Giriraj Amarnath

Detecting spatio-temporal changes in the extent of seasonal and annual flooding in South Asia using multi-resolution satellite data

This paper presents algorithm for flood inundation mapping to understand seasonal and annual changes in the flood extent and in the context of emergency response. Time-series profiles of Land Surface Water Index (LSWI), Enhanced Vegetation Index (EVI), Normalized Difference Vegetation Index (NDVI) and Normalized Difference Snow Index (NDSI) are obtained from MOD09 8-day composite time-series data (resolution 500m; time period: 2000-2011). The proposed algorithm was applied for MODIS data to produce time-series inundation maps for the ten annual flood season over the period from 2000 to 2011. The flood product has three classes as flood, mixed and long-term water bodies. The MODIS flood products were validated via comparison with ALOS AVINIR / PALSAR and Landsat TM using the flood fraction comparison method. Compared with the ALOS satellite data sets at a grid size of 10km the obtained RMSE range from 5.5 to 15 km2 and the determination coefficients range from 0.72 to 0.97. The spatial characteristics of the estimated early, peak and late and duration of inundation cycle were also determined for the period from 2000 to 2011. There are clear contracts in the distribution of the estimated flood duration of inundation cycles between large-scale floods (2008-2010) and medium and small-scale floods (2002 and 2004). Examples on the analysis of spatial extent and temporal pattern of flood-inundated areas are of prime importance for the mitigation of floods. The generic approach can be used to quantify the damage caused by floods, since floods have been increasing each year resulting in the loss of lives, property and agricultural production.

Applications of satellite-based rainfall estimates in flood inundation modeling: a case study in Mundeni Aru River Basin, Sri Lanka

The performance of Satellite Rainfall Estimate (SRE) products applied to flood inundation modelling was tested for the Mundeni Aru River Basin in eastern Sri Lanka. Three SREs (PERSIANN, TRMM, and GSMaP) were tested, with the Rainfall-Runoff-Inundation (RRI) model used as the flood inundation model. All the SREs were found to be suitable for applying to the RRI model. The simulations created by applying the SREs were generally accurate, although there were some discrepancies in discharge due to differing precipitation volumes. The volumes of precipitation of the SREs tended to be smaller than those of the gauged data, but using a scale factor to correct this improved the simulations. In particular, the SRE, i.e., the GSMaP yielding the best simulation that correlated most closely with the flood inundation extent from the satellite data, was considered the most appropriate to apply to the model calculation. The application procedures and suggestions shown in this study could help authorities to make better-informed decisions when giving early flood warnings and making rapid flood forecasts, especially in areas where in-situ observations are limited.

Time-series cloud noise mapping and reduction algorithm for improved vegetation and drought monitoring

Moderate Resolution Imaging Spectro-radiometer (MODIS) time-series Normalized Differential Vegetation Index (NDVI) products are regularly used for vegetation monitoring missions and climate change analysis. However, satellite observation is affected by the atmospheric condition, cloud state and shadows introducing noise in the data. MODIS state flag helps in understanding pixel quality but overestimates the noise and hence its usability requires further scrutiny. This study has analyzed MODIS MOD09A1 annual data set over Sri Lanka. The study presents a simple and effective noise mapping method which integrates four state flag parameters (i.e. cloud state, cloud shadow, cirrus detected, and internal cloud algorithm flag) to estimate Cloud Possibility Index (CPI). Usability of CPI is analyzed along with NDVI for noise elimination. Then the gaps generated due to noise elimination are reconstructed and performance of the reconstruction model is assessed over simulated data with five different levels of random gaps (10–50%) and four different statistical measures (i.e. Root mean square error, mean absolute error, mean bias error, and mean absolute percentage error). The sample-based analysis over homogeneous and heterogeneous pixels have revealed that CPI-based noise elimination has increased the detection accuracy of number of growing cycle from 45–60% to 85–95% in vegetated regions. The study cautions that usage of time-series NDVI data without proper cloud correction mechanism would result in wrong estimation about spatial distribution and intensity of drought, and in our study 50% of area is wrongly reported to be under drought though there was no major drought in 2014.

An evaluation of flood inundation mapping from MODIS and ALOS satellites for Pakistan

ABSTRACT The paper presents a moderate resolution imaging spectroradiometer (MODIS) time-series imagery-based algorithm for detection and mapping of seasonal and annual changes in flood extent, and tests this using the flooding of the Indus River Basin in 2010 – one of the greatest recent disasters that affected more than 25 million people in Pakistan. The algorithm was applied to produce inundation maps for 10 annual flood seasons over the period from 2000 to 2011. The MODIS flood products were validated in comparison with advanced land observing system (ALOS) sensors, which have both advanced visible and near infrared radiometer and phased array type L-band synthetic images using the flood fraction comparison method. A simple threshold method is created to cluster the data to identify the flood pixels in the imagery. Calculations are then made to estimate a flood area for each resolution. A statistical study is performed to analyze false positive and false negative rates using the ALOS sensors as ‘ground truth’. Comparison of two flood products at a grid size of 10 km resulted in the coefficient of determination range of 0.72–0.97. This research points to a relevant spatial resolution that could be effectively used to obtain accurate mapped products of the extent of the inundated area. The approach can be used to quantify the damage caused by floods.

A Decadal Historical Satellite Data and Rainfall Trend Analysis (2001–2016) for Flood Hazard Mapping in Sri Lanka

Critical information on a flood-affected area is needed in a short time frame to initiate rapid response operations and develop long-term flood management strategies. This study combined rainfall trend analysis using Asian Precipitation—Highly Resolved Observational Data Integration towards Evaluation of Water Resources (APHRODITE) gridded rainfall data with flood maps derived from Synthetic Aperture Radar (SAR) and multispectral satellite to arrive at holistic spatio-temporal patterns of floods in Sri Lanka. Advanced Land Observing Satellite Phased Array type L-band Synthetic Aperture Radar (ALOS PALSAR) data were used to map flood extents for emergency relief operations while eight-day Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance data for the time period from 2001 to 2016 were used to map long term flood-affected areas. The inundation maps produced for rapid response were published within three hours upon the availability of satellite imagery in web platforms, with the aim of supporting a wide range of stakeholders in emergency response and flood relief operations. The aggregated time series of flood extents mapped using MODIS data were used to develop a flood occurrence map (2001–2016) for Sri Lanka. Flood hotpots identified using both optical and synthetic aperture average of 325 km2 for the years 2006–2015 and exceptional flooding in 2016 with inundation extent of approximately 1400 km2. The time series rainfall data explains increasing trend in the extreme rainfall indices with similar observation derived from satellite imagery. The results demonstrate the feasibility of using multi-sensor flood mapping approaches, which will aid Disaster Management Center (DMC) and other multi-lateral agencies involved in managing rapid response operations and preparing mitigation measures.

Adapting to Climate Variability and Change in India

Responding to rainfall variability has always been one of the most critical risks facing farmers. It is also an integral part of the job of water managers, whether it be designing interventions for flood management, improving the reliability of water supply for irrigation or advising on priorities during drought conditions. The conventional tools and approaches employed are no longer sufficient to manage the increasing uncertainty and incidence of extreme climate events, and the consequent effects these have on human vulnerability and food security. To be effective, the technological advances need to be matched with physical, institutional and management innovations that transcend sectors, and place adaptation and responsiveness to variability at the centre of the approach. This chapter examines a number of these challenges and possible solutions at a range of scales, from ‘climate-smart villages’ to national policy, with a focus on Asia and India, in particular.

Application of a flood inundation model to analyze the potential impacts of a flood control plan in Mundeni Aru river basin, Sri Lanka

Capturing inundation extent by floods is indispensable for decision making for mitigating hazard. Satellite images have commonly been used for flood mapping, but there are limitations such as unavailability due to satellite’s orbital period or cloud cover. Additionally, it would also be beneficial for policy makers to figure out the impact of water management measures such as water storage options on flood mitigation and irrigation water strengthening. Utilization of flood inundation models would support providing information for these demands. In this study, the rainfall–runoff inundation (RRI) model was applied in a flood-prone basin in eastern Sri Lanka, and its applicability was discussed. The RRI model was capable of simulating discharge and inundation extent during flood events, although it should be noted that the model had been calibrated targeting only the flooding period. Satellite-observed rainfall data corrected with a scale factor were able to be used as the model input to simulate long-term trends in runoff just as well as when gauged rainfall data were applied. The calibrated model was also capable of evaluating flood mitigation effects of existing and proposed water storage options by simulating discharge with and without flood capture operations. By reproducing long-term inflow to the storage facilities using satellite rainfall data, it was possible to determine that water would reach the maximum level of the proposed storage facilities even during low-rainfall years.

Mapping multiple climate-related hazards in South Asia

RESEARCH PROGRAM ON Water, Land and Ecosystems Using the ranking procedure, we found that most of the divisions in Bangladesh, and some divisions in India, Sri Lanka, Pakistan and Nepal are extreme-risk areas. Some cities are highly affected by frequent disasters in spite of their high adaptive capacity, because the adaptive capacities of those cities are not sufficient due to high population densities and significant exposure to the hazards.