Decision Support Tools

These high quality user-tailored decision support tools and applications have been developed to address on-the-ground issues, empowering decision-makers to act locally on climate-sensitive challenges such as disasters, agriculture, water management ecosystem protection and land use.


Decision Support Tools

Topic


Biophysical M&E Dashboard
Application Purpose   Biophysical M&E Dashboard monitors, evaluates and reports landscape improvements in Cambodia. Developed to addresses the demand from USAID, Cambodia to monitor and evaluate projects that target biophysical improvement and enhance biodiversity conservation on-the-ground. It offers a wide range of biophysical parameters such as number of hectares of forest cover change in significant areas showing improved biophysical conditions as result of project assistance, and information of forest alerts, forest fire hotspot and land cover change by year information for users to choose from in order to review the changes by over a selected area, for a certain time period. Information from this service could benefit for NGO and conservation projects in Cambodia to monitor the improvement of biophysical condition and achievement of sustainable landscape management. This service can be replicated for other USAID Missions as well as other development partners.   Application Uses   Monitoring and managing large scale sustainable landscape management projects by using satellite data to determine landscape conditions before and after project intervention. The dashboard can also be used to monitor forest cover change and provide a robust platform to evaluate the performance of REDD+ in Cambodia. It can be replicated for other USAID Missions as well as other development partners.

Application Purpose   Biophysical M&E Dashboard monitors, evaluates and reports landscape improvements in Cambodia. Developed to addresses the demand from USAID, Cambodia to monitor and evaluate projects that target biophysical improvement and enhance biodiversity conservation on-the-ground. It offers a wide range of biophysical parameters such as number of hectares of forest cover change in significant areas showing improved biophysical conditions as result of project assistance, and information of forest alerts, forest fire hotspot and land cover change by year information for users to choose from in order to review the changes by over a selected area, for a certain time period. Information from this service could benefit for NGO and conservation projects in Cambodia to monitor the improvement of biophysical condition and achievement of sustainable landscape management. This service can be replicated for other USAID Missions as well as other development partners.   Application Uses   Monitoring and managing large scale sustainable landscape management projects by using satellite data to determine landscape conditions before and after project intervention. The dashboard can also be used to monitor forest cover change and provide a robust platform to evaluate the performance of REDD+ in Cambodia. It can be replicated for other USAID Missions as well as other development partners.



Gender Equality Monitoring (GEM) Platform
Application Purpose   The Gender Equality Monitoring (GEM) platform offers open access to officially published data and periodically updated sex disaggregated data repository. It visualizes gender gaps at subnational level in various sectors such as education, health, employment, access to information, intra-household decisionmaking, political participation, as well as gender inequality index (GII). These are starting points to further explore the causes and more complex dynamics of gender inequality.   Application Uses   Data in the GEM Platform can be used for free by gender advocates, policymakers, researchers and development practitioners to: Identify gender data gaps in sectors at sub-national level Establish baseline data for tracking gender inequality  Understand subnational level dynamics that contribute to gender inequality Increase interministerial cooperation on data collection, data sharing, gender analysis and gender-responsive planning

Application Purpose   The Gender Equality Monitoring (GEM) platform offers open access to officially published data and periodically updated sex disaggregated data repository. It visualizes gender gaps at subnational level in various sectors such as education, health, employment, access to information, intra-household decisionmaking, political participation, as well as gender inequality index (GII). These are starting points to further explore the causes and more complex dynamics of gender inequality.   Application Uses   Data in the GEM Platform can be used for free by gender advocates, policymakers, researchers and development practitioners to: Identify gender data gaps in sectors at sub-national level Establish baseline data for tracking gender inequality  Understand subnational level dynamics that contribute to gender inequality Increase interministerial cooperation on data collection, data sharing, gender analysis and gender-responsive planning



Dancing Rivers
Application Purpose   The purpose of this seasonal river morphological monitoring system is to provide a high-quality map of erosion and deposition areas in the village tracts along the Ayeyarwady River, Myanmar after the end of every monsoon season. Leveraging the power of Google Earth Engine to create a long-term historical morphological change map from 1988 – current year, this tool is also vital to identify morphologically active and stable channels. The system has been customized to readily upscale the tool and associated analytics to other river systems.    Application Uses   This tool is useful for broad spectrum of stakeholders in Myanmar with mandates to manage river and land systems. The Directorate of Water Resources and Improvement of River Systems (DWIR), is planning to use this tool to undertake preliminary assessment of morphological changes before initiating detailed post-monsoon survey of river sections needing bank protection. The tool is also useful for General Administration Department (GAD) to identify new land areas created by deposition process. GAD has the mandate to allocate the new land for livelihood purposes. This dataset is useful for Department of Disaster Management (DDM) to identify affected settlements and come up with a preliminary estimate of compensation. The long term (30 year) historical dataset on erosion and deposition areas can help DWIR to identify hotspots and plan appropriate for mitigation and adaptation efforts. With the possibility of addition of census, infrastructure and socio-economic layers, information provided to users can be further enhanced.

Application Purpose   The purpose of this seasonal river morphological monitoring system is to provide a high-quality map of erosion and deposition areas in the village tracts along the Ayeyarwady River, Myanmar after the end of every monsoon season. Leveraging the power of Google Earth Engine to create a long-term historical morphological change map from 1988 – current year, this tool is also vital to identify morphologically active and stable channels. The system has been customized to readily upscale the tool and associated analytics to other river systems.    Application Uses   This tool is useful for broad spectrum of stakeholders in Myanmar with mandates to manage river and land systems. The Directorate of Water Resources and Improvement of River Systems (DWIR), is planning to use this tool to undertake preliminary assessment of morphological changes before initiating detailed post-monsoon survey of river sections needing bank protection. The tool is also useful for General Administration Department (GAD) to identify new land areas created by deposition process. GAD has the mandate to allocate the new land for livelihood purposes. This dataset is useful for Department of Disaster Management (DDM) to identify affected settlements and come up with a preliminary estimate of compensation. The long term (30 year) historical dataset on erosion and deposition areas can help DWIR to identify hotspots and plan appropriate for mitigation and adaptation efforts. With the possibility of addition of census, infrastructure and socio-economic layers, information provided to users can be further enhanced.



Rainstorm Tracker
Application Purpose   Extreme rainfall events are the main triggering factor for landslides and flash floods in Southeast Asia. According to the Mekong River Commission (MRC), the average annual cost of floods in the Lower Mekong Basin (LMB) ranges from US$60-70 million.  In order to support extreme rainfall analysis of regional organizations for flash floods early warning system,  SERVIR-Mekong  developed the Rainstorm Tracker.  This operational web-based platform is designed to monitor and alert decision-makers about the severity of storm events over the Lower Mekong basin in near-real and real-time.      Application Uses   Rainstorm Tracker integrates historical and real-time rainfall data into the MRC’s flash flood guidance system and alerts MRC officials about the probability of rain storms generating flash floods. This accurate and timely information helps the MRC improve the reliability of its flash flood warnings. The Rainstorm Tracker also includes data on population distribution and critical infrastructure such as roads and hospitals. Combining such exposure and vulnerability data with rainstorm severity enables decision makers to estimate the risk faced by people and infrastructure at the district level. As a result, decision makers can take preventive measures to reduce loss of life and property and increase the resilience of communities to climate change across the region.

Application Purpose   Extreme rainfall events are the main triggering factor for landslides and flash floods in Southeast Asia. According to the Mekong River Commission (MRC), the average annual cost of floods in the Lower Mekong Basin (LMB) ranges from US$60-70 million.  In order to support extreme rainfall analysis of regional organizations for flash floods early warning system,  SERVIR-Mekong  developed the Rainstorm Tracker.  This operational web-based platform is designed to monitor and alert decision-makers about the severity of storm events over the Lower Mekong basin in near-real and real-time.      Application Uses   Rainstorm Tracker integrates historical and real-time rainfall data into the MRC’s flash flood guidance system and alerts MRC officials about the probability of rain storms generating flash floods. This accurate and timely information helps the MRC improve the reliability of its flash flood warnings. The Rainstorm Tracker also includes data on population distribution and critical infrastructure such as roads and hospitals. Combining such exposure and vulnerability data with rainstorm severity enables decision makers to estimate the risk faced by people and infrastructure at the district level. As a result, decision makers can take preventive measures to reduce loss of life and property and increase the resilience of communities to climate change across the region.



Reservoir Assessment Tool for Lower Mekong Basin (RAT-Mekong)
The Reservoir Assessment Tool for the Lower Mekong Basin (RAT-Mekong)  supports the Mekong River Commission and its member countries with reservoir monitoring. It enables decision making and planning processes for flood, and drought management in the Lower Mekong Basin (LMB). Reservoir monitoring includes reservoir state of storage change, surface area, inflow and outflow, and inferred reservoir rule curve (based on long-term records). The RAT-Mekong tool, which is open-source, replicable and scalable, aims to mitigate the challenges of data-scarce or data-restricted regions of the Mekong Basin and help the MRC and its member countries derive a broader picture of reservoir monitoring, reservoir operations and the potential impact of operations on floods, drought, natural river flow variability as a function of climate, hydrologic regime, and socio-economic indicators. With transboundary water resource management being a contentious topic in the Mekong region, this tool can facilitate transboundary regional cooperation and governance by providing unbiased data for all parties, needed to drive a fair and transparent water-sharing agreement and improving the decision-making on flood and drought management in the Lower Mekong Region.management in the Lower Mekong Region. Application Purpose     To provide near-real-time reservoir monitoring information including regularly estimating changes in reservoir storage, surface area, inflows, outflows, and inferred reservoir rule curve (Bonnema et al., 2016; 2017). This supports decision making, and planning process for drought and flood management in the Lower Mekong Basin.    Application Uses   Reservoir monitoring, water resources management, flood and drought management

The Reservoir Assessment Tool for the Lower Mekong Basin (RAT-Mekong)  supports the Mekong River Commission and its member countries with reservoir monitoring. It enables decision making and planning processes for flood, and drought management in the Lower Mekong Basin (LMB). Reservoir monitoring includes reservoir state of storage change, surface area, inflow and outflow, and inferred reservoir rule curve (based on long-term records). The RAT-Mekong tool, which is open-source, replicable and scalable, aims to mitigate the challenges of data-scarce or data-restricted regions of the Mekong Basin and help the MRC and its member countries derive a broader picture of reservoir monitoring, reservoir operations and the potential impact of operations on floods, drought, natural river flow variability as a function of climate, hydrologic regime, and socio-economic indicators. With transboundary water resource management being a contentious topic in the Mekong region, this tool can facilitate transboundary regional cooperation and governance by providing unbiased data for all parties, needed to drive a fair and transparent water-sharing agreement and improving the decision-making on flood and drought management in the Lower Mekong Region.management in the Lower Mekong Region. Application Purpose     To provide near-real-time reservoir monitoring information including regularly estimating changes in reservoir storage, surface area, inflows, outflows, and inferred reservoir rule curve (Bonnema et al., 2016; 2017). This supports decision making, and planning process for drought and flood management in the Lower Mekong Basin.    Application Uses   Reservoir monitoring, water resources management, flood and drought management



Cambodia Protected Area Alerts System
Application Purpose   Cambodia’s protected areas contribute to the country’s economy through the sustainable use of its biological and natural resources. However, this network of protected areas is under threat. In recent years, there has been accelerated deforestation due to the rapid increase in demand for agricultural land, fuel wood, and non-timber forest products. Additionally, hydropower, urban and industrial development have exerted great pressure on these protected ecosystems. The Ministry of Environment (MOE) in Cambodia is developing the Cambodia Environment Management Information System (CEMIS) to collect, integrate and display environmental data including historical and near real-time data from the Cambodian protected areas system. The near real-time forest monitoring provided by this service will enable the MOE, Provincial Department of Environment (PDoE), civil society and the private sector to take immediate action to protect and ensure effective management of Cambodia’s protected areas.   Application Uses   Cambodia Protected Area Alerts System empowers forest officials, civil society and the private sector to make data-driven decisions to promote forest conservation. This tool enables them to prioritize scarce resources for more efficient patrol planning and better protection of the 6.2-million-hectares Prey Lang protected area.  It can be replicated for other USAID Missions as well as other development partners.

Application Purpose   Cambodia’s protected areas contribute to the country’s economy through the sustainable use of its biological and natural resources. However, this network of protected areas is under threat. In recent years, there has been accelerated deforestation due to the rapid increase in demand for agricultural land, fuel wood, and non-timber forest products. Additionally, hydropower, urban and industrial development have exerted great pressure on these protected ecosystems. The Ministry of Environment (MOE) in Cambodia is developing the Cambodia Environment Management Information System (CEMIS) to collect, integrate and display environmental data including historical and near real-time data from the Cambodian protected areas system. The near real-time forest monitoring provided by this service will enable the MOE, Provincial Department of Environment (PDoE), civil society and the private sector to take immediate action to protect and ensure effective management of Cambodia’s protected areas.   Application Uses   Cambodia Protected Area Alerts System empowers forest officials, civil society and the private sector to make data-driven decisions to promote forest conservation. This tool enables them to prioritize scarce resources for more efficient patrol planning and better protection of the 6.2-million-hectares Prey Lang protected area.  It can be replicated for other USAID Missions as well as other development partners.



Mekong X-Ray
Risk is a product of hazard, exposure and vulnerability. Mekong X-Ray provides a conceptual framework for disaster management using big earth data together with different components of the disaster risk assessment framework. Big Earth Data includes earth observations, social networks, Internet of Things (IoT), and other relevant data sources for gathering information on risk components i.e.: hazard, exposure and vulnerability. The exposure component comprises of physical, environmental, social, and economic parameters. Data is translated into insights as part of the risk assessment process. This information can be used to support decision-making for disaster prevention, mitigation, preparedness, response and, recovery. Application Purpose   To improve flood risk assessment and provide local communities, governments, and humanitarian organizations with insightful information for better flood preparedness and response.   Application Uses   Flood vulnerability assessment, Flood risk management

Risk is a product of hazard, exposure and vulnerability. Mekong X-Ray provides a conceptual framework for disaster management using big earth data together with different components of the disaster risk assessment framework. Big Earth Data includes earth observations, social networks, Internet of Things (IoT), and other relevant data sources for gathering information on risk components i.e.: hazard, exposure and vulnerability. The exposure component comprises of physical, environmental, social, and economic parameters. Data is translated into insights as part of the risk assessment process. This information can be used to support decision-making for disaster prevention, mitigation, preparedness, response and, recovery. Application Purpose   To improve flood risk assessment and provide local communities, governments, and humanitarian organizations with insightful information for better flood preparedness and response.   Application Uses   Flood vulnerability assessment, Flood risk management



Landslide Hazard Assessment for Situational Awareness for Lower Mekong Region (LHASA-Mekong)
The LHASA-Mekong maps areas of potential landslide hazard in real-time to mitigate disaster risk in the Lower Mekong Region (LMR). The application incorporates landslide inventories within a machine-learning framework to estimate the relative probability of occurrence. The decision support tool relies on the following variables: slope, relief, distance to rivers, the distance to roads, lithological strength, precipitation, and soil moisture. Precipitation is estimated using the Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (GPM-IMERG), while precipitation forecasts are derived from from CHIRPS-GEFS, which merges observations from the Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) data set with the Global Ensemble Forecast System (GEFS). It also estimates the potential exposure of local communities to landslide hazard. The model has been validated with reference to new inventories of rainfall-triggered landslide events. LHASA-Mekong is open-source and is produces landslide probability at a 1 kilometer resolution Application Purpose LHASA-Mekong estimates the probability of landslide occurrence at a 1 kilometer resolution along with the potential exposure of local communities to landslide hazard Application Uses Landslide monitoring and forecasting, Landslide risk management

The LHASA-Mekong maps areas of potential landslide hazard in real-time to mitigate disaster risk in the Lower Mekong Region (LMR). The application incorporates landslide inventories within a machine-learning framework to estimate the relative probability of occurrence. The decision support tool relies on the following variables: slope, relief, distance to rivers, the distance to roads, lithological strength, precipitation, and soil moisture. Precipitation is estimated using the Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (GPM-IMERG), while precipitation forecasts are derived from from CHIRPS-GEFS, which merges observations from the Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) data set with the Global Ensemble Forecast System (GEFS). It also estimates the potential exposure of local communities to landslide hazard. The model has been validated with reference to new inventories of rainfall-triggered landslide events. LHASA-Mekong is open-source and is produces landslide probability at a 1 kilometer resolution Application Purpose LHASA-Mekong estimates the probability of landslide occurrence at a 1 kilometer resolution along with the potential exposure of local communities to landslide hazard Application Uses Landslide monitoring and forecasting, Landslide risk management



Mekong Air Quality Explorer
Application Purpose This regional web-based information system application is aimed to:1. Visualize historical and near real-time satellite observations of air quality and fires from the MODIS and VIIRS satellite sensors2. Access and visualize 3-day air quality forecast parameters derived from NASA's GEOS global forecast model and bias corrected using machine learning algorithm3. Support national/regional decision-makers in air quality monitoring, analysis, and forecasting; and4. Provide policymakers and local governors with current and forecast air quality situation to facilitate decision-making within the current forest fire season Application Uses The application allows users to intercompare historic, near real-time satellite observations, ground-based air quality observations, and forecasted conditions to assess how well they intercompare and perform. This ability enables partners such as the Pollution Control Department (PCD), Geo-Informatics & Space Technology Development Agency (GISTDA) as well as decision-makers to leverage satellite observations to more effectively monitor air quality in Thailand and Lower Mekong Region.

Application Purpose This regional web-based information system application is aimed to:1. Visualize historical and near real-time satellite observations of air quality and fires from the MODIS and VIIRS satellite sensors2. Access and visualize 3-day air quality forecast parameters derived from NASA's GEOS global forecast model and bias corrected using machine learning algorithm3. Support national/regional decision-makers in air quality monitoring, analysis, and forecasting; and4. Provide policymakers and local governors with current and forecast air quality situation to facilitate decision-making within the current forest fire season Application Uses The application allows users to intercompare historic, near real-time satellite observations, ground-based air quality observations, and forecasted conditions to assess how well they intercompare and perform. This ability enables partners such as the Pollution Control Department (PCD), Geo-Informatics & Space Technology Development Agency (GISTDA) as well as decision-makers to leverage satellite observations to more effectively monitor air quality in Thailand and Lower Mekong Region.



The Hydrologic Remote Sensing Analysis for Floods (HYDRAFloods)
HYDRAFloods is an open source Python application for downloading, processing, and delivering surface water maps derived from remote sensing data using sensor agnostic approaches. It leverages multiple remote sensing datasets to provide daily surface water maps for improved flood response. The HYDRAFloods application is built using Google Earth Engine and Google Cloud Platform (GCP) to leverage cloud computing for large-scale computations and handling high data volume outputs. The goal of the package is to allow access to high-quality, cloud-based surface water mapping algorithms with minimal effort. To achieve this goal, HYDRAFloods provides a high-level API on top of the Earth Engine Python API to reduce code duplication, such as filtering or carrying metadata for image processing, and provides complex surface water algorithms. Furthermore, the package provides some GCP functionality to read and transfer data to be used within Earth Engine. Application Purpose   To provide information on potential flood water maps, flood ages, and flood depths to improve decision-making on flood preparedness and humanitarian response in the Lower Mekong Region   Application Uses   Flood monitoring Flood response Flood risk management

HYDRAFloods is an open source Python application for downloading, processing, and delivering surface water maps derived from remote sensing data using sensor agnostic approaches. It leverages multiple remote sensing datasets to provide daily surface water maps for improved flood response. The HYDRAFloods application is built using Google Earth Engine and Google Cloud Platform (GCP) to leverage cloud computing for large-scale computations and handling high data volume outputs. The goal of the package is to allow access to high-quality, cloud-based surface water mapping algorithms with minimal effort. To achieve this goal, HYDRAFloods provides a high-level API on top of the Earth Engine Python API to reduce code duplication, such as filtering or carrying metadata for image processing, and provides complex surface water algorithms. Furthermore, the package provides some GCP functionality to read and transfer data to be used within Earth Engine. Application Purpose   To provide information on potential flood water maps, flood ages, and flood depths to improve decision-making on flood preparedness and humanitarian response in the Lower Mekong Region   Application Uses   Flood monitoring Flood response Flood risk management



ClimateSERV
In SERVIR regions, where long-term ground observations of rainfall are sparse, there is a critical need for satellite and model-derived rainfall data for predicting droughts, estimating crop yields, and more. Decision-makers need a way to accurately assess how severe a drought will be, how it compares to past droughts, and its potential effect on crop yields. Such assessments require accurate estimations of rainfall variations in space and time. It is important to place an evolving dryer-than-normal season into historical context in order to analyze the severity of rainfall deficits. Until now, such analyses used rainfall data from specific points on the Earth's surface. However, that data fails to show the region-wide variability that reveals comprehensive rainfall patterns.  Application Purpose   SERVIR has created a user-friendly, web-based tool -- ClimateSERV -- that provides three important datasets together in one system to help decision-makers in SERVIR’s data-sparse regions assess the evolving situation via holistic analysis of water and agriculture. Using ClimateSERV, development practitioners, scientists/researchers, and government decision-makers can readily analyze historical rainfall for the past 30 years and compare it with the best available forecasts for the next 180 days for their defined area of interest to improve understanding of, and make improved decisions for, issues related to agriculture and water availability. The three key datasets that ClimateSERV provides to make this possible are as follows: (1) Climate Hazards group IR Precipitation with Stations (CHIRPS): Scientists at Famine and Early Warning System (FEWS NET) who are members of the SERVIR Applied Sciences Team used 30 years’ (1982- present) worth of multiple satellite data sources and ground observations to produce an unprecedented, global, spatially and temporally consistent and continuous 30-year record of satellite-derived rainfall data. This CHIRPS global dataset makes it possible to accurately assess and monitor large-scale rainfall patterns and analyze how they may be affected by climate change. The data are updated to the latest available rainfall estimates.   (2)  North American Multi-Model Ensemble (NMME) dataset: Forecasts of future precipitation are also critical to decision-makers. The NMME dataset, a compilation by National Oceanic and Atmospheric Administration (NOAA), reflects cutting edge work on seasonal forecasting. A SERVIR AST project has taken the NMME data and performed bias correction and spatial disaggregation using standard, well-accepted techniques to generate daily, 180-day temperature and precipitation forecasts for the entire globe. These seasonal forecasts, along with the CHIRPS historical rainfall data, provide an overall perspective to connect rainfall patterns from the past to future rainfall (up to 180 days out) as projected by NMME.    (3)  MODIS-derived Normalized Difference Vegetation Index (eMODIS NDVI): SERVIR is also piloting the USGS pentadal eMODIS NDVI dataset at 250m spatial resolution over West Africa. NDVI, a measure of vegetation condition, provides a proxy for agricultural productivity by showing photosynthetic activity. By providing this 15 year dataset, SERVIR is enabling Ministries of Agriculture and the international donor community to explore how CHIRPS and NMME data link to vegetation growth and health. (The NDVI dataset is being expanded to other parts of Africa and beyond.)   ClimateSERV enables decision-makers to link historical precipitation trends (CHIRPS) to past vegetation trends (NDVI) to gain insight into potential vegetation growth and health based on seasonal (up to 180 days) precipitation and temperature forecasts (NMME).   Application Uses   With this tool, decision-makers can download, view, graph, and interpret the CHIRPS, eMODIS NDVI, and NMME seasonal forecast data in a web-based user interface. ClimateSERV can help decision-makers assess and monitor large-scale rainfall patterns, analyze how those patterns may be affected by climate change, determine likelihood of drought, and infer crop condition. Kenya Meteorological Service field offices are already using the data to provide climate resilience guidance to farmers. For example, KMS’s Kericho office is using the CHIRPS dataset to downscale seasonal climate outlooks for farmers’ use in planning crop cultivars and planting times. SERVIR hubs plan to train end-users in their regions to use and analyze the CHIRPS, NMME, and NDVI data through ClimateSERV. Use of seasonal forecasts by end-users has been increasing.  ClimateSERV will assist in the conversion of these large datasets into actionable information. 

In SERVIR regions, where long-term ground observations of rainfall are sparse, there is a critical need for satellite and model-derived rainfall data for predicting droughts, estimating crop yields, and more. Decision-makers need a way to accurately assess how severe a drought will be, how it compares to past droughts, and its potential effect on crop yields. Such assessments require accurate estimations of rainfall variations in space and time. It is important to place an evolving dryer-than-normal season into historical context in order to analyze the severity of rainfall deficits. Until now, such analyses used rainfall data from specific points on the Earth's surface. However, that data fails to show the region-wide variability that reveals comprehensive rainfall patterns.  Application Purpose   SERVIR has created a user-friendly, web-based tool -- ClimateSERV -- that provides three important datasets together in one system to help decision-makers in SERVIR’s data-sparse regions assess the evolving situation via holistic analysis of water and agriculture. Using ClimateSERV, development practitioners, scientists/researchers, and government decision-makers can readily analyze historical rainfall for the past 30 years and compare it with the best available forecasts for the next 180 days for their defined area of interest to improve understanding of, and make improved decisions for, issues related to agriculture and water availability. The three key datasets that ClimateSERV provides to make this possible are as follows: (1) Climate Hazards group IR Precipitation with Stations (CHIRPS): Scientists at Famine and Early Warning System (FEWS NET) who are members of the SERVIR Applied Sciences Team used 30 years’ (1982- present) worth of multiple satellite data sources and ground observations to produce an unprecedented, global, spatially and temporally consistent and continuous 30-year record of satellite-derived rainfall data. This CHIRPS global dataset makes it possible to accurately assess and monitor large-scale rainfall patterns and analyze how they may be affected by climate change. The data are updated to the latest available rainfall estimates.   (2)  North American Multi-Model Ensemble (NMME) dataset: Forecasts of future precipitation are also critical to decision-makers. The NMME dataset, a compilation by National Oceanic and Atmospheric Administration (NOAA), reflects cutting edge work on seasonal forecasting. A SERVIR AST project has taken the NMME data and performed bias correction and spatial disaggregation using standard, well-accepted techniques to generate daily, 180-day temperature and precipitation forecasts for the entire globe. These seasonal forecasts, along with the CHIRPS historical rainfall data, provide an overall perspective to connect rainfall patterns from the past to future rainfall (up to 180 days out) as projected by NMME.    (3)  MODIS-derived Normalized Difference Vegetation Index (eMODIS NDVI): SERVIR is also piloting the USGS pentadal eMODIS NDVI dataset at 250m spatial resolution over West Africa. NDVI, a measure of vegetation condition, provides a proxy for agricultural productivity by showing photosynthetic activity. By providing this 15 year dataset, SERVIR is enabling Ministries of Agriculture and the international donor community to explore how CHIRPS and NMME data link to vegetation growth and health. (The NDVI dataset is being expanded to other parts of Africa and beyond.)   ClimateSERV enables decision-makers to link historical precipitation trends (CHIRPS) to past vegetation trends (NDVI) to gain insight into potential vegetation growth and health based on seasonal (up to 180 days) precipitation and temperature forecasts (NMME).   Application Uses   With this tool, decision-makers can download, view, graph, and interpret the CHIRPS, eMODIS NDVI, and NMME seasonal forecast data in a web-based user interface. ClimateSERV can help decision-makers assess and monitor large-scale rainfall patterns, analyze how those patterns may be affected by climate change, determine likelihood of drought, and infer crop condition. Kenya Meteorological Service field offices are already using the data to provide climate resilience guidance to farmers. For example, KMS’s Kericho office is using the CHIRPS dataset to downscale seasonal climate outlooks for farmers’ use in planning crop cultivars and planting times. SERVIR hubs plan to train end-users in their regions to use and analyze the CHIRPS, NMME, and NDVI data through ClimateSERV. Use of seasonal forecasts by end-users has been increasing.  ClimateSERV will assist in the conversion of these large datasets into actionable information. 



Historical Flood Analysis Tool
The Historical Flood Analysis tool facilitates the analysis of the locations and temporal distribution of surface water from 1984 to 2018 and provides statistics on the extent and change of those water surfaces. Globally, this data is generated using more than three million scenes from Landsat 5, 7 and 8 observations acquired between 16 March 1984 and 31 December 2018. Surface water frequency is calculated from the monthly data product. Interpreting “water recurrence” as seen by satellite allows for the distinction between episodic (less frequent but more extreme) and regular (or predictable) water.  Results can be downloaded in GeoTIFF format.   Application Purpose   The Historical Flood Analysis Tool is designed to provide the information regarding flood prone areas (e.g. frequency of seasonal flooding cycles) in the Lower Mekong Region. In regards to flood disaster risk management, flood frequency is important for disaster preparedness, especially in the context of advanced relief resource provision. For preparedness related to response to severe droughts, this tool is potentially useful for identifying areas of permanent water in the greater Mekong region.   Application Uses   With this tool, variable risk for floods and potentially for droughts can be found with identification of areas particularly prone to such disasters. This information can help with preparedness for prevention and response to flood disasters. With the possibility of addition of census, infrastructure and socio-economic layers, information provided to users can be further enhanced. Presently, the Department of Disaster Management (DDM) in Myanmar is planning to use a modified version suited to their particular needs and requirements. 

The Historical Flood Analysis tool facilitates the analysis of the locations and temporal distribution of surface water from 1984 to 2018 and provides statistics on the extent and change of those water surfaces. Globally, this data is generated using more than three million scenes from Landsat 5, 7 and 8 observations acquired between 16 March 1984 and 31 December 2018. Surface water frequency is calculated from the monthly data product. Interpreting “water recurrence” as seen by satellite allows for the distinction between episodic (less frequent but more extreme) and regular (or predictable) water.  Results can be downloaded in GeoTIFF format.   Application Purpose   The Historical Flood Analysis Tool is designed to provide the information regarding flood prone areas (e.g. frequency of seasonal flooding cycles) in the Lower Mekong Region. In regards to flood disaster risk management, flood frequency is important for disaster preparedness, especially in the context of advanced relief resource provision. For preparedness related to response to severe droughts, this tool is potentially useful for identifying areas of permanent water in the greater Mekong region.   Application Uses   With this tool, variable risk for floods and potentially for droughts can be found with identification of areas particularly prone to such disasters. This information can help with preparedness for prevention and response to flood disasters. With the possibility of addition of census, infrastructure and socio-economic layers, information provided to users can be further enhanced. Presently, the Department of Disaster Management (DDM) in Myanmar is planning to use a modified version suited to their particular needs and requirements.