In terms of the results for climatechange for the strips – there is greater variability E-W as opposed to N-S. This suggests the influence of Atlantic derived frontal systems and how these may change in the RCMs.
In terms of the effect of landusechange then variation due to subtle ‘real changes’ in historic landuse (between LCM 2000 and LCM 2007) is small. Extremes of landusechange are predicted to result in significant change but these scenarios are very unlikely to be realised. For the Dee, Hampshire Avon, Tees and the Usk the change in recharge for landusechange to climatechange is comparable with the Ely-Ouse and Trent less and the Stour and England and Wales as a whole greater. This was investigated further by swapping out different landuse types, i.e. arable to forested and showed much less variation than for the single landuse runs. The original question that the modelling work was to address relates to the relative changes in recharge related to climatechange as opposed to landusechange. Taking England and Wales as a whole then the order of change in recharge due to landuse variation is: socio-economic landuse (LCM2000 w.r.t. LCM2007) is less than spatial replacement whose magnitude of change in recharge is less than wholesale replacement (i.e. all one landuse type for England and Wales). Comparing the magnitude of these changes with those resulting from climatechange show that variation of recharge related to climatechange variation falls in the middle of landusechange. However, the variation of recharge due to the use of different RCMs is comparable with the overall variation of landusechange, although this is tempered by the underestimation of recharge by the RCMs.
Abstract. Within the GLOWA Jordan River project, a first- time overview of the current and possible future land and wa- ter conditions of a major part of the Eastern Mediterranean region (ca. 100 000 km 2 ) is given. First, we applied the hy- drological model TRAIN to simulate current water avail- ability (runoff and groundwaterrecharge) and irrigation wa- ter demand on a 1 km×1 km spatial resolution. The results demonstrate the scarcity of water resources in the study re- gion, with extremely low values of wateravailability in the semi-arid and arid parts. Then, a set of four divergent scenar- ios on the future of water has been developed using a stake- holder driven approach. Relevant drivers for land-use/land- cover change were fed into the LandSHIFT.R model to pro- duce land-use and land-cover maps for the different scenar- ios. These maps were used as input to TRAIN in order to generate scenarios of wateravailability and irrigation water demand for the region. For this study, two intermediate sce- narios were selected, with projected developments ranging between optimistic and pessimistic futures (with regard to social and economic conditions in the region). Given that climate conditions remain unchanged, the simulations show both increases and decreases in wateravailability, depending on the future pattern of natural and agricultural vegetation and the related dominance of hydrological processes.
The average daily observed minimum and maximum temperature was 3.2 ◦ C and 14.2 ◦ C, respectively. The average annual total water yield from the baseline simulation was 1279 mm. The total water yield is the total amount of water produced in the HRU that enters the main channel, which essentially is the sum of surface runoff, lateral ﬂow, and return ﬂow minus the transmission loss (water lost from tribu- tary channels in the HRU via transmission through the bed). Average annual ET was 548 mm, average monthly soil water content was 129 mm, and the average annual groundwaterrecharge was 15 mm. In addition to the estimates provided in Table 4 , the annual average transmission loss was 11.41 mm and groundwater revap (movement of water from shallow aquifer back to the overlying unsaturated zone) was 7.55 mm. Although the transmission loss and groundwater revap are considered minor compo- nents of the overall hydrological balance ( Jha et al., 2006 ), they are important in equalizing the water balance. The amount of water lost through transmission becomes recharge for the shallow aquifer therefore can be added to groundwaterrecharge; whereas, the groundwater revap accounts for water that moves from the shallow aquifer into the overlying unsaturated zone and, thus, needs to be sub- tracted from the groundwaterrecharge. In equalizing the water balance during the baseline period, the annual average basin water output was computed as the summation of water yield, ET, ground- waterrecharge, and transmission loss minus the groundwater revap, which was equal to 1846 mm compared to the average annual input precipitation of 1849 mm. The 3-mm difference between the input and output of water in the water balance could be attributed to 1-mm gain in the soil water content at the end of the cycle ( Table 4 ) and to rounding of the numbers in Table 4 .
“Wateravailability” is a particularly complex term meaning different things to different people, but usually refers to a quantity of water and, as such, was expanded by also including the term “water quantity” in the REA search protocol. Wateravailability was taken by the REA team to mean “water potentially available for human use”, which is usually water available for abstraction from rivers, lakes/reservoirs or aquifers, rather than green water (rainwater or soil moisture) and crop wateruse which was the focus of some original articles. An assessment of wateravailability in a natural environment needs to consider the variation of the water distribution in time (seasonally) and space (geographically), the accessibility of the water (whether the locations of the available water and the water demand correspond), and the associated costs of redistributing it if this is not the case), and the quality of the water. This available water can then be exploited for use by people, for agriculture, for industry, for power generation, and for the environment. Where wateravailability exceeds water demands, there are no problems, but in the contrary situation it is necessary to make more water available and/or reduce the demands upon that water. Some of these issues are aspects of water management and demand management, which are not the focus of this REA review, which is concerned with whether landuse and land management can affect the spatial and temporal distribution of surface water (runoff and flow) and groundwater (recharge) resources to make more water available. In this review, a systematic comparison of the different studies was hindered by the lack of a consistent and standardised experimental methodology and approach to quantifying impacts on wateravailability, or on the various surrogates for wateravailability such as runoff, flow, groundwater level, etc, and the relationships between these quantities.
SWAT allows users to adjust CO2 concentration, weather parameters (e.g., temperature, precipita- tion, radiation and humidity), and landuse, and includes approaches describing how those parameters affect plant growth, ET, snow, and runoff generation. SWAT has been found to be suitable for large basins such as the Brahmaputra, and has often been used as a tool to investigate climate and landusechange effects on freshwater availability around the world ( Abbaspour et al., 2009; Gosain et al., 2006; Jha et al., 2006; Montenegro and Ragab, 2010; Rossi et al., 2009; Schuol et al., 2008; Siderius et al., 2013 ). The primary goal of this study was to assess long-term patterns of freshwater availability in the Brahmaputra basin under climate and landuse and land cover change scenarios. To fulﬁll the goal, we calibrated the model using the sequential uncertainty ﬁtting II (SUFI2) algorithm ( Abbaspour et al., 2004 ). We then quantiﬁed the sensitivity of the hydrological variables such as total water yield, soil water content, ET, streamﬂow, and groundwaterrecharge to a group of various climatechange scenarios including changes in CO 2 concentration, temperature, and precipitation. We assessed the long-term patterns in the hydrological variables with Phase 3 of the Coupled Model Intercompari- son Project (CMIP3) downscaled precipitation and downscaled Integrated Model to Assess the Global Environment (IMAGE) landusechange scenarios for the 21st century under the A1B and A2 scenarios ( Nakicenovic and Swart, 2000 ). In brief, the A1B storyline assumes a future world of very rapid eco- nomic growth, low population growth, and rapid introduction of new and more efﬁcient technology with the development balanced across fossil fuel and non-fossil fuel energy sources. In contrast, the A2 storyline assumes a very heterogeneous world where population growth is high, economic devel- opment is primarily regionally oriented, and per capita economic growth and technological change are more fragmented and slower than in A1B.
During the 1990s and 2000s the growth of soybeans also began to contribute substantially to the clearance of Amazon forests. Rather than being directly consumed by humans, soybean crop is mainly used to feed cattle, pigs and chicken. In the late 1990s, new cultivars enabled farmers to grow soybeans in regions that had not previously been climatically suitable, leading to rapid expansion of soy farms into the Amazon forest (Fearnside 2001; Nepstad et al. 2006). In response to growing environmental concerns, a moratorium was announced by the exporters and processors of soybeans stating that they would not buy crops grown on farmland within the Brazilian Amazon that had been deforested since June 2006. Since its implementation the soy moratorium appears to have been successful, with most new soy expansion occurring on previously cleared land (Rudorff et al. 2011; Gibbs et al. 2015) and has contributed to the overall decline in Brazilian deforestation rates (Hansen et al. 2013).
impervious surface areas that drive urban stormwater response to intense rainfall events. Most stormwater models that use percent impervious area (PIA) are spatially lumped models and do not require precise locations of building roofs, as in other applications of building maps, but do require accurate estimates of total impervious areas within the geographic units of observation (e.g. city blocks or sub-watershed units). Two- dimensional mapping of buildings from aerial imagery requires laborious efforts from image analysts or elaborate image analysis techniques using high spatial resolution imagery. Moreover, large uncertainties exist where tall, dense vegetation obscures the structures. Analyzing LiDAR point-cloud data, however, can distinguish buildings from vegetation canopy and facilitate the mapping of buildings. This paper presents a new building extraction approach that is based on and optimized for estimating building impervious areas (BIA) for hydrologic purposes and can be used with standard GIS software to identify building roofs under tall, thick canopy. Accuracy assessment
predominant natural land cover in the Piedmont ecoregion of North Carolina is oak-hickory- pine forests (Griffith et al., 2002a; Griffith et al., 2002b). Forest soils allow precipitation that is not used in transpiration, to infiltrate to the groundwater, thereby sustaining the base flow in surface water systems and recharging groundwater (Booth, 1991). Forested riparian areas stabilize stream banks, keep water cool by providing shade, and contribute wood and leaf litter for habitat, cover, and food for aquatic organisms (Dolloff and Melvin, 2003; Quinn et al., 2001). Forested wetlands have the added benefit of trapping contaminants and sediment rather than letting them wash downstream (Johnston, 1991).
pastureland that is in rotation with cultivated crops. Noncultivated cropland: permanent hayland and horti- cultural cropland. Pasture: vegetative cover of grasses, legumes, and/or forbs, or other forage plants that is managed principally for livestock grazing. Range: includes grasslands, savannas, tundra, and some wetlands and deserts; plant cover is primarily native grasses, grasslike plants, shrubs or forbs, or introduced forage species that are managed like traditional rangeland; practices such as deferred grazing, burning, chaining, and rotational grazing may be used, but fertilizer and chemicals are generally not applied. Forest: U.S. De- partment of Agriculture ( 2015 ) defines forest as land that is “at least 10 percent stocked by single-stemmed woody species of any size that will be at least 4 meters (13 feet) tall at maturity. Also included is land bear- ing evidence of natural regeneration of tree cover (cut over forest or abandoned farmland) and not currently developed for non-forest use. Ten percent stocked, when viewed from a vertical direction, equates to an areal canopy cover of leaves and branches of 25 percent or greater. The minimum area for classification as forestland is 1 acre, and the area must be at least 100 feet wide.” Developed: also from U.S. Department of Agriculture ( 2015 ), the “developed land category includes (a) large tracts of urban and built-up land; (b) small tracts of built-up land of less than 10 acres; and (c) land outside of these built-up areas that is in a rural trans- portation corridor (roads, railroads, and associated rights-of-way).” CRP: land enrolled in the Conservation Reserve Program.
Abstract: This study uses a scenario-based approach to ask what are the varying impacts to forest extent and biodiversity from sixteen climatechange and forest conversion scenario combinations, and what do they suggest about future forest conservation policy directions? We projected these combinations onto existing forests in South Korea and grouped them into four forest categories. We used species distribution models for 1,031 climate vulnerable plant species as a biodiversity index, and found that species richness loss due to forest conversion could be reduced significantly by deploying the scenarios which preserve forest areas that are climatically suitable for these species. Climate-suitable forest areas declined sharply and moved northward as future temperatures increase, and climate-suitable areas lost the highest proportion of forest extent under the current trend of forest conversion. We suggest climate refugia, defined as existing forests with suitable future climates be protected from landuse conversion as a way to preserve forest biodiversity. These spatially explicit results can be used for developing forest conservation policies, and the methods may be applicable to other forested regions. However, planners should consider the assumptions and uncertainties of climate projections, species distribution models, and landuse trends when addressing forest biodiversity conservation.
We adopt the IPCC AR5 (Myhre et al., 2013) definitions of adjusted RF and effective RF (ERF) and calculate the ad- justed RFs for each forcing agent (ERFs for aerosol forcings) relative to a preindustrial state (year 1850), with modeled radiative transfer or previously published expressions. Our choice of preindustrial reference year is constrained by the available land cover change data sets, which start in 1850. However, large-scale anthropogenic land cover change be- gan centuries before 1850, and preindustrial changes could have an additional impact on present-day climate, perhaps accounting for nearly 10 % of historical anthropogenic global surface temperature change (Pongratz and Caldiera, 2012). In our study, the RF of LULCC relative to the year 1850 is then compared to the RFs of other anthropogenic activ- ities, dominated by fossil fuel burning. RFs due to non- LULCC activities are calculated in this study for RCP4.5 non-LULCC emissions with identical methodology to that used for LULCC emissions. All future LULCC RFs are cal- culated assuming background concentrations of trace gases and aerosols characteristic of RCP4.5. With this approach we can examine the impacts of the range in projected LULCC on RF independent of other anthropogenic activities. However, we are not able to report, for example, the RF of projected LULCC from the RCP8.5 scenario in the context of RCP8.5 fossil fuel emissions. Using a different projection to provide the background concentrations would modify the resulting LULCC RFs.
Bangladesh is one of the most vulnerable countries in the world. The long-term data showed an increasing trend of both maximum and minimum temperatures although the maximum was more distinctly increased than minimum temperature. As a result, the diurnal temperature range (DTR) is increasing, which is not good for plant survival and production. Similarly, the total annual rainfall showed increasing trend, but the seasonal distribution was significantly different. In general, more than 70% rainfall occurs during the month June to August and most of times of dry season remain rainless. The long-term data indicated that monsoon season rainfall increased by 66%, while it decreased by 29% during dry season. Large year-to-year variation in monthly distribution of rainfall in Indo-Gangetic Region has been observed . In the recent year, it was observed prolong drought during dry season and high rainfall during monsoon . Therefore, this patchy rainfall is not good for resource conservation and agriculture production. Fluctuation of rainfall is associated with increasing temperatures .Rainfall anomalies and high fluctuation of temperature were observed in coastal area which was responsible for reducing crop productivity (19%) despite the technological development . A warmer climate scenario with uncertainty of rainfall is likely to affect crop production, irrigation system and other resources of the ecosystem .
The utilized procedure to select cases for simulation was intended to obtain a homogeneous set of days with similar meteorological conditions that were thought to favor the land surface impact on precipitation. A large spread among re- sponses to landuse and temperature scenarios was found be- tween the cases, however, so the intended comparability was not fully accomplished. This could be a model artefact or a realistic response showing how differently the atmosphere reacts to similar conditions, thus showing natural variabil- ity. Nevertheless, the majority of cases have a similar sign in their response. By averaging the results we find a more representable response then the response of any single case could be. Our estimates could be biased by the selection pro- cedure that selected cases with rather strong convective ac- tivity. Consequently, convection will always be triggered in the selected cases and a potential feedback increasing precip- itation through enhanced triggering was excluded. Examples of this feedback can be found in Findell and Eltahir (2003), Santanello et al. (2011), Taylor et al. (2012), and others. The Netherlands is however not located in a region where strong feedbacks of this type are expected (Seneviratne et al., 2006; The GLACE Team et al., 2004) and the influence of changes in climate, SST, or circulation are likely more important (At- tema et al., 2014; van Haren et al., 2013). If the selection procedure had been more successful in identifying similar events, we could have made a composite event by averaging the initial and boundary conditions, similar to Mahoney et al. (2012). Their procedure sounds promising, because it could reduce simulation time and provide a more representative re- sponse, but the selection of cases to average is apparently not straightforward.
pasture area determined from the Instituto Brasileiro de Geografia e Estatı´stica (IBGE) municipal agricultural production database for the given years (IBGE 2010). Because data on pasture area are not available for the year 2001, it was estimated, through linear interpolation, from the 1996 and 2006 data. A total of 13 crop types and 2 pasture types were considered in the confection of the land-use maps. Only areas depicted as deforested or as Cerrado (because land-cover changes of this latter are not tracked by satellites as the deforestation of the Amazon) could have the assignment of crops or pasture. Crops had priority over pasture for oc- cupation of grid cells, whereas only one dominant land-use type can occur in one grid cell. The allocation procedure followed a preference list of grid cells, which was built based on a 2000 map on the geographical distribution of crop/pasture areas, also on 5-arc-min resolution (Monfreda et al. 2008; Ramankutty et al. 2008). Grid cells with higher fraction of a given crop type in the map by Monfreda et al. (Monfreda et al. 2008) had preference for assignment of that crop type in our land-use map. Disambiguation within one crop type (e.g., when the Monfreda et al. map for soybeans had several grid cells with the same area) or between different crop types (i.e., when Monfreda et al. maps for two or more different crop types had exactly the same value in a given grid cell) was performed using a multicriteria analysis (MCA) of slope, potential productivity of the given crop type (or grassland for pasture), distance from settlements, soil type, and distance from paved roads (for data sources, see Soares-Filho et al. 2006). However, this MCA was needed only in a minor fraction (,1%) of the grid cells that later were assigned as crop or pasture. Therefore, the maps of Monfreda et al. (for crops) and Ram- ankutty et al. (for pastures) played the major role in the allocation of land uses in our base maps. Urban areas were assigned to those grid cells having a population density higher than 2000 people per square kilometer (Erb et al. 2007), using the History Database of the Global Environment (HYDE) map of population distri- bution (Goldewijk 2005), with no distinction between the years 2001 and 2006.
- ecological networks providing recreation, peace, inspiration
- Aesthetic values: ecological solutions are not always aesthetically attractive - Nitrogen and carbon sequestration in urban green spaces
- New innovations needed in e.g. storm water issue: green roofs
Protecting and restoring aquatic ecosystems is a priority in Europe that has been formalized by the European Water Framework Directive (2000/60/EC) (EC, 2000). The Water Framework Directive (WFD) aims to maintain and improve the aquatic environment partly by ensuring a good water quality status through the implementation of river basin management plans (RBMPs). A recent report by the European Commission (EC, 2012) indicated that over 90% of RBMPs mentioned agriculture to be a signiﬁcant pressure in their basin by contributing, for example, to excess organic matter, nutrients and pesticides. Farm management practices are an integral part of RBMPs because numerous ﬁeld operations, such as fertilizer management, can address non-point source pollution (Cherry et al., 2008). In basins where agricultural activities dominate, it is common for the quality of water to be com- promised (Green et al., 2014; Patoine et al., 2012; Volk et al., 2009). For example, in Denmark, Nielsen et al. (2012) found a high correlation between the amount of agricultural land and the total nitrogen (N) and total phosphorus (TP) concentrations in adjacent lakes. Donner (2003) determined the area of maize in the U.S. to be strongly correlated to N loads, and to a lesser extent to phosphorus (P).
tree species. The landusechange decisions (afforestation or deforestation) are made by comparing the net present value of managed forest with income by alternative landuse in the same place. The forest management decisions are driven first by wood demand, then forest productivity, net present value of forestry in comparison to income through alternative land uses, proximity to populated places, etc. (Gusti 2010 ). Since the model does not represent either forest markets or other economic sectors, it has to rely on other sources – models or databases – for information such as wood prices, land rents, urban sprawl, natural disturbances, and land available for afforestation. As output, G4M produces estimates of: forest area changes, carbon sequestration and emissions from forests, impacts of carbon incentives (e.g. avoided deforestation), and supply of biomass for timber and energy. For Europe, the initial forest aboveground biomass per grid cell was taken from the European forest biomass map from Gallaun et al. ( 2010 ). Species-specific biomass expansion factors (Teobaldelli et al. 2009 ) were used for growing stock-biomass conversion. The main forest management options considered by G4M are variation of thinning and choice of rotation length. The rotation length can be individually chosen but the model can estimate optimal rotation lengths to maximize increment, stocking biomass, or harvestable biomass.
cover change over eastern China and increasing CO2 with a GCM (NCAR CCM3) coupled with the Biosphere-Atmosphere Transfer Scheme (BATS). They suggest that land cover changes have a comparable effect on climate to that of historical increases in greenhouse gases at the regional scale. By using a global atmospheric GCM (CAM4.0) coupled with an urban canopy parameterization scheme, Chen and Zhang (2013) found warming effects of large-scale urbanization in eastern China and its influence on the East Asian winter monsoon. Also, regional models are used to investigate feedbacks of land cover to climate at the regional scale. Compared with GCMs, regional climate models allow for a more detailed investigation of the interactions between land cover modification and the atmospheric conditions, because regional models provide higher spatial resolution and capture physical processes and feedbacks occurring at the regional scale, which GCMs are not able to describe due to their coarse resolution (Anav et al. 2010; Fu 2003; Li et al. 2013b; Myoung et al. 2012; Shi and Wang 2003; Wang et al. 2012; Zheng et al. 2002). For instance, a regional climate model (RegCM2) coupled with BATS was used to estimate the climate effects of historical (Gao et al. 2003) and other possible land cover changes such as desertification, afforestation, and vegetation degradation (Zheng et al. 2002) in China. There is a resulting decrease in mean annual precipitation over northwest China and a decrease in temperature in coastal areas as a result of historical land cover changes. Vegetation degradation may increase flood events over the Yangtze-Huai River valleys and intensify droughts in northern China.
The ways forward listed in the previous section will only be the first stage of a process towards improved LULCC representation in climatechange assessments. Rather than improving de-coupled data products and models on an in- dividual basis and connecting them offline through the ex- change of files, we argue that landuse, land cover, and the climate system need to be studied in an integrated model- ing framework. As we have shown in this paper, most of the challenges and related uncertainties originate in the dis- parate disciplinary treatment of the individual aspects. Al- though sophisticated models have been developed during the past decades within each community, the current offline cou- pling seems overly limited, accumulating an increasing level of uncertainty along the modeling chain. Integration of these different types of models, where anthropogenic activity on the land system is considered as an integral part of ESMs, instead of an external boundary condition, might help to re- duce these uncertainties, although it will certainly further complicate the interpretation of model responses. For ex- ample, Di Vittorio et al. (2014) report preliminary results of the iESM (Collins et al., 2015), an advanced coupling of an IAM and an ESM implementing two-way feedbacks be- tween the human and environmental systems, and show how this improved coupling can increase the accuracy of infor-
Regionally, the impacts on water resources from changes in global atmospheric circulation and climate overlap with the impacts from land-use and water-use changes (Lobell and Field, 2007). For instance, in arid and semi-arid re- gions, wateravailability critically limits water-demanding agricultural expansion and economic growth, making such regions particularly vulnerable to impacts of expected fu- ture climate changes (IPCC, 2007). The different overlapping causes of freshwater resource changes make it hard to dis- tinguish between various hydrological cause-effect relations and impacts (Milly et al., 2002; Piao et al., 2007; Destouni et al., 2008). However, for all water resource changes that are driven by different types of change at the surface of a hydrological basin, hydro-climatic change projections can be considerably improved by honoring and accounting for the water flux bounds implied by the basic basin-scale wa- ter balance equation ET = P − R − 1S. Such bounds on the commonly difficult to measure and quantify vapour flux by evapotranspiration (ET) at the land surface can then be de- rived on basin scales from directly measured and/or model- interpreted data on precipitation (P ) at the basin surface, runoff (R) at the basin outlet, and storage change (1S) within the basin (Shibuo et al., 2007; Asokan et al., 2010; Destouni et al., 2010; T¨ornqvist and Jarsj¨o, 2012). Without such condi- tioning to water balance components, the Penman-Monteith type of evapotranspiration (ET) models can yield errors of 30 % to 50 % (Kite and Droogers, 2000), which is consider- ably larger than the errors of 10 % to 15 % that are involved