Water Resource Management in Dry Zonal Paddy Cultivation in Mahaweli River Basin, Sri Lanka: An Analysis of Spatial and Temporal Climate Change Impacts and Traditional Knowledge

Withanachchi, Sisira Saddhamangala; Köpke, Sören; Withanachchi, Chandana Rohana; Pathiranage, Ruwan; Ploeger, Angelika

doi:10.3390/cli2040329

Cited by 49 publications

(45 citation statements)

References 31 publications

(50 reference statements)

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“…Increasing numbers of dry zone farmers pump groundwater to irrigate other field crops [18]. Field size is small in Sri Lanka, with over 70 percent of farmers cultivating less than 2.5 acres of land [19]. Persistent cloud cover year-round significantly reduces remotely sensed data availability.…”

Section: Experimental Variable: Locationmentioning

confidence: 99%

A machine-learning approach to forecasting remotely sensed vegetation health

Nay

Burchfield²,

Gilligan

2017

International Journal of Remote Sensing

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Drought threatens food and water security around the world, and this threat is likely to become more severe under climate change. High resolution predictive information can help farmers, water managers, and others to manage the effects of drought. We have created an open source tool to produce short-term forecasts of vegetation health at high spatial resolution, using data that are global in coverage.The tool automates downloading and processing Moderate Resolution Imaging Spectroradiometer (MODIS) datasets, and training gradient-boosted machine models on hundreds of millions of observations to predict future values of the Enhanced Vegetation Index. We compared the predictive power of different sets of variables (raw spectral MODIS data and Level-3 MODIS products) in two regions with distinct agro-ecological systems, climates, and cloud coverage: Sri Lanka and California. Our tool provides considerably greater predictive power on held-out datasets than simpler baseline models.

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Section: Experimental Variable: Locationmentioning

confidence: 99%

A machine-learning approach to forecasting remotely sensed vegetation health

Nay

Burchfield²,

Gilligan

2017

International Journal of Remote Sensing

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“…During water scarce dry seasons, farmers cultivate other field crops such as soy, maize, and grain. Field size is small in Sri Lanka, with over 70 percent of farmers cultivating less than 2.5 acres of land (Withananachchi et al, 2014). …”

Section: Sri Lankamentioning

confidence: 99%

Application of Machine Learning to the Prediction of Vegetation Health

Burchfield¹,

Nay²,

Gilligan³

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:This project applies machine learning techniques to remotely sensed imagery to train and validate predictive models of vegetation health in Bangladesh and Sri Lanka. For both locations, we downloaded and processed eleven years of imagery from multiple MODIS datasets which were combined and transformed into two-dimensional matrices. We applied a gradient boosted machines model to the lagged dataset values to forecast future values of the Enhanced Vegetation Index (EVI). The predictive power of raw spectral data MODIS products were compared across time periods and land use categories. Our models have significantly more predictive power on held-out datasets than a baseline. Though the tool was built to increase capacity to monitor vegetation health in data scarce regions like South Asia, users may include ancillary spatiotemporal datasets relevant to their region of interest to increase predictive power and to facilitate interpretation of model results. The tool can automatically update predictions as new MODIS data is made available by NASA. The tool is particularly well-suited for decision makers interested in understanding and predicting vegetation health dynamics in countries in which environmental data is scarce and cloud cover is a significant concern.

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“…Irna van der Molen analyzed the functioning of the Farmer Organizations in a changing institutional environment [21]. Most recently, Sisira Withanachchi and others conducted an analysis of the impact of climate change on traditional knowledge on water resource management in paddy cultivation areas [38].…”

Section: Introductionmentioning

confidence: 99%

Indigenous Agricultural Systems in the Dry Zone of Sri Lanka: Management Transformation Assessment and Sustainability

et al. 2019

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The tank-based irrigated agricultural system in the Dry Zone of Sri Lanka is one of the oldest historically evolved agricultural systems in the world. The main component of the system consists of a connected series of man-made tanks constructed in shallow valleys to store, convey and utilize water for paddy cultivation. Up to 10,000 tanks originating from the heydays of ancient kingdoms are still integrated in the current agricultural landscape. During the last two millennia, this indigenous system has undergone many changes in technological, management and socio-cultural norms. This research aimed to analyze the current management practices and existing indigenous aspects of the Dry Zone irrigated agricultural system from the viewpoint of farmers who are the main stakeholders of the system. Altogether, 49 semi-structured interviews were conducted in seven villages in the Anuradhapura district and a detailed survey was conducted in the village of Manewa with a mixed research approach. The basic elements of the indigenous landscape, agricultural practices and management structures based on Farmer Organizations were mapped and examined in detail. The analysis of results shows that the sustainability of the indigenous agricultural system is vulnerable to rapid changes due to modernization, market changes, education levels, and inconsistent management decisions. The case study demonstrates the value of preserving indigenous agricultural systems and the negative outcomes of current management interventions that neglect the indigenous system. Therefore, careful interventions and innovations are needed to adapt the tank-based indigenous agricultural system of the Dry Zone of Sri Lanka so as to preserve ecological and socio-economic sustainability.

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Water Resource Management in Dry Zonal Paddy Cultivation in Mahaweli River Basin, Sri Lanka: An Analysis of Spatial and Temporal Climate Change Impacts and Traditional Knowledge

Cited by 49 publications

References 31 publications

A machine-learning approach to forecasting remotely sensed vegetation health

A machine-learning approach to forecasting remotely sensed vegetation health

Application of Machine Learning to the Prediction of Vegetation Health

Indigenous Agricultural Systems in the Dry Zone of Sri Lanka: Management Transformation Assessment and Sustainability

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