Integrated Use of GLEAMS and GIS to Prevent Groundwater Pollution Caused by Agricultural Disposal of Animal Waste

/ In modern intensive animal farming the disposal of a large amount of waste is of great concern, as, if not properly performed, it can cause the pollution of water, mainly because of the high content of nitrate and phosphate. This paper presents the results of a study intended to assess the environmental sustainability of animal waste disposal on agricultural soils in the alluvial plain of the River Chiana (Tuscany, Italy), a particularly sensitive area because of the high vulnerability of the shallow aquifer and of the intensive agricultural and breeding activities. With this aim, a strategy has been employed, that consists of the integrated use of a management model and GISs. The consequences on groundwater of applying animal waste to different kind of soils and crop arrangements have been simulated by means of the management model GLEAMS (Groundwater Loading Effects of Agricultural Management Systems, ver 2.01). As the huge amount of data required by such a sophisticated model does not allow applications at a scale larger than the field size, IDRISI and GRASS GIS packages have been used to divide the study area into land units, with homogeneous environmental characteristics, and then to generalize on these units the outputs of the model. The main conclusions can be synthesized as follows: The amount of animal waste produced in some of the investigated areas (i.e., municipal territory) is greater than that disposable on their own agricultural soil with no risks to the groundwater; consequently a cooperative approach among municipalities is necessary in order to plan waste disposal in a comprehensive and centralized way.KEY WORDS: Land use; Animal waste disposal; Groundwater protection; GIS, Management models     

SEASONAL PUMPING VARIATION EFFECTS ON WELLHEAD PROTECTION AREA DELINEATION1

ABSTRACT: Delineation of a welihead protection area (WHPA) is the key element in welihead protection programs for drinking water supplies. WHPAs are often delineated under idealized conditions using simple steady-state assumptions, which lead to an incorrect estimation of area and geometry. In this paper, we compare the results from a simple steady-state procedure commonly employed in WHPA delineation with a more complex transient approach that allows consideration of seasonal variation in pumping rates. We also introduce a transient procedure to delineate time-related capture zones using a numerical ground water flow and transport model. Welihead delineation is examined for two municipal wells in Tipton, Oklahoma, using a ten-year time-of-travel criterion. In the steady-state procedure, where we assumed constant pumping rates, we used GPTRAC, a semi-analytical model, and MOC, a numerical model. The capture zone delineated by GPTRAC is comparable in shape with the capture zone delineated by MOC but not in size due to the differences in solution schemes. In the transient procedure, we used MOC and considered the seasonal variation in pumping rates. The capture zones delineated in this procedure were larger than the capture zones delineated by the steady-state procedure using the same model. Further analysis showed that a higher drawdown was predicted in the transient procedure than in the steady-state procedure, which is the reason for larger capture zones.     

A methodology of characterizing status and trend of land changes in oases: a case study of Sangong River watershed, Xinjiang, China

Land change is often studied with Markov models to develop a probability transition matrix. The existing methods dependent on such matrixes cannot effectively characterize some important aspects associated with land change such as status, direction, trend and regional variations. This study presents mathematical models to quantify these elements, defining unbalanced, quasi-balanced and balanced status, one- and two-way transitions and the rising or falling trends. Using these models and remote-sensing imageries, the landscape was studied for a case area, the oasis of Sangong River in Xinjiang, Northwest China where typical arid conditions prevail. Land expansion and contraction among various land types and for the entire oasis were analyzed for the periods of 1978-1987, 1978-1998 and 1987-1998. The changes were closely related to a strong economic growth after the land-reform campaign and adoption of the market economy in China in the 1980s to early 1990s, a process not strictly Markovian that requires stationarity and randomness. Information on land-change status and trend is important for a better understanding of the underlying driving processes but also for land-use planning and decision-making.     

WATERSHED MODELS AND THEIR APPLICABILITY TO CONJUNCTIVE USE MANAGEMENT1

ABSTRACT: Techniques employed to simulate infiltration and subsurface ground-water flow were examined for a number of available watershed models. The large number of processes that these models simulate prohibits detailed analysis of subsurface flow, due to excessive computer and data requirements. Such models emphasize surface flow and include only that portion of water lost to the subsurface and the portion returned to the stream as baseflow. Problems were examined in adopting conjunctive use models, which allow the coordinated exploitation and management of both surface and ground-water resources. The application of conjunctive use models in water resources management is expected to increase dramatically over the next decade.     

Predicting Debris Yield From Burned Watersheds: Comparison of Statistical and Artificial Neural Network Models1

Abstract: Alluvial fans in southern California are continuously being developed for residential, industrial, commercial, and agricultural purposes. Development and alteration of alluvial fans often require consideration of mud and debris flows from burned mountain watersheds. Accurate prediction of sediment (hyper‐concentrated sediment or debris) yield is essential for the design, operation, and maintenance of debris basins to safeguard properly the general population. This paper presents results based on a statistical model and Artificial Neural Network (ANN) models. The models predict sediment yield caused by storms following wildfire events in burned mountainous watersheds. Both sediment yield prediction models have been developed for use in relatively small watersheds (50‐800 ha) in the greater Los Angeles area. The statistical model was developed using multiple regression analysis on sediment yield data collected from 1938 to 1983. Following the multiple regression analysis, a method for multi‐sequence sediment yield prediction under burned watershed conditions was developed. The statistical model was then calibrated based on 17 years of sediment yield, fire, and precipitation data collected between 1984 and 2000. The present study also evaluated ANN models created to predict the sediment yields. The training of the ANN models utilized single storm event data generated for the 17‐year period between 1984 and 2000 as the training input data. Training patterns and neural network architectures were varied to further study the ANN performance. Results from these models were compared with the available field data obtained from several debris basins within Los Angeles County. Both predictive models were then applied for hind‐casting the sediment prediction of several post 2000 events. Both the statistical and ANN models yield remarkably consistent results when compared with the measured field data. The results show that these models are very useful tools for predicting sediment yield sequences. The results can be used for scheduling cleanout operation of debris basins. It can be of great help in the planning of emergency response for burned areas to minimize the damage to properties and lives.