Prasanth Valayamkunnath

Scopus Publications

Advances in land surface models: Enhancing drought monitoring, prediction, and water management
Prasanth Valayamkunnath
Global Drought and Sustainability, 2026
Performance evaluation of CMIP6 global climate models using ERA5 over Indian Monsoon Region
Arun Gnanamony Sreekumar, Prasanth Valayamkunnath
Climate Dynamics, 2025
Automated calibration of Noah-MP land surface model for improved irrigation representation in the North China Plain
Danqiong Dai, Yanping Li, Liang Chen, Fei Chen, Zhenhua Li, Zhe Zhang, Prasanth Valayamkunnath, Li Xu, Cenlin He, Zhuguo Ma
Journal of Hydrology, 2025
Enhancing the Community Noah-MP Land Model Capabilities for Earth Sciences and Applications
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, Tzu-Shun Lin, Prasanth Valayamkunnath, Michael Ek, Dev Niyogi
Bulletin of the American Meteorological Society, 2023
in real-time
Modernizing the open-source community Noah with multi-parameterization options (Noah-MP) land surface model (version 5.0) with enhanced modularity, interoperability, and applicability
Cenlin He, Prasanth Valayamkunnath, Michael Barlage, Fei Chen, David Gochis, Ryan Cabell, Tim Schneider, Roy Rasmussen, Guo-Yue Niu, Zong-Liang Yang, Dev Niyogi, Michael Ek
Geoscientific Model Development, 2023
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.
Developing spring wheat in the Noah-MP land surface model (v4.4) for growing season dynamics and responses to temperature stress
Zhe Zhang, Yanping Li, Fei Chen, Phillip Harder, Warren Helgason, James Famiglietti, Prasanth Valayamkunnath, Cenlin He, Zhenhua Li
Geoscientific Model Development, 2023
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.
Modeling the Hydrologic Influence of Subsurface Tile Drainage Using the National Water Model
Prasanth Valayamkunnath, David J. Gochis, Fei Chen, Michael Barlage, Kristie J. Franz
Water Resources Research, 2022
Subsurface 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.
Mapping of 30-meter resolution tile-drained croplands using a geospatial modeling approach
Prasanth Valayamkunnath, Michael Barlage, Fei Chen, David J. Gochis, Kristie J. Franz
Scientific Data, 2020
Tile 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.
Corrigendum to ‘Field-scale intercomparison analysis of ecosystems in partitioning surface energy balance components in a semi-arid environment’: (Ecohydrology & Hydrobiology (2019) 19(1) (24–37), (S1642359317301866), (10.1016/j.ecohyd.2018.06.005))
Venkataramana Sridhar, Mirza M. Billah, Prasanth Valayamkunnath
Ecohydrology and Hydrobiology, 2020
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](S0168192317302812)(10.1016/j.agrformet.2017.08.025)
Prasanth Valayamkunnath, Venkataramana Sridhar, Wenguang Zhao, Richard G. Allen
Agricultural and Forest Meteorology, 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](S0048969718335721)(10.1016/j.scitotenv.2018.09.130)
Prasanth Valayamkunnath, Venkataramana Sridhar, Wenguang Zhao, Richard G. Allen
Science of the Total Environment, 2019
A comprehensive analysis of interseasonal and interannual energy and water balance dynamics in semiarid shrubland and forest ecosystems
Prasanth Valayamkunnath, Venkataramana Sridhar, Wenguang Zhao, Richard G. Allen
Science of the Total Environment, 2019
Field-scale intercomparison analysis of ecosystems in partitioning surface energy balance components in a semi-arid environment
Venkataramana Sridhar, Mirza M. Billah, Prasanth Valayamkunnath
Ecohydrology and Hydrobiology, 2019
Precipitation Extremes and Flood Frequency in a Changing Climate in Southeastern Virginia
Venkataramana Sridhar, Parthkumar Modi, Mirza M. Billah, Prasanth Valayamkunnath, Jonathan L. Goodall
Journal of the American Water Resources Association, 2019
Intercomparison of surface energy fluxes, soil moisture, and evapotranspiration from eddy covariance, large-aperture scintillometer, and modeling across three ecosystems in a semiarid climate
Prasanth Valayamkunnath, Venkataramana Sridhar, Wenguang Zhao, Richard G. Allen
Agricultural and Forest Meteorology, 2018

Prasanth Valayamkunnath

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Scopus Publications