Mahanadi River Research Articles

Development of algorithms for evaluating performance of flood simulation models with satellite-derived flood

Abstract Flood inundation simulation models are widely used for simulating severe events of flood, generating hazard maps, risk assessment, and to identify flood vulnerable locations. It is important to assess the degree of accuracy of flood model results as these results may be one of the triggering parameters considered in developing flood hazard maps, flood mitigation policies, and land using planning where multi-criteria analysis is approached. In the present study, an algorithm is developed in order to know the performance of flood models by validating it with flood footprints extracted from synthetic aperture radar (SAR) images using multi-segmentation and Otsu's thresholding technique. Evaluation of the performance of the model is based on two best fit criteria called F1 and F2. For this, HEC-RAS model is used for simulating the severe event of flood witnessed in Mahanadi River in Odisha stretching between Tikarpara and Mundali during September 2008. Three simulations were made by considering three different Manning's roughness for river and floodplain. The model gives appreciable results and best fit F1 = 0.85 and F2 = 0.74 was found for Manning's roughness 0.020.

Characterization of dredged soil collected from island of Chilika lake

Chilika lake, the second largest brackish water lagoon in the world, is situated at the east coast of India. It is an estuarine lake connected with the Bay of Bengal. Hydrology of Chilika depends on Mahanadi River in the north side, 52 channels of water in the western side and Bay of Bengal in eastern side. Various rivers dispose their water into the lake causing siltation, which ultimately results in decrease in depth of water body leading to ecological imbalance of the lake and the surrounding area. This necessitates dredging to be carried out in Chilika and presently the dredged material is dumped inside the lake creating islands. Proper utilization of the dredged material rather than treating it as a waste material is the need of the hour. In the present study an attempt has been made to explore the utilization potential of dredged material through its characterization. This is achieved through X-Ray Diffraction test, scanning electron microscope test, Fourier transform infrared test, geotechnical and agronomical characterization tests on the soil collected from dredged island of Chilika lake. For this purpose, representative samples were collected from two dredged islands situated in the central sector of the lake. In this investigation presence of Quartz as the main component with some amount of Phengite, Sanidine etc. are found. Based on the characterization, some suggestions regarding the potential utilization of the dredged soil are also discussed.

Estimation of flood frequency using statistical method: Mahanadi River basin, India

Abstract Estimating stream flow has a substantial financial influence, because this can be of assistance in water resources management and provides safety from scarcity of water and conceivable flood destruction. Four common statistical methods, namely, Normal, Gumbel max, Log-Pearson III (LP III), and Gen. extreme value method are employed for 10, 20, 30, 35, 40, 50, 60, 70, 75, 100, 150 years to forecast stream flow. Monthly flow data from four stations on Mahanadi River, in Eastern Central India, namely, Rampur, Sundargarh, Jondhra, and Basantpur, are used in the study. Results show that Gumbel max gives better flow discharge value than the Normal, LP III, and Gen. extreme value methods for all four gauge stations. Estimated flood values for Rampur, Sundargarh, Jondhra, and Basantpur stations are 372.361 m3/sec, 530.415 m3/sec, 2,133.888 m3/sec, and 3,836.22 m3/sec, respectively, considering Gumbel max. Goodness-of-fit tests for four statistical distribution techniques applied in the present study are also evaluated using Kolmogorov–Smirov, Anderson–Darling, Chi-squared tests at critical value 0.05 for the four proposed gauge stations. Goodness-of-fit test results show that Gen. extreme value gives best results at Rampur, Sundergarh, and Jondhra gauge stations followed by LP III, whereas LP III is the best fit for Basantpur, followed by Gen. extreme value.

Simulation of monthly streamflow using the SWAT model of the Ib River watershed, India

The objective of the study is simulating the streamflow and predicting water balance components using Soil and Water Assessment Tool (SWAT) model for Ib River watershed in Mahanadi River basin. Tropical Rainfall Measuring Mission (TRMM) daily rainfall and monthly streamflow data were used to develop the model from 1993 to 2011. The calibration showed better response than validation in which the determination of coefficient, Nash–Sutcliffe efficiency and percent of bias were 0.77; 0.75 and −19.1, respectively. The temporal and spatial variability of water balance components over study area were determined. This study shows that almost 26–50% of the rainfall was lost by evapotranspiration. The spatial variability of surface runoff was higher on the southern region, groundwater was lower due to the dominance of tile drainage, and lateral flow was higher on southern region. This study will enhance the understanding of hydrological components behaviour on the sparse rain network.

Studying land use dynamics using decadal satellite images and Dyna-CLUE model in the Mahanadi River basin, India.

Population growth rate indicates the proportional rate of settlement expansion and landscape modification in any river basin. The Mahanadi River basin (MRB), which is a densely populated, cropland and forest-dominated landscape, is selected as a case study area for studying the nature of built-up expansion and the corresponding land cover modifications. Satellite data-derived land use/land cover (LU/LC) maps for the years 1995, 2005, and 2015 were used for identification of landscape changes during the past three decades. One of themajor LU/LC changes are observed in terms of increase in the water, which may be attributed to construction of new dams at the cost of the croplands and forest areas. Conversion of forest to cropland and expansion and densification of built-up areas in and around the existing built-up areas are also identified as a major LU/LC change. The geostatistical analysis was performed to identify the relationship between LU/LC classes with drivers, which showed that built-up areas were more in topographically flat terrain with higher soil depth, and expanded more around the existing built-up areas; cropland areas were more at lower elevation and less sloppy terrain, and forest areas were more at higher elevation. The LU/LC scenario of 2025 was projected using a spatially explicit dynamic conversion of land use and its effects (Dyna-CLUE) modeling platform with the LU/LC change trends of past 10 years (2005-2015) and 20 years (1995-2015). The major LU/LC changes observed during 2005-2015 were built-up expansion by 36.53% and deciduous forest and cropland reduction by 0.35% and 0.45%, respectively. Thus, the corresponding predicted change during 2015-2025 estimated built-up expansion by 25.70% and deciduous forest and croplands loss by 0.43% and 0.35%, respectively. On the other hand, during 1995 to 2015, the total built-up expansion and deciduous forest and cropland reduction were observed 50.79%, 0.45%, and 0.73%, respectively. Thus, the predicted changes during 2015-2025 were estimated as 18.48% built-up expansion and 0.22% and 0.21% deciduous forest and cropland loss. However, with the conditions of restricted deforestation and less landscape modification, the LU/LC projections show less built-up area expansion, reducing the cropland, fallow land, plantation, and waste land. The reduced numbers of land cover conversions types during 2005-2015 compared with 1995-2005 indicate more stabilized landscape. The input LU/LC maps and statistical analysis demonstrated the landscape modifications and causes observed in the basin. The model projected LU/LC maps are giving insights to possible changes under multiple pathways, which will help the agriculture, forest, urban, and water resource planners and managers in improved policy-making processes.

Phytoplankton biomass in relation to flow dynamics: the case of a tropical river Mahanadi, India

This present study was conducted to extend our understanding on the relationship between Chlorophyll a (Chl a) concentration and hydrological parameters typically flow velocity, environmental variables and trophic state conditions using lotic water as experimental system. River Mahanadi, India was selected as a model for the present study. Based on elevation from mean sea level, twelve sampling sites from Sambalpur to Naraj were grouped into three zones viz. upper zone (71 km), middle zone (142 km) and lower zone (103 km). Carlson Trophic State Index (CTSI) revealed eutrophic state in middle and lower zone and with mesotrophic condition in the upper zone. Along the river stretch, a negative correlation was observed between Chl a concentration and flow velocity at all three zones during post monsoon (r = − 0.15) and monsoon (r = − 0.31). Seasonal study revealed higher Chl a concentration (16.16–283.85 μg l−1) in post-monsoon with recorded mean flow velocity (0.28 ± 0.19 ms−1) as compared to monsoon season with lower Chl a (18.68–232.24 μg l−1) and mean flow velocity (0.54 ± 0.21 ms−1). Among environmental variables, water temperature and pH showed a positive correlation with Chl a. Overall, the findings of this study illustrated the occurrence of higher phytoplankton biomass (quantified as Chl a) during post-monsoon season coincides with low water velocity. The dataset of this work can be implicated for the hydrological and the ecological framework of the tropical river Mahanadi in the Indian Territory.

Impact of cyclone Fani on the breeding success of sandbar-nesting birds along the Mahanadi River in Odisha, India

Cyclonic storms have detrimental effect on birds particularly those inhabit and breed in open and exposed areas like sea and riverine habitats. This note reports the effect of ‘Fani’, one of the severe cyclones that affected Odisha in May 2019 on sandbar- nesting birds along the Mahanadi River. The study found significant relative decline in population, complete failure of nests and survival of chicks. As tropical cyclones are expected to be more frequent in future, it may emerge as new threats for survival of sandbar-nesting birds.

A new bivariate risk classifier for flood management considering hazard and socio-economic dimensions

Identification of flood-risk dynamics is pivotal for refurbishing the existing and future flood-management options. The present study quantifies the marginal and compound contributions of hazard and vulnerability to flood-risk through an innovative concept of Risk-classifier, designed in the form of a 5 × 5 choropleth. The proposed framework is demonstrated at the finest administrative scale of village-level over Jagatsinghpur district in Mahanadi River basin, Odisha (India) for two-time frames: Scenario-I (1970–2011) and Scenario-II (1970–2001). An increase in high and very high hazard and vulnerable villages is noticed in Scenario-I, the majority of them lying in the coastal stretches (S-E region) and adjoining flood plains of Mahanadi River (N–W region). Scenario-I is characterized by the majority of hazard-driven and compound (both hazard and vulnerability) risk villages, while Scenario II is characterized by a majority of vulnerability driven-risk villages. For the vulnerability-driven risk villages, rigorous enforcement of policies and mitigation schemes are recommended, while for hazard-driven risk villages, enhancement of structural measures and flood-plain zoning should be exercised. Such exhaustive flood-risk information may serve as a valuable cartographic product for the civic authorities and stakeholders and help in prioritizing flood mitigation actions for improved environmental planning and management.

An Empirical Formula for Design Flood Estimation of Un-Gauged Catchments in Brahmani Basin, Odisha

Several statistical methods, viz. flood frequency analysis and hydro-meteorological methods (i.e., unit hydrograph approach) are available for estimation of design flood in un-gauged catchments. Till today, the empirical formulae are relevant as the latest methods require adequate field observation and collection of sufficient data. The available formulae such as Dicken’s formula, Ryves formula and Inglis formula are applicable, respectively, to Northern India, Western India and Southern India. But no such empirical formula is available for eastern India, especially for Odisha region, for which designers depend on the established formulae for other regions. This necessitated developing an empirical formula for computation of design flood for this zone. An empirical formula is developed for computation of design flood of un-gauged catchments in Brahmani Basin of Odisha and presented in this paper. Relation between the peak discharges of the unit hydrograph and the design discharges of individual sub-basins is established. Eight sub-basins are considered for this study. The derived formula is validated through upper and lower envelope curves for probable maximum flood for Mahanadi and adjoining river basins, recommended by Central Water Commission. This would provide guideline in planning any future water resources projects in Brahmani basin. The study would form a part of final work of derivation of a general equation applicable to the State/Eastern zone of India.

Forecasting Monthly Discharge Using Machine Learning Techniques

Discharge prediction methods play crucial role in providing early warnings and helping local people and government agencies to prepare well before flood or managing available water for various purposes. The ability to predict future river flows helps people anticipate and plan for upcoming flooding, preventing deaths and decreasing property destruction. Different hydrological models supporting these predictions have different characteristics, driven by available data and the research area. This study applied two different types of Machine learning techniques to the Tikarpara station present in the lower end of the Mahanadi river basin India. The two Machine learning techniques include Multi-layer perception (MLP) and support vector regression (SVR) MLP has shown great deal of accuracy as compared to SVR across the cases used in the study; based on available data and the study area, MLP showed the best applicability, compared to SVR techniques. MLP out performed SVR model with r2 = 0.75 and lowest RMSE = 0.58.MLP can be used as a promising tool for forecasting monthly discharge at the selected station.

The Study of Morphological Characteristics for Best Management Practices Over the Rampur Watershed of Mahanadi River Basin Using Prioritization

A morphometric analysis has been carried out for Rampur watershed of Mahanadi river basin, and its nine sub-watersheds have been prioritized according to the linear, aerial and relief characteristics. Based on digital elevation model, delineation of sub-watersheds (SWs) has been done by using ArcMap 10.2.2. The drainage analysis and their respective parameters such as stream order, stream length, stream frequency, drainage density, texture ratio, form factor, circulatory ratio, elongation ratio, bifurcation ratio and compactness coefficient for the sub-watersheds have been evaluated by applying remote sensing and geospatial techniques. Based on the behaviour of morphometric parameters of each sub-watershed (SW), the prioritized score has been allocated, and thereafter, the most sensitive parameters have been distinguished by the evaluated combined scores. The results achieved from the analysis show that the stream order varies from 1 to 6 and the first order covers 30.5% of the total catchment area. The watershed has 2087 stream segment numbers of all order. Based on morphometric analysis, prioritizing each SW has been the primary objective of this study. The final score of each sub-watershed has been allocated as per erosion threat. The highest priority has been allocated to the SW which shows the least compound parameter value. Eventually, the grouping of SWs has been done according to the high (4.0–4.7), medium (4.8–5.3) and low (> 5.4) priority classes depending on their prioritized score, maximum (6.0) and minimum (4.0).

Breeding Ecology of the Endangered Black-Bellied Tern (Sterna acuticauda) in Eastern India and Implications for Conservation

The global population of Black-bellied Tern (Sterna acuticauda) is declining, and insufficient information is available on its breeding ecology. The aim of this study was to record its breeding ecology and factors affecting breeding success along the Mahanadi River in Odisha, eastern India. Breeding was observed during January-April 2018, with peak activity in March. Among 24 nests recorded on seven sandy islands, only 11 nests successfully produced chicks. Factors affecting survival of nests were flooding due to anthropogenic changes in water levels (54%), predation (31%), and trampling (15%). Dredging of canals that use water from the river is recommended to reduce fluctuations in water levels. Targeted long-term studies along the entire length of the Mahanadi River and other large rivers can aid in identifying other breeding sites and ongoing threats to this rare and endangered species. Outreach in communities near breeding sites and recruiting of local residents as nest guardians can also help in protection of nesting sites.

Novel technique for developing flood hazard map by using AHP: a study on part of Mahanadi River in Odisha

In India, Odisha state experienced major natural hazards like floods and cyclones. Floods were experienced fifteen times during 2001–2018 and had two major catastrophic cyclones (super-cyclone 1999 and Phailin 2013). Flood is triggered due to meteorological, hydrological, and terrain conditions at any given place. Considering the terrain parameters namely elevation, slope, drainage density, soil and land use/land cover as the contributing factors for flood inundation, a new technique is developed to calculate flood hazard using analytical hierarchy process (AHP). Using multi-temporal satellite derived flood extent, spatial flood frequency map (SFFM) were generated based on the frequency of inundation. SFFM which represents the observed flood extent on ground at different magnitudes of flood and the terrain conditions on the ground are used synergistically for generating the hazard map. Part of Mahanadi River in Odisha which is highly flood-prone is taken for analysis in this study. More than 100 satellite data sets both microwave SAR and optical data acquired at different magnitudes of flood during 2001–2018 were analysed to prepare the SFFM maps. Pair wise comparison rating was given among flood producing factors and weightage of each factor was computed by using GIS based AHP and multi-criteria evaluation techniques. The individual derived weights (in percentage) of the contributing factors obtained were 50.874 for SFFM, 22.775 for elevation, 10.966 for drainage density, 7.225 for slope, 5.914 for land use and 2.936 for soil. Further, the layers were used in weighted overlay tool and respective weights in percentage were given for generating the flood hazard map in GIS environment. The flood hazard was classified into five hazard classes i.e. very high, high, moderate, low, and very low and corresponding hazard area was estimated to be 7467 ha, 12,871 ha, 28,700 ha, 35,518 ha and 292 ha respectively. These statistics and hazard map will help the concerned local authorities for formulating strategic policies for land use planning and minimize the effect of flood on agricultural land and human lives.

Growing Industrialisation and its Cumulative Impacts: Insights from the Mahanadi River Basin

The Mahanadi river faces large-scale ecological disaster due to a variety of anthropogenic stresses. A prime factor is rapid industrialisation and coal-fired power generation plans that are being encouraged by the states of Odisha and Chhattisgarh. These are not only impacting the flow of the river’s waters and damaging the health of its basin but have also made the area susceptible to climate change. The industrial growth-based development has already polluted the Mahanadi to an irrecoverable extent. The over-allocation of water to industries has adversely effected the region’s irrigation and agriculture leading to a bitter contestation between industry and the farming community. As this comment emphasises, both state governments are taking advantage of the Mahanadi river for industrial use to maximise revenue generation but are at the same time are being insensitive to the adverse environmental and ecological consequences that such exploitation will surely lead to.

Analysis of persistence in the flood timing and the role of catchment wetness on flood generation in a large river basin in India

Mahanadi River Basin (MRB) is one of the largest tropical pluvial river basin systems in India contributing the major source of freshwater to more than 71 million people in east-central India. Being located in the monsoon “core” region (18–28° N latitude and 73–82° E longitude) and its proximity to Bay of Bengal, Mahanadi River Basin (MRB) system is highly vulnerable to tropical depression-induced severe storms and extreme precipitation-induced fluvial floods during southwest monsoon as reflected in several successive and catastrophic flood episodes in recent years (2001, 2003, 2006, 2008, 2011, 2013, 2014, 2016, 2019). While previous studies so far focused on analyzing either flood trends or frequency and show the role of precipitation in flood generating mechanism over MRB using both instrumental records and climate model simulations, this study for the first time examines space-time coherence in floods and the role catchment wetness in flood response (i.e., magnitude and the timing of floods) over the basin. We examine the incidence of flooding in three different time windows: 1970–2016 (whole 47 years), 1970–2006, and 2007–2016 (post-2007s) using monsoonal maxima peak discharge (MMPD) and peak over threshold (POT) series at 24 stream gauges spatially distributed over the basin. Our analysis reveals the mean dates of floods for most of the gauges are temporally clustered during the month of August irrespective of the type of flood series and the choice of time frames. Further, we observe sensitiveness of runoff responses (flood magnitude, FM and the flood timing, FT) to lagged d-day mean catchment wetness (CW), suggesting a physical association between them. We also note FT is more strongly correlated (as manifested by statistically significant correlations) to CW rather than FM. Overall, we observe, the correlation of CW versus FT is negative, where the flood timing is relatively irregular. The outcomes of the study help to improve the predictability of floods, which can, in turn, enhance existing flood warning techniques.

Long short-term memory (LSTM) recurrent neural network for low-flow hydrological time series forecasting

This article explores the suitability of a long short-term memory recurrent neural network (LSTM-RNN) and artificial intelligence (AI) method for low-flow time series forecasting. The long short-term memory works on the sequential framework which considers all of the predecessor data. This forecasting method used daily discharged data collected from the Basantapur gauging station located on the Mahanadi River basin, India. Different metrics [root-mean-square error (RMSE), Nash–Sutcliffe efficiency (ENS), correlation coefficient (R) and mean absolute error] were selected to assess the performance of the model. Additionally, recurrent neural network (RNN) model is also used to compare the adaptability of LSTM-RNN over RNN and naive method. The results conclude that the LSTM-RNN model (R = 0.943, ENS = 0.878, RMSE = 0.487) outperformed RNN model (R = 0.935, ENS = 0.843, RMSE = 0.516) and naive method (R = 0.866, ENS = 0.704, RMSE = 0.793). The finding of this research concludes that LSTM-RNN can be used as new reliable AI technique for low-flow forecasting.

Geospatial Technology Application on Sustainable Wetland Management & Monitoring: A Case of Ansupa Lake, Odisha

Ansupa, the largest freshwater lake and a wetland of Odisha lie between 20 0 25’ to 20 0 30’ N latitude and 85 0 35’ to 85 0 37’ E longitude in the Banki Subdivision of Cuttack district. Ansupa, the so-called sister lake of Chilika once was looking like a heavenly picture gallery with small fishing boat on the blue waters and hills on the banks, birds of different colors and above all lotus and lilies. The lake is bounded by Saranda hills on its western side, Bishnupur hills on its northeast side and surrounded by villages like Subarnapur, Kadalibadi, etc. Previously during the high floods, the flood water of the river Mahanadi used to pass through the lake from its southeastern side driving away from the weeds through the south – western side. So, it was a process of natural deweeding. But, when the water enters into the lake, cannot be discharged due to the erection of embankment, silting up of the lake, etc. The Chilika Development Authority, which is under the Department of Forest and environment of Odisha has taken a very vital step to clear the weed from the lake for sustain the livelihood of the community by using an instrument Weed cuter under the project Integrated Coastal Zone Management of Odisha. Geospatial Technology has used for regular monitoring the successful management as a case study during the year 1999 to 2019. Keywords: Coastal Zone, embankment, freshwater, geospatial, heavenly Cite this Article Jajnaseni Rout, Adikanda Ojha. Geospatial Technology Application on Sustainable Wetland Management & Monitoring: A Case of Ansupa Lake, Odisha. Journal of Remote Sensing & GIS. 2019; 10(2): 13–18p.

Isolation, occurrence, screening and characterization of rock phosphate solubilizing microbes

The present experiment is conducted on ‘isolation, Occurrence, Screening & Characterization of Rock phosphate solubilizing Microbes’. Phosphorus is the least mobile element in plant and soil contrary to other macronutrients. It precipitates in soil as orthophosphate or is absorbed by Fe and Al oxides through legend exchange. Phosphorus solubilizing bacteria play role in phosphorus nutrition by enhancing its availability to plants through release from inorganic and organic soil P pools by solubilization and mineralization Rock Phosphate is the cheapest and abundant Phosphatic fertilizer available but due to its sparse solubility. Most agricultural soils contain large reserves of phosphorus (P), a considerable part of which accumulates as a consequence of regular applications of P fertilizers. However, a greater part of soil phosphorus, approximately 95–99% is present in the form of insoluble phosphates and hence cannot be utilized by the plants combined application of rock phosphate with phosphate solubilizing microorganisms has emerged as a logical solution to this issue. The selection of microorganisms capable of solubilizing phosphorus (P) from rock phosphates (RP) may contribute to reduce the dependence of imported fertilizers in grain crops, reducing the costs of agricultural production, and also the environmental impacts. Principal mechanism in soil for mineral phosphate solubilization is lowering of soil pH by microbial production of organic acids and mineralization of organic P by acid phosphatase. Use of phosphorus solubilizing bacteria as inoculants increases P uptake. These bacteria also increase prospects of using phosphatic rocks in crop production. Greater efficiency of P solubilizing bacteria has been shown through co-inoculation with other beneficial bacteria and mycorrhiza In the present study bacterial and fungal strains were isolated from waste water, soil and sediment sample of ETP of Paradeep phosphate limited (PPL) and from Mahanadi river estuary (Paradeep), having potential to solubilize insoluble inorganic phosphates were characterized. Each isolates were tested for their tricalcium phosphate (TCP) and low grade rock phosphate (RP) solubilization efficiency in both solid and liquid medium. From the experiment it is found that out of 22 fungal isolates more than 50% were classified as efficient. There were significant differences in the availability of P among strains. From the Experiment it is also revealed that the solubilizing activity of both phosphates was associated with a reduction of pH which suggests that the acidification of the culture medium can be one of the mechanisms involved in the solubilization of P. Total number of 27 bacterial strains and 29fungal strains are taken for experiment and studied for maximum solubilization of insoluble inorganic phosphorous.. From the above study, it is concluded that More than 40% isolates of bacterial and fungal strains show the effect of phosphate solubilizing activity. The bacterial strain with 11B and fungal strain with 6F has highest phosphorous solubilization. From the maximum specific growth rate it can be concluded that the microbial isolates 6F and 11B are copiotrophs.

Identification of seawater intrusion signatures through geochemical evolution of groundwater: a case study based on coastal region of the Mahanadi delta, Bay of Bengal, India

The study dealt with seawater intrusion process in a coastal aquifer system in the Mahanadi river delta region in the east coast of India along the Bay of Bengal. The aquifers of Mahanadi delta are characterized as shallow aquifers ( 50 m). Electrical conductivity (EC) of groundwater varied from a fresh of 146 μS/cm (NW of the Mahanadi delta) to a saline of 33,900 μS/cm (close to sea coast) with cation dominance in the order Na+ > Ca2+ > Mg2+ > K+ and anion dominance of Cl− > $$ {\text{HCO}}_{3}^{ - } $$ > $$ {\text{SO}}_{4}^{2 - } $$ . The hydrochemical facies changed from Ca–Mg–Na–HCO3 type to Na–Cl type along the groundwater flow direction due to ion exchange processes. A strong positive correlation (r > 0.9) between Cl− with EC, Na+, Mg2+, Ca2+, $$ {\text{SO}}_{4}^{2 - } $$ and K+ was observed, which indicated the influence of seawater on coastal aquifer. The ionic ratios (Na+/Cl−, $$ {\text{HCO}}_{3}^{ - } $$ /Cl−, Mg2+/Ca2+, $$ {\text{SO}}_{4}^{2 - } $$ /Cl−, Ca2+/( $$ {\text{HCO}}_{3}^{ - } $$ / $$ {\text{SO}}_{4}^{2 - } $$ )) also suggested that the groundwater is affected by seawater intrusion. Stable isotope compositions (δ18O and δ2H) varied from − 1.86 to − 6.87 ‰ for δ18O and from − 10.79 to − 45.42 ‰ for δ2H, implying the mixing of saline water and fresh groundwater in the coastal region of the Mahanadi delta. The proportion of seawater in groundwater was estimated to vary from 0% in the upper-delta formation to 72% in the lower-delta formation of the Mahanadi delta (close to seacoast), which was due to inland intrusion of seawater. In a first ever study on this coastal aquifer along the Bay of Bengal, where a large population is dependent on agriculture, seawater intrusion into the fresh groundwater has been quantified. The issue of seawater intrusion into the coastal aquifer in this region may become a serious disaster, if appropriate management strategies are not implemented in time.

Application of Soft Computing Techniques for River Flow Prediction in the Downstream Catchment of Mahanadi River Basin Using Partial Duration Series, India

The delta region of the Mahanadi river basin is faced with high floods having arisen many questions like (1) due to the release of Hirakud dam, (2) due to the middle region catchment contribution (Tel tributary at Kantamal station and Ong tributary at Salebhata station) and (3) due to an increase in heavy rainfalls over the basin. To answer this, we choose the partial duration series (PDS) having an average number of flood peaks per year (λ = 3) at Naraj based on the capacity of the existing embankment which mitigates floods up to 28,400 m3/s and analyze the trends of peak discharge of PDS having λ = 3 at the Naraj, Hirakud dam and middle region to know the flow behavior which is responsible for flooding problem in the delta of Mahanadi basin. Subsequently, the trend of extreme rainfall over the entire basin and at the middle region of the basin is analyzed. Analysis of trends of extreme floods in relation to the trends of extreme rainfall to have a proper understanding of the causes of changing floods over the basin is important. Then, runoff at Naraj is estimated based on two techniques: artificial neural networks with different algorithms and an adaptive neuro-fuzzy inference system (ANFIS) using five different input combinations of peak runoff data and rainfall data of upstream locations and compare their performance based on different indices, namely correlation coefficient (R), index of agreement (d), root-mean-square error and Nash–Sutcliffe efficiency (E) to obtain the best input–output model. From the analysis, it is shown that ANFIS techniques as well as the neural network with Levenberg–Marquardt algorithm performs better for estimating runoff at Naraj and that gives the clear idea about the delta flooding. Also, the analysis revealed the answer arisen by many questions regarding the delta flood problem and which are due to the middle region catchment contribution and an increase in extreme rainfall.