[教学设计]小学生春季营养早餐

re as . C atu Received 1 May 2010 Keywords: Taita hills Water resources Simulation models use a very complex system. The understanding of the interconnecting relations involved in this system is an ent in accurate assessment of water demand and distribution is crucial to improve water management and avoid scarcity. Currently roughly 70% of freshwater withdrawals are used for agriculture (FAO, 2005). In most Sub-Saharan African countries agriculture is themain economic activity, representing about 40% of sustainability of agricultural systems. It is expected that without proper investments tomitigate the impacts of climate change, global irrigationwater needsmay increase by roughly 20%by2080 (Fischer et al., 2007). Climate change may also adversely affect agricultural production, access to food and stability of food supplies, having direct impacts on food security (Schmidhuber and Tubiello, 2007). Improvements on models and computer capacity in the past decades made available important tools to deal with these prob- lems, allowing an increasing number of studies aiming at the * Corresponding author. Tel.: þ358 44 2082876. Contents lists availab Journal of Environm ls Journal of Environmental Management 92 (2011) 982e993 E-mail address: eduardo.maeda@helsinki.fi (E.E. Maeda). existence. It is essential for the economy, the social order and life itself. Although global withdrawals of water resources are still below the critical limit, more than two billion people live in highly water-stressed areas due to the uneven distribution of this resource in time and space (Oki and Kanae, 2006). Simulations carried out in previous studies indicate that up to 59% of the world population will face some sort of water shortage by 2050 (Rockstrom et al., 2009). In Kenya, over 55% of the rural population do not have access to quality drinkable water (FAO, 2005). In such regions, the Consequently, a careful control of the water used for irrigation is a key aspect to be considered in order to ensure a proper distri- bution of the available resources between residential, industrial and agricultural use. Moreover, scientific evidence indicates that anthropogenic changes in the environment are affecting global climate and oper- ating as an accelerator for environmental disturbances such as flooding and droughts (IPCC, 2007). Changes in precipitation and temperature patterns will likely have direct impacts on the Agricultural expansion Climate change 1. Introduction Freshwater is a fundamental elem 0301-4797/$ e see front matter � 2010 Elsevier Ltd. doi:10.1016/j.jenvman.2010.11.005 in the Taita Hills, Kenya. The framework comprised a land use change simulation model, a reference evapotranspiration model and synthetic precipitation datasets generated through a Monte Carlo simu- lation. In order to generate plausible climate change scenarios, outputs from General Climate Models were used as reference to perturbing the Monte Carlo simulations. The results indicate that throughout the next 20 years the low availability of arable lands in the hills will drive agricultural expansion to areas with higher IWR in the foothills. If current trends persist, agricultural areas will occupy roughly 60% of the study area by 2030. This expansion will increase by approximately 40% the annual water volume necessary for irrigation. Climate change may slightly decrease crops’ IWR in April and November by 2030, while in May a small increase will likely be observed. The integrated assessment of these envi- ronmental changes allowed a clear identification of priority regions for land use allocation policies and water resources management. � 2010 Elsevier Ltd. All rights reserved. every aspect of human their gross domestic product (Barrios et al., 2008). It is estimated that between the years 1975 and 2000 the agricultural areas increased 57% in sub-Saharan Africa (Brink and Eva, 2009). Accepted 1 November 2010 Available online 15 December 2010 resources. In this study, an integrated modelling framework was assembled in order to investigate potential impacts of agricultural expansion and climate changes on Irrigation Water Requirements (IWR) Received in revised form 11 October 2010 essential step for elaborating public policies that can effectively lead to the sustainable use of water Prospective changes in irrigation water expansion and climate changes in the e Eduardo Eiji Maeda*, Petri K.E. Pellikka, Barnaby J.F Department of Geosciences and Geography, University of Helsinki, Gustaf Hällströmin k a r t i c l e i n f o Article history: a b s t r a c t Water resources and land journal homepage: www.e All rights reserved. quirements caused by agricultural tern arc mountains of Kenya lark, Mika Siljander 2, 00014 Helsinki, Finland are closely linked with each other and with regional climate, assembling le at ScienceDirect ental Management evier .com/locate/ jenvman sustainable use of natural resources and land use planning. For instance, land use and land cover change (LUCC) simulation models might provide robust frameworks to cope with the complexity of land use systems (Veldkamp and Verburg, 2004). Such models are considered efficient tools to project alternative scenarios into the future and to test the stability of interrelated ecological systems (Koomen et al., 2008). In irrigation water management, the development of hydro- meteorological models to estimate Evapotranspiration (ET) resul- ted in important contributions at global, regional and local scales. ET is defined as the combination of two separate processes, in which water is lost on the one hand from the soil surface by evaporation and on the other hand from the crop by transpiration (Allen et al., 1998). Reliable estimates of ET are essential to identify temporal variations on irrigation requirements, improve water resource allocation and evaluate the effect of land use and management changes on the water balance (Ortega-Farias et al., 2009). Quantification of ET is a basic component for the design, operation and management of irrigation systems. Several studies have shown that careful irrigation management can considerably improve crops’ water use efficiency without causing yield reduc- tion (Du et al., 2010; Hassanli et al., 2010). Nevertheless, land use and water resources are closely linked with each other and with regional climate, assembling a very Tsavo plains of the Coast Province, Kenya (Fig. 1). The Taita Hills cover an area of approximately 850 km2. The indigenous cloud forests have suffered substantial loss and degradation for several centuries as they have been converted to agriculture, because of the abundant rainfall and rich soils that provide good conditions for agricultural production (Clark and Pellikka, 2009). Approximately half of the cloud forests in the hills have been cleared for agricul- tural lands since 1955. Currently, it is estimated that only 1% of the original forested area remains preserved (Pellikka et al., 2009). Located in the inter-tropical convergence zone, the area has a bimodal rainfall pattern, the long rains occurring in MarcheMay and short rains in NovembereDecember. The agriculture in the hills is intensive small-scale subsistence farming. In the lower highland zone and in upper midland zone, the typical crops aremaize, beans, peas, potatoes, cabbages, tomatoes, cassava and banana. In the slopes and lower parts of the hills with average annual rainfall between 600 and 900 mm, early maturing maize species and sorghum and millet species are cultivated. In the lower midland zones with average rainfall between 500 and 700 mm, dryland maize types and onions are cultivated, among others. The Eastern Arc Mountains sustain some of the richest concentrations of endemic animals and plants on Earth, and thus it is considered one of the world’s 25 biodiversity hotspots (Myers et al., 2000). Although only a small fraction of the indigenous E.E. Maeda et al. / Journal of Environmental Management 92 (2011) 982e993 983 complex system. Although many studies have been undertaken to separately understand each of these components, scientists currently face the challenge to integrate these studies into more complex frameworks. The understanding of these interconnecting relations is an essential step for elaborating public policies that can effectively lead to the sustainable use of water resources. In this study, an integrated modelling framework was assem- bled in order to investigate the potential impacts of agricultural expansion and climate changes on irrigationwater requirements in the Taita Hills, Kenya. 2. Study area The Taita Hills are located in the northernmost part of the Eastern Arc Mountains of Kenya and Tanzania, in the middle of the Fig. 1. Geographic location of the Taita Hills. The upper-right corner of the figure shows cloud forest is preserved in the Taita Hills, it continues to have an outstanding diversity of flora and fauna and a high level of ende- mism (Burgess et al., 2007). Hence, the region is considered to have high scientific interest and there is a high potential for succeeding in the connectivity development and community based natural resource management (Himberg et al., 2009). 3. Material and methods In this study, future agricultural expansion and climate change scenarios were simulated in order to evaluate their potential impacts on Irrigation Water Requirements (IWR) in the Taita Hills, Kenya. To achieve this objective a modelling framework was assembled by coupling a LUCC simulation model, a reference ET (ETo) model and synthetic precipitation datasets generated the Digital Elevation Model of the study area and the location of the main towns. through a Monte Carlo simulation (Rubinstein and Kroese, 2007). The synthetic precipitation datasets refers to artificially created precipitation data, which simulate precipitation patterns of observed historical events or potential changes caused by external forcing. The purpose of this framework was to identify tendencies and patterns in agricultural expansion and climate changes that can potentially affect the IWR in the study area. Remote sensing and GIS techniques were combined to provide the necessary inputs for the modelling framework. A flow chart illustrating the components of the modelling framework is pre- sented in Fig. 2, and the main components involved in the study are described in detail in the following text. 3.1. Agricultural expansion model Dynamic models operating on a cellular automata basis have arisen as a feasible alternative for the analysis of land use dynamics and in the exploration of future landscape scenarios. In this study, a spatially explicit simulation model of landscape dynamics, DINAMICA-EGO (Soares-Filho et al., 2002, 2007), was applied to simulate future scenarios of land use in the Taita Hills. The model receives as inputs land use transition rates, landscape variables and precipitation and distance to already established croplands. All landscape attributes were represented by raster imageswith a 20m spatial resolution. After the transition rates are defined and the role of each land- scape attributed evaluated, the model uses stochastic algorithms to allocate land changes and simulate landscape scenarios (Almeida et al., 2008). In the particular case of this study, the LULCM from the year 2003 was considered to be the initial landscape and the model was applied to simulate land changes up to 2030. In this case, an exploratory scenario was simulated. An exploratory scenario is a sequence of emerging events (Alcamo, 2001). Namely, the average agricultural expansion rates observed from 1987 to 2003 in the study areawere used to build an exploratory scenariowith constant land change rates up to the year 2030. The LUCC model performance for this specific study area was evaluated in a previous study (Maeda et al., 2010a) using an adap- tation of the method proposed by Hagen (2003), in which multiple resolution windows are used to compare the simulated and the reference maps within a neighbourhood context. The performance achieved in the LUCCmodel calibrationwas considered satisfactory, achieving spatialfittings from75%at a spatial resolutionof 100m,up to 90% at a spatial resolution of 380 m. Approaches considering E.E. Maeda et al. / Journal of Environmental Management 92 (2011) 982e993984 landscape parameters. The landscape parameters are intrinsic spatially distributed features, such as soil type and slope, which are kept constant during the simulation process. The landscape vari- ables are spatiotemporal dynamic features that are subjected to changes by decisionmakers, for instance roads and protected areas. Themodel was driven by land use and land cover maps (LULCM) from two selected dates: 1987 (initial landscape) and 2003 (final landscape), which are used as inputs to represent the historical land use transitions in the study area. The dates of the LULCM were chosen based on two criteria. The first criterion was that the landscape changes between the initial and final landscape should accurately represent the ongoing land change activities in the study area. That is to say, the agricultural expansion rates between 1987 and 2003 were assumed to retrieve a consistent figure of the current trends. The second criterion relied on the availability of cloud free satellite images to assemble the LULCM. In total, ten landscape attributes (variables/parameters) were used as inputs for the model: distance to roads, distance to markets, altitude, distance to rivers, protected areas, soil type, slope, insolation, mean annual Fig. 2. Flow chart illustrating the integr neighborhood contexts are useful in comparing maps that do not exactly match on a cell-by-cell basis, but still present similar spatial patterns within a certain cell vicinity (Soares-Filho et al., 2002). 3.2. Irrigation water requirement Crop water requirement (CWR) is defined as the amount of water required to compensate the evapotranspiration loss from a cropped field (Allen et al., 1998). In cases where all the water needed for optimal growth of the crop is provided by rainfall, irrigation is not required and the Irrigation Water Requirement (IWR) is equal to zero. In cases where all water has to be supplied by irrigation the IWR is equal to the CWR. However, when part of the CWR is supplied by rainfall and the remaining part by irrigation, the IWR is equal to the difference between the crop evapotranspiration (ETc) and the Effective Precipitation (Peff). In such cases, the IWR was computed using the following equation (FAO, 1997): IWRm ¼ ðKcm � ETom � 30Þ � Peffm (1) ated modelling framework concept. men where: IWRm ¼ Monthly average crop water requirement in monthm, [mm];Kcm¼Crop coefficient inmonthm, [ ];ETom¼Mean daily Reference Evapotranspiration in month m, [mm.day�1]; Peffm ¼ Average effective precipitation in month m, [mm]. Reference Evapotranspiration (ETo) is defined as the ET rate from a reference surface, where the reference surface is a hypothetical grasswith specific andwell knowncharacteristics (Allenet al.,1998). Effective precipitation (Peff) is defined as the fraction of rainfall retained in the root zone,which canbe effectivelyusedby theplants. That is, the portion of precipitation that is not lost by runoff, evap- oration or deep percolation. The monthly total rainfall was con- verted to Peff using a simplified method proposed by Brouwer and Heibloem (1986), which is based on empirical observations and requires only the total monthly volume of precipitation. The parameters for this calculation, published in Brouwer andHeibloem (1986), can be accessed at http://www.fao.org/documents/. 3.2.1. Evapotranspiration The concept of ETo was introduced to study the evaporative demand of the atmosphere independently of crop type, crop phenology and management practices. Several empirically and physically based ETo models have been developed during the past decades, varying in complexity and data requirements. Generally, complex physically basedmodels incorporate amore comprehensive set of variables and parameters, which allows the model to perform well in a greater variety of climatic conditions. Unfortunately, such methods demand very detailed meteorological data, which are frequently missing from meteorological stations (Jabloun and Sahli, 2008). For this reason, the Hargreaves model, which is an empirical model that requires only temperature data,was chosen for this study. The Hargreaves method was developed by Hargreaves and Samani (1985), using eight years of daily lysimeter data from Davis, Cal- ifornia, and tested in different locations such as Australia, Haiti and Bangladesh. Since then, the method has been successfully applied worldwide (e.g. Gavilán et al., 2006). The Hargreaves equation requires only daily mean, maximum and minimum air temperature and extraterrestrial radiation. The equation can be written as: ETo ¼ 0:0023$RA$ � ðTmax� TminÞ0:5$ðTmeanþ 17:8Þ � (2) where: RA ¼ extraterrestrial radiation (mm day�1); Tmean ¼Mean temperature (oC); Tmin ¼ Minimum temperature (oC); Tmax ¼ Maximum temperature (oC). The common methodology used for estimating ETo is to use weather data retrieved from ground meteorological stations as input for the model. However, data available from ground stations are frequently insufficient to represent the spatial distribution of the ET process in detailed scales. To overcome this problem, Land Surface Temperature (LST) data obtained by the Moderate Resolu- tion Imaging Spectroradiometer (MODIS) were used as input to the Hargreaves model. The combined use of the Hargreaves model and MODIS LST data for estimating ETo in the Taita Hills was evaluated in a previous study (Maeda et al., 2010b). The results of this study indicated that the approach is appropriate for this particular study area, achieving an average RMSE of 0.47 mm d�1 and a correlation coefficient of 0.67 in comparison with the FAO PenmaneMonteith (FAO-PM) method. The FAO-PMmethod is recommended as the standard ETo method and has been accepted by the scientific community as the most precise one for its good results when compared with other equations in different regions worldwide (Cai et al., 2007; Jabloun and Sahli, 2008). In order to calculate the Crop Evapotranspiration (ETc) for E.E. Maeda et al. / Journal of Environ a determined ETo condition, the ETo values are multiplied by a crop coefficient (Kc). The Kc aims to incorporate into the equation the crop type, variety and development stage, enabling the represen- tation of the spatiotemporal distributions of croplands. In general, three Kc values are used to describe the crops phenological changes during an agricultural season: those during the initial stage (Kci), the mid-season stage (Kcm) and at the end of the late season stage (Kce). The Kc values used in the present study were obtained from tables recommended by FAO (Allen et al., 1998). Nevertheless, to assign the appropriate Kc values it is essential to identify the agriculture calendar in the study area; that is, the period of the year when crops are planted, grown and harvested. For this, monthly Normalized Difference Vegetation Index (NDVI) obtained from satellite images were used to identify the phenological stages of croplands during the year. The NDVI imagery were obtained from the MOD13Q1 product (Justice et al., 2002), which provides 16-day composite imagery from the MODIS Terra/Aqua sensors. The MODIS sensor offers almost daily imagerywith a spatial resolution of 250m in the visible red and near-infrared wavelengths. These bands were specifically designed to detect land cover change dynamics (Townshend and Justice, 1988). After the NDVI imagery was acquired, random points were distributed along the agricultural areas. The monthly average NDVI values in each of these points were observed throughout the year in order to identify the