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20 pages, 3685 KiB  
Article
Land Transformations in Irpinia (Southern Italy): A Tale on the Socio-Economic Dynamics Acting in a Marginal Area of the Mediterranean Europe
by Maria Ragosta, Giada Daniele, Vito Imbrenda, Rosa Coluzzi, Mariagrazia D’Emilio, Maria Lanfredi and Nadia Matarazzo
Sustainability 2024, 16(19), 8724; https://doi.org/10.3390/su16198724 - 9 Oct 2024
Viewed by 534
Abstract
Marginal areas in economically advanced countries are a critical issue that European and national policies have been addressing for some time. These areas are affected by depopulation, infrastructural gaps and labor systems that do not reach the corresponding national levels and where often [...] Read more.
Marginal areas in economically advanced countries are a critical issue that European and national policies have been addressing for some time. These areas are affected by depopulation, infrastructural gaps and labor systems that do not reach the corresponding national levels and where often agriculture still plays a critical role. In Italy, despite the fact that the National Strategy for Inner Areas (SNAI) has been active for about a decade with the aim of increasing the territorial cohesion of these fragile areas, rather limited results have been achieved in terms of halting economic marginalization and demographic decline. In this specific context, our work is aimed at analyzing land use changes, the loss of ecosystem services and demographic trends in a Mediterranean region (Irpinia—Southern Italy) on district and municipal scales in the last 30 years (1990–2018) to capture current, subtle socio-economic dynamics. The analysis carried out has indicated a substantial increase in urban areas due to the development of new industrial areas and discontinuous urban fabric (urban sprawl) at the expense of natural areas (mainly meadows and shrublands). The agricultural areas have remained substantially unchanged in terms of extension, with a slight increase in heterogeneous agricultural areas and an expansion of high-value crops (vineyards), that are the most suitable for multifunctional agriculture activities (experiential and rural tourism). The analysis of the demographic trend has highlighted a widespread phenomenon of depopulation, with the exception of those municipalities who economically orbit around the provincial capital of Avellino. The municipalities in depopulation are mostly located in the inner areas characterized by a more rugged morphology and infrastructural gaps. Unexpectedly, most of municipalities show a significant anticorrelation among the population and agricultural areas which is an indicator of social and economic phenomena as complex as they are underestimated. As a final step, this analysis highlights also a loss of carbon storage mainly attributable to the soil sealing of large areas. This study can help to comprehensively understand the conditions of marginal areas in Mediterranean Europe over recent decades in the light of the main socio-economic dynamics to better direct efforts towards the containment of the human capital hemorrhage, consisting of persistently negative natural and migratory rates, and the sustainable empowerment of these geo-economic peripheries. Full article
(This article belongs to the Section Environmental Sustainability and Applications)
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<p>Study area: (<b>a</b>) Irpinia (Avellino province) embedded within the Campania region (Southern Italy), (<b>b</b>) DEM (Digital Elevation Model) overlapped to the municipal boundaries of the study area.</p>
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<p>Reclassification of 1990 and 2018 CLC in 9 macro-classes (<b>a</b>) and respective losses and gains recorded in Irpinia in the time-frame analyzed (<b>b</b>).</p>
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<p>Main land use trajectories which occurred in Irpinia in the period 1990–2018.</p>
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<p>The demographic dimension of Irpinia: on the left, the population trends (1990–2018); on the right, the 2018 population density.</p>
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<p>Carbon storage capacity for CLC 1990 (<b>a</b>) and CLC 2018 (<b>b</b>) eco-mosaics and the respective balance (<b>c</b>).</p>
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<p>Municipalities belonging to the C3 class showing positive demographic trend.</p>
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26 pages, 30053 KiB  
Article
Evaluating MPAS-A Performance for Mesoscale Simulation in a Tropical Region: A Case Study of Extreme Heat in Jakarta, Indonesia
by Faiz Rohman Fajary, Han Soo Lee, Vinayak Bhanage, Radyan Putra Pradana, Tetsu Kubota and Hideyo Nimiya
Atmosphere 2024, 15(10), 1202; https://doi.org/10.3390/atmos15101202 - 8 Oct 2024
Viewed by 745
Abstract
The Model for Prediction Across Scales–Atmosphere (MPAS-A) has been widely used for larger scale simulations, but its performance in mesoscale, particularly in tropical regions, is less evaluated. This study aimed to assess MPAS-A in simulating extreme surface air temperature in Jakarta during the [...] Read more.
The Model for Prediction Across Scales–Atmosphere (MPAS-A) has been widely used for larger scale simulations, but its performance in mesoscale, particularly in tropical regions, is less evaluated. This study aimed to assess MPAS-A in simulating extreme surface air temperature in Jakarta during the hot spells of October 2023 with eight different simulation setups. Several validation metrics were applied to near-surface meteorological variables, land surface temperature (LST), and vertical atmospheric profile. From the eight simulations, MPAS-A captured diurnal patterns of the near-surface variables well, except for wind direction. The model also performed well in LST simulations. Moreover, the biases in the vertical profiles varied with height and were sensitive to the initial/boundary conditions used. Simulations with modified terrestrial datasets showed higher LST and air temperatures over the sprawling urban areas. MPAS-A successfully simulated the extreme event, showing higher air temperatures in southern Jakarta (over 36 °C) compared to the northern part. Negative temperature advection by sea breeze helped lower air temperature in the northern area. This study highlights the role of sea breezes as natural cooling mechanisms in coastal cities. Additionally, MPAS-A is feasible for several applications for urban climate studies and climate projection, although further development is needed. Full article
(This article belongs to the Section Atmospheric Techniques, Instruments, and Modeling)
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<p>(<b>a</b>) Terrain height (m) in the MPAS-A with mesh resolution of 3 km over the study area. Black dots with letters (A–G) show the locations of ground-based observation stations for validation (data source: BMKG, Indonesia). Orange polygon shows province boundary, while black indicates regency/municipality boundary (data source: GIA, Indonesia [<a href="#B41-atmosphere-15-01202" class="html-bibr">41</a>]). (<b>b</b>) Monthly climatology of maximum (red), mean (black), and minimum (blue) temperatures. (<b>c</b>) Maximum values by month of daily maximum, mean, and minimum temperatures in September (solid lines) and October (dashed lines) for each year from 1987 to 2023. (<b>d</b>) Daily (maximum, mean, and minimum) temperatures in October 2023. The data in Tangerang Selatan (marked as D in (<b>a</b>)) are used for (<b>b</b>–<b>d</b>).</p>
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<p>Default (<b>a</b>,<b>c</b>,<b>e</b>) and modified (<b>b</b>,<b>d</b>,<b>f</b>) terrestrial datasets used for MPAS-A inputs, namely land use and land cover (LULC; first row), green vegetation fraction (GVF; second row) in October, and albedo in October. The default datasets are provided on the WRF preprocessing system’s website (WPS). The modified datasets are only applied to the area inside the gray polygon on each map. (<b>g</b>) Albedo distribution over urban and built-up pixels in the study area extracted from the default dataset (<b>e</b>).</p>
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<p>Time series of surface meteorological variables from observation (dot marker symbol and solid line in black) and eight simulations (red, green, blue, and yellow dot or triangle marker symbols and solid or dashed lines in red, green, blue, and yellow) for seven station points (<b>A</b>–<b>G</b>). The shaded areas indicate nighttime. The meteorological variables are air temperature at 2 m (T2m), relative humidity (RH), surface pressure (SP), wind speed (WS), and wind direction (WD).</p>
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<p>Time correlation value (<b>top panel</b>) and mean absolute error (MAE; (<b>bottom panel</b>)) for each surface meteorological variable between observation and simulation output. The two metrics are calculated from combined samples from the seven stations.</p>
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<p>(<b>a</b>–<b>h</b>) Spatial distribution of the time correlation value of land surface temperature (LST) between observation (advanced Himawari imager—AHI) and each simulation output. White areas inside the targeted study area have insignificant correlation values with <span class="html-italic">p</span>-values greater than 5%. (<b>i</b>) Box plots of the correlation coefficients from all grids for each simulation. The black dot with a dashed line shows the area average of the correlation coefficients.</p>
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<p>(<b>a</b>–<b>h</b>) Spatial distribution of MAE of land surface temperature (LST) between observation (advanced Himawari imager—AHI) and each simulation output. (<b>i</b>) Box plots of the MAE from all grids for each simulation. The black dot with a dashed line shows the area average of the MAEs.</p>
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<p>(<b>a</b>–<b>c</b>) Vertical profile of (<b>a</b>) temperature, (<b>b</b>) relative humidity, and (<b>c</b>) mixing ratio in the Soekarno Hatta station (point A in <a href="#atmosphere-15-01202-f001" class="html-fig">Figure 1</a>a) for four time points: 12 UTC 16 October 2023 (red line), 00 UTC 17 October 2023 (green line), 12 UTC 17 October 2023 (blue line), and 00 UTC 18 October 2023 (black line). (<b>d</b>–<b>f</b>) Vertical profile of time average of (<b>d</b>) temperature, (<b>e</b>) relative humidity, and (<b>f</b>) mixing ratio biases in the Soekarno Hatta station for four time points between simulation output and observation.</p>
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<p>The time average of LST (<b>left panel</b>) and T2m (<b>right panel</b>) differences between simulation outputs with modified and default terrestrial input datasets for each grid. Row-wise plots show simulation combinations with the same simulation domain and initial condition but different inputs for the terrestrial datasets (details shown in <a href="#atmosphere-15-01202-t001" class="html-table">Table 1</a>). Regions with no color inside the gray polygon have insignificant values with <span class="html-italic">p</span>-values greater than 5% using a two-tailed Student’s <span class="html-italic">t</span>-test.</p>
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<p>(<b>a</b>–<b>i</b>) Spatial pattern of hourly surface air temperature at 2 m (color shading) and horizontal wind at 10 m (vector) during the day of the extreme event (17 October 2023) from 9 to 17 at local time (LT). Those datasets are outputs from simulation 8. Three colored boxes show the regions of the northern urban area of Jakarta (purple box), the southern urban area of Jakarta (black box), and the agricultural field (green box). Areas inside those boxes are used for area averaging of meteorological variables, as shown in <a href="#atmosphere-15-01202-f010" class="html-fig">Figure 10</a>.</p>
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<p>Hourly variation in area averaging of (<b>a</b>) T2m, (<b>b</b>) radiation budget [net radiation (Q), shortwave radiation (SW), and longwave radiation (LW)], (<b>c</b>) sensible and latent heat, (<b>d</b>) time derivative in T2m, (<b>e</b>) temperature advection, (<b>f</b>) other factors that contributed to the temperature changes over time that are defined by (<b>d</b>) minus (<b>e</b>), (<b>g</b>) zonal component of temperature advection, (<b>h</b>) meridional component of temperature advection, and (<b>i</b>) zonal and meridional wind at 10 m. The reference regions for area averaging are shown in the three boxes in <a href="#atmosphere-15-01202-f009" class="html-fig">Figure 9</a>. The line colors are consistent with the colors of the boxes showing the regions of the northern urban area of Jakarta (purple line), the southern urban area of Jakarta (black line), and the agricultural field (green line). The lines (purple, black, and green) are averages from the eight simulations, and the shadings show the ranges from the eight simulations (ensemble spread). In (<b>a</b>), red and blue dashed lines show T2m at stations A (Soekarno Hatta, located in the northern urban) and D (Banten, located in the southern urban), respectively.</p>
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24 pages, 7829 KiB  
Article
Urban Sprawl and Imbalance between Supply and Demand of Ecosystem Services: Evidence from China’s Yangtze River Delta Urban Agglomerations
by Huan Wang and Qiao Sun
Sustainability 2024, 16(18), 8269; https://doi.org/10.3390/su16188269 - 23 Sep 2024
Viewed by 688
Abstract
The contradiction between ecological resource protection and urban sprawl in urban agglomeration areas is becoming more and more prominent, facing a serious imbalance between the supply and demand of ecosystem services. To analyze the impact of urban agglomeration expansion on regional ecosystem services, [...] Read more.
The contradiction between ecological resource protection and urban sprawl in urban agglomeration areas is becoming more and more prominent, facing a serious imbalance between the supply and demand of ecosystem services. To analyze the impact of urban agglomeration expansion on regional ecosystem services, based on multi-source data, an assessment model of supply and demand of ecosystem services for water conservation, carbon sequestration, soil conservation and crop production was constructed. With the help of value transformation model and spatial analysis method, this paper explores the risk of ecosystem service supply and demand imbalance faced by the Yangtze River Delta urban agglomeration in the process of expansion. This study found that the supply capacity of ecosystem services in the YRDUA has continued to decline at the spatial pixel scale; ecosystem service value deficits are a common problem in the YRDUA, with cities around Taihu Lake, such as Shanghai and Suzhou, being the most serious; the value surplus areas are concentrated in the southern cities, such as Xuancheng and Chizhou, but the balance between the supply of and demand for ecosystem services in these cities is also facing a challenge as the cities are expanding. This study analyzed the spatial pattern changes in the Yangtze River Delta region in the context of urban sprawl from the perspective of ecosystem service supply and demand, which helps to clarify the changing ecosystem service dynamics of the region and guide the formulation of urban planning policies and to achieve a balance between ecological supply and demand as well as sustainable development. Full article
(This article belongs to the Special Issue Urbanization and Environmental Sustainability—2nd Edition)
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<p>The information of the YRDUA.</p>
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<p>Processing flowchart of the data.</p>
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<p>Spatial pixel distributions of ecosystem services.</p>
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<p>Patterns of ecosystem service supply and demand in the YRDUA at the pixel scale.</p>
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<p>Surplus of supply and demand of total value of ecosystem services of cities in YRDUA.</p>
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<p>ESR spatial agglomeration characteristics of the YRDUA at the county scale.</p>
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18 pages, 736 KiB  
Review
Hegemony and Colonialization in the Water Management Sector: Issues and Lessons for IWRM
by Neil Grigg
Water 2024, 16(18), 2624; https://doi.org/10.3390/w16182624 - 16 Sep 2024
Viewed by 535
Abstract
Water resources management and the broad concept of Integrated Water Resources Management (IWRM) attract varied perspectives about their effectiveness and equity as they address diverse needs across sectors and contextual situations. Managers in the water sector generally support their current governance models, while [...] Read more.
Water resources management and the broad concept of Integrated Water Resources Management (IWRM) attract varied perspectives about their effectiveness and equity as they address diverse needs across sectors and contextual situations. Managers in the water sector generally support their current governance models, while anti-poverty advocates seek more equity in the distribution of resources. Another group of stakeholders claims a lack of inclusivity in decision-making, leading to inequitable outcomes due to hegemony and colonialization of the water management domain by sector experts, officials, and other actors. IWRM focuses on reforms in water governance to achieve greater participation and sharing of power by all sectors of society in decision-making. It can facilitate the involvement of all groups of stakeholders, including those who may in some cases need to engage in social action to address water issues. This paper reviews the claims about the validity of IWRM and analyzes them according to management scenarios where water is a connector among sector issues. The scenarios show that participation in utility and local government decisions is the main pathway for urban water, wastewater, and stormwater management, while the same pathway is more difficult to organize in dispersed situations for domestic supply and irrigation in rural areas, some cases of aquifer management, and management of sprawling flood risk zones. The body of knowledge about participation in water resources management is robust, but organizational and financial capacities among existing entities pose barriers. Water resources management and IWRM do involve hegemony, and the field of practice has been colonialized, but the existential issues and complexity of the decisions and systems involved challenge society to manage successfully while assuring equity and participation through governance reform. The debates over hegemony and colonialization in water management provide an opportunity to continue improving the norms of practice and water resources education. Full article
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<p>Varied settings of water resources management.</p>
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<p>Stakeholder group alignment by governance and priorities.</p>
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<p>Hierarchy of water needs with management scenarios.</p>
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30 pages, 17772 KiB  
Article
The Effects of Flood Damage on Urban Road Networks in Italy: The Critical Function of Underpasses
by Laura Turconi, Barbara Bono, Rebecca Genta and Fabio Luino
Land 2024, 13(9), 1493; https://doi.org/10.3390/land13091493 - 14 Sep 2024
Viewed by 1402
Abstract
The urban areas of Mediterranean Europe, and particularly Italy, have experienced considerable expansion since the late 19th century in terms of settlements, structures, and infrastructure, especially in large population centers. In such areas, the geohydrological risk is high not only for inhabited areas [...] Read more.
The urban areas of Mediterranean Europe, and particularly Italy, have experienced considerable expansion since the late 19th century in terms of settlements, structures, and infrastructure, especially in large population centers. In such areas, the geohydrological risk is high not only for inhabited areas but also along roadways exposed to flooding. This scenario is worrying, especially in road underpass sections, where drivers are unlikely to perceive a real risk due to the high degree of confidence that comes from the habit of driving. Underpasses have been widely used to obviate the need to find shorter alternative routes and manage vehicular traffic in urban settings impeded by previous anthropogenic and natural constraints. To assess the numerical consistency, frequency, and areal distribution of flood risk around road underpasses, several hundred pieces of data were selected (mostly from international, national and local newspapers, CNR IRPI archive and local archives) and cataloged in a thematic database, referring mainly to the Italian territory. The behavioral aspects in the face of risk were also examined in order to provide a better understanding and raise awareness for preventive purposes. The results of this specific CNR research, which lasted about two years, confirm the exposure of underpasses to extreme risk events, affecting road users. In Italy alone, between 1942 and 2023, 698 underpasses were identified as having experienced a flooding event at least once. The database shows that 680 vehicles were involved in Italy, with a total of at least 812 individuals, of whom 19 died. Despite incomplete and uneven information, the findings of the analysis regarding the increment in underpasses flooding and the drivers action in front of a flooded underpass may be useful for undertaking the appropriate mitigation strategies. Full article
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<p>Flooded underpasses analyzed in the dataset [<a href="#B40-land-13-01493" class="html-bibr">40</a>,<a href="#B41-land-13-01493" class="html-bibr">41</a>,<a href="#B42-land-13-01493" class="html-bibr">42</a>,<a href="#B43-land-13-01493" class="html-bibr">43</a>,<a href="#B44-land-13-01493" class="html-bibr">44</a>,<a href="#B45-land-13-01493" class="html-bibr">45</a>] that occurred in Italian areas in the past and in recent years. (<b>a</b>) Historical image of flooded underpass that occurred in Turin city (Piedmont, northwestern Italy) published in a national newspaper in 1983; (<b>b</b>) Sant’Elena, near Padua city, in Oriental Alps River Basin Districts in 2014; (<b>c</b>) Tradate city, in Po River Basin Districts in 2017; (<b>d</b>) Palermo city, in Sicily River Basin Districts in 2018; (<b>e</b>) Castellanza city, near Varese, in Po River Basin Districts in 2023; (<b>f</b>) Riccione city, in Central Appennine River Basin Districts in 2023.</p>
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<p>Subdivision of Italy by RBDs according to Floods Directive 2007/60/EC [<a href="#B48-land-13-01493" class="html-bibr">48</a>,<a href="#B49-land-13-01493" class="html-bibr">49</a>].</p>
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<p>Diachronic mapping of built-up area in a representative urban region, Grosseto city, Tuscany, North Appennine RBD (data source: CNR-IRPI archive). From left to right, the use of historical maps (1843) and remote sensing data in the form of aerial photographs, from 1954 (as reported in the central image of the figure) to early 2000s, and satellite imagery from the early 2000s to today is presented.</p>
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<p>Comparison of urbanized areas per CLC, 1990 and 2018 [<a href="#B51-land-13-01493" class="html-bibr">51</a>], per RBD. For each RBD, there is an increment on urbanized square kilometers, especially Po, Oriental Alps, and South Appennine RBD.</p>
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<p>Number of floods in whole Italian territory caused by heavy rain. Since 2018, there is a significant increment of this type of events and the increment is still ongoing [<a href="#B77-land-13-01493" class="html-bibr">77</a>].</p>
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<p>Example of integrated analysis of data planimetric variations of a short stretch of Stura di Lanzo River, northwest of Torino (Piemonte Region, Po RBD), period 1878–2000, obtained from the transposition of pattern in a GIS project of historical maps and aerial photographs found at CNR-IRPI in Turin and satellite images.</p>
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<p>Width reductions in terminal stretches of Roja River in coastal plains (Ventimiglia, Liguria, North Appennine RBD) measured via GIS using historical maps (1836) and current satellite images (2023).</p>
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<p>Distribution of underpasses obtained from technical maps [<a href="#B91-land-13-01493" class="html-bibr">91</a>] overlapping flooded areas in 2016 flood in Piedmont (Po RBD). Black dots indicate the underpasses manually individuated, while red dots indicate the underpasses present in Piedmont Cadastre. This flood could have a significant increment of flooded underpasses reported. The red outline indicates the urbanized area (as per 2018 CLC).</p>
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<p>Distribution of flooded underpasses in RBD areas, 1942–2023 (identified by the color of the outline shown in <a href="#land-13-01493-f002" class="html-fig">Figure 2</a>).</p>
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<p>Trends in Italian population (black line) and number of vehicles registered in Italy (green line) since 1942, in relation to surveyed flooded underpasses (red line). A steady increase in vehicles and higher number of floods since 2010 can be seen. The availability of online news and easier retrieval of data allowed details for the last 15 years. This graph does not consider the effect of changes in rainfall.</p>
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<p>Annual distribution of flooding events at surveyed underpasses in Italy by RBD, 2014–2023.</p>
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<p>Seasonal distribution of flooding events of surveyed underpasses in Italy by RBD.</p>
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<p>Starting from aerial and satellite images (<b>a</b>,<b>d</b>), proceeding with the restitution in flooded areas maps (<b>b</b>,<b>e</b>), finally it is possible to identify the underpasses affected by the flooding events (<b>c</b>,<b>f</b>). The example in the figure illustrates the 1994 (<b>a</b>–<b>c</b>) and the 2016 (<b>d</b>–<b>f</b>) flood event in Alessandria municipality (Piemonte region, Po RBD) (areas in blue), captured by aerial photography or satellite images (CNR IRPI archives and [<a href="#B92-land-13-01493" class="html-bibr">92</a>]). The dots in light blue indicate the underpasses flooded, while the red ones indicate the remaining ones recorded in the Piemonte Cadastre. In this area, the database obtained from the search of newspaper sources alone did not identify any underpasses involved neither in the 1994 event nor in the 2016 one. The areas affected by floods are in the same location most of the time (<b>g</b>), causing a reiteration in flooded underpasses (<b>h</b>). All flooded underpasses are located in a PGRA class [<a href="#B48-land-13-01493" class="html-bibr">48</a>,<a href="#B49-land-13-01493" class="html-bibr">49</a>].</p>
Full article ">Figure 13 Cont.
<p>Starting from aerial and satellite images (<b>a</b>,<b>d</b>), proceeding with the restitution in flooded areas maps (<b>b</b>,<b>e</b>), finally it is possible to identify the underpasses affected by the flooding events (<b>c</b>,<b>f</b>). The example in the figure illustrates the 1994 (<b>a</b>–<b>c</b>) and the 2016 (<b>d</b>–<b>f</b>) flood event in Alessandria municipality (Piemonte region, Po RBD) (areas in blue), captured by aerial photography or satellite images (CNR IRPI archives and [<a href="#B92-land-13-01493" class="html-bibr">92</a>]). The dots in light blue indicate the underpasses flooded, while the red ones indicate the remaining ones recorded in the Piemonte Cadastre. In this area, the database obtained from the search of newspaper sources alone did not identify any underpasses involved neither in the 1994 event nor in the 2016 one. The areas affected by floods are in the same location most of the time (<b>g</b>), causing a reiteration in flooded underpasses (<b>h</b>). All flooded underpasses are located in a PGRA class [<a href="#B48-land-13-01493" class="html-bibr">48</a>,<a href="#B49-land-13-01493" class="html-bibr">49</a>].</p>
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<p>Percentage distribution of the underpasses flooding causes for the complete database (period 1942–2023) (<b>a</b>) and for the reduced period 2010–2023 (<b>b</b>) in the four categories considered (pluvial flooding, urban flooding, fluvial flooding, coastal flooding).</p>
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<p>Underpass flooding in each RBD, 2010–2023, in relation to cumulative annual rainfall.</p>
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<p>Distribution of underpass flooding events by flood warning system and PGRA class (%). Red indicates underpasses without preventive measures, and green indicates underpasses with prevention.</p>
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<p>Distribution of vehicle involvement during flooding events of surveyed underpasses by time slot: 00:01 to 06:00 a.m., 06:01 to 12:00 p.m., 12:01 to 06:00 p.m., and 06:01 to 12:00 a.m. (night, morning, afternoon, and evening, respectively). The sample includes only the 195 events for which information was available.</p>
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<p>Behavior of some drivers in front of flooded underpass during event of 10 March 2024 in Monza province (Northern Italy, Po RBD). Different reactions can be observed: Vehicles in red circles pass, encouraged by van’s passing, which is higher off the ground than cars. The vehicle in the yellow circle stops, but it is unclear whether it will proceed further. A similar indication may have been apparent for the observer filming from the opposite side of the scene. There is nothing to suggest whether he too passed through flooded subway or merely filmed the scene. There is no reason to assume that the observer called for help or dissuaded drivers from going toward the underpass from his direction. The vehicle in the green circle was the only one to leave the underpass, reversing its direction, probably seeking an alternative route. No guards had been put in place by responsible parties or volunteers. There is no indication as to whether this underpass has signs warning of potential flooding (modified video frame from [<a href="#B99-land-13-01493" class="html-bibr">99</a>]).</p>
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<p>Flooded underpass during the event of May 2024 near Milan (Po RBD).</p>
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22 pages, 1854 KiB  
Article
Green Belt Legislation Regulation: Comparative Legal Research
by Natalia Lisina, Aleksandra Ushakova and Svetlana Ivanova
Laws 2024, 13(5), 58; https://doi.org/10.3390/laws13050058 - 9 Sep 2024
Viewed by 594
Abstract
Recently, legislative acts on the protection of the green belt have been increasingly adopted in various states. Using the legislation examples of the United Kingdom, the Canadian province of Ontario, and Russia, we have identified public relations that can be the subject of [...] Read more.
Recently, legislative acts on the protection of the green belt have been increasingly adopted in various states. Using the legislation examples of the United Kingdom, the Canadian province of Ontario, and Russia, we have identified public relations that can be the subject of regulation of such legislation. Based on the analysis of typical legal conflicts, the problem areas which need the most attention of the legislator have been identified. The methods of differentiation of the legal regime for various areas within the green belt are investigated, taking into account their geographical features and specific management goals. The most promising areas for legal regulation that require the increased attention of legislators speak to the establishment of the procedures and criteria for excluding land plots from the green belt, the regulation of village development processes within the green belt, the establishment of a comprehensive list of agricultural types of permitted use, and the establishment of the procedure for the development of specialized plans or strategies for the use and protection of the green belt. The article offers solutions to these issues. The methodology of comparative law, including the functional method, was used in the study. Full article
(This article belongs to the Topic Energy Policy, Regulation and Sustainable Development)
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<p>Three parts of Ontario’s green belt. Niagara Escarpment (pale green), Oak Ridges Moraine (pale blue-green) and protected countryside (bright green). Map source: green belt Plan 2017.<a href="#fn014-laws-13-00058" class="html-fn">14</a></p>
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<p>The layout of the territories of concentrated urban development activity (blue areas). Source: The scheme of territorial planning of the Moscow region—the main provisions of urban development, approved By Decree of the Government of the Moscow Region No. 517/23 dated 11 July 2007. Available online: <a href="http://pravo.gov.ru/proxy/ips/?doc_itself=&amp;backlink=1&amp;nd=112028834&amp;page=1&amp;rdk=1#I0" target="_blank">http://pravo.gov.ru/proxy/ips/?doc_itself=&amp;backlink=1&amp;nd=112028834&amp;page=1&amp;rdk=1#I0</a> (accessed on 18 May 2023).</p>
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<p>A map of the planned specially protected natural areas of regional importance (green areas). Source: The scheme of territorial planning of the Moscow region—the main provisions of urban development, approved By Decree of the Government of the Moscow Region No. 517/23 dated 11 July 2007. Available online: <a href="http://pravo.gov.ru/proxy/ips/?doc_itself=&amp;backlink=1&amp;nd=112028834&amp;page=1&amp;rdk=1#I0" target="_blank">http://pravo.gov.ru/proxy/ips/?doc_itself=&amp;backlink=1&amp;nd=112028834&amp;page=1&amp;rdk=1#I0</a> (accessed on 18 May 2023).</p>
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20 pages, 717 KiB  
Article
Barriers, Challenges, and Opportunities in the Adoption of the Circular Economy in Mexico: An Analysis through Social Perception
by Alejandro Padilla-Rivera, Magdalena Morales Brizard, Nicolas Merveille and Leonor Patricia Güereca-Hernandez
Recycling 2024, 9(5), 71; https://doi.org/10.3390/recycling9050071 - 27 Aug 2024
Viewed by 836
Abstract
This study explores the transition toward sustainable economic models through the circular economy (CE) in Mexico. Utilizing a mixed-methods approach, this research incorporates a comprehensive literature review and analyzes responses from 42 stakeholders, gathered through surveys and focus groups. These stakeholders comprise a [...] Read more.
This study explores the transition toward sustainable economic models through the circular economy (CE) in Mexico. Utilizing a mixed-methods approach, this research incorporates a comprehensive literature review and analyzes responses from 42 stakeholders, gathered through surveys and focus groups. These stakeholders comprise a diverse group including PhD students, professors, researchers, industry professionals in sustainability and the environment, and government advisors and coordinators from the Mexican Secretary of Environment. This representative sample provides a broad perspective on the barriers, opportunities, and societal perceptions regarding CE. The findings reveal significant challenges such as economic barriers, regulatory inadequacies, and a lack of awareness and education, all of which hinder the adoption of CE practices. Despite these challenges, there is a generally optimistic view among stakeholders about CE’s potential to positively impact societal needs, suggesting robust opportunities for innovation and policy enhancement to foster sustainable development. Key recommendations include intensifying educational programs to elevate public understanding and engagement, formulating supportive policies that facilitate CE adoption, and promoting intersectoral collaboration to leverage collective expertise and resources. Additionally, the research underscores the necessity of integrating CE principles into urban planning and policy frameworks to effectively address specific local challenges such as waste management, pollution, and urban sprawl. By providing a detailed analysis of the current state and potential of CE in Mexico, this paper contributes valuable insights to the global discourse on sustainability. It proposes strategic actions to overcome existing hurdles and capitalize on opportunities within the CE framework, charting a path forward for Mexico and serving as a model for other regions facing similar sustainability challenges Full article
(This article belongs to the Special Issue Sustainability of the Circular Economy)
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<p>(<b>a</b>) Summary graph of the main answers obtained from the respondents (Q1–Q4). Note: Q4 is represented, only the primary question is shown, excluding its five detailed subsections as originally presented in <a href="#recycling-09-00071-t002" class="html-table">Table 2</a>. (<b>b</b>) Summary graph of the main answers obtained from the respondents (Q5–Q8). (<b>c</b>) Summary graph of the main answers obtained from the respondents (Q9–Q11).</p>
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<p>(<b>a</b>) Summary graph of the main answers obtained from the respondents (Q1–Q4). Note: Q4 is represented, only the primary question is shown, excluding its five detailed subsections as originally presented in <a href="#recycling-09-00071-t002" class="html-table">Table 2</a>. (<b>b</b>) Summary graph of the main answers obtained from the respondents (Q5–Q8). (<b>c</b>) Summary graph of the main answers obtained from the respondents (Q9–Q11).</p>
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19 pages, 3746 KiB  
Article
Managing Risk Mitigation in Urban Expansion Areas of Argentina’s Drylands: The Gap between Perception and Environmental Behaviour
by Romina Sales and Alejandro J. Rescia
Land 2024, 13(8), 1216; https://doi.org/10.3390/land13081216 - 6 Aug 2024
Viewed by 686
Abstract
Accessibility to rigorous scientific information to promote risk mitigation measures by citizens is crucial, especially in the context of climate change and extreme weather events. This study focuses on the perception of flood risk and the implementation of mitigation strategies by residents in [...] Read more.
Accessibility to rigorous scientific information to promote risk mitigation measures by citizens is crucial, especially in the context of climate change and extreme weather events. This study focuses on the perception of flood risk and the implementation of mitigation strategies by residents in drylands urban sprawl areas. Risk perception, defined as the subjective assessment of the likelihood and potential consequences of flooding, is a key element of mitigation. While many studies have explored the link between risk perception and behaviour, this research addresses gaps in understanding how public information affects these perceptions and actions. In areas of rapid urban expansion, where regulation often overlooks environmental features, the lack of adequate information poses significant barriers to effective risk mitigation. This research reveals that although residents claim to understand flooding, their descriptions often indicate a lack of understanding of the phenomenon. This ‘passive optimism’ could be mitigated by providing rigorous information and specific technical recommendations. This study highlights the disconnect between residents’ awareness of flood risks and the inadequacy of preventive measures, underlining the importance of targeted information and collaboration between the scientific community, government sectors, and local populations. Full article
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<p>Location of the study area and elevation profile. Based on data from Secretaría de Ambiente y Ordenamiento Territorial, Mendoza and Google Earth, 2024.</p>
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<p>Areas likely to be most affected by a landslide by type of neighbourhood. Based on survey data, 2024.</p>
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<p>Relationship between the possibility of a flood occurring in an area and the consideration of this perception in decisions about the place of residence. Based on survey data, 2024.</p>
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<p>Synthesis of the steps to generate insights in spatial planning and risk management. Based on survey data, 2024.</p>
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22 pages, 10349 KiB  
Article
Space Efficiency in North American Skyscrapers
by Hüseyin Emre Ilgın and Özlem Nur Aslantamer
Buildings 2024, 14(8), 2382; https://doi.org/10.3390/buildings14082382 - 1 Aug 2024
Cited by 1 | Viewed by 699
Abstract
Space efficiency in North American skyscrapers is crucial due to financial, societal, and ecological reasons. High land prices in major cities require maximizing every square foot for financial viability. Skyscrapers must accommodate growing populations within limited spaces, reducing urban sprawl and its associated [...] Read more.
Space efficiency in North American skyscrapers is crucial due to financial, societal, and ecological reasons. High land prices in major cities require maximizing every square foot for financial viability. Skyscrapers must accommodate growing populations within limited spaces, reducing urban sprawl and its associated issues. Efficient designs also support environmental sustainability and enhance city aesthetics, while optimizing infrastructure and services. However, no comprehensive study has examined the key architectural and structural features impacting the space efficiency of these towers in North America. This paper fills this gap by analyzing data from 31 case study skyscrapers. Findings indicated that (1) central core was frequently employed in the organization of service core; (2) most common forms were setback, prismatic, and tapered configurations; (3) outriggered frame and shear walled frame systems were mostly used; (4) concrete was the material in most cases; and (5) average space efficiency was 76%, and the percentage of core area to gross floor area (GFA) averaged 21%, from the lowest of 62% and 13% to the highest of 84% and 31%. It is expected that this paper will aid architectural and structural designers, and builders involved in shaping skyscrapers in North America. Full article
(This article belongs to the Section Building Energy, Physics, Environment, and Systems)
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<p>Research method flowchart (by authors).</p>
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<p>Case studies on the map of North America (by authors).</p>
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<p>Classifications by (<b>a</b>) core planning; (<b>b</b>) form; and (<b>c</b>) structural system (by authors).</p>
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<p>North American skyscrapers by core planning.</p>
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<p>North American skyscrapers by building form.</p>
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<p>North American skyscrapers by structural material.</p>
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<p>North American skyscrapers by structural system.</p>
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<p>Interrelation of space efficiency and completion period.</p>
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<p>Interrelation of space efficiency and core type.</p>
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<p>Interrelation between space efficiency and building form.</p>
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<p>Interrelation between space efficiency and structural system.</p>
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<p>Interrelationship between space efficiency and structural material.</p>
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22 pages, 7309 KiB  
Article
Simulation of Urban Growth Boundary under the Guidance of Stock Development: A Case Study of Wuhan City
by Yang Zhang, Xiaojiang Xia, Jiandong Li, Luge Xing, Chengchao Yang, Haofeng Wang, Xiaoai Dai and Jue Wang
Land 2024, 13(8), 1174; https://doi.org/10.3390/land13081174 - 30 Jul 2024
Viewed by 789
Abstract
The implementation of an urban growth boundary (UGB) can effectively control urban sprawl and promote efficient land use, which is crucial for future urban development. However, most of existing studies overlook the reuse of existing idle and inefficient land within the city in [...] Read more.
The implementation of an urban growth boundary (UGB) can effectively control urban sprawl and promote efficient land use, which is crucial for future urban development. However, most of existing studies overlook the reuse of existing idle and inefficient land within the city in the delineation of UGBs. With China’s urban construction shifting from incremental development to stock development, this study focuses on Wuhan and presents a set of technical approaches for delineating UGBs with a stock development orientation. First, a built-up area composite index (POI&ISA) is constructed based on point of interest (POI) kernel density analysis and impervious surface index extraction to evaluate constructive levels in 2010 and 2020 and identify the urban vitality zone. Then, we combine the current land use status and control policies to divide the urban spatial development potential into five categories: urban vitality land, urban non-vitality land, other vitality land, other non-vitality land, and restricted development land. Finally, the PLUS model is applied in the analysis of the driving forces of land use change in Wuhan, simulating the UGBs in three stages of incremental development (2020–2030), incremental and stock development (2030–2040), and stock development (2040–2050). Finally, the PLUS model simulation projects the UGB areas to be 436.436 km2, 474.617 km2, and 520.396 km2 for the years 2030, 2040, and 2050, respectively. The predicted timespan of urban development extends up to 30 years, serving as a reliable reference for Wuhan’s long-term and near-term planning. Full article
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<p>Study area.</p>
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<p>Research processes.</p>
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<p>Kernel density of POIs in Wuhan. (<b>a</b>) 2010, (<b>b</b>) 2020.</p>
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<p>Impervious surface index in Wuhan. (<b>a</b>) 2010, (<b>b</b>) 2020.</p>
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<p>POI&amp;ISA index in Wuhan. (<b>a</b>) 2010, (<b>b</b>) 2020.</p>
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<p>Densi-Graph diagram of Wuhan. (<b>a</b>) 2010, (<b>b</b>) 2020.</p>
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<p>Urban vitality zone extraction results in Wuhan. (<b>a</b>) 2010, (<b>b</b>) 2020.</p>
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<p>Land types of urban spatial development potential in Wuhan. (<b>a</b>) 2010, (<b>b</b>) 2020.</p>
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<p>Spatial distribution of driving factors. (<b>a</b>) PD, (<b>b</b>) per capita GDP, (<b>c</b>) elevation, (<b>d</b>) slope, (<b>e</b>) primary road, (<b>f</b>) secondary road, (<b>g</b>) highway, (<b>h</b>) rail transit.</p>
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<p>Development probabilities of five land types. (<b>a</b>) Urban vitality land, (<b>b</b>) urban non-vitality land, (<b>c</b>) other vitality land, (<b>d</b>) other non-vitality land, (<b>e</b>) restricted development land.</p>
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<p>Contribution of each driving factor to different land types.</p>
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<p>Simulation results of land use in Wuhan at different stages. (<b>a</b>) Incremental development stage from 2020 to 2030, (<b>b</b>) incremental and stock development stage from 2030 to 2040, (<b>c</b>) stock development stage from 2040 to 2050.</p>
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<p>UGBs of Wuhan at different stages. (<b>a</b>) UGB in 2030, (<b>b</b>) UGB in 2040, (<b>c</b>) UGB in 2050.</p>
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24 pages, 7740 KiB  
Article
Balancing Environmental and Human Needs: Geographic Information System-Based Analytical Hierarchy Process Land Suitability Planning for Emerging Urban Areas in Bni Bouayach Amid Urban Transformation
by Abdelmonaim Okacha, Adil Salhi, Kamal Abdelrahman, Hamid Fattasse, Kamal Lahrichi, Kaoutar Bakhouya and Biraj Kanti Mondal
Sustainability 2024, 16(15), 6497; https://doi.org/10.3390/su16156497 - 30 Jul 2024
Viewed by 717
Abstract
Urbanization in Bni Bouayach, Morocco, threatens vital irrigated areas and agricultural land, raising concerns about environmental sustainability. This study employs a GIS-based Analytical Hierarchy Process (GIS-AHP) framework to assess land suitability for sustainable development. It addresses knowledge gaps in urban planning as follows: [...] Read more.
Urbanization in Bni Bouayach, Morocco, threatens vital irrigated areas and agricultural land, raising concerns about environmental sustainability. This study employs a GIS-based Analytical Hierarchy Process (GIS-AHP) framework to assess land suitability for sustainable development. It addresses knowledge gaps in urban planning as follows: (i) Evaluating land suitability for sustainable development: this analysis identifies areas appropriate for urban expansion while minimizing environmental impact. (ii) Balancing environmental and human needs: the framework integrates ten criteria encompassing accessibility, economic, social, geomorphological, and environmental factors. This comprehensive approach results in a Land Suitability Map with five categories: prohibited/unfit, extremely unsuitable, moderately unsuitable, adequately suitable, and highly suitable. Notably, 39.5% of the area falls within the adequately suitable or highly suitable categories, primarily consisting of accessible bare lands and pastures. These findings provide valuable insights for policymakers to guide Bni Bouayach towards sustainable urban development, ensuring balanced growth that respects both environmental preservation and resident needs. Full article
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<p>Localization of the study area. 1: Bni Bouayach Commune. 2: built-up areas.</p>
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<p><b>Evolution of Land Use/Land Cover (LULC) classes in Bni Bouayach Commune, 1964–2014:</b> (<b>a</b>): Pre-1960s. (<b>b</b>): 1964–1982. (<b>c</b>): 1982–1994. (<b>d</b>): 1994–2014. 1: built-up land. 2: irrigated areas. 3: cereal cultivation. 4: olive tree plantation. 5: arboriculture. 6: forest. 7: bare land and pasture.</p>
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<p>Physical–environmental factors. (<b>a</b>): aspect (direction of the slope). (<b>b</b>): slope. (<b>c</b>): LULC. (<b>d</b>): Environmental Context Map. (<b>e</b>): elevation (m).</p>
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<p>Accessibility and socioeconomic factors. (<b>a</b>): distance from schools (m). (<b>b</b>): distance from residential zones (m). (<b>c</b>): distance from National Road No. 2 (m). (<b>d</b>): distance from industry/commerce (m). (<b>e</b>): distance from health centers (m).</p>
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<p><b>Land Suitability Map.</b> 1: prohibited or unfit. 2: extremely unsuitable. 3: moderately unsuitable. 4: adequately suitable. 5: highly suitable.</p>
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<p>Suitability levels for urban expansion in Bni Bouayach Town (Radar Chart, in hectares).</p>
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<p>Planche 1: Panoramic View of Emerging Town of Bni Bouayach Shot: 2 November 2020; Abdelmonaim Okacha.</p>
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22 pages, 2521 KiB  
Article
Investigating of Spatial Urban Growth Pattern and Associated Landscape Dynamics in Congolese Mining Cities Bordering Zambia from 1990 to 2023
by Yannick Useni Sikuzani, Médard Mpanda Mukenza, François Malaisse and Jan Bogaert
Resources 2024, 13(8), 107; https://doi.org/10.3390/resources13080107 - 29 Jul 2024
Cited by 1 | Viewed by 868
Abstract
This study investigates the spatial urban growth patterns of cities along the Democratic Republic of the Congo (DRC) and Zambia border, a region of significant economic importance characterized by cross-border trade. This activity has led to rapid but unplanned urban growth. The objective [...] Read more.
This study investigates the spatial urban growth patterns of cities along the Democratic Republic of the Congo (DRC) and Zambia border, a region of significant economic importance characterized by cross-border trade. This activity has led to rapid but unplanned urban growth. The objective is to quantify the spatial expansion of Congolese cities (Kipushi, Kasumbalesa, Mokambo, and Sakania) bordering Zambia and to evaluate associated landscape changes. The methodology of this study includes the supervised classification of Landsat images with a spatial resolution of 30 m for the years 1990, 2000, 2010, and 2023. This classification was validated using field data. Subsequently, landscape metrics such as class area, patch number, Shannon diversity index, disturbance index, urban expansion intensity index, largest patch index, and mean Euclidean distance were calculated for each city and each date. The results reveal substantial landscape transformations in the border cities between 1990 and 2023. These changes are primarily driven by rapid urban expansion, particularly pronounced in Kasumbalesa. Between 1990 and 2023, forest cover declined from 70% to less than 15% in Kipushi, from 80% to 10% in Kasumbalesa, from 90% to 30% in Mokambo, and from 80% to 15% in Sakania. This forest cover loss is accompanied by an increase in landscape element diversity, as indicated by the Shannon diversity index, except in Kipushi, suggesting a transition towards more heterogeneous landscapes. In these border cities, landscape dynamics are also characterized by the expansion of agriculture and savannas, highlighted by an increase in the disturbance index. Analysis of spatial pattern changes shows that built-up areas, agriculture, and savannas exhibit trends of patch creation or aggregation, whereas forests are undergoing processes of dissection and patch attrition. Congolese cities bordering Zambia are undergoing substantial spatial changes propelled by intricate interactions between economic, demographic, and infrastructural factors. Our results underscore the need for sustainable development strategies to address urban sprawl through smart growth policies and mixed-use developments, mitigate deforestation via stricter land use regulations and reforestation projects, and enhance cross-border cooperation through joint environmental management and collaborative research initiatives. Full article
(This article belongs to the Special Issue Minerals and Land-Use Planning: Sustainable Narratives and Practices)
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<p>Geographical location of the Congolese cities (2) (red circles) bordering Zambia: Kipushi, Kasumbalesa, Mokambo, and Sakania in the south-eastern region of the Democratic Republic of the Congo (1) (DRC). A geometric center (centroid) was defined for each city. From this center, a 15 km radius was drawn, covering the built-up area and the city’s periphery, which were then analyzed. This area corresponds to 307.22 km<sup>2</sup>, 534.87 km<sup>2</sup>, 424.64 km<sup>2</sup>, and 468.60 km<sup>2</sup>, respectively, for the cities of Kipushi, Kasumbalesa, Mokambo, and Sakania, respectively. The yellow line corresponds to the roads.</p>
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<p>Mapping spatial land cover dynamics in Kipushi (<b>A</b>), Kasumbalesa (<b>B</b>), Mokambo (<b>C</b>), and Sakania (<b>D</b>) landscapes from 1990 to 2023 using supervised classification of Landsat images with the Random Forest classifier.</p>
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<p>Landscape composition evolution in Congolese Cities (Kipushi, Kasumbalesa, Mokambo, and Sakania) bordering Zambia from 1990 to 2023. The total landscape proportion for each city does not sum to 100%, as other land cover classes were excluded from the analyses due to their relatively stable nature. The dynamics of landscape composition are evidenced by deforestation alongside the expansion of built-up and bare soil, agriculture, and savannas.</p>
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<p>Landscape diversity dynamics of the cities of Kipushi, Kasumbalesa, Mokambo, and Sakania between July-1990 and July-2023. The studied cities are characterized by a transition marked by the shift from a less diversified landscape to a heterogeneous landscape over time.</p>
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<p>Variation in the urban expansion intensity index between 1990 and 2000, 2000 and 2010, and 2010 and 2023 within the landscapes of the border cities of Kipushi, Kasumbalesa, Mokambo, and Sakania. Urbanization is significantly more intense in Kasumbalesa, whereas relative stability was noted in Kipushi between 1990 and 2023.</p>
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<p>Evolution of the landscape disturbance index of the cities of Kipushi, Kasumbalesa, Mokambo, and Sakania between July-1990 and July-2023. There is an increase in the disturbance index across the time, reflecting a significant intensification of human activity in these cities.</p>
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23 pages, 5272 KiB  
Article
Patterns of Urban Sprawl and Agricultural Land Loss in Sub-Saharan Africa: The Cases of the Ugandan Cities of Kampala and Mbarara
by Ronald O. Muchelo, Thomas F. A. Bishop, Sabastine U. Ugbaje and Stephen I. C. Akpa
Land 2024, 13(7), 1056; https://doi.org/10.3390/land13071056 - 15 Jul 2024
Viewed by 780
Abstract
Sub-Saharan Africa (SSA) is undergoing rapid urbanization, yet research comparing urban expansion and agricultural land loss in peri-urban areas is scarce. This study utilizes multi-temporal Landsat imagery to examine the impact of urban growth on agricultural land and fragile ecosystems in Kampala (a [...] Read more.
Sub-Saharan Africa (SSA) is undergoing rapid urbanization, yet research comparing urban expansion and agricultural land loss in peri-urban areas is scarce. This study utilizes multi-temporal Landsat imagery to examine the impact of urban growth on agricultural land and fragile ecosystems in Kampala (a mega city) and Mbarara (a regional urban center) in Uganda. We distinguish between random and systematic land-use and land-cover (LULC) transitions in the landscape. The results reveal substantial urban expansion. Kampala’s urban area surged from 7.14% in 1989 to 55.10% in 2015, while Mbarara increased from 6.37% in 2002 to 30.95% in 2016. Correspondingly, agricultural land decreased, from 48.02% to 16.69% in Kampala, and from 39.92% to 32.08% in Mbarara. Notably, a significant proportion of urban growth in both cities encroached upon agricultural land (66.7% in Kampala and 57.8% in Mbarara). The transition from agricultural to built-up areas accounted for 14.72% to 28.45% of the landscapes. Additionally, unsustainable practices led to the conversion of wetlands and forests to agricultural land, with approximately 13% of wetlands and 23% of Savannah and forests being converted between 2001 and 2015. These findings underscore the necessity of monitoring LULC changes for sustainable urban growth management, emphasizing the importance of preserving agricultural land and ecosystems to ensure present and future food security. This research contributes to the understanding of urbanization’s impact on peri-urban agricultural land and ecosystems in SSA, providing insights that are crucial for informed urban planning and policy formulation aimed at sustainable development in the region. Full article
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<p>Location of Kampala and Mbarara urban centers in Uganda. (Source: <a href="https://energy-gis.ug/gis-maps" target="_blank">https://energy-gis.ug/gis-maps</a> (accessed on 20 November 2020)).</p>
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<p>Schematic flow of image analysis of land-use classification (Source: authors’ illustration).</p>
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<p>Land-use and cover changes between 1989 and 2015 in Kampala.</p>
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<p>Gain, loss, and persistence for agricultural land 1989 to 2001 and 1989 to 2015, Kampala.</p>
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<p>Gain, loss, and persistence for agricultural land from 2002 to 2016 (Mbarara).</p>
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25 pages, 5835 KiB  
Review
Multiple Roles of Green Space in the Resilience, Sustainability and Equity of Aotearoa New Zealand’s Cities
by Paul Blaschke, Maibritt Pedersen Zari, Ralph Chapman, Edward Randal, Meredith Perry, Philippa Howden-Chapman and Elaine Gyde
Land 2024, 13(7), 1022; https://doi.org/10.3390/land13071022 - 8 Jul 2024
Cited by 1 | Viewed by 980
Abstract
Green space is needed in urban areas to increase resilience to climate change and other shocks, as well as for human health and wellbeing. Urban green space (UGS) is increasingly considered as green infrastructure and highly complementary to engineered urban infrastructure, such as [...] Read more.
Green space is needed in urban areas to increase resilience to climate change and other shocks, as well as for human health and wellbeing. Urban green space (UGS) is increasingly considered as green infrastructure and highly complementary to engineered urban infrastructure, such as water and transport networks. The needs for resilient, sustainable and equitable future wellbeing require strategic planning, designing and upgrading of UGS, especially in areas where it has been underprovided. We explore the implications of these needs for urban development through a detailed review of cited UGS analyses conducted on the larger cities in Aotearoa New Zealand (AoNZ). There are important differences in UGS availability (i.e., quantity), accessibility and quality within and between cities. Some of these differences stem from ad hoc patterns of development, as well as topography. They contribute to apparently growing inequities in the availability and accessibility of UGS. Broader health and wellbeing considerations, encompassing Indigenous and community values, should be at the heart of UGS design and decisionmaking. Most of AoNZ’s cities aim (at least to some extent) at densification and decarbonisation to accommodate a growing population without costly sprawl; however, to date, sprawl continues. Our findings indicate a clear need for the design and provision of high-quality, well-integrated UGS within and servicing areas of denser housing, which are typically areas in cities with a demonstrable UGS deficiency. Full article
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<p>Locations of Aotearoa New Zealand’s cities for which studies are cited in this review.</p>
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<p>The Wellington Town Belt provides multiple ecosystem services, including temperature moderation, improvements in water and air qualities, carbon sequestration, biodiversity, human health and wellbeing, amenities and amelioration of noise nuisances. Photo: Wellington City Council (with permission).</p>
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<p>An urban park acting as a stormwater detention area in Auckland following the February 2023 flooding events described in <a href="#sec1-land-13-01022" class="html-sec">Section 1</a>. Photo: Auckland Council (with permission).</p>
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<p>Green space offering wetland restoration, recreation and public art. Chaffers Park, Wellington. Photo: Authors’.</p>
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<p>Establishing locally rare native dryland species in a specially prepared stressed niche (very-free-draining exposed substrate) on the Christchurch Southern Motorway extension. Photo: C. Meurk (with permission).</p>
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<p>Green spaces as disaster recovery areas. Latimer Square, Christchurch, after the February 2011 earthquake. Photo: CHC-EQR-USAR-Camp-17, with acknowledgement to Christchurch City Libraries.</p>
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<p>A newly created “pocket park” with universally accessible seating and water fountain. Note the prevalence of impervious surfaces. Denton Park, Wellington. Photo: Authors’.</p>
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<p>Green wall of ground-rooted native vine species at a new Auckland suburban rail station, 2020. The larger-leafed vine in the right-hand foreground is critically threatened in its native habitat. Photo: R. Simcock (with permission).</p>
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<p>A green roof at the Auckland Botanic Gardens, forming part of the Auckland Sustainable Stormwater Trail. Photo: R. Simcock (with permission).</p>
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<p>Oruaiti Reserve, Wellington. Located within a harbourside suburb, the reserve is owned by an iwi trust and co-managed with Wellington City Council. It is a significant cultural site that also has important recreational and ecological values. Photo: N. Price for Wellington City Council (with permission).</p>
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<p>Raingarden in Auckland waterfront development showing the diverse ground and canopy covers of native species. Photo: R. Simcock (with permission).</p>
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17 pages, 4786 KiB  
Article
Distribution Profile of Benthic Macroinvertebrates in Some Rivers of Yaoundé City and Its Surroundings Using Self Organizing Map and Indicator value methods
by Marie Anita Temgoua Zemo, Samuel Foto Menbohan, Bernard Tossou Atchrimi, Delagnon Assou, Belmond Eric Biram à Ngon, Noel Christiane Wilfreid Betsi, Serge Gwos Nhiomock, Harissou, Nathaniel Larry Lactio, Bolivar Far Ndourwe, Mathias Nwaha, Donald l’or Nyame Mbia, Laure Yvonne Tchouapi, Ghislain Ulric Tchouta, Blaise Rollinat Mboye and Jean Dzavi
Diversity 2024, 16(7), 385; https://doi.org/10.3390/d16070385 - 2 Jul 2024
Viewed by 1370
Abstract
Urban sprawl leads to the degradation of aquatic environments and, consequently, to the destruction of biodiversity. With the aim of highlighting the distribution profile of benthic macroinvertebrates in the city of Yaoundé and its surroundings according to the level of degradation, this study [...] Read more.
Urban sprawl leads to the degradation of aquatic environments and, consequently, to the destruction of biodiversity. With the aim of highlighting the distribution profile of benthic macroinvertebrates in the city of Yaoundé and its surroundings according to the level of degradation, this study was carried out in seven rivers. A total of 144 taxa of benthic macroinvertebrates, belonging to 74 families, 15 orders, five classes, and three phyla, were collected from seven rivers in urban, peri-urban, and forest environments on Yaoundé and its surroundings. The self-organizing map (SOM) analysis tool was used to group the collected taxa from all stations into three clusters or affinity cores. The indicator value analysis (IndVal) method was employed to determine, based on their ecological preferences, which organisms were most likely to belong to each group. Out of the 144 collected taxa, only 44 were indicated to represent the three different groups. Thus, three communities were defined: the Hydropsyche community, with Hydropsyche sp. as the predominant taxon in Group III, characterizing well-oxygenated and low-mineralized stations; the Hydrocyrius community, where the species Hydrocyrius sp. predominates in Group I, describing stations with low oxygenation and moderate mineralization; and the Lumbriculidae community, where Lumbriculidae is the taxon associated with environments with high mineralization and critical oxygenation. These two methods contribute to the biomonitoring of tropical aquatic environments, firstly by grouping organisms by affinity and then identifying those that reflect the environment conditions. This facilitates the detection of changes in the quality of hydrosystems and guides management and conservation efforts. Full article
(This article belongs to the Special Issue Freshwater Zoobenthos Biodiversity, Evolution and Ecology)
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Figure 1

Figure 1
<p>Map of the study site and locations of different streams.</p>
Full article ">Figure 2
<p>(<b>a</b>) Distribution of samples on the SOM map and the different groups formed from the benthic macroinvertebrate abundance matrix. 1 = Group I, 2 = Group II, and 3 = Group III. The acronyms in the hexagonal units represent samples (station code and month). (<b>b</b>) aur = A<span class="html-italic">uriculata</span> sp., thr = <span class="html-italic">Thraulus</span> sp., mah = <span class="html-italic">Maheathraulus</span> sp., thj = <span class="html-italic">Thalersphyrus</span> josettae, afm = <span class="html-italic">Afronurus matitensis</span>, asp = <span class="html-italic">Afronurus</span> sp., caenis.sp = <span class="html-italic">Caenis</span> sp., afr = <span class="html-italic">Afrocaenis</span> sp., din = <span class="html-italic">Dinocras</span> sp., eop = <span class="html-italic">Eoperla</span> sp., bla, Blaberidae, hsy = <span class="html-italic">Hydropsyche</span> sp., dip = <span class="html-italic">Diplectrona</span> sp., let = <span class="html-italic">Leptocerus</span> sp., hal = <span class="html-italic">Halesus</span> sp., pop = <span class="html-italic">Polycentropu</span>s sp., hyt = <span class="html-italic">Hypothyacophila</span> sp., oli = <span class="html-italic">Oligotrichia striata</span>, ors = <span class="html-italic">Orthotrichia</span> sp., lep = <span class="html-italic">Lepidostoma</span> sp., caf = <span class="html-italic">Caridina africana</span>, Sou = <span class="html-italic">Soudanautes</span> sp., mac = <span class="html-italic">Macrobrachium niloticus</span>, spa = Sparganoplilidae, hap = Haplotaxidae, lub = Lumbriculidae, brs = <span class="html-italic">Branchiura sowerbyi</span>, Lcb = Lumbricidae, ent = Enchytraeidae, nai = Naididae, pro = Proppapidae, hae = <span class="html-italic">Haementeria costata</span>, hem = <span class="html-italic">Hemiclepsis marginata</span>, bat = <span class="html-italic">Batracobdella</span> sp., glo = <span class="html-italic">Glossiphonia</span> sp., pha = <span class="html-italic">Physa acuta</span>, tru = <span class="html-italic">Lymnaea truncatula</span>, nat = <span class="html-italic">Lymnaea natalensis</span>, bul = Bulinidae, Hyg = Hygrobiidae, tom = <span class="html-italic">Tomichia</span> sp., hyd = <span class="html-italic">Hydrobia</span> sp., mel = <span class="html-italic">Melonoides</span> sp., lan = <span class="html-italic">Lanites</span> sp., pln = Planorbidae. (<b>c</b>) Lbl = <span class="html-italic">Libellula</span> sp., Syt = <span class="html-italic">Sympetrum</span> sp., Xyp = <span class="html-italic">Xyzomma petiolatum</span>, brl = <span class="html-italic">Brachythemis lacustris</span>, ort = <span class="html-italic">Orthetrum</span> sp., Oph = <span class="html-italic">Ophiogomphus</span> sp., les = <span class="html-italic">Lestinogomphus angus</span>, phg = <span class="html-italic">Phyllogomphus brunneus</span>, chv = <span class="html-italic">Chalcolestes viridis</span>, ict = <span class="html-italic">Ictinogomphus</span> sp., cal = <span class="html-italic">Calopteryx</span> sp., brc = <span class="html-italic">Brachythemis leucostica</span>, oxc = <span class="html-italic">Oxygastra curtisil</span>, epb = <span class="html-italic">Epitheca bimaculata</span>, hol = <span class="html-italic">Hemicordulia olympica</span>, pha = <span class="html-italic">Phyllomacromia picta</span>, sop = <span class="html-italic">Somatochlora pro parte</span>, nas = <span class="html-italic">Nehalennia speciosa</span>, ens = <span class="html-italic">Enallagma spermatum</span>, enc = <span class="html-italic">Enallagma cyathigerum</span>, erp = <span class="html-italic">Erythromma pro parte</span>, eng = <span class="html-italic">Enallagma glaucum</span>, cog = <span class="html-italic">Cordulegaster</span> sp., coe = <span class="html-italic">Coenagrion</span> sp., pse = <span class="html-italic">Pseudagrion</span> pla = Platycnemididae, mas = <span class="html-italic">Macromiia splendens</span>, hyd = <span class="html-italic">Hydatiscus</span> sp., plb = <span class="html-italic">Platambus</span> sp., ere <span class="html-italic">= Eretes</span> sp., Dyt = <span class="html-italic">Dytiscus</span> sp., Lac = <span class="html-italic">Laccophilus</span> sp., hyv = Hydrovatus sp., hyp = <span class="html-italic">Hydrocyphon</span> sp., mic = <span class="html-italic">Microcara</span> sp., elo = <span class="html-italic">Elodes</span> sp., ore = <span class="html-italic">Orectochilus</span> sp., amp = <span class="html-italic">Amphiops</span> sp., hyb = <span class="html-italic">Hydrobius</span> sp., hyd = <span class="html-italic">Hydrochara</span> sp., enh = <span class="html-italic">Enochrus</span> sp., lab = <span class="html-italic">Laccobius</span> sp., Neo = <span class="html-italic">Neohydrophilus</span> sp., chr = Chrysomelidae, dry = <span class="html-italic">Drops</span> sp., Lim = <span class="html-italic">Limnebius</span> sp., hyn = <span class="html-italic">Hydraena</span> sp., hpn = <span class="html-italic">Hydraenopsis</span> sp., (<b>d</b>) Distribution of samples in the SOM based on benthic macroinvertebrate presence–absence data at the different sampling stations and distribution profile of benthic macroinvertebrate taxa in the different groups. The scale bars indicate the weight vector of each taxon (i.e., the abundance of the taxon) in the corresponding SOM units. Dark bars represent a high abundance of taxa, while light bars indicate a low abundance of taxa. lnu <span class="html-italic">= Limnius</span> sp., elm <span class="html-italic">= Elmis</span> sp., Po<span class="html-italic">t = Potamophilus</span> sp., psx <span class="html-italic">= Pseudancyronyx</span> sp., oul <span class="html-italic">= Oulimnius</span> sp., not <span class="html-italic">= Noterus</span> sp., nta <span class="html-italic">= Notonecta</span> sp., ani <span class="html-italic">= Anisops</span> sp., ger <span class="html-italic">= Gerris</span> sp., eum <span class="html-italic">= Eurymetra</span> sp., aqu <span class="html-italic">= Aquarius</span> sp., rha <span class="html-italic">= Rhagotarsis</span> sp., teg <span class="html-italic">= Tenagogonus</span> sp., neo <span class="html-italic">= Neogerris</span> sp., mic <span class="html-italic">= Microvelia</span> sp., rhg <span class="html-italic">= Rhagovelia</span> sp., vel <span class="html-italic">= Velia</span> sp., ang <span class="html-italic">= Angelia</span> sp., oce <span class="html-italic">= Ocelovelia</span> sp., mes <span class="html-italic">= Mesovelia</span> sp., ran <span class="html-italic">= Ranatra</span> sp., Lph <span class="html-italic">= Laccotrephes</span> sp., nep <span class="html-italic">= Nepa</span> sp., hdm <span class="html-italic">= Hydrometra</span> sp., ncr <span class="html-italic">= Naucoris</span> sp., nsp <span class="html-italic">= Neomacrocoris</span> sp., coi <span class="html-italic">= Corixa</span> sp., poo <span class="html-italic">= Hydrocyrius</span> sp., app <span class="html-italic">= Appasus</span> sp., chi <span class="html-italic">= Chironomus</span> sp., syh = Syrphidae, eph = Ephydridae, cer = Ceratopogonidae, Pch = Psychodidae, Sci = Sciomyzidae, cha = Chaoboridae, tab <span class="html-italic">=</span> Tabanidae, Sim = Simuliidae, dix <span class="html-italic">=</span> Dixidae, sca = Scatophagidae, cul <span class="html-italic">= Culex</span> sp., tip = Tipulidae, cle <span class="html-italic">= Cloeon</span> sp., bae <span class="html-italic">= Baetis</span> sp., rhi <span class="html-italic">= Rhitrocloeon</span> sp., adp <span class="html-italic">= Adenophlebia</span> sp., ade <span class="html-italic">= adenophlebiodes</span> sp., Syl <span class="html-italic">= Sylvatica</span> sp.</p>
Full article ">Figure 2 Cont.
<p>(<b>a</b>) Distribution of samples on the SOM map and the different groups formed from the benthic macroinvertebrate abundance matrix. 1 = Group I, 2 = Group II, and 3 = Group III. The acronyms in the hexagonal units represent samples (station code and month). (<b>b</b>) aur = A<span class="html-italic">uriculata</span> sp., thr = <span class="html-italic">Thraulus</span> sp., mah = <span class="html-italic">Maheathraulus</span> sp., thj = <span class="html-italic">Thalersphyrus</span> josettae, afm = <span class="html-italic">Afronurus matitensis</span>, asp = <span class="html-italic">Afronurus</span> sp., caenis.sp = <span class="html-italic">Caenis</span> sp., afr = <span class="html-italic">Afrocaenis</span> sp., din = <span class="html-italic">Dinocras</span> sp., eop = <span class="html-italic">Eoperla</span> sp., bla, Blaberidae, hsy = <span class="html-italic">Hydropsyche</span> sp., dip = <span class="html-italic">Diplectrona</span> sp., let = <span class="html-italic">Leptocerus</span> sp., hal = <span class="html-italic">Halesus</span> sp., pop = <span class="html-italic">Polycentropu</span>s sp., hyt = <span class="html-italic">Hypothyacophila</span> sp., oli = <span class="html-italic">Oligotrichia striata</span>, ors = <span class="html-italic">Orthotrichia</span> sp., lep = <span class="html-italic">Lepidostoma</span> sp., caf = <span class="html-italic">Caridina africana</span>, Sou = <span class="html-italic">Soudanautes</span> sp., mac = <span class="html-italic">Macrobrachium niloticus</span>, spa = Sparganoplilidae, hap = Haplotaxidae, lub = Lumbriculidae, brs = <span class="html-italic">Branchiura sowerbyi</span>, Lcb = Lumbricidae, ent = Enchytraeidae, nai = Naididae, pro = Proppapidae, hae = <span class="html-italic">Haementeria costata</span>, hem = <span class="html-italic">Hemiclepsis marginata</span>, bat = <span class="html-italic">Batracobdella</span> sp., glo = <span class="html-italic">Glossiphonia</span> sp., pha = <span class="html-italic">Physa acuta</span>, tru = <span class="html-italic">Lymnaea truncatula</span>, nat = <span class="html-italic">Lymnaea natalensis</span>, bul = Bulinidae, Hyg = Hygrobiidae, tom = <span class="html-italic">Tomichia</span> sp., hyd = <span class="html-italic">Hydrobia</span> sp., mel = <span class="html-italic">Melonoides</span> sp., lan = <span class="html-italic">Lanites</span> sp., pln = Planorbidae. (<b>c</b>) Lbl = <span class="html-italic">Libellula</span> sp., Syt = <span class="html-italic">Sympetrum</span> sp., Xyp = <span class="html-italic">Xyzomma petiolatum</span>, brl = <span class="html-italic">Brachythemis lacustris</span>, ort = <span class="html-italic">Orthetrum</span> sp., Oph = <span class="html-italic">Ophiogomphus</span> sp., les = <span class="html-italic">Lestinogomphus angus</span>, phg = <span class="html-italic">Phyllogomphus brunneus</span>, chv = <span class="html-italic">Chalcolestes viridis</span>, ict = <span class="html-italic">Ictinogomphus</span> sp., cal = <span class="html-italic">Calopteryx</span> sp., brc = <span class="html-italic">Brachythemis leucostica</span>, oxc = <span class="html-italic">Oxygastra curtisil</span>, epb = <span class="html-italic">Epitheca bimaculata</span>, hol = <span class="html-italic">Hemicordulia olympica</span>, pha = <span class="html-italic">Phyllomacromia picta</span>, sop = <span class="html-italic">Somatochlora pro parte</span>, nas = <span class="html-italic">Nehalennia speciosa</span>, ens = <span class="html-italic">Enallagma spermatum</span>, enc = <span class="html-italic">Enallagma cyathigerum</span>, erp = <span class="html-italic">Erythromma pro parte</span>, eng = <span class="html-italic">Enallagma glaucum</span>, cog = <span class="html-italic">Cordulegaster</span> sp., coe = <span class="html-italic">Coenagrion</span> sp., pse = <span class="html-italic">Pseudagrion</span> pla = Platycnemididae, mas = <span class="html-italic">Macromiia splendens</span>, hyd = <span class="html-italic">Hydatiscus</span> sp., plb = <span class="html-italic">Platambus</span> sp., ere <span class="html-italic">= Eretes</span> sp., Dyt = <span class="html-italic">Dytiscus</span> sp., Lac = <span class="html-italic">Laccophilus</span> sp., hyv = Hydrovatus sp., hyp = <span class="html-italic">Hydrocyphon</span> sp., mic = <span class="html-italic">Microcara</span> sp., elo = <span class="html-italic">Elodes</span> sp., ore = <span class="html-italic">Orectochilus</span> sp., amp = <span class="html-italic">Amphiops</span> sp., hyb = <span class="html-italic">Hydrobius</span> sp., hyd = <span class="html-italic">Hydrochara</span> sp., enh = <span class="html-italic">Enochrus</span> sp., lab = <span class="html-italic">Laccobius</span> sp., Neo = <span class="html-italic">Neohydrophilus</span> sp., chr = Chrysomelidae, dry = <span class="html-italic">Drops</span> sp., Lim = <span class="html-italic">Limnebius</span> sp., hyn = <span class="html-italic">Hydraena</span> sp., hpn = <span class="html-italic">Hydraenopsis</span> sp., (<b>d</b>) Distribution of samples in the SOM based on benthic macroinvertebrate presence–absence data at the different sampling stations and distribution profile of benthic macroinvertebrate taxa in the different groups. The scale bars indicate the weight vector of each taxon (i.e., the abundance of the taxon) in the corresponding SOM units. Dark bars represent a high abundance of taxa, while light bars indicate a low abundance of taxa. lnu <span class="html-italic">= Limnius</span> sp., elm <span class="html-italic">= Elmis</span> sp., Po<span class="html-italic">t = Potamophilus</span> sp., psx <span class="html-italic">= Pseudancyronyx</span> sp., oul <span class="html-italic">= Oulimnius</span> sp., not <span class="html-italic">= Noterus</span> sp., nta <span class="html-italic">= Notonecta</span> sp., ani <span class="html-italic">= Anisops</span> sp., ger <span class="html-italic">= Gerris</span> sp., eum <span class="html-italic">= Eurymetra</span> sp., aqu <span class="html-italic">= Aquarius</span> sp., rha <span class="html-italic">= Rhagotarsis</span> sp., teg <span class="html-italic">= Tenagogonus</span> sp., neo <span class="html-italic">= Neogerris</span> sp., mic <span class="html-italic">= Microvelia</span> sp., rhg <span class="html-italic">= Rhagovelia</span> sp., vel <span class="html-italic">= Velia</span> sp., ang <span class="html-italic">= Angelia</span> sp., oce <span class="html-italic">= Ocelovelia</span> sp., mes <span class="html-italic">= Mesovelia</span> sp., ran <span class="html-italic">= Ranatra</span> sp., Lph <span class="html-italic">= Laccotrephes</span> sp., nep <span class="html-italic">= Nepa</span> sp., hdm <span class="html-italic">= Hydrometra</span> sp., ncr <span class="html-italic">= Naucoris</span> sp., nsp <span class="html-italic">= Neomacrocoris</span> sp., coi <span class="html-italic">= Corixa</span> sp., poo <span class="html-italic">= Hydrocyrius</span> sp., app <span class="html-italic">= Appasus</span> sp., chi <span class="html-italic">= Chironomus</span> sp., syh = Syrphidae, eph = Ephydridae, cer = Ceratopogonidae, Pch = Psychodidae, Sci = Sciomyzidae, cha = Chaoboridae, tab <span class="html-italic">=</span> Tabanidae, Sim = Simuliidae, dix <span class="html-italic">=</span> Dixidae, sca = Scatophagidae, cul <span class="html-italic">= Culex</span> sp., tip = Tipulidae, cle <span class="html-italic">= Cloeon</span> sp., bae <span class="html-italic">= Baetis</span> sp., rhi <span class="html-italic">= Rhitrocloeon</span> sp., adp <span class="html-italic">= Adenophlebia</span> sp., ade <span class="html-italic">= adenophlebiodes</span> sp., Syl <span class="html-italic">= Sylvatica</span> sp.</p>
Full article ">Figure 2 Cont.
<p>(<b>a</b>) Distribution of samples on the SOM map and the different groups formed from the benthic macroinvertebrate abundance matrix. 1 = Group I, 2 = Group II, and 3 = Group III. The acronyms in the hexagonal units represent samples (station code and month). (<b>b</b>) aur = A<span class="html-italic">uriculata</span> sp., thr = <span class="html-italic">Thraulus</span> sp., mah = <span class="html-italic">Maheathraulus</span> sp., thj = <span class="html-italic">Thalersphyrus</span> josettae, afm = <span class="html-italic">Afronurus matitensis</span>, asp = <span class="html-italic">Afronurus</span> sp., caenis.sp = <span class="html-italic">Caenis</span> sp., afr = <span class="html-italic">Afrocaenis</span> sp., din = <span class="html-italic">Dinocras</span> sp., eop = <span class="html-italic">Eoperla</span> sp., bla, Blaberidae, hsy = <span class="html-italic">Hydropsyche</span> sp., dip = <span class="html-italic">Diplectrona</span> sp., let = <span class="html-italic">Leptocerus</span> sp., hal = <span class="html-italic">Halesus</span> sp., pop = <span class="html-italic">Polycentropu</span>s sp., hyt = <span class="html-italic">Hypothyacophila</span> sp., oli = <span class="html-italic">Oligotrichia striata</span>, ors = <span class="html-italic">Orthotrichia</span> sp., lep = <span class="html-italic">Lepidostoma</span> sp., caf = <span class="html-italic">Caridina africana</span>, Sou = <span class="html-italic">Soudanautes</span> sp., mac = <span class="html-italic">Macrobrachium niloticus</span>, spa = Sparganoplilidae, hap = Haplotaxidae, lub = Lumbriculidae, brs = <span class="html-italic">Branchiura sowerbyi</span>, Lcb = Lumbricidae, ent = Enchytraeidae, nai = Naididae, pro = Proppapidae, hae = <span class="html-italic">Haementeria costata</span>, hem = <span class="html-italic">Hemiclepsis marginata</span>, bat = <span class="html-italic">Batracobdella</span> sp., glo = <span class="html-italic">Glossiphonia</span> sp., pha = <span class="html-italic">Physa acuta</span>, tru = <span class="html-italic">Lymnaea truncatula</span>, nat = <span class="html-italic">Lymnaea natalensis</span>, bul = Bulinidae, Hyg = Hygrobiidae, tom = <span class="html-italic">Tomichia</span> sp., hyd = <span class="html-italic">Hydrobia</span> sp., mel = <span class="html-italic">Melonoides</span> sp., lan = <span class="html-italic">Lanites</span> sp., pln = Planorbidae. (<b>c</b>) Lbl = <span class="html-italic">Libellula</span> sp., Syt = <span class="html-italic">Sympetrum</span> sp., Xyp = <span class="html-italic">Xyzomma petiolatum</span>, brl = <span class="html-italic">Brachythemis lacustris</span>, ort = <span class="html-italic">Orthetrum</span> sp., Oph = <span class="html-italic">Ophiogomphus</span> sp., les = <span class="html-italic">Lestinogomphus angus</span>, phg = <span class="html-italic">Phyllogomphus brunneus</span>, chv = <span class="html-italic">Chalcolestes viridis</span>, ict = <span class="html-italic">Ictinogomphus</span> sp., cal = <span class="html-italic">Calopteryx</span> sp., brc = <span class="html-italic">Brachythemis leucostica</span>, oxc = <span class="html-italic">Oxygastra curtisil</span>, epb = <span class="html-italic">Epitheca bimaculata</span>, hol = <span class="html-italic">Hemicordulia olympica</span>, pha = <span class="html-italic">Phyllomacromia picta</span>, sop = <span class="html-italic">Somatochlora pro parte</span>, nas = <span class="html-italic">Nehalennia speciosa</span>, ens = <span class="html-italic">Enallagma spermatum</span>, enc = <span class="html-italic">Enallagma cyathigerum</span>, erp = <span class="html-italic">Erythromma pro parte</span>, eng = <span class="html-italic">Enallagma glaucum</span>, cog = <span class="html-italic">Cordulegaster</span> sp., coe = <span class="html-italic">Coenagrion</span> sp., pse = <span class="html-italic">Pseudagrion</span> pla = Platycnemididae, mas = <span class="html-italic">Macromiia splendens</span>, hyd = <span class="html-italic">Hydatiscus</span> sp., plb = <span class="html-italic">Platambus</span> sp., ere <span class="html-italic">= Eretes</span> sp., Dyt = <span class="html-italic">Dytiscus</span> sp., Lac = <span class="html-italic">Laccophilus</span> sp., hyv = Hydrovatus sp., hyp = <span class="html-italic">Hydrocyphon</span> sp., mic = <span class="html-italic">Microcara</span> sp., elo = <span class="html-italic">Elodes</span> sp., ore = <span class="html-italic">Orectochilus</span> sp., amp = <span class="html-italic">Amphiops</span> sp., hyb = <span class="html-italic">Hydrobius</span> sp., hyd = <span class="html-italic">Hydrochara</span> sp., enh = <span class="html-italic">Enochrus</span> sp., lab = <span class="html-italic">Laccobius</span> sp., Neo = <span class="html-italic">Neohydrophilus</span> sp., chr = Chrysomelidae, dry = <span class="html-italic">Drops</span> sp., Lim = <span class="html-italic">Limnebius</span> sp., hyn = <span class="html-italic">Hydraena</span> sp., hpn = <span class="html-italic">Hydraenopsis</span> sp., (<b>d</b>) Distribution of samples in the SOM based on benthic macroinvertebrate presence–absence data at the different sampling stations and distribution profile of benthic macroinvertebrate taxa in the different groups. The scale bars indicate the weight vector of each taxon (i.e., the abundance of the taxon) in the corresponding SOM units. Dark bars represent a high abundance of taxa, while light bars indicate a low abundance of taxa. lnu <span class="html-italic">= Limnius</span> sp., elm <span class="html-italic">= Elmis</span> sp., Po<span class="html-italic">t = Potamophilus</span> sp., psx <span class="html-italic">= Pseudancyronyx</span> sp., oul <span class="html-italic">= Oulimnius</span> sp., not <span class="html-italic">= Noterus</span> sp., nta <span class="html-italic">= Notonecta</span> sp., ani <span class="html-italic">= Anisops</span> sp., ger <span class="html-italic">= Gerris</span> sp., eum <span class="html-italic">= Eurymetra</span> sp., aqu <span class="html-italic">= Aquarius</span> sp., rha <span class="html-italic">= Rhagotarsis</span> sp., teg <span class="html-italic">= Tenagogonus</span> sp., neo <span class="html-italic">= Neogerris</span> sp., mic <span class="html-italic">= Microvelia</span> sp., rhg <span class="html-italic">= Rhagovelia</span> sp., vel <span class="html-italic">= Velia</span> sp., ang <span class="html-italic">= Angelia</span> sp., oce <span class="html-italic">= Ocelovelia</span> sp., mes <span class="html-italic">= Mesovelia</span> sp., ran <span class="html-italic">= Ranatra</span> sp., Lph <span class="html-italic">= Laccotrephes</span> sp., nep <span class="html-italic">= Nepa</span> sp., hdm <span class="html-italic">= Hydrometra</span> sp., ncr <span class="html-italic">= Naucoris</span> sp., nsp <span class="html-italic">= Neomacrocoris</span> sp., coi <span class="html-italic">= Corixa</span> sp., poo <span class="html-italic">= Hydrocyrius</span> sp., app <span class="html-italic">= Appasus</span> sp., chi <span class="html-italic">= Chironomus</span> sp., syh = Syrphidae, eph = Ephydridae, cer = Ceratopogonidae, Pch = Psychodidae, Sci = Sciomyzidae, cha = Chaoboridae, tab <span class="html-italic">=</span> Tabanidae, Sim = Simuliidae, dix <span class="html-italic">=</span> Dixidae, sca = Scatophagidae, cul <span class="html-italic">= Culex</span> sp., tip = Tipulidae, cle <span class="html-italic">= Cloeon</span> sp., bae <span class="html-italic">= Baetis</span> sp., rhi <span class="html-italic">= Rhitrocloeon</span> sp., adp <span class="html-italic">= Adenophlebia</span> sp., ade <span class="html-italic">= adenophlebiodes</span> sp., Syl <span class="html-italic">= Sylvatica</span> sp.</p>
Full article ">Figure 2 Cont.
<p>(<b>a</b>) Distribution of samples on the SOM map and the different groups formed from the benthic macroinvertebrate abundance matrix. 1 = Group I, 2 = Group II, and 3 = Group III. The acronyms in the hexagonal units represent samples (station code and month). (<b>b</b>) aur = A<span class="html-italic">uriculata</span> sp., thr = <span class="html-italic">Thraulus</span> sp., mah = <span class="html-italic">Maheathraulus</span> sp., thj = <span class="html-italic">Thalersphyrus</span> josettae, afm = <span class="html-italic">Afronurus matitensis</span>, asp = <span class="html-italic">Afronurus</span> sp., caenis.sp = <span class="html-italic">Caenis</span> sp., afr = <span class="html-italic">Afrocaenis</span> sp., din = <span class="html-italic">Dinocras</span> sp., eop = <span class="html-italic">Eoperla</span> sp., bla, Blaberidae, hsy = <span class="html-italic">Hydropsyche</span> sp., dip = <span class="html-italic">Diplectrona</span> sp., let = <span class="html-italic">Leptocerus</span> sp., hal = <span class="html-italic">Halesus</span> sp., pop = <span class="html-italic">Polycentropu</span>s sp., hyt = <span class="html-italic">Hypothyacophila</span> sp., oli = <span class="html-italic">Oligotrichia striata</span>, ors = <span class="html-italic">Orthotrichia</span> sp., lep = <span class="html-italic">Lepidostoma</span> sp., caf = <span class="html-italic">Caridina africana</span>, Sou = <span class="html-italic">Soudanautes</span> sp., mac = <span class="html-italic">Macrobrachium niloticus</span>, spa = Sparganoplilidae, hap = Haplotaxidae, lub = Lumbriculidae, brs = <span class="html-italic">Branchiura sowerbyi</span>, Lcb = Lumbricidae, ent = Enchytraeidae, nai = Naididae, pro = Proppapidae, hae = <span class="html-italic">Haementeria costata</span>, hem = <span class="html-italic">Hemiclepsis marginata</span>, bat = <span class="html-italic">Batracobdella</span> sp., glo = <span class="html-italic">Glossiphonia</span> sp., pha = <span class="html-italic">Physa acuta</span>, tru = <span class="html-italic">Lymnaea truncatula</span>, nat = <span class="html-italic">Lymnaea natalensis</span>, bul = Bulinidae, Hyg = Hygrobiidae, tom = <span class="html-italic">Tomichia</span> sp., hyd = <span class="html-italic">Hydrobia</span> sp., mel = <span class="html-italic">Melonoides</span> sp., lan = <span class="html-italic">Lanites</span> sp., pln = Planorbidae. (<b>c</b>) Lbl = <span class="html-italic">Libellula</span> sp., Syt = <span class="html-italic">Sympetrum</span> sp., Xyp = <span class="html-italic">Xyzomma petiolatum</span>, brl = <span class="html-italic">Brachythemis lacustris</span>, ort = <span class="html-italic">Orthetrum</span> sp., Oph = <span class="html-italic">Ophiogomphus</span> sp., les = <span class="html-italic">Lestinogomphus angus</span>, phg = <span class="html-italic">Phyllogomphus brunneus</span>, chv = <span class="html-italic">Chalcolestes viridis</span>, ict = <span class="html-italic">Ictinogomphus</span> sp., cal = <span class="html-italic">Calopteryx</span> sp., brc = <span class="html-italic">Brachythemis leucostica</span>, oxc = <span class="html-italic">Oxygastra curtisil</span>, epb = <span class="html-italic">Epitheca bimaculata</span>, hol = <span class="html-italic">Hemicordulia olympica</span>, pha = <span class="html-italic">Phyllomacromia picta</span>, sop = <span class="html-italic">Somatochlora pro parte</span>, nas = <span class="html-italic">Nehalennia speciosa</span>, ens = <span class="html-italic">Enallagma spermatum</span>, enc = <span class="html-italic">Enallagma cyathigerum</span>, erp = <span class="html-italic">Erythromma pro parte</span>, eng = <span class="html-italic">Enallagma glaucum</span>, cog = <span class="html-italic">Cordulegaster</span> sp., coe = <span class="html-italic">Coenagrion</span> sp., pse = <span class="html-italic">Pseudagrion</span> pla = Platycnemididae, mas = <span class="html-italic">Macromiia splendens</span>, hyd = <span class="html-italic">Hydatiscus</span> sp., plb = <span class="html-italic">Platambus</span> sp., ere <span class="html-italic">= Eretes</span> sp., Dyt = <span class="html-italic">Dytiscus</span> sp., Lac = <span class="html-italic">Laccophilus</span> sp., hyv = Hydrovatus sp., hyp = <span class="html-italic">Hydrocyphon</span> sp., mic = <span class="html-italic">Microcara</span> sp., elo = <span class="html-italic">Elodes</span> sp., ore = <span class="html-italic">Orectochilus</span> sp., amp = <span class="html-italic">Amphiops</span> sp., hyb = <span class="html-italic">Hydrobius</span> sp., hyd = <span class="html-italic">Hydrochara</span> sp., enh = <span class="html-italic">Enochrus</span> sp., lab = <span class="html-italic">Laccobius</span> sp., Neo = <span class="html-italic">Neohydrophilus</span> sp., chr = Chrysomelidae, dry = <span class="html-italic">Drops</span> sp., Lim = <span class="html-italic">Limnebius</span> sp., hyn = <span class="html-italic">Hydraena</span> sp., hpn = <span class="html-italic">Hydraenopsis</span> sp., (<b>d</b>) Distribution of samples in the SOM based on benthic macroinvertebrate presence–absence data at the different sampling stations and distribution profile of benthic macroinvertebrate taxa in the different groups. The scale bars indicate the weight vector of each taxon (i.e., the abundance of the taxon) in the corresponding SOM units. Dark bars represent a high abundance of taxa, while light bars indicate a low abundance of taxa. lnu <span class="html-italic">= Limnius</span> sp., elm <span class="html-italic">= Elmis</span> sp., Po<span class="html-italic">t = Potamophilus</span> sp., psx <span class="html-italic">= Pseudancyronyx</span> sp., oul <span class="html-italic">= Oulimnius</span> sp., not <span class="html-italic">= Noterus</span> sp., nta <span class="html-italic">= Notonecta</span> sp., ani <span class="html-italic">= Anisops</span> sp., ger <span class="html-italic">= Gerris</span> sp., eum <span class="html-italic">= Eurymetra</span> sp., aqu <span class="html-italic">= Aquarius</span> sp., rha <span class="html-italic">= Rhagotarsis</span> sp., teg <span class="html-italic">= Tenagogonus</span> sp., neo <span class="html-italic">= Neogerris</span> sp., mic <span class="html-italic">= Microvelia</span> sp., rhg <span class="html-italic">= Rhagovelia</span> sp., vel <span class="html-italic">= Velia</span> sp., ang <span class="html-italic">= Angelia</span> sp., oce <span class="html-italic">= Ocelovelia</span> sp., mes <span class="html-italic">= Mesovelia</span> sp., ran <span class="html-italic">= Ranatra</span> sp., Lph <span class="html-italic">= Laccotrephes</span> sp., nep <span class="html-italic">= Nepa</span> sp., hdm <span class="html-italic">= Hydrometra</span> sp., ncr <span class="html-italic">= Naucoris</span> sp., nsp <span class="html-italic">= Neomacrocoris</span> sp., coi <span class="html-italic">= Corixa</span> sp., poo <span class="html-italic">= Hydrocyrius</span> sp., app <span class="html-italic">= Appasus</span> sp., chi <span class="html-italic">= Chironomus</span> sp., syh = Syrphidae, eph = Ephydridae, cer = Ceratopogonidae, Pch = Psychodidae, Sci = Sciomyzidae, cha = Chaoboridae, tab <span class="html-italic">=</span> Tabanidae, Sim = Simuliidae, dix <span class="html-italic">=</span> Dixidae, sca = Scatophagidae, cul <span class="html-italic">= Culex</span> sp., tip = Tipulidae, cle <span class="html-italic">= Cloeon</span> sp., bae <span class="html-italic">= Baetis</span> sp., rhi <span class="html-italic">= Rhitrocloeon</span> sp., adp <span class="html-italic">= Adenophlebia</span> sp., ade <span class="html-italic">= adenophlebiodes</span> sp., Syl <span class="html-italic">= Sylvatica</span> sp.</p>
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