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Dr. Venkataramana Sridhar
Virginia Polytechnic Institute and State University , Blacksburg, USA

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0 Climate Change
0 Hydrology
0 Water Resources
0 Remote Sensing and Gis
0 Floods & Droughts

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Journal article
Published: 30 April 2021 in Remote Sensing
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Accessible, low-cost technologies and tools are needed in the developing world to support community planning, disaster risk assessment, and land tenure. Enterprise-scale geographic information system (GIS) software and high-resolution aerial or satellite imagery are tools which are typically not available to or affordable for resource-limited communities. In this paper, we present a concept of aerial data collection, 3D cadastre modeling, and disaster risk assessment using low-cost drones and adapted open-source software. Computer vision/machine learning methods are used to create a classified 3D cadastre that contextualizes and quantifies potential natural disaster risk to existing or planned infrastructure. Building type and integrity are determined from aerial imagery. Potential flood damage risk to a building is evaluated as a function of three mechanisms: undermining (erosion) of the foundation, hydraulic pressure damage, and building collapse due to water load. Use of Soil and Water Assessment Tool (SWAT) provides water runoff estimates that are improved using classified land features (urban ecology, erosion marks) to improve flow direction estimates. A convolutional neural network (CNN) is trained to find these flood-induced erosion marks from high-resolution drone imagery. A flood damage potential metric scaled by property value estimates results in individual and community property risk assessments.

ACS Style

Daniel Whitehurst; Brianna Friedman; Kevin Kochersberger; Venkat Sridhar; James Weeks. Drone-Based Community Assessment, Planning, and Disaster Risk Management for Sustainable Development. Remote Sensing 2021, 13, 1739 .

AMA Style

Daniel Whitehurst, Brianna Friedman, Kevin Kochersberger, Venkat Sridhar, James Weeks. Drone-Based Community Assessment, Planning, and Disaster Risk Management for Sustainable Development. Remote Sensing. 2021; 13 (9):1739.

Chicago/Turabian Style

Daniel Whitehurst; Brianna Friedman; Kevin Kochersberger; Venkat Sridhar; James Weeks. 2021. "Drone-Based Community Assessment, Planning, and Disaster Risk Management for Sustainable Development." Remote Sensing 13, no. 9: 1739.

Research article
Published: 14 April 2021 in International Journal of Climatology
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India is one of the most drought‐ravaged countries in the world and faces at least one drought in one region or another in every 3 years. There is no single reliable approach in characterizing future droughts. To understand future drought risk, potential changes of drought properties and characteristics are analysed in this study. Using Fuzzy c‐means clustering approach, homogeneous drought regions are identified in the Godavari river basin and therefore, optimum number of clusters were assigned as four. The 12‐month standardized precipitation index (SPI) using precipitation data from India Meteorological Department (IMD) and Global Climate Model (GCM)—MIROC‐ESM‐CHEM is calculated for the homogeneous regions of the Godavari basin. The best fit copula for observed and simulated severity and duration are: Region 1—Clayton, Regions 2 and 3—Gumbel, Region 4—Frank copula. Severity‐duration‐frequency (SDF) and severity‐area‐frequency (SAF) curves were developed and analysed using the best fit copulas. The research findings conclude that moderate and severe droughts are frequently increasing for future periods (2006–2099) compared to the historic period (1962–2005). Droughts with high severity and high mean interarrival time are observed as expected in the future. For the Godavari basin, the SDF curves were concave upwards indicating an increase in severity with an increase in duration. The rate of increase of severity is small for shorter durations compared to that of longer‐duration drought. Thus, more prolonged drought events in the 21st century are likely to occur. The SAF curves with steeper slopes and high variability in topographical and hydrological characteristics have been observed over the Godavari basin. From these curves, for a specified percentage of area and return period, the drought severity can be calculated and the information can be used for crop management and agricultural water demands. Overall, the findings of this research offer a view of likely scenarios of drought in the Godavari basin.

ACS Style

Kuruva Satish Kumar; Pallakury AnandRaj; Koppala Sreelatha; Venkataramana Sridhar. Regional analysis of drought severity‐duration‐frequency and severity‐area‐frequency curves in the Godavari River Basin, India. International Journal of Climatology 2021, 1 .

AMA Style

Kuruva Satish Kumar, Pallakury AnandRaj, Koppala Sreelatha, Venkataramana Sridhar. Regional analysis of drought severity‐duration‐frequency and severity‐area‐frequency curves in the Godavari River Basin, India. International Journal of Climatology. 2021; ():1.

Chicago/Turabian Style

Kuruva Satish Kumar; Pallakury AnandRaj; Koppala Sreelatha; Venkataramana Sridhar. 2021. "Regional analysis of drought severity‐duration‐frequency and severity‐area‐frequency curves in the Godavari River Basin, India." International Journal of Climatology , no. : 1.

Journal article
Published: 03 April 2021 in Climate
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Traditional drought monitoring is based on observed data from both meteorological and hydrological stations. Due to the scarcity of station observation data, it is difficult to obtain accurate drought distribution characteristics, and also tedious to replicate the large-scale information of drought. Thus, Gravity Recovery and Climate Experiment (GRACE) data are utilized in monitoring and characterizing regional droughts where ground station data is limited. In this study, we analyzed and assessed the drought characteristics utilizing the GRACE Groundwater Drought Index (GGDI) over four major river basins in India during the period of 2003–2016. The spatial distribution, temporal evolution of drought, and trend characteristics were analyzed using GGDI. Then, the relationship between GGDI and climate factors were evaluated by the method of wavelet coherence. The results indicate the following points: GRACE’s quantitative results were consistent and robust for drought assessment; out of the four basins, severe drought was noticed in the Cauvery river basin between 2012 and 2015, with severity of −27 and duration of 42 months; other than Godavari river basin, the remaining three basins displayed significant negative trends at monthly and seasonal scales; the wavelet coherence method revealed that climate factors had a substantial effect on GGDI, and the impact of Southern Oscillation Index (SOI) on drought was significantly high, followed by Sea Surface Temperature (SST) Index (namely, NINO3.4) and Multivariate El Niño–Southern Oscillation Index (MEI) in all the basins. This study provides reliable and robust quantitative result of GRACE water storage variations that shares new insights for further drought investigation.

ACS Style

Kuruva Satish Kumar; Pallakury AnandRaj; Koppala Sreelatha; Deepak Bisht; Venkataramana Sridhar. Monthly and Seasonal Drought Characterization Using GRACE-Based Groundwater Drought Index and Its Link to Teleconnections across South Indian River Basins. Climate 2021, 9, 56 .

AMA Style

Kuruva Satish Kumar, Pallakury AnandRaj, Koppala Sreelatha, Deepak Bisht, Venkataramana Sridhar. Monthly and Seasonal Drought Characterization Using GRACE-Based Groundwater Drought Index and Its Link to Teleconnections across South Indian River Basins. Climate. 2021; 9 (4):56.

Chicago/Turabian Style

Kuruva Satish Kumar; Pallakury AnandRaj; Koppala Sreelatha; Deepak Bisht; Venkataramana Sridhar. 2021. "Monthly and Seasonal Drought Characterization Using GRACE-Based Groundwater Drought Index and Its Link to Teleconnections across South Indian River Basins." Climate 9, no. 4: 56.

Journal article
Published: 01 April 2021 in Science of The Total Environment
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Drought is a complex natural hazard that affects ecosystems and society in several ways and it is important to quantify drought at the river basin scale. Assessment of drought requires both hydrological observations and simulation models as the data are generally scarce. Therefore, we use remote sensing products to help understand drought conditions in four basins in South India. This study analysed the correlation among five drought indices for four seasons: gravity recovery and climate experiment - drought severity index (GRACE-DSI), standardized precipitation index (SPI), self-calibrated palmer drought severity index (sc_PDSI), standardized precipitation-evapotranspiration index (SPEI), and combined climatologic deviation index (CCDI) with GRACE terrestrial water storage anomalies (TWSA) using the Pearson correlation coefficient (r) from 2002 to 2016 over the Godavari, Krishna, Pennar, and Cauvery river basins. Basin scale drought events are evaluated using CCDI, GRACEDSI, sc_PDSI, SPI12, and SPEI12 at seasonal and monthly time scale. Characteristics of drought event analysis are calculated for CCDI monthly. The results showed that GRACE TWS is highly correlated with GRACE-DSI, CCDI, and sc_PDSI. Seasonally, high spatial correlations between CCDI and GRACE-DSI with GRACE TWS are evident for all the river basins. Additionally, correlation is found to exist between sc_PDSI and GRACE TWS as soil moisture content is an operating variable between them. The 12-month SPI and SPEI correlated better with GRACE TWS than the 3 and 6-month periods. Among the four basins, droughts in the Krishna Basin lasted 29 months, longer than in the rest of the basins between 2003 and 2005. Overall, CCDI and GRACE-DSI indices are found to be effective for examining and evaluating the drought conditions at the basin scale.

ACS Style

K. Satish Kumar; E. Venkata Rathnam; Venkataramana Sridhar. Tracking seasonal and monthly drought with GRACE-based terrestrial water storage assessments over major river basins in South India. Science of The Total Environment 2021, 763, 142994 .

AMA Style

K. Satish Kumar, E. Venkata Rathnam, Venkataramana Sridhar. Tracking seasonal and monthly drought with GRACE-based terrestrial water storage assessments over major river basins in South India. Science of The Total Environment. 2021; 763 ():142994.

Chicago/Turabian Style

K. Satish Kumar; E. Venkata Rathnam; Venkataramana Sridhar. 2021. "Tracking seasonal and monthly drought with GRACE-based terrestrial water storage assessments over major river basins in South India." Science of The Total Environment 763, no. : 142994.

Journal article
Published: 05 March 2021 in Hydrology
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Landslides are a common geologic hazard that disrupts the social and economic balance of the affected society. Therefore, identifying zones prone to landslides is necessary for safe living and the minimal disruption of economic activities in the event of the hazard. The factors causing landslides are often a function of the local geo-environmental set-up and need a region-specific study. This study evaluates the site characteristics primarily altered by anthropogenic activities to understand and identify the various factors causing landslides in Coonoor Taluk of Uthagamandalam District in Tamil Nadu, India. Studies on landslide susceptibility show that slope gradient, aspect, relative relief, topographic wetness index, soil type, and land use of the region influence slope instability. Rainfall characteristics have also played a significant role in causing landslides. Logistic Regression, a popular statistical tool used for predictive analysis, is employed to assess the various selected factors’ impact on landslide susceptibility. The factors are weighted and combined in a GIS platform to develop the region’s landslide susceptibility map. This region has a direct link between natural physical systems, hydrology, and humans from the socio-hydrological perspective. The landslide susceptibility map derived using the watershed’s physical and environmental conditions offers the best tool for planning the developmental activities and prioritizing areas for mitigation activities in the region. The Coonoor region’s tourism and agriculture sectors can significantly benefit from identifying zones prone to landslides for their economic stability and growth.

ACS Style

Evangelin Sujatha; Venkataramana Sridhar. Landslide Susceptibility Analysis: A Logistic Regression Model Case Study in Coonoor, India. Hydrology 2021, 8, 41 .

AMA Style

Evangelin Sujatha, Venkataramana Sridhar. Landslide Susceptibility Analysis: A Logistic Regression Model Case Study in Coonoor, India. Hydrology. 2021; 8 (1):41.

Chicago/Turabian Style

Evangelin Sujatha; Venkataramana Sridhar. 2021. "Landslide Susceptibility Analysis: A Logistic Regression Model Case Study in Coonoor, India." Hydrology 8, no. 1: 41.

Article
Published: 18 January 2021
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A multi-model hydrological assessment in the Congo Basin is performed to assess water availability conditions for historical and future periods (1913–2099). With models limited by scarce in situ observations, a combination of GRACE satellite data and soil-moisture-based drought indices is shown to be capable of estimating water budget, streamflow, and drought and storage variability. Changes in land use and land cover played a role in modifying the hydrologic responses but were found to be within the uncertainties of other inputs, including weather, soil, and model parameters. Seasonal and annual variability in total water storage anomalies (TWSAs) and the modified Palmer drought severity index (MPDSI) display a good correlation with each other. A selected set of global climate models is used to characterize the future temperature and precipitation patterns. It is expected that subbasin-scale variability in future temperature and precipitation increases will result in increased evapotranspiration, decreased runoff, and more drought events in the Congo Basin.

ACS Style

Venkataramana Sridhar; Hyunwoo Kang; Syed A Ali; Gode B Bola; Raphael M Tshimanga; Venkataraman Lakshmi. Water Budgets and Droughts under Current and Future Conditions in the Congo River Basin. 2021, 1 .

AMA Style

Venkataramana Sridhar, Hyunwoo Kang, Syed A Ali, Gode B Bola, Raphael M Tshimanga, Venkataraman Lakshmi. Water Budgets and Droughts under Current and Future Conditions in the Congo River Basin. . 2021; ():1.

Chicago/Turabian Style

Venkataramana Sridhar; Hyunwoo Kang; Syed A Ali; Gode B Bola; Raphael M Tshimanga; Venkataraman Lakshmi. 2021. "Water Budgets and Droughts under Current and Future Conditions in the Congo River Basin." , no. : 1.

Journal article
Published: 01 January 2021 in Journal of Hydrologic Engineering
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ACS Style

S. Setti; R. Maheswaran; D. Radha; V. Sridhar; K. K. Barik; M. L. Narasimham. Erratum for “Attribution of Hydrologic Changes in a Tropical River Basin to Rainfall Variability and Land-Use Change: Case Study from India” by S. Setti, R. Maheswaran, D. Radha, V. Sridhar, K. K. Barik, and M. L. Narasimham. Journal of Hydrologic Engineering 2021, 26, 08220004 .

AMA Style

S. Setti, R. Maheswaran, D. Radha, V. Sridhar, K. K. Barik, M. L. Narasimham. Erratum for “Attribution of Hydrologic Changes in a Tropical River Basin to Rainfall Variability and Land-Use Change: Case Study from India” by S. Setti, R. Maheswaran, D. Radha, V. Sridhar, K. K. Barik, and M. L. Narasimham. Journal of Hydrologic Engineering. 2021; 26 (1):08220004.

Chicago/Turabian Style

S. Setti; R. Maheswaran; D. Radha; V. Sridhar; K. K. Barik; M. L. Narasimham. 2021. "Erratum for “Attribution of Hydrologic Changes in a Tropical River Basin to Rainfall Variability and Land-Use Change: Case Study from India” by S. Setti, R. Maheswaran, D. Radha, V. Sridhar, K. K. Barik, and M. L. Narasimham." Journal of Hydrologic Engineering 26, no. 1: 08220004.

Journal article
Published: 30 November 2020 in Atmosphere
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Tropical Cyclones (TCs) are the most disastrous natural weather phenomenon, that have a significant impact on the socioeconomic development of the country. In the past two decades, Numerical Weather Prediction (NWP) models (e.g., Advanced Research WRF (ARW)) have been used for the prediction of TCs. Extensive studies were carried out on the prediction of TCs using the ARW model. However, these studies are limited to a single cyclone with varying physics schemes, or single physics schemes to more than one cyclone. Hence, there is a need to compare different physics schemes on multiple TCs to understand their effectiveness. In the present study, a total of 56 sensitivity experiments are conducted to investigate the impact of seven microphysical parameterization schemes on eight post-monsoon TCs formed over the North Indian Ocean (NIO) using the ARW model. The performance of the Ferrier, Lin, Morrison, Thompson, WSM3, WSM5, and WSM6 are evaluated using error metrics, namely Mean Absolute Error (MAE), Mean Square Error (MSE), Skill Score (SS), and average track error. The results are compared with Indian Meteorological Department (IMD) observations. From the sensitivity experiments, it is observed that the WSM3 scheme simulated the cyclones Nilofar, Kyant, Daye, and Phethai well, whereas the cyclones Hudhud, Titli, and Ockhi are best simulated by WSM6. The present study suggests that the WSM3 scheme can be used as the first best scheme for the prediction of post-monsoon tropical cyclones over the NIO.

ACS Style

G. Venkata Rao; K. Venkata Reddy; Venkataramana Sridhar. Sensitivity of Microphysical Schemes on the Simulation of Post-Monsoon Tropical Cyclones over the North Indian Ocean. Atmosphere 2020, 11, 1297 .

AMA Style

G. Venkata Rao, K. Venkata Reddy, Venkataramana Sridhar. Sensitivity of Microphysical Schemes on the Simulation of Post-Monsoon Tropical Cyclones over the North Indian Ocean. Atmosphere. 2020; 11 (12):1297.

Chicago/Turabian Style

G. Venkata Rao; K. Venkata Reddy; Venkataramana Sridhar. 2020. "Sensitivity of Microphysical Schemes on the Simulation of Post-Monsoon Tropical Cyclones over the North Indian Ocean." Atmosphere 11, no. 12: 1297.

Journal article
Published: 20 November 2020 in Atmosphere
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Precipitation is essential for modeling the hydrologic behavior of watersheds. There exist multiple precipitation products of different sources and precision. We evaluate the influence of different precipitation product on model parameters and streamflow predictive uncertainty using a soil water assessment tool (SWAT) model for a forest dominated catchment in India. We used IMD (gridded rainfall dataset), TRMM (satellite product), bias-corrected TRMM (corrected satellite product) and NCEP-CFSR (reanalysis dataset) over a period from 1998–2012 for simulating streamflow. The precipitation analysis using statistical measures revealed that the TRMM and CFSR data slightly overestimate rainfall compared to the ground-based IMD data. However, the TRMM estimates improved, applying a bias correction. The Nash–Sutcliffe (and R2) values for TRMM, TRMMbias and CFSR, are 0.58 (0.62), 0.62 (0.63) and 0.52 (0.54), respectively at model calibrated with IMD data (Scenario A). The models of each precipitation product (Scenario B) yielded Nash–Sutcliffe (and R2) values 0.71 (0.76), 0.74 (0.78) and 0.76 (0.77) for TRMM, TRMMbias and CFSR datasets, respectively. Thus, the hydrological model-based evaluation revealed that the model calibration with individual rainfall data as input showed increased accuracy in the streamflow simulation. IMD and TRMM forced models to perform better in capturing the streamflow simulations than the CFSR reanalysis-driven model. Overall, our results showed that TRMM data after proper correction could be a good alternative for ground observations for driving hydrological models.

ACS Style

Sridhara Setti; Rathinasamy Maheswaran; Venkataramana Sridhar; Kamal Barik; Bruno Merz; Ankit Agarwal. Inter-Comparison of Gauge-Based Gridded Data, Reanalysis and Satellite Precipitation Product with an Emphasis on Hydrological Modeling. Atmosphere 2020, 11, 1252 .

AMA Style

Sridhara Setti, Rathinasamy Maheswaran, Venkataramana Sridhar, Kamal Barik, Bruno Merz, Ankit Agarwal. Inter-Comparison of Gauge-Based Gridded Data, Reanalysis and Satellite Precipitation Product with an Emphasis on Hydrological Modeling. Atmosphere. 2020; 11 (11):1252.

Chicago/Turabian Style

Sridhara Setti; Rathinasamy Maheswaran; Venkataramana Sridhar; Kamal Barik; Bruno Merz; Ankit Agarwal. 2020. "Inter-Comparison of Gauge-Based Gridded Data, Reanalysis and Satellite Precipitation Product with an Emphasis on Hydrological Modeling." Atmosphere 11, no. 11: 1252.

Research article
Published: 01 October 2020 in International Journal of Climatology
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A seasonal drought forecasting approach using a high‐resolution meteorological forcing and hydrologic model was developed in the Mekong River Basin (MRB), where ground‐based observations are sparse. The Soil and Water Assessment Tool (SWAT) model was used to simulate soil moisture, runoff, and evapotranspiration, which were then used to compute three drought indices for historical drought assessment (1953–2016) and seasonal forecasting of drought. In the absence of observed soil moisture data, SWAT was first calibrated with streamflow data to derive reliable soil moisture estimates, and historical drought events were validated with the available reference drought index. Based on the calibrated results, the Modified Palmer Drought Severity Index (MPDSI), Standardized Soil Moisture Index (SSI) and Multivariate Standardized Drought Index (MSDI) were estimated to evaluate the meteorological, agricultural and multivariate aspects, respectively, of drought. The total drought durations were 105 to 220 months, according to the reference drought index, and the estimated drought index captured 60% to 76% of these drought events. The three drought indices perform somewhat differently, but the 1991–1994 and 2015–2016 droughts were the worst drought events in the last 64 years based on analysis of the severity, duration and area of the meteorological and multivariate aspects of drought. The extreme droughts occurred in the Upper and Mid sections of the Lower Mekong sub‐basins and the 3S region when there were consecutive precipitation and temperature anomalies that continued for more than two years. Spatial variability in precipitation deficits and temperature increases were random because these variables affect soil moisture differentially. In addition, 68.4% to 76.1% of the areas with increased drought were explained by the areas’ precipitation decrease or temperature increase. Validation of the multiple drought indices at high resolution can inform sub‐regional variability in drought conditions and hence food and water security in this important transboundary basin.

ACS Style

Hyunwoo Kang; Venkataramana Sridhar. A near‐term drought assessment using hydrological and climate forecasting in the Mekong River Basin. International Journal of Climatology 2020, 41, 1 .

AMA Style

Hyunwoo Kang, Venkataramana Sridhar. A near‐term drought assessment using hydrological and climate forecasting in the Mekong River Basin. International Journal of Climatology. 2020; 41 (S1):1.

Chicago/Turabian Style

Hyunwoo Kang; Venkataramana Sridhar. 2020. "A near‐term drought assessment using hydrological and climate forecasting in the Mekong River Basin." International Journal of Climatology 41, no. S1: 1.

Journal article
Published: 29 May 2020 in Weather and Climate Extremes
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Understanding the spatio-temporal distribution of rainfall characteristics has a major role in assessing the availability of water resources over a catchment. Therefore, it is necessary to understand the changes in rainfall characteristics using gridded precipitation data and robust statistical analysis for making decisions. In this study, the trends in rainfall and rainfall extremes over the Nagavali and Vamsadhara river basins are studied at three time steps (long-term-1901-2018, pre-1950, and post-1950) with four different Mann-Kendall (MK) tests using daily gridded rainfall data of 118 years (1901–2018). The spatial patterns of the trends are evaluated with the kriging interpolation method. Magnitude in rainfall and rainfall extremes (CDD, CWD, PRCPTOT, R10MM, R20MM, R40MM, R95PTOT, RX1DAY, and RX5DAY) are analyzed using the Sen's slope method. Except in the monsoon season, a decreasing trend is observed in all the rainfall extremes in post-1950 compared to pre-1950 period. Whereas, in the monsoon an increasing trend is observed for the extremes in post-1950 period. Overall period (i.e, 1901–2018) an increasing trend is observed for rainfall and rainfall extremes in the pre-monsoon (March–May), monsoon (June–Sep) seasons and a decreasing trend in the winter season (Dec–Feb) for both the basins. No obvious trends are evident in the post-monsoon season (Oct–Nov). At the annual scale, rainfall and rainfall extremes exhibited an increasing trend. Overall, the Nagavali basin experienced more extreme rainfall events indicating the higher vulnerability of floods while the middle and lower portions of the Vamsadhara basin shown increase in extremes. When linked with hydrological analysis, insights gained from this study are useful for flood vulnerability mapping and risk assessment for both the basins.

ACS Style

G. Venkata Rao; K. Venkata Reddy; Raghavan Srinivasan; Venkataramana Sridhar; N.V. Umamahesh; Deva Pratap. Spatio-temporal analysis of rainfall extremes in the flood-prone Nagavali and Vamsadhara Basins in eastern India. Weather and Climate Extremes 2020, 29, 100265 .

AMA Style

G. Venkata Rao, K. Venkata Reddy, Raghavan Srinivasan, Venkataramana Sridhar, N.V. Umamahesh, Deva Pratap. Spatio-temporal analysis of rainfall extremes in the flood-prone Nagavali and Vamsadhara Basins in eastern India. Weather and Climate Extremes. 2020; 29 ():100265.

Chicago/Turabian Style

G. Venkata Rao; K. Venkata Reddy; Raghavan Srinivasan; Venkataramana Sridhar; N.V. Umamahesh; Deva Pratap. 2020. "Spatio-temporal analysis of rainfall extremes in the flood-prone Nagavali and Vamsadhara Basins in eastern India." Weather and Climate Extremes 29, no. : 100265.

Journal article
Published: 11 January 2020 in Water
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The objective of this study is to evaluate the homogeneity, trend, and trend change points in the rainfall data. Daily rainfall data was collected for the arid district of Ananthapuramu, Andhra Pradesh state, India from 1981 to 2016 at the subdistrict level and aggregated to monthly, annual, seasonal rainfall totals, and the number of rainy days. After quality checks and homogeneity analysis, a total of 27 rain gauge locations were considered for trend analysis. A serial correlation test was applied to all the time series to identify serially independent series. NonParametric Mann–Kendall test and Spearman’s rank correlation tests were applied to serially independent series. The magnitude of the trend was calculated using Sen’s slope method. For the data influenced by serial correlation, various modified versions of Mann–Kendall tests (pre-whitening, trend-free pre-whitening, bias-corrected pre-whitening, and two variants of variance correction approaches) were applied. A significant increasing summer rainfall trend is observed in six out of 27 stations. Significant decreasing trends are observed at two stations during the southwest monsoon season and at two stations during the northeast monsoon season. To identify the trend change points in the time series, distribution−free cumulative sum test, and sequential Mann–Kendall tests were applied. Two open−source library packages were developed in R language namely, ”modifiedmk” and ”trendchange” to implement the statistical tests mentioned in this paper. The study results benefit water resource management, drought mitigation, socio−economic development, and sustainable agricultural planning in the region.

ACS Style

Sandeep Kumar Patakamuri; Krishnaveni Muthiah; Venkataramana Sridhar. Long-Term Homogeneity, Trend, and Change-Point Analysis of Rainfall in the Arid District of Ananthapuramu, Andhra Pradesh State, India. Water 2020, 12, 211 .

AMA Style

Sandeep Kumar Patakamuri, Krishnaveni Muthiah, Venkataramana Sridhar. Long-Term Homogeneity, Trend, and Change-Point Analysis of Rainfall in the Arid District of Ananthapuramu, Andhra Pradesh State, India. Water. 2020; 12 (1):211.

Chicago/Turabian Style

Sandeep Kumar Patakamuri; Krishnaveni Muthiah; Venkataramana Sridhar. 2020. "Long-Term Homogeneity, Trend, and Change-Point Analysis of Rainfall in the Arid District of Ananthapuramu, Andhra Pradesh State, India." Water 12, no. 1: 211.

Erratum
Published: 01 January 2020 in Ecohydrology & Hydrobiology
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ACS Style

Venkataramana Sridhar; Mirza M. Billah; Prasanth Valayamkunnath. Corrigendum to ‘Field-scale intercomparison analysis of ecosystems in partitioning surface energy balance components in a semi-arid environment’. Ecohydrology & Hydrobiology 2020, 20, 151 .

AMA Style

Venkataramana Sridhar, Mirza M. Billah, Prasanth Valayamkunnath. Corrigendum to ‘Field-scale intercomparison analysis of ecosystems in partitioning surface energy balance components in a semi-arid environment’. Ecohydrology & Hydrobiology. 2020; 20 (1):151.

Chicago/Turabian Style

Venkataramana Sridhar; Mirza M. Billah; Prasanth Valayamkunnath. 2020. "Corrigendum to ‘Field-scale intercomparison analysis of ecosystems in partitioning surface energy balance components in a semi-arid environment’." Ecohydrology & Hydrobiology 20, no. 1: 151.

Preprint
Published: 17 December 2019
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Daily rainfall data was collected for the arid district of Ananthapuramu, Andhra Pradesh state, India from 1981 to 2016 at the sub-district level and aggregated to monthly, annual and seasonal rainfall totals and the number of rainy days. The objective of this study is to evaluate the homogeneity, trend, and trend change points in the rainfall data. After quality checks and homogeneity analysis, a total of 27 rain gauge locations were considered for trend analysis. A serial correlation test was applied to all the time series to identify serially independent series. Non-Parametric Mann-Kendall test and Spearman’s rank correlation tests were applied to serially independent series. The magnitude of the trend was calculated using Sen’s slope method. For the data influenced by serial correlation, various modified versions of Mann-Kendall tests (Pre-Whitening, Trend Free Pre-Whitening, Bias Corrected Pre-Whitening and two variants of Variance Correction Approaches) were applied. A significant increasing summer rainfall trend is observed in 6 out of 27 stations. Significant decreasing trends are observed at two stations in the south-west monsoon season and at two stations in the north-east monsoon season. To identify the trend change-points in the time series, distribution-free Cumulative SUM test and sequential Mann-Kendall tests were applied. Two open-source library packages were developed in R language namely, ‘modifiedmk’ and ‘trendchange’ to implement the statistical tests mentioned in this paper. The study will benefit water resource management, drought mitigation, socio-economic development and sustainable agricultural planning in the region.

ACS Style

Sandeep Kumar Patakamuri; Krishnaveni Muthiah; Venkataramana Sridhar. Long Term Homogeneity, Trend and Change-Point Analysis of Rainfall in the Arid District of Ananthapuramu, Andhra Pradesh State, India. 2019, 1 .

AMA Style

Sandeep Kumar Patakamuri, Krishnaveni Muthiah, Venkataramana Sridhar. Long Term Homogeneity, Trend and Change-Point Analysis of Rainfall in the Arid District of Ananthapuramu, Andhra Pradesh State, India. . 2019; ():1.

Chicago/Turabian Style

Sandeep Kumar Patakamuri; Krishnaveni Muthiah; Venkataramana Sridhar. 2019. "Long Term Homogeneity, Trend and Change-Point Analysis of Rainfall in the Arid District of Ananthapuramu, Andhra Pradesh State, India." , no. : 1.

Journal article
Published: 03 December 2019 in Remote Sensing
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The Mekong River basin supported a large population and ecosystem with abundant water and nutrient supply. However, the impoundments in the river can substantially alter the flow downstream and its timing. Using limited observations, this study demonstrated an approach to derive dam characteristics, including storage and flow rate, from remote-sensing-based data. Global Reservoir and Lake Monitor (GRLM), River-Lake Hydrology (RLH), and ICESat-GLAS, which generated altimetry from Jason series and inundation areas from Landsat 8, were used to estimate the reservoir surface area and change in storage over time. The inflow simulated by the variable infiltration capacity (VIC) model from 2008 to 2016 and the reservoir storage change were used in the mass balance equation to calculate outflows for three dams in the basin. Estimated reservoir total storage closely resembled the observed data, with a Nash-Sutcliffe efficiency and coefficient of determination more than 0.90 and 0.95, respectively. An average decrease of 55% in outflows was estimated during the wet season and an increase of up to 94% in the dry season for the Lam Pao. The estimated decrease in outflows during the wet season was 70% and 60% for Sirindhorn and Ubol Ratana, respectively, along with a 36% increase in the dry season for Sirindhorn. Basin-wide demand for evapotranspiration, about 935 mm, implicitly matched with the annual water diversion from 1000 to 2300 million m3. From the storage–discharge rating curves, minimum storage was also evident in the monsoon season (June–July), and it reached the highest in November. This study demonstrated the utility of remote sensing products to assess the impacts of dams on flows in the Mekong River basin.

ACS Style

Syed A. Ali; Venkataramana Sridhar. Deriving the Reservoir Conditions for Better Water Resource Management Using Satellite-Based Earth Observations in the Lower Mekong River Basin. Remote Sensing 2019, 11, 2872 .

AMA Style

Syed A. Ali, Venkataramana Sridhar. Deriving the Reservoir Conditions for Better Water Resource Management Using Satellite-Based Earth Observations in the Lower Mekong River Basin. Remote Sensing. 2019; 11 (23):2872.

Chicago/Turabian Style

Syed A. Ali; Venkataramana Sridhar. 2019. "Deriving the Reservoir Conditions for Better Water Resource Management Using Satellite-Based Earth Observations in the Lower Mekong River Basin." Remote Sensing 11, no. 23: 2872.

Erratum
Published: 04 July 2019 in Agricultural and Forest Meteorology
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ACS Style

Prasanth Valayamkunnath; Venkataramana Sridhar; Wenguang Zhao; Richard G. Allen. Corrigendum to “Intercomparison of surface energy fluxes, soil moisture, and evapotranspiration from eddy covariance, large-aperture scintillometer, and modeling across three ecosystems in a semiarid climate” [Agric. For. Meteorol. 248 (2018) 22–47]. Agricultural and Forest Meteorology 2019, 278, 107646 .

AMA Style

Prasanth Valayamkunnath, Venkataramana Sridhar, Wenguang Zhao, Richard G. Allen. Corrigendum to “Intercomparison of surface energy fluxes, soil moisture, and evapotranspiration from eddy covariance, large-aperture scintillometer, and modeling across three ecosystems in a semiarid climate” [Agric. For. Meteorol. 248 (2018) 22–47]. Agricultural and Forest Meteorology. 2019; 278 ():107646.

Chicago/Turabian Style

Prasanth Valayamkunnath; Venkataramana Sridhar; Wenguang Zhao; Richard G. Allen. 2019. "Corrigendum to “Intercomparison of surface energy fluxes, soil moisture, and evapotranspiration from eddy covariance, large-aperture scintillometer, and modeling across three ecosystems in a semiarid climate” [Agric. For. Meteorol. 248 (2018) 22–47]." Agricultural and Forest Meteorology 278, no. : 107646.

Journal article
Published: 03 July 2019 in Environmental Modelling & Software
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An integrated framework of surface and groundwater models is essential for the comprehensive understanding of impacts of droughts due to surface water and subsurface exchanges. We evaluate the response of soil moisture and groundwater dynamics during drought in the Northern Atlantic Coastal Plain in the Chesapeake Bay Watershed. The Variable Infiltration Capacity model coupled with MODFLOW (VICMF) was implemented and several variables were used in the Multivariate Standardized Drought Index (MSDI) that consider multivariate perspectives of droughts. Three drought indices were derived (MSDI_PSV, MSDI_PSM, MSDI_PWM) (PSV: Precipitation and Soil moisture from VIC; PSM: Precipitation and Soil moisture from VICMF, PWM: Precipitation and WTE from VICMF), and the accuracy of the results was verified using a performance measure (Drought area Agreement (%); DA) and a statistical test to evaluate spatial extent of the drought areas. The MSDI_PWM showed better results for predicting drought events as it captured overall drought conditions.

ACS Style

Hyunwoo Kang; Venkataramana Sridhar. Drought assessment with a surface-groundwater coupled model in the Chesapeake Bay watershed. Environmental Modelling & Software 2019, 119, 379 -389.

AMA Style

Hyunwoo Kang, Venkataramana Sridhar. Drought assessment with a surface-groundwater coupled model in the Chesapeake Bay watershed. Environmental Modelling & Software. 2019; 119 ():379-389.

Chicago/Turabian Style

Hyunwoo Kang; Venkataramana Sridhar. 2019. "Drought assessment with a surface-groundwater coupled model in the Chesapeake Bay watershed." Environmental Modelling & Software 119, no. : 379-389.

Journal article
Published: 25 June 2019 in Water
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The Mekong River Basin (MRB) is one of the significant river basins in the world. For political and economic reasons, it has remained mostly in its natural condition. However, with population increases and rapid industrial growth in the Mekong region, the river has recently become a hotbed of hydropower development projects. This study evaluated these changing hydrological conditions, primarily driven by climate as well as land use and land cover change between 1992 and 2015 and into the future. A 3% increase in croplands and a 1–2% decrease in grasslands, shrublands, and forests was evident in the basin. Similarly, an increase in temperature of 1–6 °C and in precipitation of 15% was projected for 2015–2099. These natural and climate-induced changes were incorporated into two hydrological models to evaluate impacts on water budget components, particularly streamflow. Wet season flows increased by up to 10%; no significant change in dry season flows under natural conditions was evident. Anomaly in streamflows due to climate change was present in the Chiang Saen and Luang Prabang, and the remaining flow stations showed up to a 5% increase. A coefficient of variation

ACS Style

Venkataramana Sridhar; Hyunwoo Kang; Syed A. Ali. Human-Induced Alterations to Land Use and Climate and Their Responses for Hydrology and Water Management in the Mekong River Basin. Water 2019, 11, 1307 .

AMA Style

Venkataramana Sridhar, Hyunwoo Kang, Syed A. Ali. Human-Induced Alterations to Land Use and Climate and Their Responses for Hydrology and Water Management in the Mekong River Basin. Water. 2019; 11 (6):1307.

Chicago/Turabian Style

Venkataramana Sridhar; Hyunwoo Kang; Syed A. Ali. 2019. "Human-Induced Alterations to Land Use and Climate and Their Responses for Hydrology and Water Management in the Mekong River Basin." Water 11, no. 6: 1307.

Erratum
Published: 11 June 2019 in Science of The Total Environment
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ACS Style

Prasanth Valayamkunnath; Venkataramana Sridhar; Wenguang Zhao; Richard G. Allen. Corrigendum to "A comprehensive analysis of interseasonal and interannual energy and water balance dynamics in semiarid shrubland and forest ecosystems" [Sci. Total Environ. 651 (2019) 381-398]. Science of The Total Environment 2019, 686, 847 .

AMA Style

Prasanth Valayamkunnath, Venkataramana Sridhar, Wenguang Zhao, Richard G. Allen. Corrigendum to "A comprehensive analysis of interseasonal and interannual energy and water balance dynamics in semiarid shrubland and forest ecosystems" [Sci. Total Environ. 651 (2019) 381-398]. Science of The Total Environment. 2019; 686 ():847.

Chicago/Turabian Style

Prasanth Valayamkunnath; Venkataramana Sridhar; Wenguang Zhao; Richard G. Allen. 2019. "Corrigendum to "A comprehensive analysis of interseasonal and interannual energy and water balance dynamics in semiarid shrubland and forest ecosystems" [Sci. Total Environ. 651 (2019) 381-398]." Science of The Total Environment 686, no. : 847.

Journal article
Published: 28 May 2019 in Journal of Hydrology: Regional Studies
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The Chesapeake Bay is the largest estuary in the United States, and its catchment has heterogeneous hydrological and geomorphologic characteristics. It includes seven major river basins: James, Patuxent, Potomac, Rappahannock, Susquehanna, Western Shore, Eastern Shore, and York. Remote sensing data, along with in-situ observations of streamflow and simulated water budget components, can provide significant understanding of variability in water resources availability in this diverse watershed. In this study, we quantify the terrestrial water storage using both remote sensing and in-situ data and hydrologic model outputs in the Chesapeake Bay watershed. Total water storage change (TWSC) was calculated based on the combination of three methods to identify the best approach in estimating TWSC. These methods evaluated different sources of data, including Parameter elevation Regression on Independent Slopes Model (PRISM) precipitation, MODIS ET, U.S. Geological Survey observed streamflow, and the Variable Infiltration Capacity (VIC) model. Estimated TWSC were in close agreement with GRACE-derived TWSC when we employed VIC-simulated streamflow after calibration with observed streamflow. However, the use of VIC-simulated ET or MODIS-derived ET yielded similar results for TWSC. Assessment of TWSC during extreme events (drought) during the summer months revealed that predicting ET is critical for TWSC in June–August and that VIC-simulated TWSC could be a reliable proxy for GRACE data to assess the water availability in the watershed.

ACS Style

Venkataramana Sridhar; Syed Azhar Ali; Venkataraman Lakshmi. Assessment and validation of total water storage in the Chesapeake Bay watershed using GRACE. Journal of Hydrology: Regional Studies 2019, 24, 100607 .

AMA Style

Venkataramana Sridhar, Syed Azhar Ali, Venkataraman Lakshmi. Assessment and validation of total water storage in the Chesapeake Bay watershed using GRACE. Journal of Hydrology: Regional Studies. 2019; 24 ():100607.

Chicago/Turabian Style

Venkataramana Sridhar; Syed Azhar Ali; Venkataraman Lakshmi. 2019. "Assessment and validation of total water storage in the Chesapeake Bay watershed using GRACE." Journal of Hydrology: Regional Studies 24, no. : 100607.