Alternative Land Cover Research Articles

‘Kesho’ Scenario Development for Supporting Water-Energy Food Security under Future Conditions in Zanzibar

Social-ecological interactions mediate water–energy–food security in small developing islands, but community-scale insights are underrepresented in nexus research. These interactions are dynamic in their response to environmental and anthropogenic pressures and need to be understood to inform sustainable land use planning into the future. This study centered on bringing together diverse stakeholders to explore water–energy–food futures using the “Kesho” (meaning “tomorrow” in Kiswahili) scenario tool for two of the largest islands that comprise the Zanzibar Archipelago. The methodology comprised four core stages: (1) exploration of how past drivers of change impacted water–energy–food security; (2) modeling of a Business as Usual Scenario for land cover change; (3) narrative development to describe alternative futures for 2030 based on themes developed at the community scale; and (4) predictions about how narratives would shape land cover and its implications for the nexus. These results were used to model alternate land cover scenarios in TerrSet IDRISI (v. 18.31) and produce visual representations of expected change. Findings demonstrated that deforestation, saltwater incursion, and a reduction in permanent waterbodies were projected by 2030 in a Business as Usual Scenario. Three alternative scenario narratives were developed, these included Adaptation, Ecosystem Management, and Settlement Planning. The results demonstrate that the effectiveness of actions under the scenario options differ between the islands, indicating the importance of understanding the suitability of national policies across considered scales. Synergies across the alternative scenario narratives also emerged, including integrated approaches for managing environmental change, community participation in decision making, effective protection of forests, cultural sensitivity to settlement planning, and poverty alleviation. These synergies could be used to plan strategic action towards effectively strengthening water–energy–food security in Zanzibar.

GIS-Based Scientific Workflows for Automated Spatially Driven Sea Level Rise Modeling

Sea level rise (SLR) poses a significant threat to shorelines and the environment in terms of flooding densely populated areas and associated coastal ecosystems. Scenario analysis is often used to investigate potential SLR consequences, which can help stakeholders make informed decisions on climate change mitigation policies or guidelines. However, SLR scenario analysis requires considerable geospatial data analytics and repetitive execution of SLR models for alternative scenarios. Having to run SLR models many times for scenario analysis studies leads to heavy computational needs as well as a large investment of time and effort. This study explores the benefits of incorporating cyberinfrastructure technologies, represented by scientific workflows and high-performance computing, into spatially explicit SLR modeling. We propose a scientific workflow-driven approach to modeling the potential loss of marshland in response to different SLR scenarios. Our study area is the central South Carolina coastal region, USA. The scientific workflow approach allows for automating the geospatial data processing for SLR modeling, while repetitive modeling and data analytics are accelerated by leveraging high-performance and parallel computing. With support from automation and acceleration, this scientific workflow-driven approach allows us to conduct computationally intensive scenario analysis experiments to evaluate the impact of SLR on alternative land cover types including marshes and tidal flats as well as their spatial characteristics.

No more silent (and uncoloured) springs in vineyards? Experimental evidence for positive impact of alternate inter‐row management on birds and butterflies

Abstract Agricultural intensification is a main threat to biodiversity, and vineyards are particularly concerning because of their increasing extent and intensive management. Management strategies that mitigate vineyard impacts on biodiversity are urgently needed. In a major wine area in northern Italy, we tested in a 3‐year experiment the effect of alternate management of vineyard ground cover (mowing or tillage depending on the usual management system adopted by each farmer). After a first year (2017) with no implementation (baseline), in the two subsequent spring–summer periods (2018 and 2019), alternate management was adopted in a varying number of sites, providing an ideal BACI design. Birds and butterflies were selected as target groups, and surveyed by means of 200‐m linear transects scattered over both conventional and organic vineyards, with and without alternate management. We evaluated whether the implementation of alternate management resulted in an increase in species richness per transect. We also evaluated the effect of alternate management, year and land cover on different functional avian guilds (functional insectivores, seed eaters, potential grape eaters), considering both richness and abundance. For both birds and butterflies, we found a positive effect of alternate management on the number of species per transect. The implementation of alternate management also promoted richness and abundance of functional insectivores and abundance of seed eaters; a positive but less supported association was also found between alternate management and richness and abundance of potential grape eaters. The most relevant land cover for the supply of ecosystem services by birds was likely shrubland cover, which increased richness and abundance of insectivores and seed eaters, while not supporting potential grape eaters. Farmers reported no adverse effects of alternate management, and a positive impact on farm perception by consumers. Synthesis and applications. The very easy‐to‐implement alternate inter‐row management has the potential to rapidly increase the suitability of vineyards for biodiversity, while enhancing ecosystem services and attractiveness of farms for nature‐based recreation, contributing to their multi‐functionality. Alternate management could contribute to shape, for example, new interventions within the coming Common Agricultural Policy, and its benefits may be maximized by sympathetic landscape strategies.

Drivers of irrigated olive grove expansion in Mediterranean landscapes and associated biodiversity impacts

Over the last 30 years, olive farming has experienced a fast and large-scale intensification process across its Mediterranean range, that is reshaping Mediterranean farmland landscapes with associated impacts on biodiversity and ecosystem services. This study aims to analyze irrigated olive grove spatial expansion patterns across a 27–year period in a region of Southern Portugal, by exploring its drivers, describing the involved land-use dynamics, and to evaluating its potential biodiversity impacts. Land cover spatio-temporal dynamics were characterized by comparing recent land cover maps 2017 with those before the intensification upsurge (1990), using a grid of sampling points across the area. To investigate the drivers of intensification, these points were characterized using 10 variables reflecting policy context, previous land use (in 1990), biophysical features, and farm structure, which were used to model land cover transitions resulting in intensive olive farming. Finally, we used a counterfactual approach to assess the impacts of the olive intensification process on biodiversity in relation to alternative land cover change pathways, using farmland birds as an indicator group. We confirmed a large-scale expansion of irrigated olive groves in the region between 1990 and 2017, from being practically absent to covering ca. 6% of the study area. This expansion was made mostly at the expenses of open rainfed annual crops (63%) and, to a lesser degree, traditional rainfed olive groves (21%). Change was driven mainly by the combined effect of the availability of public irrigation water and large farms, although other factors related to legal constraints to land-use change, biophysical context and previous land management were also involved. During the study period, land cover transitions conducting to intensive olive farming were the most harmful for biodiversity, when compared to alternative land cover change pathways. By providing a quantitative insight into the underlying mechanisms and environmental consequences of the olive farming intensification process currently affecting biodiversity-rich Mediterranean farmland landscapes, our study contributes with valuable information that can be used by policy makers to better plan and manage the on-going expansion of intensive olive farming.

Carbon dioxide dynamics in a residential lawn of a tropical city

Turfgrass is an important component of the urban landscape frequently considered as an alternative land cover to offset anthropogenic CO2 emissions. However, quantitative information of the potential to directly remove CO2 from the atmosphere by turfgrass systems is lacking, especially in the tropics. Most assessments have considered the carbon accumulated by grass shoots and soil, but not the release of CO2 to the atmosphere by soil respiration (i.e., soil CO2 efflux). Here, we measured at high-temporal resolution (30-min) soil CO2 efflux, production, and storage rate for nearly three years in a residential lawn of Singapore. Furthermore, we quantified the carbon capture related to biomass production and CO2 emissions from fossil fuel consumption associated with maintenance activities (e.g., mowing equipment). Warm and humid conditions resulted in relatively constant rates of soil CO2 efflux, CO2 storage in soil, and aboveground biomass production (3370, 652, 1671 Mg CO2 km−2 yr−1; respectively), while the systematic use of mowing machinery emitted 27 Mg CO2 km−2 yr−1. Soil CO2 efflux and CO2 mowing emissions represent carbon losses to the atmosphere, while CO2 storage in soil and biomass productivity represent gains of carbon into the ecosystem. Under a steady state in which soil CO2 losses are only compensated by atmospheric CO2 uptake by photosynthesis, an ideal clipping waste disposal management, in which no CO2 molecule returns to the atmosphere (i.e., clippings are not burnt), and a 3-week mowing regime, this site can act as a sink of 2296 Mg CO2 km−2 yr−1. In the scenario of incinerating all clippings, the lawn acts as an emission source of 1046 Mg CO2 km−2 yr−1. Thus, management practices that reduce mowing frequency together with clipping disposal practices that minimize greenhouse gas emissions are needed to make urban lawns a potential natural solution to mitigate global environmental change.

Characterizing Land Use/Land Cover Using Multi-Sensor Time Series from the Perspective of Land Surface Phenology

Due to a rapid increase in accessible Earth observation data coupled with high computing and storage capabilities, multiple efforts over the past few years have aimed to map land use/land cover using image time series with promising outcomes. Here, we evaluate the comparative performance of alternative land cover classifications generated by using only (1) phenological metrics derived from either of two land surface phenology models, or (2) a suite of spectral band percentiles and normalized ratios (spectral variables), or (3) a combination of phenological metrics and spectral variables. First, several annual time series of remotely sensed data were assembled: Accumulated growing degree-days (AGDD) from the MODerate resolution Imaging Spectroradiometer (MODIS) 8-day land surface temperature products, 2-band Enhanced Vegetation Index (EVI2), and the spectral variables from the Harmonized Landsat Sentinel-2, as well as from the U.S. Landsat Analysis Ready Data surface reflectance products. Then, at each pixel, EVI2 time series were fitted using two different land surface phenology models: The Convex Quadratic model (CxQ), in which EVI2 = f(AGDD) and the Hybrid Piecewise Logistic Model (HPLM), in which EVI2 = f(day of year). Phenometrics and spectral variables were submitted separately and together to Random Forest Classifiers (RFC) to depict land use/land cover in Roberts County, South Dakota. HPLM RFC models showed slightly better accuracy than CxQ RFC models (about 1% relative higher in overall accuracy). Compared to phenometrically-based RFC models, spectrally-based RFC models yielded more accurate land cover maps, especially for non-crop cover types. However, the RFC models built from spectral variables could not accurately classify the wheat class, which contained mostly spring wheat with some fields in durum or winter varieties. The most accurate RFC models were obtained when using both phenometrics and spectral variables as inputs. The combined-variable RFC models overcame weaknesses of both phenometrically-based classification (low accuracy for non-vegetated covers) and spectrally-based classification (low accuracy for wheat). The analysis of important variables indicated that land cover classification for this study area was strongly driven by variables related to the initial green-up phase of seasonal growth and maximum fitted EVI2. For a deeper evaluation of RFC performance, RFC classifications were also executed with several alternative sampling scenarios, including different spatiotemporal filters to improve accuracy of sample pools and different sample sizes. Results indicated that a sample pool with less filtering yielded the most accurate predicted land cover map and a stratified random sample dataset covering approximately 0.25% or more of the study area were required to achieve an accurate land cover map. In case of data scarcity, a smaller dataset might be acceptable, but should not smaller than 0.05% of the study area.

Do Uncertainties in the Reconstruction of Land Cover Affect the Simulation of Air Temperature and Rainfall in the CORDEX Region of East Asia?

AbstractLand cover type reconstructions, required in climate models, commonly utilize remote sensing products. There are inevitable misclassifications in land cover reconstructions due to the retrieval process. We use the Weather Research and Forecasting model to determine whether these misclassifications can affect the simulations of air temperature and rainfall over the Coordinated Regional Climate Downscaling Experiment (CORDEX) East Asia region, where the accuracy of the land cover classification is low. The Moderate Resolution Imaging Spectroradiometer land cover map is used for the control simulations and is then replaced by the most likely alternative land cover type at pixels where the classification confidence is below various threshold values. Results show that misclassification‐induced land cover change can affect key biogeophysical characteristics (albedo, leaf area index, and roughness length) and these can affect the sensible and latent heat fluxes at regional scales. However, the impact on air temperature is very limited and is restricted to the Tibetan Plateau where warming of up to 2 °C occurs associated with the replacement of barren or sparsely vegetated land to grassland. The impact on rainfall is negligible, and most changes are likely caused by model internal variability rather than land cover change. Overall, uncertainties in the reconstruction of land cover have negligible impacts, and the Moderate Resolution Imaging Spectroradiometer land cover product can be used in regional simulations over East Asia. However, we note that land cover change experiments incorporating uncertainties must utilize large numbers of simulations if air temperature and rainfall changes are to be examined robustly.

Landscape aesthetic modelling using Bayesian networks: Conceptual framework and participatory indicator weighting

Landscape aesthetics provides humans with health and social benefits contributing to overall well-being, thus representing a cultural ecosystem service. Landscape biophysical and social attributes create information that is interpreted as either beauty or blight by the mind of the beholder. The ARtificial Intelligence for Ecosystem Services (ARIES) modelling platform is quite suited to the landscape aesthetics paradigm, since it is characterized by both a strong focus on the spatial connectivity between ecosystems and beneficiaries and by the employment of Bayesian networks to quantify and communicate uncertainty. A conceptual framework based on landscape aesthetic abstraction levels was proposed to build these Bayesian networks, progressively linking tangible indicators to abstract dimensions and concepts. As input to ARIES, a simple and rapid participatory methodology was designed to weight indicators according to stakeholder preferences, from which values the probabilities were derived for use in canonical probabilistic models. The participatory indicator identification methodology generated both abstract and concrete terms, suggesting that the process should be supervised to obtain clear and tangible indicators. A sensitivity analysis revealed that individual visual blight indicators had more profound impacts on landscape aesthetic while the effect of beauty indicators was more subtle and balanced. Although the methodology may require a relatively large number of participants to derive probabilities, the procedure was not overly challenging for the participants. This methodology has the potential to be implemented widely, in various contexts and for different periods, accounting for alternative spatiotemporal variations and land cover contexts.

Impact of alternative land cover descriptions on urban hydrological model simulations

ABSTRACTAvailability of remotely sensed and openly available land cover datasets is rapidly improving. This opens promising possibilities for utilizing such data in urban hydrological assessments. However, it remains unknown how the performance of readily available land cover data compares with manually collated information when used to construct detailed model parameterizations required in urban hydrological models. In this study, model runs with alternative land cover data are conducted for three small study areas with varying urban densities in Helsinki, Finland. The study demonstrates how different spatial data sources with varying resolutions produce different urban runoff simulation results. The results suggest that an openly available detailed land cover description can perform equally well with a laboriously collated manual land cover description. However, mixed land cover types of the pan-European Urban Atlas dataset are problematic in describing directly connected impervious areas, which leads to poor modelling results in low density urban areas.

Multi-scale effects of land cover and urbanization on the habitat suitability of an endangered toad

Habitat degradation, entwined with land cover change, is a major driver of biodiversity loss. Effects of land cover change on species can be direct (when habitat is converted to alternative land cover types) or indirect (when land outside of the species habitat is altered). Hydrologic and ecological connections between terrestrial and aquatic systems are well understood, exemplifying how spatially disparate land cover conditions may influence aquatic habitats, but are rarely examined. We sought to quantify relative effects of land cover at two different but interacting scales on habitat suitability for the endangered arroyo toad (Anaxyrus californicus). Based on an existing distribution model for the arroyo toad and available land cover data, we estimated effects of land cover along streams and within entire watersheds on habitat suitability using structural equation modeling. Relationships between land cover and habitat suitability differed between scales, and broader, watershed-scale conditions influenced land cover along the embedded stream networks. We found anthropogenic development and forest cover at the watershed-scale negatively impacted habitat suitability, but development along stream networks was positively associated with suitability. The positive association between development along streams and habitat suitability may be attributable to increased spatial heterogeneity along urbanized streams, or related factors including policies designed to conserve riparian habitats amidst development. These findings show arroyo toad habitat is influenced by land cover across multiple scales, and can inform conservation of the species. Furthermore, our methodology can help elucidate similar dynamics with other taxa, particularly those reliant on both terrestrial and aquatic environments.

A landscape index of ecological integrity to inform landscape conservation

Conservation planning is increasingly using “coarse filters” based on the idea of conserving “nature’s stage”. One such approach is based on ecosystems and the concept of ecological integrity, although myriad ways exist to measure ecological integrity. To describe our ecosystem-based index of ecological integrity (IEI) and its derivative index of ecological impact (ecoImpact), and illustrate their applications for conservation assessment and planning in the northeastern United States. We characterized the biophysical setting of the landscape at the 30 m cell resolution using a parsimonious suite of settings variables. Based on these settings variables and mapped ecosystems, we computed a suite of anthropogenic stressor metrics reflecting intactness (i.e., freedom from anthropogenic stressors) and resiliency metrics (i.e., connectivity to similar neighboring ecological settings), quantile-rescaled them by ecosystem and geographic extent, and combined them in a weighted linear model to create IEI. We used the change in IEI over time under a land use scenario to compute ecoImpact. We illustrated the calculation of IEI and ecoImpact to compare the ecological integrity consequences of a 70-year projection of urban growth to an alternative scenario involving securing a network of conservation core areas (reserves) from future development. IEI and ecoImpact offer an effective way to assess ecological integrity across the landscape and examine the potential ecological consequences of alternative land use and land cover scenarios to inform conservation decision making.

Monitoring landscape-scale environmental changes with citizen scientists: Twenty years of land use change in Great Britain

Citizen science is increasingly recognised as one of the most cost-effective means of achieving large-scale and long-term biodiversity monitoring. Here we assess the potential for citizen scientists to contribute to the long-term monitoring of land cover, land use and habitat change through ongoing field data collection.Land cover monitoring is most commonly carried out via remote sensing or professional surveys but these can be costly, low detail or spatiotemporally limited. We used ongoing habitat data collection by citizen scientists participating in a structured survey of breeding birds to assess whether there is the potential for citizen scientists more broadly to play a role in the long-term monitoring of habitat extent and condition. Categorical habitat data has been collected annually by over 2500 volunteers as part of the UK Breeding Bird Survey since 1994 and we used this to quantify temporal variation in the reporting of different habitats in the British countryside. Where possible we validated our estimates of habitat cover and change using independent estimates from professional surveys and other datasets.We detected increases in woodland cover, in particular mixed woodland, and declines in farmland cover, particularly livestock farming. Our habitat cover estimates closely matched alternative land cover estimates but there was little correspondence in estimates of change between survey types and we discuss why discrepancies may occur.Although the data we used were not initially designed for this purpose, our results suggest that there is considerable potential to use citizen science for cost-effective identification of temporal patterns in land use, habitat-type and management in ways that could complement other methods.

Large Mammal Use of Seminatural Grasslands and Implications for Aviation Strike Risk

Abstract Large mammals pose a significant risk to U.S. aircraft safety within airport operation areas and cost airlines millions of dollars in repairs annually. Native warm-season grass polycultures and switchgrass monocultures offer alternative land covers for airports that could benefit current risk-mitigation efforts in addition to offering economic and environmental benefits. We compared use of a native warm-season grass polyculture and switchgrass Panicum virgatum monoculture by white-tailed deer (deer; Odocoileus virginianus) and coyote Canis latrans, using remote cameras in Mississippi, during 2011–2012. Coyotes and deer were observed 27% and 51% less in switchgrass monoculture than in native warm-season grass polyculture, respectively. However, November detections and cumulative hazard score demonstrated the greatest differences between treatments, especially for deer. Considering deer and coyotes are among the most hazardous mammal species to aircraft, switchgrass monocultures could be a better alternative land cover than native warm-season grass polycultures for some airport turf areas. Increased land coverage of switchgrass monocultures could benefit airport wildlife-hazard mitigation but needs validation by comparing alternative land covers to more traditional airport land covers.

Uncertainty in plant functional type distributions and its impact on land surface models

The spatial distribution and fractional cover of plant functional types (PFTs) is a key uncertainty in land surface models (LSMs) that is closely linked to uncertainties in global carbon, hydrology and energy budgets. Land cover is considered to be an Essential Climate Variable because changes in it can result in local, regional or global scale impacts on climate. In LSMs, land cover (LC) class maps are converted to PFT fractional maps using a cross-walking (CW) table by prescribing the fraction of each PFT that occurs within each LC class. In this study we assess the largest plausible range of PFT uncertainty derived from remotely sensed LC maps produced under the European Space Agency Land Cover Climate Change Initiative on simulations of land surface fluxes using 3 leading LSMs. We evaluate the impact of uncertainty due to both LC classification algorithms, and CW procedure, on energy, moisture and carbon fluxes in LSMs. We investigate the maximum plausible range of uncertainty deriving from both LC and CW within the context of a potential biomass scale (bare ground-grass-shrub-tree), representing a gradient from low to high biomass PFTs. More specifically, plausible alternative land cover maps and associated PFT fractional distributions were produced to prioritise low or high biomass vegetation in the LC classification (uncertainty in LC), and subsequently in the assignment of PFT fractions for each LC class (uncertainty in CW), relative to a reference PFT distribution.We examined the impact of PFT uncertainty on 3 key variables in the carbon, water and energy cycles (gross primary production (GPP), evapo-transpiration (ET), and albedo), for 3 LSMs (JSBACH, JULES and ORCHIDEE) at global scale. Results showed a greater uncertainty in PFT fraction due to CW as opposed to LC uncertainty, for all three variables. CW uncertainty in tree fraction was found to be particularly important in the northern boreal forests for simulated LSM albedo. Uncertainty in the balance between grass and bare soil fraction in arid parts of Africa, central Asia, and central Australia was also found to influence albedo and ET in all models. The spread due to PFT uncertainty for albedo was between 30 and 105% of inter-model uncertainty, for GPP between 20 and 90%, and for ET 0–30%. Each model had a different sensitivity to PFT uncertainty, for example, GPP in JSBACH was found to have a much higher sensitivity to PFT uncertainty in the tropics than JULES and ORCHIDEE, whereas the inverse was true for ET.These results show that inter-model uncertainty for key variables in LSMs can be reduced by more accurate representation of PFT distributions. Future efforts in land cover mapping should therefore be focused on reducing CW uncertainty through better understanding of the fractional cover of PFTs within a land cover class. Efforts to reduce LC uncertainty should particularly be focused on more accurate mapping of grass and bare soil fractions in arid areas. In the context of Land Surface Models, these results demonstrate that prescribed vegetation distribution in models is a key source of uncertainty that is comparable to the spread between models for key model state variables.

A Model-Based Assessment of Potential Impacts of Man-Made Reservoirs on Precipitation

Abstract Land-use land-cover change (LULCC) plays an important role in weather and climate systems. Human modifications of land cover include building reservoirs and thus creating artificial lakes for multipurpose use. In this research, the authors have completed a Weather Research and Forecasting (WRF) Model–based assessment of impacts of two large parallel lakes on precipitation. This area is located in the western part of the states of Kentucky and Tennessee and known as the Land between the Lakes (LBL). To determine the impacts, this study has replaced the lakes with grass, deciduous forests, and bare soil and conducted model simulations for three precipitation events of different magnitudes. The analysis suggests that precipitation increased in some cases and reduced in others. One of the key impacts of LULCC in the LBL area is the relocation of precipitation cells and also the timing of precipitation. Local precipitation amounts increased or decreased with these relocations. In summary, establishment of lakes or replacement of lakes with alternate land cover may modify local precipitation in the LBL area.

Flood risk reduction and flow buffering as ecosystem services – Part 2: Land use and rainfall intensity effects in Southeast Asia

Abstract. Watersheds buffer the temporal pattern of river flow relative to the temporal pattern of rainfall. This ecosystem service is inherent to geology and climate, but buffering also responds to human use and misuse of the landscape. Buffering can be part of management feedback loops if salient, credible and legitimate indicators are used. The flow persistence parameter Fp in a parsimonious recursive model of river flow (Part 1, van Noordwijk et al., 2017) couples the transmission of extreme rainfall events (1 − Fp), to the annual base-flow fraction of a watershed (Fp). Here we compare Fp estimates from four meso-scale watersheds in Indonesia (Cidanau, Way Besai and Bialo) and Thailand (Mae Chaem), with varying climate, geology and land cover history, at a decadal timescale. The likely response in each of these four to variation in rainfall properties (including the maximum hourly rainfall intensity) and land cover (comparing scenarios with either more or less forest and tree cover than the current situation) was explored through a basic daily water-balance model, GenRiver. This model was calibrated for each site on existing data, before being used for alternative land cover and rainfall parameter settings. In both data and model runs, the wet-season (3-monthly) Fp values were consistently lower than dry-season values for all four sites. Across the four catchments Fp values decreased with increasing annual rainfall, but specific aspects of watersheds, such as the riparian swamp (peat soils) in Cidanau reduced effects of land use change in the upper watershed. Increasing the mean rainfall intensity (at constant monthly totals for rainfall) around the values considered typical for each landscape was predicted to cause a decrease in Fp values by between 0.047 (Bialo) and 0.261 (Mae Chaem). Sensitivity of Fp to changes in land use change plus changes in rainfall intensity depends on other characteristics of the watersheds, and generalisations made on the basis of one or two case studies may not hold, even within the same climatic zone. A wet-season Fp value above 0.7 was achievable in forest–agroforestry mosaic case studies. Inter-annual variability in Fp is large relative to effects of land cover change. Multiple (5–10) years of paired-plot data would generally be needed to reject no-change null hypotheses on the effects of land use change (degradation and restoration). Fp trends over time serve as a holistic scale-dependent performance indicator of degrading/recovering watershed health and can be tested for acceptability and acceptance in a wider social-ecological context.

MODIS land cover uncertainty in regional climate simulations

MODIS land cover datasets are used extensively across the climate modeling community, but inherent uncertainties and associated propagating impacts are rarely discussed. This paper modeled uncertainties embedded within the annual MODIS Land Cover Type (MCD12Q1) products and propagated these uncertainties through the Regional Atmospheric Modeling System (RAMS). First, land cover uncertainties were modeled using pixel-based trajectory analyses from a time series of MCD12Q1 for Urumqi, China. Second, alternative land cover maps were produced based on these categorical uncertainties and passed into RAMS. Finally, simulations from RAMS were analyzed temporally and spatially to reveal impacts. Our study found that MCD12Q1 struggles to discriminate between grasslands and croplands or grasslands and barren in this study area. Such categorical uncertainties have significant impacts on regional climate model outputs. All climate variables examined demonstrated impact across the various regions, with latent heat flux affected most with a magnitude of 4.32 W/m2 in domain average. Impacted areas were spatially connected to locations of greater land cover uncertainty. Both biophysical characteristics and soil moisture settings in regard to land cover types contribute to the variations among simulations. These results indicate that formal land cover uncertainty analysis should be included in MCD12Q1-fed climate modeling as a routine procedure.

The Influence of Groundwater Depletion from Irrigated Agriculture on the Tradeoffs between Ecosystem Services and Economic Returns.

An irrigated agricultural landscape experiencing groundwater overdraft generates economic returns and a suite of ecosystem services (in particular, groundwater supply, greenhouse gases reduction, and surface water quality). Alternative land cover choices indicate tradeoffs among the value of ecosystem services created and the economic returns. These tradeoffs are explored using efficiency frontiers that determine the least value in ecosystem services that must be given up to generate additional economic returns. Agricultural producers may switch to irrigation with surface water using on-farm reservoirs and tail water recovery systems in response to groundwater overdraft, and this has consequences for the bundle of ecosystem service values and economic returns achievable from the landscape. Planning that accounts for both ecosystem service value and economic returns can achieve more value for society, as does the adoption of reservoirs though lowering the costs of irrigation, increasing groundwater levels, and reducing fuel combustion and associated GHG emissions from groundwater pumping. Sensitivity analyses of per unit value of ecosystem services, crop prices, and the groundwater and water purification model parameters indicate tradeoff among ecosystems service values, such as the use of a high-end social cost of carbon ultimately lowers groundwater supply and water purification value by more than 15%.

Modelling Deforestation and Land Cover Transitions of Tropical Peatlands in Sumatra, Indonesia Using Remote Sensed Land Cover Data Sets

In Southeast Asia land use change associated with forest loss and degradation is a major source of greenhouse gas (GHG) emissions. This is of particular concern where deforestation occurs on peat soils. A business-as-usual (BAU) land change model was developed using Dinamica EGO© for a REDD+ Demonstration Activity area in south-east Jambi Province, Sumatra, Indonesia containing Berbak National Park (NP). The model output will be used as baseline land change predictions for comparison with alternative land cover management scenarios as part of a REDD+ feasibility study. The study area is approximately 376,000 ha with approximately 50% on peat soils. The model uses published 2000 and 2010 land cover maps as input and projects land cover change for thirty years until 2040. The model predicted that under a BAU scenario the forest area, 185,000 ha in 2010, will decline by 37% by 2040. In protected forest areas, approximately 50% of the study area, forest cover will reduce by 25%. Peat swamp forest will reduce by almost 37%. The greatest land cover category increases are plantation/regrowth areas (which includes oil palm) and open areas which each increase by 30,000 ha. These results indicate that the site has great potential as an Indonesian REDD+ Demonstration Activity.

Impacts of Land Cover and Climate Changes on Peak Floods Probability Distribution Function

Climate and land cover are the main regulators of discharge formation. We combined here 5 alternative land cover case with 2 different precipitation scenarios. Land cover scenarios are: (a) present land cover according to CORINE 2006 maps, (b) ICES scenario at 2050, (c) IMAGE scenario at 2050, (d) cementification scenario C1 and (e) cementification scenario C2. The ICES and IMAGE scenarios are based on consideration of economy and population growth, cementification scenarios 1 and 2 are intended as exemplificative what-if scenarios for urbanization of rural areas. Precipitation is modelled as (a) the observed one and (b) increasing of 10% the average value. Peak flood probability distributions are analytically for each of the 10 land cover-climate combinations. The case study is the Secchia river basin in Emilia Romagna region. Results show that in absence of changes in the precipitation regime, the peak flood associated to low return period (