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1

Flood susceptibility mapping using qualitative and statistical methods in a semi-arid basin: case of the Manouba–Sijoumi watershed, Northeastern Tunisia

Khadraoui Nahed, Dahri Noura, Bouamrane Ali, Pham Quoc Bao, Abida Habib

Acta Geophysica

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2023

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Vol. 71, no. 5

2307--2323

EN

Floods are considered among the gravest natural disasters worldwide and have resulted in enormous human and material damage. The Manouba–Sijoumi basin (Northeast of Tunisia) is often flooded due to urban expansion, population growth and unplanned land use. This study aims to identify and to define the flood-prone areas of this basin for the 2003 and 2018 extreme events based on a Geographic Information System, a qualitative method (analytic network process-ANP) and a statistical model (frequency ratio-FR). The flood risk maps obtained by both models were validated using the receiver operating characteristic, the area under the curve (AUC) and inventory map. Areas of high and very high flood sensitivity are located mainly in urban settings, with an increase in risk between 2003 and 2018. The AUC values for both models were of the same significance (98%) for the year 2003 while those for the year 2018 were 94% and 98% for the ANP and FR models, respectively. This would imply that both models yielded reasonable results. However, the FR model showed an ability to reduce the uncertainty associated with expert judgements. The results indicate that the most influential factor on flooding in this area was land use/cover. Indeed, populations were largely settled in unsuitable sites for urbanization and in potentially flood-prone areas located mainly around the Sijoumi Sebkha, especially to the west and south of it. The findings of the study are of great value for policy makers and state authorities to achieve greater awareness and adopt strategies for environmental preparedness and management.

2

Flow structure investigation over a pool-rife sequence in a variable width river

Tabarestani Ehsan Shahiri, Afzalimehr Hossein, Pham Quoc Bao

Acta Geophysica

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2022

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Vol. 70, no 2

713--727

EN

A comprehensive overview of flow characteristics in natural channels with bedforms is a vital issue in river management projects. Pool-rife sequences as common bedforms in the gravel-bed rivers significantly impact flow characteristics and turbulence intensity. The present study was taken by field investigation in the Babolroud River, Iran. A 95 m reach with variable width was chosen in this river and velocity components and shear stress were obtained in different sections. Quadrant analysis was also applied to determine the dominant bursting event in the pool section. The results revealed a phase shift for stream-wise velocity, near-bed velocities, and bed shear stress versus bed profile. In the pool, vertical velocity components were oriented downward near the bed and upward near the water surface, while in the rife section vectors were oriented towards the bed. The findings of quadrant analysis demonstrated the ejections and sweeps as a dominant event close to the bed and water surface, respectively.

3

Evaluating the variability in long-term rainfall over India with advanced statistical techniques

Ahmed Ishita Afreen, Salam Roquia, Naikoo Mohd Waseem, Rahman Atiqur, Praveen Bushra, Hoai Pham Ngoc, Pham Quoc Bao, Anh Duong Tran, Tri Doan Quang, Elkhrachy Ismail

Acta Geophysica

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2022

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Vol. 70, no 2

801--818

EN

Climate change has been a significant subject in recent years all around the world. Statistical analysis of climatic parameters such as rainfall can investigate the actual status of the atmosphere. As a result, this study aimed to look at the pattern of mean annual rainfall in India from 1901 to 2016, considering 34 meteorological subdivisions. The Mann–Kendall (MK) test, Modified Mann–Kendall (MMK) test, Bootstrapped MK (BMK) test, and Innovative Trend Analysis (ITA) were used to find trends in yearly rainfall time-series results. Rainfall forecasting was evaluated using detrended fluctuation analysis (DFA). Because the research comprised 34 meteorological subdivisions, it may be challenging to convey the general climatic conditions of India in a nutshell. The MK, MMK, and BMK tests showed a significant (p < 0.01 to p < 0.1) negative trend in 9, 8, and 9 sub-divisions, respectively. According to the ITA, a negative trend was found in 17 sub-divisions, with 9 sub-divisions showing a significance level of 0.01 to 0.1. The ITA outperformed the other three trend test techniques. The results of DFA showed that 20 sub-divisions would decrease in future rainfall, suggesting that there was a link between past and future rainfall trends. Results show that highly negative or decreasing rainfall trends have been found in broad regions of India, which could be related to climate change, according to the results. ITA and DFA techniques to discover patterns in 34 sub-divisions across India have yet to be implemented. In developing management plans for sustainable water resource management in the face of climate change, this research is a valuable resource for climate scientists, water resource scientists, and government officials.

4

Applicability of machine learning techniques for multi-time step ahead runof forecasting

Bajirao Tarate Suryakant, Elbeltagi Ahmed, Kumar Manish, Pham Quoc Bao

Acta Geophysica

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2022

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Vol. 70, no 2

757--776

EN

Precise and reliable runoff forecasting is crucial for water resources planning and management. The present study was conducted to test the applicability of different data-driven techniques including artificial neural networks (ANN), support vector machine (SVM), random forest (RF) and M5P models for runoff forecasting for the lead time of 1 day and 2 days in the Koyna River basin, India. The best input variables for the development of the models were selected by applying the Gamma test (GT). Two different scenarios were considered to select the input variables for 2 days ahead runoff forecasting. In the first scenario, the output of 1 day ahead runoff (t+1) was not used as an input while it was also used as an input along with other input variables for the development of the models in the second scenario. For 2 days ahead runoff forecasting, the models developed by adopting the second scenario performed more accurately than that of the first scenario. The RF model performed the best for 1 day ahead runoff forecasting with root mean square error (RMSE), coefficient of efficiency (CE), correlation coefficient (r) and coefficient of determination (R2 ) values of 168.94 m3 /s, 0.67, 0.84 and 0.704, respectively, during the test period. For 2 days ahead runoff forecasting, RF and ANN models performed the best in the first and second scenario, respectively. In 2 days ahead runoff forecasting, RMSE, CE, r and R2 values were observed to be 169.72 m3 /s, 0.67, 0.84, 0.7023 and 148.55 m3 /s, 0.74, 0.87, 0.76 in the first and second scenarios, respectively, during the test period. Finally, the results revealed that the addition of 1 day ahead runoff forecast increased the forecast accuracy of 2 days ahead runoff forecasts. In addition, the dependability of the various models was determined using the uncertainty analysis.

5

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

Sahoo Satiprasad, Chakraborty Subha, Pham Quoc Bao, Sharif Ehsan, Sammen Saad Sh., Vojtek Matej, Vojteková Jana, Elkhrachy Ismail, Costache Romulus, Linh Nguyen Thi Thuy

Acta Geophysica

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2021

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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.

6

Monitoring forest landcover changes in the Eastern Sundarban of Bangladesh from 1989 to 2019

Kumar Mithun, Mondal Ismail, Pham Quoc Bao

Acta Geophysica

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2021

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Vol. 69, no. 2

561--577

EN

The present study aims to estimate areal extent of the mangrove forest cover in the eastern Sundarban of Bangladesh from 1989 to 2019 to understand mangrove dynamic over the last 30 years. Freely available Landsat TM of 1989, 2014, and L8 OLI imagery were used to generate land use/land cover (LU/LC) map for the study area using maximum likelihood (MaxLike) algorithm. Results of previous investigations among diferent scientists and researchers were used to develop a conceptual background and also included in this paper to fnd out the causes that relate to forest cover change in the study area. Study results show that the vegetation cover of Sharankhola range in Sundarban has decreased by 0.44% over last 30 years (from 1989 to 2019). Water body has increased (1.30%) with the decrease in vegetation cover. Classifed map of 2014 and 2019 shows that 2.66% vegetation cover of the study area was lost in 2014 based on 1989 while 2.22% vegetation cover was gained in 2019 based on 2014. The overall accuracy of Landsat TM (1989), TM (2014), and L8 OLI (2019) were 80%, 82.85%, and 84.28%, respectively. Its accuracy would increase if it is supplemented by extensive ground verifcation data and hybrid satellite data of diferent spectral and spatial resolution.

7

Groundwater potential assessment as a preliminary step to solving water scarcity challenges in Ekpoma, Edo State, Nigeria

Ajibade Fidelis Odedishemi, Olajire Olabanji Olatona, Ajibade Temitope Fausat, Fadugba Olaolu George, Idowu Temitope Ezekiel, Adelodun Bashir, Opafola Omobolaji Taofeek, Lasisi Kayode Hassan, Adewumi James Rotimi, Pham Quoc Bao

Acta Geophysica

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2021

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Vol. 69, no. 4

1367--1381

EN

Water scarcity is a major challenge around the world, particularly in Ekpoma community, Edo State, Nigeria. The population depends on water vendors and reservoir tanks as a means of water supply. This study aims to make an assessment of groundwater potentials for efective and sustainable water resources management in Ekpoma. Seven criteria were considered to determine groundwater potentiality including slope, rainfall, land use, drainage density, distance to lineament, soil, and geology. According to their impact on groundwater, the parameters were grouped into fuzzy membership categories. The groundwater potentiality map was generated by overlaying the fuzzy members. Of the 101.2 km2 area of Ekpoma, the high, medium, and low potential zones cover 7.9, 6.4, and 85.7% of the total area, respectively. High and medium groundwater zones were identifed mostly on the outskirt of the built-up areas. These groundwater potential areas were discovered to be predominant around the lineament areas suggesting that lineament plays a major role in the potential for groundwater in the study area. Reservoirs can be assigned in these high potential areas. Conclusively, the generated groundwater prospective map can be exploited for hydrological policy making and also by water supply engineers to predict the availability of groundwater.

8

Flood prediction based on climatic signals using wavelet neural network

Linh Nguyen Thi Thuy, Ruigar Hossein, Golian Saeed, Bawoke Getnet Taye, Gupta Vivek, Rahman Khalil Ur, Sankaran Adarsh, Pham Quoc Bao

Acta Geophysica

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2021

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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.

9

Application of entropy weighting method for urban flood hazard mapping

Malekinezhad Hossein, Sepehri Mehdi, Pham Quoc Bao, Hosseini Seyed Zeynalabedin, Meshram Sarita Gajbhiye, Vojtek Matej, Vojteková Jana

Acta Geophysica

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2021

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Vol. 69, no. 3

841--854

EN

Flooding is one of the most frequently occurring natural hazards worldwide. Mapping and assessment of possible flood hazards are critical components of the evaluation and mitigation of flood risk. In this study, six flood-related indices, i.e., slope, elevation, distance to discharge channel, runof volume, street-drainage network intersection, index of the development and persistence of the drainage network (IDPR), were used to assess the flood hazard. The entropy weighting method was used for assigning the weights to flood-related indices and combining them to prepare urban food hazard mapping in Hamadan city. The produced map showed that nearly 20% of the study area (14.7 km2 ) corresponded to very high susceptibility to flooding, 19.4% (143 km2 ) to high susceptibility and 20.3%, 20.7% and 19.6% regard the moderate, low and very low susceptibility to flooding, respectively. Finally, two methods were used to evaluate the accuracy of the produced food susceptibility map. The frst method is related to assessing the behavior of the map by making and propagating error in foodrelated indices and used model (entropy weighting method), and the second method is superimposing method. The results showed that by making and propagation of error, the behavior of producing food susceptibility mapping, the produced map has a robust behavior either in ranking importance of flood-related indices and percentage of food susceptibility areas. On the other hand, regarding the result of the superimposing method, the accuracy of the flood susceptibility map was 72%, which also suggests an acceptable result.

10

Seasonality shift and streamfow fow variability trends in central India

Kuriqi Alban, Ali Rawshan, Pham Quoc Bao, Montenegro Gambini Julio, Gupta Vivek, Malik Anurag, Linh Nguyen Thi Thuy, Joshi Yogesh, Anh Duong Tran, Nam Thai Van, Dong Xiaohua

Acta Geophysica

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2020

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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.