Sediment deposition in the flood plain of Stemple Creek Watershed, northern California

Over the past 150 years, major land use changes have occurred in the Stemple Creek Watershed in northern California that have caused erosion to move soils from the upland to the flood plain, stream channels, and the bay. The purpose of this study is to document the recent (1954 to present) sediment deposition patterns in the flood plain area adjacent to Stemple Creek using the ¹³⁷Cesium technique. Sediment deposition ranged from 0.26 to 1.84 cm year⁻¹ for the period from 1964 to 2002 with an average of 0.85±0.41 cm year⁻¹. Sediment deposition rates were higher for the 1954 to 1964 period with a range of 0.31–3.50 cm year⁻¹ and an average of 1.29±1.04 cm year⁻¹. These data indicate that sediment deposition in the flood plain has decreased since the middle 1950s, probably related to reduction in row crop agriculture and an increase in pasturelands. This study shows that the flood plains in the Stemple Creek Watershed are a significant sink for the soils being eroded from the upland area. Given the significance of the flood plain for trapping eroded materials before they reach the stream channels or the bay, efforts need to be made to manage these flood plain areas to insure that they do not change and become a source rather than a sink for eroded materials as improved management practices on the upland areas reduce sediment input to the flood plain.     

Fractal Analysis of the Complexity of United States Coastlines

Coastlines have long been used as a principal example of a natural feature that exhibits fractal structure. With the advent of large digitized databases, it has become possible to examine in detail large regions of coast and to examine differences in complexity, as measured by the fractal dimension, among regions. In this study, we have determined the fractal dimension of the Atlantic and Pacific coastlines of the conterminous United States. The traditional divider method was used in obtaining the fractal dimension of each coastline arc from the NOAA Medium-Resolution Shoreline Data Set. On average, the Atlantic coast has much higher fractal dimension than the Pacific coast. The results also indicate that the complexity of the Atlantic coast increases toward low latitudes. These results have implications for the interpretation of species distributions and diversity patterns along the coast and for the understanding of the dynamics of biotic recovery from mass extinctions.     

Wind energy estimates by use of a diagnostic model

A diagnostic model is a relatively simple and practical tool for modeling the wind flow of the boundary layer in complex terrain. The model begins with a wind analysis based on available surface wind reports and geostrophic winds (computed from pressure data). The height of the boundary layer top (upper surface of the computational domain) is prescribed to fit local conditions. Using the continuity equation in terrain-following coordinates, the winds at mesh points are adjusted to produce nondivergence while maintaining the original vertical component of vorticity. The method of computing the nondivergent winds uses direct alterations. This method may be useful for other modeling purposes and will be described. Data for a long period (usually a year) are analyzed to obtain eigenvectors and the associated time series of their coefficients at each observation time. The model is run only for the five or six eigenvectors that explain most of the variance. The wind field at any particular time is reconstructed from the eigenvector solutions and their appropriate coefficients. Comparisons of model results with measured winds at sites representing different types of terrain will be shown. The accuracy and economy of the model make it a useful tool for estimating wind energy and also for giving wind fields for low-level diffusion models.     

Climate Change and Water Resources

Current perspectives on global climate change based on recent reports of the Intergovernmental Panel on Climate Change (IPCC) are presented. Impacts of a greenhouse warming that are likely to affect water planning and evaluation include changes in precipitation and runoff patterns, sea level rise, land use and population shifts following from these effects, and changes in water demands. Irrigation water demands are particularly sensitive to changes in precipitation, temperature, and carbon dioxide levels. Despite recent advances in climate change science, great uncertainty remains as to how and when climate will change and how these changes will affect the supply and demand for water at the river basin and watershed levels, which are of most interest to planners. To place the climate-induced uncertainties in perspective, the influence on the supply and demand for water of non-climate factors such as population, technology, economic conditions, social and political factors, and the values society places on alternative water uses are considered.     

Multi-Source Emission Determination Using an Inverse-Dispersion Technique

Inverse-dispersion calculations can be used to infer atmospheric emission rates through a combination of downwind gas concentrations and dispersion model predictions. With multiple concentration sensors downwind of a compound source (whose component positions are known) it is possible to calculate the component emissions. With this in mind, a field experiment was conducted to examine the feasibility of such multi-source inferences, using four synthetic area sources and eight concentration sensors arranged in different configurations. Multi-source problems tend to be mathematically ill-conditioned, as expressed by the condition number κ. In our most successful configuration (average κ = 4.2) the total emissions from all sources were deduced to within 10% on average, while component emissions were deduced to within 50%. In our least successful configuration (average κ = 91) the total emissions were calculated to within only 50%, and component calculations were highly inaccurate. Our study indicates that the most accurate multi-source inferences will occur if each sensor is influenced by only a single source. A “progressive” layout is the next best: one sensor is positioned to “see” only one source, the next sensor is placed to see the first source and another, a third sensor is placed to see the previous two plus a third, and so on. When it is not possible to isolate any sources κ is large and the accuracy of a multi-source inference is doubtful.     

Accumulation and speciation of selenium in evaporation basins in California,USA

The accumulation of selenium in evaporation basins (or ponds) in the San Joaquin Valley, California is of a great concern due to its potential hazards to environments. In this study, the accumulation, speciation and concentrations of Se were examined in waters as well as sediments in a system of the evaporation ponds. A significant decrease in the total dissolved Se concentration in Cell 1 in which drainage water with higher Se concentration was pumped from Inlet Channels indicated that the immobilization of Se was active in the Cell 1 and resulted in the higher Se concentration in sediments compared to the terminal cell such as Cell 9. The percentage of reduced Se species such as selenite [Se(IV)] and org-Se of total Se in drainage waters was also found increased in Cell 1 compared to Inlet Channels. The total dissolved Se concentrations in water along flow paths from Cell 1 were relatively constant except for terminal cells such as Cells 9 and 10, which showed higher total dissolved Se concentrations due to evapoconcentration. The percentage of reduced Se forms of total Se was inversely proportional to the percentage of Se(VI) depending on the redox condition of evaporation ponds along the flow paths. Sequential extractions of Se species in sediments indicated that organic associated Se and elemental Se were prevalent forms in sediments in the ponds system. The higher concentrations of elemental Se and organic associated Se in sediments in Cell 1 indicated that the immobilization of Se was active in the sediments compared to Cell 9, while the percentage of both fractions of total Se in sediments in Cells 1 and 9 was relatively constant. The organic materials from algae might provide carbon sources for Se reduction and Se sink in sediments in its elemental and organic associated forms.     

A copula-based multivariate analysis of Canadian RCM projected changes to flood characteristics for northeastern Canada

In the present work, climate change impacts on three spring (March–June) flood characteristics, i.e. peak, volume and duration, for 21 northeast Canadian basins are evaluated, based on Canadian regional climate model (CRCM) simulations. Conventional univariate frequency analysis for each flood characteristic and copula based bivariate frequency analysis for mutually correlated pairs of flood characteristics (i.e. peak–volume, peak–duration and volume–duration) are carried out. While univariate analysis is focused on return levels of selected return periods (5-, 20- and 50-year), the bivariate analysis is focused on the joint occurrence probabilities P1 and P2 of the three pairs of flood characteristics, where P1 is the probability of any one characteristic in a pair exceeding its threshold and P2 is the probability of both characteristics in a pair exceeding their respective thresholds at the same time. The performance of CRCM is assessed by comparing ERA40 (the European Centre for Medium-Range Weather Forecasts 40-year reanalysis) driven CRCM simulated flood statistics and univariate and bivariate frequency analysis results for the current 1970–1999 period with those observed at selected 16 gauging stations for the same time period. The Generalized Extreme Value distribution is selected as the marginal distribution for flood characteristics and the Clayton copula for developing bivariate distribution functions. The CRCM performs well in simulating mean, standard deviation, and 5-, 20- and 50-year return levels of flood characteristics. The joint occurrence probabilities are also simulated well by the CRCM. A five-member ensemble of the CRCM simulated streamflow for the current (1970–1999) and future (2041–2070) periods, driven by five different members of a Canadian Global Climate Model ensemble, are used in the assessment of projected changes, where future simulations correspond to A2 scenario. The results of projected changes, in general, indicate increases in the marginal values, i.e. return levels of flood characteristics, and the joint occurrence probabilities P1 and P2. It is found that the future marginal values of flood characteristics and P1 and P2 values corresponding to longer return periods will be affected more by anthropogenic climate change than those corresponding to shorter return periods but the former ones are subjected to higher uncertainties.     

Anasazi (Pre-Columbian Native-American) Migrations During The Middle-12Th and Late-13th Centuries – Were they Drought Induced?

Severe droughts in the middle-12th and late-13th centuries appear to have affected Anasazi (pre-Columbian Native American) populations. During the first drought most of the great houses in the central San Juan Basin were vacated; the second drought resulted in the abandonment of the Four Corners region. During the first drought, villages may not have been completely abandoned. The multi-year drought periods probably were characterized by reductions in both winter and summer precipitation. Maize is dependent on winter precipitation for its germination and initial growth and on summer (monsoonal) precipitation for its continued growth. Reductions in precipitation are hypothesized to have resulted in low yields of maize, the dietary staple of the Anasazi. A comparison of historic climate data and tree-ring-based reconstructions of precipitation in the Four Corners region with tree-ring-based reconstructions of the Pacific Decadal Oscillation (PDO) and the Atlantic Multidecadal Oscillation (AMO) indicate that severe and persistent drought in the Four Corners region occurs when the PDO is negative and the AMO is positive. Historic climate data from the greater San Juan Basin indicate that a negative PDO is characterized by reductions in both water-year and summer precipitation, reinforcing the concept that at least some multi-year droughts involved weakening of the summer monsoon with attendant decreases in the yields of maize.     

Evaluation of a Photosynthesis-Based Canopy Resistance Formulation in the Noah Land-Surface Model

Accurately representing complex land-surface processes balancing complexity and realism remains one challenge that the weather modelling community is facing nowadays. In this study, a photosynthesis-based Gas-exchange Evapotranspiration Model (GEM) is integrated into the Noah land-surface model replacing the traditional Jarvis scheme for estimating the canopy resistance and transpiration. Using 18-month simulations from the High Resolution Land Data Assimilation System (HRLDAS), the impact of the photosynthesis-based approach on the simulated canopy resistance, surface heat fluxes, soil moisture, and soil temperature over different vegetation types is evaluated using data from the Atmospheric Radiation Measurement (ARM) site, Oklahoma Mesonet, 2002 International H₂O Project (IHOP_2002), and three Ameriflux sites. Incorporation of GEM into Noah improves the surface energy fluxes as well as the associated diurnal cycle of soil moisture and soil temperature during both wet and dry periods. An analysis of midday, average canopy resistance shows similar day-to-day trends in the model fields as seen in observed patterns. Bias and standard deviation analyses for soil temperature and surface fluxes show that GEM responds somewhat better than the Jarvis scheme, mainly because the Jarvis approach relies on a parametrised minimum canopy resistance and meteorological variables such as air temperature and incident radiation. The analyses suggest that adding a photosynthesis-based transpiration scheme such as GEM improves the ability of the land-data assimilation system to simulate evaporation and transpiration under a range of soil and vegetation conditions.