The system is composed of three stages, with each subsequent stage more uncertain than the last. Reason being is that there is a computational limit on the accuracy of any system, especially if the scope is of great magnitude. The further one wishes to peer in the future, the less uncertain the result will be; nevertheless, this system’s basis of producing results is thoroughly supported by numerous sources of data and pattern recognizing algorithms to ensure the most accurate and effective way to produce accurate results. The system will be able to analyze the initial conditions and the aftermath of the trigger event, flood, in order to juxtapose it to similar prerequisites to already occurred indirect consequences. The system will identify these patterns along with multiple sources and data to accurately decide whether or not the situation will escalate or dissipate. That was stage one, processing and deciding based on data and similar details in past indirect effects. If the system decides that the scenario will escalate, stage two will begin to identify the type of indirect effect that may result from the trigger event. Given the initial data, it will then process localized data in an effort to isolate the immediate zone of impact; which requires for the trigger event to subside or relocate.

The system will take new data in stage two, which as described below in more detail, and narrow down the possible indirect effects. After identifying the indirect effects and calculating possible indirect consequences/lead time, the system will decide whether or not the situation will escalate or dissipate. If escalated, the situation will proceed to stage three. Stage three uses both stage one and stage two data along with developing data to give the most accurate prediction of the indirect consequences. There are certain exceptions on how smoothly the system can advance through each stage. That is why it is important to note that the system’s approximate time to make a prediction depends entirely on the scenario. The duration of flash floods may only take hours allowing the system to advance to stage two in merely a day, whereas long term trigger events can be unpredictable in their duration leaving the system to either wait for the trigger event or move on to stage two. By moving on to stage two, the system recognizes that the current trigger event has supplied adequate data that is indifferent to what is being gathered. This gives the system the capability to project the trigger event and draw results from the long term effects in the zone of impact; effectively allowing the system to move onto the following stages. The initial conditions that the system will consider falls under two broad categories, time and location. Under those two categories the system will begin to process more specific data relating to the zone of impact. Categories under location include: position on earth, topography, vegetation, type of environment, urbanization, population density, type of infrastructure, climate, socioeconomic level, resources, human use. Time on the other hand can be separated into: time in day, yearly season, significance to society(I.E holiday), politics, media, social events, end start or occurring of a project(project can refer to business, research, demand, regulation, public service, supply), occupational task, etc. All this data plays a role into the development and overall severity of the indirect effect and its subsequent consequences. However, all this data may not be available to the computational system or may not even be required. Especially if the system has a global scope, that is why the system will have the capability to narrow downs its available resources too what is required; in accord to the type of trigger event and subsequent indirect effects. Once the system is finished analyzing stage one’s initial data, it will compare the data to similar trigger events and decide, based on the ration of similarities to differences, whether or not the situation will escalate or dissipate. This ratio will change as the system process more and more trigger events and its indirect consequences. It will do this through computational machine learning, with each subsequent scenario, the better and more accurate the variables and ratios become. These factors improve the overall algorithm which leads to a more accurate prediction from the system’s success and mistakes. As the system learns the lead time will become more accurate and precise, especially as it advances through each stage. Each situation will become a part of recorded data that the computer will use in statistical analysis, each following scenario will gain a more unique lead time and indirect consequence prediction. 

As this methodology is fully based on an automated information network, The computer must be able to gather and evaluate data in high speed. If this method will be implemented, using a supercomputer would be the choice. When the warnings are obtained successfully using the system, it would be largely beneficial if the warnings are given out to the people in the impact zone identified. 

Flood and cyclone and Heavy rain related events

The flood is caused as a result of a storm, excessive amount of rain, overflowing reservoirs. Even though flood is considered as a trigger event, it also can cause floods in far away areas as a result of interconnected reservoir systems. This interconnected reservoirs are common in many developed countries as well as in many developing nations. When reservoirs in higher elevations receive more rainfall, they unintentionally release more water to control the amount of water that they store in the reservoir. This causes the lower elevations to be flooded. So, a flood could also trigger more floods indirectly one where in the same region. This increases the area affected by flooding. The indirect ripple events of floods are discussed on why and how each of them are caused and identified. 

Droughts and Heatwaves

Main reason for occurring droughts is because of lack of rainfall in a specific region. Just like flood it has a series of direct and indirect effects. Direct influences includes lack of water in the region, and drying water reservoirs. As trigger event the worst case scenarios would be death due to lack of water, causing riots in the area and as well as large scale migration. Labor utilization is also considered as an indicator of the indirect impacts of drought. Both self-employment and hired employment are presumed to be related to water availability and, therefore will be impacted by water scarcity. By identifying many unexpected aftershocks of this event can strongly minimize the impacts. 

Heatwave is pertaining of high temperature on a region for a extended period of time. This event gives out similar impacts to droughts. As trigger event the worst case scenarios would be death due to high temperature, causing riots in the area and as well as large scale migration. 

stage one (before the indirect effect)

trigger: Droughts[D], Heatwaves[H]

Initial data: water loss, Rainfall, temperature, 

similar occurred events: 

California Droughts, Dry seasonal drought of india,Chinese Drought 1941

 escalate or dissipate: If the trigger event isn’t strong enough to cause any indirect events, it will not go to stage II.

Possible Stage II events: impact on agriculture, livestock, businesses, Large scale migration

Floods

stage one (before the indirect effect)

trigger:Flood [F]

Initial data: Topography, vegetation, infrastructure, climate, human use, resources 

The automated system should be able access the Historical climatological data any possible region in the world. If the system gather new information about a flood, hurricane or a cyclone, it will be programmed to access historical climatic records of that area. By analyzing, it would be able to identify if the recent climatic event event’s severity and whether it can cause any possible indirect effects.

similar occurred events: Monsoons in south and eastern asia,High tides coinciding with extreme low pressure systems in central Europe, heavy rainfall in tropical regions around the world(brazil, philippines, india etc.

escalate or dissipate: If the trigger event isn’t strong enough to cause any indirect events, it will not go to stage II.

Possible Stage II events: infrastructure damage, nutrient runoff, pollution of water, ecosystem damage, spread of infectious diseases, harmful algae growth, impact on agriculture and livestock, impact on local business 

Blizzards

stage one (before the indirect effect)

trigger: Blizzards[B]

Initial data: Rainfall, temperature, precipitation 

similar occurred events: 

The automated system should be able access the Historical climatological data any possible region in the world. If the system obtained information regarding a recent blizzard, it will be programmed to access historical climatic records of that area. By analyzing, it would be able to identify if the recent climatic event event’s severity and whether it can cause any possible indirect effects.

escalate or dissipate: If the trigger event isn’t strong enough to cause any indirect events, it will not go to stage II.

Possible Stage II events: impact on agriculture, livestock, businesses, transport

Indirect effects

I) vector borne diseases

vector borne diseases are diseases spread through Mosquitos which usually spreads along favorable climate regions for mosquito reproduction. After a water/climatic shocks such as storm events, floods, and Tsunamis, the spread of vector borne diseases is noticeable yet not a direct impact from it. After such events, there’s an increase in the amount of mosquito breeding places. 

stage two(immediately after indirect effect):[F][C]

Indirect event: Spread of Vector borne diseases

subsequent data/events: Frequency of spreading possible vector borne disease, Vegetation, land cover, rainfall and temperature

identify the indirect effect:

It is able to use knowledge of favorable mosquito breeding environment when reviewing satellite data. By closely monitoring the vegetation in the region affected by increased rainfall,it is possible to identify the actual areas affected by outbreaks for mosquito-borne disease that can be fatal to humans and animals. it is possible to use satellite images to show regions that are greener (and wetter) than normal or more brown (and drier) than normal.

impact zone:

The initial impact zone is limited to all flood zones issued by federal emergency management agency. The impact zone of the vector borne diseases will then spread towards areas where the climate and conditions are suitable for mosquito breeding. The algorithm should be created using various data such as land cover, temperature and precipitation.

stage three(after indirect consequences):

possible indirect consequences: negative effects on the economy, The taxes go higher, the 

record and store data:

lead time and possible outcome:

II) algae growth

The floodwaters deposit sediments, into reservoirs and causes increase the amount of nitrogen or phosphorus on lakes. Nutrient loading, or the accumulation of too much plant nutrients (nitrogen or phosphorus), causes algal blooms or an explosion of algae growth, followed by algal death and decay. The decay processes consumes the available free oxygen in the water and kills aquatic life. This would endanger the aquatic animals and also might cause the fresh water fishing industry to decline. This will eventually cause a negative impact on the country’s economy as many countries obtain a considerable income out of fishing industry. Not only that, but also harmful algae growth in reservoirs will also affect human health of the people settling around the lake/reservoir obtaining water for household purposes. Algal growth also cause threats to the hydroelectricity production.  

stage two(immediately after indirect effect): [F][C]

Indirect event: Algae Growth

subsequent data/events: NASA's SeaWiFS satellite data, NASA’s Moderate Resolution Imaging Spectroradiometer, NOAA reservoir information supply information. 

identify the indirect effect: 

This harmful algal bloom forecasting system based on the NASA’s Moderate Resolution Imaging Spectroradiometer. NASA's SeaWiFS satellite data can also be used to measure the amount of chlorophyll-a, a pigment present in algae. Both system data can be used collect cyanobacterial cell counts in freshwater systems. Algae growth monitoring can be used to keep track of water quality and human health.It is possible to use the satellite data to pinpoint locations that have a slight growth of algae on areas near a climate/ water shock. To keep monitoring levels and provide quality, the community can plant a 3 sensing devices on the reservoir that will keep track of the nutrient flow to the river. If the data obtained from the devices provide any signs of nutrient loading, the local agriculture and maintenance offices will be contacted before the algae bloom happens. This way it would provide a big lead time. 

impact zone: The impact zone has been calculated by assessing the area where the water body with the algae supply water. The data can be obtained by local agricultural departments. If the lake is located in US, the data is already available in NOAA. 

lead time and possible outcome: 

The lead time of the practical sensing system will be more than few weeks in advance. The satellite information method can identify the places with algae bloom very quickly and also identify whether it’s the harmful type or not. But lead time for this is lower than that of the sensing application. Yet the stage 3 impact’s could be identified with a lead time of few weeks as well. We can’t be exactly certain until the full methodology has been implemented. 

stage three(after indirect consequences):

possible indirect consequences: kills aquatic life, impact on fishery industry, human health effects

record and store data: How much area affected, How fast the algae bloom happened after the trigger event

lead time and possible outcome: The lead time for this can be identified even before the algae started to appear in a certain reservoir with the use of sensors. With the use of the satellite data, 

III) impact on international, local businesses and the government 

Apart from all the above effect, the floods cause a lot of economically related impacts. Such as business disruption, loss of income, loss in tax revenues, transportation delays, and spread of physical and mental illnesses. Both self employed and hired employees will be greatly affected by the flood as of them. This leads to output losses. Output losses are caused due to complex interactions between businesses, such as production bottlenecks when one element of a supply chain is affected and paralyze the entire production process. Floods also affect government-funded programs. Each tax dollar spent on flood response, relief, and recovery is a dollar not spent on other publicly funded programs. 

stage two(immediately after indirect effect) :[F][C][H][D][B]

Indirect event: Impact on Local and international Business

subsequent data/events: Local and international business list in the affected area, Price fluctuation data

impact zone: The impact area is found through the area affected by the disaster. The business related impacts are considered too big as an area

lead time : The lead time could be informed few weeks before any other businesses are affected. 

stage three(after indirect consequences):

possible indirect consequences: price fluctuation in foreign and local businesses, GDP of nation, stock market indications

record and store data: The system will learn and improve its computational algorithms with each passing event. Improving accuracy and precision in its future predictions. price fluctuation data is recorded for further use

lead time and possible outcome: The lead time varies as the system determines and analyzes stage one and two along with any recorded stage three events.

IV)Impact on agriculture and livestock

Stage two(after indirect effect)

identify the indirect effect: 

The following paragraph gives a detailed description of the process that will predict one or more scenarios that indirectly resulted from the trigger event. The introduction of excess water onto a crop field has detrimental impacts that can resonate through all of society. Excess water can inhibit the growth of a plant or outright terminate its existence. The damage that this can have an a regional scale is tremendous, especially if it is situated in a rural developing region. These regions are more prone to sudden climate and water shocks compared to more developed regions. These and many more factors are processed through the system, in order to narrow down the indirect effect, in this case the impact on agriculture and livestock. The system can come to this conclusion if the amount of precipitation and duration onto vulnerable location occurred. This locations, for example, may be at lower elevation exposing the area to flash floods and right after a slash and burn process. The system aware of all this can identify that the agriculture and livestock are in great risk, once it has identified the indirect effect(s); it will move onto stage three.

narrow down impact zone: The system narrows down the primary zone of impact through weather surveillance radars and geographical simulations of water flow(the technology used in augmented reality sandboxes). By implanting sensors into at risk areas, the amount of precipitation can be calculated and this data can be fed into the system as a live simulation of the impact zone’s topography allow for a more efficient method to record and analyze data. Overall improving the data gathering and accuracy of stage three’s indirect consequences. 

lead time and possible outcome: The lead time will be based of the data that the system processes. With each passing scenario,estimations will become more accurate and precise.

escalate or dissipate: If the situation is as severe as described above, the situation will escalate to stage three.

stage three(after indirect consequences):

possible indirect consequences: The possible ripple effects of a damaged agriculture and livestock is dependent on stage one and two’s data. As we described above, the rural underdeveloped location, with lets say, a high population density; may have unprecedented ripples on the whole region. This would most certainly lead to famine or food shortages in the area. Due to its rural roads, sending humanitarian supply would almost seem impossible; vehicles would have a difficult time traversing through the region as the slick mud would inhibit them from doing so. The region would require great amount of time and resources that could of or was being used on national advancement. These setbacks could result into a monetary issues that could inturn devastate the country financially in the long run. using the vast data that the system is able to process. Data based predictions become more accurate and more precise. With each passing event the system will learn and adapt in order to carry its task, three stages, out more efficiently.

record and store data: The system will learn and improve its computational algorithms with each passing event. Improving accuracy and precision in its future predictions.

lead time and possible outcome: The lead time varies as the system determines and analyzes stage one and two along with any recorded stage three events.

• Ground water data – national water information system - http://waterdata.usgs.gov/nwis/
• Temperature – climate data online - http://catalog.data.gov/dataset?groups=climate5434&#topic=climate_navigation
• Crop data – cropland data layer - http://nassgeodata.gmu.edu/CropScape/
• Mapping and Planning- enviroatlas - http://enviroatlas.epa.gov/enviroatlas/atlas.html
• Storms and drought – Severe weather data - data.gov – http://catalog.data.gov/dataset/storm-events-databasen
• Coastal flooding – Digital coast/ coastal digital analysis –http://coast.noaa.gov/digicoast/data/ccap
• Mapping interfaces - Giovanni - Interactive Visualization and Analysis -http://disc.sci.gsfc.nasa.gov/giovanni 
• NASA Worldview (GIBS client) - https://earthdata.nasa.gov/labs/worldview 
• Air quality - Land Atmosphere near Real-Time Capacity for EOS (LANCE) -https://earthdata.nasa.gov/data/near-real-time-data/data/hazards-and-disasters/air-quality 
• Land cover - NASA Earth Exchange - https://nex.nasa.gov/nex/ 
• Perceptible water product layer - NASA Earth Exchange- https://nex.nasa.gov/nex/ 
• Aerosol profile – CALIPSO - https://eosweb.larc.nasa.gov/project/calipso/calipso_table 
• Land cover and surface temperature – MODIS - http://modis.gsfc.nasa.gov/data/ 
• Algae monitoring – MODIS - http://oceancolor.gsfc.nasa.gov/ 
• Algae monitoring – SeaWiFs - http://oceancolor.gsfc.nasa.gov/cgi/algorithms.cgi
• Geographical data-http://toolkit.climate.gov/