The Mangrove Landscape and Zonation following Soil Properties and Water Inundation Distribution in Segara Anakan Cilacap

The mangrove zoning and landscape express the correlation between mangrove vegetation (density, biodiversity and species distribution) with environment factors like as water inundation, seatide, and soil properties. The research was conducted in Segara Anakan Lagoon to analysis community structure and mangrove landscape based on species distribution, biodiversity, environment factors, and mangrove zoning. The results showed that (a) Segara Anakan Lagoon had 4 mangrove zone's were dominated by Sonneratia alba, Rhizophora mucronata, Avicennia marina, Rhizophora apiculata, Rhizophora styllosa, and Nypa frutican; (b) the structure of ecosystem was showed by trend of mangrove ecosystem with equation y = 35.34x2 923.85x + 12817 with x = time (year) and y = mangrove area (ha), mangrove density between 1333367 ind ha-1 (West Segara Anakan) and 899–567 ind ha-1 (East Segara Anakan), dominated species were Nypa frutican, Rhizophora stylosa, Rhizophora apiculata, and Aegiceras corniculatum and mangrove biodiversity between 2,572,65 (moderate); (c) structure of environment factor showed single and semi double-type tides, water debit between 0.360.73 m s-1; water depth between 0.20–23.7 m and water inundation between 480cm; soil texture was clay and loam, soil nitrate of 1.5 mg 100 g-1, soil phosphate of 1.5 mg 100 g-1, C organic of 1.31%, soil pH of 6–7, and soil salinity of 6.5–10 ppt.

Impact of Coastal Flood on Building, Infrastructure, and Community Adaptation in Bukit Bestari Tanjungpinang

Coastal flood in Indonesia, namely as banjir rob, is a phenomenon that increases seawater to inundate around the tidal area. In Tanjungpinang, cases of coastal floods become a serious problem for people living in this area. This research aims to model the coastal flood inundation by modeling water inundation with a maximum level increase scenario. Its model was used to estimate coastal floods' impact on houses, buildings, and infrastructures with scenario 2 meters of sea-level rise. On the other hand, the budget loss for restoration was estimated to study the effort of community adaptations with the ECLAC RAB method and observation to understand community adaptation. It was found that the spatial model succeeded in zoning inundation areas, which had a significant impact on houses, buildings, worship places, schools, and industrial at many 4112 units. From this case, the budget loss for the restoration of the physical environment was estimated at around 61994014.75 USD. In addition, the survey revealed the existing condition before and after the coastal flood. Several community efforts for adaptation were developing houses on stilt and hoarding the lowest land on-site location for build houses.

Using MODIS-NDVI Time Series to Quantify the Vegetation Responses to River Hydro-Geomorphology in the Wandering River Floodplain in an Arid Region

Vegetation, hydrology and geomorphology are three major elements of the floodplain ecosystem on Earth. Although the normalized difference vegetation index (NDVI) has been used extensively to characterize floodplain vegetation growth, vigour and biomass, methods for quantifying the various distinct responses of floodplain vegetation to hydro-geomorphological changes in different lateral belts in arid regions are still needed. In this study, the Linhe reach was divided into four lateral belts based on their hydro-geomorphological characteristics, and the Moderate Resolution Imaging Spectroradiometer (MODIS)-NDVI time series statistical indicators were used to characterise the distinct changing the patterns of vegetation growth in different belts. The response of floodplain vegetation to river hydro-geomorphology in each belt was analysed. The result showed that the average maximum NDVI value in the regular inundation area was 0.23 and higher than that in the other lateral belts. The correlation between the water persistence time and peak NDVI value in the regular water inundation area was significant (ρ = 0.84), indicating that in contrast to highly frequent or extremely rare water inundation, regular water inundation provides significant benefits to floodplains. Continuous or highly frequent inundation may cause decreased vegetation productivity. Overall, our results suggest that the vegetation greenness response to the river hydro-geomorphology is different from the river to the edge of the floodplain. Thus, a better understanding of the interactions between the floodplain vegetation and river hydro-morphology and river water resource management in arid-region floodplains.

Pemetaan Potensi Kerawanan Banjir Rob di Kabupaten Gianyar

Tidal flood is a water inundation phenomenon happened on the coast of the mainland or the coast which is caused by the tides of the sea. Tidal flood causes inundation on the certain parts of the mainland due to land altitude is lower than sea level at high tides. Some beaches around Gianyar, Bali, potentially experience a tidal flood. There is no research about tidal flood in Gianyar regency yet gives impact to the information about areas that potentially experience a tidal flood. This study aimed to determine the distribution of flood-prone areas in Gianyar Regency. Remote sensing and other spatial data by using scoring methods were utilized to determine prone areas of tidal flood. The parameters used included land elevation, distance area from shoreline, distance area from river, slope, land cover, and soil types. Result of the study shows that the 1104 ha of the total research area 66,37 ha or 6,02% are vulnerable areas, 684,51 ha or 62,00% are quite vulnerable area, and 353,12 ha or 31,98% are classified as non-prone areas. Spatial distribution of tidal flood potential in Gianyar Regency includes Rangkan Beach, around Purnama Beach, Saba Beach, Keramas Beach, Pering Beach, Lebih Beach and the west side of Siyut Beach . Observations and interviews toward vulnerable areas were conducted as the validation of the result of the study.

Application of Hybrid Prediction Methods in Spatial Assessment of Inland Excess Water Hazard

Inland excess water is temporary water inundation that occurs in flat-lands due to both precipitation and groundwater emerging on the surface as substantial sources. Inland excess water is an interrelated natural and human induced land degradation phenomenon, which causes several problems in the flat-land regions of Hungary covering nearly half of the country. Identification of areas with high risk requires spatial modelling, that is mapping of the specific natural hazard. Various external environmental factors determine the behavior of the occurrence, frequency of inland excess water. Spatial auxiliary information representing inland excess water forming environmental factors were taken into account to support the spatial inference of the locally experienced inland excess water frequency observations. Two hybrid spatial prediction approaches were tested to construct reliable maps, namely Regression Kriging (RK) and Random Forest with Ordinary Kriging (RFK) using spatially exhaustive auxiliary data on soil, geology, topography, land use, and climate. Comparing the results of the two approaches, we did not find significant differences in their accuracy. Although both methods are appropriate for predicting inland excess water hazard, we suggest the usage of RFK, since (i) it is more suitable for revealing non-linear and more complex relations than RK, (ii) it requires less presupposition on and preprocessing of the applied data, (iii) and keeps the range of the reference data, while RK tends more heavily to smooth the estimations, while (iv) it provides a variable rank, providing explicit information on the importance of the used predictors.

Global partitioning of runoff generation mechanisms using remote sensing data

A set of complex processes contribute to generate river runoff, which in the hydrological sciences are typically divided into two major categories: surface runoff, sometimes called Hortonian flow, and baseflow-driven runoff or Dunne flow. In this study, we examine the covariance of global satellite-based surface water inundation (SWI) observations with two remotely sensed hydrological variables, precipitation, and terrestrial water storage, to better understand how apparent runoff generation responds to these two dominant forcing mechanisms in different regions of the world. Terrestrial water storage observations come from NASA’s Gravity Recovery and Climate Experiment (GRACE) mission, while precipitation comes from the Global Precipitation Climatology Project (GPCP) combined product, and surface inundation levels from the NASA Surface WAter Microwave Product Series (SWAMPS) product. We evaluate the statistical relationship between surface water inundation, total water storage anomalies (TWS; TWSAs), and precipitation values under different time lag and quality control adjustments between the data products. We find that the global estimation of surface inundation improves when considering a quality control threshold of 50 % reliability for the SWAMPS data and after applying time lags ranging from 1 to 5 months. Precipitation and total water storage equally control the majority of surface inundation developments across the globe. The model tends to underestimate and overestimate at locations with high interannual variability and with low inundation measurements, respectively.

Automatic Extraction of Water Inundation Areas Using Sentinel-1 Data for Large Plain Areas

Accurately quantifying water inundation dynamics in terms of both spatial distributions and temporal variability is essential for water resources management. Currently, the water map is usually derived from synthetic aperture radar (SAR) data with the support of auxiliary datasets, using thresholding methods and followed by morphological operations to further refine the results. However, auxiliary datasets may lose efficacy on large plain areas, whilst the parameters of morphological operations are hard to be decided in different situations. Here, a heuristic and automatic water extraction (HAWE) method is proposed to extract the water map from Sentinel-1 SAR data. In the HAWE, we integrate tile-based thresholding and the active contour model, in which the former provides a convincing initial water map used as a heuristic input, and the latter refines the initial map by using image gradient information. The proposed approach was tested on the Dongting Lake plain (China) by comparing the extracted water map with the reference data derived from the Sentinel-2 dataset. For the two selected test sites, the overall accuracy of water classification is between 94.90% and 97.21% whilst the Kappa coefficient is within the range of 0.89 and 0.94. For the entire study area, the overall accuracy is between 94.32% and 96.7% and the Kappa coefficient ranges from 0.80 to 0.90. The results show that the proposed method is capable of extracting water inundations with satisfying accuracy.

Global partitioning of runoff generation mechanisms using remote sensing data

A set of complex processes contribute to generate river runoff, which in the hydrological sciences are typically divided into two major categories: surface runoff, sometimes called Hortonian flow, and baseflow-driven runoff or Dunne flow. In this study, we examine the covariance of global satellite-based surface water inundation observations with two remotely sensed hydrological variables, precipitation, and terrestrial water storage, to better understand how apparent runoff generation responds to these two dominant forcing mechanisms. Terrestrial water storage observations come from NASA's GRACE mission, while precipitation comes from the GPCP combined product, and surface inundation levels from the NASA SWAMPS product. We evaluate the statistical relationship between surface water inundation, total water storage anomalies, and precipitation values under different time lag and quality control adjustments between the data products. We find that the global prediction of surface inundation improves when considering a quality control threshold of 50 % reliability for the SWAMPS data, and after applying time lags ranging from 1 to 5 months. Precipitation tends to be the dominant driver of surface water formation at zero time lag in most locations, while very wet tropical locations and high latitudes also contain a storage driven runoff component at variable time lags.

CLIMATE CHANGE AND ITS INTERACTION WITH NATURAL, ECONOMIC AND SOCIAL PROCESSES

Human-made GHGs work against us when they trap too much sunlight and block outward radiation. Scientists worry that the accumulation of these gases in the atmosphere has changed and will continue to change the climate. Potential climate risks include more severe weather patterns; hobbled ecosystems, with less biodiversity; changes in patterns of drought and flood, with less potable water; inundation of coastal areas from rising sea levels; and a greater spread of infectious diseases such as malaria, yellow fever, and cholera. On the plus side, climate change might benefit agriculture and forestry in certain locations by increasing productivity as a result of longer growing seasons and increased fertilization. Although climate change is not the same as day-to-day or even year-to-year fluctuations in the weather, the nature of these fluctuations could be altered by climate change.