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.

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.

How far spatial resolution affects the ensemble machine learning based flood susceptibility prediction in data sparse region.

Although the effect of digital elevation model (DEM) and its spatial resolution on flood simulation modeling has been well studied, the effect of coarse and finer resolution image and DEM data on machine learning ensemble flood susceptibility prediction has not been investigated, particularly in data sparse conditions. The present work was, therefore, to investigate the performance of the resolution effects, such as coarse (Landsat and SRTM) and high (Sentinel-2 and ALOS PALSAR) resolution data on the flood susceptible models. Another motive of this study was to construct very high precision and robust flood susceptible models using standalone and ensemble machine learning algorithms. In the present study, fifteen flood conditioning parameters were generated from both coarse and high resolution datasets. Then, the ANN-multilayer perceptron (MLP), random forest (RF), bagging (B)-MLP, B-gaussian processes (B-GP) and B-SMOreg algorithms were used to integrate the flood conditioning parameters for generating the flood susceptible models. Furthermore, the influence of flood conditioning parameters on the modelling of flood susceptibility was investigated by proposing an ROC based sensitivity analysis. The validation of flood susceptibility models is also another challenge. In the present study, we proposed an index of flood vulnerability model to validate flood susceptibility models along with conventional statistical techniques, such as the ROC curve. Results showed that the coarse resolution based flood susceptibility MLP model has appeared as the best model (area under curve: 0.94) and it has predicted 11.65 % of the area as very high flood susceptible zones (FSz), followed by RF, B-MLP, B-GP, and B-SMOreg. Similarly, the high resolution based flood susceptibility model using MLP has predicted 19.34 % of areas as very high flood susceptible zones, followed by RF (14.32 %),B-MLP (14.88 %), B-GP, and B-SMOreg. On the other hand, ROC based sensitivity analysis showed that elevation influences flood susceptibility largely for coarse and high resolution based models, followed by drainage densityand flow accumulation. In addition, the accuracy assessment using the IFV model revealed that the MLP model outperformed all other models in the case of a high resolution imageThe coarser resolution image's performance level is acceptable but quite low. So, the study recommended the use of high resolution images for developing a machine learning algorithm based flood susceptibility model. As the study has clearly identified the areas of higher flood susceptibility and the dominant influencing factors for flooding, this could be used as a good database for flood management.

Effect of channel morphological changes on wetland transformation.

Keeping aside the traditional approaches to investigating floodplain wetland transformation, the current study investigated various aspects of it through changes in river channel morphology and drainage pattern. The study analyzed wetland transformation using satellite image-based machine learning and intensive fieldwork. Ordinary Least Square (OLS) regression was applied to identify dominant influencing factors among 24 contributing factors under six clusters to eight dependent phenomena of transformation. The result showed that 57 % of wetland area lost since 1991, and existing wetland has also experiencing hydrological scarcity. From 1991 to 2021, the area under low water depth (<1 m.) inflated from 18.55 % to 50.54 %, the hydro-period narrowed down, and the appearance of water become inconsistent. The OLS result showed that changes in channel morphology (bottle neck channel, embankment-driven carrying capacity enhancement, etc.), interruptions in river and wetland connecting channels (source closure, breaching the continuity, conversion in to agricultural land, etc.), and changes in flood ambience (regulated by dam construction, erection of embankments, etc.) majorly contributed to wetland transformation. Very high explainability was found in the cases of rate of wetland loss, decreasing water depth under greater depth, narrowing hydro-period (R2 > 0.9). The findings of this work would be a good policy document for floodplain wetland management.

Wetland transformation and its impact on the livelihood of the fishing community in a flood plain river basin of India.

Discretely wetland transformations and livelihood vulnerability related works are profoundly found worldwide, but their linkage is not investigated often. The present study aimed to explore the after damming transformation of wetland's eco-hydrological status and water quality and assessed its effects on livelihood vulnerability state of the fishermen community in the lower part of the Tangon river basin. Based on 15 field and satellite image-driven indicators of transformation, multiple machine learning (ML) algorithms were used to model the eco-hydrological state (EHS) of the wetland. Livelihood Vulnerability Index (LVI) of 45 fishing-dominated villages was computed using a balanced weighted LVI score. The result revealed that 60.55 % wetland area was obliterated between the pre- dam and post-dam periods, and the existing wetland area (21.06 km2) witnessed noticeable eco-hydrological and water quality degradation. Correlation and kernel density estimation (KDE) plot clearly revealed that rate of EHS degradation and water quality changes was negatively associated (at ≤0.01 level of significance) and both controlled LVI. So, such changes not only pose pressure on the aquatic species like fishes but also hampered the well-being of the fishermen communities evolving. The findings of the work would be useful in this transition while deciding the alternative strategies to build a resilient community. Moreover, since the eco-hydrological state were explored this would be effective for wetland restoration planning.

Linking river flow modification with wetland hydrological instability, habitat condition, and ecological responses.

Flow modification pursuing dams is widely found. Some works also focused on its impact on floodplain wetland hydrology. However, how this change can pose an impact on habitat conditions, ecological conditions, and trophic state is also a matter of investigation. The very least attention has been paid to this so far. Therefore, the present study focused on these, taking the dam-induced Lower Tangon river basin of India and Bangladesh as a case. The degree of flow alteration in the river was presented in a heat map. Multi-parametric machine learning (ML) approaches were applied to model hydrological instability and habitat condition. The ecological consequences like evaluating eco-deficit using flow duration curve (FDC) approach, trophic state using trophic state index (TSI), fish habitat zone using image-based hydrological parameters, etc. were measured. The study exhibited that after damming, the degree of river flow modification was about 41%. Consequently, the wetland hydrological instability and habitat conditions were degraded. In the post-dam period, > 50% of wetland area was lost, and hydrological instability was enhanced considerably over wider parts of the wetland. Habitat conditions of the existing wetland also witnessed fragility (poor and very poor areas increased by about 22.23 and 9.34%). As a result of this, adverse ecological responses were found. For instance, the eco-deficit area was increased by 36.19%, a good proportion (100%) of wetlands was witnessed the transformation of TSI from oligotrophic to mesotrophic state, and optimum fish habitat area was declined. The ecological strength map integrating all the cause-effect model parameters showed that good ecological strength was reduced from 49 to 2% in the post-dam. The result of the study would be very useful for wetland restoration for ecological and human well-being.

Predicting wetland area and water depth in Barind plain of India.

The present study attempts to delineate wetlands in the lower Tangon river basin in the Barind flood plain region using spectral water body extraction indices. The main objectives of this present study are simulating and predicting wetland areas using the advanced artificial neural network-based cellular automata (ANN-CA) model and water depth using statistical (adaptive exponential smoothing) as well as advanced machine learning algorithms such as Bagging, Random Subspace, Random Forest, Support vector machine, etc. The result shows that RmNDWI and NDWI are the representative wetland delineating indices. NDWI map was used for water depth prediction. Regarding the prediction of wetland areas, a remarkable decline is likely to be identified in the upcoming two decades. The small wetland patches away from the master stream are expected to dry out during the predicted period, where the major wetland patches nearer to the master stream with greater water depth are rather sustainable, but their depth of water is predicted to be reduced in the next decades. All models show satisfactory performance for wetland depth mapping, but the random subspace model was identified as the best-suited water depth predicting method with an acceptable prediction accuracy (root mean square error <0.34 in all the years) and the machine learning models explored better result than adaptive exponential smoothing. This recent study will be very helpful for the policymakers for managing wetland landscape as well as the natural environment.

Integration of satellite image-derived temperature and water depth for assessing fish habitability in dam controlled flood plain wetland.

The present study attempted to investigate the changes in temperature conducive to fish habitability during the summer months in a hydrologically modified wetland following damming over a river. Satellite image-driven temperature and depth data calibrated with field data were used to analyse fish habitability and the presence of thermally optimum habitable zones in some fishes, such as labeo rohita, cirrhinus mrigala, tilapia fish, small shrimp, and catfish. The study was conducted both at the water's surface and at the optimum depth of survival. It is very obvious from the analysis that a larger part of the wetland has become an area that destroyed aquatic habitat during the post-dam period, and existing wetlands have suffered significant shallowing of water depth. This has resulted in a shrinking of the thermally optimum area of fish survival in relation to surface water temperature (from 100.09 to 74.24 km2 before the dam to 93.97 to 0 km2 after the dam) and an improvement in the optimum habitable condition in the comfortable depth niche of survival. In the post-dam period, it increased from 75.49 to 99.76%. Since the damming effect causes a 30.53 to 100% depletion of the optimum depth niche, improving the thermal environment has no effect on fish habitability. More water must be released from dams for restoration. Image-driven depth and temperature data calibrated with field information has been successfully applied in data sparse conditions, and it is further recommended in future work.