Developing wetland landscape insecurity and hydrological security models and measuring their spatial linkages

Transformation trajectory of wetland and suitability of migratory water bird habitat in the moribund Ganges delta

Wetland is a suitable habitat for water birds, and it enhances cultural ecosystem services. But the rapid transformation of such habitat, especially in floodplain environments, is an emerging crisis. Wetland reclamation and fragmentation are two major issues leading to poor habitat and landscape. The present paper aimed to explore the spatio-temporal changes in the suitability of wetland bird habitat, wetland landscape pattern, and the connection between them. Two wetlands, including a wetland of national importance, were taken as cases for this study. Time series Landsat and Sentinel images were taken for developing modeling parameters and Land Use Land Cover (LULC) for the years 2016 and 2020. The first transformation of wetland was accounted from the LULC maps of both years. Machine learning algorithm-based spatial models were developed for mapping the poor landscape condition of the existing wetland parts. Finally, semi-subjective analytic hierarchy approach (AHP)-based models were developed for assessing waterbird habitat suitability. Results demarcated more than 48% area belonging primarily to edges and tiny patches of wetlands under a poor state in 2020. Although the total wetland area was reduced between 2016 and 2020, the wetland area found to be highly suitable habitat increased from 25.5 to 59.44% of the total area during that period. The suitability of edge-preferring bird habitat showed a 10% increase. The increasing poverty of the landscape was caused by declining edge-preferring bird habitat suitability. From 1990 to 2020, 27% of wetlands were converted to single-cropped lands, and 5% were converted to multi-cropped agricultural land. Since the study spatially identified the potential suitable area and trend of wetland habitat transformation, this could help policymakers define suitable planning for the restoration and conservation of such promising bird habitat.

How hydrological components of urban blue space influence the thermal milieu?

Present study examines the possible improvement of thermal discomfort mitigation. Unlike prior researches, which focused primarily on cooling effects of urban blue space, this study, instead of physical presence of blue space considers its hydrological components. The aim of the study is to better understand the role hydrological components like water consistency depth etc. In temperature regulation. The work uses field surveys and modeling to demonstrate how these hydrological factors influence the cooling effect of blue space, providing insights on urban thermal management. To fulfill the purpose, spatial association of hydrological components blue space with its thermal environment and cooling effects was assessed. The control of hydrological components on the surrounding air temperature was examined by conducting case studies. RESULTS: reveals greater hydro-duration, deeper water, and higher Water Presence Frequency (WPF) produce greater cooling effects. The study demonstrates a favorable correlation between hydrological richness and temperature reduction. The study also analyzes how land use and wetland size affect temperature, emphasizing the significance of hydrological conservation and restoration for successful temperature mitigation. Due to their hydrology, larger wetlands are able to moderate temperature to some extent, whereas smaller, fragmented wetlands being hydrologically poor are not so influential in this regard. With these results, the present study reaches beyond to the general understanding regarding the cooling effects of the urban blue spaces. While the previous studies primarily focused on estimating the cooling effect of urban blue space, the current one shows its synchronization with the hydrological characteristics. Novelty also entrusts here, through the modeling and field survey current study demonstrates deeper and consistent water coverage in the urban blue space for maximum period of a year pronounces the cooling effect. In addition, in this cooling effect, the role of land use which is a strong determinant of many aspects of the urban environment is also highlighted. Since all these findings define specific hydrological feature, the study has several practical implications. Mare restoration of urban blue space is not enough to mitigate the thermal discomfort. In order to optimize the cooling effect, the conservation of the hydrological richness is essential. The hydrological richness of the smaller wetlands and the edge of the larger wetlands is to be improved. The connection of these wetlands with the adjacent mighty may strengthen the hydrology. The vegetation was found to promote the cooling effect whereas shorter building helped in spreading the cooling effect. Such finding drives to incorporate the blue space with the green infrastructure along with restricting the building height atleast at the edge of the blue space.

Anthropogenic impacts on urban blue space and its reciprocal effect on human and socio-ecological health

Quantifying anthropogenic impacts on blue space (BS) and its effect on human and socio-ecological health was least explored. The present study aimed to do this in reference to the urban BS transformation scenario of Eastern India. To measure BS transformation, Landsat image-based water indices were run from 1990 to 2021. Anthropogenic impact score (AIS) and 7 components scores of 78 selected BS on 70 parameters related data driven from the field. Total 345 respondents were taken for human and socio-ecological health assessment. For this, depression (DEP), anxiety (ANX), stress (STR), physical activities (PA), social capital (SC), therapeutic landscape (TL) and environment building (EB) parameters were taken. The result exhibited that BS was reduced. About 50% of urban core BS was reported highly impacted. Human and socio-ecological health was identified as good in proximity to BS, but it was observed better in the cases of larger peripheral BS. AIS on BS was found to be positively associated with mental health (0.47-0.63) and negatively associated with PA, SC, TL and EB (-0.50 to -0.90). Standard residual in ordinary least square was reported low (-1.5 to 1.5) in 95% BS. Therefore, BS health restoration and management is crucial for sustaining the living environment.

How far the types and wetland hydrological conditions influence its provisioning services in the Indian mature Ganges delta

Present work intended to explore how far the Provisioning Service Value (PSV) of the mature Ganges deltaic wetlands is determined by its typology and a few physical attributes like hydrology and aquatic vegetations. Firstly, a field investigation was carried out in the representative sample sites, and field-measured PSV was calibrated with wetland types, hydrological security, and aquatic plant biomass to perform spatial estimation and mapping of PSV. The estimation yielded average annual PSV of entire wetlands as 146.5 × 10⁵ Indian Rupee (INR)/km²/year, with the highest over bheries (embankments for fish and shrimp aquaculture) 176 × 10⁵ INR/km²/year and lowest over marshy wetlands 107 × 10⁵ INR/km²/year. Sensitivity analysis of this estimation showed in cases of 55% field visited sites, the field-measured PSV was outside the range of low standard regression residuals (-0.5 to 0.5). While searching for the reason behind such error in the estimation, the variability of the field-measured PSV was measured. Various inequality measures showed high inequality in inter and intra-hydrological conditions of the wetland. Analysis of variance (ANOVA) proved statistical significance of within-class variability. To explain the variability of PSV, Kernel Density Estimation (KDE) plotting was performed, incorporating a few other regional conditioning factors like wetland size, fish and shrimp aquaculture, perenniality, expenditure, and external feeding from the experience of the field. From this excesize, external feeding and expenditure were essential factors that should be incorporated along with the wetland characteristics and physical attributes for accurate estimation. Since producing spatial data layers of these factors with a finer resolution is difficult, the study suggests case-specific estimation of PSV instead of general spatial mapping.

Modelling hydrological strength and alteration in moribund deltaic India

The Ganga-Brahmaputra moribund deltaic floodplain region hosted many socio-ecologically precious freshwater wetland ecosystems experiencing hydrological alteration. The present study aimed to model hydrological strength (HS) to show the spatial difference and account for the degree and direction of hydrological alteration of Indian moribund deltaic wetland in three phases e.g. (1) phase I (1988-1997), (2) phase II (1998-2007) and phase III (2008-2017). Three key hydrological parameters, such as Water Presence Frequency (WPF), water depth, and hydro-period were considered for hydrological strength modelling using two ensemble Machine Learning (ML) techniques (Random Forest (RF) and XGBoost). Image algebra was employed for phasal change detection. Hydrological strength models show that around 75% of the wetland area was lost in-between phases I to III and the loss was found more intensive in moderate and weak HS zones. Existing wetland shows a clear spatial difference of HS between wetland core and periphery and river linked and delinked or not linked wetlands. Regarding the suitability of the ML models, both are acceptable, however, the XGBoost outperformed in reference to applied 15 statistical validation techniques and field evidence. HS models based on change detection clarified that more than 22% and 55% of the weak HS zone in phases II and III respectively were turned into non-wetland. The degree of alteration revealed that about 40% of wetland areas experienced a negative alteration during phases I to II, and this proportion increased to 63% in between phases II to III. Since the study figured out the spatial nature of HS, degree and direction of alteration at a spatial scale, these findings would be instrumental for adopting rational planning towards wetland conservation and restoration.

Image-driven hydrological parameter coupled identification of flood plain wetland conservation and restoration sites

A good many works focus on wetland vulnerability; some works also explore restoration sites at a very limited spatial extent. But the satellite image-driven hydrological data-based approach adopted in this work is absolutely new. Moreover, existing work only focused on identifying restoration sites in the present context, but for devising long-term sustainable planning, predicted hydrological parameters based on possible restoration sites may be an effective tool. Considering this, the present work focused on exploring hydrological data (water presence frequency (WPF), hydro-period (HP) and water depth (WD)) from time-series satellite images. This exploration may resolve the hydrological data scarcity of wetland over the wider geographical areas. Using these parameters, we developed wetland restoration and conservation sites for different historical years (2008, 2018) and predicted years (2028) using ensemble machine learning (EML) models. From the analysis, it was found that water depth, hydro-period and WPF became poorer over the period, and the trend may seem to continue in predicted years. Among the applied EML models, Random Subspace (RS) predicted wetland restoration and conservation sites precisely over others. The predicted area under high-priority restoration sites is 34% in 2018, which was 14% in 2008. In 2028, 12% more areas may fall in this priority level. Wetland away from main streams (mainly ortho-fluvial wetland) and fringe wetland parts should be given more priority for restoration. These present and predicted information will effectively help to frame sustainable wetland restoration planning.

Refining Assignment of Runoff Control Targets with a Landscape Statistical Model: A Case Study in the Beijing Urban Sub-Center, China

Rapid urbanization has triggered large changes to both the urban landscape and the yield and degree of confluence of runoff. The annual runoff volume control rate (ARVCR) is the key target identified in sponge city overall planning and is based on local natural and social conditions. However, the large impact that landscape patterns have on the runoff process causes the capacity to implement the targets to differ between those patterns. Refinement of ARVCR targets based on landscape pattern indices is therefore needed. This study identified statistical relationships between landscape indices and runoff control targets in the delta pilot region of the Beijing urban sub-center and extended the statistical model to the Beijing urban sub-center, an area almost 20 times larger than the pilot region. Landscape factors were quantified based on their area, shape, and distribution. In the delta pilot region, the runoff control volume for each block was obtained from a simulation using the SWMM model, and the correlation between landscape indices and runoff control volume capacity in different functional land-use blocks was identified by multiple linear stepwise regression. Because the distributions of landscape indices were similar in the pilot delta area and the Beijing urban sub-center, the model could be extended to the much larger study area. The statistical model provided a runoff control scheme that produced a refined assignment of the total annual runoff control target and provided guidance that could be implemented in land-use planning.