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NASA NTRS2021

Colorado River Basin Water Resources: Pairing Phenology Citizen Science Observations with NASA Earth Observations to Monitor Riparian Vegetation

Native plant species in the Colorado River Basin are currently threatened by non-native species that are changing resource competition dynamics. Monitoring native species is essential for understanding changes in plant health throughout the year as well as how species’ distributions change inter-annually. However, current monitoring of species is limited to local efforts and cannot be efficiently conducted across more extensive regions of the basin. Project partners, including the USA National Phenology Network, United States Geological Survey, and Educating Children Outdoors, provided data and on-the-ground expertise to assist the project. NASA Earth observations, including Landsat 8 Operational Land Imager (OLI), Aqua and Terra Moderate Resolution Imaging Spectroradiometer (MODIS), and Sentinel-2 MultiSpectral Instrument (MSI) from the European Space Agency, provided remote sensing data that covers the large spatial extent of the basin. This study combined both data sources over the study period of 2013 – 2019 and explored the relationship between on-the-ground phenology observations and remotely sensed phenology signals. The team found that pairing the datasets required spatial coordinates to be accurately logged and that it was necessary to have consistent observations throughout the year by citizen scientists. The team found promising relationships between phenology observations and the two vegetation indices, normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI), when study locations were in an area with a high proportion of vegetation within a satellite pixel. The results and methods outlined in this study create new opportunities to leverage remote sensing alongside citizen science data for widespread monitoring of non-native species.

Tyler Pantle↗
NASA NTRS2004

Eastern Sahara Geology from Orbital Radar: Potential Analog to Mars

Much of the surface of Mars has been intensely reworked by aeolian processes and key evidence about the history of the Martian environment seems to be hidden beneath a widespread layer of debris (paleo lakes and rivers, faults, impact craters). In the same way, the recent geological and hydrological history of the eastern Sahara is still mainly hidden under large regions of wind-blown sand which represent a possible terrestrial analog to Mars. The subsurface geology there is generally invisible to optical remote sensing techniques, but radar images obtained from the Shuttle Imaging Radar (SIR) missions were able to penetrate the superficial sand layer to reveal parts of paleohydrological networks in southern Egypt.

Farr, T. G.↗
NASA NTRS1984

Drainage Evolution in the Debris Avalanche Deposits Near Mount Saint Helens, Washington

The 18 May 1980 eruption of Mount St. Helens was initiated by a massive rockslide-debris avalanche which completely transformed the upper 25 km of the North Fork Toutle River valley. The debris was generated by one of the largest gravitational mass movements ever recorded on Earth. Moving at an average velocity of 35 m/s, the debris avalanche buried approximately 60 sq km of terrain to an average depth of 45 m with unconsolidated, poorly sorted volcaniclastic material, all within a period of 10 minutes. Where exposed and unaltered by subsequent lahars and pyroclastic flows, the new terrain surface was characterized predominantly by hummocks, closed depressions, and the absence of an identifiable channel network. Following emplacement of the debris avalanche, a complex interrelationship of fluvial and mass wasting processes immediately began operating to return the impacted area to an equilibrium status through the removal of material (potential energy) and re-establishment of graded conditions. In an attempt to chronicle the morphologic evolution of this unique environmental setting, a systematic series of interpretative maps of several selected areas was produced. These maps, which document the rate and character of active geomorphic processes, are discussed.

Gary L Beach↗
NASA NTRS2021

The Analysis of Using Satellite Soil Moisture Observations for Flood Detection, Evaluating Over the Thailand’s Great Flood of 2011

A flood monitoring and warning system provides critical information that can protect property and save lives. A basin-scale flood monitoring system requires an effective observation platform that offers extensive ground coverage of flood conditions, low latency, and high spatiotemporal resolution. While satellite imagery offers substantial spatial flood extent in detail due to its high spatial resolution, the coarse temporal resolution and cloud obstruction limit its near real-time application. Daily soil moisture data derived from satellite sensors at a scale of a few km can be used to monitor extreme wet surface conditions arising in flood occurrences. This study analyses the flood detection capabilities of several sources of soil moisture information, including the Soil Moisture and Ocean Salinity mission (SMOS), the Advanced Microwave Scanning Radiometer on EOS (AMSR-E), the Advanced SCATterometer (ASCAT) on MetOp, the Global Land Data Assimilation System (GLDAS), and the WaterGAP Global Hydrology Model (WGHM). In addition to soil moisture, the analysis includes measurements of surface reflectance from the Moderate Resolution Imaging Spectroradiometer (MODIS), precipitation measurements from the Tropical Rainfall Measuring Mission (TRMM), and terrestrial water storage estimates from the Gravity Recovery And Climate Experiment (GRACE) as proxies for flood inundations. The analysis was conducted over the Chao Phraya River Basin (CPB) in Thailand, where the Great Flood of 2011 led to one of the most significant economic losses in the country's history. Satellite-derived soil moisture exhibits a stronger correlation with the flood inundations than the precipitation, model-derived soil moisture, and terrestrial water storage data. SMOS soil moisture observation agrees best with the MODIS-derived flood extent/occurrence, both in terms of spatial distribution and timing, and providing approximated flood lead-time of one week or longer. A neural network constructed from SMOS and MODIS data is used to predict flood intensity/occurrence (given soil moisture input) with a predicted time window from eight days to thirty-two days. The short-term prediction (e.g., eight days) achieves the highest accuracy with an averaged recovery rate of approximately 60% (correlation coefficient). This study's results suggest a potential application of satellite soil moisture data in assisting flood monitoring and warning systems.

Satellite soil moisture↗
NASA NTRS2023

Potomac River Basin Water Resources: Assessing Water Quality and Quantity in the National Capital Region Using NASA Earth Observations

The Potomac River Basin (PRB) is responsible for providing drinking water to over 5 million residents and plays a significant role in the health of the Chesapeake Bay. Therefore, it is important to understand the relationship between water quality, landcover, and the hydrological cycle within the PRB. The National Park Service (NPS) has monitored 37 streams within the National Park Units in Maryland, Virginia, West Virginia and Washington, D.C. This project aimed to help the NPS better understand trends in water quality to supplement their ability to monitor changes in the National Capital Region Network (NCRN). Google Earth Engine, ArcGIS Pro, R, and Python were used for data retrieval, visualization, and analysis. Earth observations included Landsat 5 TM and Landsat 8 OLI/TIRS imagery. Ancillary data included the USDA Cropland Data Layer, Climate Hazards Group InfraRed Precipitation with Station Data (CHIRPS), and soil moisture data from the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS). We compared Land use/land cover (LULC), Normalized Difference Vegetation Index (NDVI), precipitation and soil moisture data to water quality data provided by the NPS at a watershed level. LULC change maps were also generated for the PRB between 2008 and 2022. We found significant correlations between precipitation, soil moisture, NDVI, and water quality. Correlations were found between certain land use types and water quality metrics, but findings varied greatly between watersheds. These insights emphasize the imperative of strategic watershed management in preserving the integrity of key aquatic systems.

Landsat↗
NASA NTRS2006

Jet Propulsion Laboratory: Annual Report 2005

What an amazing host of new sights 2005 brought us. With impeccable choreography, one spacecraft sent an impactor slamming into a comet, reversing the traditional view of these space wayfarers by revealing it to be buried in deep drifts of a fine talcum-like powder. Another spacecraft delivered a probe from our European partners to the surface of Saturn's haze-shrouded moon Titan, disclosing a landscape eerily like Earth's, if we had methane rivers cascading down hillsides of ice. An orbiting observatory for the first time showed us the light from planets circling other stars, which astronomers previously knew to exist only from indirect clues. Throughout the year we also amassed continually expanding views of Earth as well as Mars, by far the most-explored planet after our own. In all, 18 spacecraft and five instruments were stationed across the solar system, studying our own world, other planets, comets and the deeper universe. These missions were enabled by the efforts of everyone at JPL. The Deep Space Network of communications complexes across three continents continued to experience a period of remarkable activity. Others were at work creating technologies both for NASA missions and other uses. JPL's contingent of scientific researchers was equally busy coordinating the science activities of our missions or pursuing independent investigations. None of this would be possible without the support of world-class business and administrative teams. All of our missions in one way or another support our nations Vision for Space Exploration, which envisages a gradually widening robotic and human presence across the solar system in the years ahead. The year was not without its challenges. NASA set forth to implement the Vision for Space Exploration, which resulted in some flight projects and technology efforts being terminated. To adjust to this new direction, it was necessary for us to reduce the JPL workforce by about five percent. Taking steps like this is painful, but we tried to make the process as orderly as possible. In the end, the adjustments made have left JPL on a healthy footing for the years ahead.

Jet Propulsion Laboratory (JPL)↗
NASA NTRS1974

Water-management models in Florida from ERTS-1 data

A prototype multiparameter data acquisition network, installed and operated by the U.S. Geological Survey is a viable approach for obtaining near real-time data needed to solve hydrologic problems confronting nearly 2.5 million residents of south Florida. Selected water quantity and quality data obtained from ground stations are transmitted for relay via ERTS-1 to NASA receiving stations in virtual real time. This data relay system has been very reliable and, by coupling the ground information with ERTS imagery, a modeling technique is available for water resource management in south Florida. An ecological model has been designed for the Shark River Slough in Everglades National Park.

Higer, A. L.↗
NASA NTRS2022

Could Road Constructions Be More Hazardous Than an Earthquake in Terms of Mass Movement?

Roads can have a significant impact on the frequency of mass wasting events in mountainous areas. However, characterizing the extent and pervasiveness of mass movements over time has rarely been documented due to limitations in available data sources to consistently map such events. We monitored the evolution of a road network and assessed its effect on mass movements for a 11-year window in Arhavi, Turkey. The main road construction projects run in the area are associated with a hydroelectric power plant as well as other road extension works and are clearly associated with the vast majority (90.1%) of mass movements in the area. We also notice that the overall number and size of the mass movements are much larger than in the naturally-occurring comparison area. This means that the sediment load originating from the anthropogenically induced mass movements is larger than its counterpart associated with naturally occurred landslides. Notably, this extra sediment load could cause river channel aggregation, reduce accommodation space and as a consequence, it could lead to an increase in the probability and severity of flooding along the river channel. This marks a strong and negative effect of human activities on the natural course of earth surface processes. We also compare frequency-area distributions of human-induced mass movements mapped in this study and co-seismic landslide inventories from the literature. By doing so, we aim to better understand the consequences of human effects on mass movements in a comparative manner. Our findings show that the damage generated by the road construction in terms of sediment loads to river channels is compatible with the possible effect of a theoretical earthquake with a magnitude greater than Mw=6.0.

Hakan Tanyas↗
NASA NTRSDate not supplied

The Application of Remote Sensing and Machine Learning to Improve Early Warning Systems for Harmful Algal Events in the Highland Lake Chain, TX

Beginning in 2019, harmful algal events have caused canine deaths in both Lady Bird Lake and Lake Travis located near Austin, Texas. These two reservoirs are part of the larger Highland Lakes chain, managed by the City of Austin Department of Watershed Protection (COA DWP) and the Lower Colorado River Authority (LCRA), which fulfill municipal, commercial, and agricultural water demands. Given the recent increase in favorable environmental conditions for algal events in central Texas, NASA DEVELOP partnered with LCRA and COA DWP to improve monitoring and early detection of algal events, utilizing satellite remote sensing and machine learning. Spatially and temporally varied chlorophyll a concentrations, cyanobacteria detections, turbidity, and water surface temperature products are used as environmental proxies. Landsat 8 Operational Land Imager (OLI) and the Sentinel-2 MultiSpectral Instrument (MSI) data were used to provide a combined revisit time of up to ~3 days and < 30 m per pixel products. Chlorophyll a concentrations were estimated using a pre-trained Mixture Density Network, and cyanobacteria detection was accomplished using the Broad Wavelength Algae Index, which can differentiate algal blooms from algal proliferations (mats). In situ data were used to validate remotely sensed measurements and quantify uncertainties. Preliminary results show a good fit between the modeled output and in situ observations, suggesting that remote sensing data can be used to retrieve biogeochemical properties and/or inherent optical properties (IOPs) of water columns in these inland human-made lakes. Uncertainties were introduced from the sensitivity to atmospheric correction, inherent mismatch between satellite and sampling data, and a relatively lower signal-to-noise ratio over water. The resulting products enable near real-time monitoring of environmental proxies relevant to algal event presence in the Highland Lakes chain, and will ultimately support water management, decision making, and risk communication.

Shuyu Chang↗
NASA NTRS2023

ENSO and MJO Modulation of U.S. Cloud-to-Ground Lightning Activity

Cloud-to-ground (CG) lightning substantially impacts human health and property. However, the relations between U.S. lightning activity and the Madden-Julian Oscillation (MJO) and El Niño-Southern Oscillation (ENSO), two predictable drivers of global climate variability, remain uncertain, in part because most lightning datasets have short records that cannot robustly reveal MJO- and ENSO-related patterns. To overcome this limitation, we developed an empirical model of 6-hourly lightning flash count over the contiguous U.S. (CONUS) using environmental variables (convective available potential energy and precipitation) andNational Lightning Detection Network data for 2003–2016. This model is shown to reproduce the observed daily and seasonal variability of lightning over most of CONUS. Then, the empirical model was applied to construct a proxy lightning dataset for the period 1979–2021, which was used to investigate the summer MJO-lightning relationship at daily resolution and the winter-spring ENSO-lightning relationship at seasonal resolution. Overall, no robust relationship between MJO phase and lightning patterns was found when seasonality was taken into consideration. El Niño is associated with increased lightning activity over the Coastal Southeast U.S. during early winter, the Southwest during winter through spring, and the Northwest during late spring, whereas La Niña is associated with increased lightning activity over the Tennessee River Valley during winter.

Lightning↗
NASA NTRS2011

Observation of Hydrological Processes Using Remote Sensing: Volume 2: The Science of Hydrology - Chapter 2.14

Improving water management can make a significant contribution to achieving most of the Millennium Development Goals established by the UN General Assembly in 2000, especially those related to poverty, hunger, and major diseases. The World Summit on Sustainable Development (WSSD) in 2002 recognized this need. Water and sanitation in particular received great attention from the Summit. The Johannesburg Plan of Implementation recommended to improve water resources management and scientific understanding of the water cycle through joint cooperation and research. For this purpose, it is recommended to promote knowledge sharing, provide capacity building, and facilitate the transfer of technology including remote-sensing (RS) and satellite technologies, especially to developing countries and countries with economies in transition, and to support these countries in their efforts to monitor and assess the quantity and quality of water resources, for example, by establishing and/or further developing national monitoring networks and water resources databases and by developing relevant national indicators. The Johannesburg Plan also adopted integrated water resources management as the overarching concept in addressing and solving water-related issues. As a result of the commitments made in the Johannesburg Plan of Implementation, several global and regional initiatives have emerged. Current international initiatives such as the Global Monitoring for Environment and Security (GMES) program of the European Commission and the European Space Agency (ESA), and the Global Earth Observation System of Systems (GEOSS) 10-Year Implementation Plan, have all identified Earth observation (EO) of the water cycle as the key in helping to solve the world s water problems. The availability of spatial information on water quantity and quality will also enable closure of the water budget at river basin and continental scales to the point where effective water management is essential (e.g., as requested by the European Union s Water Framework Directive (WFD), as well as national policies). Geo-information science and EO are vital in achieving a better understanding of the water cycle and better monitoring, analysis, prediction, and management of the world s water resources. The major components of the water cycle of the Earth system and their possible observations are presented. Such observations are essential to understand the global water cycle and its variability, both spatially and temporally, and can only be achieved consistently by means of EOs. Additionally, such observations are essential to advance our understanding of coupling between the terrestrial, atmospheric, and oceanic branches of the water cycle, and how this coupling may influence climate variability and predictability. Water resources management directly interferes with the natural water cycle in the forms of building dams, reservoirs, water transfer systems, and irrigation systems that divert and redistribute part of the water storages and fluxes on land. The water cycle is mainly driven and coupled to the energy cycle in terms of phase changes of water (changes among liquid, water vapor, and solid phases) and transport of water by winds in addition to gravity and diffusion processes. The water-cycle components can be observed with in situ sensors as well as airborne and satellite sensors in terms of radiative quantities. Processing and conversion of these radiative signals are necessary to retrieve the water-cycle components.

Wilder, Peter↗
NASA NTRS2021

Highland Lakes Water Resources: Using NASA Earth Observations to Improve Detection Systems for Harmful Algal Events in the Highland Lakes in Central Texas

Beginning in 2019, harmful algal events in Austin, Texas, caused canine deaths in the Lady Bird Lake and Lake Travis reservoirs. These reservoirs are part of the larger Highland Lakes chain, managed by the Lower Colorado River Authority (LCRA) and the City of Austin Department of Watershed Protection (CoA DWP), which fulfill municipal, commercial, and agricultural water demands. Given the recent increase in favorable algal event conditions in central Texas, the LCRA and CoA DWP partnered with NASA DEVELOP to improve algal event early-warning systems through the application of remote sensing and machine learning. An Earth observation-based algal monitoring system will assist the responsible agencies in predicting algal conditions and communicating hazards to the public. The NASA DEVELOP team utilized Landsat 8 Operational Land Imager (OLI) and Sentinel-2 Multispectral Instrument (MSI) data to produce products including chlorophyll-a concentrations, cyanobacteria detections, turbidity, and water surface temperature. Chlorophyll-a concentrations were retrieved with a pre-trained machine learning model (mixture density network) and spectral indices, while the other products were derived from spectral indices. In situ field data were used to validate and quantify uncertainties for each product. The validations show strong correlations for chlorophyll-a and water surface temperature. Time series analyses of chlorophyll-a concentrations show peaks in the severe drought years (2015 and 2016). This project's resulting products enable monitoring of environmental proxies relevant to algal event presence in the Highland Lakes chain and will ultimately support water management, decision making, and risk communication.

Kaitlynn Hietpas↗
NASA NTRS2015

A Module for Assimilating Hyperspectral Infrared Retrieved Profiles into the Gridpoint Statistical Interpolation System for Unique Forecasting Applications

Hyperspectral infrared sounder radiance data are assimilated into operational modeling systems however the process is computationally expensive and only approximately 1% of available data are assimilated due to data thinning as well as the fact that radiances are restricted to cloud-free fields of view. In contrast, the number of hyperspectral infrared profiles assimilated is much higher since the retrieved profiles can be assimilated in some partly cloudy scenes due to profile coupling other data, such as microwave or neural networks, as first guesses to the retrieval process. As the operational data assimilation community attempts to assimilate cloud-affected radiances, it is possible that the use of retrieved profiles might offer an alternative methodology that is less complex and more computationally efficient to solve this problem. The NASA Short-term Prediction Research and Transition (SPoRT) Center has assimilated hyperspectral infrared retrieved profiles into Weather Research and Forecasting Model (WRF) simulations using the Gridpoint Statistical Interpolation (GSI) System. Early research at SPoRT demonstrated improved initial conditions when assimilating Atmospheric Infrared Sounder (AIRS) thermodynamic profiles into WRF (using WRF-Var and assigning more appropriate error weighting to the profiles) to improve regional analysis and heavy precipitation forecasts. Successful early work has led to more recent research utilizing WRF and GSI for applications including the assimilation of AIRS profiles to improve WRF forecasts of atmospheric rivers and assimilation of AIRS, Cross-track Infrared and Microwave Sounding Suite (CrIMSS), and Infrared Atmospheric Sounding Interferometer (IASI) profiles to improve model representation of tropopause folds and associated non-convective wind events. Although more hyperspectral infrared retrieved profiles can be assimilated into model forecasts, one disadvantage is the retrieved profiles have traditionally been assigned the same error values as the rawinsonde observations when assimilated with GSI. Typically, satellitederived profile errors are larger and more difficult to quantify than traditional rawinsonde observations (especially in the boundary layer), so it is important to appropriately assign observation errors within GSI to eliminate potential spurious innovations and analysis increments that can sometimes arise when using retrieved profiles. The goal of this study is to describe modifications to the GSI source code to more appropriately assimilate hyperspectral infrared retrieved profiles and outline preliminary results that show the differences between a model simulation that assimilated the profiles as rawinsonde observations and one that assimilated the profiles in a module with the appropriate error values.

Berndt, Emily↗
NASA NTRS2004

Anthropogenic Land-use Change and the Dynamics of Amazon Forest Biomass

This project was focused on assessing the effects of prevailing land uses, such as habitat fragmentation, selective logging, and fire, on biomass and carbon storage in Amazonian forests, and on the dynamics of carbon sequestration in regenerating forests. Ancillary goals included developing GIs models to help predict the future condition of Amazonian forests, and assessing the effects of anthropogenic climate change and ENS0 droughts on intact and fragmented forests. Ground-based studies using networks of permanent plots were linked with remote-sensing data (including Landsat TM and AVHRR) at regional scales, and higher-resolution techniques (IKONOS imagery, videography, LIDAR, aerial photographs) at landscape and local scales. The project s specific goals were quite eclectic and included: Determining the effects of habitat fragmentation on forest dynamics, floristic composition, and the various components of above- and below-ground biomass. Assessing historical and physical factors that affect trajectories of forest regeneration and carbon sequestration on abandoned lands. Extrapolating results from local studies of biomass dynamics in fragmented and regenerating forests to landscape and regional scales in Amazonia, using remote sensing and GIS. Testing the hypothesis that intact Amazonian forests are functioning as a significant carbon sink. Examining destructive synergisms between forest fragmentation and fire. Assessing the short-term impacts of selective logging on aboveground biomass. Developing GIS models that integrate current spatial data on forest cover, deforestation, logging, mining, highway and roads, navigable rivers, vulnerability to wild fires, protected areas, and existing and planned infrastructure projects, in an effort to predict the future condition of Brazilian Amazonian forests over the next 20-25 years. Devising predictive spatial models to assess the influence of varied biophysical and anthropogenic predictors on Amazonian deforestation.

Laurance, William F.↗
NASA NTRS2021

Cincinnati & Covington Urban Development II: Assessing Flooding and Landslide Susceptibility Along the Ohio-Kentucky Border

Landslides and flooding are reoccurring environmental hazards that lead to health risks and economic burdens in the urban areas of Cincinnati, Ohio and Covington, Kentucky. These communities share underlying natural and artificial conditions that make them vulnerable to these hazards, including excessive precipitation, weak lithology, high impervious surface levels, and steep slopes. Despite the human and economic risks associated with these environmental hazards, the areas of highest vulnerability within the region remain unknown. NASA DEVELOP partnered with Groundwork USA and Groundwork Ohio River Valley (ORV) to assess the region’s susceptibility to landslides and flooding. The team utilized NASA Earth observations, including the Landsat 8 Operational Land Imager (OLI), Landsat 8 Thermal Infrared Sensor (TIRS), and Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrieval for GPM (IMERG), alongside ancillary datasets to map landslide susceptibility and exposure throughout the study area. The team also used ancillary data to map surface runoff and runoff retention using the Natural Capital Project’s Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) Urban Flood Risk Mitigation Model. The resulting landslide susceptibility and exposure maps highlight the neighborhoods around Avondale and Fairmount as areas of particularly high landslide exposure. Meanwhile, the InVEST outputs demonstrate that Downtown Cincinnati and the Queensgate neighborhood retain the least amount of rainfall. This research provides partners with a more complete hazard analysis of the greater Cincinnati area while also producing refined methodologies to enhance future flood and landslide vulnerability mapping throughout Groundwork USA’s nationwide network of communities.

Paxton LaJoie↗
NASA NTRS2000

Lidar Remote Sensing for Industry and Environment Monitoring

Contents include the following: 1. Keynote paper: Overview of lidar technology for industrial and environmental monitoring in Japan. 2. lidar technology I: NASA's future active remote sensing mission for earth science. Geometrical detector consideration s in laser sensing application (invited paper). 3. Lidar technology II: High-power femtosecond light strings as novel atmospheric probes (invited paper). Design of a compact high-sensitivity aerosol profiling lidar. 4. Lasers for lidars: High-energy 2 microns laser for multiple lidar applications. New submount requirement of conductively cooled laser diodes for lidar applications. 5. Tropospheric aerosols and clouds I: Lidar monitoring of clouds and aerosols at the facility for atmospheric remote sensing (invited paper). Measurement of asian dust by using multiwavelength lidar. Global monitoring of clouds and aerosols using a network of micropulse lidar systems. 6. Troposphere aerosols and clouds II: Scanning lidar measurements of marine aerosol fields at a coastal site in Hawaii. 7. Tropospheric aerosols and clouds III: Formation of ice cloud from asian dust particles in the upper troposphere. Atmospheric boundary layer observation by ground-based lidar at KMITL, Thailand (13 deg N, 100 deg. E). 8. Boundary layer, urban pollution: Studies of the spatial correlation between urban aerosols and local traffic congestion using a slant angle scanning on the research vessel Mirai. 9. Middle atmosphere: Lidar-observed arctic PSC's over Svalbard (invited paper). Sodium temperature lidar measurements of the mesopause region over Syowa Station. 10. Differential absorption lidar (dIAL) and DOAS: Airborne UV DIAL measurements of ozone and aerosols (invited paper). Measurement of water vapor, surface ozone, and ethylene using differential absorption lidar. 12. Space lidar I: Lightweight lidar telescopes for space applications (invited paper). Coherent lidar development for Doppler wind measurement from the International Space Station. 13. Space lidar II: Using coherent Doppler lidar to estimate river discharge. 14. Poster session: Lidar technology, optics for lidar. Laser for lidar. Middle atmosphere observations. Tropospheric observations (aerosols, clouds). Boundary layer, urban pollution. Differential absorption lidar. Doppler lidar. and Space lidar.

Singh, Upendra N.↗
NASA NTRS2017

Upper Blue Nile Basin Water Budget from a Multi-Model Perspective

Improved understanding of the water balance in the Blue Nile is of critical importance because of increasingly frequent hydroclimatic extremes under a changing climate. The intercomparison and evaluation of multiple land surface models (LSMs) associated with different meteorological forcing and precipitation datasets can offer a moderate range of water budget variable estimates. In this context, two LSMs, Noah version 3.3 (Noah3.3) and Catchment LSM version Fortuna 2.5 (CLSMF2.5) coupled with the Hydrological Modeling and Analysis Platform (HyMAP) river routing scheme are used to produce hydrological estimates over the region. The two LSMs were forced with different combinations of two reanalysis-based meteorological datasets from the Modern-Era Retrospective analysis for Research and Applications datasets (i.e., MERRA-Land and MERRA-2) and three observation-based precipitation datasets, generating a total of 16 experiments. Modeled evapotranspiration (ET), streamflow, and terrestrial water storage estimates were evaluated against the Atmosphere-Land Exchange Inverse (ALEXI) ET, in-situ streamflow observations, and NASA Gravity Recovery and Climate Experiment (GRACE) products, respectively. Results show that CLSMF2.5 provided better representation of the water budget variables than Noah3.3 in terms of Nash-Sutcliffe coefficient when considering all meteorological forcing datasets and precipitation datasets. The model experiments forced with observation-based products, the Climate Hazards group Infrared Precipitation with Stations (CHIRPS) and the Tropical Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA), outperform those run with MERRA-Land and MERRA-2 precipitation. The results presented in this paper would suggest that the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System incorporate CLSMF2.5 and HyMAP routing scheme to better represent the water balance in this region.

Rainfall↗
NASA NTRS2004

Observing the Global Water Cycle from Space

This paper presents an approach to measuring all major components of the water cycle from space. Key elements of the global water cycle are discussed in terms of the storage of water-in the ocean, air, cloud and precipitation, in soil, ground water, snow and ice, and in lakes and rivers, and in terms of the global fluxes of water between these reservoirs. Approaches to measuring or otherwise evaluating the global water cycle are presented, and the limitations on known accuracy for many components of the water cycle are discussed, as are the characteristic spatial and temporal scales of the different water cycle components. Using these observational requirements for a global water cycle observing system, an approach to measuring the global water cycle from space is developed. The capabilities of various active and passive microwave instruments are discussed, as is the potential of supporting measurements from other sources. Examples of space observational systems, including TRMM/GPM precipitation measurement, cloud radars, soil moisture, sea surface salinity, temperature and humidity profiling, other measurement approaches and assimilation of the microwave and other data into interpretative computer models are discussed to develop the observational possibilities. The selection of orbits is then addressed, for orbit selection and antenna size/beamwidth considerations determine the sampling characteristics for satellite measurement systems. These considerations dictate a particular set of measurement possibilities, which are then matched to the observational sampling requirements based on the science. The results define a network of satellite instrumentation systems, many in low Earth orbit, a few in geostationary orbit, and all tied together through a sampling network that feeds the observations into a data-assimilative computer model.

Hildebrand, P. H.↗