Human activities have modified the environment over the years. Urbanization, agriculture lumbering, mining and other land uses have substantially altered the Earth's surface. Land use and the resultant change in land cover have significant effects on ecological, environmental and hydrologic systems and processes. An understanding of past and present land-cover change, together with an analysis of potential future change, is necessary for proper management; thus, the need for models. Hydrologic models are primarily used for hydrologic prediction and for understanding hydrologic processes. With recent technological advances, technological based tools such as GIS are incorporated into hydrologic models for assessing the impacts of various land use/cover. Hydrologic models incorporated with GIS can be used to project future land uses/cover to provide an increased clarity, probability or likelihood of potential consequences on ecosystem services such as biodiversity, water quality and climate. This paper critically examines land use/cover, effects of impacts of land use/cover and the use of hydrologic models to assess the impact of land us/cover on runoff and sediment yield. Hence it calls for their use by watershed managers and decision maker as management tool especially in developing countries.
In this work, a low cost six-multipoint temperature data logger was developed. It was designed using LM35 as the sensor, and arduino Uno as the data processing element. The response time of the sensor was observed to be between three and four minutes. The comparative evaluation of the system with other established thermometers show that no two temperature meters gave the same values. However the most important thing is that all the systems recorded the same temperature flow pattern. This indicates that the systems actually senses change in the surrounding effectively. In relation to the alcohol thermometer, the designed system shows an accuracy of +0.4 o C at temperatures below 30 0 C and +1.85 0 C at temperatures above 30 0 C. The system gives room for effective and adjustable temperature data logging procedure. Hence, the system is recommended for use in monitoring low temperature systems.
The success and profitability of aquaculture production is highly dependent on the proper management employed during the culture period. This management is synonymous with water management since water of suitable quality and quantity is a prerequisite for any successful aquacultural production. The knowledge of the environment (water) enhances better management. However, the complexity of an aquaculture system which result from multiple interactions makes it difficult to predict how the aquatic community will respond to changes with simple methods of analysis, especially if the methods address a single stressor at a time. These necessitated the development of numerous aquatic ecosystem models such as the Fish Simulation Culture Model (FIS-C), Tilapia Farming Support Tool (TFST), Farm Aquaculture Resource Management (FARM), Pond-Water Availability Period (PWAP) model, AquaFarm and Raceway design and simulation system (RDSS)),which have been used for years as tools to interpret, predict and better understand water quality changes. This paper reviewed existing simulation models of aquacultural production systems with the aim of adopting a suitable one for predicting the environment, performance of African cultured indigenous fish species under different management scenarioes. The reviewed models were found to have addressed specific problem that pertain to some foreign species, production systems and locality. There was none that could be used to model the effects of different management scenario and their effects on African Catfish (Clarials gariepinus) cultured in intensive static aquacultural system. Hence, the problem of predicting the environmental condition, so as to determine point of diminishing returns and optimize yield in an economical way still remains elusive for most fish farms in Nigeria. There is therefore a serious need to develop models that can predict the effects of environment on the performance of indigenous fish species. This will aid stake holders in predicting different management scenario so as to achieve a better crop (fish) yield.
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