scholarly journals Multi-criteria parameter estimation for the unified land model

2012 ◽  
Vol 9 (4) ◽  
pp. 4417-4463 ◽  
Author(s):  
B. Livneh ◽  
D. P. Lettenmaier
Keyword(s):  
United States ◽  
Remote Sensing ◽  
Parameter Estimation ◽  
Land Surface ◽  
Large Scale ◽  
The United States ◽  
Limiting Factor ◽  
Surface Model ◽  
United States Geological Survey ◽  
Skill Scores

Abstract. We describe a parameter estimation framework for the Unified Land Model (ULM) that utilizes multiple independent data sets over the Continental United States. These include a satellite-based evapotranspiration (ET) product based on MODerate resolution Imaging Spectroradiometer (MODIS) and Geostationary Operation Environmental Satellites (GOES) imagery, an atmospheric-water balance based ET estimate that utilizes North American Regional Reanalysis (NARR) atmospheric fields, terrestrial water storage content (TWSC) data from the Gravity Recovery and Climate Experiment (GRACE), and streamflow (Q) primarily from the United States Geological Survey (USGS) stream gauges. The study domain includes 10 large-scale (≥105 km2) river basins and 250 smaller-scale (<104 km2) tributary basins. ULM, which is essentially a merger of the Noah Land Surface Model and Sacramento Soil Moisture Accounting model, is the basis for these experiments. Calibrations were made using each of the criteria individually, in addition to combinations of multiple criteria, with multi-criteria skill scores computed for all cases. At large-scales calibration to Q resulted in the best overall performance, whereas certain combinations of ET and TWSC calibrations lead to large errors in other criteria. At small scales, about one-third of the basins had their highest Q performance from multi-criteria calibrations (to Q and ET) suggesting that traditional calibration to Q may benefit by supplementing observed Q with remote sensing estimates of ET. Model streamflow errors using optimized parameters were mostly due to over (under) estimation of low (high) flows. Overall, uncertainties in remote-sensing data proved to be a limiting factor in the utility of multi-criteria parameter estimation.

scholarly journals Multi-criteria parameter estimation for the Unified Land Model

2012 ◽  
Vol 16 (8) ◽  
pp. 3029-3048 ◽  
Author(s):  
B. Livneh ◽  
D. P. Lettenmaier
Keyword(s):  
United States ◽  
Remote Sensing ◽  
Parameter Estimation ◽  
Land Surface ◽  
The United States ◽  
Limiting Factor ◽  
Surface Model ◽  
United States Geological Survey ◽  
Data Sets ◽  
Skill Scores

Abstract. We describe a parameter estimation framework for the Unified Land Model (ULM) that utilizes multiple independent data sets over the continental United States. These include a satellite-based evapotranspiration (ET) product based on MODerate resolution Imaging Spectroradiometer (MODIS) and Geostationary Operational Environmental Satellites (GOES) imagery, an atmospheric-water balance based ET estimate that utilizes North American Regional Reanalysis (NARR) atmospheric fields, terrestrial water storage content (TWSC) data from the Gravity Recovery and Climate Experiment (GRACE), and streamflow (Q) primarily from the United States Geological Survey (USGS) stream gauges. The study domain includes 10 large-scale (≥105 km2) river basins and 250 smaller-scale (<104 km2) tributary basins. ULM, which is essentially a merger of the Noah Land Surface Model and Sacramento Soil Moisture Accounting Model, is the basis for these experiments. Calibrations were made using each of the data sets individually, in addition to combinations of multiple criteria, with multi-criteria skill scores computed for all cases. At large scales, calibration to Q resulted in the best overall performance, whereas certain combinations of ET and TWSC calibrations lead to large errors in other criteria. At small scales, about one-third of the basins had their highest Q performance from multi-criteria calibrations (to Q and ET) suggesting that traditional calibration to Q may benefit by supplementing observed Q with remote sensing estimates of ET. Model streamflow errors using optimized parameters were mostly due to over (under) estimation of low (high) flows. Overall, uncertainties in remote-sensing data proved to be a limiting factor in the utility of multi-criteria parameter estimation.

scholarly journals Diagnosing Moisture Sources for Flash Floods in the United States. Part II: Terrestrial and Oceanic Sources of Moisture

2019 ◽  
Vol 20 (8) ◽  
pp. 1511-1531 ◽  
Author(s):  
Jessica M. Erlingis ◽  
Jonathan J. Gourley ◽  
Jeffrey B. Basara
Keyword(s):  
United States ◽  
Boundary Layer ◽  
Land Surface ◽  
Flash Flood ◽  
The United States ◽  
Flash Floods ◽  
Heat Fluxes ◽  
Surface Model ◽  
Positive Anomaly ◽  
The Impact

Abstract Backward trajectories were derived from North American Regional Reanalysis data for 19 253 flash flood reports published by the National Weather Service to determine the along-path contribution of the land surface to the moisture budget for flash flood events in the conterminous United States. The impact of land surface interactions was evaluated seasonally and for six regions: the West Coast, Arizona, the Front Range, Flash Flood Alley, the Missouri Valley, and the Appalachians. Parcels were released from locations that were impacted by flash floods and traced backward in time for 120 h. The boundary layer height was used to determine whether moisture increases occurred within the boundary layer or above it. Moisture increases occurring within the boundary layer were attributed to evapotranspiration from the land surface, and surface properties were recorded from an offline run of the Noah land surface model. In general, moisture increases attributed to the land surface were associated with anomalously high surface latent heat fluxes and anomalously low sensible heat fluxes (resulting in a positive anomaly of evaporative fraction) as well as positive anomalies in top-layer soil moisture. Over the ocean, uptakes were associated with positive anomalies in sea surface temperatures, the magnitude of which varies both regionally and seasonally. Major oceanic surface-based source regions of moisture for flash floods in the United States include the Gulf of Mexico and the Gulf of California, while boundary layer moisture increases in the southern plains are attributable in part to interactions between the land surface and the atmosphere.

The calibration-free complementary relationship (CR) approach aids large-scale ET estimation

2020 ◽  
Author(s):  
Ning Ma ◽  
Jozsef Szilagyi ◽  
Yinsheng Zhang
Keyword(s):  
Remote Sensing ◽  
Water Balance ◽  
Eddy Covariance ◽  
Land Surface ◽  
Large Scale ◽  
Relative Bias ◽  
Dew Point ◽  
Surface Model ◽  
Complementary Relationship ◽  
Calibration Free

&lt;p&gt;Having recognized the limitations in spatial representativeness and/or temporal coverage of (i) current ground evapotranspiration (ET&lt;sub&gt;a&lt;/sub&gt;) observations, and; (ii) land surface model (LSM) and remote sensing (RS) based ET&lt;sub&gt;a&lt;/sub&gt; estimates due to uncertainties in soil and vegetation parameters, a calibration-free nonlinear complementary relationship (CR) model is employed with inputs of air and dew-point temperature, wind speed, and net radiation to estimate monthly ET&lt;sub&gt;a&lt;/sub&gt; over conterminous United States during 1979&amp;#8211;2015. Similar estimates of three land surface models (Noah, VIC, Mosaic), two reanalysis products (NCEP-II, ERA-Interim), two remote-sensing-based (GLEAM, PML) algorithms, and the spatially upscaled eddy-covariance ET&lt;sub&gt;a&lt;/sub&gt; measurements of FLUXNET-MTE plus this new result from CR were validated against water-balance-derived results. We found that the CR outperforms all other methods in the multiyear mean annual HUC2-averaged ET&lt;sub&gt;a&lt;/sub&gt; estimates with RMSE = 51 mm yr&lt;sup&gt;&amp;#8722;1&lt;/sup&gt;, R = 0.98, relative bias of &amp;#8722;1 %, and NSE = 0.94, respectively. Inclusion of the GRACE data into the annual water balances for the considerably shorter 2003&amp;#8211;2015 period does not have much effect on model performance. Besides, the CR outperforms all other models for the linear trends in annual ET rates over the HUC2 basins. Over the significantly smaller HUC6 basins where the water-balance validation is more uncertain, the CR still outperforms all other models except FLUXNET-MTE, which has the advantage of possible local ET&lt;sub&gt;a&lt;/sub&gt; measurements, a benefit that clearly diminishes at the HUC2 scale.&lt;/p&gt;&lt;p&gt;&amp;#160;&amp;#160; Because the employed CR method is calibration-free and requires only very few meteorological inputs, yet it yields superior ET performance at the regional scale, we further employed this method to develop a new 34-year (1982-2015) ET&lt;sub&gt;a&lt;/sub&gt; product for China. The new Chinese ET&lt;sub&gt;a&lt;/sub&gt; product was first validated against 13 eddy-covariance measurements in China, producing NSE values in the range of 0.72&amp;#8211;0.95. On the basin scale, the modeled ET&lt;sub&gt;a&lt;/sub&gt; values yielded a relative bias of 6%, and an NSE value of 0.80 in comparison with water-balance-derived evapotranspiration rates across ten major river basins in China, indicating the CR-simulated ET&lt;sub&gt;a&lt;/sub&gt; rates reliable over China. Further evaluations suggest that the CR-based ET&lt;sub&gt;a&lt;/sub&gt; product is more accurate than seven other mainstream ET&lt;sub&gt;a&lt;/sub&gt; products. During last three decades, our new ET&lt;sub&gt;a&lt;/sub&gt; product showed that annual ET&lt;sub&gt;a&lt;/sub&gt; increased significantly over most parts of western and northeastern China, but decreased significantly in many regions of the North China Plain as well as in the eastern and southern coastal regions of China. This new CR-derived ET&lt;sub&gt;a&lt;/sub&gt; product would benefit the community for long-term large-scale hydroclimatological studies.&lt;/p&gt;

scholarly journals Constructing a Large-scale Landslide Database Across Heterogeneous Environments Using Task-Specific Model Updates

2021 ◽  
Author(s):  
savinay nagendra ◽  
srikanth banagere manjunatha ◽  
daniel kifer ◽  
te pei ◽  
weixin li ◽  
...  
Keyword(s):  
United States ◽  
Large Scale ◽  
Satellite Images ◽  
The United States ◽  
Specific Model ◽  
Google Earth ◽  
Landslide Inventory ◽  
United States Geological Survey ◽  
Heterogeneous Environments ◽  
Extensive Data

We use the landslide inventory database provided by the United States Geological Survey. USGS maintains a database of landslide reports with approximate locations and times, but no images. This is the most extensive data of its kind. We extract satellite images from Google Earth by using this inventory.<br>

scholarly journals A SMAP-Based Drought Monitoring Index for the United States

2018 ◽  
Author(s):  
Sara Sadri ◽  
Eric F. Wood ◽  
Ming Pan
Keyword(s):  
United States ◽  
Soil Moisture ◽  
Land Surface ◽  
Surface Soil ◽  
Root Zone ◽  
Warm Season ◽  
The United States ◽  
Surface Model ◽  
Surface Soil Moisture ◽  
Near Surface

Abstract. Since April 2015, NASA's Soil Moisture Active Passive (SMAP) mission has monitored near-surface soil moisture, mapping the globe between the latitude bands of 85.044° N/S in 2–3 days depending on location. SMAP Level 3 passive radiometer product (SPL3SMP) measures the amount of water in the top 5 cm of soil except for regions of heavy vegetation (vegetation water content >4.5 kg/m2) and frozen or snow covered locations. SPL3SMP retrievals are spatially and temporally discontinuous, so the 33 months offers a short SMAP record length and poses a statistical challenge for meaningful assessment of its indices. The SMAP SPL4SMAU data product provides global surface and root zone soil moisture at 9-km resolution based on assimilating the SPL3SMP product into the NASA Catchment land surface model. Of particular interest to SMAP-based agricultural applications is a monitoring product that assesses the SMAP near-surface soil moisture in terms of probability percentiles for dry and wet conditions. We describe here SMAP-based indices over the continental United States (CONUS) based on both near-surface and root zone soil moisture percentiles. The percentiles are based on fitting a Beta distribution to the retrieved moisture values. To assess the data adequacy, a statistical comparison is made between fitting the distribution to VIC soil moisture values for the days when SPL3SMP are available, versus fitting to a 1979–2017 VIC data record. For the cold season (November–April), 57 % of grids were deemed to be consistent between the periods, and 68 % in the warm season (May–October), based on a Kolmogorov–Smirnov statistical test. It is assumed that if grids passed the consistency test using VIC data, then the grid had sufficient SMAP data. Our near-surface and root zone drought index on maps are shown to be similar to those produced by the U.S. Drought Monitor (from D0-D4) and GRACE. In a similar manner, we extend the index to include pluvial conditions using indices W0-W4. This study is a step forward towards building a national and international soil moisture monitoring system, without which, quantitative measures of drought and pluvial conditions will remain difficult to judge.

scholarly journals Analysis Ready Data: Enabling Analysis of the Landsat Archive

2018 ◽  
Author(s):  
John Dwyer ◽  
David Roy ◽  
Brian Sauer ◽  
Calli Jenkerson ◽  
Hankui Zhang ◽  
...  
Keyword(s):  
United States ◽  
Land Cover ◽  
Large Scale ◽  
The United States ◽  
Poor Quality ◽  
Thematic Mapper ◽  
United States Geological Survey ◽  
Landsat 8 ◽  
Landsat 7 ◽  
Data Provision

Data that have been processed to allow analysis with a minimum of additional user effort are often referred to as Analysis Ready Data (ARD). The ability to perform large scale Landsat analysis relies on the ability to access observations that are geometrically and radiometrically consistent, and have had non-target features (clouds) and poor quality observations flagged so that they can be excluded. The United States Geological Survey (USGS) has processed all of the Landsat 4 and 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) archive over the conterminous United States (CONUS), Alaska, and Hawaii, into Landsat ARD. The ARD are available to significantly reduce the burden of pre-processing on users of Landsat data. Provision of pre-prepared ARD is intended to make it easier for users to produce Landsat-based maps of land cover and land-cover change and other derived geophysical and biophysical products. The ARD are provided as tiled, georegistered, top of atmosphere and atmospherically corrected products defined in a common equal area projection, accompanied by spatially explicit quality assessment information, and

Simulating the Effects of Irrigation over the United States in a Land Surface Model Based on Satellite-Derived Agricultural Data

2010 ◽  
Vol 11 (1) ◽  
pp. 171-184 ◽  
Author(s):  
Mutlu Ozdogan ◽  
Matthew Rodell ◽  
Hiroko Kato Beaudoing ◽  
David L. Toll
Keyword(s):  
United States ◽  
Heat Flux ◽  
Land Surface ◽  
Agricultural Land ◽  
Weather Prediction ◽  
Land Surface Model ◽  
The United States ◽  
Surface Model ◽  
Irrigation Scheme ◽  
Ground Heat Flux

Abstract A novel method is introduced for integrating satellite-derived irrigation data and high-resolution crop-type information into a land surface model (LSM). The objective is to improve the simulation of land surface states and fluxes through better representation of agricultural land use. Ultimately, this scheme could enable numerical weather prediction (NWP) models to capture land–atmosphere feedbacks in managed lands more accurately and thus improve forecast skill. Here, it is shown that the application of the new irrigation scheme over the continental United States significantly influences the surface water and energy balances by modulating the partitioning of water between the surface and the atmosphere. In this experiment, irrigation caused a 12% increase in evapotranspiration (QLE) and an equivalent reduction in the sensible heat flux (QH) averaged over all irrigated areas in the continental United States during the 2003 growing season. Local effects were more extreme: irrigation shifted more than 100 W m−2 from QH to QLE in many locations in California, eastern Idaho, southern Washington, and southern Colorado during peak crop growth. In these cases, the changes in ground heat flux (QG), net radiation (RNET), evapotranspiration (ET), runoff (R), and soil moisture (SM) were more than 3 W m−2, 20 W m−2, 5 mm day−1, 0.3 mm day−1, and 100 mm, respectively. These results are highly relevant to continental-to-global-scale water and energy cycle studies that, to date, have struggled to quantify the effects of agricultural management practices such as irrigation. On the basis of the results presented here, it is expected that better representation of managed lands will lead to improved weather and climate forecasting skill when the new irrigation scheme is incorporated into NWP models such as NOAA’s Global Forecast System (GFS).

scholarly journals Effects of Irrigation on Summer Precipitation over the United States

2016 ◽  
Vol 29 (10) ◽  
pp. 3541-3558 ◽  
Author(s):  
Lisi Pei ◽  
Nathan Moore ◽  
Shiyuan Zhong ◽  
Anthony D. Kendall ◽  
Zhiqiu Gao ◽  
...  
Keyword(s):  
United States ◽  
Irrigation Water ◽  
Land Surface ◽  
Large Scale ◽  
The United States ◽  
High Plains ◽  
Pressure System ◽  
Typical Application ◽  
Central United States ◽  
Mean Square Errors

Abstract Irrigation’s effects on precipitation during an exceptionally dry summer (June–August 2012) in the United States were quantified by incorporating a novel dynamic irrigation scheme into the Weather Research and Forecasting (WRF) Model. The scheme is designed to represent a typical application strategy for farmlands across the conterminous United States (CONUS) and a satellite-derived irrigation map was incorporated into the WRF-Noah-Mosaic module to realistically trigger the irrigation. Results show that this new irrigation approach can dynamically generate irrigation water amounts that are in close agreement with the actual irrigation water amounts across the high plains (HP), where the prescribed scheme best matches real-world irrigation practices. Surface energy and water budgets have been substantially altered by irrigation, leading to modified large-scale atmospheric circulations. In the studied dry summer, irrigation was found to strengthen the dominant interior high pressure system over the southern and central United States and deepen the trough over the upper Midwest. For the HP and central United States, the rainfall amount is slightly reduced over irrigated areas, likely as a result of a reduction in both local convection and large-scale moisture convergence resulting from interactions and feedbacks between the land surface and atmosphere. In areas downwind of heavily irrigated regions, precipitation is enhanced, resulting in a 20%–100% reduction in the dry biases (relative to the observations) simulated over a large portion of the downwind areas without irrigation in the model. The introduction of irrigation reduces the overall mean biases and root-mean-square errors in the simulated daily precipitation over the CONUS.

scholarly journals The Status of Air Quality in the United States During the COVID-19 Pandemic: A Remote Sensing Perspective

2021 ◽  
Vol 13 (3) ◽  
pp. 369
Author(s):  
Yasin F. Elshorbany ◽  
Hannah C. Kapper ◽  
Jerald R. Ziemke ◽  
Scott A. Parr
Keyword(s):  
United States ◽  
Remote Sensing ◽  
New York ◽  
Air Quality ◽  
Large Scale ◽  
The United States ◽  
Pollution Source ◽  
Traffic Volume ◽  
Industrial Emissions ◽  
High Traffic Volume

The recent COVID-19 pandemic has prompted global governments to take several measures to limit and contain the spread of the novel virus. In the United States (US), most states have imposed a partial to complete lockdown that has led to decreased traffic volumes and reduced vehicle emissions. In this study, we investigate the impacts of the pandemic-related lockdown on air quality in the US using remote sensing products for nitrogen dioxide tropospheric column (NO2), carbon monoxide atmospheric column (CO), tropospheric ozone column (O3), and aerosol optical depth (AOD). We focus on states with distinctive anomalies and high traffic volume, New York (NY), Illinois (IL), Florida (FL), Texas (TX), and California (CA). We evaluate the effectiveness of reduced traffic volume to improve air quality by comparing the significant reductions during the pandemic to the interannual variability (IAV) of a respective reference period for each pollutant. We also investigate and address the potential factors that might have contributed to changes in air quality during the pandemic. As a result of the lockdown and the significant reduction in traffic volume, there have been reductions in CO and NO2. These reductions were, in many instances, compensated by local emissions and, or affected by meteorological conditions. Ozone was reduced by varying magnitude in all cases related to the decrease or increase of NO2 concentrations, depending on ozone photochemical sensitivity. Regarding the policy impacts of this large-scale experiment, our results indicate that reduction of traffic volume during the pandemic was effective in improving air quality in regions where traffic is the main pollution source, such as in New York City and FL, while was not effective in reducing pollution events where other pollution sources dominate, such as in IL, TX and CA. Therefore, policies to reduce other emissions sources (e.g., industrial emissions) should also be considered, especially in places where the reduction in traffic volume was not effective in improving air quality (AQ).

scholarly journals Development of the Pan-Arctic Snowfall Reconstruction: New Land-Based Solid Precipitation Estimates for 1940–99

2007 ◽  
Vol 8 (6) ◽  
pp. 1243-1263 ◽  
Author(s):  
J. E. Cherry ◽  
L-B. Tremblay ◽  
M. Stieglitz ◽  
G. Gong ◽  
S. J. Déry
Keyword(s):  
Land Surface ◽  
Snow Depth ◽  
Large Scale ◽  
Land Surface Model ◽  
Seasonal Prediction ◽  
The United States ◽  
Cold Season ◽  
Surface Model ◽  
Blowing Snow ◽  
Solid Precipitation

Abstract A new product, the Pan-Arctic Snowfall Reconstruction (PASR), is developed to address the problem of cold season precipitation gauge biases for the 1940–99 period. The method used to create the PASR is different from methods used in other large-scale precipitation data products and has not previously been employed for estimating pan-arctic snowfall. The NASA Interannual-to-Seasonal Prediction Project Catchment Land Surface Model is used to reconstruct solid precipitation from observed snow depth and surface air temperatures. The method is tested at four stations in the United States and Canada where results are examined in depth. Reconstructed snowfall at Dease Lake, British Columbia, and Barrow, Alaska, is higher than gauge observations. Reconstructed snowfall at Regina, Saskatchewan, and Minot, North Dakota, is lower than gauge observations, probably because snow is transported by wind out of the Prairie region and enters the hydrometeorological cycle elsewhere. These results are similar to gauge biases estimated by a water budget approach. Reconstructed snowfall is consistently higher than snowfall from the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) but does not have a consistent relationship with snowfall derived from the WMO Solid Precipitation Intercomparison Project correction algorithms. Advantages of the PASR approach include that 1) the assimilation of snow depth observations captures blowing snow where it is deposited and 2) the modeling approach takes into account physical snowpack evolution. These advantages suggest that the PASR product could be a valuable alternative to statistical gauge corrections and that arctic ground-based solid precipitation observing networks might emphasize snow depth measurements over gauges.

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