I am a hydrologist for the USGS – New Mexico Water Science Center and in charge of all technical watershed-modeling activities. I have developed data processing and analysis techniques and serve as the center's technical authority on all precipitation runoff modeling system (PRMS) and snow hydrology activities. I am currently developing and implementing novel characterizations of forest canopy as well as snow modeling tools in order to quantify the effects of canopy disturbance on snow water resources from a recently awarded competitive grant from the South Central Climate Science Center.
C. David Moeser
300 Hermosa DR NE
Albuquerque, NM 87108 USA
+1 (77five) 3five7- 66six8
PhD •December 2015
Surface Water Hydrology•Department of Environmental Systems Science
Dissertation: The Influence of Forest Canopy Structure on Snow Hydrology. Download hereFunding: Successful Swiss National Science Foundation Grant Proposal. Download here
Surface Water Hydrology•Department of Hydrologic Sciences
Thesis: Development, Analysis and Use of a Distributed Wireless Sensor Network for Quantifying Spatial Trends of Snow Depth and Snow Water Equivalence. Download here
Environmental Geology / Chemistry minor•Department of Geosciences
Thesis: Discriminating Pre- and Post- Mining Effects on The Middle Fork of Mineral Creek, Silverton, CO, Using Tree Core Analysis
Awarded outstanding senior in the earth sciences (Eugene M. Shoemaker Award)
New Mexico Water Science Center
Hydrologist • July 2016 - Present
Snow Hydrologist / PhD Candidate • February 2012 - February 2016
Contract Hydrologist • September 2011 - February 2012
Snow Hydrology Intern• January 2011 - July 2011
Research Assistant• September 2008 - December 2010
Public Lands Center - Durango, Colorado
Hydrologic Technician• 2005 - 2007
(2013 - 2014) Department of Environment Systems Science
'Environmental Measurement Laboratory' (701)
(2008 - 2010) Department of Natural Resources and Environmental Science
'Principles of Ecohydrology' (295)
'Ecohydrology Field Camp (400)
(2009 - 2010) 'Discover your Future'
•Moeser, D., G. Mazzotti, N. Helbig, T. Jonas; Representing spatial variability of forest snow: Implementation of a new interception model, 2016; Water Resources Research, doi: 10.1002/2015WR017961 (link)
•Moeser, D., M. Stähli, T. Jonas; Improved snow interception modeling using novel canopy parameters from airborne LIDAR data, 2015; Water Resources Research, doi: 10.1002/2014WR016724 (link)
•Moeser, D., F. Morsdorf, T. Jonas; Novel forest structure metrics from airborne LiDAR data for improved snow interception estimation, 2015; Agriculture and Forest Meteorology, doi: 10.1016/j.agrformet.2015.04.013 (link)
•Moeser, D., J. Roubinek, P. Schleppi, F. Morsdorf, T. Jonas; Canopy closure, LAI and radiation transfer from airborne LiDAR synthetic images; 2014; Agricultural and Forest Meteorology, doi: 10.1016/j.agrformet.2014.06.008 (link)
•South Central Climate Science Center – ‘The Effects of Wildfire on Snow Water Resources Under Multiple Climate Conditions (2017): ~375,000 USD
•Swiss National Science Foundation – ‘Snow Distribution Dynamics under Forest Canopy’ (2012) (link) : ~175,000 USD
•Agriculture Research Service – ‘Recommended Procedure for Assessing Soil Disturbances in Vegetation Management Projects within Sensitive Areas of the Lake Tahoe Basin’ (2008)
•Moeser, D., M. Stähli; ‘Forest Canopy Controls on Snow Hydrology,’ poster presentation, Western Snow Conference, Boise, Idaho, March 2017
•Moeser, D.; ‘Forest snow hydrology,’ Department colloquium series, Department of Earth and Environmental Science, New Mexico Tech, Socorro, New Mexico, January 2017
•Moeser, D.; ‘The influence of forest canopy structure on snow hydrology: Novel modeling and visualization approaches,’ Department colloquium series, Department of Earth and Planetary Sciences, University of New Mexico, Albuquerque, New Mexico, December 2016
•Moeser, D., M. Stähli; ‘The influence of canopy structure on snow,’ poster presentation, American Geophysical Union meeting, San Francisco, California, December 2016
•Moeser, D., M. Stähli, T. Jonas; ‘Snow interception modeling,’ oral presentation, The International Union of Geodesy and Geophysics, Prague, Czech Republic, June 2015
•Moeser, D., F. Morsdorf, T. Jonas; ‘Improving snow interception modeling using LiDAR data,’ poster presentation, American Geophysical Union meeting, San Francisco, CA, December 2014
•Moeser, D., J. Roubinek, F. Morsdorf, T. Jonas; ‘Snow distribution dynamics under forest canopy,’ poster presentation, American Geophysical Union meeting, San Francisco, CA, December 2013
•Moeser, D., T. Jonas, F. Morsdorf; ‘Linking snow accumulation patterns in forests with LiDAR derived canopy structure data,’ oral presentation, Davos Atmosphere and Cryosphere Assembly – The International Union of Geodesy and Geophysics, Davos, Switzerland, July 2013
•Jonas, T., D. Moeser, F. Morsdorf; ‘Linking forest snow distribution measurements with canopy structure data,’ Presented by Dr. Tobias Jonas at the American Geophysical Union meeting, San Francisco, California, December 2012
•Jonas, T., D. Moeser, J. Magnusson, M. Bavay; ‘Validation of multiple approaches for modeling SWE Distribution and subsequent snowmelt in a small alpine watershed,’ Presented by Dr. Tobias Jonas at the International Union of Geodesy and Geophysics, Melbourne, Australia, July 2011
•Moeser, D., M. Walker, C. Skalka, J. Frolik; ‘A distributed wireless sensor network for quantifying spatial trends of snow depth and snow water equivalent,’ Presented by Dr. Mark Walker at the 79th Annual Western Snow Conference, Stateline, NV, April 2011
•Moeser, D., M. Walker, C. Skalka, J. Frolik; ‘Development, analysis & sse of a distributed wireless sensor network for quantifying spatial trends of snow,’ Presented by Dr. Mark Walker at the Nevada Water Resources Association, Annual conference Reno, NV, February 2011
•Moeser, D., Skalka, C., M. Walker, J. Frolik; ‘Snowcloud: development of a distributed in situ instrument for snowpack monitoring,’ Poster presentation, American Geophysical Union meeting, San Francisco, California, December 2009
•Scripting / Coding is and has been an integral part of my work flow for over ten years. After I open my email each morning, I typically then open the command line window and start a blank matlab script. My scripting activities range from daily data analysis to dyanmically programmed interfaces and stand alone programs to process and analyze environmental data. I have several packages for novel LiDAR data manipulation, analysis and visualization available upon request. I coinisder myself an expert in Matlab, highly proficient in R, and have a base foundation in Python and Fortran as well as HTML and CSS.
• Deployment and Development of Meteorological Equipment
The recovery timing of burned watersheds, or the time the watershed takes to return to pre-fire peak flow state, is a function of many processes and can range from just a few years to decades. The watershed recovery continuum (the initialization, duration, rate and plenum) is based upon many interrelated aspects such as the portion of the watershed initially affected by fire, fire severity, storm duration, storm timing and storm intensity. However, recovery can be indirectly quantified by runoff efficiency, which is defined as the percent of precipitation that collects and creates runoff in a stream channel. Runoff efficiency is related to runoff travel time, and in general runoff travel time decreases as post-fire runoff efficiency increases following a wildfire compared to pre-fire values.
Stay tuned for an upcoming ISI joiurnal article which analyzes watershed response and recovery in a small SW US watershed.Wildifire, Watershed, Modeling, Recovery
I was responsible for the development of a Precipitation Runoff Modeling System (PRMS) created for a small southwestern US watershed to determine post-fire wildfire effects of the hydrologic system. I created and calibrated two models: one model for pre-fire conditions and one model for post-fire conditions. The post-fire model was able to accurately model post-fire watershed response primarily from the manipulation of 5 manually calibrated parameters (PRMS has over 130 tunable parameters), which includes 2 canopy density parameters, shortwave radiation transmission through the canopy, soil recharge capacity and soil-water storage capacity.
There are dramatic diferences in pre- and post- wildfire watershed response. The above graph compares overland flow between the pre- and post- fire models at diferent precipiation events. Interestingly, the larget diference between the two models were at median soil moisture capacities. Stay tuned for an upcoming USGS Scientific Investigations Report and an ISI Journal article.PRMS modeling, post-fire watershed response
I was recently awarded a competetvie grant from the South Central Cliamte Center to qauntify the effects of forest canopy disturbance on the underlying snow water resources.
Snow accounts for approximately 70% of total streamflow from the South Western US region’s primary water arteries, the Colorado River and Rio Grande. Forests within these watersheds are affected by climate change, modifications in land management, and a variety of natural disturbances such as wildfire and bark beetle attacks, all of which create uncertainty regarding the fate of this major water source. No studies have characterized or quantified the effects of forest fire on snow-water resources under a range of meteorological conditions that represent potential future climate scenarios. Until recently, forest snow models have been ill equipped to accurately quantify under-canopy snow accumulation and melt processes as they relate to the overlying forest canopy structure. Without tools to simulate and analyze potential impacts of wildfire on snow-water resources, effective water-resource planning, watershed protection, post-wildfire risk assessments, and future forest gap and growth analyses will have limited scientific basis or applicability in regions with wildfire potential. In order to better constrain forest-snow processes, a new snow-melt model has been developed that directly integrates LiDAR data for a high resolution representation of the modeling domain. A new process-based snow-interception model has also been developed that integrates LiDAR data to characterize the forest canopy.High Resolution Snow Modeling, Forest Canopy Disturbace, Aerial LiDAR
The Water Cycle is affected by our changing world and climate; as such, we need more accurate measures to quantify the distribution of this critical resource over the landscape.
Please do not hesitate to contact me with any questions, comments or ideas. Collaboration keeps it fun!
Just 0.3% of total freshwater ( less than 0.007% of total water) is seen on the surface as rivers and lakes.The United Nations World Water Assesment Programme
Water scarcity affects > 40 percent of the global population, over 1.7 billion people are living in river basins where water use exceeds recharge.The United Nations World Water Assesment Programme
On average the water footprint of a U.S. citizen is 32,911 glasses of water a day. CHECK YOUR PERSONAL WATER FOOTPRINT HERE.The Water Footprint Network
Look forward to hearing from you!