Land Use/land Cover Change Scenarios Research Articles

Soil erosion in the United States: Present and future (2020–2050)

Brought on by anthropogenic actions, accelerated soil erosion inflicts extreme changes in terrestrial and aquatic ecosystems. These field-scale (30 m) changes have neither been fully surveyed in the present, nor predicted for a probable future. Water-driven soil erosion (i.e., sheet and rill erosion) rates across the contiguous United States were estimated for the present, and then predicted for the future using three alternative Shared Socioeconomic Pathway and Representative Concentration Pathway (SSP-RCP) scenarios (2.6, 4.5, and 8.5) of the Coupled Model Intercomparison Project Phase 6 (CMIP6). The G2 erosion model which is integrated with Machine Learning (ML) and Remote Sensing (RS) techniques were used to estimate soil erosion based on gauge observations of long-term precipitation, and climate and land use land cover (LULC) scenarios. The baseline model (2020) estimated soil erosion rates of 2.32 Mg ha−1yr−1 under current conservation agriculture practices (CPs). Maintaining current CPs, future scenarios predict an 8 % to 21 % increase in soil erosion under different combinations of SSP-RCP climate and LULC change scenarios. The findings of this study can help policy makers for future conservation planning on maintaining soil fertility, mitigating environmental impacts, and promoting food security.

LUCST: A novel toolkit for Land Use Land Cover change assessment in SWAT+ to support flood management decisions

Land Use Land Cover (LULC) change is widely recognised as one of the most important factors impacting the hydrological response of river basins. SWAT+, the latest version of the Soil and Water Assessment Tool, has been used extensively to assess the hydrological impacts of LULC change. However, the process of making and assessing such changes in SWAT+ is often cumbersome and non-intuitive, thereby reducing its usability amongst a wider pool of applied users. We address this issue by developing a user-friendly toolkit, Land Use Change SWAT + Toolkit (LUCST), that will: (1) allow the end-user to define various LULC change scenarios in their study catchment, (2) run the SWAT+ model with the specified LULC changes, and (3) enable interactive visualisation of the different SWAT+ output variables. A good System Usability Score (79.8) and positive feedback from end-users promises the potential for adopting LUCST in future LULC change studies. Video abstracthttps://youtu.be/QygBidYr4cQ.

Optimal Operation of Nashe Hydropower Reservoir under Land Use Land Cover Change in Blue Nile River Basin

Changes in LULC (land use land cover), which significantly influence the spatial and temporal distribution of hydrological processes and water resources in general, have a substantial impact on hydropower generation. The utilization of an optimization approach in order to analyze the operation of reservoirs is an important concern in the planning and management of water resources. The SWAT (Soil and Water Assessment Tool) and the HEC-ResPRM (Hydrologic Engineering Center reservoir evaluation system Prescriptive Reservoir Model) were combined to model and optimize the Nashe hydropower reservoir operation in the Blue Nile River Basin (BNRB). The stream flow into the reservoir was determined using the SWAT model, considering the current and future impacts of LULC changes. The HEC-ResPRM model has been utilized in order to generate the optimal hydropower reservoir operation by using the results of the SWAT calibrated and validated stream flow as input data. This study proposes a method for integrating the HEC-ResPRM and SWAT models to examine the effects of historical and future land use land cover change on the watershed’s hydrological processes and reservoir operation. Therefore, the study aimed to investigate the current and future optimal reservoir operation scenarios for water resources management concerning hydropower generation under the effect of LULC changes. The results reveal that both the 2035 and 2050 LULC change scenarios show the increased operation of hydropower reservoirs with increasing reservoir inflows, releases, storage, and reservoir elevation in the future. The effects of LULC change on the study area’s hydrological components reveal an increase in surface runoff until 2035, and its decrease from 2035 to 2050. The average annual reservoir storage and elevation in the 2050 LULC scenario increased by 7.25% and 2.27%, respectively, when compared to the current optimized scenario. Therefore, changes in LULC have a significant effect on hydropower development by changing the total annual and monthly reservoir inflow volumes and their seasonal distribution. Reservoir operating rule curves have been commonly implemented in the operation of hydropower reservoirs, since they help operators to make essential, optimal decisions with available stream flow. Moreover, the generated future reservoir rule curves can be utilized as a reference for the long-term prediction of hydropower generation capacity, and assist concerned authorities in the successful operation of the reservoir under the impact of LULC changes.

Modeling Potential Impacts of Climate Change on the Distribution of Wooly Wolf (Canis lupus chanco)

The Central Asian wolves form a cohort within the wolf-dog clade known as the wooly wolf (Canis lupus chanco). These wolves are poorly studied and their current extent and distribution remain unknown. Apex predators already existing at higher elevations like wooly wolves can be severely affected by climate change because of the absence of suitable refuge. Concomitantly, in the era of Anthropocene, the change in land use land cover (LULC) is rapidly increasing. Even the most adaptable species occurring in human-dominated landscapes may fail to survive under the combined impact of both climate change and human pressure. We collected 3,776 presence locations of the wooly wolf across its range from published literature and compiled 39 predictor variables for species distribution modeling, which included anthropogenic factors, climatic, vegetation, and topographic features. We predicted the change in their distribution under different anthropogenic factors, climate change, and land-use land-cover change scenarios. Wolf showed affinity toward areas with low to moderately warm temperatures and higher precipitations. It showed negative relationships with forests and farmlands. Our future projections showed an expansion of wolf distribution and habitat suitability under the combined effects of future climate and LULC change. Myanmar and Russia had the introduction of high and medium suitability areas for the wooly wolf in future scenarios. Uzbekistan and Kazakhstan showed the consistent loss in high suitability areas while Mongolia and Bhutan had the largest gain in high suitability areas. The study holds great significance for the protection and management of this species and also provides opportunities to explore the impact on associated species.

Evaluating the joint effects of climate and land use change on runoff and pollutant loading in a rapidly developing watershed

Communities are coping with changes in runoff quantity and quality, stemming mainly from changes in climate and land use/land cover (LULC); there is a need to identify the most adaptable strategies that improve community resilience. However, the joint impacts of climate and LULC change have rarely been assessed at local scales. To address these needs, we assessed the response of runoff and pollutant loads from Broad Run, a rapidly developing watershed in northern Virginia, to projected climate and LULC change. Climate data from two downscaled Global Climate Models (GCMs) were used to force an urban watershed model, the Storm Water Management Model (SWMM), while forecasts of LULC change were derived from the Chesapeake Bay Land Change Model (CBLCM). Two Representative Concentration Pathways (RCPs), 4.5 and 8.5, a historic baseline (1995–2020), and projected periods (2040–2065); and four LULC change scenarios designated agricultural conservation (AC), forest conservation (FC), growth management (GM), and historical trend (HT) were used to create a series of ensemble simulations of coupled LULC and climate change. Results indicated that, under RCP 8.5, annual precipitation is projected to increase substantially more than RCP 4.5. Projected LULC change resulted in a projected increase in imperviousness from 6.3% to 13.1%. Results indicated that climate change will likely increase the seasonal variability of runoff, Total Suspended Solids (TSS), Total Nitrogen (TN), and Total Phosphorus (TP) for both RCPs. The largest increase for a single LULC change (without climate change) scenario for runoff, TSS, TN, and TP was 32.6%, 33.4%, 31.6%, and 35.8%, respectively. which occurred with the HT scenario. Results of LULC change also indicated that more pollutant loads were associated with increased imperviousness from increased urban development and loss of deciduous forests and grasslands. The largest increase for climate and LULC change scenarios in runoff, TSS, TN, and TP was 67.6%, 66.7%, 63.4%, and 69.4%, respectively, which occurred with the RCP 8.5 and HT scenarios. Similar, but smaller increases were obtained for other scenarios, suggesting that climate and LULC change may be synergistic, likely undermining watershed restoration efforts. The results of our study also indicate that runoff, TSS, TN, and TP are expected to be more affected by changes in future LULC than by projected changes in climate. Our study can be used to inform watershed restoration efforts, urban planning, and environmental policy. The combined impact of climate and LULC change will likely generate increased runoff, and nutrients and sediment loading, indicating that robust mitigation strategies are needed for watershed restoration to succeed.

Hydrological Responses of Watershed to Historical and Future Land Use Land Cover Change Dynamics of Nashe Watershed, Ethiopia

Land use land cover (LULC) change is the crucial driving force that affects the hydrological processes of a watershed. The changes of LULC have an important influence and are the main factor for monitoring the water balances. The assessment of LULC change is indispensable for sustainable development of land and water resources. Understanding the watershed responses to environmental changes and impacts of LULC classes on hydrological components is vigorous for planning water resources, land resource utilization, and hydrological balance sustaining. In this study, LULC effects on hydrological parameters of the Nashe watershed, Blue Nile River Basin are investigated. For this, historical and future LULC change scenarios in the Nashe watershed are implemented into a calibrated Soil and Water Assessment Tool (SWAT) model. Five LULC scenarios have been developed that represent baseline, current, and future periods corresponding to the map of 1990, 2005, 2019, 2035, and 2050. The predicted increase of agricultural and urban land by decreasing mainly forest land will lead till 2035 to an increase of 2.33% in surface runoff and a decline in ground water flow, lateral flow, and evapotranspiration. Between 2035 and 2050, a gradual increase of grass land and range land could mitigate the undesired tendency. The applied combination of LULC prognosis with process-based hydrologic modeling provide valuable data about the current and future understanding of variation in hydrological parameters and assist concerned bodies to improve land and water management in formulating approaches to minimize the conceivable increment of surface runoff.

Predicting changes in land use/land cover and seasonal land surface temperature using multi-temporal landsat images in the northwest region of Bangladesh

Land use/land cover (LULC) variations are accelerated by rapid urbanization and significantly impacted global Land Surface Temperature (LST). The dynamic increase in LST results in the Urban Heat Island (UHI) effect. In this study, future LULC change scenarios, seasonal (summer & winter) LST variations, and LST distribution over different LULC classes were predicted using Landsat satellite images for 1999, 2009, and 2019 in Rajshahi District, Bangladesh. Cellular Automata (CA) and Artificial Neural Network (ANN) procedures were used to predict the LULC changes and seasonal LST variations for 2029 and 2039. In addition, Focus Group Discussions (FGDs) and Key Informants Interviews (KIIs) were conducted to identify the possible impacts of LULC change, LST shifts, and climate change on agricultural productivity and developed a sustainable land use management plan for the study area. Validation of the CA model demonstrated an excellent accuracy with a kappa value of 0.82. Similarly, the ANN model's validation using Mean Square Error (0.523 and 0.796 for summer) and Correlation coefficient (0.6023 and 0.831 for winter) values demonstrated a good prediction accuracy. The LULC prediction result indicated that the built-up area will be expanded by 58.03 km2 and 79.90 km2, respectively, from 2019 to 2029 and 2039. The predicted seasonal LST indicated that in 2029 and 2039, more than 23.30 % and 50.46 % of the summer and 3.02 % and 13.02 % of the winter seasons will likely be experienced LSTs greater than 35 °C. The results of public participation exposed that changes in LULC classes, variations in LST, and climate change significantly impact the regional biodiversity (loss of farmland and water bodies), reduce agricultural productivity, and increase extreme weather events (flood, heavy rainfall, and cold/warm temperature). This study provides the useful guidelines for agricultural officers, urban planners, and environmental engineers to understand the spatial configurations of built-up area enlargement and provide effective policy measures to conserve farming lands to ensure environmental sustainability.

LAND USE/LAND COVER CHANGE PREDICTION USING MULTI-TEMPORAL SATELLITE IMAGERY AND MULTI-LAYER PERCEPTRON MARKOV MODEL

Abstract. This paper aims to predict the trend of land use land cover (LULC) changes in Dak Nong province over time. Data from Landsat images captured in 2009, 2015, and 2018 was employed to analyze and predict the spatial distributions of LULC categories. The Random Forest (RF) was adopted to classify the images into ten different LULC classes. Besides, integration of Multi-Layer Perceptron Markov Neural Network (MLP-NN) with Markov Chain (MC) was applied to predict the future LULC changes in the region based on the change detection over the previous years. For all classified images, overall accuracy (OA) ranged from 77.35% to 84.55% with kappa (K) coefficient index ranging from 0.75 to 0.8. The results revealed that the annual population growth together with social-economic development was regarded as major drives for land conversion in the area. The predicted map showed a significant decrease trend inthe forest classes by 2025, accounting for 23 thousand ha. However, residential areas, rubber, and agricultural land classes are predicted to rise to 460 ha, 3,000 ha, and 20,000 ha, respectively. The simulated model and calibrated area data may be a vital contribution to sustainable development efforts of the local based on the dynamics of LULC and future LULC change scenarios. Overall, ascertaining the complex interface related to changes in land use and its major drivers over time provides useful information predict to explore the future trend of LULC changes, establish alternative land-use schemes and serve as guidelines for urban planning policymakers.

The Impact of Land Use/Land Cover Change (LULCC) on Water Resources in a Tropical Catchment in Tanzania under Different Climate Change Scenarios

Many parts of sub-Saharan Africa (SSA) are prone to land use and land cover change (LULCC). In many cases, natural systems are converted into agricultural land to feed the growing population. However, despite climate change being a major focus nowadays, the impacts of these conversions on water resources, which are essential for agricultural production, is still often neglected, jeopardizing the sustainability of the socio-ecological system. This study investigates historic land use/land cover (LULC) patterns as well as potential future LULCC and its effect on water quantities in a complex tropical catchment in Tanzania. It then compares the results using two climate change scenarios. The Land Change Modeler (LCM) is used to analyze and to project LULC patterns until 2030 and the Soil and Water Assessment Tool (SWAT) is utilized to simulate the water balance under various LULC conditions. Results show decreasing low flows by 6–8% for the LULC scenarios, whereas high flows increase by up to 84% for the combined LULC and climate change scenarios. The effect of climate change is stronger compared to the effect of LULCC, but also contains higher uncertainties. The effects of LULCC are more distinct, although crop specific effects show diverging effects on water balance components. This study develops a methodology for quantifying the impact of land use and climate change and therefore contributes to the sustainable management of the investigated catchment, as it shows the impact of environmental change on hydrological extremes (low flow and floods) and determines hot spots, which are critical for environmental development.

Dramatic Loss of Agricultural Land Due to Urban Expansion Threatens Food Security in the Nile Delta, Egypt

Egypt has one of the largest and fastest growing populations in the world. However, nearly 96% of the total land area is uninhabited desert and 96% of the population is concentrated around the River Nile valley and the Delta. This unbalanced distribution and dramatically rising population have caused severe socio-economic problems. In this research, 24 land use/land cover (LULC) maps from 1992 to 2015 were used to monitor LULC changes in the Nile Delta and quantify the rates and types of LULC transitions. The results show that 74,600 hectares of fertile agricultural land in the Nile Delta (Old Lands) was lost to urban expansion over the 24 year period at an average rate of 3108 ha year−1, whilst 206,100 hectares of bare land was converted to agricultural land (New Lands) at an average rate of 8588 ha year−1. A Cellular Automata-Markov (CA-Markov) integrated model was used to simulate future alternative LULC change scenarios. Under a Business as Usual scenario, 87,000 hectares of land transitioned from agricultural land to urban areas by 2030, posing a threat to the agricultural sector sustainability and food security in Egypt. Three alternative future scenarios were developed to promote urban development elsewhere, hence, with potential to preserve the fertile soils of the Nile Delta. A scenario which permitted urban expansion into the desert only preserved the largest amount of agricultural land in the Nile Delta. However, a scenario that encouraged urban expansion into the desert and adjacent to areas of existing high population density resulted in almost the same area of agricultural land being preserved. The alternative future scenarios are valuable for supporting policy development and planning decisions in Egypt and demonstrating that continued urban development is possible while minimising the threats to environmental sustainability and national food security.

Impact of water demand on hydrological regime under climate and LULC change scenarios

The present study focuses on an assessment of the impact of future water demand on the hydrological regime under land use/land cover (LULC) and climate change scenarios. The impact has been quantified in terms of streamflow and groundwater recharge in the Gandherswari River basin, West Bengal, India. dynamic conversion of land use and its effects (Dyna-CLUE) and statistical downscaling model (SDSM) are used for quantifying the future LULC and climate change scenarios, respectively. Physical-based semi-distributed model Soil and Water Assessment Tool (SWAT) is used for estimating future streamflow and spatiotemporally distributed groundwater recharge. Model calibration and validation have been performed using discharge data (1990–2016). The impacts of LULC and climate change on hydrological variables are evaluated with three scenarios (for the years 2030, 2050 and 2080). Temperature Vegetation Dyrness Index (TVDI) and evapotranspiration (ET) are considered for estimation of water-deficit conditions in the river basin. Exceedance probability and recurrence interval representation are considered for uncertainty analysis. The results show increased discharge in case of monsoon season and decreased discharge in case of the non-monsoon season for the years 2030 and 2050. However, a reverse trend is obtained for the year 2080. The overall increase in groundwater recharge is visible for all the years. This analysis provides valuable information for the irrigation water management framework.

Quantifying the Spatiotemporal Pattern of Urban Expansion and Hazard and Risk Area Identification in the Kaski District of Nepal

The present study utilized time-series Landsat images to explore the spatiotemporal dynamics of urbanization and land use/land-cover (LULC) change in the Kaski District of Nepal from 1988 to 2016. For the specific overtime analysis of change, the LULC transition was clustered into six time periods: 1988–1996, 1996–2000, 2000–2004, 2004–2008, 2008–2013, and 2013–2016. The classification was carried out using a support vector machine (SVM) algorithm and 11 LULC categories were identified. The classified images were further used to predict LULC change scenarios for 2025 and 2035 using the hybrid cellular automata Markov chain (CA-Markov) model. Major hazard risk areas were identified using available databases, satellite images, literature surveys, and field observations. Extensive field visits were carried out for ground truth data acquisition to verify the LULC maps and identify multihazard risk areas. The overall classification accuracy of the LULC map for each year was observed to be from 85% to 93%. We explored the remarkable increase in urban/built-up areas from 24.06 km2 in 1988 to 60.74 km2 by 2016. A majority of urban/built-up areas were sourced from cultivated land. For the six time periods, totals of 91.04%, 78.68%, 75.90%, 90.44%, 92.35%, and 99.46% of the newly expanded urban land were sourced from cultivated land. Various settlements within and away from the city of Pokhara and cultivated land at the river banks were found at risk. A fragile geological setting, unstable slopes, high precipitation, dense settlement, rampant urbanization, and discrete LULC change are primarily accountable for the increased susceptibility to hazards. The predicted results showed that the urban area is likely to continue to grow by 2025 and 2035. Despite the significant transformation of LULC and the prevalence of multiple hazards, no previous studies have undertaken a long-term time-series and simulation of the LULC scenario. Updated district-level databases of urbanization and hazards related to the Kaski District were lacking. Hence, the research results will assist future researchers and planners in developing sustainable expansion policies that may ensure disaster-resilient sustainable urban development of the study area.

Characterizing the relationship between land use land cover change and land surface temperature

Exploring changes in land use land cover (LULC) to understand the urban heat island (UHI) effect is valuable for both communities and local governments in cities in developing countries, where urbanization and industrialization often take place rapidly but where coherent planning and control policies have not been applied. This work aims at determining and analyzing the relationship between LULC change and land surface temperature (LST) patterns in the context of urbanization. We first explore the relationship between LST and vegetation, man-made features, and cropland using normalized vegetation, and built-up indices within each LULC type. Afterwards, we assess the impacts of LULC change and urbanization in UHI using hot spot analysis (Getis-Ord Gi∗ statistics) and urban landscape analysis. Finally, we propose a model applying non-parametric regression to estimate future urban climate patterns using predicted land cover and land use change. Results from this work provide an effective methodology for UHI characterization, showing that (a) LST depends on a nonlinear way of LULC types; (b) hotspot analysis using Getis Ord Gi∗ statistics allows to analyze the LST pattern change through time; (c) UHI is influenced by both urban landscape and urban development type; (d) LST pattern forecast and UHI effect examination can be done by the proposed model using nonlinear regression and simulated LULC change scenarios. We chose an inner city area of Hanoi as a case-study, a small and flat plain area where LULC change is significant due to urbanization and industrialization. The methodology presented in this paper can be broadly applied in other cities which exhibit a similar dynamic growth. Our findings can represent an useful tool for policy makers and the community awareness by providing a scientific basis for sustainable urban planning and management.

How many RCM ensemble members provide confidence in the impact of land‐use land cover change?

ABSTRACTRegional climate models (RCMs) include both terrestrial and atmospheric compartments and thereby allow studying land–atmosphere feedback, in particular, the impact of land‐use land cover driven by biogeophysical processes on regional climate. In this study, a method is developed to separate the signals from the noise in RCM simulations of the effects of changes in land use, using perturbed initial boundary conditions (PICs). We want to know how many ensemble members are required to identify robust and statistically significant land‐use land cover change (LULCC) effects from RCM LULCC studies. The method is applied to a case study of urbanization and deforestation, for which LULCC scenarios are implemented in the RCM Weather Research and Forecasting (WRF). Based on WRF ensemble simulations with PICs for 2010, the signal‐to‐noise ratio (SNR) is used to identify areas with pronounced effect of an LULCC or, rather, the parametrization of the land‐use classes. While in the urbanization scenarios clear and statistically significant signals are found for air temperature and for both latent‐ and sensible heat (SNR values up to 24), the effects are less pronounced for precipitation, and for deforestation in general (SNR values < 1). For the case study of urbanization and precipitation, the impact of the ensemble size is studied in order to derive robust conclusions about the effects of LULCC on precipitation. We conclude that single RCM realizations of different land‐use representations are not sufficient to derive LULCC‐induced signals, particularly not for precipitation. Small ensemble sizes led to concluding there were significant LULCC‐induced precipitation signals, but these disappeared when the ensemble size was increased. Our regional analysis suggests the need for ensemble sizes well above 10 for precipitation.

Reforestation and crop land conversion impacts on future regional air quality in the Southeastern U.S.

Land in the southeastern U.S. is expected to change, e.g., given the potential demand to develop forest-to-fuel technologies or, conversely, cropification of current forests to increase food production. Possible future PM2.5 and O3 air quality for two land use/land cover change (LULCC) scenarios, reforestation and cropland conversion, are compared to a reference case scenario for the year 2050 using the Weather Research and Forecasting (WRF) and Community Multi-scale Air Quality (CMAQ) models. Changes in air quality driven by changes in climate, deposition and emissions relating to the LULCC are investigated. Reforestation in the Southeast tends to decrease the ambient O3 mixing ratio while slightly increasing summertime PM2.5 in the Southeastern U.S. Results of a climate and deposition (CD) sensitivity simulation are provided for the two alternative LULCC scenarios to isolate the impact of changing climate and deposition on PM2.5 and O3 air quality. The sensitivity results indicate that deposition and emissions changes associated with reforestation impact O3 and PM2.5 concentrations as much as, and in most cases more than, changes in meteorology. Conversion of forest to cropland in the Southeast, on the other hand, tends to increase O3 and increase PM2.5 year-round. Cropland conversion leads to increased NOX emissions and increases in the 4th highest maximum daily 8-h O3 (MDA8) of the year by up to 10ppb despite the tendency for increased deposition and decreased temperature to reduce the MDA8 mixing ratio. The results of this study show that O3 and aerosol concentrations are sensitive to reforestation and cropland conversion in the Southeast and these land use changes should be considered in air quality management plans. Further, they show the sensitivity of such calculations to land cover properties.

Change analysis of land use/land cover and modelling urban growth in Greater Doha, Qatar

Urban population growth has been accelerating in the developing countries and this exerts tremendous pressure on available land resources in major cities and urban centres. Greater Doha, which is a metropolis area where 78% of the country’s population live, has witnessed a substantial population growth and a large urban expansion. This study is the first of its kind in the State of Qatar and it aims to provide an understanding of the land use/land cover (LULC) change and its spatio-temporal pattern in Greater Doha between the years 1997 and 2010. Parcel data and census statistics from these two dates were used in the analysis. LULC change statistics indicate a very large increase of 289% in the built-up areas and a larger increase of 426% of recreational spaces which shows a progressive planning approach towards improving living standards in Greater Doha. As a result of this development demands, the open spaces have been substantially reduced from 77% to 25%, population density increased in many parts of the metropolis and high-rise building started to dominate the skylines in certain parts of the metropolis. MARKOV model was used to generate future LULC change scenarios for the year 2020. The analysis predicts a 20% increase of built-up areas which will take place in the open spaces, and this modest increase may be explained by model constraints resulting from limited availability of land within the current boundaries of Greater Doha for future development.

Impacts of Postdam Land Use/Land Cover Changes on Modification of Extreme Precipitation in Contrasting Hydroclimate and Terrain Features

Abstract Understanding the impact of postdam climate feedbacks, resulting from land use/land cover (LULC) variability, on modification of extreme precipitation (EP) remains a challenge for a twenty-first-century approach to dam design and operation. In this study, the Regional Atmospheric Modeling System (RAMS, version 6.0) was used, involving a number of predefined LULC scenarios to address the important question regarding dams and their impoundments: How sensitive are the hydroclimatology and terrain features of a region in modulating the postdam response of climate feedbacks to EP? The study region covered the Owyhee Dam/Reservoir on the Owyhee River watershed (ORW), located in eastern Oregon. A systematic perturbation of the relative humidity in the initial and boundary condition of the model was carried out to simulate EP. Among the different LULC scenarios used in the simulation over the ORW, irrigation expansion in the postdam era resulted in an increase in EP up to 6% in the 72-h precipitation total. The contribution of the reservoir on EP added 8% to the 72-h total when compared to the predam LULC conditions. To address the science question, a previously completed investigation on the Folsom Dam [American River watershed (ARW)] in California was compared with the ORW findings on the basis of contrasting differences in hydroclimatology and terrain features. The results indicate that the postdam LULC change scenarios impact EP of ORW (Owyhee Dam) much greater than the EP of the ARW (Folsom Dam) because of its semiarid climate and flat terrain. EP was less sensitive to LULC changes on the windward side of the mountainous terrain of ARW as compared to the leeward side of the flat terrain of ORW.

Quantifying climate change mitigation potential in the United States Great Plains wetlands for three greenhouse gas emission scenarios

We examined opportunities for avoided loss of wetland carbon stocks in the Great Plains of the United States in the context of future agricultural expansion through analysis of land-use land-cover (LULC) change scenarios, baseline carbon datasets and biogeochemical model outputs. A wetland map that classifies wetlands according to carbon pools was created to describe future patterns of carbon loss and potential carbon savings. Wetland avoided loss scenarios, superimposed upon LULC change scenarios, quantified carbon stocks preserved under criteria of carbon densities or land value plus cropland suitability. Up to 3420 km2 of wetlands may be lost in the region by 2050, mainly due to conversion of herbaceous wetlands in the Temperate Prairies where soil organic carbon (SOC) is highest. SOC loss would be approximately 0.20 ± 0.15 megagrams of carbon per hectare per year (MgC ha−1 yr−1), depending upon tillage practices on converted wetlands, and total ecosystem carbon loss in woody wetlands would be approximately 0.81 ± 0.41 MgC ha−1 yr−1, based on biogeochemical model results. Among wetlands vulnerable to conversion, wetlands in the Northern Glaciated Plains and Lake Agassiz Plains ecoregions exhibit very high mean SOC and on average, relatively low land values, potentially creating economically competitive opportunities for avoided carbon loss. This mitigation scenarios approach may be adapted by managers using their own preferred criteria to select sites that best meet their objectives. Results can help prioritize field-based assessments, where site-level investigations of carbon stocks, land value, and consideration of local priorities for climate change mitigation programs are needed.