In this era of limited government funding for new initiatives, we need to start with leveraging current and largely successful approaches such as satellite observation of LULCC. Specifically, for example, continuation of Landsat, Terra, Aqua, Sentinel, and other similar satellite missions by various nations and space agency policies that allow free access to Earth observation data are needed for international transparency for monitoring LULCC (De Sy et al. 2012; Herold and Johns 2007). These activities should include database development and easy access to quality-assured data. Spaceborne observa- tion and monitoring platforms could be particularly useful for developing nations where historical data may not be available (Herold et al. 2011). We rec- ognize that processing and analysis of the data still require resources and budgetary support. However, the level of funding needed for these steps is rela- tively small even in an already con strained national budget. As shown in Brazil’s approach to the reduc- tion in deforestation, moni toring transparency and appropriate policies can lead to significant lowering of adverse impacts of LULCCs (Instituto Nacional de Pesquisas Especias 2013).
and were described in the PAAMCC (12 references). The related text passages were from ecosystem-based strategies such as sustainable watershed management, soil conservation and reforestation, and thus most of the co-benefits were linked to environment and biodiversity, as well as to forestry. Any additional mitigation co-benefits that might occur from these strategies were not mentioned. Other co-benefits, such as livelihood and agriculture, were only mentioned in six and five text passages, respectively. One example from the Adaptation and Mitigation Action Plan was a proposal to implement adaptation measures in prioritized watersheds in Mayo, Santa, Piura, Mantaro, Caplina, Locumba, Chili and Ica. The main objective was to implement integrated watershed management to reduce the adverse effects of climatechange, desertification and drought. Activities included the improvement and recovery of wetlands, grasslands and degraded soils in basins, the restoration of vegetation, and reforestation, all of which are expected to bring co-benefits related to environment and biodiversity. The Integrated Watershed Management Plans to be developed are also expected to include payment for ecosystem services (PES) schemes and capacity-building programs to foster sustainable development projects (co-benefit for development).
is at its minimum from July to September, however future minimal values occur earlier in the year near April to June.
8.3 Model Response to LandUseLandCoverChange Only
Figure 17 shows the average monthly flow and sediment loading under future LULC change only for each carbon emission scenario compared to the baseline. Runoff is virtually unaltered by the changes made to landuse; future quantities match the present baseline with monthly differences equaling no more than ± 20 cms, occurring from August – October. Sediment loading is more significantly affected, with monthly averages differing from the baseline ± 185 tonnes / day (Figure 17(b)). Further, a distinct response to the specific carbon scenarios is more easily identifiable. A2 predicts an increase in loading for all months. The largest increase (33%) occurs in March. A1B produces similar monthly averages compared to the baseline, and B1 results in a decrease in sediment loading. The largest deviation between the baseline and B1 also occurs in March, when the sediment loading is projected to reduce by 18%. Over the course of the entire 30 year simulation, the sediment percent change for A2, A1B, and B1 from the baseline are +43.8, -0.4, and -20.8, respectively. Percent change for runoff is < 1% for all three scenarios. In general, the seasonal fluctuations of future streamflow and sediment loading remain consistent with present day conditions.
these simulations: secondary LULCC and negative emissions using bioenergy with carbon capture and storage (BECCS). The carbon changes from secondary landuse changes (for instance natural to managed forest, which is not accounted for in this model) can be substantial and may account for more carbon emissions than primary landuse changes [Shevliakova et al., 2009; Hurtt et al., 2011; Lawrence et al., 2012]. Similarly, BECCS for the RCP2.6 scenario could give negative emissions of between 43.8 and 160.6 GtC [Kato and Yamagata, 2014]. According to those projections, the potential of BECCS is likely to be bigger than the net land carbon change in any of the three RCPs considered here (8, 101, or 83 GtC for the three RCPs, respectively; see Figure 5a). Therefore, the lack of representation of secondary LULCC and BECCS is a considerable limitation to this study. It is also notable that the total land carbon change (including
Data from the contingency analysis (table 2) indicates that many new areas were planted but also that some former plantations were reconverted to crop land, including settlements, or left idle after cutting. Farmers confirmed that such reconversion is occurring although the results indicate a magnitude bigger than would be expected. About 70% of the cropland area in 2001 was classified as crop land in 2015 while the corresponding Figure for tree plantations was 10-15% only. From a methodological aspect it is a challenge separating real changes from other discrepancies caused in the process of classifying landuse in a fragmented landscape on temporal images. A conservative approach while evaluating the dynamics based upon the image data only is therefore advocated, but it could well be used as a base for discussions with the local farmers.
GCMs are considered to be the most appropriate means for projecting climatechange. However, due to their coarse spatial resolution, it is essential to use downscaled GCM outputs rather than raw output for impact studies ( Chu et al., 2010; Wilby et al., 1999 ), because local scale forcings, pro- cesses, and feedbacks are not well represented in GCM experiments ( Hewitson and Crane, 2006; Wetterhall et al., 2009 ). We used statistically downscaled precipitation for both A1B and A2 scenarios on the basis of empirical statistical relationships established in the SDSM ( Wilby et al., 2002 ) between historical (1988–2004) large-scale circulation patterns and atmospheric moisture variables from the NCEP reanalysis dataset ( Kalnay et al., 1996 ) and locally observed precipitation from the GSOD dataset for the same time period ( Pervez and Henebry, 2014 ). The 21st century daily precipitation was then modeled through a stochastic weather generator applying the established relationships with the prob- ability of the precipitation depending on CGCM3.1 predictor variables. The comparison of observed precipitation with CGCM3.1 projected raw and downscaled precipitation concluded that downscaled precipitation provided consistency and attenuated uncertainties while simulating future precipita- tion ( Pervez and Henebry, 2014 ). The precipitation was downscaled at the subbasin level and daily time-series were created and assigned to each subbasins’ centroid to be used in the calibrated SWAT model.
caused warming of the global climate by modifying radiative forcings ( Houghton et al., 2001 ). Because of the coupling between water and energy balance, any changes in climate will affect the hydrological cycle and the spatial and temporal distribution and intensity of precipitation ( Immerzeel, 2008; Labat et al., 2004 ). The primary source of precipitation in the Brahmaputra basin is the Indian summer mon- soon, which is projected to be impacted by global warming ( Kripalani et al., 2007; Sabade et al., 2011 ). Average monsoon precipitation is projected to increase with a possible extension of the monsoon period ( Kripalani et al., 2007 ). Such intensiﬁcation has been demonstrated to increase the severity of droughts in some parts of India but enhance the intensity of ﬂoods in other parts of the country ( Gosain et al., 2006 ). The Indian summer monsoon is linked to a complex set of natural phenomena, including the El Ni ˜ no–Southern Oscillation (ENSO), Indian Ocean Dipole (IOD) ( Ashok et al., 2004; Ashok and Saji, 2007 ), and Eurasian snow depth levels ( Immerzeel, 2008 ). However, the projected inﬂuence of ENSO and IOD on the Indian monsoon is unclear ( Cai et al., 2013; Immerzeel, 2008; Jourdain et al., 2013 ).
Landuse and cover data are the basis of investigation on global environmental change and also the key factor in the study of Earth surface activity progress; the exploration in- volves fields like biochemical circle, plant biomass distribu- tion, climatechange and atmospheric circulation. Many re- search results suggested that recent landuse is associated with a large number of industrial and agricultural activities; landuse has experienced a great and fast change in the past 50 years, especially in the width and depth. These changes are usually accompanied by the economic increase and pop- ulation boom as well as the change of production and living (Fuller et al., 2012).
Keywords: landuse/landcover; optimal fingerprinting; ARMA modeling; climatechange
Extensive landuse/landcover changes (LULC) and their climate forcing represent an important human influence on atmospheric temperature trends . Several studies using both modeled and observed data have documented the perturbation and impacts of LULC changes on climate (e.g., [2–12]). Authors in  expressed the importance of detecting LULC changes accurately at appropriate scales as so to better understand their impacts on climate and provide improved prediction of future climate. In this study, we attempt to detect the signature of the external forcing of LULC change on the regional climate of the High Plains. The proxy investigated for the forcing on the regional climate due to LULC change is the Normalized Difference Vegetation Index (NDVI). The index has been widely used to study and monitor vegetation coverage, change, and development in several ecosystems. On a daily basis, vegetation as a component of the biosphere interacts with the atmosphere through its direct influence on the partitioning between latent and sensible heat fluxes [13,14]. In addition to LULC change, we included atmospheric carbon dioxide (CO 2 ) concentrations as a proxy of greenhouse gas
Changing landcover from prairie grasslands to intensive, primarily cereal agriculture, over the North American Great Plains since the mid-19 th century, has had a hydrological and climatological impact on that ecosystem (Pielke, Sr., et al., 2011). Agriculture has introduced timed harvest seasons, irrigation, and C3 photosynthesizing crops with poorer water efficiency than the grasses it replaced. All of these changes have been linked to exacerbated drought conditions and warmer temperatures; however, few studies have quantified this relationship at the continental scale. In order to evaluate the change imposed by this shift in landuse and landcover, the observation based 20 th Century Reanalysis Project (20CR) was used to quantify the climatological differences in temperature and humidity between areas of natural prairie and agriculture over the 20 th century. An additional analysis used the Observation Minus Reanalysis (OMR) technique to isolate the surface climate signal found in the 20CR. We find indications that changing landcover had an impact on climate. However, using observation based data returned no evidence of a statistically significant change due to the small landuse and landcoverchange (LULCC) signal within the larger climate noise. Therefore, an idealised modelling experiment was undertaken using the Geophysical Fluid Dynamics Laboratory (GFDL) AM2- LM2 atmosphere-land model to remove these other influences. This experiment compared the results of two model simulations: one where the entirety of the prairie was preserved as grassland (GRASS), and another where the entire prairies had been converted into an agricultural area (AGRIC). Relative to GRASS, the AGRIC simulation has reduced surface albedo and root zone depth, and increased roughness length over the prairies, which collectively cause a significant summer drying. This occurs when the shallower rooting zone limited potential
In India only few studies were conducted by the scholars mainly for some metropolitan cities like Mumbai (Grover and Singh, 2015), Chennai (Lilly Rose and Devadas, 2009), Jaipur (Jalan and Sharma, 2014), Delhi (Mallick et al., 2008; Grover and Singh, 2015). But no such works have carried out for the small towns those are also started nucleating heating problem. Monitor- ing of those can help to provide early step for adopting suitable policies for either over come or minimize the problems. Keeping this concern in mind, present work is based on highly populated and rapidly growing English Bazar Municipality and its surround- ing areas of Malda district of West Bengal. Moreover, In India average density of meteorological density in plain region it is 1/520 sq.km. which is too sparse and from this data it is difficult to conduct any high resolution work related to atmospheric or land surface temperature change. From land surface temperature one can roughly predict atmospheric temperature based on many of such works showing relation between land surface and atmospheric temperature. Kawashima et al. (2000) documented a relation between mean air temperature and mean surface tem- perature. Also they rightly mentioned that this relation varies in different altitudinal range. They recorded that mean air tempera- ture is 7 ° to 9.6 ° C greater than mean surface temperature and obviously it is high in the lower elevation. Adjusted R 2
Wu et al. (2004) explored the influence of cropping pattern changes in the mid-west U.S. on regional water quality and ultimately on hypoxia potential in the Gulf of Mexico. They found that changes in cropping patterns (e.g. more corn-less pasture) and practices (e.g. minimum tillage) affected the run-off and erosion levels within the region. Although climatechange was not explicitly examined, the underlying modeling included the influence of differences in weather variables across the region. A number of studies have addressed the relationship between forest cover, riparian zone health and water quality. For example, Watanabe et al. (2006) examined such relationships in the Pacific Northwest. The water quality parameters of interests were stream temperatures, which if elevated can adversely affect cold water species such as salmonids. The study noted that even active management of such landscapes, such as tree planting or riparian zone protection, have limited potential to reduce water temperatures to desired levels. Other studies, such as Langpap et al. (2011) or Seedang et al. (2008) also noted the difficulty (high costs) of obtaining reductions in water temperature through forest and
Objective long-term records of the past vegetation/land- cover changes are, however, limited. Palaeoecological data, particularly fossil pollen records, have been used to describe vegetation changes regionally and globally (e.g. Prentice and Jolly, 2000; Williams et al., 2008), but unfortunately they have been of little use for the assessment of human impacts on vegetation and landcover (Anderson et al., 2006; Gail- lard et al., 2008). The development of databases of human- induced changes in landcover based on historical records, remotely-sensed images, land census and modelling (Klein Goldewijk, 2001, 2007; Ramankutty and Foley, 1999; Olof- sson and Hickler, 2008) has been useful to evaluate the ef- fects of anthropogenic land-cover changes on the past cli- mate (e.g. Brovkin et al., 2006; Olofsson and Hickler, 2008). However, the most used databases to date (i.e. the Klein Goldewijk’s database in particular) cover relatively short pe- riods. Recently developed scenarios of anthropogenic landcoverchange (ALCC) (Pongratz et al., 2008; Kaplan et al., 2009; Lemmen, 2009) include longer time periods. Notably, all these datasets show inconsistent estimates of landcover during key time periods of the past. Therefore, the devel- opment of tools to quantify and synthesize records of veg- etation/landcoverchange based on palaeoecological data is essential to evaluate model-based scenarios of ALCC and to improve their reliability.
Oleson, K. W., D. M. Lawrence, G. B. Bonan, M. G. Flanner, E. Kluzek, P. J. Lawrence, S. Levis, S. C. Swenson, and P. E. Thornton, 2010: Technical description of version 4.0 of the Community Land Model (CLM). NCAR, 266 pp. [Available online at http://www.cesm.ucar.edu/models/ccsm4.0/clm/CLM4_Tech_Note.pdf] Oleson, K.W., Bonan, G.B., Levis, S. et al., 2004. Climate Dynamics 23: 117.
estimated the relationship between the land surface temperature and vegetation abundance, different vegetation indices such as normalized vegetation index (NDVI) and fractional vegetation cover were used to indicate vegetation abundance. The results revealed that the negative correlation between land surface temperature and NDVI, and the cooling effect of green areas. Two methods were usually used to observe the relationship between vegetation index and LST: statistical analysis and the temperature/vegetation index (TVX)approach. TVX is a method of combining LST and a vegetation index in a scatterplot. The initial location of the migrating pixels in the TVX space determined the magnitude and direction of the path.Carlson and Sanches-Azofeifa analyzed the effect of rapid urbanization on surface climate using TVX method. Amiri provided a method for addressing the uncertainty in the TVX space.
Human land degradation which removes the vegetation cover increases the vulnerability of the region for wind erosion (Cook et al., 2009). The loss of vegetation, accompanied by cropland expansion, plays the main role in exposing the topsoil of Kaftahumera for wind erosion. The severity of loss of vegetal cover is aggravating in most woodland areas of Kaftahumera. Due to the extended landuse changes, there might be a reduction in the capacity of the land to provide ecosystem goods and services. Image analysis confirmed the conversion of about 47% of the woody vegetation to other landuse types affecting the natural vegetation. The intensive mechanized farming, mainly for production of oil crops for international markets, has contributed to the shrinking of the woodlands (Lemenih et al. 2014; Zewdie & Csaplovics, 2014). In addition, the expansion of subsistence agriculture with ever increasing population pressure competes with the natural vegetation of the region (Zewdie & Csaplovics, 2015). The continuous exposure of the landscape for degradation eases the loss of soil from the region. The soils being blown away from the landscape drain the soil through transporting sediments and nutrients. Studies on climatechange also show a stronger link between vegetation change and dust aerosols in which precipitation of the Sahel region is reduced due to changes in vegetation cover and increased occurrence of dust storms (Yoshioka et al., 2007).
Concerns about the availability of freshwater inflows have been increasing as many of the World’s estuaries are experiencing a decrease in freshwater input. The rate of freshwater inflows is highly influenced by landuse and landcover (LULC) change, upstream water diversion for human uses, and the effects of climatechange (Russell et al., 2006). These changes affect estuarine chemical and physical properties (Xu and Wu, 2006). Salinity is used as an indicator of estuary condition because it strongly affects estuaries’ productivity. Upstream water diversions and extractions may impact the natural salinity of estuaries both on an annual and seasonal basis. For example, increasing salinity and decreasing Mulinia coloradoensis population in the Colorado River Delta of Baja California, Mexico, were the result of extensive upstream diversion on the Colorado River (Rodriguez et al., 2001). In addition, reducing freshwater influx to the Colorado River Estuary has accelerated the growth of two main bivalve mollusk species in the northern Gulf of California by 6 to 28% (Schone et al., 2003).
The change in carbon stored and sequestered can be quantified by an existing model that uses primary data to build a sample tree inventories of forests given relevant biological data inputs. This research brings quantitative modelling LULCC research into the scope of policy with a specific local case study of considering climate ecosystem services in urban planning. The temporal element of ecosystem service loss is captured as a baseline snapshot prior to planned, projected change as the study area undergoes deforestation and subsequent urbanisation.
Remotely sensed (RS) imagery is increasingly being adopted in investigations and applications outside of traditional land-useland-coverchange (LUCC) studies. This is due to the increased awareness by governments, NGOs and Indus- try that earth observation data provide important and useful spatial and temporal information that can be used to make better decisions, design policies and address problems that range in scale from local to global. Additionally, citizens are increasingly adopting spatial analysis into their work as they utilize a suite of readily available geospatial tools. This pa- per examines some of the ways remotely sensed images and derived maps are being extended beyond LUCC to areas such as fire modeling, coastal and marine applications, infrastructure and urbanization, archeology, and to ecological, or infrastructure footprint analysis. Given the interdisciplinary approach of such work, this paper organizes selected stud- ies into broad categories identified above. Findings demonstrate that RS data and technologies are being widely used in many fields, ranging from fishing to war fighting. As technology improves, costs go down, quality increases and data become increasingly available, greater numbers of organizations and local citizens will be using RS in important eve- ryday applications.
Regarding new urban development types, infill development has the highest mean temperature while leapfrog development has the lowest temperature (Table 4). This can be explained in two ways. First, urban infill areas are surrounded by high LST landuse type such as built-up areas, which positively influences the infill area's LST. In contrast, leapfrog urban areas are surrounded by other landuse types with lower LST such as agriculture land. This helps to reduce the leapfrog area mean LST. In addition, leapfrog urban areas often have good planning policies that incorporate an appropriate percentage of public areas like parks and lakes. This "good" LULC structure contributes to the decrease in LST. An urban area under extension often has higher LST than a leapfrog area and lower LST than infill area because its surrounding areas include both built-up areas and other LULC types. The correspondence of differences in mean LST to urban development types provides important feedback. Process of filling up the open land in a city has many negative impacts. It critically increases the urban warming effect within the city and reduces living conditions by decreasing public space. In such a situation, UHI cannot be avoided, but its effect can be reduced by applying more appropriate urban development types. Constructing new urban areas with proper LULC structure instead of filling up the existing urban space is an efficient solution for reducing the UHI effect.