Feifei Pan

Gap Winds and Wakes: SAR Observations and Numerical Simulations

The nature of terrain-induced gap winds and wakes in the atmosphere is examined using surface wind data from synthetic aperture radar (SAR) and the shallow water equations. The shallow water model is used to predict the types of wake‐jet wind patterns that might occur behind an idealized pair of bell-shaped hills with a gap between them. A regime diagram is constructed based on the width of the gap and the upstream Froude number. Specific predictions of the model are found to compare moderately well with SAR data from four examples of airflow near Unimak Island in the Aleutian Chain. The model predicts the observed wakes and jets, including jets that exceed the upstream speed. Theoretical analysis considers the relative importance of rising terrain and narrowing valley walls in the acceleration of gap winds. Wind speeds in the wake region are controlled by the Bernoulli function and regional pressure. Gap winds therefore are streams of air that have avoided Bernoulli loss over the terrain by passing through gaps. The speed of gap winds can exceed the upstream speed only in ridgelike situations when the regional leeside pressure is lower than the upstream pressure.

Recent changes in nitrate and dissolved organic carbon export from the upper Kuparuk River, North Slope, Alaska

[1] Export of nitrate and dissolved organic carbon (DOC) from the upper Kuparuk River between the late 1970s and early 2000s was evaluated using long-term ecological research (LTER) data in combination with solute flux and catchment hydrology models. The USGS Load Estimator (LOADEST) was used to calculate June–August export from 1978 forward. LOADEST was then coupled with a catchment-based land surface model (CLSM) to estimate total annual export from 1991 to 2001. Simulations using the LOADEST/CLSM combination indicate that annual nitrate export from the upper Kuparuk River increased by � 5 fold and annual DOC export decreased by about one half from 1991 to 2001. The decrease in DOC export was focused in May and was primarily attributed to a decrease in river discharge. In contrast, increased nitrate export was evident from May to September and was primarily attributed to increased nitrate concentrations. Increased nitrate concentrations are evident across a wide range of discharge conditions, indicating that higher values do not simply reflect lower discharge in recent years but a significant shift to higher concentration per unit discharge. Nitrate concentrations remained elevated after 2001. However, extraordinarily low discharge during June 2004 and June–August 2005 outweighed the influence of higher concentrations in determining export during these years. The mechanism responsible for the recent increase in nitrate concentrations is uncertain but may relate to changes in soils and vegetation associated with regional warming. While changes in nitrate and DOC export from arctic rivers reflect changes in terrestrial ecosystems, they also have significant implications for Arctic Ocean ecosystems.

A comparison of geographical information systems-based algorithms for computing the TOPMODEL topographic index

[1] The performance of six geographical information systems (GIS)-based topographic index algorithms is evaluated by computing root-mean-square errors of the computed and the theoretical topographic indices of three idealized hillslopes: planar, convergent, and divergent. In addition to these three idealized cases, two divergent hillslopes with varying slopes, i.e., concave (slopes decrease from top to bottom) and convex (slopes increase from top to bottom) are also tested. The six GIS-based topographic index algorithms are combinations of flow direction and slope algorithms: i.e., single flow direction (SFD), biflow direction (BFD), and multiple flow direction (MFD) plus methods that determine slope values in flat areas, e.g., W-M method [Wolock and McCabe, 1995] and tracking flow direction (TFD) method. Two combinations of horizontal resolution and vertical resolution of the idealized terrain data are used to evaluate those methods. Among those algorithms the MFD algorithm is the most accurate followed by the BFD algorithm and the SFD algorithm. As the vertical resolution increases, the errors in the computed topographic index for all algorithms decrease. We found that the orientation of the contour lines of planar hillslopes significantly influences the SFD’s computed topographic index. If the contour lines are not parallel to one of eight possible flow directions, the errors in the SFD’s computed topographic index are significant. If mean slope is small, TFD becomes more accurate because slope values in flat areas are better estimated. INDEX TERMS: 1899 Hydrology: General or miscellaneous; 1824 Hydrology: Geomorphology (1625); 1832 Hydrology: Groundwater transport; KEYWORDS: GIS, TOPMODEL, topographic index, single flow direction algorithm, biflow direction algorithm, multiple flow direction algorithm

River export of nutrients and organic matter from the North Slope of Alaska to the Beaufort Sea

While river-borne materials are recognized as important resources supporting coastal ecosystems around the world, estimates of river export from the North Slope of Alaska have been limited by a scarcity of water chemistry and river discharge data. This paper quantifies water, nutrient, and organic matter export from the three largest rivers (Sagavanirktok, Kuparuk, and Colville) that drain Alaskas North Slope and discusses the potential importance of river inputs for biological production in coastal waters of the Alaskan Beaufort Sea. Together these rivers export ∼297,000 metric tons of organic carbon and ∼18,000 metric tons of organic nitrogen each year. Annual fluxes of nitrate-N, ammonium-N, and soluble reactive phosphorus are approximately 1750, 200, and 140 metric tons per year, respectively. Constituent export from Alaskas North Slope is dominated by the Colville River. This is in part due to its larger size, but also because constituent yields are greater in the Colville watershed. River-supplied nitrogen may be more important to productivity along the Alaskan Beaufort Sea coast than previously thought. However, given the dominance of organic nitrogen export, the potential role of river-supplied nitrogen in support of primary production depends strongly on remineralization mechanisms. Although rivers draining the North Slope of Alaska make only a small contribution to overall river export from the pan-arctic watershed, comparisons with major arctic rivers reveal unique regional characteristics as well as remarkable similarities among different regions and scales. Such information is crucial for development of robust river export models that represent the arctic system as a whole.

Application of the inundation area-lake level rating curves constructed from the SRTM DEM to retrieving lake levels from satellite measured inundation areas

Remote sensing technology has great potential for measuring lake inundation areas and lake levels, and providing important lake water quantity and quality information which can be used for improving our understanding of climate change impacts on the global water cycle, and assessing the influence of the projected future climate change on the global water resources. One remote sensing approach is to estimate lake level from satellite measured inundation area based on the inundation area-lake level rating (IALLR) curves. However, this approach is not easy to implement because of a lack of data for constructing the IALLR curves. In this study, an innovative and robust approach to construct the IALLR curves from the digital elevation model (DEM) data collected during the Shuttle Radar Topography Mission (SRTM) was developed and tested. It was shown that the IALLR curves derived from the SRTM DEM data could be used to retrieve lake level from satellite measured inundation area. Applying the constructed IALLR curve to the estimated inundation areas from 16 Landsat Thematic Mapper (TM) images, 16 lake levels of Lake Champlain in Vermont were obtained. The root mean square error (RMSE) of the estimated lake levels compared to the observed water levels at the U.S. Geological Survey (USGS) gauging station (04294500) at Burlington, Vermont is about 0.12m.

Estimating FPAR of maize canopy using airborne discrete-return LiDAR data

The fraction of absorbed photosynthetically active radiation (FPAR) is a key parameter for ecosystem modeling, crop growth monitoring and yield prediction. Ground-based FPAR measurements are time consuming and labor intensive. Remote sensing provides an alternative method to obtain repeated, rapid and inexpensive estimates of FPAR over large areas. LiDAR is an active remote sensing technology and can be used to extract accurate canopy structure parameters. A method to estimating FPAR of maize from airborne discrete-return LiDAR data was developed and tested in this study. The raw LiDAR point clouds were processed to separate ground returns from vegetation returns using a filter method over a maize field in the Heihe River Basin, northwest China. The fractional cover (fCover) of maize canopy was computed using the ratio of canopy return counts or intensity sums to the total of returns or intensities. FPAR estimation models were established based on linear regression analysis between the LiDAR-derived fCover and the field-measured FPAR (R(2) = 0.90, RMSE = 0.032, p < 0.001). The reliability of the constructed regression model was assessed using the leave-one-out cross-validation procedure and results show that the regression model is not overfitting the data and has a good generalization capability. Finally, 15 independent field-measured FPARs were used to evaluate accuracy of the LiDAR-predicted FPARs and results show that the LiDAR-predicted FPAR has a high accuracy (R(2) = 0.89, RMSE = 0.034). In summary, this study suggests that the airborne discrete-return LiDAR data could be adopted to accurately estimate FPAR of maize.

Estimating Daily Surface Soil Moisture Using a Daily Diagnostic Soil Moisture Equation

AbstractOne common problem is associated with water balance calculation methods for determining soil moisture for scheduling irrigation: errors in the estimated soil moisture are cumulative and frequent recalibrations are needed. A simple and robust approach to estimation of daily soil moisture using a daily diagnostic soil moisture equation is suggested and studied. The estimated soil moisture is a function of the time-weighted summation of the ratio of historical precipitation rate to soil moisture loss coefficient. To capture the seasonal variation in soil moisture loss coefficient, a sinusoidal wave function of the day of year (DOY) is used to represent the seasonal variation in loss coefficient. A 3-year continuous data set of daily soil moisture and daily precipitation collected at each of four Soil Climate Analysis Network sites—AR2091; in Arkansas, GA2013 in Georgia, NM2107 in New Mexico, and PR2052 in Puerto Rico—is applied to test the proposed method. The land cover/land use of these four sites ...

The DOE Water Cycle Pilot Study

Two simulations, control and land use change, were performed for an eight week period (2 April-16 May 1990) to determine the net sensitivity of the local climate around the Three Gorges Dam. The analysis indicates that the large reservoir acts as a potential evaporating surface that decreases the surface temperature, cools the lower atmosphere, decreasing upward motion, and increasing sinking air mass. Such sinking results in low level moisture divergence, decreasing cloudiness, and increasing net downward radiation, which increases the surface temperature. However, results indicate that evaporative cooling dominates radiative warming in this initial study. The strong evaporation also supplies moisture to the atmosphere, suggesting an increase in precipitation, but the sinking moist air diverges away from the TGD region with no net change in precipitation. This numerical study represents an initial methodology for quantification of the impact of the Three Gorges Dam on the local climate and a more comprehensive, fine-scale set of multi-season simulations with additional observational data is needed for a more complete analysis.

Downscaling 1-km Topographic Index Distributions to a Finer Resolution for the TOPMODEL-Based GCM Hydrological Modeling

TOPMODEL predictions of surface runoff and subsurface flow are fundamentally developed on the basis of the topographic index distribution (TID). The scale dependency of the TID (i.e., dependency on the resolution of the digital elevation model (DEM) data used to compute the topographic indexes) determines that downscaling of the TID computed from a coarser resolution DEM to a finer resolution is needed before the TOPMODEL concepts can be applied to simulate hydrological processes at some larger scales than the scale of hillslopes. It was found that adjusting only the meanvalues cannot achieve an accurate downscaling of the TID because the difference between 2-m TIDs and the downscaled TIDs from a coarser resolution to 2 m through adjusting only mean values resulted in overestimation of the fraction of the saturation area and surface runoff under wet conditions and underestimation under dry conditions. It was found that downscaling by correcting for scale-dependencies in the first three moments of TIDs produced better predictions. A series of empirical relationships among mean, standard deviation, and coefficient of skewness of TIDs of nine catchments in eastern Tennessee at resolutions of 2, 10, and 100 m and 205 watersheds across the contiguous United States at resolutions of 10 m and 1 km were developed for downscaling TIDs from 1 km to 2 m through approximating TIDs by a 3-parameter gamma distribution function. The errors in the downscaled TIDs from 1 km to 10 m over 205 watersheds across the contiguous United States decreased with increasing watershed size and approached a minimum (approximately 6%) as the watershed drainage area was larger than approximately 500 km 2 . With the constructed empirical relationships, topographic indexes computed from 1-km DEM can be scaled down to 2 m for reducing errors and uncertainties in the TOPMODEL-based general circulation model (GCM) hydrological simulations. DOI: 10.1061/(ASCE)HE.1943-5584.0000438.

Signal-to-noise ratio–based quality assessment method for ICESat/GLAS waveform data

Abstract. Data quality determines the accuracy of results associated with remote sensing data processing and applications. However, few effective studies have been carried out on quality assessment methods for the full-waveform light detecting and ranging data. Using the geoscience laser altimeter system (GLAS) waveform data as an example, a signal-to-noise ratio (SNR)-based waveform quality assessment method is proposed to analyze the relationship between the SNR and its controlling factors, i.e., laser type, laser using time, topographic relief, and land cover type, and study the impacts of these factors on the quality of the GLAS waveform data. Results show that the SNR-based data quality assessment method can quantitatively and effectively assess the GLAS waveform data quality. The SNR linearly attenuates with the laser using time, and the attenuation rate varies with laser type. The topographic relief is inversely correlated with the SNR of the GLAS data. As the land cover structure (especially the vertical structure) becomes more complex, the SNR of the GLAS data decreases. It was found that land cover types in descending order of the SNR values are desert, farmland, water body, grassland, city, and forest.