Prasanth Valayamkunnath
@iisertvm.ac.in
Assistant Professor, School of Earth, Environmental and Sustainability Sciences
Indian Institute of Science Education and Research Thiruvananthapuram
RESEARCH, TEACHING, or OTHER INTERESTS
Atmospheric Science, Agricultural and Biological Sciences, Computers in Earth Sciences, Earth-Surface Processes
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Cenlin He, Fei Chen, Michael Barlage, Zong-Liang Yang, Jerry W. Wegiel, Guo-Yue Niu, David Gochis, David M. Mocko, Ronnie Abolafia-Rosenzweig, Zhe Zhang,et al.
American Meteorological Society
in real-time
Cenlin He, Prasanth Valayamkunnath, Michael Barlage, Fei Chen, David Gochis, Ryan Cabell, Tim Schneider, Roy Rasmussen, Guo-Yue Niu, Zong-Liang Yang,et al.
Copernicus GmbH
Abstract. The widely used open-source community Noah with multi-parameterization options (Noah-MP) land surface model (LSM) is designed for applications ranging from uncoupled land surface hydrometeorological and ecohydrological process studies to coupled numerical weather prediction and decadal global or regional climate simulations. It has been used in many coupled community weather, climate, and hydrology models. In this study, we modernize and refactor the Noah-MP LSM by adopting modern Fortran code standards and data structures, which substantially enhance the model modularity, interoperability, and applicability. The modernized Noah-MP is released as the version 5.0 (v5.0), which has five key features: (1) enhanced modularization as a result of re-organizing model physics into individual process-level Fortran module files, (2) an enhanced data structure with new hierarchical data types and optimized variable declaration and initialization structures, (3) an enhanced code structure and calling workflow as a result of leveraging the new data structure and modularization, (4) enhanced (descriptive and self-explanatory) model variable naming standards, and (5) enhanced driver and interface structures to be coupled with the host weather, climate, and hydrology models. In addition, we create a comprehensive technical documentation of the Noah-MP v5.0 and a set of model benchmark and reference datasets. The Noah-MP v5.0 will be coupled to various weather, climate, and hydrology models in the future. Overall, the modernized Noah-MP allows a more efficient and convenient process for future model developments and applications.
Zhe Zhang, Yanping Li, Fei Chen, Phillip Harder, Warren Helgason, James Famiglietti, Prasanth Valayamkunnath, Cenlin He, and Zhenhua Li
Copernicus GmbH
Abstract. The US Northern Great Plains and the Canadian Prairies are known as the world's breadbaskets for their large spring wheat production and exports to the world. It is essential to accurately represent spring wheat growing dynamics and final yield and improve our ability to predict food production under climate change. This study attempts to incorporate spring wheat growth dynamics into the Noah-MP crop model for a long time period (13 years) and fine spatial scale (4 km). The study focuses on three aspects: (1) developing and calibrating the spring wheat model at a point scale, (2) applying a dynamic planting and harvest date to facilitate large-scale simulations, and (3) applying a temperature stress function to assess crop responses to heat stress amid extreme heat. Model results are evaluated using field observations, satellite leaf area index (LAI), and census data from Statistics Canada and the United States Department of Agriculture (USDA). Results suggest that incorporating a dynamic planting and harvest threshold can better constrain the growing season, especially the peak timing and magnitude of wheat LAI, as well as obtain realistic yield compared to prescribing a static province/state-level map. Results also demonstrate an evident control of heat stress upon wheat yield in three Canadian Prairies Provinces, which are reasonably captured in the new temperature stress function. This study has important implications in terms of estimating crop yields, modeling the land–atmosphere interactions in agricultural areas, and predicting crop growth responses to increasing temperatures amidst climate change.
Prasanth Valayamkunnath, David J. Gochis, Fei Chen, Michael Barlage, and Kristie J. Franz
American Geophysical Union (AGU)
AbstractSubsurface tile drainage (TD) is a dominant agriculture water management practice in the United States (US) to enhance crop production in poorly drained soils. Assessments of field‐level or watershed‐level (<50 km2) hydrologic impacts of TD are becoming common; however, a major gap exists in our understanding of regional (>105 km2) impacts of TD on hydrology. The National Water Model (NWM) is a distributed 1‐km resolution hydrological model designed to provide accurate streamflow forecasts at 2.7 million reaches across the US. The current NWM lacks TD representation which adds considerable uncertainty to streamflow forecasts in heavily tile‐drained areas. In this study, we quantify the performance of the NWM with a newly incorporated tile‐drainage scheme over the heavily tile‐drained Midwestern US. Employing a TD scheme enhanced the uncalibrated NWM performance by about 20–50% of the fully calibrated NWM (Calib). The calibrated NWM with tile drainage (CalibTD) showed enhanced accuracy with higher event hit rates and lower false alarm rates than Calib. CalibTD showed better performance in high‐flow estimations as TD increased streamflow peaks (14%), volume (2.3%), and baseflow (11%). Regional water balance analysis indicated that TD significantly reduced surface runoff (−7% to −29%), groundwater recharge (−43% to −50%), evapotranspiration (−7% to −13%), and soil moisture content (−2% to −3%). However, TD significantly increased soil profile lateral flow (27.7%) along with infiltration and soil water storage potential. Overall, our findings highlight the importance of incorporating the TD process into the operational configuration of the NWM.
Prasanth Valayamkunnath, Michael Barlage, Fei Chen, David J. Gochis, and Kristie J. Franz
Springer Science and Business Media LLC
AbstractTile drainage is one of the dominant agricultural management practices in the United States and has greatly expanded since the late 1990s. It has proven effects on land surface water balance and quantity and quality of streamflow at the local scale. The effect of tile drainage on crop production, hydrology, and the environment on a regional scale is elusive due to lack of high-resolution, spatially-explicit tile drainage area information for the Contiguous United States (CONUS). We developed a 30-m resolution tile drainage map of the most-likely tile-drained area of the CONUS (AgTile-US) from county-level tile drainage census using a geospatial model that uses soil drainage information and topographic slope as inputs. Validation of AgTile-US with 16000 ground truth points indicated 86.03% accuracy at the CONUS-scale. Over the heavily tile-drained midwestern regions of the U.S., the accuracy ranges from 82.7% to 93.6%. These data can be used to study and model the hydrologic and water quality responses of tile drainage and to enhance streamflow forecasting in tile drainage dominant regions.
Venkataramana Sridhar, Mirza M. Billah, and Prasanth Valayamkunnath
Elsevier BV
Prasanth Valayamkunnath, Venkataramana Sridhar, Wenguang Zhao, and Richard G. Allen
Elsevier BV
Prasanth Valayamkunnath, Venkataramana Sridhar, Wenguang Zhao, and Richard G. Allen
Elsevier BV
Prasanth Valayamkunnath, Venkataramana Sridhar, Wenguang Zhao, and Richard G. Allen
Elsevier BV
Venkataramana Sridhar, Parthkumar Modi, Mirza M. Billah, Prasanth Valayamkunnath, and Jonathan L. Goodall
Wiley
Despite the advances in climate change modeling, extreme events pose a challenge to develop approaches that are relevant for urban stormwater infrastructure designs and best management practices. The study first investigates the statistical methods applied to the land‐based daily precipitation series acquired from the Global Historical Climatology Network‐Daily (GHCN‐D). Additional analysis was carried out on the simulated Multivariate Adaptive Constructed Analogs (MACA)‐based downscaled daily extreme precipitation of 15 General Circulation Models and Weather Research and Forecasting‐based hourly extreme precipitation of North American Regional Reanalysis to discern the return period of 24‐hr and 48‐hr events. We infer that the GHCN‐D and MACA‐based precipitation reveals increasing trends in annual and seasonal extreme daily precipitation. Both BCC‐CSM1‐1‐m and GFDL‐ESM2M models revealed that the magnitude and frequency of extreme precipitation events are projected to increase between 2016 and 2099. We conclude that the future scenarios show an increase in magnitudes of extreme precipitation up to three times across southeastern Virginia resulting in increased discharge rates at selected gauge locations. The depth‐duration‐frequency curve predicted an increase of 2–3 times in 24‐ and 48‐h precipitation intensity, higher peaks, and indicated an increase of up to 50% in flood magnitude in future scenarios.
Venkataramana Sridhar, Mirza M. Billah, and Prasanth Valayamkunnath
Elsevier BV
Prasanth Valayamkunnath, Venkataramana Sridhar, Wenguang Zhao, and Richard G. Allen
Elsevier BV