability (A)
Flood prone points should be first identified based on
reports/complaints filed by citizens, or by direct observations, and
reported
into a central control room. Monitoring stations (in charge of
specific jurisdictions) should regularly monitor instances of flooding
in the respective wards/zones, as mentioned above. Data should
be captured by time, date, location and extent of flooding.
Minimum frequency of measurement
of performance indicator
Smallest geographical jurisdiction for measurement of performance
Measurement Annually Measurement Ward level

14. Sl.
No
Urban Service Frequency of
Measuremen
t by
ULB/Utility
Frequency of
Reporting
within
ULB/Utility
Frequency of
Reporting to
State/Central
Govt.
Jurisdiction for
Measurement
by ULB /Utility
Jurisdiction for
Reporting
within
ULB/Utility
Jurisdiction
for Reporting
to
State/Central
Govt.
1 Coverage of storm water
drainage network
Annually Annually Annually Ward Ward ULB
2 Incidence of water
logging/flooding
Quarterly Quarterly Annually Ward Ward ULB
SUGGESTED FREQUENCY AND JURISDICTION OF REPORTING

15. Indicator Parameter Source of data Remark
Coverage Drain length pucca or
covered (road width of
3.5 m)
1. Existing drainage
map (updated).
2. Data captured by
infrastructure DPR
(like water,
sewerage, drainage
or road etc).
3. Physical
measurement (using
bike)
•Only pucca covered
drains should be
considered.
•Road with both side
drain should be
considered once.
Water Logging No. of incidence in a year 1. Complaint records.
2. Other related
records.
3. Physical verification
during monsoon.
4. Experience of the
local people.
5. Disaggregated level
(supervisors).
• Along roads
(50 mt length or more)
and
Locality (affecting 50 HH
or more)
Incidence of storm water
mixing in the sewerage
Capacity of storm water
drains.
1. Disaggregated level
(supervisors).
2. Complaint records.

16. Summary of SLB Indicators – Storm Water Drainage
Source: MoUD SLB performance data.

17. Average of Storm Water Drainage in Pilot Cities
Indicator Benchmark Pilot Cities Average
1. Drainage network
coverage
100% 31.3
2. Incidence of water logging 0 66.7

18. STORM WATER DRAINAGE
(case study of Bangalore)
Observations/Comments
•Exact length of tertiary drains needs to be assessed.
•Lack of regular updation of information on drains, water stagnation points etc.
•No centralized monitoring system is in place.
•Ward level road history is to be maintained. This is to be linked GIS.
Performance Indicator Benchmark Status Reliability
Coverage 100% 5 C
Incidence of water logging 0 number 135 number B

19. SWD: Areas identified for improvement
Indicator Reasons for low
reliability
ISIP
Coverage No comprehensive
records on drains
• Ward level road history register to
be updated
• Developing a GIS based road
database
• Maintenance of records
Incidence of
water logging
No records are being
updated on occurance of
flooding
• Identification of flood prone areas
• Integration of traffic data and GIS
based data
• Updation of records on occurance
of flooding
• Participatory reporting for flooding
incidence
• Establishmet of rain gauge
recording system & integration of
rain fall data

20. Key issues and possible solutions
Key Issues Likely Difficulties Possible Solutions
Choice of
Indicators and
Definitions
Difficulties in arriving on a
universally accepted set
of indicators
•Choose number and type of indicators carefully based
on relevance and usefulness to a broad majority of
utilities, ease of understanding and measurability, their
likelihood to be monitored, and so on
•Customize global indicators to suit the local context
while, at the same time, retaining the flexibility to
allow international comparisons
•Communicate indicators and their definitions to
utilities clearly
Data Collection Availability and
reliability of data can
be limited
•Communicate indicator definitions,
interpretations and their calculation to utilities
clearly
•Devise methods to arrive at broad indicators
within the existing data Constraints
•Include robust quality assurance mechanisms to
grade the reliability and accuracy of data
•Improve accounting practices and put in place
incentives for utilities to collect and report
accurate data

21. Thank you