Land use and land cover (LULC) change is one of the primary contributors to hydrological change in urban watersheds and can potentially influence stream flow and flood volume. Understanding the impacts of LULC change on urban hydrological processes is critical to effective urban water management and minimizing flood risks. In this context, this study aims to determine the impacts of LULC change on hydrological response in a fast transitioning watershed for the predicted years of 2050 and 2080. This research employs the hybrid land use classification technique, Cellular Automata–Markov (CA–Markov) model to predict land use changes, utilizing land use data from 2001, 2013, and 2021. Additionally, it incorporates a calibrated, event-specific hydrologic model known as the Personal Computer Storm Water Management Model (PCSWMM) to assess alterations in hydrological responses for storm events of various magnitudes. The findings indicate a transition of the watershed into an urbanized landscape, replacing the previous dominance of agriculture and forested areas. The initial urban area, constituting 11.6% of the total area in 2021, expands to cover 34.1% and 44.2% of the total area by 2050 and 2080, respectively. Due to the LULC changes, there are increases in peak discharge of 5% and 6.8% and in runoff volume of 8% and 13.3% for the years 2050 and 2080 for a 100-year return period storm event. Yet, the extent of these changes intensifies notably during storm events with lower return periods. This heightened impact is directly attributed to the swift urbanization of the watershed. These results underscore the pressing necessity to regulate LULC change to preserve the hydrological equilibrium.

Deforestation, the widespread clearance of forests for various land uses, has become a significant global environmental issue with far-reaching consequences. Deforestation in the study area was identified, categorized, assessed, and interpreted using Landsat 5, 7, and 8 from the years 2008, 2014, and 2020, respectively. A geographic information system (GIS) database of land use and land cover categories and their changes were created. The results showed that several anthropogenic human activities, including agriculture and wood harvesting, were driving a general retreat of the forest area. The study further shows that between 2008 and 2020, forest area decreased by 8.5% with an annual rate of 0.33%, other vegetative areas increased by 2% with an annual rate of 0.077%, and non-vegetative areas increased by 1.5% with an annual rate of 0.58%. The hotspot map shows that the rate at which the reserve is deteriorating and the conversion of the forest area to other vegetation and bare ground are only a matter of time. The study recommended that the government should adopt rigorous policies to protect forest reserves from unauthorized habitation by encouraging the use of alternative firewood fuel sources to reduce the pressure on the forest.

Quantification of the impact of land cover and climate change on water and sediment yield in sub-tropical Himalayas in upstream Teesta river basin, Sikkim

The impact of land use land cover change on hydropower potential in northern Ethiopia

Impacts of anthropogenic land use and land cover change on climate extremes based on CMIP6-LUMIP experiments: part II. Future period

Modeling the impact of historical and future land use land cover changes on the hydrological response of an Ethiopian watershed

This study investigates soil loss erosion dynamics in the Nan River Basin, Thailand, focusing on the impact of land cover changes. Utilizing the Universal Soil Loss Equation (USLE) model, key factors, including rainfall erosivity, soil erodibility, topography, and land cover, are analyzed for the years 2001 to 2019. The findings reveal a substantial increase in human-induced soil erosion, emphasizing the pressing need for effective mitigation measures. Severity classification demonstrates shifting patterns, prompting targeted conservation strategies. The examination of land cover changes indicates significant alterations in the satellite image (MODIS), particularly an increase in Deciduous forest (~13.21%), Agriculture (~0.18%), and Paddy (~0.43%), and decrease in Evergreen Forest (~13.73%) and Water (~0.12%) cover types. Deciduous forest and Agriculture, associated with the highest soil loss rates, underscore the environmental consequences of specific land use practices. Notably, the increase in Deciduous forest and Agriculture significantly contributes to changes in soil loss rates, revealing the interconnectedness of land cover changes and soil erosion in ~18.05% and ~8.67%, respectively. This study contributes valuable insights for informed land management decisions and lays a foundation for future research in soil erosion dynamics. Additionally, the percentage increase in Agriculture corresponds to a notable rise in soil loss rates, underscoring the urgency for sustainable land use practices.

Land use changes can majorly affect many parameters that are directly or indirectly interlinked to various human-environmental systems, including hydrological processes and flood risks. The knowledge of future land cover changes is crucial for better managing human-environmental interactions and addressing potential environmental challenges, such as floods. In this work, the impact of future land cover changes in flood inundation is assessed, using a case study in northeast Indiana, US. A Cellular Automata Markov (CAM) model is applied, combining Geographic Information Systems (GIS) and Python, to predict land changes and provide future land cover maps, along with statistical validation measures. The land use map outputs are then used in a HEC-RAS hydraulic model, to test the different flooding impacts under a design storm, using the rain-on-grid routine. The results indicate that even slightly more urbanized and deforested areas can increase the potential flood extent. Furthermore, the impacts of these forecasted land cover changes are quantified in monetary terms, based on a spatial Ecosystem Services Valuation (ESV) model. The findings indicate that as certain land uses (mainly wetlands, followed by forests) give their place to build-up areas, barren land, or even agricultural lands, the ‘lost’ value due can reach 1.5 million USD in 2051. The novelty of this study lies in int integrated character, combining for the first time to our knowledge land cover forecast with hydrologic-hydraulic modelling and spatial ESV, showing thus the future changes, risks, and potential economic losses, respectively. This application uses the minimum necessary input data to perform the analyses, and all data and codes are publicly available, contributing thus to the transferability and reproducibility of the approach.

With increasing concerns regarding water resource management and environmental sustainability, understanding land use change and the hydrological health of watersheds is critical for informed decision-making. This study aimed to explore the effect of land use changes on hydrological health resilience in the Rejoso watershed, East Java, using remote sensing techniques and geographic information system tools to characterize the various land cover types. Landsat ETM 7+ satellite imagery was used to describe land cover classes. Hydrological health indicators such as water transmission, water availability during the dry season, and peak rainfall buffer events were systematically analyzed in relation to land cover change conditions. Regression statistical methods were used to measure the impact of land cover changes on hydrological parameters. The study results showed that the area of forest land in the Rejoso watershed tended to increase from 2011 to 2021; on the other hand, the area of agroforestry land tended to decrease along with the increase in upland land. In general, the hydrological health of the Rejoso watershed is decreasing from year to year. The increase in forest area, agroforestry, settlements, and ponds provides a positive response to the hydrological health of the watershed. On the other hand, the increase in moorland and rice fields has a negative impact on the hydrological health of the watershed.

This study focuses on identifying land cover to create models for hydrological analysis.Data from 2012 and 2019 is used.The Land Change Modeler in TerrSet software is used for modeling scenarios like Business as Usual and Conservation.Hydrological modeling is done using the Soil and Water Analysis Tool integrated with QGIS software.Analyzing the impact of land cover changes on discharge outcomes is done using Geographically Weighted Regression.Results show differences between land cover projections under BAU and Conservation scenarios.Conservation scenario reduces urbanization but benefits nature reserves and protected forests.The Umbulan spring had a simulated output of 4030 liters per second in 2012, with other rivers showing varying annual discharges.The validation test resulted in an r value of 0.89.Typology analysis revealed that conversion of annual crops to residential areas was the main cause of discharge decrease at Umbulan Spring.GWR modeling led to local regression equations for each sub-basin, with an average r value of 54.42 percent.Variables like population density, land cover changes, and household water usage negatively impact Umbulan spring discharge.An optimal scenario aims to promote economic development while ensuring environmental sustainability and protecting protected areas from potential damage.This scenario could potentially reduce Umbulan spring discharge decline by up to 88.32 percent in certain zones.

Climate and Land Use Land Cover Change Impact on Groundwater Recharge Potential in the Pra and Ankobra River Basins, Ghana

This study used multi-platform remote sensing datasets to assess the impacts of land use and land cover (LULC) change on forest biomass density across the Three Gorges Reservoir (TGR) region along the Yangtze River. This study maps land use and land cover (LULC) change and forest aboveground biomass estimation (AGB) using Sentinel-2A and GEDI-L4B data based on observations between 2019 and 2022. The data analysis results of monitoring revealed a strong association frequency between land-use change and forest land cover since the significant relationship with forest aboveground biomass density has also been explored. The total deforestation decreased significantly in 2022. The accuracy assessment results show a classification accuracy of between 90.4% and 94.1%, with a kappa coefficient of 82.4%–92.1%. For the TGR region forest, the minimum to maximum mean above-ground biomass values are from 15.81 to 373.66 Mgha-1, respectively. Moreover, the fitting performance of the statistical analysis of aboveground biomass density of forest mean for the forest was quite decent with training (R 2: 0.92, RMSE: 12.30 Mg/ha) and testing (R 2: 0.79, RMSE: 18.07 Mg/ha) datasets. The results highlight LULC change impacts, particularly forest land class over forest AGB stands, and the effectiveness of the GEDI-L4B product in regional biomass estimation in the TGR region.

Abstract This study analyzed the impact of land use and land cover (LULC) changes and increased in greenhouse gases (GHGs) on surface variables in the climate of the metropolitan region of Manaus (MRM). The numerical experiments were carried out using the BRAMS regional model for the MRM rainy season period and divided into four categories, namely: actual land cover, sensitivity to deforestation and urbanization expansions, sensitivity to increased GHGs, and a combined experiment driven by an extreme scenario. Changes in LULC produced local alterations in the energy and radiation balances and in surface temperature. In addition, the diurnal cycle of the precipitation showed an increase after peak hours over the urban area. In the scenario of increasing GHGs, significant changes in the components of the radiation and energy balances resulted in a positive surface temperature anomaly (∼10 °C) and a negative precipitation anomaly (∼50%). These changes were slightly intensified in the combined experiment. It was found that MRM's climate is more sensitive to an increase in GHGs than to a local change in LULC. Our results reinforce the urgent need to take measures to contain the global increase in GHGs because, in the face of such a scenario, the maintenance of the forest, its ecological processes, and its environmental services would be impossible.

We evaluated the landscape of the North and Northwest of Rio de Janeiro, determining changes in forest cover by phytophysiognomy using collection 6 of MapBiomas (1985-2020). We worked in the R environment and QGIS. Among the phytophysiognomies, the Lowland forests showed the greatest area loss until 1985 (93%), becoming highly fragmented and isolated in the landscape. Between 1985 and 2020 the loss of forest was reduced. However, this result is related to the balance of secondary vegetation increase that covers up the losses of mature vegetation, with risks to biodiversity. The main driver of vegetation loss was agriculture, and currently less than 8% of the vegetation is protected. The procrastination in the establishment of conservation units and restoration of permanent protection areas will have serious negative consequences for the conservation of the vegetation in this region.

In this study, the impact of change in land use and land cover (LULC) on runoff estimation in the Kidangoor watershed was assessed using the SCS-CN technique. Recent flood-like natural disasters in Kerala are thought to be driven by changes in rainfall patterns and LULC. The accurate calculation of runoff from watersheds is urgently needed. In ArcGIS 10.5, the supervised classification approach is used to classify satellite images from 2000, 2011, 2013, and 2017. Similarly, the Inverse Distance Weighted (IDW) technique is used to produce spatial distribution maps of rainfall for each antecedent moisture condition (AMC). The runoff maps were generated by superimposing the distributed rainfall, LULC, and Hydrological Soil Group (HSG) maps. It was observed that the built-up area expanded by 168% between 2000 and 2017, whereas other classes decreased by 10–23%. However, compared to 2000, both with and without a change in LULC, runoff generation increased by just 31%, and 27% in 2017. The SCS-CN technique for runoff estimation indicates that the change in LULC in the Kidangoor watershed is insignificant. Thus, this study will help land use planners and decision-makers in limiting the potential damage from flooding when it comes to flood management techniques.

<p>The impact of historical land use and land cover change (LULCC) on the mean climate has been extensively studied, but its impact on temperature extremes is not well understood. This study investigates the biophysical effect of LULCC on temperature extremes using two sets of model simulations – one with land use fixed at 1850 level and the other with historical LULCC from 1850 to 2014. We find that the historical LULCC has two asymmetric effects: (i) it decreases the temperature of coldest day (–0.56 ± 0.23 K; mean ± std. error) more than that of the hottest day (–0.21 ± 0.07 K) at the mid-latitudes of northern hemisphere; and (ii) it has a stronger impact in the mid-latitudes of northern hemisphere relative to the tropical region. These changes result largely from an indirect effect of LULCC via changes in clouds, circulations, and the downward longwave radiation. We stress that the indirect effects from climate feedback of LULCC should be considered when implementing reforestation policy.</p>

The challenges posed by changes in land use and land cover are greatly affecting agricultural productivity in the semi-arid region of Nigeria. Land use and land cover change affects ecosystem functions and services, which leads to people migrating from one place to another, predominantly rural dwellers. The research studies the impact of land use and land cover change on agricultural productivity and the factors influencing the changes. To achieve this, a remote sensing technique was employed. The Landsat Thematic Mapper Image of 1990, Landsat Enhanced Thematic Mapper Image of 2005 and Landsat Enhanced Thematic Mapper Image of 2020 were collected and analysed. Six classes of land use and land cover were obtained using Earth Resource Development Assessment System Imagery 9.1 software. The result shows that there was a decrease in the land area occupied by wetland, shrubland and water body, while there was an increase in the land area occupied by bare lands/dunes, settlement and scattered cultivation from the year 1990 to 2020. Increase in bare land/dune, scattered cultivation and settlement were mainly as a result of climate change resulting from overgrazing, a southward movement of dunes from the Sahara desert, increasing farming activities and increasing demand for shelter by the increasing population. The decrease in shrubland, water body, and wetland were also linked to climate change resulting from increasing deforestation, and increasing use of wetland for farming activities. Based on the results of the analysis, it is recommended that measures should be taken to integrate sustainable land management and climate change adaptation options for sustainable Agriculture.

The increasing need for land has resulted in a higher rate of land conversion and urbanization, leading to a rise in urban density and the occurrence of an Urban Heat Island (UHI) effect. The application of remote sensing and GIS can serve as a substitute for data collection in monitoring the UHI phenomena. This work utilizes Landsat 8 OLI satellite image data, namely band 10, to analyze Land Surface Temperature (LST). Bands 5 and 4 are employed to assess the distribution of Normalized Difference Vegetation Index (NDVI) in Bekasi Regency during the years 2014 and 2020. The relationship between NDVI and LST is highly correlated as they can effectively forecast the influence of areas with sparse vegetation on temperature. The guided classification approach, employing the maximum likelihood algorithm and kappa validation, is utilized to evaluate alterations in land use. The kappa accuracy test yielded a score of 0.90% for 2014 and 0.99% for 2020. The research conducted between 2014 and 2020 revealed changes in land distribution. Specifically, the built-up land area increased by 99.92 Km2, empty land expanded by 280.82 Km2, bodies of water covered an additional 46.13 Km2, and vegetation expanded by 293.91 Km^2. According to the UHI research, it is evident that there has been a rise in surface temperature in Bekasi Regency from 2014 to 2020. In 2014, the minimum temperature reached 30 °C, and the maximum temperature reached 51 °C. In 2020, the minimum temperature was recorded at 34 °C, while the maximum temperature reached 52 °C.

Estimation of the surface urban heat island intensity across 1031 global cities using the regression-modification-estimation (RME) method

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