Land Use/land Cover Scenarios Research Articles

Dynamic shifts of functional diversity through climate-Resilient strategies and farmland restoration in a mountain protected area

Land-use land-cover (LULC) change contributes to major ecological impacts, particularly in areas undergoing land abandonment, inducing modifications on habitat structure and species distributions. Alternative land-use policies are potential solutions to alleviate the negative impacts of contemporary tendencies of LULC change on biodiversity.This work analyzes these tendencies in the Montesinho Natural Park (Portugal), an area representative of European abandoned mountain rural areas. We built ecological niche models for 226 species of vertebrates (amphibians, reptiles, birds, and mammals) and vascular plants, using a consensus modelling approach available in the R package ‘biomod2’. We projected the models to contemporary (2018) and future (2050) LULC scenarios, under four scenarios aiming to secure relevant ecosystem services and biodiversity conservation for 2050: an afforestation and a rewilding scenario, focused on climate-smart management strategies, and a farmland and an agroforestry recovery scenario, based on re-establishing human traditional activities. We quantified the influences of these scenarios on biodiversity through species habitat suitability changes for 2018–2050. We analyzed how these management strategies could influence indices of functional diversity (functional richness, functional evenness and functional dispersion) within the park.Habitat suitability changes revealed complementary patterns among scenarios. Afforestation and rewilding scenarios benefited more species adapted to habitats with low human influence, such as forests and open woodlands. The highest functional richness and dispersion was predicted for rewilding scenarios, which could improve landscape restoration and provide opportunities for the expansion and recolonization of forest areas by native species. The recovery of traditional farming and agroforestry activities results in the lowest values of functional richness, but these strategies contribute to complex landscape matrices with diversified habitats and resources. Moreover, this strategy could offer opportunities for fire suppression and increase landscape fire resistance. An integrative approach reconciling rewilding initiatives with the recovery of extensive agricultural and agroforestry activities is potentially an harmonious strategy for supporting the provision of ecosystem services while securing biodiversity conservation and functional diversity within the natural park.

Projecting the response of carbon sink potential to land use/land cover change in ecologically fragile regions

Introduction: The carbon storage service of ecosystems in ecologically fragile areas is highly sensitive to regional land use/land cover (LULC) changes. Predicting changes in regional carbon storage under different LULC scenarios is crucial for land use management decisions and exploring carbon sink potential. This study focuses on the Luan River Basin, a typical ecologically fragile area, to analyze the impact of LULC changes on carbon storage.Methods: The PLUS-InVEST model was employed to simulate LULC patterns for the year 2030 under three scenarios: natural development, cropland protection and urban development, and ecological protection. The model projected the future carbon sink potential of the basin under these scenarios.Results: From 2000 to 2020, carbon storage showed a trend of decrease followed by an increase. By 2030, compared to 2020, carbon storage is projected to increase by 16.97% under the ecological protection scenario and decrease by 22.14% under the cropland protection and urban development scenario. The increase in carbon storage was primarily due to the conversion of cropland and grassland to forestland, while the decrease was mainly associated with the conversion of forestland to grassland and cropland, and the transformation of grassland to cropland and construction land. In the potential LULC scenarios of 2030, certain regions within the basin exhibited unstable carbon sink potential, strongly influenced by LULC changes. These areas were predominantly characterized by artificially cultivated forests, shrubs, and agricultural land. Implementing appropriate forest management measures and optimizing agricultural land management practices are essential to enhance carbon sink potential in these regions. Population density, annual average temperature, and DEM (Digital Elevation Model) were the dominant factors driving the spatial variation of carbon sink potential in the Luan River Basin.Discussion: The research results provide a theoretical basis for rational planning of land use and the enhancement of carbon sink potential in ecologically fragile regions.

Alterations in Hydrological Responses under Changing Climate and Land Use/Land Cover across Contrasting Agroecological Environments: A Case Study on the Chemoga Watershed in the Upper Blue Nile Basin, Ethiopia

We analyzed hydrological responses to changing climate and land use/land cover (LULC) for the past (1985–2020) and future (2021–2080) in the Chemoga watershed of the Upper Blue Nile Basin. The watershed comprises four agroecological environments: Moist Kolla, Moist Weyna Dega, Moist Dega, and Wet Wurch. Past and projected LULC changes under business-as-usual (BAU) and land conservation (LC) scenarios were utilized. Climate projections from 2021 to 2080, under two Shared Socioeconomic Pathways (SSP2-4.5 and SSP5-8.5), were downscaled from Global Climate Models. Utilizing the Soil and Water Analysis Tool, we assessed impacts on mean annual surface runoff (SR) and evapotranspiration (ET). Maximum and minimum temperatures increased significantly in the past and future climate scenarios, with a significant rainfall increase observed under SSP5-8.5. Historical trends revealed a 16.6% increase in SR and 7% in ET from 1983–2002 to 2003–2020. Under BAU LULC with the SSP2-4.5 (SSP5-8.5) climate scenario, SR increased by 24% (26.1%) and ET by 3.1% (4.4%) from 2003–2020 to 2021–2050, followed by a subsequent SR rise of 13.7% (14.0%) and ET increase of 6.0% (5.7%) from 2021–2050 to 2051–2080. Conversely, the LC LULC with SSP2-4.5 (SSP5-8.5) resulted in a 5.3% (4.2%) SR decrease and ET increase of 9.7% (11.3%) from 2003–2020 to 2021–2050 and a further SR decrease of 1% (0.7%) and 6.1% (6.9%) ET increase from 2021–2050 to 2051–2080. The Moist Kolla agroecology experienced the highest SR increase due to vegetation clearances for commercial farming. Meanwhile, the LC scenario indicated substantial decreases in SR and marginal increases in ET in the Moist Weyna Dega agroecology due to forest restoration on steep slopes. Overall, SR showed greater sensitivity to LULC changes, while ET was more responsive to climate changes. The results emphasize considering diverse agroecological contexts for effective water resource management under changing climate and LULC scenarios.

Modeling the effects of land use/land cover changes on river runoff using SWAT models: A case study of the Danjiang River source area, China

Land use/land cover (LULC) is a crucial factor that directly influences the hydrology and water resources of a watershed. In order to assess the impacts of LULC changes on river runoff in the Danjiang River source area, we analyzed the characteristics of LULC data for three time periods (2000, 2010, and 2020). The LULC changes during these periods were quantified, and three Soil and Water Assessment Tool (SWAT) models were established and combined with eight LULC scenarios to quantitatively analyze the effects of LULC changes on river runoff. The results revealed a decrease in the cropland area and an increase in the forest, grassland, and urban land areas from 2000 to 2020. Grassland, forest, and cropland collectively accounted for over 94% of the total area, and conversions among these land types were frequent. The SWAT models constructed based on the LULC data demonstrated good calibration and validation results. Based on the LULC data in three periods, the area of each LULC type changed slightly, so the simulation results were not significantly different. In the subsequent LULC scenarios, we found that the expansion of cropland, grassland, and urban areas was associated with increased river runoff, while an increase in forest area led to a decrease in river runoff. Among the various LULC types, urban land exerted the greatest influence on changes in river runoff. This study establishes three SWAT models and combines multiple LULC scenarios, which is novel and innovative. It can provide scientific basis for the rational allocation of water resources and the optimization of LULC structure in the Danjiang River source area.

Modelling and forecasting of urban flood under changing climate and land use land cover

Abstract Chennai is a rapidly urbanizing Indian mega-city and experiences flooding frequently. Literature state that climate change and land use change have a significant impact on the runoff generated every year, making it essential to study the historical trend and forecast changes in land use land cover (LULC) and climate to model runoff. This study considered Adyar watershed for LULC change detection, climate change analysis, and flood forecasting for 2030 and 2050 based on LULC and runoff from 2005 and 2015. A coupled hydrologic–hydraulic model (HEC-HMS and HEC-RAS) was developed to assess flooding for future LULC and climate scenarios. LULC analysis shows an increase in built-up cover by 6%. Climate analysis shows 74% probability of increase in precipitation intensity between 2015 and 2050 compared to 2015. It was observed that depth of flooding increased by 19.4% in 2030 and 60.4% in 2050 compared to 2015. This study makes a structural proposition for flood mitigation through flood carrier canals on the downstream reach of the river which flows through Chennai. The canals were found to significantly reduce overbanking, providing protection against flooding. This is the best measure for providing the highest flood reduction for the study area.

Can land use/land cover optimization improve the environmental flow guarantee in regulated basins?

Land use/land cover (LULC) change is an important driver of changes in hydrologic processes in watersheds. Many studies have focused on the disruptive effects of human activity-induced urban sprawl and increased impervious surfaces on river runoff and environmental flow (e-flow), and little has been reported on whether LULC changes can improve e-flow security. Therefore, we proposed a hypothesis on whether LULC optimization can improve the e-flow guarantee using the Shayinghe River watershed as an example. To test the hypothesis, we evaluated the runoff response and e-flow security status of seven LULC scenarios (four historical scenarios and three possible optimization scenarios) by constructing the Soil and Water Assessment Tool (SWAT) model to establish the statistical relationship between a single LULC type and the e-flow guarantee rate (EGR) and e-flow deviation rate (EDR) and screened the best land optimization scenario for the Shayinghe River watershed. The results showed that: (1) forest land and grassland have positive effects on EGR, and LULC optimization can counteract the risk of dry season e-flow security caused by urban expansion and increased construction land to a certain extent; (2) construction land was the most active LULC type and inhibited the dry season flow and EGR of rivers, leading to moderate changes in the hydrological process of Shayinghe River; (3) the Grain for Green Project was the best LULC optimization scenario for the Shayinghe River basin, which converts 1342 km2 of cultivated land in the western part of the basin to forest land, resulting in the highest dry season EGR increases of 3.23 % and 2.26 % in Zhoukou and Fuyang, respectively. Our study proposes a relatively simple and easily replicable approach in other watersheds, providing a new reference for rational LULC planning and environmental flow protection in highly regulated watersheds.

Enhancing basin sustainability: Integrated RUSLE and SLCC in land use decision-making

Sustainable agriculture and eco-environment conservation face constant threats from soil erosion in mountainous regions. This study utilizes remote sensing and GIS support to predict soil erosion and sediment yield by applying the Revise Universal Equation Soil Loss (RUSLE) and Sediment Delivery Ratio (SDR) models. The findings highlight that the basin adversely experiences an average annual soil erosion rate of 108.47 t ha−1 yr−1, leading to a significant sediment influx of 206.03 × 106 t/yr at downstream. The RUSLE-SDR model performs satisfactorily, with percent bias (PBIAS) values below 25%. Based on the RUSLE output, the study integrates a threshold of over 100 t ha−1 yr−1 into the adapted slopeland capability classification (SLCC), along with slope gradient derived from Digital Elevation Model (DEM) and soil depth, to categorize the entire basin into five capability classes. Each class is associated with specific soil erosion control treatments for agricultural activities and forestry. The study suggests the four land use/land cover (LULC) scenarios with different prioritizations, aimed to optimize land resource utilization and conserve the eco-environment. Scenarios #3 and #4, in particular, demonstrate promising benefits for the eco-environment by substantially increasing forest coverage to 81.27% and 85.07% of the total area, respectively. In contrast, scenarios #1 and #2 prioritize agricultural development. Due to the challenge posed by rugged terrains, the application of LULC scenarios must be adhered strictly, considering a specific class of the SLCC with its treatments. The study offers a timely and feasible approach for soil erosion investigation and land use adjustment to support effective basin management. However, ensuring the health and sustainability of the basin ecosystem may necessitate additional measures, such as proper planning for riparian zones and engineering solutions.

A multi-layer perceptron–Markov chain based LULC change analysis and prediction using remote sensing data in Prayagraj district, India

Land use land cover (LULC) mapping and temporal observations are indispensable drivers for sustainable development. This research showed the growth trends and land use transition for the Prayagraj district in the last three decades. Supervised classification of Landsat images was performed on 5-year temporal intervals using a maximum likelihood classifier. All satellite images were organized into six major LULC feature classes viz agriculture/open land, barren land, built-up, forest, sand, and water. The overall accuracy of LULC classification was achieved by more than 89% in all seven temporal points. Furthermore, the accuracy of the classified maps was estimated through area-based error matrix. The Land Change Modeler tool of TerrSet 2020 software was used to analyze the transition of classes and to incorporate the multi-layer perceptron-Markov chain (MLP-MC) technique. The transition potentials were included in MLP-MC with the help of sensitive explanatory variables and significant transitions of classes. Furthermore, these transition potentials and the Markov chain transition matrix were used to predict the future LULC dynamics and vulnerability. The change analysis revealed that a significant portion of the agriculture/open land gradually decreased and got converted to built-up land. The results depicted that agriculture/open land was reduced by 8.03% in the last three decades while the built-up region was grown by 199.61%. Forest area was continuously decreasing while the sand area increased due to river meandering. Overall, more than 75% of accuracy was achieved in MLP. The prediction model was first validated with observed data, and then the LULC scenario of 2035 and 2050 was simulated. LULC of 2050 showed that the built-up area would likely reach 13.90% of district area whereas the forest area would remain only 0.79%. The prediction model has given the output in the form of future LULC map along with projected potential transition maps. This would be useful for sustainable urban planning to deal with the alarming rate of built-up growthand agriculture/open landshrinkage.

Quantifying the Impacts of High‐Resolution Urban Information on the Urban Thermal Environment

AbstractDetailed urban information, including land use/land cover (LULC), anthropogenic heat (AH) release, and urban canopy parameters (UCP), play critical roles in meteorological field simulations. It is particularly relevant for the Weather Research and Forecasting (WRF) model coupled with the single‐layer urban canopy model (SLUCM). Thus, we develop high‐resolution LULC, AH, and UCP data sets for Nanjing, a megacity in China, and conduct a series of numerical experiments with WRF/SLUCM to evaluate the impacts of urban parameters on the urban thermal environment. Model simulations with LULC scenarios have good agreement with the observed 2‐m temperature with a correlation coefficient of around 0.85, and present strong spatial homogeneity due to the more realistic representation of urban categories. The LULC change directly decreases the surface wind speed and increases (decreases) the sensible (latent) heat flux ( ()) in urban areas during the daytime; meanwhile increases and releases ground heat storage () during the nighttime, resulting in urban warming by 0.91°C in urban areas, compared with the control simulation (CTL) that does not take into account urban surfaces. In the LULC experiments combined with the UCP or AH, the UCP change enhances and releases more during nighttime, which increases by 0.13°C relative to LULC simulation. Also, the UCP effect reduces surface roughness and increases the width of the urban canopy, resulting in slightly enhanced wind speed, which is favorable for a warming environment in the urban area; the AH change contributes to increasing by 0.19°C through directly enhancing relative to LULC simulation. AH combined with the UCP further strengthens the UCP effect in the urban area. Overall, the influence of urban parameters on the is more pronounced during nighttime than daytime, which presents a decreasing trend with an increase in wind speed and spatial humidity in the urban area.

Analysis of the impacts of land use land cover change on streamflow and surface water availability in Awash Basin, Ethiopia

The current study analyzed the impact of Land Use Land Cover (LULC) change on streamflow and surface water availability in the Akaki catchment of the Awash Basin, Ethiopia. Soil and Water Assessment Tool (SWAT) and Climatic Water Balance (CWB) models were used. The SWAT model calibrated and validated the daily streamflow. The results indicated that from 1993 to 2016, built-up and barren land areas increased by 5.3% and 3.4%, respectively. The SWAT model over-simulated the peak flows and best simulated the medium and low flows. Based on the calibrated daily streamflow, the runoff was 236.01 mm and 272.59 mm under the 1993 and 2016 LULC scenarios. The total water yield of the catchment was 366.7 mm and 382.01 mm for the 1993 and 2016 LULC scenarios, respectively. The seasonal CWB of the catchment depicted decreasing trend except for the Tsedey (SON) season. Based on the current study’s findings, since LULC change enhanced surface runoff and total water yield in the catchment, proactive flood management is recommended to reduce the impact of the flood hazard on life and property.

Modelling Impact of Urban Expansion on Ecosystem Services: A Scenario-Based Approach in a Mixed Natural/Urbanised Landscape

The present study aims at predicting future land use/land cover (LULC) and quantifying and mapping the ecosystem services (ESs) of water yield, outdoor recreation opportunity and food production in current (here, 2017) and future landscapes in Northern Iran, using the InVEST, Recreation Opportunity Spectrum (ROS) and yield models. To that end, two LULC scenarios known as business as usual (BAU) and protection-based (PB) plan were applied for 2028, using the Markov Artificial Neural Network and Multi-objective land allocation (MOLA) models. The results show that rapid urbanisation, caused by the expansion of human settlements and industrial areas, has led to a decline in the ESs in the region. Compared to the ESs in 2017, the service of water yield increases as urban expansion increases, whereas food production and recreation services decrease as urban expansion increases, under the BAU scenario. On the other hand, in the PB scenario, relatively better conditions can be observed for all three ESs. Considering that the ecological structures of this region have been severely affected by rapid urban expansion, the results of this research will be useful for maintaining the existing ESs and can greatly affect planning and decision-making regarding future development towards urban sustainability.

Adaptation measures under the impacts of climate and land-use/land-cover changes using HSPF model simulation: Application to Gongola river basin, Nigeria

Recently, there is an upsurge in flood emergencies in Nigeria, in which their frequencies and impacts are expected to exacerbate in the future due to land-use/land cover (LULC) and climate change stressors. The separate and combined forces of these stressors on the Gongola river basin is feebly understood and the probable future impacts are not clear. Accordingly, this study uses a process-based watershed modelling approach – the Hydrological Simulation Program FORTRAN (HSPF) (i) to understand the basin's current and future hydrological fluxes and (ii) to quantify the effectiveness of five management options as adaptation measures for the impacts of the stressors. The ensemble means of the three models derived from the Coupled Model Intercomparison Project Phase 5 (CMIP5) are employed for generating future climate scenarios, considering three distinct radiative forcing peculiar to the study area. Also, the historical and future LULC (developed from the hybrid of Cellular Automata and Markov Chain model) are used to produce the LULC scenarios for the basin. The effective calibration, uncertainty and sensitivity analyses are used for optimising the parameters of the model and the validated result implies a plausible model with efficiency of up to 75 %. Consequently, the results of individual impacts of the stressors yield amplification of the peak flows, with more profound impacts from climate stressor than the LULC. Therefore, the climate impact may trigger a marked peak discharge that is 48 % higher as compared to the historical peak flows which are equivalent to 10,000-year flood event. Whilst the combine impacts may further amplify this value by 27 % depending on the scenario. The proposed management interventions such as planned reforestation and reservoir at Dindima should attenuate the disastrous peak discharges by almost 36 %. Furthermore, the land management option should promote the carbon-sequestering project of the Paris agreement ratified by Nigeria. While the reservoir would serve secondary functions of energy production; employment opportunities, aside other social aspects. These measures are therefore expected to mitigate feasibly the negative impacts anticipated from the stressors and the approach can be employed in other river basins in Africa confronted with similar challenges.

Collaborative scenario building: Engaging stakeholders to unravel opportunities for urban adaptation planning

Uncertainties arising from the climate crisis and socioeconomic challenges require decision makers to advance climate change adaptation planning and practice. In response, many cities have adopted climate change adaptation strategies, which is also the case in the Czech Republic. Although adaptation planning should be prospective, requiring long-term, future-oriented approaches, the use of future scenarios have not been fully acknowledged yet. In this methodological paper, we introduce a combined approach for delivering spatially explicit future land use/land cover (LULC) scenarios developed as supporting tools for adaptation planning in three major Czech cities—Prague, Brno, and Ostrava. The methodological approach consists of (1) Shared Socioeconomic Pathways (SSPs) downscaled to city level, to capture underpinning social, environmental, and economic dynamics; (2) collaborative processes with stakeholders; and (3) computer modelling of LULC scenarios. While collaborative process collects the perceptions of local needs, challenges, and opportunities, spatially explicit modelling of LULC changes allows for an easy-to-read visualization of the plausible futures. Nesting the scenarios in SSPs makes them consistent with global socioeconomic drivers. The introduced methodological approach can support adaptation planning by engaging stakeholders directly in the scenario building process, to elaborate on plausible future urban developments while considering an array of different stakes and challenges.

Future Land Use/Land Cover Change Has Nontrivial and Potentially Dominant Impact on Global Gross Primary Productivity

AbstractAnthropogenic land use/land cover (LULC) change alters terrestrial gross primary productivity (GPP), the largest land‐atmosphere carbon exchanges. Identifying the impacts of LULC changes on future terrestrial GPP has been challenging due to the scarcity of standardized future LULC for ecosystem models. Here, we present eight scenario‐based projections of global spatially explicit LULC at 1‐km resolution over the period 2015–2100 with a Future Land Use Simulation model—consistent with the Shared Socioeconomic Pathways and Representative Concentration Pathways. Twenty computational experiments with different LULC patterns, climate forcing, and CO2 concentrations were conducted to quantify their contributions to future GPP dynamics. Results show that the global terrestrial GPP would decline in the 21st century in most LULC scenarios due to urbanization, agricultural expansion, and deforestation. Moreover, the contribution of LULC changes to global GPP dynamics ranges from 3.43% to 10.78% when CO2 fertilization effect (CFE) is not modeled during 2000–2100 (7%–9% of the terrestrial area is dominated by LULC change). However, this value may range from 10.92% to 16.16% during 2000–2050 and 1.41%–14.57% during 2050–2100. The contribution of LULC even reached 56.08% during 2050–2100 in Southeast Asia due to deforestation. Despite the relatively important role of LULC to GPP dynamics, it becomes trivial globally when incorporating CFE into the model (i.e., LULC accounts for 1.24%–2.51% during 2000–2100). Our findings emphasize the strategic role of CFE in enhancing global GPP and highlight the quantitatively nontrivial role of LULC at the regional scale.

Impacts and Implications of Land Use Land Cover Dynamics on Groundwater Recharge and Surface Runoff in East African Watershed

Assessing the spatiotemporal dynamics of land use land cover (LULC) change on water resources is vital for watershed sustainability and developing proper management strategies. Evaluating LULC scenarios synergistically with hydrologic modeling affords substantial evidence of factors that govern hydrologic processes. Hence, this study assessed the spatiotemporal effects and implications of LULC dynamics on groundwater recharge and surface runoff in Gilgel Gibe, an East African watershed, using the Soil and Water Assessment Tool (SWAT) model. Three different LULC maps (2000, 2010, and 2020) were derived from Landsat images, and the comparisons pointed out that the land-use pattern had changed significantly. The agricultural land and grassland cover increased by 3.76% and 1.36%, respectively, from 2000 to 2020. The implications acquired for 2000 show that forested land covers decreased by 5.49% in 2020. The SWAT simulation process was executed using a digital elevation model, soil, LULC, and weather data. The model was calibrated and validated using streamflow data to understand the surface runoff and groundwater recharge responses of each Hydrologic Response Units on reference simulation periods using the Calibration and Uncertainty Program (SWAT-CUP), Sequential Uncertainty Fitting (SUFI-2) algorithm. The observed and simulated streamflows were checked for performance indices of coefficient of determination (R2), Nash–Sutcliffe model efficiency (NSE), and percent bias (PBIAS) on monthly time steps. The results show that there is good agreement for all LULC simulations, both calibration and validation periods (R2 & NSE ≥ 0.84, −15 < PBIAS < +15). This reveals that for the LULC assessment of any hydrological modeling, the simulation of each reference period should be calibrated to have reasonable outputs. The study indicated that surface runoff has increased while groundwater decreased over the last two decades. The temporal variation revealed that the highest recharge and runoff occurred during the wet seasons. Thus, the study can support maximizing water management strategies and reducing adverse driving environmental forces.

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.

Impact of Land Use/Land Cover Change on Landslide Susceptibility in Rangamati Municipality of Rangamati District, Bangladesh

Landslide susceptibility depends on various causal factors such as geology, land use/land cover (LULC), slope, and elevation. Unlike other factors that are relatively stable over time, LULC is a dynamic factor associated with human activities. This study evaluates the impact of LULC change on landslide susceptibility in the Rangamati municipality of Rangamati district, Bangladesh, based on three LULC scenarios—the existing (2018) LULC, the proposed LULC (proposed in 2010, but not yet implemented), and the simulated LULC of 2028—using artificial neural network (ANN)-based cellular automata. The random forest model was used for landslide susceptibility mapping. The model showed good accuracy for all three LULC scenarios (existing: 82.7%; proposed: 81.4%; and 2028: 78.3%) and strong positive correlations (>0.8) between different landslide susceptibility maps. LULC is either the third or fourth most important factor in these scenarios, suggesting that is has a moderate impact on landslide susceptibility. Future LULC changes will likely increase landslide susceptibility, with up to 14.5% increases in the high susceptibility zone for both the proposed and simulated LULC scenarios. These findings may help policymakers carry out proper urban planning and highlight the importance of considering landslide susceptibility in LULC planning.

A Framework for Calculating Peak Discharge and Flood Inundation in Ungauged Urban Watersheds Using Remotely Sensed Precipitation Data: A Case Study in Freetown, Sierra Leone

As the human population increases, land cover is converted from vegetation to urban development, causing increased runoff from precipitation events. Additional runoff leads to more frequent and more intense floods. In urban areas, these flood events are often catastrophic due to infrastructure built along the riverbank and within the floodplains. Sufficient data allow for flood modeling used to implement proper warning signals and evacuation plans, however, in least developed countries (LDC), the lack of field data for precipitation and river flows makes hydrologic and hydraulic modeling difficult. Within the most recent data revolution, the availability of remotely sensed data for land use/land cover (LULC), flood mapping, and precipitation estimates has increased, however, flood mapping in urban areas of LDC is still limited due to low resolution of remotely sensed data (LULC, soil properties, and terrain), cloud cover, and the lack of field data for model calibration. This study utilizes remotely sensed precipitation, LULC, soil properties, and digital elevation model data to estimate peak discharge and map simulated flood extents of urban rivers in ungauged watersheds for current and future LULC scenarios. A normalized difference vegetation index (NDVI) analysis was proposed to predict a future LULC. Additionally, return period precipitation events were calculated using the theoretical extreme value distribution approach with two remotely sensed precipitation datasets. Three calculation methods for peak discharge (curve number and lag method, curve number and graphical TR-55 method, and the rational equation) were performed and compared to a separate Soil and Water Assessment Tool (SWAT) analysis to determine the method that best represents urban rivers. HEC-RAS was then used to map the simulated flood extents from the peak discharges and ArcGIS helped to determine infrastructure and population affected by the floods. Finally, the simulated flood extents from HEC-RAS were compared to historic flood event points, images of flood events, and global surface water maximum water extent data. This analysis indicates that where field data are absent, remotely sensed monthly precipitation data from Integrated Multi-satellitE Retrievals for GPM (IMERG) where GPM is the Global Precipitation Mission can be used with the curve number and lag method to approximate peak discharges and input into HEC-RAS to represent the simulated flood extents experienced. This work contains a case study for seven urban rivers in Freetown, Sierra Leone.

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.

Modeling the relationship between land use/land cover and land surface temperature in Dhaka, Bangladesh using CA-ANN algorithm

Rises in land surface temperature (LST) significantly impacted by land use/land cover (LULC) changes. The increase in LST massively impacted the urban biodiversity, ecosystem and population health. This study aims to estimate the changes in LULC classes and identify their impacts on LST in Dhaka city, Bangladesh using Landsat satellite images from 2000 to 2020. Based on the past estimated change maps of LULC and LST, the study finally predicted the future LULC and LST scenario for the year 2030. The support vector machine algorithm was applied to perform the LULC classification. Artificial neural network and cellular automata algorithms were used to predict the LST and LULC changes for 2030. Results suggested a significant reduction in vegetation cover (5%) and an increase in built-up area (14%) from 2000 to 2020. Due to this massive increase in built-up areas, the LST increment took place by 7.24 °C in the last two decades. The maximum temperature was recorded in built-up areas (34 °C), and water bodies (19 °C) exhibited minimum temperature. A strong positive correlation was found between LST and Normalized Difference Built-up Index (NDBI), where negative relation estimated between LST and Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI). The predicted results for 2030 also exhibit significant loss of urban green cover areas by 13% and rises in built-up areas by 21%. The maximum LST will likely be increased by 9.29 °C in the predicted year. For ensuring sustainable urban development and minimizing the urban heat island effects, this study will play a significant role by providing effective guidelines for urban planners, policymakers and respective authorities of Dhaka city.