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Browsing by Author "Sridhar, Venkataramana"

Now showing 1 - 20 of 61
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    Access to Water: Advancement of Multidimensional, Multiscalar, and Participatory Methods of Measurement in the Global South
    Prince, Breeanna Carroll (Virginia Tech, 2018-06-29)
    This project deploys a modified Water Poverty Index (WPI) in villages reconstructed after the 2004 tsunami in southeastern India. While previous measurements of access to water have advanced understandings of waterscape complexities, this modified WPI improves past efforts and deconstructs some of the previous misunderstandings and notions regarding access to water. The traditional WPI is multidimensional and seeks to measure water access in a holistic fashion; the WPI presented here employs this approach, but is adapted to include new place-based indicators (e.g., Secondary Sources). Furthermore, unlike previous iterations of the WPI, our modified index incorporates water quality testing, three weight schemes, and operates at several scales. Ultimately, the construction and arrangement of our modified WPI enables statistical analyses, geospatial analyses, and water poverty mapping -- which are absent in most prior studies-- while still remaining easy to populate and descriptively analyze among non-academicians. Statistical tests of original household level data from a total of 24 villages in Nagapattinam District, Tamil Nadu, and Karaikal District, Puducherry, indicate significant differences between the two districts in indicator scores as well as total WPI score. Additionally, the urban and rural areas within each district were found to be significantly different in level of water poverty, and trends were similar across the three weight schemes. Multiple linear regressions show correlation of independent socioeconomic variables (i.e., Income, Education, and Assets-Networks) with the dependent indicator of Capacity, but not with the other indicators or total WPI score. Global Moran's I tests indicate positive spatial autocorrelation, demonstrating that indicator and WPI scores tend to cluster in space. Overall, the results match what was anticipated, yet serve to challenge commonly held assumptions on urban-rural hierarchies and the role of socioeconomic variables in determining water poverty. The construction, deployment, and analytical potential of this modified WPI can be used by scholars to improve existing conceptualizations and measurements of access to water, while the results can be used by local governments and nonprofits to improve resource allocation and inform spatially-targeted interventions.
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    Advancing the Global Land Grant Institution: Creating a Virtual Environment to Re-envision Extension and Advance GSS-related Research, Education, and Collaboration
    Hall, Ralph P.; Polys, Nicholas F.; Sforza, Peter M.; Eubank, Stephen D.; Lewis, Bryan L.; Krometis, Leigh-Anne H.; Pollyea, Ryan M.; Schoenholtz, Stephen H.; Sridhar, Venkataramana; Crowder, Van; Lipsey, John; Christie, Maria Elisa; Glasson, George E.; Scherer, Hannah H.; Davis, A. Jack; Dunay, Robert J.; King, Nathan T.; Muelenaer, Andre A.; Muelenaer, Penelope; Rist, Cassidy; Wenzel, Sophie (Virginia Tech, 2017-05-15)
    The vision for this project has emerged from several years of research, teaching, and service in Africa and holds the potential to internationalize education at Virginia Tech and in our partner institutions in Malawi. The vision is simple, to develop a state-of-the-art, data rich, virtual decision-support and learning environment that enables local-, regional-, and national-level actors in developed and developing regions to make decisions that improve resilience and sustainability. Achieving these objectives will require a system that can combine biogeophysical and sociocultural data in a way that enables actors to understand and leverage these data to enhance decision-making at various levels. The project will begin by focusing on water, agricultural, and health systems in Malawi, and can be expanded over time to include any sector or system in any country. The core ideas are inherently scalable...
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    Analysis of Land Use and Land Cover Using Machine Learning Algorithms on Google Earth Engine for Munneru River Basin, India
    Loukika, Kotapati Narayana; Keesara, Venkata Reddy; Sridhar, Venkataramana (MDPI, 2021-12-13)
    The growing human population accelerates alterations in land use and land cover (LULC) over time, putting tremendous strain on natural resources. Monitoring and assessing LULC change over large areas is critical in a variety of fields, including natural resource management and climate change research. LULC change has emerged as a critical concern for policymakers and environmentalists. As the need for the reliable estimation of LULC maps from remote sensing data grows, it is critical to comprehend how different machine learning classifiers perform. The primary goal of the present study was to classify LULC on the Google Earth Engine platform using three different machine learning algorithms—namely, support vector machine (SVM), random forest (RF), and classification and regression trees (CART)—and to compare their performance using accuracy assessments. The LULC of the study area was classified via supervised classification. For improved classification accuracy, NDVI (normalized difference vegetation index) and NDWI (normalized difference water index) indices were also derived and included. For the years 2016, 2018, and 2020, multitemporal Sentinel-2 and Landsat-8 data with spatial resolutions of 10 m and 30 m were used for the LULC classification. ‘Water bodies’, ‘forest’, ‘barren land’, ‘vegetation’, and ‘built-up’ were the major land use classes. The average overall accuracy of SVM, RF, and CART classifiers for Landsat-8 images was 90.88%, 94.85%, and 82.88%, respectively, and 93.8%, 95.8%, and 86.4% for Sentinel-2 images. These results indicate that RF classifiers outperform both SVM and CART classifiers in terms of accuracy.
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    Analysis of the causes of extreme precipitation in major cities of Peninsular India using remotely sensed data
    Kotrike, Tharani; Keesara, Venkata Reddy; Sridhar, Venkataramana (Elsevier, 2024-01)
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    Assessing the impacts of a water, sanitation, and hygiene (WASH) intervention on changing behavior in Bihar, India
    Wilcox, Emily Rose (Virginia Tech, 2023-06-07)
    Access to safe water, sanitation, and hygiene (WASH) is a fundamental human right and a critical component of public and environmental health. Inadequate access to WASH facilities and practices can give rise to preventable diarrheal and waterborne diseases, which can have severe consequences on individuals' health and well-being. This is especially true in low- and middle-income countries such as India. To address these issues, the S.M. Sehgal Foundation identified water quality and hygiene needs in Bihar, India, and thus launched a behavior change intervention called "WASH for Healthy Homes." The intervention aimed to promote the use of silver-ceramic pot filters and safe handwashing practices in five communities of the Vaishali District. While behavior change is a common approach to address WASH issues, evaluating the outcomes of such interventions is crucial for determining the most effective strategies and conditions under which they can be successful. Therefore, this study assessed the effectiveness of the WASH for Healthy Homes intervention and identified factors that influenced its success. A mixed methods approach was utilized that combined statistical analyses of pre- and post-intervention survey data with a thematic analysis of interview and focus group discussion data. Results demonstrated that the intervention was successful in increasing the adoption of the silver-ceramic pot filter and overall safe handwashing practices within the study communities. Success of the WASH for Health Homes intervention was facilitated by participants' health concerns, trust in the field coordinator and social peers, the aesthetic appeal of the treated water, and repeated intervention messaging. However, adoption of intervention behaviors was hindered by several factors, including economic barriers, gender roles in decision-making, the effects of children and elderly in the household, and low attendance during intervention sessions. The research findings provide valuable insights that can help nonprofits better design and execute behavior change interventions, especially in the face of increasing WASH challenges.
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    Assessment and validation of total water storage in the Chesapeake Bay watershed using GRACE
    Sridhar, Venkataramana; Ali, Syed Azhar; Lakshmi, Venkataraman (Elsevier, 2019-05-22)
    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.
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    Assessment of rice yield gap under a changing climate in India
    Debnath, Subhankar; Mishra, Ashok; Mailapalli, D. R.; Raghuwanshi, N. S.; Sridhar, Venkataramana (2021-06)
    Climate change evokes future food security concerns and needs for sustainable intensification of agriculture. The explicit knowledge about crop yield gap at country level may help in identifying management strategies for sustainable agricultural production to meet future food demand. In this study, we assessed the rice yield gap under projected climate change scenario in India at 0.25 degrees x 0.25 degrees spatial resolution by using the Decision Support System for Agrotechnology Transfer (DSSAT) model. The simulated spatial yield results show that mean actual yield under rainfed conditions (Y-a) will reduce from 2.13 t/ha in historical period 1981-2005 to 1.67 t/ha during the 2030s (2016-2040) and 2040s (2026-2050), respectively, under the RCP 8.5 scenario. On the other hand, mean rainfed yield gap shows no change (approximate to 1.49 t/ha) in the future. Temporal analysis of yield indicates that Y-a is expected to decrease in the considerably large portion of the study area (30-60%) under expected future climate conditions. As a result, yield gap is expected to either stagnate or increase in 50.6 and 48.7% of the study area during the two future periods, respectively. The research outcome indicates the need for identifying plausible best management strategies to reduce the yield gap under expected future climate conditions for sustainable rice production in India.
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    Assessment of Sediment and Salinity in the Lower Mekong River Basin
    Chowdhury, Md Mahabub Arefin (Virginia Tech, 2023-01-06)
    The Mekong River Basin (MRB) is famous for its rice farming and export and produces more than 20 million tons of rice per year. Rice production depends on climate, irrigation, soil fertility. However, this region is adversely impacted by several environmental concerns like nutrient deficiency from sediment and saltwater intrusion. The decrease in sediment deposition in the Mekong basin is caused by a number of factors. In China, Lao PDR, and Vietnam, the hydropower generation from dams has improved people's overall living standards, leading in more dams being built or planned in the future. However, dam construction work is adversely impacting the overall salinity condition in this region by reducing upstream flow. Upstream lower flows during the dry season contributes to the increased salinity in the lower Mekong Delta. In addition to these, multiple dams in the upper and middle region of the Mekong basin are trapping sediments and decreasing it in the lower zones. This study found that the reservoirs, built by China between 2008-2015, has reduced the sediment load at all five stations considered in the study. When a reservoir is removed from the model, the sediment load is increased which showed the substantial impact of reservoir construction on sediment load in this area. The landuse pattern is another factor for variability of the sediment yield in the study area. Forest area contributes to higher sediment production whereas agricultural area results in lower sediment yield. The GFDL RCP (4.5) and GFDL RCP (8.5) future climate change projection scenarios used in this study also demonstrated substantial variability in the precipitation pattern for the study region. GFDL RCP (4.5) scenario resulted in a lower sediment yield during the dry season. On contrary to that, GFDL RCP (8.5) showed higher sediment yield due to higher precipitation during the wet season. The severe salinity impact was observed in the Cai Nuoc, Nam Can, and Thanh Phu districts. In Ca Mau province, the observed salinity is highest among the provinces of the study area during dry season (February to May), about 12-14 PPT (parts per thousand) whereas the lowest level of salinity (less than 1 PPT) was observed in the Dong Thap and Vinh Long provinces. This salinity intrusion is adversely impacting the rice production in the study area. In the year 2000, rice production in the Ca Mau province was about 100-150 thousand tons. But salinity intrusion is drastically reducing the rice production in this area, about 10-30thousand tons per year during 2015-2017. Rice production is increasing in the upper deltaic part of the Mekong Delta region where preventive measures were taken.
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    Assessment of the Impact of Climate Change on Streamflow and Sediment in the Nagavali and Vamsadhara Watersheds in India
    Nagireddy, Nageswara Reddy; Keesara, Venkata Reddy; Venkata Rao, Gundapuneni; Sridhar, Venkataramana; Srinivasan, Raghavan (MDPI, 2023-06-26)
    Climate-induced changes in precipitation and temperature can have a profound impact on watershed hydrological regimes, ultimately affecting agricultural yields and the quantity and quality of surface water systems. In India, the majority of the watersheds are facing water quality and quantity issues due to changes in the precipitation and temperature, which requires assessment and adaptive measures. This study seeks to evaluate the effects of climate change on the water quality and quantity at a regional scale in the Nagavali and Vamsadhara watersheds of eastern India. The impact rainfall variations in the study watersheds were modeled using the Soil and Water Assessment Tool (SWAT) with bias-corrected, statistically downscaled models from Coupled Model Intercomparison Project-6 (CMIP-6) data for historical (1975–2014), near future (2022–2060), and far future (2061–2100) timeframes using three Shared Socioeconomic Pathways (SSP) scenarios. The range of projected changes in percentage of mean annual precipitation and mean temperature varies from 0 to 41.7% and 0.7 °C to 2.7 °C in the future climate, which indicates a warmer and wetter climate in the Nagavali and Vamsadhara watersheds. Under SSP245, the average monthly changes in precipitation range from a decrease of 4.6% to an increase of 25.5%, while the corresponding changes in streamflow and sediment yield range from −11.2% to 41.2% and −15.6% to 44.9%, respectively. Similarly, under SSP370, the average monthly change in precipitation ranges from −3.6% to 36.4%, while the corresponding changes in streamflow and sediment yield range from −21.53% to 77.71% and −28.6% to 129.8%. Under SSP585, the average monthly change in precipitation ranges from −2.5% to 60.5%, while the corresponding changes in streamflow and sediment yield range from −15.8% to 134.4% and −21% to 166.5%. In the Nagavali and Vamsadhara watersheds, historical simulations indicate that 2438 and 5120 sq. km of basin areas, respectively, were subjected to high soil erosion. In contrast, under the far future Cold-Wet SSP585 scenario, 7468 and 9426 sq. km of basin areas in the Nagavali and Vamsadhara watersheds, respectively, are projected to experience high soil erosion. These results indicate that increased rainfall in the future (compared to the present) will lead to higher streamflow and sediment yield in both watersheds. This could have negative impacts on soil properties, agricultural lands, and reservoir capacity. Therefore, it is important to implement soil and water management practices in these river basins to reduce sediment loadings and mitigate these negative impacts.
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    Bridging the Data Gap between the GRACE Missions and Assessment of Groundwater Storage Variations for Telangana State, India
    Kumar, Kuruva Satish; Sridhar, Venkataramana; Varaprasad, Bellamkonda Jaya Sankar; Chinnapa Reddy, Konudula (MDPI, 2022-11-26)
    Because of changing climatic conditions, uneven distribution of rainfall occurs throughout India. As a result, dependence on groundwater for irrigation has increased tremendously for industrial and domestic purposes. In India approximately 89% of agricultural demands are met through groundwater. Due to increases in population, demand for groundwater and lack of effective utilization have resulted in rapid depletion of groundwater in most parts of the country. Therefore, quantifying groundwater resources is a serious concern in populated states of India, because it is now difficult to supply enough water to every citizen, and will remain so in the future. Because of difficulties in accessing observation data, researchers have begun to depend on satellite-based remote sensing information to deal with groundwater variations. The present study deals with filling the data gap between Gravity Recovery And Climate Experiment (GRACE) and GRACE Follow On (GRACE FO) missions using multilayer perceptron’s (MLPs) during 2017–2018 to obtain a continuous terrestrial water storage anomaly (TWSA) series from 2003 to 2020 for Telangana state, India. The MLP model performed well in predicting the TWSA, with a correlation coefficient of r = 0.96 between modeled TWSA and GRACE TWSA during the test period. Telangana state observed negative TWSAs (annual) in the years 2003, 2004, 2005, 2009, 2012, 2015, and 2016–19. This TWSA series (2003–2020) was then used to evaluate regional groundwater storage anomalies (GWSAs) in Telangana state, which is considered to be one of the water stress regions in India. The TWSAs were converted to GWSAs using Global Land Data Assimilation System (GLDAS) parameters. The Telangana state experienced decreasing GWSA in the years 2005, 2009, and 2012, and from 2015 to 2019, leading to severe droughts. Groundwater well measurements were obtained from the Central Groundwater Board (CGWB) and converted to GWSA at a seasonal scale. The GWSAs obtained from GRACE (GWSAGRACE) were converted to seasonal values and compared with GWSAs obtained from observation well data (GWSAobs). The performance metrics of r = 0.74, RMSE = 5.3, and NSE = 0.62 were obtained between (GWSAGRACE) and (GWSAobs), representing a good correlation among them. Over the past decade, Telangana state has significantly relied on groundwater resources for irrigation, domestic, and industrial purposes. As a result, evaluating groundwater storage variations at a regional scale may help policy makers and water resource researchers in the sustainable utilization and management of groundwater resources.
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    Characterization of future drought conditions in the Lower Mekong River Basin
    Thilakarathne, Madusanka; Sridhar, Venkataramana (Elsevier, 2017-07-29)
    This study evaluates future changes to drought characteristics in the Lower Mekong River Basin using climate model projections. The Lower Mekong Basin (LMB), covering Thailand, Cambodia, Laos and Vietnam, is vulnerable to increasing droughts. Univariate analysis was employed in this study to compare drought characteristics associated with different return periods for the historical period 1964–2005 and future scenarios (RCP 4.5 2016–2057, RCP 4.5 2058–2099, RCP 8.5 2016–2057 and RCP 8.5 2058–2099). Because a single drought event is defined by several correlated characteristics, drought risk assessment by a multivariate analysis was deemed appropriate, and a multivariate analysis of droughts was conducted using copula functions to investigate the differences in the trivariate joint occurrence probabilities of the historical period and future scenarios. The Standardized Precipitation Index (SPI) was selected as the drought index because of its ability to detect and compare metrological droughts across time and space scales. Historical precipitation data from 1964 to 2005 and future precipitation projections from 2016 to 2099 for 15 global circulation models (GCMs) obtained from the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset were employed. In all future scenarios, the Lower LMB and 3S subbasins were expected to experience more severe and intense droughts. The multivariate drought risk assessment revealed an increase in drought risks in the LMB. However, the Chi-Mun subbasin may experience an alleviation of future drought characteristics. Because the basin was expected to experience an increase in average monthly precipitation in most months, the variability in magnitude suggested that the LMB region requires adaptation strategies to address future drought occurrences.
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    Climate Change Impact on Water Resources of Tank Cascade Systems in the Godavari Sub-Basin, India
    Ramabrahmam, Koppuravuri; Keesara, Venkata Reddy; Srinivasan, Raghavan; Pratap, Deva; Sridhar, Venkataramana (Springer, 2023-05-01)
    The availability of water at the regional and river basin scales in the future will be significantly impacted by climate change. Effective water management in the sub-basin is essential for ensuring long-term sustainability in the face of changing climatic conditions. The Maner River basin is a significant contributor to the Godavari River, and agriculture serves as the primary source of income for the majority of individuals residing in the subbasin. Nearly 50–65% of irrigational fields in the Maner basin are cultivated using local Tank Cascade Systems (TCS) and reservoirs that are managed by monsoon precipitation. The regional level climate change impact on the water resources of these tank cascade systems is important for sustainable management of water resources. In this study, The NEX-GDDP RCM models of CCSM4, MPI-ESM-LR and MIROC-ESM-CHEM were utilized to examine climate patterns during historical and future periods under RCP 4.5 and RCP 8.5 scenarios. The Maner sub-basin and KTCS (Katakshapur Tank Cascade System) were modeled using the SWAT hydrological model to simulate runoff and water availability. The average monsoon (July-October) streamflow increase in the Maner basin during the near, mid, and far futures is projected to be 47%, 66%, and 114% under the RCP 4.5 scenario, and 53%, 72%, and 69% under the RCP 8.5 scenario, respectively. Excess flow may overflow from Ramchandrapur, Mallampalli, and Dharmaraopalli tanks to the downstream Katakshapur tank since it can accommodate the up to 18.91 Mm3. To enhance water management in response to climate change, one potential adaptation strategy is to utilize the surplus inflow to refill downstream artificial ponds, which can aid in the replenishment of groundwater and the provision of water supply to tail end tanks.
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    Climate change impacts on conventional and flash droughts in the Mekong River Basin
    Kang, Hyunwoo; Sridhar, Venkataramana; Ali, Syed A. (Elsevier, 2022-09-10)
    Recent drought events in the Mekong River Basin (MRB) have resulted in devastating environmental and economic losses, and climate change and human-induced alterations have exacerbated drought conditions. Using hydrologic models and multiple climate change scenarios, this study quantified the future climate change impacts on conventional and flash drought conditions in the MRB. The Soil and Water Assessment Tool (SWAT) and Variable Infiltration Capacity (VIC) models were applied to estimate long-term drought indices for conventional and flash drought conditions over historical and future periods (1966–2099), using two emission scenarios (RCP 4.5 and RCP8.5), and four climate models from the Coupled Model Intercomparison Project Phase 5 (CMIP5). For the conventional drought assessment, monthly scale drought indices were estimated, and pentad-scale (5 days) drought indices were computed for the flash drought evaluations. There were overall increases in droughts from the SWAT model for the conventional drought conditions and overall decreases from the VIC model. For the flash drought conditions, the SWAT-driven drought indices showed overall increases in drought occurrences (up to 165%). On the contrary, the VIC-driven drought indices presented decreases in drought occurrences (up to −44%). The conventional and flash drought evaluations differ between these models as they partition the water budget, specifically soil moisture differently. We conclude that the proposed framework, which includes hydrologic models, various emission scenarios, and projections, allows us to assess the various perspectives on drought conditions. Basin countries have differential impacts, so targeted future adaptation strategy is required.
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    Climate Change Impacts on Streamflow in the Krishna River Basin, India: Uncertainty and Multi-Site Analysis
    Naga Sowjanya, Ponguru; Keesara, Venkata Reddy; Mesapam, Shashi; Das, Jew; Sridhar, Venkataramana (MDPI, 2022-12-01)
    In Peninsular India, the Krishna River basin is the second largest river basin that is overutilized and more vulnerable to climate change. The main aim of this study is to determine the future projection of monthly streamflows in the Krishna River basin for Historic (1980–2004) and Future (2020–2044, 2045–2069, 2070–2094) climate scenarios (RCP 4.5 and 8.5, respectively), with the help of the Soil Water and Assessment Tool (SWAT). SWAT model parameters are optimized using SWAT-CUP during calibration (1975 to 1990) and validation (1991–2003) periods using observed discharge data at 5 gauging stations. The Cordinated Regional Downscaling EXperiment (CORDEX) provides the future projections for meteorological variables with different high-resolution Global Climate Models (GCM). Reliability Ensemble Averaging (REA) is used to analyze the uncertainty of meteorological variables associated within the multiple GCMs for simulating streamflow. REA-projected climate parameters are validated with IMD-simulated data. The results indicate that REA performs well throughout the basin, with the exception of the area near the Krishna River’s headwaters. For the RCP 4.5 scenario, the simulated monsoon streamflow values at Mantralayam gauge station are 716.3 m3/s per month for the historic period (1980–2004), 615.6 m3/s per month for the future1 period (2020–2044), 658.4 m3/s per month for the future2 period (2045–2069), and 748.9 m3/s per month for the future3 period (2070–2094). Under the RCP 4.5 scenario, lower values of about 50% are simulated during the winter. Future streamflow projections at Mantralayam and Pondhugala gauge stations are lower by 30 to 50% when compared to historic streamflow under RCP 4.5. When compared to the other two future periods, trends in streamflow throughout the basin show a decreasing trend in the first future period. Water managers in developing water management can use the recommendations made in this study as preliminary information and adaptation practices for the Krishna River basin.
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    Combined statistical and spatially distributed hydrological model for evaluating future drought indices in Virginia
    Kang, Hyunwoo; Sridhar, Venkataramana (Elsevier, 2017-06-06)
    Study region: Virginia, United States. Study focus: Climate change is expected to impact the intensity and severity of droughts; therefore, it is necessary to simulate future drought conditions using temperature and precipitation projections and hydrological models to derive reliable hydrological variables and drought indices. The objective of this study was to evaluate climate change influences on future drought potential and water resources in five major river basins in Virginia. In this study, the Soil and Water Assessment Tool (SWAT) and Coupled Model Intercomparison Project Phase 5 (CMIP5) climate models were used to compute a Standardized Soil Moisture Index (SSI), a Multivariate Standardized Drought Index (MSDI), and a Modified Palmer Drought Severity Index (MPDSI) for both historic and future periods. The drought conditions were evaluated, and their occurrences were determined at river basin scales. New hydrological insights for the region: The results of the ensemble mean of SSI indicated that there was an overall increase in agricultural drought occurrences projected in the New (> 1.3 times) and Rappahannock (> 1.13 times) river basins due to increases in evapotranspiration and surface and groundwater flow. However, MSDI and MPDSI exhibited a decrease in projected future drought, despite increases in precipitation, which suggests that it is essential to use hybridmodeling approaches and to interpret application-specific drought indices that consider both precipitation and temperature changes.
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    Compartmental Process-based Model for Estimating Ammonia Emission from Stored Scraped Liquid Dairy Manure
    Karunarathne, Sampath Ashoka (Virginia Tech, 2017-07-06)
    The biogeochemical processes responsible for production and emission of ammonia from stored liquid dairy manure are governed by environmental factors (e.g. manure temperature, moisture) and manure characteristics (e.g. total ammoniacal nitrogen concentration, pH). These environmental factors and manure characteristics vary spatially as a result of spatially heterogeneous physical, chemical, and biological properties of manure. Existing process-based models used for estimating ammonia emission consider stored manure as a homogeneous system and do not consider these spatial variations leading to inaccurate estimations. In this study, a one-dimensional compartmental biogeochemical model was developed to (i) estimate spatial variation of temperature and substrate concentration (ii) estimate spatial variations and rates of biogeochemical processes, and (iii) estimate production and emission of ammonia from stored scraped liquid dairy manure. A one-dimension compartmentalized modeling approach was used whereby manure storage is partitioned into several sections in vertical domain assuming that the conditions are spatially uniform within the horizontal domain. Spatial variation of temperature and substrate concentration were estimated using established principles of heat and mass transfer. Pertinent biogeochemical processes were assigned to each compartment to estimate the production and emission of ammonia. Model performance was conducted using experimental data obtained from National Air Emissions Monitoring Study conducted by the United States Environmental Protection Agency. A sensitivity analysis was performed and air temperature, manure pH, wind speed, and manure total ammoniacal nitrogen concentration were identified as the most sensitive model inputs. The model was used to estimate ammonia emission from a liquid dairy manure storage of a dairy farm located in Rockingham and Franklin counties in Virginia. Ammonia emission was estimated under different management and weather scenarios: two different manure storage periods from November to April and May to October using historical weather data of the two counties. Results suggest greater ammonia emissions and manure nitrogen loss for the manure storage period in warm season from May to October compared to the storage period in cold season from November to April.
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    Deriving the Reservoir Conditions for Better Water Resource Management Using Satellite-Based Earth Observations in the Lower Mekong River Basin
    Ali, Syed Azhar; Sridhar, Venkataramana (MDPI, 2019-12-03)
    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.
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    Description of future drought indices in Virginia
    Kang, Hyunwoo; Sridhar, Venkataramana (Elsevier, 2017-07-20)
    This article presents projected future drought occurrences in five river basins in Virginia. The Soil and Water Assessment Tool (SWAT) and the Coupled Model Intercomparison Project Phase 5 (CMIP5) climate models were used to derive input variables of multiple drought indices, such as the Standardized Soil Moisture index (SSI), the Multivariate Standardized Drought Index (MSDI), and the Modified Palmer Drought Severity Index (MPDSI) for both historic and future periods. The results of SSI indicate that there was an overall increase in agricultural drought occurrences and that these were caused by increases in evapotranspiration and runoff. However, the results of the MSDI and MPDSI projected a decrease in drought occurrences in future periods due to a greater increase in precipitation in the future. Furthermore, GCM-downscaled products (precipitation and temperature) were verified using comparisons with historic observations, and the results of uncertainty analyses suggest that the lower and upper bounds of future drought projections agree with historic conditions.
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    Drone Imagery Applied to Enhance Flood Modeling
    Friedman, Brianna (Virginia Tech, 2021-06-01)
    Accessible flood modeling for low-resource, data-scarce communities currently does not exist. This paper proposes using drone imagery to compensate for the lack of other flood modeling data (i.e. streamflow measurements). Three flood models were run for Dzaleka Refugee Camp, located in Dowa, Malawi. Two of the models (the Soil and Water Assessment Tool (SWAT) and the Hydrologic Engineering Center River Analysis System (HEC-RAS)) are commonly used hydrological-hydraulic based models. The third model, the Water Caused Erosion Patterns (WCEP) model, was proposed by the author to capitalize on the high-resolution drone imagery using geological-geomorphological information. The drone imagery used in this study has a resolution of 3.5cm and shows erosion patterns throughout the refugee camp. By comparing the erosion patterns to flow direction of the surface, the erosion patterns were determined to be water caused or not water caused, the erosion patterns considered water caused were defined as high-risk flood areas, creating the WCEP model. The three models were compared using locations of collapsed houses throughout the camp. It was found that the WCEP model represents the location of collapsed houses significantly better (misclassification rate below 17%) than the SWAT or HEC-RAS models (misclassification rate below 54%, and 67% respectively). The WCEP model was combined with the best hydrological-hydraulic model (SWAT) to create a hydrogeomorphological model which capitalizes on both the drone imagery and the hydrological process.
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    Drone-Based Community Assessment, Planning, and Disaster Risk Management for Sustainable Development
    Whitehurst, Daniel; Friedman, Brianna; Kochersberger, Kevin B.; Sridhar, Venkataramana; Weeks, James (MDPI, 2021-04-30)
    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.