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Assessing the multi-decadal shoreline dynamics along the Purba Medinipur Balasore coastal stretch, India by integrating remote sensing and statistical methods SK

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Hossain A. , Mondal I. , Thakur S. , Linh N. T. T. , Anh D. T.

Acta Geophysica

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2022

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tom Vol. 70, no 4

1701--1715

EN

Monitoring decadal shoreline change is essential to understand the influence of coastal processes on the coastline. The shoreline is constantly shaped by natural and anthropogenic factors, and so, it is critical to understand decadal trends. The prediction of future shoreline positions is a must for effective long-term coastal zone management. This study was conducted along a 90-km-stretch of the coastline from the mouth of the Haldi River (Purba Medinipur) in the Northeast to the Subarnarekha estuary (Balasore) in the Southwest. The primary objectives of the study were to analyze the decadal shoreline migration using the End Point Rate (EPR) method and then predict future shoreline change prediction using the Kalman Filter method. Shoreline positions were digitized after extracting the shorelines using Principal Component Analysis (PCA) from Multi-temporal (1990, 2000, 2010, and 2020) and Multisensor (Landsat TM, ETM+, and OLI) satellite data. A total of 887 transects were cast to compute change statistics of the time series shoreline. It was observed that the average shoreline change rate was −8.41 m/year in the periods of 1990–2000 and 2000–2010, and −8.80 m/year from 2010 to 2020. Accretion along this coastal stretch is caused by the growth of morphological features such as sand bars, beaches, and dunes. We also found that erosion occurred from 1990 to 2000 along the coastline of Bhograi, Ramnagar-I, Ramnagar-II, a few parts of Contai-I, Khejuri-I, and the Nandigram-I coastal block. Accretion mostly occurred due to Land reclamation in the Northern portion of Bhograi, Contai-1 blocks and Nandigram- I block from 2000 to 2010 and 2010 to2020. Root mean square error (RMSE) and Regression Coefficient values were computed for the future shoreline prediction of 2031 and 2041. The calculated RMSE value of±4.7 m and value of 0.97 shows a good relationship between the actual and predicted coastline of 2020. This study concludes that the coastline of Purba Medinipur-Balasore experienced severe erosion and needs management action and also proves the efficiency of the Digital Shoreline Analysis System (DSAS) tool for decadal analysis and prediction of shoreline change. The findings of this study may help the coastal planners, environmentalists, and coastal managers in preparing both short-term and long-term coastal zone management plans.

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Developing a new multi-criteria decision-making for flood prioritization of sub-watersheds using concept of D numbers

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Sepehri M. , Linh N. T. T. , Pouya H. N. , Bahramloo R. , Sadeghian J. , Ghermezcheshme B. , Talebi A. , Peyrovan H. , Thanh P. N.

Acta Geophysica

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2024

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tom Vol. 72, no. 3

2027--2039

EN

Floods are one of the most dangerous natural disasters that humanity has ever faced. In this study, a modified version of D number technique as a suitable form of multi-criteria decision-making (MCDM) approaches was proposed to prioritize flooding in the Sad-Kalan watershed of Iran using some flood related criteria. The proposed method can overcome some shortcomings and uncertainties of the existing MCDM methods. In order to evaluate the performance of the method regarding flood prioritization, its results were compared with the analytic hierarchy process (AHP) technique as mostly frequently used MCDM method. The findings demonstrate that the modified version of D number method provides better results than AHP method. In spite of inherent advantages of D number method, the advantages of the proposed method in relation to existing MCDM are as follows: 1- considering the local and global importance of used criteria, 2- reducing the uncertainty in decision makers’ judgments using employing the concept of Picture fuzzy-AHP, 3- considering the degree of consistency in evaluation of decision makers into calculations. Furthermore, the method is flexible and can be used in any region of the world.

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Flood prediction based on climatic signals using wavelet neural network

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Linh N. T. T. , Ruigar H. , Golian S. , Bawoke G. T. , Gupta V. , Rahman K. U. , Sankaran A. , Pham Q. B.

Acta Geophysica

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2021

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tom Vol. 69, no. 4

1413--1426

EN

Large-scale climatic circulation modulates the weather patterns around the world. Understanding the teleconnections between large-scale circulation and local hydro-climatological variables has been a major thrust area of hydro-climatology research. The large-scale circulation is often quantifed in terms of sea surface temperature (SST) and sea-level pressure (SLP). In this paper, we investigate the potential of wavelet neural network (WNN) hybrid model to predict maximum monthly discharge of the Madarsoo watershed, North of Iran considering two large-scale climatic signals like SST and SLP as inputs. Error measures like root-mean-square error (RMSE), and mean absolute error along with the correlation measures like coefcient of correlation (R), and Nash–Sutclife coefcient (CNS) were used to quantify the performance of prediction of maximum monthly discharge of three diferent hydrometry stations of the watershed. In all the cases, the WNN hybrid machine learning model was found to be giving superior performance consistently against the standalone artifcial neural network (ANN) model and multiple linear regression model to predict the food discharges of March and August months. The prediction of food for August which is more devastating is found to be slightly better than the prediction of foods of March, in the stations served with smaller drainage area. The RMSE, R and CNS of Tamer hydrometry station in August were found to be 0.68, 0.996, and 0.99 m3 /s, respectively, for the test period by using WNN model against 1.55, 0.989 and 0.95 by ANN model. Moreover, when evaluated for predicting the maximum monthly discharge in March and August between 2012 and 2013, the wavelet-based neural networks performed remarkably well than the ANN.

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Recognition of district wise groundwater stress zones using the GLDAS-2 catchment land surface model during lean season in the Indian state of West Bengal

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Sahoo S. , Chakraborty S. , Pham Q. B. , Sharif E. , Sammen S. S. , Vojtek M. , Vojteková J. , Elkhrachy I. , Costache R. , Linh N. T. T.

Acta Geophysica

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2021

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tom Vol. 69, no. 1

175--198

EN

Water is essential for irrigation, drinking and industrial purposes from global to the regional scale. The groundwater considered a signifcant water resource specifcally in regions where the surface water is not sufcient. Therefore, the research problem is focused on district-wise sustainable groundwater management due to urbanization. The number of impervious surface areas like roofng on built-up areas, concrete and asphalt road surface were increased due to the level of urban development. Thus, these surface areas can inhibit infltration and surface retention by the impact of urbanization because vegeta tion/forest areas are decreased. The present research examines the district-wise spatiotemporal groundwater storage (GWS) changes under terrestrial water storage using the global land data assimilation system-2 (GLDAS-2) catchment land surface model (CLSM) from 2000 to 2014 in West Bengal, India. The objective of the research is mainly focused on the delineation of groundwater stress zones (GWSZs) based on ten biophysical and hydrological factors according to the defciency of groundwater storage using the analytic hierarchy process by the GIS platform. Additionally, the spatiotemporal soil moisture (surface soil moisture, root zone soil moisture, and profle soil moisture) changes for the identifcation of water stress areas using CLSM were studied. Finally, generated results were validated by the observed groundwater level and groundwater recharge data. The sensitivity analysis has been performed for GWSZs mapping due to the defcit of groundwater storage. Three correlation coefcient methods (Kendall, Pearson and Spearman) are applied for the interrelationship between the most signifcant parameters for the generation of GWSZ from sensitivity analysis. The results show that the northeastern (max: 1097.35 mm) and the southern (max: 993.22 mm) parts have high groundwater storage due to higher amount of soil moisture and forest cover compared to other parts of the state. The results also show that the maximum and minimum total annual groundwater recharge shown in Paschim Medinipore [(361,148.51 hectare-meter (ham)] and Howrah (31,510.46 ham) from 2012 to 2013. The generated outcome can create the best sustainable groundwater management practices based upon the human attitude toward risk.

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Seasonality shift and streamfow fow variability trends in central India

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Kuriqi A. , Ali R. , Pham Q. B. , Montenegro Gambini J. , Gupta V. , Malik A. , Linh N. T. T. , Joshi Y. , Anh D. T. , Nam T. V. , Dong X.

Acta Geophysica

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2020

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tom Vol. 68, no. 5

1461--1475

EN

A better understanding of intra/inter-annual streamfow variability and trends enables more efective water resources planning and management for current and future needs. This paper investigates the variability and trends of streamfow data from fve stations (i.e. Ashti, Chindnar, Pathgudem, Polavaram, and Tekra) in Godavari river basin, India. The streamfow data were obtained from the Indian Central Water Commission and cover more than 30 years of mean daily records (i.e. 1972–2011). The streamfow data were statistically assessed using Gamma, Generalised Extreme Value and Normal distributions to under stand the probability distribution features of data at inter-annual time-scale. Quantifable changes in observed streamfow data were identifed by Sen’s slope method. Two other nonparametric, Mann–Kendall and Innovative Trend Analysis methods were also applied to validate fndings from Sen’s slope trend analysis. The mean fow discharge for each month (i.e. January to December), seasonal variation (i.e. Spring, Summer, Autumn, and Winter) as well as an annual mean, annual maximum and minimum fows were analysed for each station. The results show that three stations (i.e. Ashti, Tekra, and Polavaram) demonstrate an increasing trend, notably during Winter and Spring. In contrast, two other stations (i.e. Pathgudem, Chindnar) revealed a decreasing trend almost at all seasons. A signifcant decreasing trend was observed at all station over Summer and Autumn seasons. Notably, all stations showed a decreasing trend in maximum fows; remarkably, Tekra station revealed the highest decreasing magnitude. Signifcant decrease in minimum fows was observed in two stations only, Chindnar and Pathgudem. Findings resulted from this study might be useful for water managers and decision-makers to propose more sustainable water management recommendations and practices.