Spatial assemblage of shorebirds (Aves: Charadriiformes) in an altered wetland of the southern coast of Sri Lanka

To demonstrate potential future consequences of land cover and land use changes beyond those for physical climate and the carbon cycle, we present an analysis of large‐scale impacts of land cover and land use changes on atmospheric chemistry using the chemistry‐climate model EMAC (ECHAM5/MESSy Atmospheric Chemistry) constrained with present‐day and 2050 land cover, land use, and anthropogenic emissions scenarios. Future land use and land cover changes are expected to result in an increase in global annual soil NO emissions by ∼1.2 TgN yr−1 (9%), whereas isoprene emissions decrease by ∼50 TgC yr−1 (−12%). The analysis shows increases in simulated boundary layer ozone mixing ratios up to ∼9 ppbv and more than a doubling in hydroxyl radical concentrations over deforested areas in Africa. Small changes in global atmosphere‐biosphere fluxes of NOx and ozone point to compensating effects. Decreases in soil NO emissions in deforested regions are counteracted by a larger canopy release of NOx caused by reduced foliage uptake. Despite this decrease in foliage uptake, the ozone deposition flux does not decrease since surface layer mixing ratios increase because of a reduced oxidation of isoprene by ozone. Our study indicates that the simulated impact of land cover and land use changes on atmospheric chemistry depends on a consistent representation of emissions, deposition, and canopy interactions and their dependence on meteorological, hydrological, and biological drivers to account for these compensating effects. It results in negligible changes in the atmospheric oxidizing capacity and, consequently, in the lifetime of methane. Conversely, we expect a pronounced increase in oxidizing capacity as a consequence of anthropogenic emission increases.

Land cover change (LCC) and climate change (CC) impacts on streamflow in high elevated catchments are a great challenge to sustainable management and the development of water resources. This study evaluates the possible future impacts of both land cover and climate change on the streamflows in the Mohmand Dam catchment, Pakistan, by utilizing the semi-distributed hydrological model known as the Soil and Water Assessment Tool (SWAT), along with the latest Coupled Model Intercomparison Project phase 6 (CMIP6) dataset of different global climate models (GCMs). The downscaling of the precipitation and temperature data was performed by the CMhyd software. The downscaled precipitation and temperature projections from the best performing GCM, out of four GCMs, under two shared socioeconomic pathways (SSP2 and SSP5) and future land cover conditions were forced in a calibrated hydrological model (SWAT model). Compared to the baseline period (1990–2015), the outputs from the selected GCM indicated an increase in the average monthly precipitation, and the maximum and minimum temperature in the study area under both the SSP2 and SSP5 scenarios, by the end of the 21st century. It is expected that the increase in precipitation for the period 2016–2100 is 10.5% and 11.4% under the SSP2 and SSP5 scenarios, respectively. Simulated results from the SWAT model showed significant impacts from the projected climate and land cover changes on Mohmand Dam flows that include: (a) an increase in the overall mean annual flow ranging from 13.7% to 34.8%, whereas the mean monthly flows of June, July and August decreased, and (b) a shift in the peak flows in the Mohmand catchment from July to June. It is concluded that the projected climate changes can substantially influence the seasonality of flows at the Mohmand Dam site. Climate and land cover change impacts are significant, so project planners and managers must include CC and LCC impacts in the proposed operational strategy.

A series of 17-yr equilibrium simulations using the NCAR CCM3 (T42 resolution) were performed to investigate the regional scale impacts of land cover change and increasing CO2 over China. Simulations with natural and current land cover at CO2 levels of 280, 355, 430, and 505 ppmv were conducted. Results show statistically significant changes in major climate fields (e.g. temperature and surface wind speed) on a 15-yr average following land cover change. We also found increases in the maximum temperature and in the diurnal temperature range due to land cover change. Increases in CO2 affect both the maximum and minimum temperature so that changes in the diurnal range are small. Both land cover change and CO2 change also impact the frequency distribution of precipitation with increasing CO2 tending to lead to more intense precipitation and land cover change leading to less intense precipitation—indeed, the impact of land cover change typically had the opposite effect versus the impacts of CO2. Our results provide support for the inclusion of future land cover change scenarios in long-term transitory climate modelling experiments of the 21st Century. Our results also support the inclusion of land surface models that can represent future land cover changes resulting from an ecological response to natural climate variability or increasing CO2. Overall, we show that land cover change can have a significant impact on the regional scale climate of China, and that regionally, this impact is of a similar magnitude to increases in CO2 of up to about 430 ppmv. This means that that the impact of land cover change must be accounted for in detection and attribution studies over China.

The combined effects of climate and land cover changes influence hydrologic responses of a basin in an offsetting or synergistic manner depending on the nature and severity of the changes. As such, estimating the impacts of these environmental changes on hydrologic responses is crucial for planning water resources management. However, such a comprehensive study is missing in most basins of Ethiopia, particularly in the Dhidhessa River basin (DRB). The aim of this study is, therefore, to quantify the combined and separate impacts of land cover and climate changes on multiple hydrologic variables for the DRB. The Calibrated Soil and Water Analysis Tool (SWAT) model and statistical techniques were integrated for this study. Quantifying the separate and combined effects of land cover and climate changes on multiple hydrologic responses at a local scale, and determining the relative contribution of the changes are the strength of this study. The result indicated better performance of the SWAT model in simulating water balance components for the DRB. Significant changes in hydrologic responses were observed in response to the land cover changes, and the increasing trends of temperature and rainfall observed during the last 30 years in DRB. The result showed increasing actual evapotranspiration (AET), streamflow, and surface runoff while decreasing groundwater recharge. Surface runoff was more affected by land cover change than by climate change, whereas streamflow and AET were more affected by climate change than land cover change during the last 30 years in the basin. The combined effects of land cover and climate changes played an offsetting effect on groundwater recharge and AET. Overall, the simulated hydrologic responses will have negative effects on water resource availability and agricultural production in the basin and the surroundings. Therefore, implementing integrated watershed management strategies, such as soil and water conservation and afforestation, could minimize the negative impact.

Ecosystem services are vital services that support life and are the basis for human socio-economic progress. However, changes in land use and land cover (LULC) brought about by urban expansion degrade them. Thus, analysing the impact of land use and land cover (LULC) change on ecosystem service values (ESVs) is crucial for understanding and informing resource policy decisions. This study aims to analyse the impact of land use and land cover changes on ecosystem service values in Mwanza City, Tanzania. To achieve that, the benefits transfer approach was employed to analyse the changes in ESV in response to LULC. We estimated and analysed changes in ESV using satellite image datasets from 1999, 2009, and 2019. The LULC classes that were identified are vegetated land, agricultural land, waterbodies, built-up area, and bareland. The results exhibit that Mwanza City experienced significant LULC changes. While vegetated land, agricultural land, and bareland decreased by 49%, 15%, and 36%, respectively, the built-up area and water bodies increased by 568% and 48%, respectively, during the two decades. The total ESV decreased from 31.35 million US dollars to 26.3 million US dollars between 1999 and 2009 and to 23.96 million US dollars between 2009 and 2019. The waterbodies increased due to the increased volume of water in streams that expanded the floodplains, which resulted from surface runoff attributed to increased paved surfaces as more land was converted into a built-up environment upstream. The built-up area and bareland contributed nothing to ESV. However, the built-up area was the driving force behind the reduction of ESV in other LULC classes, as it was encroaching on them. The study concludes that the decrease in ESV reflects the degradation of ecosystem services due to the change in LULC. Hence, it is recommended that sustainable management of ecosystems be adhered for the proper functioning of the earth’s life-support system.

Frequent channel migrations of the Yellow River, coupled with increasing human disturbances, have driven significant land cover changes in the Yellow River Delta (YRD) over time. Accurate estimation of aboveground biomass (AGB) and clarification of the impact of land cover changes on AGB are crucial for monitoring vegetation dynamics and supporting ecological management. However, field-based biomass samples are often time-consuming and labor-intensive, and the quantity and quality of such samples greatly affect the accuracy of AGB estimation. This study developed a robust AGB estimation framework for the YRD by synthesizing 4717 field-measured samples from the published scientific literature and integrating two critical ecological indicators: leaf area index (LAI) and length of growing season (LGS). A random forest (RF) model was employed to estimate AGB for the YRD from 2001 to 2022, achieving high accuracy (R2 = 0.74). The results revealed a continuous spatial expansion of AGB over the past two decades, with higher biomass consistently observed in western cropland and along the Yellow River, whereas lower biomass levels were concentrated in areas south of the Yellow River. AGB followed a fluctuating upward trend, reaching a minimum of 204.07 g/m2 in 2007, peaking at 230.79 g/m2 in 2016, and stabilizing thereafter. Spatially, western areas showed positive trends, with an average annual increase of approximately 10 g/m2, whereas central and coastal zones exhibited localized declines of around 5 g/m2. Among the changes in land cover, cropland and wetland changes were the main contributors to AGB increases, accounting for 54.2% and 52.67%, respectively. In contrast, grassland change exhibited limited or even suppressive effects, contributing −6.87% to the AGB change. Wetland showed the greatest volatility in the interaction between area change and biomass density change, which is the most uncertain factor in the dynamic change in AGB.

This study provides a detailed assessment of land cover (LC) changes on the water balance components on data constrained Kikafu-Weruweru-Karanga (KWK) watershed, using the integrated approaches of hydrologic modeling and partial least squares regression (PLSR). The soil and water assessment tool (SWAT) model was validated and used to simulate hydrologic responses of water balance components response to changes in LC in spatial and temporal scale. PLSR was further used to assess the influence of individual LC classes on hydrologic components. PLSR results revealed that expansion in cultivation land and built-up area are the main attributes in the changes in water yield, surface runoff, evapotranspiration (ET), and groundwater flow. The study findings suggest that improving the vegetation cover on the hillside and abandoned land area could help to reduce the direct surface runoff in the KWK watershed, thus, reducing flooding recurring in the area, and that with the ongoing expansion in agricultural land and built-up areas, there will be profound negative impacts in the water balance of the watershed in the near future (2030). This study provides a forecast of the future hydrological parameters in the study area based on changes in land cover if the current land cover changes go unattended. This study provides useful information for the advancement of our policies and practices essential for sustainable water management planning.

The Godavari river basin (GRB), the second largest river basin (312,800 \(\hbox {km}^{2}\)) in India, was considered in this study to quantify the relative hydrological impact of recent land cover (LC) changes and rainfall trends using the variable infiltration capacity hydrologic model. Three scenarios, namely, (i) LC change, (ii) climate change and (iii) LC and climate changes, were considered to isolate the hydrological implications of the LC changes from those of climate change. Results revealed that evapotranspiration is predominantly governed by LC change and that small changes in rainfall cause greater changes in the runoff. Although the spatial extent of LC change is higher, the climate change is the dominant driver of hydrological changes within the GRB. Thus, climate projections are the key inputs to study the impact on the river basin hydrology. The results provide insights into the impacts of the climate and LC changes on the basin. The methodology and results of the present study can be further considered for water resource planning within the river basin in view of the changing environment.

Understanding the effects of land cover changes on ecosystem carbon stocks is essential for ecosystem management and environmental protection, particularly in the transboundary region that has undergone marked changes. This study aimed to examine the impacts of land cover changes on ecosystem carbon stocks in the transboundary Tumen River Basin (TTRB). We extracted the spatial information from Landsat Thematic Imager (TM) and Operational Land Imager (OLI) images for the years 1990 and 2015 and obtained convincing estimates of terrestrial biomass and soil carbon stocks with the InVEST model. The results showed that forestland, cropland and built-up land increased by 57.5, 429.7 and 128.9 km2, respectively, while grassland, wetland and barren land declined by 24.9, 548.0 and 43.0 km2, respectively in the TTRB from 1990 to 2015. The total carbon stocks encompassing aboveground, belowground, soil and litter layer carbon storage pools have declined from 831.48 Tg C in 1990 to 831.42 Tg C in 2015 due to land cover changes. In detail, the carbon stocks decreased by 3.13 Tg C and 0.44 Tg C in Democratic People’s Republic of Korea (North Korea) and Russia, respectively, while increased by 3.51 Tg C in China. Furthermore, economic development, and national policy accounted for most land cover changes in the TTRB. Our results imply that effective wetland and forestland protection policies among China, North Korea, and Russia are much needed for protecting the natural resources, promoting local ecosystem services and regional sustainable development in the transnational area.

A series of 17‐year integrations using the NCAR CCM3 (at about 2.8° × 2.8° resolution) were performed to investigate the regional‐scale impact of land cover change. Our aim was to determine the impact of historical land cover change on the regional‐scale climate over the regions where most change occurred: Europe, India and China. The change from natural to current land cover was estimated using BIOME3 to predict the natural vegetation type, and then using remotely sensed data to estimate the locations where land cover had been changed through human activity. Results show statistically significant changes in the 15‐year averaged 1000 hPa wind field, mean near‐surface air temperature, maximum near‐surface air temperature and the latent heat flux over the regions where land cover change was imposed. These changes disappeared if the land cover over a particular region was omitted, indicating that our results cannot be explained by model variability. An analysis of changes on an averaged monthly time scale showed large changes in the maximum daily temperature in (Northern Hemisphere) summer and little change in the minimum daily temperature, resulting in changes in the diurnal temperature range. The change in the diurnal temperature range could be positive or negative depending on region, time of year and the precise nature of the land cover changes. Our results indicate that the inclusion of land cover change scenarios in simulations of the 20th century may lead to improved results. The impact of land cover changes on regional climates also provides support for the inclusion of land surface models that can represent future land cover changes resulting from an ecological response to natural climate variability or increasing carbon dioxide. Copyright © 2002 Royal Meteorological Society.

Increasing carbon dioxide (CO2) concentrations affect vegetation physiologically (through stomatal conductance) and structurally (through changes in leaf area index). It may be important to include these biospheric feedbacks in experiments that explore atmosphere‐surface interactions including the impact of historical land cover change (LCC) within the climate system. In this paper, we show that the biospheric feedback affects the simulation of historical human‐induced LCC over Australia for January. The biospheric feedbacks reduce the simulated impact of LCC on latent heat flux and temperature. Further, we show that the magnitude of these feedbacks is non‐negligible and can be comparable, at regional scales, to changes caused by increases in radiative forcing simulated by a climate model over the same time period. We suggest that exploring the impact of historical LCC, for example on 20th Century climate, without including the biospheric feedbacks may incorrectly assess the impact of LCC.

A general issue in climate science is the handling of big data and running complex and computationally heavy simulations. In this paper, we explore the potential of using machine learning (ML) to spare computational time and optimize data usage. The paper analyzes the effects of changes in land cover (LC), such as deforestation or urbanization, on local climate. Along with green house gas emission, LC changes are known to be important causes of climate change. ML methods were trained to learn the relation between LC changes and temperature changes. The results showed that random forest (RF) outperformed other ML methods, and especially linear regression models representing current practice in the literature. Explainable artificial intelligence (XAI) was further used to interpret the RF method and analyze the impact of different LC changes on temperature. The results mainly agree with the climate science literature, but also reveal new and interesting findings, demonstrating that ML methods in combination with XAI can be useful in analyzing the climate effects of LC changes. All parts of the analysis pipeline are explained including data pre-processing, feature extraction, ML training, performance evaluation, and XAI.

In cold and humid climates, warming temperatures will result in longer growing seasons, leading to land cover changes that could have long‐term impacts on groundwater recharge (GWR), in addition to the direct impacts of climate change. The objective of this study was therefore to investigate whether land cover (LC) changes need to be considered when simulating long‐term regional‐scale potential GWR in cold and humid climates by (1) quantifying how LC changes impact simulated GWR and (2) quantifying the combined impacts of LC and climate changes on the future GWR changes. Using the region of southern Quebec (Canada) as a case study and a water budget model, this work proposes an innovative coupling of land cover change scenarios and specific future climate conditions to simulate spatially distributed transient GWR over the 1951–2100 period. The results showed that including LC changes in long‐term GWR simulations produced statistically significant increases in GWR compared to using a constant LC through time (average of +13 mm). Massive afforestation taking place on agricultural lands simulated for one of the scenario chains (RCP4.5) increased GWR by reducing runoff during the snow‐dominated period (average − 17 mm). The results also showed that GWR was more sensitive to climate change for scenarios that included intense land cover changes. Additionally, the spatial distribution of the LC changes influenced their simulated impacts on GWR. Considering that the methodology was computationally feasible and entirely transferrable to the new CMIP6 ensemble, LC changes should be considered systematically in long‐term groundwater resources simulations.

<p>Deforestation in Colombia has drastically increased in recent years. At the same time, droughts and floods are affecting the country more frequently due to climate change. Analyzing the impacts and interactions of deforestation and global warming is challenging due to the terrain’s complexity and the high climate variability along with the severe lack of regional climate modelling.</p><p>Here, we quantify the impact of historical anthropogenic global warming (CC) and land cover changes (LCC) on precipitation, temperature and the surface energy balance in Colombia by running the Weather Research and Forecasting model WRF v3.9.1.1. across different land cover and climate scenarios during the study period 2009-2011 for Colombia.</p><p>We find that precipitation is increased by CC with a stronger effect over forests. LCC implies a small reduction of precipitation which is strongly enhanced above deforested areas. LCC is found to be a strong driver of regional precipitation changes representing up to 25% and 60% of the CC effects magnitude in Coastal Caribbean and Andean regions, respectively. CC causes a temperature increase across the whole domain, in particular with increasing altitude. Surprisingly however, WRF simulates a slight cooling after deforestation which is not in line with almost all observations and modelling studies regarding biophysical effects of tropical deforestation. This apparent bias is further investigated across different WRF schemes and parameters because of its great importance for climate studies using WRF with default parametrization in tropical contexts.</p><p> </p>

Changes in land use/land cover are a major driver of biodiversity change in the Mediterranean region. Understanding how animal populations respond to these landscape changes often requires using landscape mosaics as the unit of investigation, but few previous studies have measured both response and explanatory variables at the land mosaic level. Here, we used a “whole-landscape” approach to assess the influence of regional variation in the land cover composition of 81 farmland mosaics (mean area of 2900 ha) on the population density of a threatened bird, the little bustard (Tetrax tetrax), in southern Portugal. Results showed that ca. 50% of the regional variability in the density of little bustards could be explained by three variables summarising the land cover composition and diversity in the studied mosaics. Little bustard breeding males attained higher population density in land mosaics with a low land cover diversity, with less forests, and dominated by grasslands. Land mosaic composition gradients showed that agricultural intensification was not reflected in a loss of land cover diversity, as in many other regions of Europe. On the contrary, it led to the introduction of new land cover types in homogenous farmland, which increased land cover diversity but reduced overall landscape suitability for the species. Based on these results, the impact of recent land cover changes in Europe on the little bustard populations is evaluated.