Developing a Composite Map of Vulnerability to Rainfall Extremes in Sri Lanka

Hemachandra, E. M. G. P.; Dayawansa, N. D. K.; Silva, R. P. De

doi:10.1007/978-3-030-47786-8_4

Cited by 3 publications

(1 citation statement)

References 12 publications

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“…Yala is the minor cultivation season that falls from March to August and overlaps with the FIM and SWM periods (Chithranayana and Punyawardena, 2014). Several research studies have highlighted the climatic variability that Sri Lanka has undergone in the recent past (Wickramagamage, 2016;Hemachandra et al, 2020). Only a few studies have looked into seasonal streamflow characteristics, the variation of streamflow extremes, and their relationships with major climatic variables in Sri Lanka.…”

Section: Introductionmentioning

confidence: 99%

Rainfall and Streamflow Trends in the Upper <em>Maduru Oya</em> Sub-watershed in Sri Lanka: A Comparative Study Using Different Statistical Methods

2023

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Section: Introductionmentioning

confidence: 99%

Rainfall and Streamflow Trends in the Upper <em>Maduru Oya</em> Sub-watershed in Sri Lanka: A Comparative Study Using Different Statistical Methods

2023

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Granger Causality-Based Forecasting Model for Rainfall at Ratnapura Area, Sri Lanka: A Deep Learning Approach

Saubhagya,

Tilakaratne,

Lakraj

et al. 2024

Forecasting

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Rainfall forecasting, especially extreme rainfall forecasting, is one of crucial tasks in weather forecasting since it has direct impact on accompanying devastating events such as flash floods and fast-moving landslides. However, obtaining rainfall forecasts with high accuracy, especially for extreme rainfall occurrences, is a challenging task. This study focuses on developing a forecasting model which is capable of forecasting rainfall, including extreme rainfall values. The rainfall forecasting was achieved through sequence learning capability of the Long Short-Term Memory (LSTM) method. The identification of the optimal set of features for the LSTM model was conducted using Random Forest and Granger Causality tests. Then, that best set of features was fed into Stacked LSTM, Bidirectional LSTM, and Encoder-Decoder LSTM models to obtain three days-ahead forecasts of rainfall with the input of the past fourteen days-values of selected features. Out of the three models, the best model was taken through post hoc residual analysis and extra validation approaches. This entire approach was illustrated utilizing rainfall and weather-related measurements obtained from the gauging station located in the city of Ratnapura, Sri Lanka. Originally, twenty-three features were collected including relative humidity, ssunshine hours, and mean sea level pressure. The performances of the three models were compared using RMSE. The Bidirectional LSTM model outperformed the other methods (RMSE < 5 mm and MAE < 3 mm) and this model has the capability to forecast extreme rainfall values with high accuracy.

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Spatial and Temporal Changes in Surface Water Area of Sri Lanka over a 30-Year Period

et al. 2020

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Sri Lanka contains a large number of natural and man-made water bodies, which play an essential role in irrigation and domestic use. The island has recently been identified as a global hotspot of climate change extremes. However, the extent, spatial distribution, and the impact of climate and anthropogenic activities on these water bodies have remained unknown. We investigated the distribution, spatial and temporal changes, and the impacts of climatic and anthropogenic drivers on water dynamics in Dry, Intermediate, and Wet zones of the island. We used Landsat 5 and Landsat 8 images to generate per-pixel seasonal and annual water occurrence frequency maps for the period of 1988–2019. The results of the study demonstrated high inter- and intra-annual variations in water with a rapid increase. Further, results showed strong zonal differences in water dynamics, with most dramatic variations in the Dry zone. Our results revealed that 1607.73 km2 of the land area of the island is covered by water bodies, among this 882.01 km2 (54.86%) is permanent and 725.72 km2 (45.14%) is seasonal water area. Total inland seasonal water increased with a dramatic annual growth rate of 7.06 ± 1.97 km2 compared to that of permanent water (4.47 ± 2.08 km2/year). Sri Lanka has the highest permanent water area during December–February (1045.97 km2), and drops to the lowest in May–September (761.92 km2) when the seasonal water (846.46 km2) is higher than permanent water. The surface water area was positively related to both precipitation and Gross Domestic Product, while negatively related to the temperature. Findings of our study provide important insights into possible spatiotemporal changes in surface water availability in Sri Lanka under certain climate change and anthropogenic activities.

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Developing a Composite Map of Vulnerability to Rainfall Extremes in Sri Lanka

Cited by 3 publications

References 12 publications

Rainfall and Streamflow Trends in the Upper <em>Maduru Oya</em> Sub-watershed in Sri Lanka: A Comparative Study Using Different Statistical Methods

Rainfall and Streamflow Trends in the Upper <em>Maduru Oya</em> Sub-watershed in Sri Lanka: A Comparative Study Using Different Statistical Methods

Granger Causality-Based Forecasting Model for Rainfall at Ratnapura Area, Sri Lanka: A Deep Learning Approach

Spatial and Temporal Changes in Surface Water Area of Sri Lanka over a 30-Year Period

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