Urban Expansion Assessment in Huaihe River Basin, China, from 1998 to 2013 Using Remote Sensing Data

Urbanization reflects the overall behavior of human society; thus, characterization of its associated spatial and temporal trends has been extensively researched. This study examines the process of urban expansion in the Huaihe River Basin (HRB) which is a key transition region within China’s urban system. In order to grasp the urban expansion process in different temporal sequences objectively, rapidly, and accurately, we used remote sensing data to assess the urban expansion in time and space. Urban expansion rules were defined for the urban area, urbanization intensification, extended dynamic degree, and spatial pattern. The research findings show that the urban area expansion speed was at medium level throughout the entire HRB and within each province. Presently, the formation of a whole urban agglomeration or urban system is not complete in the HRB; urban expansion in the HRB displayed space-time disequilibrium tendencies during 1998–2013

Recent trends in vegetation greenness in China significantly altered annual evapotranspiration and water yield

There has been growing evidence that vegetation greenness has been increasing in many parts of the northern middle and high latitudes including China during the last three to four decades. However, the effects of increasing vegetation greenness particularly afforestation on the hydrological cycle have been controversial. We used a process-based ecosystem model and a satellite-derived leaf area index (LAI) dataset to examine how the changes in vegetation greenness affected annual evapotranspiration (ET) and water yield for China over the period from 2000 to 2014. Significant trends in vegetation greenness were observed in 26.1% of China\u27s land area. We used two model simulations driven with original and detrended LAI, respectively, to assess the effects of vegetation \u27greening\u27 and \u27browning\u27 on terrestrial ET and water yield. On a per-pixel basis, vegetation greening increased annual ET and decreased water yield, while vegetation browning reduced ET and increased water yield. At the large river basin and national scales, the greening trends also had positive effects on annual ET and had negative effects on water yield. Our results showed that the effects of the changes in vegetation greenness on the hydrological cycle varied with spatial scale. Afforestation efforts perhaps should focus on southern China with larger water supply given the water crisis in northern China and the negative effects of vegetation greening on water yield. Future studies on the effects of the greenness changes on the hydrological cycle are needed to account for the feedbacks to the climate

GIS Analysis of Flood Vulnerable Areas In Benin- Owena River Basin, Nigeria

The frequency and intensity of flood disasters have become serious issues in the national development process of Nigeria as flood disasters have caused serious environmental damages, loss of human lives and other heavy economic losses;  putting the issue of disaster reduction and risk management higher on the policy agenda of affected governments, multilateral agencies and NGOs. The starting point of concrete flood disaster mitigation efforts is to identify the areas with higher risk levels and fashion out appropriate preventive and response mechanisms. This research paper explored the potentials of Geographic Information System (GIS) in data capture, processing and analysis in identifying flood-prone areas for the purpose of planning for disaster mitigation and preparedness, using Benin-Owena river basin of Nigeria as a unit of analysis. The data used in this study were obtained from FORMECU and were entered and use to develop a flood risk information system. Analysis and capability of the developed system was illustrated and shown graphically. The research showed that over one thousand settlements harbouring over ten million people located in the study area are at grave risk of flooding. Key words: Flood, Risk, Vulnerability, Geographical Information System (GIS), River -Basi

Evaluation and Prediction of Land-Use Changes using the CA_Markov Model

AbstractThe purpose of this study was to model and predict temporal and spatial patterns of land-use change in the Zayandehrud basin. In this research, the CA-Markov prediction model was used to simulate and predict land-use change. First, the land-use changes from 1996 to 2018 were studied and then the future changes for 2030 and 2050 were simulated. Afterward, the future land-use scenarios were designed. The model was validated by comparing the simulated map of 2018 with the real map, and the kappa coefficient of 94 % was utilized to evaluate the model. Based on the results, the Built-up land-use was altered from 13016 hectares in 1996 to 154194 hectares in 2050. This outcome necessitates the management of the future development of the city. Furthermore, the amount of agricultural land was varied from 177067 hectares in 1996 to 40,000 hectares in 2050. Among all the changes, agricultural lands attracted the most attention and concerns. The results indicated the land-use changes in the form of urban areas and reducing area of agricultural lands. Such alterations were taken place in two distinct stages: urban lands have been developing since 2013, with a direct impact on the reduction of vegetation due to the conversion of agricultural lands into other land-uses. The dynamic trend of changes has also been confirmed and intensified since 1996. In 2018, a significant area of agricultural lands was converted into urban and industrial areas. In addition, the agricultural and orchard lands were 74057 hectares in 2018 and can be reduced to 40,000 hectares by 2050. It revealed 34057 hectares lost as compared to the agricultural and orchard lands in 2018. The present study depicts that the expansion of urban and industrial activities and reducing the level of agricultural land in the region requires more attention and care in land management. Extended Abstract:Introduction: Land use/Land cover (LULC) change is one of the main issues of sustainable development. To provide a rational science for regional planning decisions and sustainable development, land use pattern prediction models based on past preliminary information can be used to construct future scenarios of land-use changes. Modeling and predicting land use changes provide an interesting perspective for applications in planning units such as river basins and make it an effective tool for analyzing the causal dynamics of the future landscape under different scenarios. Land-use models are considered a powerful tool for understanding the spatio-temporal pattern of land-use changes, such as the Markov chain, cellular automation, and hybrid models based on these methods, which are widely used to simulate the spatial and temporal dimensions of land use. In the present study, land use changes prediction was performed using a combined model of cellular automation and the Markov chain (CA-Markov) to simulate temporal and spatial land-use patterns. The present study tries to predict land-use changes in the Zayandehrood river basin. The Zayandehrud basin is currently facing major environmental problems (such as water resources scarcity, population growth, urban development, and agricultural land degradation). Therefore, it is essential to evaluate land-use changes for this sensitive basin. In particular, the objectives of the research include two stages: 1) patial modeling of land-use change, and 2) predicting spatio-temporal patterns of land-use changes in the Zayandehrud river basin.Therefore, in the present research, land-use changes from 1996 to 2018 were investigated and future changes for 2030 and 2050 were simulated. Methodology: In this research, land-use changes modeling was performed in three time periods 1996 to 2013 (17-year period), 2013 to 2018 (5-year period), and 2018 to 2030 and 2050. The purpose of modeling is to determine the capabilities of the Markov chain model and integrate it with cellular automation to detect land-use changes. The images were classified into 4 classes: agriculture and gardens, built-up (urban areas, airport, and road), industrial towns, and other land uses (abandoned lands and fallow, rangeland, water areas). Finally, land-use changes modeling was performed in the period 1996 to 2050 (54-year period). The steps of the research method are as follows:Step 1: Pre-processing of satellite images: Radiometric correction was applied to the images. Next, the images were processed using the FLAASH module in ENVI5.3 software to reduce atmospheric interference. Then, by synthesizing the name and wavelength of the bands, image storage, mosaic, and mask clipping, a preprocessed remote sensing image was generated. Finally, the preprocessed remote sensing image was obtained.Step 2: Processing satellite images: Types of land use images in the area in ENVI 5.3 were extracted using visual interpretation and supervised classification methods. Land use classification algorithms were used to estimate the three main land-use classes (agriculture, urban, and industrial development). The principal component analysis method was performed on the images and was identified agricultural by high resolution. Land use classification for 1996, 2013, and 2018 was done with a classification approach based on the decision tree. To classify the images, maximum likelihood methods, artificial neural networks, and support vector machines were used. The final classification was performed using decision tree analysis. Finally, prediction of land-use changes was performed on images by performing the CA_Markov analysis in TerrSet software.Step 3: Post-processing of satellite images: Using Google Earth and cross-tab analysis, TerrSet software evaluated the accuracy of classifying land-use thematic maps. Using the existing database, a validation process was performed to ensure the accuracy of the model in predicting land-use changes for the forecasted 2018 map. The accuracy of the simulated model of land-use change in 2018 was validated and then compared with the actual map of the same year. The validation process was performed by generating the kappa coefficient. Discussion: In this study, land-use changes in the Zayandehrood basin were identified and investigated. The results showed that land-use changes are in the form of urban development and reduction of agricultural land use. Such changes have occurred in two distinct stages. First, urban land expansion has prevailed since 2013, with a direct impact on declining vegetation as a result of the conversion of agricultural land to other land uses. The dynamic trend of changes has also been confirmed and intensified since 1996. Because in 2018, a significant area of agricultural lands was converted into urban and industrial areas. Future scenarios based on the CA-Markov model provide valuable information about future land use and land cover changes in the study area. This study can identify land-use changes in different periods and depict the increase or decrease of important land uses in the region. According to the study of Motlagh et al. (2020), land-use changes were studied based on three possible scenarios (i.e. the current trend of land use growth, conservation of agricultural lands, and urban development forecast). Future scenarios for 2030 and 2050 estimate that there will be a significant reduction in vegetation and agricultural lands and orchards and continued urban and industrial development in areas along the Zayandehrood basin. Expansion of the agricultural sector along with the conservation of natural resources is not only one of the most important challenges of sustainable development in the Zayandehrud basin but is also essential for future strategic land use plans. Compilation of instructions for sustainable agricultural development can be a way to strike a balance between nature conservation and economic development in the region. Conclusion: In summary, this study demonstrates how the proposed CA-Markov model is used to better simulate land use complex and dynamic changes over time. Of all the land-use changes, the most worrying is the situation in the region for agricultural lands. If the current trend of land use continues, we estimate that by 2050, its area will be halved, and such changes in the landscape will undoubtedly change the entire ecosystem of the basin, emphasizing that the negative effects on the vegetation of the basin have a direct impact on the economic sector of the region because maintaining the quality of the environment of the Zayandehrood river basin is essential for ecotourism. Therefore, the management and planning of the basin are highly recommended to preserve its unique ecosystem, as well as to protect the vegetation in the area. The methods and results of this study will be useful for policymakers and urban planners for precise planning of the region to be able to manage the city using farms and conserving water resources and urban infrastructure development planning for environmentally sustainable development. Keywords: Land-Use Changes, Cellular Automation, the Markov Chain, Zayandehrud River Basin. References- Asgarian, A., Soffianian, A., Pourmanafi, S., & Bagheri, M. (2018). Evaluating the spatial effectiveness of alternative urban growth scenarios inprotecting cropland resources: A case of mixed agricultural-urbanized landscape in central Iran. Journal of Sustainable Cities and Society, 43, 197-207.- Assaf, C., Adamsa, C., Ferreira, F. F., & Françac, H. (2021). Land use and cover modeling as a tool for analyzing nature conservation policies – A case study of Juréia-Itatins. Journal of Land Use Policy, 100, 104895.- Aung, T. S., Fischer, T. B., & Buchanan, J. (2020). Land use and land cover changes along the China-Myanmar oil and gas pipelines-Monitoring infrastructure development in remote conflict-prone regions. PloS one, 15(8), e0237806.- Baqa, M. F., Chen, F., Lu, L., Qureshi, S., Tariq, A., Wang, S., … & Li, Q. (2021). Monitoring and modeling the patterns and trends of urban growth using urban sprawl matrix and CA-Markov model: A case study of Karachi, Pakistan. Land, 10(7), 700.- Cunha, E. R. D., Santos, C. A. G., da Silva, R. M., Bacani, V. M., & Pott, A. (2021). Future scenarios based on a CA-Markov land use and land cover simulation model for a tropical humid basin in the Cerrado/Atlantic forest ecotone of Brazil. Journal of Land Use Policy, 101, 105141.- Dey, N. N., Al Rakib, A., Kafy, A. A., & Raikwar, V. (2021). Geospatial modelling of changes in land use/land cover dynamics using Multi-layer perception Markov chain model in Rajshahi City, Bangladesh. Journal of Environmental Challenges, 4, 100148.- Gharaibeh, A., Shaamala, A., Obeidat, R., & Al-Kofahi, S. (2020). Improving land-use change modeling by integrating ANN with Cellular Automata-Markov Chain model. Heliyon, 6(9), e05092.- Ghosh, S., Chatterjee, N. D., & Dinda, S. (2021). Urban ecological security assessment and forecasting using integrated DEMATEL-ANP and CA-Markov models: A case study on Kolkata Metropolitan Area, India. Journal of Sustainable Cities and Society, 68, 102773.- Huang, Y., Yang, B., Wang, M., Liu, B., & Yang, X. (2020). Analysis of the future land cover change in Beijing using CA–Markov chain model. Journal of Environmental Earth Sciences, 79(2), 1-12.- Ji, G., Lai, Z., Xia, H., Liu, H., & Wang, Z. (2021). Future runoff variation and flood disaster prediction of the yellow river basin based on CA-Markov and SWAT. Land, 10(4), 421.- Khwarahm, N. R., Qader, S., Ararat, K., & Al-Quraishi, A. M. F. (2021). Predicting and mapping land cover/land use changes in Erbil /Iraq using CA-Markov synergy model. Journal of Earth Science Informatics, 14(1), 393–406.- Li, Q., Wang, L., Gul, H. N., & Li, D. (2021). Simulation and optimization of land use pattern to embed ecological suitability in an oasis region: A case study of Ganzhou district, Gansu province, China. Journal of Environmental Management, 287, 112321.- Matlhodi, B., Kenabatho, P. K., Parida, B. P., & Maphanyane, J. G. (2021). Analysis of the future land use land cover changes in the gaborone dam catchment using ca-markov model: Implications on water resources. Journal of Remote Sensing, 13(13), 2427.- Mitsova, D., Shuster, W., & Wang, X. (2011). A cellular automata model of land cover change to integrate urban growth with open space conservation. Journal of Landscape and Urban Planning, 99(2), 141–153.- Motlagh, Z. K., Lotfi, A., Pourmanafi, S., Ahmadizadeh, S., & Soffianian, A. (2020). Spatial modeling of land-use change in a rapidly urbanizing landscape in central Iran: Integration of remote sensing, CA Markov, and landscape metrics. Journal of Environmental Monitoring and Assessment, 192(11), 1-19.- Munthali, M. G., Mustak, S., Adeola, A., Botai, J., Singh, S. K., & Davis, N. (2020). Modelling land use and land cover dynamics of Dedza district of Malawi using hybrid Cellular Automata and Markov model. Remote Sensing Applications: Society and Environment, 17, 100276.- Nath, B., Wang, Z., Ge, Y., Islam, K., Singh, R. P., & Niu, Z. (2020). Land use and land cover change modeling and future potential landscape risk assessment using Markov-CA model and analytical hierarchy process. International Journal of Geo-Information, 9(2), 134.- Rahnama, M. R. (2021). Forecasting land-use changes in Mashhad metropolitan area using cellular Automata and Markov chain model for 2016-2030. Journal of Sustainable Cities and Society, 64, 102548.- Ruben, G. B., Zhang, K., Dong, Z., & Xia, J. (2020). Analysis and projection of land-use/land-cover dynamics through scenario-based simulations using the CA-Markov model: A case study in guanting reservoir basin, China. Sustainability, 12(9), 3747.- Sibanda, S., & Ahmed, F. (2021). Modelling historic and future land use/land cover changes and their impact on wetland area in Shashe sub‑catchment, Zimbabwe. Journal of Modeling Earth Systems and Environment, 7(1), 57–70.- Silver, D., & Silva, T. H. (2021). A Markov model of urban evolution: Neighbourhood change as a complex process. Plos One, 16(1), e0245357.- Tang, F., Fu, M., Wang, L., Song, W., Yu, J., & Wu, Y. (2021). Dynamic evolution and scenario simulation of habitat quality under the impact of land-use change in the Huaihe river economic belt, China. Plos One, 16(4), e0249566.- Tariq, A., & Shu, H. (2020). CA-Markov chain analysis of seasonal land surface temperature and land use land cover change using optical multi-temporal satellite data of Faisalabad, Pakistan Aqil Tariq and Hong Shu. Remote Sensing, 12(20), 3402.- Tavangar, Sh., Moradi, H., Massah Bavani, A., & Gholamalifard, M. (2019). A futuristic survey of the effects of LU/LC change on stream flow by CA–Markov model: A case of the Nekarood watershed, Iran. Geocarto International, 36(10), 1100-1116.- Vinayak, B., Lee, H. S., & Gedem, S. (2021). Prediction of land use and land cover changes in Mumbai city, India, using remote sensing data and a multilayer perceptron neural network-based markov chain model. Sustainability, 13(2), 471.- Wang, Q., Guan, Q., Lin, J., Luo, H., Tan, Z., & Ma, Y. (2021). Simulating land use/land cover change in an arid region with the coupling models. Journal of Ecological Indicators, 122, 107231.- Wang, H., & Hu, Y. (2021). Simulation of biocapacity and spatial-temporal evolution analysis of Loess Plateau in northern shaanxi based on the CA–Markov model. Sustainability, 13(11), 5901.- Wang, S. W., Munkhnasan, L., & Lee, W. (2021). Land use and land cover change detection and prediction in Bhutan’s high-altitude city of Thimphu, using cellular automata and Markov chain. Journal of Environmental Challenges, 2, 100017.- Zhou, L., Dang, X., Sun, Q., & Wang, S. (2020). Multi-scenario simulation of urban land change in Shanghai by random forest and CA-Markov model. Journal of Sustainable Cities and Society, 55, 102045

Relationships between Landscape Patterns and Hydrological Processes in the Subtropical Monsoon Climate Zone of Southeastern China

With rapid economic development, extensive human activity has changed landscape patterns (LPs) dramatically, which has further influenced hydrological processes. However, the effects of LPs changes on hydrological processes, especially for the streamflow–sediment relationship in the subtropical monsoon climate zone, have not been reported. In our study, 10 watersheds with different sizes in the subtropical monsoon climate zone of southeastern China were chosen as the study area, and the effect of the 14 most commonly used landscape metrics (LMs) on 4 typical hydrological indices (water yields (WY), the runoff coefficient (RC), the soil erosion modulus (SEM), and the suspended sediment concentration (SSC)) were analyzed based on land use maps and hydrological data from 1990 to 2019. The results reveal that the LP characteristics within the study area have changed significantly. The number of patches and landscape shape indices were significantly positively correlated with watershed size (p &lt; 0.01). For most watersheds, the largest patch index was negatively correlated with WY, RC, and SEM, and the perimeter area fractal dimension was positively correlated with WY, RC, SEM, and SSC. The effects of several LMs on the hydrological indices had scale effects. WY/RC and the interspersion and juxtaposition index were negatively correlated in most larger watersheds but were positively correlated in most smaller watersheds. Similar results were found for Shannon’s diversity/evenness index and SEM. In general, an increase in a small patch of landscape and in landscape diversity would increase WY, the fragmentation of LPs would result in more soil erosion, and LPs would affect the relationship between streamflow and sediment yield. As a result, a proper decrease in landscape fragmentation and physical connectivity in the subtropical monsoon climate zone of southeastern China would benefit soil erosion prevention. These results enhance the knowledge about the relationship between LPs and hydrological processes in the subtropical monsoon climate zone of southeastern China and benefit local water and soil conservation efforts.</p

Hydrologic Response to Land Use Change and Climate Variability in an Ungauged Basin, North-Western Himalaya, India

Hydrological models are overwhelmingly used for gauged basins to simulate variations in water balance components from environmental changes. In the present study, we used Soil and Water Assessment Tool (SWAT) to investigate the impacts of land use land cover (LULC) change and climate variability on hydrological regime of an ungauged river basin (Sirsa river) in north-western Himalaya, India for the period 1983–2008.  The model was calibrated and validated (2004–2008) using MODIS actual evapotranspiration data (MOD16A2) with high monthly concordance (R2=0.81). The results showed that remotely sensed evapotranspiration data could be used as a proxy of gauge discharge data to calibrate the physically-based model. The substantial increase in built-up area (6.5%) and cropland (9.8%) over forest cover and barren land caused a corresponding increase in average annual surface runoff (12%) and a decrease in lateral flow (6.7%) from base level LULC of 1989 to 2009. The climate variability alone was found significant to reduce average annual streamflow (26.5%) in monsoon season (wet), baseflow (6.5%) and lateral flow (4.6%) in the dry period.  As the water resources of the study area are expected to be adversely effected in the near future, this study will effectively benefit stakeholders and administrators for the management of water resources

Using Remote Sensing Techniques to Improve Hydrological Predictions in a Rapidly Changing World

Remotely sensed geophysical datasets are being produced at increasingly fast rates to monitor various aspects of the Earth system in a rapidly changing world. The efficient and innovative use of these datasets to understand hydrological processes in various climatic and vegetation regimes under anthropogenic impacts has become an important challenge, but with a wide range of research opportunities. The ten contributions in this Special Issue have addressed the following four research topics: (1) Evapotranspiration estimation; (2) rainfall monitoring and prediction; (3) flood simulations and predictions; and (4) monitoring of ecohydrological processes using remote sensing techniques. Moreover, the authors have provided broader discussions on how to capitalize on state-of-the-art remote sensing techniques to improve hydrological model simulations and predictions, to enhance their skills in reproducing processes for the fast-changing world

Lower Atmosphere Meteorology

The Atmosphere Special Issue “Lower Atmosphere Meteorology” deals with the meteorological processes that occur in the layer of the atmosphere close to the surface. The interaction between the biosphere and the atmosphere is made through the lower layer and can greatly influence living beings and materials. The analysis of the meteorological parameters provides a better understanding of processes within the lower atmosphere and involved in air pollution, climate, and weather. The mixed layer height, the wind speed, and the air parcel trajectory have a relevant interest due to their marked impact on population and energy production. The research also comprises aerosols, clouds, and precipitation, analysing their spatiotemporal variations. This issue addresses features of gases in the atmosphere and anthropogenic greenhouse emission estimates, which are also conditioned by the lower atmosphere meteorology