Deforestation models use a set of biophysical and socio-economic variables, such as accessibility maps (mainly roads and rivers), landscape maps (land-cover/land-use), cattle and soy prices, human population density and agricultural suitability maps, to predict where deforestation is more likely to occur in the future. Although using different methodologies, they all agree that maintaining the rate of deforestation at current levels would have devastating impacts on the ecosystem and atmosphere, and agree about the relative risk among different regions. Laurance et al. (2001a)used a simple spatial model to generate two scenarios for the future of the Amazon, with the main difference being the effectiveness of protected areas in preventing deforestation. Both scenarios suggested a dramatic landscape alteration, ranging from 28% to 42% of the region deforested or heavily degraded over the 20 year period beginning in 2001, especially in the south-eastern areas of the Brazilian Amazon. The authors concluded that the efforts to avoid deforestation by improving conservation will be overwhelmed by the destructive trends observed in this region. Soares-Filho et al. (2006), although using an improved methodology that allowed for different deforestation rates among the 47sub-regions of the Amazon, found a similar effect, albeit one that took an additional three decades to manifest. All these policy-sensitive scenarios revealed that, given a regional deforestation rate, the spatial pattern will continue to be mostly concentrated in the eastern part of the Amazon where the infrastructures are well developed. Similar results were found by Wassenaar et al. (2007a), who used the modelling environment CLUE-S to model deforestation in Central and tropical South America until 2010.
In addition to utilizing data from existing in situ and spaceborne climate monitoring platforms, new in situ monitoring networks need to be estab- lished in regions where rapid LULCC is currently underway. This effort could be undertaken in se- lected areas such as the Amazonia, Costa Rican cloud forests, Southeast Asian tropical forests, and near rapidly growing urban and agricultural areas and then expanded to other regions. This effort could consider collaborating with existing coordinated national and international efforts [e.g., Flux Network (FLUXNET; Baldocchi et al. 2001)]. Mitigation of the diverse range of effects on climate from LULCC can also begin with existing local policies and practices of land management devised for conservation efforts. For example, in the United States and China, there are certain government policies [e.g., Grain for Green Project (Fan et al. 2014)] that encourage farmers from selected re- gions to adopt conservation practices that may also
the northern edge of the PRAMS region, north of Gerald- ton, bounded by the natural coastline and Darling Fault. It has similar aquifers to PRAMS, including the Leederville- Parmelia and Yarragadee, but has complex faulting that makes them locally discontinuous. The area has low rainfall and low population, which leads to groundwater usage that is around 40 percent of the total amount available for licensing. Texture contrast soils are common in the basin with sandy and loamy top soils over clayey lower layers. Water tables are generally not close to the surface, being 10m or deeper away from coastal dunes. In keeping with the PRAMS and SWAMS modelling, the leaf area index (LAI) and rooting distribution of the cover were fixed, along with the ground- water depth. The cover types are bare ground with no vege- tation cover, perennial grass with an LAI of 1.0 and rooting depth of 1 m over a 10 m water table, and trees with an LAI of 0.5 and rooting depth of 5 m over a 10 m water table. At the Three Springs site with 15 percent less rainfall, the tree LAI was reduced to 0.33.
The Hisar district, a part of the Indo – Gangetic alluvial plain, is situated between 2853’45” to 2949’15” North latitudes and 7513’15” to 7618’15” East longitudes (Fig.1). It occupies a total area of 3983sq.km. Hisar district comprises of three major physiographic units i.e. Aeolian plain, Older alluvial plain and Chautang flood plain. The district lies in semi-arid region, which is nearly 30 km northeast of the Rajasthan desert. It generally experiences a sub-tropical, continental type of climate. According to 2011 Census, Hisar district recorded a population of 17, 42,815 persons, which made the district 2nd most populous district of the state. The district observed population density of 438 persons per square km. The Population of the district comprised of 931,535 Male and 811,280 female. The district made rapid progress in agricultural production during post Green Revolution period. As a matter of fact the dry climatic conditions of the district necessitated the development of alternative source of water, essential for cultivation of crops.
García-Ruiz JM, Lasanta T, Ruiz-Flaño P, Ortigosa L, White S, González C, Martí C. 1996. Land-use changes and sustainable development in mountain areas: A case study in the Spanish Pyrenees. Landscape Ecology 11(5):267–277. González-Martínez SC, Bravo F. 2001. Density and population structure of the natural regeneration of Scots pine (Pinus sylvestris L.) in the High Ebro Basin (Northern Spain). Annales des Sciences Forestières 58:277–288. Latron J. 2003. Estudio del funcionamiento hidrológico de una cuenca mediterránea de montaña (Vallcebre, Pirineos Catalanes) [PhD thesis]. Barcelona, Spain: Facultat de Geologia, Universitat de Barcelona. Latron J, Anderton S, White S, Llorens P, Gallart, F. 2003. Seasonal characteristics of the hydrological response in a Mediterranean mountain research catchment (Vallcebre, Catalan Pyrenees): Field investigations and modelling. In: Servat E, Wajdi N, Leduc C, Shakeel A, editors. Hydrology of the Mediterranean and Semiarid Regions. International Association of Hydrological Sciences (IAHS), Proceedings of an International Symposium, IAHS 278; Montpellier, France: International Association of Hydrological Sciences, pp 106–110.
Figure 1 below shows the location of the study area falling between longitude 36 o 44’ 39.46” E and 37 o 00’ 58.03” E and latitude 0 o 42’ 13.28” S and 1 o 01” 12.72” S, an area at the south-eastern edges of Aberdare ranges/forest in Kenya. It’s about 80 km north of Nairobi and 40 km west of Thika town and encompassing the Ndakaini Dam and its environs, administratively straddling both Gatanga and Maragua districts, Murang’a County, Kenya. Main settlement areas including Ndakaini, Makomboki, Kangari and Kariara sub-locations in Gatanga district and Kigumo division in Maragua district and together with tracts of forest areas such as Kimakia and Gatare forest stations, form the catchments of the dam. The physiographic characteristic of the region greatly influences the drainage pattern of the area and can be described in terms of three zones. The first is a zone characterized by deeply incised V- shaped valleys having slopes greater than 30% and is highly susceptible to soil erosion. This zone covers the reservoir’s main catchment area, the forest reserve, the official tea belt buffering the forest -The Nyayo Tea Zone- and the influent river zones. The second zone encompasses the settlement areas, a predominantly tea growing area and where water has been dammed to contain the main water-mass. It is characterized by steep topography and soil erosion is of major concern on arable land. The area beyond the dam all the way to the confluence of Chania River forms the third zone and is predominantly a coffee plantation zone. Ng’ethu Water Treatment Works is located in this zone. Besides the Ndakaini dam, the other main hydrological features in the study area are six major rivers namely Githika, Thika, Kayuyu, Kiama, Kimakia and Chania, all influencing the drainage pattern of the area. Ndakaini dam is fed by rivers Kayuyu, Githika and Thika River and also from underground seepage.
analyses done in the communal villages of the central Keiskamma catchment indicate increasing vegetation fragmentation manifested by an increase in smaller and less connected vegetation patches and a subsequent increase in bare and degraded soil patches which are much bigger and more connected. The differences in rangeland condition in the different communal areas and the former commercial farms were validated using the LOI. The LOI revealed very low vegetation connectivity in communal rangelands that have weak local traditional institutions. In contrast, good range conditions existed in communal rangelands with strong local institutions. These differences were investigated by Bennett and Barrett (2007) who suggested that the differences in rangeland condition are a reflection on the degree of control local communities exert on communal grazing resources. Their study reveals that grazing resources are influenced by the social and ecological heterogeneity that characterise the catchment (Bennett and Barrett, 2007). Moyo et al. (2008) concur that the rangeland conditions and grazing strategies found in the communal areas are a sequence of the interaction between social, land tenure, ecological and institutional factors. In a similar study, Ainslie (2002) suggests that dissimilarities in rangeland condition in communal grazing areas are a result of high stocking density and ineffective rangeland management methods. The strength of local institutions such as Residence Associations (RA) and traditional authorities responsible for coordinating grazing and land management in communal villages explain the disparities in rangeland condition in the central Keiskamma (Bennett and Barrett, 2007; Moyo et al., 2008). This study also confirms that former commercial farms have better rangeland condition compared to the communal areas; this is proved by higher image analyses and landscape indices which both reflect relatively high vegetation connectivity. The differences are however not significantly different from those of communal villages whose rangeland condition is still good.
Three different approaches were used to evaluate potential runoff changes. First we examined time series of model residuals, which means that we compared model simulations with parameters calibrated on a reference period with observed runoff for periods with potential change. We also compared parameter values of the best parameter sets for the different time periods. Finally we compared daily runoff peaks simulated by using the best parameter sets for the different time periods. For WS1 these different time periods could be clearly separated into the time series before and after clear-cutting. For the LOOK basin where harvesting and road construction occurred on a more protracted basis, changes were related to the harvesting history record. We used these three approaches to detect changes in flow and system behavior for the three catchments WS1, WS2, and LOOK. The catchment WS2, in which there was no harvesting activity at all, was included as a test to detect false change.
Finally, we used two matrices for change probabilities in line with the changes occurring over recent decades and with the two simulated scenarios (Table A4 ). If the expected conditions prevail, the trend scenario will see few land use changes. However, the green scenario predicts that more agricultural land will be abandoned, with the development of natural ecosystems. In Figure 6 , we show land use changes expected between 2006 and 2030, in a Green Scenario with Restrictions and Incentives (GS30-WRI). Again, we can see two opposing trends. On the one hand, there is an increase in the artificial area (reds), with an annual rate of change of +1.2870 (Table 5 ). If the expected conditions are met, urban expansion will take place in the surroundings of Sabañánigo, mainly on former agricultural land and also on scrubland. Other transitions to artificial areas can be expected in the areas that are closest to the main roads. On the other hand, natural regeneration of vegetation (green colors) is the natural evolution of ecosystems towards the climax. We can expect transitions of agricultural land (about 1000 ha) and of grassland (also about 1000 ha) towards shrubs. In ecological terms, the latter are those that most worry PA managers because focal habitats might be lost. Finally, patches of grassland and shrubs can be expected to evolve towards forest (about 2000 ha).
• Urban expansion estimated to consume 1-2 million ha yr -1 of cropland in developing
• Salinity in irrigated lands is associated with a worldwide loss of ~1.5 million ha yr -1 of arable land (est. $11 billion in production) • Up to ~40% of global croplands (~ 6 billion
Mining operations going on in the study area have been identified as one of the major driving forces causing rapid land-cover changes (the other is urbanisation). Mine sites in the area have increased from 333.9 ha (0.48% of the study area) in 1986 to 1,045.98 ha (1.52% of the study area) in 2008. The Tables 3, 4 and Figs. 3 and 4 reveal that mine sites increased at the expense of rangeland, farmland and barren land. This confirms a report that the upsurge of gold mining between 1986 and 1996 led to the increase in gold production from an annual total of 400,000 troy ounces in 1987 to 1.2million troy ounces by 1996, which established Ghana as Africa’s second largest gold producer, after South Africa [27, 28] . Moreover, the Anglo-Gold Ashanti Company also confirms that mining in the area has grown over the time and is
Land use and landcoverchange is a phenomena starting from ancient time. However, rapid and extensive landcoverchange was the major element of global environmental changes of the past three centuries. Globally cropland showed fivefold increase from 1770 up to 1990 and pastureland also increased by above six fold from 1700 to 1990. In contrast forest cover was decreased from 5000-6200 million hectares in 1700 to 4300- 5300 million hectares in 1990 (Lambin et al 2003). More rapidly than the aforementioned periods, more woody vegetation cover was converted to cropland between1950 and 1980 (MEA, 2005). But direction of land use and landcoverchange is not similar for all parts of the world. In the last two decades, the area of temperate forest was increasing by almost 3 million hectares, while the tropical forest was decreasing by 12 million hectares per year (MEA, 2005). In contrary, in Eastern and Western Africa only reduced and fragmented forestlands were left (Gutman et al 2004).
i-Tree Eco also estimates the carbon storage value as biomass through species-specific allometric equations derived from the literature (Nowak and Crane, 2002, Nowak, 1994), and if unavailable, an average of equations from the same genus, failing which broadleaf equations are used (Nowak et al., 2008a). Aboveground biomass is converted to tree biomass assuming a globally averaged root-to-shoot ratio of 0.26, a slight overestimation compared to the tropical ratio of 0.24 (Cairns et al., 1997), that may affect this study. Fresh weight biomass equations are adjusted to dry weight with species-specific equations from the literature (Nowak and Crane, 2002). Only wood biomass is considered for deciduous trees due to the annual shedding of leaves. The total dry weight biomass of trees is converted to total stored carbon by a factor of 0.5 (Chow and Rolfe, 1989).
study area were mapped mainly based on 2004 digital imagery and were field checked. It provides a good ground truth not only for the 2004 imagery, but must also be part of the reference for interpreting 1988, 1972 and 1954 orthoimages. The interpretation was carried out with respect to the forest canopy patterns that appeared on the imagery, relationships with other land covers, and DEM derived attributes such as aspects and slope declivity. For example, cool temperate rainforest appears as closed canopy in or adjacent wet forest and distributes along valleys with above 300 m elevation, especially where aspect provides the shadiest local climate. The most daunting photo interpretation challenge refers to the black and white orthoimages. Stereo models were built using stereo pairs of aerial photograph to support the interpretation. This 3D view of terrain
The FD typically manages the forest in a bureaucratic manner, justified for enhancing ecological services and biodiversity conservation, but with the challenges of rent seeking, driven by institutionalized incentives (Fleischman 2014). The FD gains revenue through the regular felling of trees in selected beats/coupes, and via plantations, usually of eucalyptus and teak. Despite claims of ecological services being enhanced by forest plantation, other research has demonstrated the problems of forest degradation and ecological damage resulting from such activities (Afreen et al. 2011; Chaturvedi et al. 2011; Das 2010). Thus, market-driven plantation based projects are problematic, and more attention should be given to plantations to meet local conditions (Vatn and Vedeld 2013). Teak plantations may provide short term economic benefits through wage labour earnings, but monocultures adversely affect the livelihood of communities dependent on a range of non-timber forest products that do not grow in such plantations. Teak plantations also impact the ecology of the landscape, converting a biodiverse dry forest into an area where tree cover is protected, but with low ecological value in terms of their overall support of wildlife, bird, and insect diversity (Mondal and Southworth 2010).
Deforestation of TDF in the municipality of San Juan de Cinco Pinos began before 1985. Since then, TDF cover has seen both increases and decreases due to various socioeconomic factors and historical events, but a net loss of 210.51 ha was observed over a 26-year period. The majority of TDF loss since 1985 was a result of forest conversion to agricultural and pastoral land. If the TDF of Cinco Pinos continues to be converted to alternate land uses at a rate of approximately 10 ha per year, the local people face the prospect of losing what remains of their TDF in a few decades. However, results of the LULCC analysis (Figure 5.1) could also indicate a tapering off of TDF conversion. Since the mid-90s, deforestation of TDF in Cinco Pinos has remained moderately constant. This leveling off of LULCC could be attributed to livelihoods being supported by remittances as a result of out-migration of labor obtaining employment in neighboring Central American countries as well as the U.S. and Spain.
Information regarding topography was taken from the global Digital Elevation Model (DEM) GTOPO30 (USGS, 2009a) with a horizontal grid spacing of 30 arc s (approxi- mately 1 km×1 km). No consistent soil and land-use/land- cover data base was available for the study region. There- fore, information from different data sources had to be in- tegrated and joined to individual maps covering the whole region. A detailed, digital soil map of Israel was elaborated at the University of Bochum, Germany (Marschner, personal communication, 2008) which is based on Singer (2007) and the soil associations map of Israel. For the rest of the study region, the global map of soil properties ISRIC-WISE (Bat- jes, 2006) was available. For both data sources, soil depth, profile available water capacity and basic hydraulic proper- ties were derived using as far as possible the same methodol- ogy. The LandSHIFT.R results were geographically mapped to the 1 km×1 km grid required by the TRAIN model. For the grid cells where no LandSHIFT.R simulation results are available (Lebanon, Syria, Egypt), information was derived from the IGBP Global LandCover Characterization GLCC (Loveland et al., 2000; USGS, 2009b) which is available on a 1 km grid size. The DEM as well as the individual soil and land-use/land-cover layers were re-projected to congru-
where R is the rainfall erosivity (MJ mm (ha hr) -1 ), K is the soil erodibility (Mg ha h (MJ ha mm) -1 ), LS is the slope length-gradient factor, C is a crop-management factor, and P is a support practise factor (explained below). Rainfall erosivity R was calculated based on an equation given in the InVEST user manual (Tallis et al. 2014) after guidelines recommended by the FAO, resulting in a value of 190 MJ mm (ha h year) -1 . A raster reporting the soil erosivity index K was acquired from the UK National Soil Map (Farewell et al. 2011). Gaps in the source data led to some soil series reporting erroneous K factors below zero; these were replaced with values of zero which the model treated as ‘no data’ pixels, and removed from subsequent analysis. The cover management factor C was parameterised for each landcover class after Morgan (2005): 0.002 for broadleaf trees; 0.004 for coniferous trees; 0.010 for grassland; 0 for buildings, water and paved surfaces; and 0.003 for suburban (25 m analysis only, as an area- weighted average of constituent classes). The support practise factor P is an index value between 0 and 1, where 1 has no effect on the equation and values less than 1 represent standard management practices that impede erosion, such as contour farming. Since this parameter is specific to row crop commercial agricul- ture practices in the United States, it was not applicable here and omitted from the study by assigning it a value of 1 for all classes.