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.

Remote sensing is an efficient technique for monitoring and extracting surface water^1,2 and has been extensively utilized and has become a primary source for numerous studies relevant to water as it provides fine spatial, multispectral, lower cost, and greater temporal views of surface water over larger areas, compared to the ground measurements.³ Many methods have been developed to extract water features from optical remote sensing data, such as thresholding and machine learning.⁴ Threshold-based water indices are broadly adapted to identify surface water due to simplification, whereas these indices are relying on a locally varying optimal threshold, which hinders the universal applicability on a broadscale.¹ Due to the availability of the high amount of data and large-scale applications, a considerable number of studies have investigated automated processes of feature recognition and reform. The machine learning methods extremely absorb data from training samples to optimally separate water from nonwater in automated manner, including artificial neural network (ANN),² random forest,⁵ decision trees,⁶ isocluster,⁷ K-nearest neighbor (KNN),⁸ discriminant analysis (DA),² fuzzy clustering,⁹ K-means clustering,¹⁰ and support vector machine (SVM).³ Studies have demonstrated that the primary benefit of these models is the higher classification accuracy in a more complex system.^11,12 During the last three decades, ANNs have undergone a rapid improvement in most research areas^13,14 due to their learning ability, relative noise tolerance, unreliableness on data distribution, and great parallel structure, including target detection, image segmentation and image classification,^{15 – 17} climate and environmental transformations,¹⁸ and remote sensing water mapping.^{19 – 21}

Addressing inland water transparency and driver effects to ensure the sustainability and provision of good quality water in Sri Lanka has been a timely prerequisite, especially under the Sustainable Development Goals 2030 agenda. Natural and anthropogenic changes lead to significant variations in water quality in the country. Therefore, an urgent need has emerged to understand the variability, spatiotemporal patterns, changing trends and impact of drivers on transparency, which are unclear to date. This study used all available Landsat 8 images from 2013 to 2020 and a quasi-analytical approach to assess the spatiotemporal Secchi disk depth (Z_SD) variability of 550 reservoirs and its relationship with natural (precipitation, wind and temperature) and anthropogenic (human activity and population density) drivers. Z_SD varied from 9.68 cm to 199.47 with an average of 64.71 cm and 93% of reservoirs had transparency below 100 cm. Overall, slightly increasing trends were shown in the annual mean Z_SD. Notable intra-annual variations were also indicating the highest and lowest Z_SD during the north-east monsoon and south-west monsoon, respectively. The highest Z_SD was found in wet zone reservoirs, while dry zone showed the least. All of the drivers were significantly affecting the water transparency in the entire island. The combined impact of natural factors on Z_SD changes was more significant (77.70%) than anthropogenic variables, whereas, specifically, human activity accounted for the highest variability across all climatic zones. The findings of this study provide the first comprehensive estimation of the Z_SD of entire reservoirs and driver contribution and also provides essential information for future sustainable water management and conservation strategies.

Understanding the spatiotemporal characteristics of drought and its impacts on vegetation is a timely prerequisite to ensuring agricultural, environmental, and socioeconomic sustainability in Sri Lanka. We investigated the drought characteristics (duration, severity, frequency, and intensity) from 1990 to 2020 by using the Standardized Precipitation Evapotranspiration Index (SPEI) at various timescales and the cumulative and lagged effects on vegetation between 2000 and 2020 across the climatic zones of Sri Lanka (Dry, Wet, and Intermediate). SPEI indexes at 1-, 3-, 6-, 12-, and 24-month scales were used to analyze the drought characteristics. Frequent droughts (~13%) were common in all zones, with a concentration in the Dry zone during the last decade. Drought occurrences mostly ranged from moderate to severe in all zones, with extreme events more common in the Dry zone. This research used SPEI and the Standardized Normalized Difference Vegetation Index (SNDVI) at 0 to 24-month scales to analyze the cumulative and lagged effects of drought on vegetation. Cumulated drought effects and vegetation had maximum correlation coefficient values concentrated in the −0.41–0.98 range in Sri Lanka. Cumulated drought effects affected 40% of Dry and 16% of Intermediate zone vegetation within 1–4 months. The maximum correlation between the lagged drought effect and vegetation SNDVI showed coefficient values from −0.31–0.94 across all zones, and the high correlation areas were primarily distributed in Dry and Intermediate zones. Over 60% of the Dry and Intermediate zones had a lagged drought impact within 0 to 1 month, while 52% of the Wet zone experienced it over 11 months. The resulting dominant shorter timescale responses indicate a higher sensitivity of vegetation to drought in Sri Lanka. The findings of this study provide important insights into possible spatiotemporal changes of droughts and their possible impact on vegetation across climate zones.