Tarendra Lakhankar
Research Scientist
Adjunct Professor
Additional Departments/Affiliated Programs
Areas of Expertise/Research
- Data Science
- Field Campaigns
- Remote Sensing and GIS
- Statistics
- Urban Extremes and Social Vulnerability
- Weather Stations Networking
Building
Steinman Hall
Office
Room 185
Phone
(212) 650 5815
Website
Tarendra Lakhankar
Profile
Dr. Lakhankar has led and coordinated various research projects contributing to the NOAA and NASA’s satellite remote sensing missions in water prediction and ecosystem services research. Dr. Tarendra Lakhankar has demonstrated exceptional leadership skills by designing and leading several major field campaign sites. These include the Snow-SAFE site in Caribou, ME, the Soil Moisture Field Campaign at CUNY-SMART in Millbrook, NY, and the NYC-Urban HydroMet Testbed project. The NYC-Urban HydroMet Testbed involved installing 21 weather stations in key research and educational outreach locations across New York City, including public schools, botanical gardens, and community colleges. These projects have provided valuable data for research, improved the accuracy of weather and water predictions, and increased public awareness of environmental issues. These projects have resulted in successful outcomes and publications in top-tier journals. Dr. Lakhankar has also served as a mentor and supervisor to numerous undergraduate and graduate research students. He has successfully organized research meetings and conferences, led grants and professional development seminars, and provided mentoring, advising, and outreach activities. His dedication and service to the City College of New York were recognized with the S.T.A.R. Award in 2022.
Dr. Tarendra Lakhankar boasts a rich teaching career at The City College of New York, where he has imparted knowledge across several courses, including Geographic Information Systems (GIS) in Water Resources, Geographical Information Systems, Introduction to Satellite Remote Sensing and Imaging, Environmental Systems Science, and Environmental Water Resources. His teaching philosophy centers around inquiry-based learning, fostering scientific inquiry, honing data analysis skills, and cultivating effective communication abilities in both written and oral forms. This approach significantly bolsters students' capacities in problem-solving and research. For over a decade, Dr. Lakhankar has dedicated his summers to teaching high school and undergraduate students, extending his educational impact beyond the regular academic year.
Publications
Peer Reviewed Papers:
- Malikah, S., Avila, S., Garcia, G., & T. Lakhankar (2024). Historical Climate Trends and Extreme Weather Events in the Tri-State Area: A Detailed Analysis of Urban and Suburban Differences. Climate, 12(3), 32.
- Trossi-Torres, G., Muñoz-Barreto, J., Morales-Vélez, A. C., Rodriguez-Fernández, E., Martínez-Sánchez, O., & T. Lakhankar (2024). Assessing streamflow forecast accuracy for flash flood events in Puerto Rico. Journal of Hydrology: Regional Studies, 52, 101697.
- Agonafir, C., Lakhankar, T., Khanbilvardi, R., Krakauer, N., Radell, D., & N. Devineni (2023). A review of recent advances in urban flood research. Water Security, 19, 100141.
- Agonafir C., Lakhankar T., Khanbilvardi R., Krakauer N., Radell D., N. Devineni (2022) A machine learning approach to evaluate the spatial variability of New York City’s 311 street flooding complaints, Computers, Environment and Urban Systems, Volume 97, 101854, ISSN 0198-9715.
- Abdelkader, M., Temimi, M., Colliander, A., Cosh, M.H., Kelly, V.R., Lakhankar, T. and A. Fares (2022). Assessing the Spatiotemporal Variability of SMAP Soil Moisture Accuracy in a Deciduous Forest Region. Remote Sensing, 14(14), p.3329.
- Agonafir C., Lakhankar T., Khanbilvardi T., Krakauer N., and D. Radell (2022) A review of recent advances in urban flood modeling and mitigation techniques, accepted in Water Security.
- Sthapit, E., Lakhankar, T., Hughes, M., Khanbilvardi, R., Cifelli, R., Mahoney, K., Currier, W.R., Viterbo, F. and A. Rafieeinasab (2022). Evaluation of Snow and Streamflows Using Noah-MP and WRF-Hydro Models in Aroostook River Basin, Maine. Water, 14(14), p.2145.
- Agonafir, C., Pabon, A. R., Lakhankar, T., Khanbilvardi, R., & N. Devineni (2022). Understanding New York City street flooding through 311 complaints. Journal of Hydrology, 605, 127300.
- Holtzman NM, Anderegg LD, Kraatz S, Mavrovic A, Sonnentag O, Pappas C, Cosh MH, Langlois A, Lakhankar T, Tesser D, N. Steiner (2021). L-band vegetation optical depth as an indicator of plant water potential in a temperate deciduous forest stand. Biogeosciences. 18(2):739-53.
- Fang, L., Zhan, X., Yin, J., Liu, J., Schull, M., Walker, J.P., Wen, J., Cosh, M.H., Lakhankar, T., Collins, C.H. and D.D. Bosch (2020). An Intercomparison Study of Algorithms for Downscaling SMAP Radiometer Soil Moisture Retrievals. Journal of Hydrometeorology, 21(8), pp.1761-1775.
- Chiu, J., Paredes-Mesa, S., Lakhankar, T., Romanov, P., Krakauer, N., Khanbilvardi, R., & R. Ferraro (2020). Intercomparison and Validation of MIRS, MSPPS, and IMS Snow Cover Products Advances in Meteorology, 2020.
- Krakauer, N. Y., Lakhankar, T., and G. H. Dars (2019). Precipitation Trends over the Indus Basin. Climate, 7(10), 116. https://doi.org/10.3390/cli7100116
- Krakauer, N. Y., Lakhankar, T., and D. Hudson (2019). Trends in Drought over the Northeast United States. Water, 11(9), 1834. https://doi.org/10.3390/w11091834
- Bhattacharjee A., Anadón JD, Lohman D., Doleck T., Lakhankar T., Shresta B., Thapa P., Devkota D., Tiwari S., Jha A., Siwakoti M., Devkota NR, Jha PK, and N. Krakauer (2017) The impact of climate change on biodiversity in Nepal: current knowledge, gaps and opportunities, Climate, 5(4), 80; doi:10.3390/cli5040080.
- Mandal R.A., Jha P.K., Krakauer N., Jha A., and T. Lakhankar (2017) Assessing Cost Effective Management Options of Eichhornia crassipes in Ecotourism Ramsar Sites, Nepal, International Journal of Agricultural Science, Research and Technology in Extension and Education Systems (IJASRT in EESs), Volume 7-2, Page 79-83.
- Olivieri J.N., Muñoz-Barreto J., Tirado-Corbalá R., Lakhankar T., and A Fisher (2017) Comparison and downscale of AMSR2 soil moisture products with in-situ measurements from the SCAN-NRCS network over Puerto Rico, Hydrology, 4(4), 46; doi:10.3390/hydrology4040046.
- Nilawar A, Calderlla C., Lakhankar T., Waikar M., and J. Muñoz, (2017), Satellite Soil Moisture Validation using Hydrological SWAT Model: A Case Study of Puerto Rico, USA, Hydrology, 4(4), 45; doi: 10.3390/hydrology4040045.
- Krakauer, N.Y.; Lakhankar, T.; J.D. Anadon (2017) Mapping and Attributing Normalized Difference Vegetation Index Trends for Nepal. Remote Sensing. Vol: 9(10): Pages 986, doi:10.3390/rs9100986.
- Seo D., Lakhankar T., Cosgrove B., Khanbilvardi R. and X. Zhan (2017) Applying SMOS Soil Moisture data into the National Weather Service (NWS)’s Research Distributed Hydrologic Model (HL-RDHM) for flash flood guidance application, In Remote Sensing Applications: Society and Environment, Volume 8, 2017, Pages 182-192, ISSN 2352-9385.
- Pérez-Díaz C.L., Lakhankar T., Romanov R., Muñoz J., Khanbilvardi J. & Yunyue Yu (2017) Evaluation of MODIS land surface temperature with in situ snow surface temperature from CREST-SAFE, International Journal of Remote Sensing, Vol. 38 , Iss. 16,2017.
- Pérez Díaz, C.L.; Muñoz, J.; Lakhankar, T.; Khanbilvardi, R.; P. Romanov (2017). Proof of Concept: Development of Snow Liquid Water Content Profiler Using CS650 Reflectometers at Caribou, ME, USA. Sensors 2017, 17, 647.
- Mandal RA, NY Krakauer, A Jha, T Lakhankar, MP Sharma, PK Jha (2016), Effect of seasonal variables on cow milk production of smallholders at Voltar, Gorusinghe and Jholpe villages in Nepal, Wayamba Journal of Animal Science, 8: 1467645923
- Jha, A.K.; Malla, R.; Sharma, M.; Panthi, J.; Lakhankar, T.; Krakauer, N.Y.; Pradhanang, S.M.; Dahal, P.; M.L. Shrestha (2016) Impact of Irrigation Method on Water Use Efficiency and Productivity of Fodder Crops in Nepal. Climate 2016, 4, 4.
- Dahal, P., Shrestha, N., Shrestha, M., Krakauer, N., Panthi, J., Pradhanang, S., Jha A. and T. Lakhankar, (2016). Drought risk assessment in central Nepal: temporal and spatial analysis. Natural Hazards, 1–20. doi:10.1007/s11069-015-2055-5
- Pérez Díaz, C.L.; Lakhankar, T.; Romanov, P.; Khanbilvardi, R.; Y. Yu (2015) Evaluation of VIIRS Land Surface Temperature Using CREST-SAFE Air, Snow Surface, and Soil Temperature Data. Geosciences, 2015, 5, 334-360.
- Corona, J.A.I.; Muñoz, J.; Lakhankar, T.; Romanov, P.; R. Khanbilvardi (2015) Evaluation of the Snow Thermal Model (SNTHERM) through Continuous in situ Observations of Snow’s Physical Properties at the CREST-SAFE Field Experiment. Geosciences, 5, 310-333.
- Krakauer, N.Y.; Pradhanang, S.M.; Panthi, J.; Lakhankar, T.; , A.K. Jha (2015), Probabilistic Precipitation Estimation with a Satellite Product. Climate, 3, pages 329-348.
- Pradhanang, U.B.; Pradhanang, S.M.; Sthapit, A.; Krakauer, N.Y.; Jha, A.; T. Lakhankar (2015) National Livestock Policy of Nepal: Needs and Opportunities. Agriculture 5, 103-131.
- Panthi, J.; Dahal, P.; Shrestha, M.L.; Aryal, S.; Krakauer, N.Y.; Pradhanang, S.M.; Lakhankar, T.; Jha, A.K.; Sharma, M.; R. Karki (2015) Spatial and Temporal Variability of Rainfall in the Gandaki River Basin of Nepal Himalaya. Climate, 3, 210-226.
- Corona, J.A.I.; Lakhankar, T.; Pradhanang, S.; R. Khanbilvardi (2014). Remote Sensing and Ground-Based Weather Forcing Data Analysis for Streamflow Simulation Hydrology, 1, 89-111.
- M Temimi, T Lakhankar, X Zhan, MH Cosh, NY Krakauer, A Fares, V Kelly, R Khanbilvardi, L Kumassi (2014), Soil Moisture Retrieval Using Ground-Based L-Band Passive Microwave Observations in Northeastern USA, Vadose Zone Research, 13(3). doi: 10.2136/vzj2013.06.0101
- Krakauer N.Y. , S. M. Pradhanang, T. Lakhankar, and A. Jha, (2013) Evaluating Satellite Products for Precipitation Estimation in Mountain Regions: A Case Study for Nepal, Remote Sensing vol. 5(8), pages 4107-4123.
- Munoz J., J.A.I. Corona, T. Lakhankar, R. Khanbilvardi, P. Romanov, N. Krakauer, A. Powell (2013) Synergistic Use of Remote Sensing for Snow Cover and Snow Water Equivalent Estimation British Journal of Environment and Climate Change., 3(4): 612-627. DOI : 10.9734/BJECC/2013/7699.
- Seo D., T. Lakhankar, J. Mejia, B. Cosgrove, and R. Khanbilvardi (2013) Evaluation of Operational National Weather Service Gridded Flash Flood Guidance over the Arkansas Red River Basin, The Journal of the American Water Resources Association, 1-12. DOI: 10.1111/jawr.12087.
- Lakhankar, T., Muñoz, J., Romanov, P., Powell, A. M., Krakauer, N. Y., Rossow, W. B., and R. M. Khanbilvardi (2013) CREST-Snow Field Experiment: analysis of snowpack properties using multi-frequency microwave remote sensing data, Hydrology Earth System Science, 17, 783-793, doi:10.5194/hess-17-783-2013.
- Chen C.,T. Lakhankar, P. Romanov, S. Helfrich, A. Powell, R. Khanbilvardi (2012) Validation of NOAA-Interactive Multisensor Snow and Ice Mapping System (IMS) by Comparison with Ground-Based Measurements over Continental United States. Remote Sensing Vol. 4(5), pages 1134-1145.
- Lakhankar T., A.E. Azar, N. Shahroudi, A. Powell, and R. Khanbilvardi (2012), Analysis of the Effects of Snowpack Properties on Satellite Microwave Brightness Temperature and Emissivity Data, Journal of Geophysics & Remote Sensing, !:101, doi: 10.4172/jgrs.1000101
- Ghedira H., J-C. Arevalo, T. Lakhankar, R. Khanbilvardi and R. Blake (2011), Snow Cover Mapping Using Satellite Remote Sensing Data, International Journal of Remote Sensing Applications, Vol.1(6), pages 37-42.
- Temimi M., T. Lacava, T. Lakhankar, V. Tramutoli, H. Ghedira, R. Ata, and R Khanbilvardi (2011), A multi-temporal analysis of AMSR-E data for flood and discharge monitoring during the 2008 flood in Iowa. Hydrological Processes, DOI: 10.1002/hyp.8020.
- Seo D., T. Lakhankar, R. Khanbilvardi (2010) Sensitivity Analysis of b-factor in Microwave Emission Model for Soil Moisture Retrieval: A Case Study for SMAP Mission Remote Sensing, Vol. 2(5), pages 1273-1286.
- Lakhankar T., A.S. Jones, C.L. Combs, M. Sengupta, T.H. Vonder Haar, and R. Khanbilvardi (2010) “Analysis of Large Scale Spatial Variability of Soil Moisture Using a Geostatistical Method. Sensors, Vol. 10(1), pages 913-932.
- Lakhnakar T., N.Y. Krakauer, R Khanbilvardi (2009), Applications of microwave remote sensing of soil moisture for agricultural applications, International Journal of Terraspace Science and Engineering, Vol. 2(1), pages 81-91.
- Lakhankar T., H. Ghedira, M. Temimi, M. Sengupta, R. Khanbilvardi, R. Blake (2009) Non-parametric Methods for Soil Moisture Retrieval from Satellite Remote Sensing Data. Remote Sensing. Vol. 1(1), pages 3-21.
- Lakhankar T., H. Ghedira, M. Temimi, A.E. Azar, R. Khanbilvardi (2009) Effect of Land Cover Heterogeneity on Soil Moisture Retrieval Using Active Microwave Remote Sensing Data. Remote Sensing. Vol.1(2) pages 80-91.
Book Chapters
- Khanbilvardi R., T. Lakhankar, A. Powell, and R. Nazari (2014), “Remote Sensing Data and Information for Hydrological Monitoring and Modeling” in Handbook of Engineering Hydrology: Vol. 2: Modeling Climate Changes and Variability, Edited by Saeid Eslamian, to be published by CRC Press of Taylor & Francis Group, 584 Pages. February 25, 2014.
Courses Taught
GIS in Water Resources (CE-G0800)
In this course, students will learn how to use the underlying concepts of Geographic Information Systems for problems in Water Resources engineering. We will start with a review of the basic concepts of GIS, such as the use of Coordinate systems, projections, data concepts, and geographic references. After refreshing this knowledge, we will apply these concepts to typical hydrologic tasks, such as Terrain modeling, watershed delineation, computation, and extraction of river and watershed networks, including spatial analysis computations. Once we have mastered that, the course will introduce some modeling concepts and expand on integrating time series and geospatial data in general. We will also add some components that address remote sensing data products and their analysis within the GIS environment. The course will be rounded out with special topics related to challenges for geospatial information systems, such as data heterogeneity, data about data (metadata), and how to search best for data.
Geographical Information System (ENGR59910)
This course aims to develop an understanding of geographic space and how maps represent geographic space. In this class, students will learn about the basic principles of maps, their specialized contents, and how to create these maps. The class will also address approaches to map projections, reference systems, and where to find locations. The class will introduce you to basic objects such as points, lines, polygons, and features and ways to organize these in classes. It will also cover principles of geographic information systems, learn about spatial analysis, and how to represent data via data models such as raster and vector formats and store and organize data in a geodatabase. You will get hands-on experience using GIS software and acquire basic skills to insert, create, and extract data from different sources and manipulate these in the GIS environment. 4 hr/wk; one 2.0-hour lecture; one 2.0-hour Lab, 3 cr.
Introduction to Satellite Remote Sensing and Imaging (ENGR 30100)
This introductory remote sensing course covers different environments where remote sensing can be applied, including discussing various space platforms and selected sensors that orbit the Earth. Emphasis is placed on the application of remote sensing on the interactions between the hydrosphere, biosphere, geosphere, and atmosphere, as well as bio-productivity and geophysical/geochemical processes in the oceans. By the end of this class, students should be able to Understand the principles of an EM wave and interactions with surface/atmosphere that are relevant to remote sensing (e.g., surface scattering vs particle scattering), Describe various types of remote sensing systems (e.g., visible, Infrared, microwave, etc.), Understand the fundamental principles for different types of remote sensing systems, Understand resolutions and viewing geometry of different remote sensing systems, and Describe different satellite orbits that are used for Earth Observations.
Environmental Systems Science (SCIE 4104E)
This course focuses on Earth as a system and explores the interdependent relationships among the atmosphere, hydrosphere, biosphere, and lithosphere. Through inquiry-based laboratories and field investigations, students learn to take scientifically valid measurements in the fields of atmosphere, hydrology, soil, and land cover/phenology. Students will gain experience in analyzing and mapping scientific data, designing and investigating their scientific inquiry, and presenting oral and written reports to their peers. The goal of this course is to provide an integrated background in earth system science and to become proficient at scientific practices, including asking questions, developing and using models, planning and carrying out investigations, analyzing and interpreting data, using mathematics and computational thinking, constructing explanations, engaging argument from evidence, and obtaining, evaluating, and communicating information.
Environmental Water Resources (CE 45100)
The goal of this course is: 1) Develop an appreciation for the components of the hydrologic cycle, how they interact, and how they transport various materials; 2) Practice quantitative techniques for estimating the magnitude of different components of the hydrologic cycle; Become more comfortable making reasonable estimates, utilizing data, and addressing open-ended questions in engineering problem solving and design; 3) Become familiar with the agencies, organizations, and institutions participating in scientific research and management of the Southern Hudson River; 4) Improve their ability to communicate technical material in written form and orally; 5) Better their ability to do research: identify and collect needed information, analyze data, draw and support appropriate conclusions, and provide recommendations for future studies; 6) Improve their ability to work as part of a team effectively; 7) Develop their skills in completing an open-ended project under time constraints using a systematic, phased approach.
Hydrology and Hydraulics Engineering (CE36500)
This course aims to give civil engineering majors a basic understanding of flow systems in closed and open hydraulic and hydrologic systems. It provides detailed computation for studying, analyzing, and designing components of hydraulic systems such as pipes, pumps, open channels, and storm collection systems. This course will include two hydraulic laboratory experiments and three computer lab experiments using commercial software (e.g., Haestad-Method© software), which help students design and visualize different hydraulic and hydrological phenomena professionally.