Project Description:

The overarching research objective of this research is to investigate aerosol-cloud-rainfall interactions in the Central Himalayas with a focus on elucidating the impact of anthropogenic aerosols on cloudiness, the space-time variability of precipitation, and ultimately the regional water cycle in the northern Indian subcontinent, and in particular the Ganges river basin.

To assess this indirect effect of aerosols, we rely on the following methodology:

1. Analysis of remote sensing aerosol, cloud and rainfall products from MODIS, TOMS and TRMM.
2. Field Campaigns for characterizing the aerosol physical and chemical properties including the thermodynamic variables and wind profiles in the region. Instrumentation for aerosol measurements like Scanning Mobility Particle Sizer (SMPS) and IC/TOC for analysis of filter data are available (through collaboration with Prof. Andrei Khlystov Research Laboratory, http://aerosol.pratt.duke.edu/) at Duke University). A wide array of instrumentation for the measurement of meteorological variables is available in our lab.
3. Numerical study using cloud resolving models with double moment microphysics, coupled with a land surface model

Himalayas

The Himalayas act as a barrier that separates a region of abundant aerosols in the Indo-Gangetic Plains (IGP) from the region of pristine air at high altitude in the Tibetan plateau. Statistical analysis of remote sensing data from multiple satellite platforms over the Himalayas shows a strong seasonal cycle of aerosol build up and decay with aerosol peaks during the pre-monsoon/winter season and scavenging of aerosols during the monsoon.

On a more local scale, the mountainous terrain of the Himalayan system is dissected by numerous river valleys. As the dissected nature of terrain is important in decoupling the winds in the valley from the synoptic scale winds above, these river valleys act as narrow pockets for the accumulation of aerosols within the ridges. Recent field experiment campaigns in Central Nepal provide new insights and confirm previous work regarding the role of mountain-valley circulations in the diurnal cycle of aerosol number and mass concentration across the Middle Himalaya range and document evidence of the role of synoptic scale transport and weather controls (haze events and rainfall) in modulating the amplitude of the diurnal cycle and day-to-day variability of aerosols in the pre-monsoon season. The study also characterizes the aerosol size distribution and chemical composition of aerosols during May~June 2009.

The numerical model we are using is a modified version of the 3-D non-hydrostatic, anelastic model which has been documented in detail by Clark (1977, 1979), Clark and Farley (1984), Clark and Hall (1991,1996). The model has been successfully used for simulating flows over complex terrains [Hoinka and Clark (1991), Clark and Miller (1991), Bhushan and Barros (2007) among many others].

This new cloud microphysical scheme was previously introduced into the model by Richard Farley. The warm rain aspects of the scheme are based on Cohard and Pinty (2000), whereas much of the ice treatment is patterned after Ferrier (1994).It assumes two classes of liquid particles, cloud water and rain, and four classes of ice particles, cloud ice crystals, snow, graupel, and hail.

Publications

• Shrestha P., A.P. Barros, 2010: Joint spatial variability of aerosol, clouds and rainfall in the Himalayas from satellite data, Atmos. Chem. Phys., 10, 8305-8317, doi:10.5194/acp-10-8305-2010
• Shrestha P., Barros A.P.,, Khlystov A., Chemical composition and aerosol size distribution of the middle mountain range in the Nepal Himalayas during the 2009 pre-monsoon season, Atmospheric Chemistry and Physics - Discussions, vol. 10 (2010), ppt. 15629-15670

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Update 1 - February 2016 (APB)
Environmental Physics Laboratory – Duke University (Ana P. Barros, Faculty Director)

The DCHM (Duke Coupled surface-subsurface Hydrology Model) is a research model that is freely available to all interested scientists. Due to the lack of appropriate funding, the model is documented in the literature, but a User’s Manual and FAQ documentation are not maintained and regularly updated. Therefore, interested users should plan to spend time in-residence at Duke toward hands-on orientation and applications using the model.

DCHM Development Timeline and Milestones
The DCHM has been in development since 1991. It started as the LSHM (Land-Surface Hydrology Model) as an independent effort by Ana P. Barros at the University of Washington to code the column land-surface used in the ECMWF model at the time following the corresponding ECMWF technical note but implemented in a finite-element framework including different numerical formulations and boundary conditions for heat and water fluxes in the subsurface. The model was used for the first time in Barros (1995, J. Climate). The 1D-LSHM was subsequently tested by undergraduate and graduate students working with Barros at Penn State, including two M.Sc. Thesis (ElRasheed-Eltayebb and Eve Devonec), with the first comprehensive manuscript documenting the column model (1D-LSHM) was published in 2002 (Devonec and Barros, 2002, J. Hydrology). Osman Yildiz developed the model into a full-distributed hydrology model including runoff and streamflow routing and surface-subsurface interactions for his Penn State doctoral dissertation (Yildiz and Barros 2005, 2007, 2009, J. Hydrology and others). Juan Garcia-Quijano worked with Barros at Harvard to introduce a dynamic vegetation module to simulate photosynthesis coupled with hydrologic processes at sub-hourly scale (Garcia-Quijano and Barros, 2005, Ecological Modeling). At Duke, Shanti Bushan coupled the 1D-LSHM to a NWP model, the Clark-Hall model, at cloud allowing resolution (1 km, Bushan and Barros, 2007, J. Hydrometeorology). Do-Hyuk Kang changed the snow physics to include multiple layers and linked the model to a forward microwave emission model (Kang and Barros, 2011a and 2011b, IEEE TGARS). Jing Tao implemented the model at high resolution and introduced an aquifer model, variable soil depths and full two-way coupling to the streamflow routing, wetland physics, and data assimilation (Tao and Barros 2013 and 2014a,b,c, Tao et al. 2016, Tao and Barros 2016, J. Hydrology and HESS and others). Lauren Lowman introduced prognostic phenology and dynamic vegetation roots (Lowman and Barros, 2016) and in preparation.

DCHM (and LSHM) Bibliography

Peer-Reviewed Journal Publications

Lowman,L.E.L, and Barros, A.P., 2016: Interplay of Drought andTropical Cyclone Activity in SE US Gross Primary Productivity. J. Geophys. Res.- Biogeosciences, pending revisions.

Tao, J., Wu, D., Gourley, J., Zhang, S.Q., Crow, W., Peters-Lidard, C.,and Barros, A.P., 2016: Operational Hydrological Forecasting during the IPHEx-IOP Campaign- Meet the
Challenge. J. Hydrology, in press.

Nogueira, M., and Barros, A.P., 2015: Transient Stochastic Downscaling of Quantitative Precipitation Estimates for Hydrological Applications. J. Hydrology, No. 529, 1407- 1421. DoI:10.1016/j.jhydrol.2015.08.041.

Tao, J. and Barros, A.P., 2014: Coupled Prediction of flood response and debris flows initiation during warm and cold season events in the Southern Appalachians, USA. Hydrol. and
Earth System Sciences. DOI:10.5194/hess-18-1-2014.

Tao, J, and Barros, A. P., 2013: Prospects for Flash Flood Forecasting In Mountainous Regions-An Investigation of Tropical Storm Fay in the Southern Appalachians. J. Hydrology,
DOI:10.1016/j.jhydrol.2013.02.052.

Kang, D.H., and Barros, A. P., 2011b: Observing System Simulation of Snow Microwave Emissions over Data Sparse Regions. Part 2: Multilayer Physics, IEEE TGRSS, doi:10.1109/TGRS.2011.2169074.

Kang, D.H., and Barros, A. P., 2011a: Observing System Simulation of Snow Microwave Emissions over Data Sparse Regions. Part 1: Single Layer Physics, IEEE TGRSS, doi:10.1109/TGRS.2011.2169073.

Yildiz, O. and Barros, A.P., 2009. Evaluating spatial variability and scale effects on hydrologic processes in a midsize river basin. Scientific Research and Essays, 4(4): 217-225.

Bhushan, S., and Barros, A.P., 2007: A Numerical Study to Investigate the Relationship Between Moisture Convergence Patterns and the Spatial Distribution of Orographic
Precipitation Features from TRMM in Central Mexico. J. of Hydromet., 8,1264-1284.

Yildiz, O, and Barros, A.P., 2007: Elucidating Vegetation Controls on the Hydroclimatology of a Mid-Latitude Basin. J. Hydrology, 333,431-448, doi:10.1016/j.jhydrol.2006.09.10.

Yildiz, O., and Barros, A.P., 2005: Climate Variability and Hydrologic Extremes – Modeling the Water and Energy Budgets in the Monongahela River Basin. Climate and Hydrology in Mountain Areas. de Jong, C., Collins, D., and Ranzi, R. (Eds.), Wiley(Pub.), 303-318.

Gebremichael, M, and Barros, A.P., 2005: Evaluation of MODIS Gross Primary productivity (GPP) in the Tropics During the Monsoon. Remote Sensing of the Environment,
doi:10.1016/j.rse.2005.10.009.

Garcia-Quijano, J. and Barros, A.P., 2005: Incorporating Vegetation Dynamics into a Hydrological Model: Photosynthesis, Dynamic Respiration and Stomatal
Sensitivity. Ecological Modeling, Vol. 185, 29-49 .

Devonec, E. and Barros, A. P., 2002: Exploring the Transferability of a Land-Surface Hydrology Model. J. Hydrology, Vol. 265, 258-282.

Barros, A.P., 1995: Adaptive Multilevel Modeling of Land-Atmosphere Interactions. J. Climate, 8, 2144-2160.

Research Reports

Nogueira, M. and Barros, A.P., 2014: The Integrated Precipitation and Hydrology Experiment. Part III: High-Resolution Ensemble Rainfall Products Precipitation Datasets. Report
EPL-2013-IPHEX-H4SE-3, EPL/Duke University (Pub.), 38pp. http://dx.doi.org/10.7924/G8MW2F2W

Tao, J. and Barros, A.P., 2014a: The Integrated Precipitation and Hydrology Experiment. Part I: Quality High-Resolution Landscape Attributes Datasets. Report EPL-2013-
IPHEX-H4SE-1, EPL/Duke University (Pub.), V.1., 60 pp. http://dx.doi.org/10.7924/G8H41PBG

Tao, J. and Barros, A.P., 2014b: The Integrated Precipitation and Hydrology Experiment. Part
II: Atmospheric Forcing and Topographic Corrections. Report EPL-2013-IPHEX-H4SE- 2, EPL/Duke University (Pub.), V.1., 80pp. http://dx.doi.org/10.7924/G8RN35S6