Does intensity of change matter? Factors affecting adoption of agri-environmental schemes in Spain

Agri-environmental schemes (AES) are the main policy instrument currently available in the EU to promote environmentally-friendly farming practices. However, the rate of adoption of these measures is still relatively low in southern Europe, and understanding how these rates can be increased is still an open issue. The goal of this paper is to increase that understanding by testing whether the factors which determine AES sign-up decisions are influenced by the intensity of change in farming practices that are brought about by adopting the scheme. A micro-economic model reflecting farmer AES sign-up decisions is proposed and applied to two schemes in Spain respectively requiring major or minor intensity of change in practices by surveying farmers eligible for both schemes. The results show that farm structural factors play a role when major practice change is required by the scheme, yet when dealing with minor change, individual farmer characteristics play a more determining role. Social capital and farmer attitude are important factors in both the AES surveyed. Therefore, it may be concluded that improving agronomic design would be an important tool to improve farmer participation in AES where major change is involved, whereas improved targeting and extension would help uptake for AES involving a lesser degree of change.     

PREDICTION OF CHLOROPHYLL A CONCENTRATIONS IN FLORIDA LAKES: THE IMPORTANCE OF PHOSPHORUS AND NITROGEN1

ABSTRACT: Models for the prediction of chlorophyll a concentrations were developed and tested using data on 223 Florida lakes. A statistical analysis showed that the best model was log (Chl a) =?2.49 + 0.269 log (TP) + 1.06 log (TN) or log (Chl a) =?2.49 + 1.06 log (TN/TP) + 1.33 log (TP) where Chl a is the chlorophyll a concentration (mg m^-3), TP is the total phosphorus concentration (mg m^-3) and TN is the total nitrogen concentration (mg m^-3). The model yields unbiased estimates of chlorophyll a concentrations over a wide range of lake types and has a 95 percent confidence interval of 29–319 percent of the calculated chlorophyll a concentrations. Other models, especially the published Dillon-Rigler and Jones-Bachmann phosphorus-chlorophyll models, are less precise when applied to Florida lakes. The data support the hypothesis that nitrogen is an important limiting nutrient in hypereutrophic lakes.     

Differences among model simulations of climate change on the scale of resource regions

The climate simulations from atmospheric general circulation models (GCMs) are often used to analyze the potential effects of climate change on environmental resources. It has been demonstrated that there are differences among the simulations from various GCMs, on spatial scales ranging from global to regional. This paper quantifies the differences in temperature and precipitation simulated by three major GCMs for four specific regions: an agricultural region (the North American winter wheat belt), a hydrologic region (the Great Basin), a demographic region (the high-density population corridor of the northeast United States), and a political region (the state of Texas). Both the current (control) climate and the climatic response to a doubling of atmospheric carbon dioxide (CO₂) are consideredIn each region, even when the data are averaged on a seasonal basis, marked differences occurred in the areal average climate simulated by the different GCMs for both the control climate and the doubled-CO₂ climate. Thus, climate impact studies based on the simulations of more than one GCM could easily yield a range of possible results     

HYDRO-POWER SYSTEM SIMULATION TECHNIQUE1

ABSTRACT. The non-power requirements of a large hydro-power system of multiple-purpose projects often conflict with the best power peaking operation. In order to schedule an optimum multiple-purpose operation, advanced procedures that necessitate the use of computers are required. Special techniques are used in a computer program developed in the Reservoir Control Center, North Pacific Division of the Corps of Engineers that provide the ability to define operating constraints in order of priority. These techniques are easily adapted to practical reservoir regulation problems so that the program is useful in daily reservoir regulation scheduling.     

CONTINUOUS SIMULATION OF RECEIVING WATER QUALITY TRANSIENTS1

: This paper presents solutions to the one-dimensional, transient conservation of mass equations for the coupled biochemical oxygen demand-dissolved oxygen (BOD-DO) reactions, based on the principle of superposition, for continuously discharging plane sources. The solutions are applied within the framework of a continuous simulation model to allow the derivation of water quality frequency curves and frequency histograms of consecutive hourly dissolved oxygen violations, for any desired standard. Receiving water response is determined for waste inputs from urban wet weather, dry weather, and upstream sources. An application to Des Moines, Iowa, and Des Moines River indicated that urban storm water impacts on the stream can be masked in the cumulative frequency curve representation, but the benefits of storm water control are clearly shown in frequency histograms of the duration of consecutive stream standard violations.     

Review of Urban Stormwater Quality Models: Deterministic,Stochastic, and Hybrid Approaches1

Abstract: The growing impact of urban stormwater on surface‐water quality has illuminated the need for more accurate modeling of stormwater pollution. Water quality based regulation and the movement towards integrated urban water management place a similar demand for improved stormwater quality model predictions. The physical, chemical, and biological processes that affect stormwater quality need to be better understood and simulated, while acknowledging the costs and benefits that such complex modeling entails. This paper reviews three approaches to stormwater quality modeling: deterministic, stochastic, and hybrid. Six deterministic, three stochastic, and three hybrid models are reviewed in detail. Hybrid approaches show strong potential for reducing stormwater quality model prediction error and uncertainty. Improved stormwater quality models will have wide ranging benefits for combined sewer overflow management, total maximum daily load development, best management practice design, land use change impact assessment, water quality trading, and integrated modeling.     

Modeling the relationships between land use and land cover on private lands in the Upper Midwest, USA

This paper presents an approach to modeling land-cover change as a function of land-use change. We argue that, in order to model the link between socio-economic change and changes in forest cover in a region that is experiencing residential and recreational development and agricultural abandonment, land-use and land-cover change need to be represented as separate processes. Forest-cover change is represented here using two transition probabilities that were calculated from Landsat imagery and that, taken together, describe a Markov transition matrix between forest and non-forest over a 10-year period. Using a three-date land-use data set, compiled and interpreted from digitized parcel boundaries, and scanned aerial photography for 136 sites (c. 2500 ha) sampled from the Upper Midwest, USA, we test functional relationships between forest-cover transition probabilities, standardized to represent changes over a decade, and land-use conditions and changes within sample sites. Regression models indicated that about 60% of the variation in the average forest-cover transition probabilities (i.e. from forest to non-forest and vice versa) can be predicted using three variables: amount of agricultural land use in a site; amount of developed land use; and the amount of area increasing in development. In further analysis, time lags were evaluated, showing that agricultural abandonment had a relatively strong time-lag effect but development did not. We demonstrate an approach to using forest-cover transition probabilities to develop spatially-constrained simulations of forest-cover change. Because the simulations are based on transition probabilities that are indexed to a particular time and place, the simulations are improved over previous applications of Markov transition models. This modeling approach can be used to predict forest-cover changes as a result of socio-economic change, by linking to models that predict land-use change on the basis of exogenous human-induced drivers.     

A MATHEMATICAL MODEL FOR OPTIMUM DESIGN AND CONTROL OF METROPOLITAN WASTEWATER MANAGEMENT SYSTEMS1

ABSTRACT: A comprehensive mathematical model (Urban Wastewater Management Model) has been developed to continuously simulate time-varying wastewater flows and qualities in complex metropolitan combined sewerage systems. The model serves three functions: (1) assessment of existing or planned system performance in relation to other wastewater discharges in either a metropolitan or river basin area; (2) determination of the optium operation or automatic control of existing or planned systems during rainstorms; and (3) determination of the most economically feasible combination of design alternatives for improving or expanding existing systems to meet specified performance criteria. The model provides an efficient engineering tool for evaluating and controlling pollutant discharges from combined sewerage systems (including treatment plants) to receiving waters, while considering the time and spacial variations of rainfall and dry-weather flows and qualities as well as economic constraints.     

CALCULATION OF NONLINEAR CONFIDENCE AND PREDICTION INTERVALS FOR GROUND-WATER FLOW MODELS1

ABSTRACT: A method is derived to efficiently compute nonlinear confidence and prediction intervals on any function of parameters derived as output from a mathematical model of a physical system. The method is applied to the problem of obtaining confidence and prediction intervals for manually-calibrated ground-water flow models. To obtain confidence and prediction intervals resulting from uncertainties in parameters, the calibrated model and information on extreme ranges and ordering of the model parameters within one or more independent groups are required. If random errors in the dependent variable are present in addition to uncertainties in parameters, then calculation of prediction intervals also requires information on the extreme range of error expected. A simple Monte Carlo method is used to compute the quantiles necessary to establish probability levels for the confidence and prediction intervals. Application of the method to a hypothetical example showed that includsion of random errors in the dependent variable in addition to uncertainties in parameters can considerably widen the prediction intervals.     

SIMULATION OF TEMPORAL CHANGES IN RAINFALL-RUNOFF CHARACTERISTICS,COON CREEK BASIN,WISCONSIN1

ABSTRACT: Streamflow for 67 years was simulated for Coon Creek at Coon Valley, Wisconsin, for three conditions in the drainage basin: (1) conditions in the 1930s; (2) conditions in the 1970s, excluding flood-detention reservoirs; and (3) conditions in the 1970s, including flood-detention reservoirs. These simulations showed that the changes in agricultural practices over 40 years (1940–80) reduced the 100-year flood by 53 percent (from 38,900 to 18,300 cubic feet per second). The flood-detention reservoirs reduced the 100-year flood by an additional 17 percent (to 15,100 cubic feet per second). The simulation was accomplished by calibrating a precipitation-runoff model to observed rainfall and runoff during two separate periods (1934–40 and 1978–81). Comparisons of model simulations showed that differences between the model calibrations for the two periods were statistically significant at the 95 percent confidence level.