Modeling land use and cover as part of global environmental change

Land use and cover changes are important elements of the larger problem of global environmental change. Landuse patterns result in landcover changes that cumulatively affect the global biosphere and climate. We describe efforts to analyze the driving forces behind land transformations and to create land use models that can be linked to other types of global change models. Two efforts to model land use in the U.S. are reviewed. One projects aggregate agricultural, forest, and range land, and the other attempts to model forest land use change at the parcel scale in two mountain landscapes. We conclude with suggestions for new approaches that could clarify the role of land use/cover change in global change and in natural resources management.     

Unsteady sedimentation in nonuniform rills

Runoff and sediment were measured from agricultural land exposed to controlled simulated rainfall. We extended the kinematic unsteady overland sedimentation theory of prismatic channels to the experiments by considering both hydraulics and sediment dynamics of rill flow for changing flow geometries of nonuniform and unsteady rill development. The characteristic unimodal concentration peak observed in the experiments and the changing channel geometry were interpreted in theoretical terms. Overland sedimentation in unsteady nonprismatic rills under uniform rainfall can be described with kinematic models of flow, entrainment and deposition applied to developing flow geometries; this is not possible with sheet-flow models. Other interpretations are considered and experimental needs are identified.     

A study of the performance of a land surface process model (LSPM)

In this paper, a reliable Land-Surface Process Model (LSPM), which is a new version of the LPM of Ji and Hu (1989), is described. The LSPM has been validated with experimental data measured at two stations in the Po Valley (Northern Italy).     

Assessing the concentration,speciation, and toxicity of dissolved metals during mixing of acid-mine drainage and ambient river water downstream of the Elizabeth Copper Mine,Vermont, USA

The authors determine the composition of a river that is impacted by acid-mine drainage, evaluate dominant physical and geochemical processes controlling the composition, and assess dissolved metal speciation and toxicity using a combination of laboratory, field and modeling studies. Values of pH increase from 3.3 to 7.6 and the sum of dissolved base metal (Cd + Co + Cu + Ni + Pb + Zn) concentrations decreases from 6270 to 100 μg/L in the dynamic mixing and reaction zone that is downstream of the river’s confluence with acid-mine drainage. Mixing diagrams and PHREEQC calculations indicate that mixing and dilution affect the concentrations of all dissolved elements in the reach, and are the dominant processes controlling dissolved Ca, K, Li, Mn and SO₄ concentrations. Additionally, dissolved Al and Fe concentrations decrease due to mineral precipitation (gibbsite, schwertmannite and ferrihydrite), whereas dissolved concentrations of Cd, Co, Cu, Ni, Pb and Zn decrease due to adsorption onto newly formed Fe precipitates.     

A method of differential field photometry by video-photographic image subtraction

A method is presented for subtracting two sequential images by a video technique. This procedure is applied to detect both rotational line-of-sight effects, and velocity fields around an active region (McMath plage No. 10181). The standard deviation of a velocity measurement was found to be ± 0.4 km/sec. This method appears to offer some advantages compared with the photographic subtraction process.     

Generous statistical tests

A common statistical problem is deciding which of two possible sources, A and B, of a contaminant is most likely the actual source. The situation considered here, based on an actual problem of polychlorinated biphenyl contamination discussed below, is one in which the data strongly supports the hypothesis that source A is responsible. The problem approach here is twofold: One, accurately estimating this extreme probability. Two, since the statistics involved will be used in a legal setting, estimating the extreme probability in such a way as to be as generous as is possible toward the defendant’s claim that the other site B could be responsible; thereby leaving little room for argument when this assertion is shown to be highly unlikely. The statistical testing for this problem is modeled by random variables {X _i } and the corresponding sample mean the problem considered is providing a bound ɛ for which for a given number a ₀. Under the hypothesis that the random variables {X _i } satisfy E(X _i ) ≤ μ, for some 0  < μ < 1, statistical tests are given, described as “generous”, because ɛ is maximized. The intent is to be able to reject the hypothesis that a ₀ is a value of the sample mean while eliminating any possible objections to the model distributions chosen for the {X _i } by choosing those distributions which maximize the value of ɛ for the test used.     

APPROXIMATING RAINFALL-RUNOFF MODELLING RESPONSE USING A STOCHASTIC INTEGRAL EQUATION

Rainfall-runoff modelling uncertainty can be analysed by the use of a stochastic integral formulation. The stochastic integral equation can be based on the rainfall–runoff model input of model rainfall or model rainfall excess. Similarly, the stochastic integral equation can be based on the rainfall–runoff model output of the modelled runoff hydrograph. The residual between actual measured runoff data and modelled runoff (from the rainfall–runoff model) is analysed here by the use of a stochastic integral equation. This approach is used to develop a set of convolution integral transfer function realizations that represent the chosen rainfall–runoff modelling error. The resulting stochastic integral component is a distribution of possible residual outcomes that may be directly added to the rainfall–runoff model's deterministic outcome, to develop a distribution of probable runoff hydrograph realizations from the chosen rainfall–runoff model.