Shelter from the storm? Use and misuse of coastal vegetation bioshields for managing natural disasters

Vegetated coastal ecosystems provide goods and services to billions of people.In the aftermath of a series of recent natural disasters, including the Indian Ocean Tsunami, Hurricane Katrina and Cyclone Nargis, coastal vegetation has been widely promoted for the purpose of reducing the impact of large storm surges and tsunami. In this paper, we review the use of coastal vegetation as a ``bioshield'' against these extreme events. Our objective is to alter bioshield policy and reduce the long-term negative consequences for biodiversity and human capital. We begin with an overview of the scientific literature, in particular focusing on studies published since the Indian Ocean Tsunami in 2004 and discuss the science of wave attenuation by vegetation. We then explore case studies from the Indian subcontinent and evaluate the detrimental impacts bioshield plantations can have upon native ecosystems, drawing a distinction between coastal restoration and the introduction of exotic species in inappropriate locations. Finally, we place bioshield policies into a political context, and outline a new direction for coastal vegetation policy and research.

Circulation and salinity distribution in coastal inlets

The mathematical model developed by Hansen and Rattray based on Pritchard's equations for a coastal-plain estuary has been analysed to study the circulation and salinity distributions in coastal inlets with constant width and depth. Numerical solutions of the basic equations have been obtained without placing any restriction on Rayleigh numbers. A noteworthy contribution of the present analysis is that solutions of equations have been obtained for higher Rayleigh numbers, which was not possible in the earlier model. It is found that the effect of higher Rayleigh numbers is to increase the vertical advection, making the salinities in the upper and lower layers more uniform with a distinct halocline near the mid-depths. Solutions are discussed for some special cases of practical interest.

Eutrophication of the Finnish coastal waters : origin, fate and effects of riverine nutrient fluxes

Suomen rannikkovesien rehevöityminen: jokivesien tuomien ravinteiden alkuperä, käyttäytyminen ja vaikutukset.

Seasonal variation in nitrification and nitrate-reduction pathways in coastal sediments in the Gulf of Finland, Baltic Sea

The Baltic Sea is one of the most eutrophic marine areas in the world. The role of nitrogen as a eutrophicating nutrient in the Baltic Sea has remained controversial, due to lack of understanding of nitrogen cycling in the area. We investigated the seasonal variation in sediment nitrification, denitrification, anaerobic ammonium oxidation (anammox), and dissimilatory nitrate reduction to ammonium (DNRA) at two coastal sites in the Gulf of Finland. In addition to the in situ rates, we assessed the potential for these processes in different seasons. The nitrification and nitrogen removal processes were maximal during the warm summer months, when the sediment organic content was highest. In colder seasons, the in situ rates of the nitrification and nitrate reduction processes decreased, but the potential for nitrification remained equal to or higher than that during the warm months. The denitrification and nitrification rates were usually higher in the accumulation basin, where the organic content of the sediment was higher, but the transportation area, despite lower denitrification rates and potential, typically had higher potential for nitrification than the accumulation basin. Anammox and DNRA were not significant nitrate sinks in any of the seasons sampled. The results also show that the denitrification rates in the coastal Gulf of Finland sediment have decreased, and that benthic denitrification might be a less important sink for fixed nitrogen than previously assumed.

Seasonal fluctuations in blue-green algae, especially in heterocystous species in polluted coastal waters off Helsinki city, in the northern part of the Gulf of Finland

Tiivistelmä

Seasonal and short-term variation in denitrification and anammox at a coastal station on the Gulf of Finland, Baltic Sea

Benthic processes were measured at a coastal deposition area in the northern Baltic Sea, covering all seasons. The N-2 production rates, 90-400 mu mol N m(-2) d(-1), were highest in autumn-early winter and lowest in spring. Heterotrophic bacterial production peaked unexpectedly late in the year, indicating that in addition to the temperature, the availability of carbon compounds suitable for the heterotrophic bacteria also plays a major role in regulating the denitrification rate. Anaerobic ammonium oxidation (anammox) was measured in spring and autumn and contributed 10% and 15%, respectively, to the total N-2 production. The low percentage did, however, result in a significant error in the total N-2 production rate estimate, calculated using the isotope pairing technique. Anammox must be taken into account in the Gulf of Finland in future sediment nitrogen cycling research.

Characterization of estuarine marine clays for coastal reclamation in Pusan, Korea

Large scale reclamation works in coastal areas of the Nakdong River plain are at various stages of progress, since early 1990's on in-situ soft marine clay deposits. These deposits are of the order of 30 to 40 m thick. A realistic rapid characterization of soft ground would ensure success of any reclamation work in this area. In order to cope with the work carried out with different agencies, it is desirable to evolve a systematic methodology. In this study, engineering properties of clays at three coastal areas, Gadukdo, Noksan and Shinho, have been generated. The analysis of data has been done within the framework of classical developments in soil mechanics. Analysis has also been made by making use of the recent developments in dealing with soft clays. The dominant factors, namely, stress, time, and environment influencing the response of clay to loading are identified.

Optimal crop planning and conjunctive use of water resources in a coastal river basin

Due to increasing trend of intensive rice cultivation in a coastal river basin, crop planning and groundwater management are imperative for the sustainable agriculture. For effective management, two models have been developed viz. groundwater balance model and optimum cropping and groundwater management model to determine optimum cropping pattern and groundwater allocation from private and government tubewells according to different soil types (saline and non-saline), type of agriculture (rainfed and irrigated) and seasons (monsoon and winter). A groundwater balance model has been developed considering mass balance approach. The components of the groundwater balance considered are recharge from rainfall, irrigated rice and non-rice fields, base flow from rivers and seepage flow from surface drains. In the second phase, a linear programming optimization model is developed for optimal cropping and groundwater management for maximizing the economic returns. The models developed were applied to a portion of coastal river basin in Orissa State, India and optimal cropping pattern for various scenarios of river flow and groundwater availability was obtained.

Prediction of Ground Water Levels in the Uplands of a Tropical Coastal Riparian Wetland using Artificial Neural Networks

Artificial Neural Networks (ANNs) have been found to be a robust tool to model many non-linear hydrological processes. The present study aims at evaluating the performance of ANN in simulating and predicting ground water levels in the uplands of a tropical coastal riparian wetland. The study involves comparison of two network architectures, Feed Forward Neural Network (FFNN) and Recurrent Neural Network (RNN) trained under five algorithms namely Levenberg Marquardt algorithm, Resilient Back propagation algorithm, BFGS Quasi Newton algorithm, Scaled Conjugate Gradient algorithm, and Fletcher Reeves Conjugate Gradient algorithm by simulating the water levels in a well in the study area. The study is analyzed in two cases-one with four inputs to the networks and two with eight inputs to the networks. The two networks-five algorithms in both the cases are compared to determine the best performing combination that could simulate and predict the process satisfactorily. Ad Hoc (Trial and Error) method is followed in optimizing network structure in all cases. On the whole, it is noticed from the results that the Artificial Neural Networks have simulated and predicted the water levels in the well with fair accuracy. This is evident from low values of Normalized Root Mean Square Error and Relative Root Mean Square Error and high values of Nash-Sutcliffe Efficiency Index and Correlation Coefficient (which are taken as the performance measures to calibrate the networks) calculated after the analysis. On comparison of ground water levels predicted with those at the observation well, FFNN trained with Fletcher Reeves Conjugate Gradient algorithm taken four inputs has outperformed all other combinations.

Sea-level changes and its impact on coastal archaeological monuments: seven pagodas of Mahabalipuram, a case study

The name `Seven Pagodas' has served as a nickname for the south Indian port of Mahabalipuram since the early European explorers used it as landmark for navigation as they could see summits of seven temples from the sea. There are many theories concerning the name Seven Pagodas. The present study has compared coastline and adjacent seven monuments illustrated in a 17th century Portolan Chart (maritime map) with recent remote sensing data. This analysis throws new light on the name ``Seven Pagodas'' for the city. This study has used DEM of the site to simulate the coastline which is similar to the one depicted in the old portolan chart. Through this, the then sea level and corresponding flooding extent according to topography of the area and their effect on monuments could be analyzed. Most importantly this work has in the process identified possibly the seven monuments that constituted the name Seven Pagodas and this provides an alternative explanation to one of the mysteries of history. This work has demonstrated unique method of studying coastal archaeological sites. As large numbers of heritage sites around the world are on coastlines, this methodology has potential to be very useful for coastal heritage preservation and management.

Characterization and Space-Time Downscaling of the Inundation Extent over the Inner Niger Delta Using GIEMS and MODIS Data

The objective in this work is to develop downscaling methodologies to obtain a long time record of inundation extent at high spatial resolution based on the existing low spatial resolution results of the Global Inundation Extent from Multi-Satellites (GIEMS) dataset. In semiarid regions, high-spatial-resolution a priori information can be provided by visible and infrared observations from the Moderate Resolution Imaging Spectroradiometer (MODIS). The study concentrates on the Inner Niger Delta where MODIS-derived inundation extent has been estimated at a 500-m resolution. The space-time variability is first analyzed using a principal component analysis (PCA). This is particularly effective to understand the inundation variability, interpolate in time, or fill in missing values. Two innovative methods are developed (linear regression and matrix inversion) both based on the PCA representation. These GIEMS downscaling techniques have been calibrated using the 500-m MODIS data. The downscaled fields show the expected space-time behaviors from MODIS. A 20-yr dataset of the inundation extent at 500 m is derived from this analysis for the Inner Niger Delta. The methods are very general and may be applied to many basins and to other variables than inundation, provided enough a priori high-spatial-resolution information is available. The derived high-spatial-resolution dataset will be used in the framework of the Surface Water Ocean Topography (SWOT) mission to develop and test the instrument simulator as well as to select the calibration validation sites (with high space-time inundation variability). In addition, once SWOT observations are available, the downscaled methodology will be calibrated on them in order to downscale the GIEMS datasets and to extend the SWOT benefits back in time to 1993.