Krishna Basin Research Articles

Flood detection in the Upper Krishna Basin through integrated geospatial analysis: leveraging decision frameworks and statistical measures

ABSTRACT Floods threaten the environment and human settlements across river basins globally, including the Upper Krishna Basin in India. This research delves into evaluating flood hazard areas within the Upper Krishna Basin utilizing the Analytical Hierarchy Process (AHP), Frequency Ratio (FR), and Statistical Index (SI). These methodologies prioritize and classify flood-prone regions by integrating spatial and non-spatial criteria. The findings reveal significant variations in flood risk classification across the Upper Krishna Basin based on the three models. The AHP model identifies 3.37% of the region as low flood risk, with 22.90% classified as moderate risk, and 68.27% as high risk. In contrast, the FR model designates 3.76% as low risk, 10.50% as moderate risk, and 42.21% as high risk. Meanwhile, the SI model identifies 1.04% of areas with low risk, 35.38% with under-high risk, and 57.87% with very high risk. Validation using Receiver Operating Characteristic-Area Under the Curve (ROC-AUC) values demonstrates the superior reliability of the SI model. These findings offer valuable insights for decision-makers to allocate resources and implement effective flood mitigation measures.

Rainfall-runoff modelling – a comparison of Artificial Neural Networks (ANNs) and Hydrologic Engineering Centre-Hydrologic Modelling System (HEC-HMS)

ABSTRACT This article presents comparison of Artificial Neural Networks (ANNs) and Hydrologic Engineering Centre-Hydrologic Modelling System (HEC-HMS) model for rainfall-runoff (R-R) process. Aim of the present work is to forecast runoff one day ahead at Shivade station of Upper Krishna Basin, India, using 17 years of daily rainfall and discharge data. The R-R modelling can be exercised using various traditional methods which generally require exogenous data in the form of basin parameters. Unavailability of such data becomes major impediment in applying these models at many basins. In such situations, soft computing techniques like ANNs have been extensively applied to model R-R process. Though ANN is now an established tool in hydrology, compared to HEC-HMS, its results are viewed with suspicion owing to its data-driven nature rather than a model-driven nature. In this study, ANN model performed reasonably well, with a higher correlation coefficient (0.87) and the lowest Root Mean Square Error (136.28 m3/s) when compared with HEC-HMS (0.76, 139.8 m3/s) respectively. Novelty of the present work lies in model development using restricted basin data. Both models showed less accuracy in predicting extreme events. Finally, it is concluded that ANN model can be used as a supplementary technique along with HEC-HMS for this phenomenon.

Estimation of spatial correlation structure of gauged rainfall in network under semi-arid situation of Krishna basin at watershed scale

Estimation of spatial correlation structure of gauged rainfall in network under semi-arid situation of Krishna basin at watershed scale

Assessment of Stream Flow of Hidkal Dam Catchment Area in Krishna Basin of India Using SIMHYD Model

A study was conducted to predict the stream flow from a catchment area of Hidkal dam situated in Krishna basin of India. The SimHYD model was selected to setup the stream flow model under limited data conditions. Daily rainfall, potential evapotranspiration (PET) and observed discharges were used as input data to setup the model. Sensitivity analysis was carried out to identify the more sensitive parameter and fixed final parameter values. A genetic algorithm was used for calibration and validation of the model with object function of Nash -Sutcliffe Equation (NSE). The performance of model during calibration and validation with monthly stream flow (m3/s) was found to be very good in terms of NSE, R2. The NSE and correlation coefficient were found to be 0.77 and 0.93 during calibration period and 0.90 and 0.95 during validation period respectively. The was a very good agreement between monthly observed and simulated stream flows of catchment area of Hidkal Dam. The NSE value of model during calibration period with daily runoff was found to be satisfactory at 0.51. It was observed that good agreement between observed and simulated daily runoff (in mm) with a correlation coefficient is 0.74. NSE and correlation coefficient during validation period using daily data are found to be 0.80 and 0.9 respectively. This study concluded that the SimHYD model can be used for assessment of stream flow with limited data.

Rendering lineament induced stream alignment in Upper Krishna Basin, India: a geospatial approach

Lineaments play an important role in drainage development, stream alignment and groundwater recharge. These are traced as surface or subsurface feature. A linear feature that is associated with dislocation and deformation is known as lineament. The present study was aimed to identify the influence of lineaments extracted from satellite images in the Upper Krishna River basin in India. Objective set for the present study is to characterize and analyse the spatial organization of lineaments from ASF-DEM and sentinel-2 satellite data which may help to determine the role of lineaments in groundwater flow and to identify groundwater potential zones. High resolution spatial data allows determining the linear features for considerable distance and play an important role to identify mineral potential areas (Mohammadpour et al. in Geotectonics 54:366–382, 2020). Comprehensive understanding of regional lineament maps is important in terms of their prospectivity for mineral exploration (Richards in SEG Newsl 42:1–20, 2000). Remote sensing datasets are considered the best option when using image enhancement techniques for extracting lineaments. Lineament studies are useful for groundwater and mineral exploration and also in the field of engineering geology (Anbazhagan in Bhu-Jal News 8:8–12, 1994; Anbazhagan et al. in New approaches to characterizing groundwater flow, 2001. http://dspace.library.iitb.ac.in/xmlui/handle/10054/16176, http://hdl.handle.net/100/2781) more generally in the geomorphological studies like forms of surface features and structural geological fields also in physical setting and control over the rocky terrain. The availability of high-resolution satellite data and image processing techniques have rendered it further convenient to map lineaments. In the present study, lineaments were extracted from Sentinel-2 images with a resolution of 10 m, using various image processing techniques. A total of 1314 lineaments were extracted from the study area with a total length of 3983.44 km. The analysis of the extracted lineaments revealed that the lineament density was higher in the upper reaches of the basin, where the undulating hilly region which is located in Western Ghats. This finding implied that these regions have a high structural deformation and a higher groundwater infiltration potential. Moreover, 15% of the total stream length was observed to be influenced by the lineaments. The maximum influence of lineaments was observed in the source region. The lineament extraction results of the present study would assist in understanding the geomorphology of this region and the structural control on the streams and groundwater potential zones, particularly as a contribution to water resource management in this region.

Analysing Decadal Land Use Land Cover Dynamics in the Sub-Upper Krishna Basin of Maharashtra, India Using Remote Sensing and GIS

Aims: This study was conducted to examine Land Use Land Cover (LULC) dynamics in Maharashtra’s sub-upper Krishna basin from 2009 to 2019 using remote sensing and geographical information system (GIS), focusing on water bodies, vegetation, soil, settlements, and their changes.
 Study Design: Employing remote sensing and GIS for LULC mapping (2009-2019) the study used a maximum likelihood classifier in supervised classification, identifying six land use categories: water bodies, open shrubs, forests, agricultural land, settlements, and fallow land.
 Place and Duration of Study: It was conducted in the sub-upper Krishna basin, Maharashtra, over ten years’ data (2009-2019).
 Methodology: The study utilised satellite remote sensing and GIS tools for LULC mapping. A supervised classification was applied with a maximum likelihood classifier to categorize land. The changes in water bodies, open shrubs, forests, agricultural land, settlements, and fallow land were analysed using GIS approach.
 Results: It was seen that, over the decade, fallow land decreased by 3.03%, while agricultural land and settlements grew by 7.32% and 4.3%, respectively. Tree cover increased by 9.85%, water bodies by 0.93%, and open scrubland decreased by 1.77%. Institutional factors, easier water access, and technological and economic factors drove these changes.
 Conclusion: The study advocates the effective use of satellite remote sensing to monitor LULC changes, identifying key drivers, including institutional and technological factors, contributes to sustainable development planning. The findings aid predictions for future land use changes, supporting effective land management and conservation strategies in the region.

Geospatial Interpolative Analytics of Gauged Rainfall in Network under Semi-arid Situation of Krishna Basin on a Watershed Scale

Kriging as a Geospatial interpolative delineation of optimal radial distance around existing rainfall stations with in the purview of flat land (1-3 %) watershed (Hti-2 4D7B3) situated in Krishna basin. The smaller (15 and 30 min) and medium (45 min to 1h 45 min) events of the were analyzed focusing on their distribution whereas events of longer duration (>2 h) analyzed with their depth across 14 gauging stations. Analysis of 14 gauged rainfall network stations and their corresponding rainfall data during 2014-2020 used to derive relationship between recurring events of classified duration (15 min to 2 h) vis-a-vis weighted influence of number and depth due to spatial morphology and limitations of watershed. The event vise rainfall network distribution of Huti-2 watershed found to be significantly uniform in number and depth within the radial distance upto 3 km around each station prevailed by strong coefficient of determinant (R2) more than 0.8.

Mapping soil property variability in oil palm (Elaeis guineensis jacq.) plantationsof Krishna basin of Andhra Pradesh through geospatial technologies

Sixty seven soil samples were collected from oil palm growing regions of NTR District of Andhra Pradesh and were analysed for different soil properties. The data was subjected to descriptive analysis to find out the variability and then to geostatistical analysis to design best fit models having minimal error for developing prediction maps. Highest variation was observed in exch. Mg and the lowest in pH. The mean values of soil pH, EC (dS/m), OC (g/kg), Olsen-P (mg/kg), NH4OAc-K (mg/kg), Exch. Ca (mg/kg), Exch. Mg (mg/kg), CaCl2-S (mg/kg) and HWB (mg/kg) were 7.32 ± 0.08, 0.25 ± 0.02, 0.87 ± 0.03, 101.47 ± 6.95, 566.14 ± 42.97, 4.72 ± 0.24, 2.46 ± 0.27, 60.86 ± 2.6 and 5.98 ± 0.25 respectively in the surface layer (0-20 cm) of the soil. Geostatistical analysis revealed wider spatial variability in surface soil properties and they had circular, spherical, stable and exponential best fit semi variogram models for evaluating dependency. The wide spatial variability of soil properties warrants site specific nutrient management for higher oil palm production. Nutrient distribution maps could be developed through kriging interpolation to interpolate the nutrient levels in unsampled areas.

Assessment and Mapping of Riverine Flood Susceptibility (RFS) in India through Coupled Multicriteria Decision Making Models and Geospatial Techniques

Progressive environmental and climatic changes have significantly increased hydrometeorological threats all over the globe. Floods have gained global significance owing to their devastating impact and their capacity to cause economic and human loss. Accurate flood forecasting and the identification of high-risk areas are essential for preventing flood impacts and implementing strategic measures to mitigate flood-related damages. In this study, an assessment of the susceptibility to riverine flooding in India was conducted utilizing Multicriteria Decision making (MCDM) and an extensive geospatial database was created through the integration of fourteen geomorphological, meteorological, hydroclimatic, and anthropogenic factors. The coupled methodology incorporates a Fuzzy Analytical Hierarchy Process (FAHP) model, which utilizes Triangular Fuzzy Numbers (TFN) to determine the Importance Weights (IWs) of various parameters and their subclasses based on the Saaty scale. Based on the determined IWs, this study identifies proximity to rivers, drainage density, and mean annual rainfall as the key factors that contribute significantly to the occurrence of riverine floods. Furthermore, as the Geographic Information System (GIS) was employed to create the Riverine Flood Susceptibility (RFS) map of India by overlaying the weighted factors, it was found that high, moderate, and low susceptibility zones across the country span of 15.33%, 26.30%, and 31.35% of the total area of the country, respectively. The regions with the highest susceptibility to flooding are primarily concentrated in the Brahmaputra, Ganga, and Indus River basins, which happen to encompass a significant portion of the country’s agricultural land (334,492 km2) potentially posing a risk to India’s food security. Approximately 28.13% of built-up area in India falls in the highly susceptible zones, including cities such as Bardhaman, Silchar, Kharagpur, Howrah, Kolkata, Patna, Munger, Bareilly, Allahabad, Varanasi, Lucknow, and Muzaffarpur, which are particularly susceptible to flooding. RFS is moderate in the Kutch-Saurashtra-Luni, Western Ghats, and Krishna basins. On the other hand, areas on the outskirts of the Ganga, Indus, and Brahmaputra basins, as well as the middle and outer portions of the peninsular basins, show a relatively low likelihood of riverine flooding. The RFS map created in this research, with an 80.2% validation accuracy assessed through AUROC analysis, will function as a valuable resource for Indian policymakers, urban planners, and emergency management agencies. It will aid them in prioritizing and executing efficient strategies to reduce flood risks effectively.

Flood frequency and flood forecasting analysis of Krishna basin Andhra Pradesh

Floods are occurrences of natural hazards, frequent during the year in many rivers across the globe. Every year, several rivers in India are vulnerable to flooding, causing loss of property and life. Krishna is one of the major rivers in India which is vulnerable to flooding in every monsoon. In this study, flood analysis was conducted for the year 2019. Rainfall data from AWS was used to estimate discharge levels in dams during the monsoon of 2019. It was observed that moderate rainfall occurred in the months of August and September, corresponding to low rainfall and that extreme flooding occurred in the same month. Compared to the flood inundation map of the satellite, it shows a close relationship with the flood map of 2019 and the affected area. The study shows that proper analysis of rainfall will be helpful in predicting downstream floods. To evaluate the flood control situation with appropriate data management in the Krishna basin, the usage of flood water is strong. Such types of studies would help to provide reliable and prompt flood forecasts and advance warning to redirect the main river flow to small canals, which will help to mitigate, excavate and remediate flooding in any area.

Are flood events really increasing? A case study of Krishna River Basin, India

There is a heightened sensitivity about the risks posed by the prospect of climate change as deduced from the results of the global-regional climate models. Notwithstanding the moderating, albeit feebly articulated, acknowledgement of variability as being an intrinsic attribute of climate, it is often claimed that intensification of large hydrological extremes such as floods is indeed emerging as a new looming reality. This has aptly given rise to fears of prospective exacerbated socio-economic vulnerability. In an attempt to investigate the veracity of whether the hydrological flood events are really intensifying across the Krishna River Basin, the present study has examined the historical floods in Krishna River Basin (KRB) using available streamflow and precipitation records along with published reports and featured news articles. The approach followed is based on quantitative and qualitative analysis of floods in KRB at the subsystem scale (K1 to K12). The quantitative analysis involved i) Development of unregulated flow series through hydrological modelling, ii) Frequency analysis of unregulated flows and precipitation, iii) Threshold selection for defining the small, medium, and large floods, iv) Identification of flood events in observed streamflow series and v) Identification of causal rainfall and its relationship with flood peak. The qualitative analysis focused on published reports and news articles to attempt a multivariate characterization of flood flow attributes and the accompanying losses. The study concludes that the hypothesis that flood events are intensifying is untenable for most subsystems of KRB except K7 (Lower Krishna Basin) being the sole exception where, in sharp contrast, flood events show signs of moderation. Interestingly, cyclic patterns analogous to Noah and Joseph Effect are seen in the case of small floods for all the subsystems except K7. Non-recurrent, standalone extremes that bear the classical signature of Erratic Noah and Joseph elements have also been observed in medium and large floods in all the subsystems except the K12 subbasin (Munneru Basin). The study confirms that qualitative analysis alone cannot lead to an incontrovertible determination of trend like features in flood records as every event in the historical record bears a unique multi-dimensional footprint arising from a mix flood characteristics, associated losses and perceived short and long-term impacts. This study can provide a guideline to identify changes in flood typology especially in basins with altered hydrologic regimes and serve as an aid in planning and formulating policies for ameliorative flood management strategies as well as in policy restructuring when deemed to be necessary.

GEB v0.1: a large-scale agent-based socio-hydrological model – simulating 10 million individual farming households in a fully distributed hydrological model

Abstract. Humans play a large role in the hydrological system, e.g. by extracting large amounts of water for irrigation, often resulting in water stress and ecosystem degradation. By implementing large-scale adaptation measures, such as the construction of irrigation reservoirs, water stress and ecosystem degradation can be reduced. Yet we know that many decisions, such as the adoption of more effective irrigation techniques or changing crop types, are made at the farm level by a heterogeneous farmer population. While these decisions are usually advantageous for an individual farmer or their community, aggregate effects of those decisions can have large effects downstream. Similarly, decisions made by other stakeholders, such as governments, often have basin-wide effects and affect each farmer differently. To fully comprehend how the human–natural water system evolves over time and space and to explore which interventions are suitable to reduce water stress, it is important to consider human behaviour and feedbacks to the hydrological system simultaneously at the local household and large basin scales. Therefore, we present the Geographical, Environmental, and Behavioural (GEB) model, a coupled agent-based hydrological model that simulates the behaviour and daily bidirectional interaction of more than 10 million individual farm households with the hydrological system on a personal laptop. Farmers exhibit autonomous heterogeneous behaviour based on their characteristics, assets, environment, management policies, and social network. Examples of behaviour are irrigation, generation of income from selling crops, and investment in adaptation measures. Meanwhile, reservoir operators manage the amount of water available for irrigation and river discharge. All actions can be taken at a daily time step and influence the hydrological system directly or indirectly. GEB is dynamically linked with the spatially distributed grid-based hydrological model CWatM at 30′′ resolution (< 1 km at the Equator). Because many smallholder farm fields are much smaller than 1 × 1 km, CWatM was specifically adapted to implement dynamically sized hydrological response units (HRUs) at the farm level, providing each agent with an independently operated hydrological environment. While the model could be applied anywhere globally at both large and small scales, we explore its implementation in the heavily managed Krishna basin in India, which encompasses ∼ 8 % of India's land area and ∼ 12.1 million farmers.

Flood inundation mapping of upper Krishna basin using hydrodynamic model

Floodplain management and mapping are new and applied methods in river engineering and for the prediction of flood hazards. Krishna river basin is the second largest river basin in Peninsular India. This basin is one of the flood prone basins in India. The purpose of this study is to focus on the analysis of HEC-RAS to assess and predict the flood depth and spatial extent of flood in the upper Krishna River basin which is drained by Krishna River. To determine extent of inundation, the hydrodynamic model HEC-RAS with ArcGIS was used. For this, discharge data of two months from August and September 2012 has been processed in the study. A methodology was applied to combine hydraulic simulation model, HEC-RAS and GIS analysis for delineation of flood extents and depths for upper Krishna basin in India. Results obtained by using HEC-RAS Model were used by integrating with Arc-GIS to floodplain maps. Through this floodplain maps, the areas that are vulnerable to hazards, have been identified. The results of this research will benefit in future of flood forecasts on a regional scale, also they will be beneficial for water resources management and planning.

Estimation of Moho Depth Beneath Southern Indian Shield by Inverting Gravity Anomalies Constrained by Seismic Data

AbstractThis study presents a high‐resolution 3D Moho structure beneath southern India and its surrounding regions from observed gravity anomalies. The global gravity disturbance model (XGM2019e) with a grid resolution of 0.1° is considered for this study. The extended Bott's inversion algorithm and Gauss‐Fast Fourier Transform based forward model are adopted to invert for the Moho undulations beneath the Indian peninsula. The inversion algorithm is tested for a synthetic model having a predefined density contrast and mean Moho depth. The robustness of this inversion algorithm is further tested for noise‐incorporated gravity data. The control points are required for estimating two hyper‐parameters, viz. density contrast, and reference depth, which play a crucial role in the precise estimation of Moho depth. In real case study, the inverted Moho depth of Southern India and its surrounding regions by seismic constraint receiver function‐driven control points show a very complex architecture of Moho topography. The observed average crustal thickness in the study region is 35.35 km, corroborating with the previously reported Moho depths. The maximum crustal thickness is 53.04 km beneath the southern part of Archean Western Dharwar Craton and west of Salem block, around 44–47 km Moho depth is observed at the south of Salem block into Madurai block till Achankovil Shear Zone, which suggests the possible continuation of the Achaean crust of Palghat‐Cauvery Shear Zone System. The lowest crustal thickness values are observed along the eastern margin of the Cuddapah basin, which overlaps with the Proterozoic Krishna basin of the Eastern Ghats Mobile belt.

Establishing Critical Leaf Nutrient Concentrations and Identification of Yield Limiting Nutrients for Precise Nutrient Prescriptions of Oil Palm (Elaeis guineensis Jacq) Plantations

African oil palm (Elaeis guineensis Jacq.) is a bulk feeder of nutrients. In this study, we aimed at devising strategies for efficient nutrient management in the oil palm plantations of the Krishna River basin located in Andhra Pradesh, India by assessing soil fertility status, establishing optimal leaf nutrient concentrations and identifying yield restrictive nutrients. In total, 67 oil palm plantations were surveyed from this area in 2020, soil samples were collected and analysed for different soil properties, including pH, EC, SOC, available P, K, S, exchangeable Ca and Mg, and hot water-soluble boron (HWB) in surface (from 0–20 cm depth), subsurface (from 20–40 cm depth) and deep (from 40–60 cm depth) soil layers. As per DRIS (Diagnosis and Recommendation Integrated System) indices estimated in this study, the order of requirement of nutrients is Nitrogen (N) > B > K > P > Mg for this area. Optimum leaf nutrient concentrations ranged between 2.07–4.29%, 0.13–0.27%, 0.52–0.94%, 0.44–0.76% and 44.97–102.70 mg/kg for N, P, K, Mg and B, respectively. In surveyed plantations, about 15, 6, 16, 9 and 12 percent of leaf samples had less than optimum concentration of N, P, K, Mg and B respectively. Nitrogen and Boron are the major yield limiting factors in this region. Leaf nutrient concentrations need to be maintained at the optimum ranges as estimated above for higher productivity in the Krishna basin area.

Flood Frequency Analysis of Lower Krishna Basin using Gumbel Method at Prakasam Barrage, Vijayawada, Andhra Pradesh

The Gumbel Distribution approach is used in this study for analyzing the flood frequency in the lower Krishna basin. It is a probability distribution method that was used to predict river annual peak discharges from 2007 through 2021 (14 years). The probability distribution function was used to calculate the return period for 2, 5, 10, 15 and 20 years and estimated discharges were produced. The flood for the next 10 years will be 1239523 Cusecs and the predicted flood discharge for the next 25 years will be 1586634 Cusecs. The expected flood from 2 to 25 years has also indicated a consistent growing tendency in selected Prakasam barrage station. This type of information would be useful in the planning of engineering projects such as reservoirs, bridges, flood mitigation structures etc. Flood hazard maps can also be created using these methodologies.

A GIS-Based Comparative Analysis of Frequency Ratio and Statistical Index Models for Flood Susceptibility Mapping in the Upper Krishna Basin, India

The Upper Krishna Basin in Maharashtra (India) is highly vulnerable to floods. This study aimed to generate a flood susceptibility map for the basin using Frequency Ratio and Statistical Index models of flood analysis. The flood hazard inventory map was created by 370 flood locations in the Upper Krishna Basin and plotted using ArcGIS 10.1 software. The 259 flood locations (70%) were selected randomly as training samples for analysis of the flood models, and for validation purposes, the remaining 111 flood locations (30%) were used. Flood susceptibility analyses were performed based on 12 flood conditioning factors. These were elevation, slope, aspect, curvature, Topographic Wetness Index, Stream Power Index, rainfall, distance from the river, stream density, soil types, land use, and distance from the road. The Statistical Index model revealed that 38% of the area of the Upper Krishna Basin is in the high- to very-high-flood-susceptibility class. The precision of the flood susceptibility map was confirmed using the receiver operating characteristic and the area under the curve value method. The area under the curve showed a 66.89% success rate and a 68% prediction rate for the Frequency Ratio model. However, the Statistical Index model provided an 82.85% success rate and 83.23% prediction rate. The comparative analysis of the Frequency Ratio and Statistical Index models revealed that the Statistical Index model was the most suitable for flood susceptibility analysis and mapping flood-prone areas in the Upper Krishna Basin. The results obtained from this research can be helpful in flood disaster mitigation and hazard preparedness in the Upper Krishna Basin.

Flood Risk Analysis of Upper Krishna Sub-Basin using GIS

Abstract: Floods are the most and repeatedly occurring destructive natural disaster due to an overflow of water submerges land which is basically dry. Generally, floods are occurring due to heavy rainfall or cloud bursting or manmade disturbance to nature, fast snowmelt, global warming or tropical cyclone or tsunami. In 2019, the Krishna River has faced very heavy rainfall and major floods which took the lives of approximately 500 people and nearly isolating 350 villages and leaving millions homeless. Here we studied the previous flood disaster at the basin of the Krishna River and processed the GIS environment using software tools. This facilitates exploring the data and methods that are mostly unexplored, and areas that have not lightened in the field of flood studies in Krishna basin. It is impossible to avoid floods and risk associated with flood, however it is possible to work on the flood reduction. Flood hazard mapping is to identify comparatively safe sites in high elevation with low risk is one of the powerful tools for this purpose. Flood hazard mapping flash flood will be beneficial for risk assessment. Management and emergency services during flood events. The objective of this paper is to generate flood hazard zonation maps of Upper Krishna sub-basin using GIS tools and satellite images. To do so, we use spatial data and SRTM DEMs with accuracy assessment is achieved by using check points, obtained by GPS observations. Runoff, surface slope, drainage density, distance to main channel and land use were considered causative factors. All used data are processed and integrated in an ArcMap and QGIS to prepare a final flood hazard map for Upper Krishna sub-basin. The areas in high risk flood zones are obtained by overlaying the flood hazard index map with the zone boundaries layer

Artificial Groundwater Recharge Zones Mapping using Remote Sensing and GIS for Sub-Watersheds of Krishna Basin

The exacerbated exploitation of groundwater resources has led to an alarming decrease in groundwater levels. It is necessary to identify areas that allow for efficient recharge so that implementation of artificial groundwater recharge can be conducted in such areas with better efficiency. This study maps zones with their varying degrees of affinity for groundwater recharge in two of the sub-watersheds of the Krishna Basin.The factors affecting the potential of groundwater recharge were determined and then thematic maps for each factor were generated in ArcGIS 10.8. Remote sensing data from sources like ‘Bhuvan’ and ‘Bhukosh’ were used for the generation of the thematic layers. The Analytic Hierarchy Process (AHP) was used while deciding the relative and subsequently overall importance of each layer. The weights derived from the AHP were applied to the thematic layers and the mapping of zones for artificial groundwater recharge was done.

Economic impact assessment of soybean rust (Phakopsora pachyrhizi) resistance breeding in India: An ex-ante analysis

Soybean rust (Phakopsora pachyrhizi Syd.) is one of the most prevalent soybean diseases particularly in Krishna basin area of Karnataka and Maharashtra, which can cause losses from 10-100%. The research conducted to estimate benefits from adoption of rust resistant soybean varieties in rust prone districts of Karnataka and Maharashtra using primary data collected from adopter and non-adopter farmers for the crop year 2017-18. Rust resistant varieties require less chemical pesticides and reduce yield loss. The study estimated potential economic benefits in terms of economic surplus for rust resistant varieties. In normal conditions, total benefits from the adoption of rust resistant soybean varieties using the economic surplus model was estimated at ` 198.8 crores per annum at a discount rate of 5% and ` 99.3 crores at 8% discount rate, respectively. Adoption of rust resistant varieties performed well over other varieties in reducing yield loss and cost of cultivation. The popularization of these varieties in rust prone areas to cope up with this biotic stress is needed.