Standardization Process for Flood Maps
in India: A Comprehensive Approach
like India. Standardization of flood maps ensures consistency, accuracy, and
interoperability of data across different regions and agencies, enabling more
effective flood risk assessment, mitigation, Flood mapping is a critical component of
disaster management and urban planning, especially in a flood-prone country and
response.
1. Introduction to Flood Mapping and
Standardization
Flood maps visually represent areas that are susceptible to flooding, indicating the
extent, depth, and probability of inundation. The standardization of these maps
involves establishing uniform methodologies, data specifications, terminology, and
presentation formats to ensure comparability and reliability nationwide. This is crucial
for developing robust disaster management plans, informing land-use policies, and
guiding infrastructural development in flood-prone areas.
In India, organizations like the National Remote Sensing Centre (NRSC), Indian
Space Research Organisation (ISRO), play a significant role in flood monitoring and
mapping using satellite remote sensing technologies. The Ministry of Jal Shakti also
advocates for Flood Plain Zoning (FPZ) as a non-structural measure for flood
management.
2. Scope
This standardization framework applies to all flood maps generated for flood hazard
assessment, flood risk assessment, and flood management planning across India. It
encompasses methodologies, data standards, terminology, symbology, and output
formats to ensure uniformity and facilitate data exchange and integration at national,
state, and local levels.
3. Key Terminologies in Flood Mapping
Understanding the following terms is essential for comprehending flood map
standardization:
3.1 Flood Plain: Land adjoining a river or channel that is inundated only
during floods.
3.2 Flood Plain Zoning (FPZ): Demarcating zones or areas likely to be
affected by floods of different magnitudes or frequencies, specifying
permissible developments within these zones to minimize damage.
3.3 Flood Hazard Map: A map that identifies areas at risk of flooding,
categorizing them by hazard levels (e.g., very low to very high).
3.4 Flood Hazard Index: A computed value reflecting the overall flood
hazard, often considering factors like hazard category, area, and intra-annual
flood variations.
3.5 Return Period (Recurrence Interval): An estimated average time
between events, indicating the probability of a flood of a certain magnitude
occurring in any given year. For example, a 100-year flood has a 1% chance
of occurring in any year.
3.6 Annual Exceedance Probability (AEP): The probability of a flood of a
given magnitude being equaled or exceeded in any single year (e.g., 1% AEP
for a 100-year flood).
3.7 Highest Flood Level (HFL): The maximum level to which a river or
stream could rise due to rainwater and runoff during a flooding event.
3.8 Digital Elevation Model (DEM): A digital representation of terrain
elevation, crucial for determining flow direction, inundation extent, and water
depth.
3.9 Geographic Information System (GIS): A powerful tool used for
assembling, managing, analyzing, and visualizing spatial data, including flood
maps.
3.10 Remote Sensing (RS): The acquisition of information about an object or
phenomenon without making physical contact, often through satellite imagery,
vital for large-scale flood monitoring and mapping.
3.11Land Use/Land Cover (LULC): The classification of different types of
land surfaces (e.g., urban, agricultural, forest), which influences flood
susceptibility.
3.12 Drainage Density (DD): A measure of the total length of streams and
rivers in a drainage basin divided by the total area, indicating runoff
characteristics.
3.13 Topographic Wetness Index (TWI): A measure used to quantify
topographic control on hydrological processes.
3.14 Probable Maximum Precipitation (PMP): The greatest depth of
precipitation for a given duration that is meteorologically possible over a given
basin at a particular time of year.
4. Stages of Flood Map Standardization
The standardization process generally involves the following key stages:
4.1Data Acquisition and Preparation
This initial phase focuses on collecting comprehensive and standardized input data:
4.1.1 Topographic Data: High-resolution DEMs obtained from sources like
LIDAR or satellite missions are fundamental for accurate flood modeling.
4.1.2 Hydro-meteorological Data: Long-term historical rainfall records, river
discharge data, and extreme event data are crucial. The India Meteorological
Department (IMD) provides hydrometeorological services, including rainfall
monitoring and quantitative precipitation forecasting (QPF).
4.1.3 Satellite Imagery: Multi-mission, multi-sensor satellite data (e.g., Indian
Remote Sensing - IRS, and foreign satellite data) are used by NRSC/ISRO for
flood inundation mapping and damage assessment.
4.1.4 Ancillary Data: This includes LULC maps, soil types, geological data,
and infrastructure layers, which are essential for comprehensive flood risk
assessment.
4.2 Flood Modeling and Hazard Assessment
This stage involves using models and statistical methods to predict flood behavior
and delineate hazard zones:
4.2.1 Hydrological and Hydraulic Modeling: These models simulate flood
events, predicting water levels, flow velocities, and inundation extents for
various return periods.
4.2.2 Flood Frequency Analysis (FFA): This statistical method estimates
flood peaks for different return periods. Commonly used probabilistic models
include:
4.2.2.1 Normal Distribution: Useful when data is symmetrically
distributed around the mean.
Formula for Predicted Discharge (Q): Q=μ+Kσ Where:
Q = predicted discharge
μ = standard mean (arithmetic mean of sample
data)
σ = standard deviation
K = frequency factor (standard normal deviate),
obtained from standard normal distribution tables
based on probability.
Probability (P) for a given Return Period (T): P=T1
4.2.2.2 Gumbel (Extreme Value Type I) Distribution: Often used for
modeling extreme events like maximum annual floods.
Formula for Abridged Variate (Yt ): Yt =−ln(−ln(T1 ))
Where:
Yt = abridged variate
T = return period
4.2.2.3 Log Pearson Type III (LP-III) Distribution: Widely applied in
flood frequency analysis, especially for skewed data.
4.2.2.4 Generalized Extreme Value (GEV) Distribution: A flexible
distribution that includes Gumbel, Frechet, and Weibull distributions as
special cases.
4.2.3 Goodness-of-Fit Tests: Statistical tests like Kolmogorov-Smirnov (KS),
Chi-Squared (CS), and Anderson-Darling (AD) are used to determine how
well a chosen distribution fits the observed flood data.
4.2.4 GIS Integration: All the processed data and model outputs are
integrated within a GIS environment to generate final flood hazard and risk
maps.
4.3 Map Delineation and Classification
Based on the modeling and hazard assessment, flood maps are delineated into
different zones. Common classifications include:
4.3.1 Flood Hazard Zones: Categorized into levels such as "Very Low,"
"Low," "Moderate," "High," and "Very High Hazard Zones". These zones are
typically based on historical flood extents and predicted inundation depths and
frequencies.
4.3.2 Floodplain Zoning Categories: The National Disaster Management
Authority (NDMA) guidelines suggest different zones based on flood
frequency, e.g., areas likely to be affected by 10-year or 25-year frequency
floods, with regulations on permissible structures.
4.4 Validation and Ratification
To ensure accuracy and credibility, flood maps undergo rigorous validation:
4.4.1 Ground Truthing: Maps are validated with actual ground observations
of flood inundation areas and ratified by relevant state disaster management
authorities or district administrations.
4.4.2 Accuracy Assessment: Statistical methods like AUROC (Area Under
the Receiver Operating Characteristic) analysis can be used to assess the
validation accuracy of the maps.
4.5 Dissemination and Utilization
Standardized flood maps are then made accessible to various stakeholders:
4.5.1Online Platforms: Creation of web-based platforms for easy access and
visualization of flood maps by government agencies, urban planners,
emergency services, and the public.
4.5.2 Policy Integration: Integration of flood maps into land-use planning
regulations, building codes, and disaster preparedness plans to minimize
future flood damages.
5 Importance of Graphs and Visualizations
Graphs and visualizations are integral to effective flood map standardization, as they
make complex data understandable and actionable:
5.1 Flood Hydrographs: Illustrate discharge over time, crucial for
understanding flood event characteristics and for calibrating hydrological
models.
5.2 Frequency Curves: Plot flood magnitudes against their return periods,
providing insights into flood probabilities. An example of a flood frequency
curve might look like this:
5.3Flood Inundation Maps: The core output, showing the spatial extent and
depth of flooding for different scenarios, often color-coded for clarity (e.g.,
blue for deeper water, lighter blue for shallower).
5.4 Hazard Zonation Maps: Similar to inundation maps but specifically
categorizing areas based on hazard levels (e.g., Very High, High, Moderate,
Low, Very Low).
5.5 Vulnerability Maps: Overlay flood hazard with elements at risk (e.g.,
population density, critical infrastructure) to highlight vulnerable areas.
These visual tools enhance decision-making by providing intuitive representations of
flood risk and potential impacts.
6. Challenges and Future Directions
Despite significant progress by organizations like NRSC/ISRO in preparing national
and state-level flood atlases, India faces challenges in fully implementing a national
flood plain zoning policy. Resistance from states due to population pressure and
livelihood concerns are key obstacles.
Future efforts towards standardization should focus on:
6.1Uniform Data Standards: Establishing national protocols for data
collection, processing, and mapping to ensure consistency across states.
6.2 Advanced Modeling: Employing more sophisticated hydrological and
hydraulic models, including those incorporating climate change impacts.
6.3 Integration of Technologies: Further integrating GIS, remote sensing,
and Artificial Intelligence/Machine Learning techniques for more accurate and
real-time flood forecasting and mapping.
6.4 Legal Frameworks: Encouraging all states to enact and implement flood
plain zoning legislation based on the model draft bill circulated by the Union
Government.
6.5 Community Engagement: Involving local communities in the mapping
and planning process to ensure ground-level validation and acceptance of the
maps and zoning regulations.
By adhering to a standardized approach, India can significantly enhance its flood
preparedness and mitigation strategies, ultimately reducing the devastating impact of
floods on lives and livelihoods.
