Knowledge of soil moisture in the root zone is critical for crop growth estimation and irrigation... more Knowledge of soil moisture in the root zone is critical for crop growth estimation and irrigation scheduling. In this study, a soil moisture diagnostic equation is applied to estimate soil moisture at depths of 0–100 cm (because the majority of crop roots are in the top 100 cm of soil) at four USDA Soil Climate Analysis Network (SCAN) sites in arid and semi-arid regions: TX2105 in northwest Texas, NM2015 and NM2108 in east New Mexico, and AZ2026 in southeast Arizona. At each site, a dataset of 5–6 years of records of daily soil moisture, daily mean air temperature, precipitation and downward solar radiation is compiled and processed. Both the sinusoidal wave function of day of year (DOY) and a linear function of the potential evapotranspiration (PET) are used to approximate the soil moisture loss coeffi- cient. The first four years of data are used to derive the soil moisture loss function and the empirical para- meters in the soil moisture diagnostic equation. The derived loss function and empirical parameters are then applied to estimate soil moisture in the last fifth or sixth year at each site. Root mean square errors (RMSEs) of the estimated volumetric soil moistures in five different soil columns (i.e., 5 cm, 10 cm, 20 or 30 cm, 50 cm, and 100 cm) are less than 3.2 (%V/V), and the accuracy of the estimated soil moistures using the sinusoidal soil moisture loss function is slightly better than the PET-based loss functions. In addition to the three advantages of this soil moisture diagnostic equation, i.e., (1) non-cumulative errors in the estimated soil moisture, (2) no regular recalibration is required to correct the cumulative errors, and (3) no numerical iteration and initial moisture inputs are needed since only precipitation data are required, this study also demonstrates that the soil moisture diagnostic equation not only can be used to estimate surface soil moisture, but also the entire root-zone soil moisture.
While river-borne materials are recognized as important resources supporting coastal ecosystems a... more 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, Colville) that drain Alaska's 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 Alaska's 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.
Remote sensing technology has great potential for measuring lake inundation areas and lake levels... more 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 0.12 m.► Constructing inundation area—lake level rating (IALLR) curves using the SRTM DEMs. ► Estimating lake levels from satellite measured inundation areas using IALLR curves. ► Monitoring, estimating, and modeling the water budget of lakes using IALLR curves.
[1] Digital elevation model (DEM) data are essential to hydrological applications and have been w... more [1] Digital elevation model (DEM) data are essential to hydrological applications and have been widely used to calculate a variety of useful topographic characteristics, e.g., slope, flow direction, flow accumulation area, stream channel network, topographic index, and others. Except for slope, none of the other topographic characteristics can be calculated until the flow direction at each pixel within a DEM is determined. However, flow direction cannot be accurately calculated until depressions and flat areas within a DEM have been rectified. This is a routine problem in hydrologic modeling, because virtually all DEMs contain flat and sink pixels, both real and artifactual, that if left untreated will prevent accurate simulation of hydrologic flow paths. Although a number of algorithms are available for rectifying flat and sink pixels in DEM data, treatment of flat areas and depressions and calculation of flow direction remain problematic for reasons of complexity and uncertainty. A new algorithm that effectively rectifies flat and sink pixels was developed and tested. The approach is to use linear interpolation between low elevation grid cells on the edge of each flat area or depression defined as outlets and higher elevation grid cells on the opposite side defined as inflow pixels. The implementation requires an iterative solution to accommodate the irregular geometry of flat areas or depressions and exceptions that arise. Linear interpolation across flat areas or depressions provides a natural way to scale elevation adjustments based on the vertical scale of the surrounding topography, thereby avoiding the addition or subtraction of arbitrary small numbers that we regard as a disadvantage in some prior techniques. Tests for two virtual terrains and one real terrain show that our algorithm effectively rectifies flat areas and depressions, even in low-relief terrain, and produces realistic patterns of flow accumulation and extracted channel networks.
[1] Forest harvest effects on streamflow generation have been well described experimentally, but ... more [1] Forest harvest effects on streamflow generation have been well described experimentally, but a clear understanding of process-level hydrological controls can be difficult to ascertain from data alone. We apply a new model, Visualizing Ecosystems for Land Management Assessments (VELMA), to elucidate how hillslope and catchment-scale processes control stream discharge in a small Pacific Northwest catchment. VELMA is a spatially distributed ecohydrology model that links hydrological and biogeochemical processes within watersheds. The study site is WS10 of the H.J. Andrews LTER, a 10 ha forested catchment clearcut in 1975. Simulated and observed daily streamflow are in good agreement for both the pre- (1969–1974) and postharvest (1975–2008) periods (Nash-Sutcliffe efficiency = 0.807 and 0.819, respectively). One hundred scenarios, where harvest amounts ranged from 2% to 100% were conducted. Results show that (1) for the case of a 100% clearcut, stream discharge initially increased by ∼29% or 345 mm but returned to preclearcut levels within 50 years, and (2) annual streamflow increased at a near linear rate of 3.5 mm year−1 for each percent of catchment harvested, irrespective of location. Thereafter, to assess the impact of harvest location on stream discharge, 20 harvest scenarios were simulated, where harvest amount was fixed at 20% but harvest location varied. Results show that the streamflow response is strongly sensitive to harvest distance from the stream channel. Specifically, a 20% clearcut area near the catchment divide resulted in an average annual streamflow increase of 53 mm, whereas a 20% clearcut near the stream resulted in an average annual streamflow increase of 92 mm.
Abstract: The objective of this work was to explain an apparent contradiction in the literature ... more Abstract: The objective of this work was to explain an apparent contradiction in the literature related to the relationship between mean and variance (or standard deviation) of soil moisture fields. Some studies found an increase in soil moisture variance with decreasing mean soil moisture, while others showed a decrease. The evidence of maximum variance in the mid-range of mean soil moisture was also reported in the literature. In this paper, we focus on the effects of spatial variability of soil texture on the relationship between variance and mean of soil moisture during soil dry-down processes. Soil texture influences soil moisture mean and variance through its direct effects on evaporation and drainage, which are two main factors controlling soil drying. A differential equation describing soil moisture dry down is proposed and studied. Our study shows that as mean soil moisture is greater than a threshold, variance increases with decreasing mean soil moisture. If mean soil moisture is less than the threshold, variance decreases with decreasing mean soil moisture. The threshold depends on soil texture and is between the field capacity and the wilting point. The soil moisture dry-down equation is also applied to explain the apparent contradiction with regard to the relationship between mean and variance of soil moisture fields reported in the literature.
[1] Export of nitrate and dissolved organic carbon (DOC) from the upper Kuparuk River between the... more [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.
Knowledge of soil moisture in the root zone is critical for crop growth estimation and irrigation... more Knowledge of soil moisture in the root zone is critical for crop growth estimation and irrigation scheduling. In this study, a soil moisture diagnostic equation is applied to estimate soil moisture at depths of 0–100 cm (because the majority of crop roots are in the top 100 cm of soil) at four USDA Soil Climate Analysis Network (SCAN) sites in arid and semi-arid regions: TX2105 in northwest Texas, NM2015 and NM2108 in east New Mexico, and AZ2026 in southeast Arizona. At each site, a dataset of 5–6 years of records of daily soil moisture, daily mean air temperature, precipitation and downward solar radiation is compiled and processed. Both the sinusoidal wave function of day of year (DOY) and a linear function of the potential evapotranspiration (PET) are used to approximate the soil moisture loss coeffi- cient. The first four years of data are used to derive the soil moisture loss function and the empirical para- meters in the soil moisture diagnostic equation. The derived loss function and empirical parameters are then applied to estimate soil moisture in the last fifth or sixth year at each site. Root mean square errors (RMSEs) of the estimated volumetric soil moistures in five different soil columns (i.e., 5 cm, 10 cm, 20 or 30 cm, 50 cm, and 100 cm) are less than 3.2 (%V/V), and the accuracy of the estimated soil moistures using the sinusoidal soil moisture loss function is slightly better than the PET-based loss functions. In addition to the three advantages of this soil moisture diagnostic equation, i.e., (1) non-cumulative errors in the estimated soil moisture, (2) no regular recalibration is required to correct the cumulative errors, and (3) no numerical iteration and initial moisture inputs are needed since only precipitation data are required, this study also demonstrates that the soil moisture diagnostic equation not only can be used to estimate surface soil moisture, but also the entire root-zone soil moisture.
While river-borne materials are recognized as important resources supporting coastal ecosystems a... more 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, Colville) that drain Alaska's 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 Alaska's 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.
Remote sensing technology has great potential for measuring lake inundation areas and lake levels... more 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 0.12 m.► Constructing inundation area—lake level rating (IALLR) curves using the SRTM DEMs. ► Estimating lake levels from satellite measured inundation areas using IALLR curves. ► Monitoring, estimating, and modeling the water budget of lakes using IALLR curves.
[1] Digital elevation model (DEM) data are essential to hydrological applications and have been w... more [1] Digital elevation model (DEM) data are essential to hydrological applications and have been widely used to calculate a variety of useful topographic characteristics, e.g., slope, flow direction, flow accumulation area, stream channel network, topographic index, and others. Except for slope, none of the other topographic characteristics can be calculated until the flow direction at each pixel within a DEM is determined. However, flow direction cannot be accurately calculated until depressions and flat areas within a DEM have been rectified. This is a routine problem in hydrologic modeling, because virtually all DEMs contain flat and sink pixels, both real and artifactual, that if left untreated will prevent accurate simulation of hydrologic flow paths. Although a number of algorithms are available for rectifying flat and sink pixels in DEM data, treatment of flat areas and depressions and calculation of flow direction remain problematic for reasons of complexity and uncertainty. A new algorithm that effectively rectifies flat and sink pixels was developed and tested. The approach is to use linear interpolation between low elevation grid cells on the edge of each flat area or depression defined as outlets and higher elevation grid cells on the opposite side defined as inflow pixels. The implementation requires an iterative solution to accommodate the irregular geometry of flat areas or depressions and exceptions that arise. Linear interpolation across flat areas or depressions provides a natural way to scale elevation adjustments based on the vertical scale of the surrounding topography, thereby avoiding the addition or subtraction of arbitrary small numbers that we regard as a disadvantage in some prior techniques. Tests for two virtual terrains and one real terrain show that our algorithm effectively rectifies flat areas and depressions, even in low-relief terrain, and produces realistic patterns of flow accumulation and extracted channel networks.
[1] Forest harvest effects on streamflow generation have been well described experimentally, but ... more [1] Forest harvest effects on streamflow generation have been well described experimentally, but a clear understanding of process-level hydrological controls can be difficult to ascertain from data alone. We apply a new model, Visualizing Ecosystems for Land Management Assessments (VELMA), to elucidate how hillslope and catchment-scale processes control stream discharge in a small Pacific Northwest catchment. VELMA is a spatially distributed ecohydrology model that links hydrological and biogeochemical processes within watersheds. The study site is WS10 of the H.J. Andrews LTER, a 10 ha forested catchment clearcut in 1975. Simulated and observed daily streamflow are in good agreement for both the pre- (1969–1974) and postharvest (1975–2008) periods (Nash-Sutcliffe efficiency = 0.807 and 0.819, respectively). One hundred scenarios, where harvest amounts ranged from 2% to 100% were conducted. Results show that (1) for the case of a 100% clearcut, stream discharge initially increased by ∼29% or 345 mm but returned to preclearcut levels within 50 years, and (2) annual streamflow increased at a near linear rate of 3.5 mm year−1 for each percent of catchment harvested, irrespective of location. Thereafter, to assess the impact of harvest location on stream discharge, 20 harvest scenarios were simulated, where harvest amount was fixed at 20% but harvest location varied. Results show that the streamflow response is strongly sensitive to harvest distance from the stream channel. Specifically, a 20% clearcut area near the catchment divide resulted in an average annual streamflow increase of 53 mm, whereas a 20% clearcut near the stream resulted in an average annual streamflow increase of 92 mm.
Abstract: The objective of this work was to explain an apparent contradiction in the literature ... more Abstract: The objective of this work was to explain an apparent contradiction in the literature related to the relationship between mean and variance (or standard deviation) of soil moisture fields. Some studies found an increase in soil moisture variance with decreasing mean soil moisture, while others showed a decrease. The evidence of maximum variance in the mid-range of mean soil moisture was also reported in the literature. In this paper, we focus on the effects of spatial variability of soil texture on the relationship between variance and mean of soil moisture during soil dry-down processes. Soil texture influences soil moisture mean and variance through its direct effects on evaporation and drainage, which are two main factors controlling soil drying. A differential equation describing soil moisture dry down is proposed and studied. Our study shows that as mean soil moisture is greater than a threshold, variance increases with decreasing mean soil moisture. If mean soil moisture is less than the threshold, variance decreases with decreasing mean soil moisture. The threshold depends on soil texture and is between the field capacity and the wilting point. The soil moisture dry-down equation is also applied to explain the apparent contradiction with regard to the relationship between mean and variance of soil moisture fields reported in the literature.
[1] Export of nitrate and dissolved organic carbon (DOC) from the upper Kuparuk River between the... more [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.
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