Agriculture, Disaster and Governance

Sustainable and Disaster Resilient Agriculture through Climate and Weather Informatics.

Agriculture, Disaster and Governance

Sustainable and Disaster Resilient Agriculture through Climate and Weather Informatics.

P Goswami

December, 2014

Agriculture and Disaster: Impact (TOI December 12, 2014)

Agriculture and Disaster: Impact (TOI December 12, 2014)

Needs holistic, inclusive and Pro-active ApproachNeeds holistic, inclusive and Pro-active Approach

Impact, intensity and frequency of disasters are increasingImpact, intensity and frequency of disasters are increasing

The Team

P Goswami: Group CoordinatorEVSP Rao (Adviser)S HimeshK V RameshK C GoudaV RakeshG N MahapatraK R BimalaS RayPost DocsPh D Students Project Scientist

www.cmmacs.ernet.in

CSIR C-MMACS (Repositioned 4PI)

Climate and Environmental Modelling Programme

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Plan of Discussion

• Disaster and Impact Continuum• Pro-active Disaster Management• Critical Components for Pro-active Management and

Mitigation• Actionable Disaster Forecasts• Integrated Disaster Assessment and Forecasting (IDAF)• Science for Integrated Disaster Assessment and

Forecasting (IDAF)• Modelling Issues• Data Optimality and Observation System Design• Preparedness• IDAF: Organization of a Platform• IDAF: Roadmap for a National Initiative• Disaster Simulation and Forecasting: Perspective

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Disaster and Impact Continuum

Disaster and Society- Loss of Life and Valuables- Medical Emergencies- Epidemic- Loss of working and schooling hours

Disaster and Economy- Loss of property- Loss of Business- Disruption

Disaster and Ecosystem

Disaster and National Security: Some Disaster ScenariosGridlock/Reduced mobility due to extreme rainfall event and flooding:

Foreknowledge available with disruptive forces for planning

Release of toxins/radiological hazards at easy access points (away from target): Knowledge of dispersion paths available with disruptive forces

Extreme rainfall events over strategic and remote locations: Periods of increased vulnerability

The scales of atmospheric motion with the phenomenon’s average size and life span. (Because the actual sizeof certain features may vary, some of the features fall into more than one category.)

The Multi-scale Structure of the Atmospheric Processes and Disasters

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Pro-active Disaster Management

Event and Disaster: An event by itself is not a disaster; it is the impact of an event on the ecological and the sociological landscapes that determine the nature of a disaster

In the absence of foreknowledge, management is post-event (Reactive)

Emerging technology of weather informatics provides a way of pro-active management of many natural and related (chemical, radiological,..) disasters

Critical Components for Pro-active Management and Mitigation:Actionable ForecastImpact Scenarios and QuantificationMitigation PlanInformation and Communication

Actionable Forecast is the most critical component of a pro-active disaster platform

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Actionable Disaster Forecasts

Actionable Knowledge: A general warning or a forecast is does not provide actionable information and knowledge

Critical Requirements: Disaster Forecasts need to meet stringent conditions

Precision in location of the eventAccuracy in intensityPrecision in Time of OnsetAccuracy in Duration (Persistence)

Current and standard Meteorological forecasts often do not meet these criteria

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Integrated Disaster Assessment and Forecasting (IDAF)

• Disaster Assessment: Planning and Design (Redesign)• Disaster Scenarios: Disaster Resilience and Preparedness• Disaster Forecasts: Pro-active Management• Disaster Nowcast: Online Coordination and Management

(Evacuation)• Disaster GIS: Real time and Post-event management

• Assembly Points and Evacuation Plan: Multiple chemical/radiation leaks

• Identification of sites for minimum hazard based on dispersion scenarios

• Recovery time for safe return/operation• Climate Resilience for Worst-case (Least-Regret) Scenarios

Disaster Simulation and Forecasting is a rapidly evolving field; needs national initiative for technological capability

Some Examples of Agricultural Processes (Plant Diseases etc.)

Agricultural Process

Weather Factors Impact

1 Yellow rust of Wheat

Low temperature, foggy weather, recent rainfall

Food Security

2 Mites of Coconut Wind velocity and direction

Commercial Plantation

Crops

3 Spices Disease Temperature,Rainfall, Humidity

International Trade

4 Oil seeds Diseases

Temperature,Rainfall, Humidity

Nutritional Security

5 Aromatic Rice Production, Secondary

Metabolites

Temperature,Rainfall, Humidity

Export Trade,

Pharmaceuticals

6 Soil Health Temperature,Rainfall, Humidity, Wind

Natural Resource

Management, Land use

Extreme is not rare!

The Distribution of “Rare” Extreme Rainfall Events

Challenges and Approaches

Technical Challenges

• Climate-Crop Interface: Model Development

• Data Optimality and Observation System Design

• Model Calibration and Validation

• Reliability of Projections

Approaches

• Multi-source, multi-variable Database on disaster

• Crop-Disaster Interface algorithms

• Modelling and Projection Strategy: Modular System

• Climate Projections over India with Reliability Quantification

Weather forecastWeather forecast

Weather monitoring

Weather monitoring

Moisture adequacy, water stress, …

Moisture adequacy, water stress, …

Plant Disease (Pest

Population) Model

Plant Disease (Pest

Population) Model

Precision AgriculturePrecision Agriculture

Crop viability (Choice)Crop viability (Choice)

Sowing ScheduleSowing Schedule

Irrigation ScheduleIrrigation Schedule

Fertilizer ScheduleFertilizer Schedule

Pesticide SchedulePesticide Schedule

Harvest ManagementHarvest Management

Agro-advisories GIS Platform

Weather Informatics in Agriculture

Meeting Challenges in Forecasting and Projection

• Scope: Process models (disease and weather)

• Accuracy: Forecast for regimes (ranges)

• Reliability: Calibrated model Forecast Configuration

• Relevance: Downscaling to relevant scale

• Applicability: Design of Advisories and ICT

10-km

10-km

30-km 30-

km

Simulation Observation

Choosing the right model configuration and model optimization is critical to forecast extreme events like Mumbai event happened during July 26-27, 2005

Observation No Assimilation Assimilation

Uttarakhand ERE June 2013

Towards Local Climate ProjectionsGeneration of Skillful Forecasts at Station Scale

The raw forecasts (yellow) hardly match the observed values (brown circles)

The debiased forecast (blue line) shows good match

Goswami and Mallick, Weather and Forecasting, 2010, Monthly Weather Review, 2011

Challenge: Typical climate projections provide fields averaged over hundreds or thousands of Km square; not directly applicable at local scale

Solution: From useless raw forecasts to skillful station-scale forecasts through downscaling and Debiasing

These algorithms and the computer codes are developed in-house and validated over multiple locations against observations

The Karnataka Initiative (Operational since July 2010)

Sh. Sharanappa has access to canal water. He prefers not to irrigate …., as canal irrigation

followed by rains will cause insect/pest attack and cost about Rs.1,000/acre to spray

pesticide; he owns 10 acres. The forecast from C-MMACS provided through KSNDMC helps

him to save this amount. (One out of many mails)

October 2010

November 2010

December 2010

Needs Scale-up

Happy Users Message from Dr V S Prakash, Director,

KSNDMC, Govt Karnataka

Message from M N Vidyashankar, Principal Secretary, Govt Karnataka

A Quick Estimate of Economical Benefit

Number of Marginal Farmers: ~ 70 LakhsCost of irrigation/episode/land parcel: Rs 2500

Saving (10% farmers) avoiding one episode of irrigation: Rs 175 Crores

Enhancing Crop Viability through Weather Informatics

Roadmap and Scale up

• From meso-scale to Micro-meteorology (100-10m)

• Model configuration for different locations and validation

• Decision support system for mitigation (Traffic diversion, evacuation etc,.)

• Inclusion of structures in flow simulations (LES)

• Inclusion of Marine and space environment

Integrated Disaster Assessment and Forecast Platform (IDAF)

Organization

Analysis

Initial and Boundary Conditions

Meso-scale Atmospheric ModelDispersion Models

•Release Scenarios• Urban topography

User Interface

•Debiasing•Ensemble Algorithm

•Downscaling Algorithm

•CRM-Meso-scale Interface

Constraints

GIS

White Space in Integrated Assessment

Colors in Spectrum of Research in IndiaColors in Spectrum of Research in India

Current Global Status of Research in Integrated Assessment

Hazard Models: International Scenario

• Quality– Do the results accurately reflect the event?– Are the limitations of the model clearly stated?– Is information presented in an orderly manner?

• Timeliness– Is the information provided when needed?– Is the information up to date?– If the scenario changes, does the model reflect the

change?• Completeness

– Is the model complete?– Does the user have access to past model runs?– Does the model provide appropriate information?– Does the model give too much information?

Montreal, Canada

Washington DC, USA

Tokyo, Japan

Melbourne, Australia

Exeter, UK

Toulouse, France

Obninsk, Russian Federation

Beijing, China

•WMO’s 8 Regional Specialized Meteorological Centres (RSMCs)

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Disaster Simulation and Forecasting: PerspectiveKnowledge-based disaster scenarios can help safe design as

well as pro-active mitigation; modern techniques of simulation, forecasting and GIS can help

Strategically, It is likely that disruptive and enemy forces will soon begin to employ these tools to identify vulnerability and plan strikes; an adequate counter measure is necessary

• Development of a quantitative Disaster Assessment and Forecasts Platform should be a National Preparedness

•Because of common drivers and interactive processes, the platform needs to integrate key processes and parameters

Disaster Forecasts need to meet stringent conditions in terms of forecast parameters like time of onset, location, intensity, life cycle and duration for actionable knowledge.

Towards a Disaster-Resilient Green

Revolution

Thank You