The adverse impacts of climate change exert mounting pressure on agriculture-dependent livelihoods of many developing and developed nations. However, integrated and spatially specific vulnerability assessments in less-developed countries like Bangladesh are rare, and insufficient to support the decision-making needed for climate-change resilience. Here, we develop an agricultural livelihood vulnerability index (ALVI) and an integrated approach, allowing for (i) mapping out the hot spots of vulnerability distribution; (ii) identifying key factors of spatially heterogeneous vulnerability; and (iii) supporting intervention planning for adaptation. This study conceptualized vulnerability as a function of exposure, sensitivity, and adaptive capacity by developing a composite index from a reliable dataset of 64 indicators comprising biophysical, agro-ecological, and socioeconomic variables. The empirical studies of coastal Bangladesh revealed that Bhola, Patuakhali, and Lakshmipur districts, around the mouth of the deltaic Meghna estuaries, are the hot spot of vulnerability distribution. Furthermore, the spatially heterogeneous vulnerability was triggered by spatial variation of erosion, cyclones, drought, rain-fed agriculture, land degradation, soil phosphorus, crop productivity, sanitation and housing condition, infant mortality, emergency shelters, adoption of agro-technology. The integrated approach could be useful for monitoring and evaluating the effectiveness of adaptation intervention by substituting various hypothetical scenarios into the ALVI framework for baseline comparison.

Rapid urban development results in visible changes in land use due to increase in impervious surfaces from human construction and decrease in pervious areas. Urbanisation influences the hydrological cycle of an area, resulting in less infiltration, higher flood peak, and surface runoff. This study analysed the impact of land use change due to urbanisation on surface runoff, using the geographic information system (GIS)-based soil conservation service curve number (SCS–CN) method, during the period of rapid urban development from 1980 to 2015 in Xiamen, located in south-eastern China. Land use change was analysed from the data obtained by classifying Landsat images from 1980, 1990, 2005, and 2015. Results indicated that farmland decreased the most by 14.01%, while built-up areas increased the most by 15.7%, from 1980 to 2015. Surface runoff was simulated using the GIS-based SCS–CN method for the rainfall return periods of 5, 10, 20, and 50 years. The spatial and temporal variation of runoff was obtained for each land use period. Results indicate that the increase in surface runoff was highest in the period of 1990–2005, with an increase of 10.63%. The effect of urbanisation can be realised from the amount of runoff, contributed by built-up land use type in the study area, that increased from 14.2% to 27.9% with the rise of urban expansion from 1980 to 2015. The relationship between land use and surface runoff showed that the rapid increase in constructed land has significantly influenced the surface runoff of the area. Therefore, the introduction of nature-based solutions such as green infrastructure could be a potential solution for runoff mitigation and reducing urban flood risks in the context of increasing urbanization.

Assessing the effects of different land use scenarios on subsequent changes in ecosystem service has great implications for sustainable land management. Here, we designed four land use/land cover (LULC) scenarios, such as business-as-usual development (BAUD), economic development priority (EDP), ecological protection priority (EPP), and afforestation development priority (ADP), through a Cellular Automata-Markov (CA-Markov) model, and their effects on ecosystem service values (ESVs) were predicted, using historical LULC maps and ESV coefficients of the Lower Meghna River Estuary, Bangladesh. Findings revealed that agricultural and mangrove forest lands experienced the greatest decreases, while rural and urban settlement land had the greatest increases, leading to a total ESV decrease of US$105.34 million during 1988-2018. The scenario analysis indicated that ESV in 2038 would also decrease by US$41.37 million and US$16.38 million under the BAUD and EDP scenarios, respectively, while ESV will increase by US$60.61 million and US$130.95 million under the EPP and ADP scenarios, respectively. However, all the future land use scenarios will lead to 1.65%, 10.21%, 7.58%, and 6.75% gaps in total food requirements, respectively. Hence, from the perspective of maximizing ESVs and minimizing the trade-offs in food gaps, the ADP scenario could be the optimal land management policy for the studied landscape.