Impervious Surfaces In Urban Areas Research Articles

Total nitrogen and phosphorus loads in surface runoff from urban land use (city of Lublin) under climate change

An expansion of impervious surfaces in urban areas leads to increases of nutrient loads discharged with the surface runoff to receivers. A study of a different density of urban development impact on total nitrogen (TN) and phosphorus (TP) loads from the city of Lublin (eastern Poland) with the use of the SWAT (Soil & Water Assessment Tool) model was performed. To distinguish between areas with high and low density of urban development (UHD and ULD), a special analysis of hydrological parameters has been proposed. Moreover, to investigate the impact of climate change, four variant scenarios were taken into account, combining the RCP (representative concentration pathway) 4.5 and 8.5 forecasts and the adopted time horizons (2026–2035 and 2046–2055). The results showed a much higher share of TN and TP from UHD compared to ULD (86%—32 022 kg/year and 89%—2574 kg/year, respectively). In addition, the variant scenarios showed that the forecasted increase in precipitation and temperature will result in increased loads of nutrients from UHD and ULD up to 30%. Furthermore, the current increase of inhabitant number, due to the Ukrainian war migration and the common tendency to convert agricultural land to residential areas, could contribute to further expansion of UHD and ULD areas and an additional increase of nutrient loads.Graphical abstract

Distribution of pyrethroid insecticides in urban storm drain structures: Catch basins, open channels, and outfalls

Surface water runoff can transport contaminants offsite to downstream aquatic ecosystems. The prevalence of impervious surfaces in urban areas enhances surface runoff and contributes to contamination of urban surface streams. Urban areas have complex drainage systems for the conveyance of drainage water, however, there is a dearth of information on the distribution of contaminants within storm drain system structures. Pyrethroid insecticides are among the most used insecticides in urban areas, and trace levels of pyrethroids are known to exert toxicity to aquatic invertebrates. To investigate pyrethroid occurrence and distribution throughout an urban drainage system, samples of water, sediment, algae, and biofilm were collected from catch basins, open channels, and outfalls in Los Angeles County, California, during the dry season. From 3 catch basins, 7 open channels, and 7 outfalls, a total of 28 water samples, 4 sediment samples, 8 algae samples, and 4 biofilm samples were collected and analyzed. Pyrethroid concentrations above the reporting limit were detected in 89% of water samples and all sediment, algae, and biofilm samples, with bifenthrin and cyfluthrin being the most frequently detected compounds. The median total pyrethroid concentrations in water, sediments, algae, and biofilms were 27 ng/L, 88 ng/g, 356 ng/g, and 3556 ng/g, respectively. Bifenthrin concentrations in catch basins were found to be significantly higher than those in open channels or outfalls. Significant correlations were found for various metrics, including between pyrethroid partitioning in water samples and total suspended solids. These findings highlight the role of underground catch basins as a sink as well as a secondary source for contaminants such as pyrethroid insecticides. Prevention of the input of these urban originated contaminants to catch basins is crucial for protecting the water quality of urban surface waters.

Two-dimensional (2D) flood analysis and calibration of stormwater drainage systems using geographic information systems.

In recent years, there has been severe flooding in urban areas as well as coastal and river flooding. Urban flooding is exacerbated by climate change, urbanization, growing population, and the increase of impervious surfaces in urban areas. Stormwater drainage systems that discharge stormwater to a safe location in urban areas are becoming increasingly important. The objective of this study is to analyze and calibrate the flood performance of stormwater drainage systems currently used in the central region of Malatya in a potential flood situation using geographic information systems and the InfoWorks ICM. The model was created using the land use type, buildings, and digital elevation model (DEM), and the analysis was performed by exposing stormwater drainage systems to rainfall events of 5, 10, and 15 min of duration for return periods of 2, 5, and 10 years. The model was then validated using field-observed rainfall and flood data and its performance was evaluated using R2, NSE, RMSE, and MAE metrics. The results showed that the eight stormwater drainage systems currently in operation cannot fully convey stormwater and may pose a risk of loss of life and property in residential areas. In addition, the severity of the flooding was found to increase with an increasing return period.

Investigation of Thermal Dynamics within Rainwater Harvesting Systems and Implications for Design

Rainwater harvesting (RWH) systems have long been used to supply water for a variety of end uses. RWH systems, when used correctly, have proven to be effective at reducing runoff volumes and improving runoff quality at the site and catchment scales. Stormwater runoff from impervious surfaces in urban areas is thermally enriched, causing detrimental impacts to receiving waters. Research has begun to identify stormwater control measures (SCMs) that can offset this thermal loading by reducing total runoff amounts or storing the water in cooler locales. To the authors’ knowledge, no evidence exists in the literature to support whether RWH systems are sources or sinks of thermal pollution or to inform the thermal dynamics within the tanks. Monitoring was undertaken to compare the thermal dynamics of paired above- and below-ground RWH systems from three locations across the eastern United States (Ohio, Virginia-North Carolina, and Florida) over a three-year period. Below-ground RWH systems had lower minimum, mean, and maximum temperatures than their above-ground counterparts. The difference in temperatures between above- and below-ground RWH systems was greatest in late spring and summer months. Water temperatures in the above-ground systems exceeded thermal sensitivity thresholds for regionally important cold-water fish for 0.7%–60.3% of the time. Below-ground tanks exceeded these limits less than 1% of the time; variance in below-ground RWH system water temperatures were 2–3 fold lower than for their above-ground counterparts. Both RWH system types formed a vertical thermal gradient with a temperature drop of 1.72°C–5.6°C from the top of the tank to the bottom. This thermal stratification suggests that RWH systems should be designed to overflow from the bottom of the tank in urbanizing watersheds that support cold-water fish species or are otherwise sensitive to spikes in receiving water temperature.

Comparison of three different numerical implementations to model rainfall‐runoff transformation on roofs

AbstractRoofs represent a high percentage of the impervious surfaces in urban areas, and hence their implementation in urban drainage models is essential for accurate results to be achieved. Current modelling approaches are based on parameters such as a roof's slope and width, its roughness coefficient, and the initial abstraction. In this study, an experimental campaign was performed in order to assess the sensitivity of the roof runoff hydrographs to these parameters. The experimental tests were carried out in a new large‐scale urban drainage facility equipped with a rainfall simulator. The experimental tests were replicated numerically using three different levels of model resolution, from a high fidelity representation with a spatial resolution of 5 mm (which can be considered a digital twin) to a lumped representation. Our experimental results show that, for practical purposes, the sensitivity of the outlet runoff hydrograph to the roof slope tested is negligible. The numerical upscaling analysis carried out showed that flat roofs present a slightly different hydrograph behaviour with greater times of concentration than sloped roofs. No significant sensitivity of the outlet hydrograph to the surface roughness coefficient was found. In terms of numerical modelling, the use of a very high spatial resolution for the roof, which implies a high computational cost, does not affect the results significantly compared to the far simpler lumped approach. The current research involves the first thorough experimental and computational analysis of the runoff over roofs to date.

Modelling the interaction between vegetation and infiltrated stormwater

A major problem associated with sealing native soils with impervious surfaces in urban areas is reduced groundwater recharge. This in turn reduces stream baseflows which has serious implications for freshwater ecosystems. To address this problem, the use of stormwater infiltration systems is becoming increasingly common worldwide. There is, however, substantial uncertainty on the fate of infiltrated stormwater and its interactions with downslope vegetation. This study aimed to investigate the role of vegetation on the amount of infiltrated stormwater reaching the stream. A model using MIKE SHE was constructed, calibrated, and validated based on a real infiltration system which features extensive vegetation between the site of stormwater infiltration and the stream. We then used the calibrated model to predict the amount of infiltrated stormwater reaching the stream in the absence of vegetation. We also predicted the impact of infiltrated stormwater on the evapotranspiration downslope of the system. The results showed that the performance of the model was satisfactory, and the model captured the overall groundwater dynamic very well. The amount of infiltrated stormwater reaching the stream increased by about 17 percent in the absence of vegetation. The model also predicted that evapotranspiration would be 13 percent lower in the warmer months if stormwater was not infiltrated upslope. The results suggest that the choice of location of infiltration systems should consider the potential of vegetation to intercept infiltrated water and impact on achievement of the design objectives, which, in this case, included restoring baseflow. Where increasing the baseflows is not a priority, the increased evapotranspiration afforded by stormwater infiltration could provide important microclimate benefits.

Spatiotemporal Continuous Impervious Surface Mapping by Fusion of Landsat Time Series Data and Google Earth Imagery

The monitoring of impervious surfaces in urban areas using remote sensing with fine spatial and temporal resolutions is crucial for monitoring urban development and environmental changes in urban areas. Spatiotemporal super-resolution mapping (STSRM) fuses fine-spatial-coarse-temporal remote sensing data with coarse-spatial-fine-temporal data, allowing for urban impervious surface mapping at both fine-spatial and fine-temporal resolutions. The STSRM involves two main steps: unmixing the coarse-spatial-fine-temporal remote sensing data to class fraction images, and downscaling the fraction images to sub-pixel land cover maps. Yet, challenges exist in each step when applying STSRM in mapping impervious surfaces. First, the impervious surfaces have high spectral variability (i.e., high intra-class and low inter-class variability), which impacts the accurate extraction of sub-pixel scale impervious surface fractions. Second, downscaling the fraction images to sub-pixel land cover maps is an ill-posed problem and would bring great uncertainty and error in the predictions. This paper proposed a new Spatiotemporal Continuous Impervious Surface Mapping (STCISM) method to deal with these challenges in fusing Landsat and Google Earth imagery. The STCISM used the Multiple Endmember Spectral Mixture Analysis and the Fisher Discriminant Analysis to minimize the within-class variability and maximize the between-class variability to reduce the spectral unmixing uncertainty. In addition, the STCISM adopted a new temporal consistency check model to incorporate temporal contextual information to reduce the uncertainty in the time-series impervious surface prediction maps. Unlike the traditional temporal consistency check model that assumed the impervious-to-pervious conversion is unlikely to happen, the new model allowed the bidirectional conversions between pervious and impervious surfaces. The temporal consistency check was used as a post-procession method to correct the errors in the prediction maps. The proposed STCISM method was used to predict time-series impervious surface maps at 5 m resolution of Google Earth image at the Landsat frequency. The results showed that the proposed STCISM outperformed the STSRM model without using the temporal consistency check and the STSRM model using the temporal consistency check based on the unidirectional pervious-to-impervious surface conversion rule.

Comparison of indices for evaluating building green values based on greenhouse gas emission reductions

The rapid increase of the amount of impervious surfaces in urban areas has generated heat islands and other adverse environmental impacts that subsequently increase greenhouse gas (GHG) emissions. Local governments are now looking for an index to encourage building owners to increase green areas, including green roofs and green walls. Currently, several building green value (BGV) indices can be used to assess building greenness, including the green coverage ratio (GCR), biotope area factor (BAF), green factor (GF), and green plot ratio (GnPR). However, these indices use different sub-indexes and weights, such that the values they output vary significantly. To analyze their applicability, this study compares output values of the BGV indices and estimated GHG emission reductions for three typical buildings under three conditions: no green roof or green wall; a green roof; and a green roof and green walls. The GCR does not include green walls and thus cannot reflect reductions in GHG emissions from green walls. The BAF values are highly correlated to associated GHG emission reductions. The trend of GF values vs. associated GHG emission reductions is less consistent than that using the BAF method because the weights of GF are not consistent with associated thermal resistance improvements. The correlation between GnPR values and associated GHG emission reductions is the strongest among all BGV indices because the ratio of leaf area indexes for green roofs to that of green walls is close to the ratio of U-value reductions from green roofs and green walls.

Assessment of Changes in Land Use/Land Cover and Land Surface Temperatures and Their Impact on Surface Urban Heat Island Phenomena in the Kathmandu Valley (1988–2018)

More than half of the world’s populations now live in rapidly expanding urban and its surrounding areas. The consequences for Land Use/Land Cover (LULC) dynamics and Surface Urban Heat Island (SUHI) phenomena are poorly understood for many new cities. We explore this issue and their inter-relationship in the Kathmandu Valley, an area of roughly 694 km2, at decadal intervals using April (summer) Landsat images of 1988, 1998, 2008, and 2018. LULC assessment was made using the Support Vector Machine algorithm. In the Kathmandu Valley, most land is either natural vegetation or agricultural land but in the study period there was a rapid expansion of impervious surfaces in urban areas. Impervious surfaces (IL) grew by 113.44 km2 (16.34% of total area), natural vegetation (VL) by 6.07 km2 (0.87% of total area), resulting in the loss of 118.29 km2 area from agricultural land (17.03% of total area) during 1988–2018. At the same time, the average land surface temperature (LST) increased by nearly 5–7 °C in the city and nearly 3–5 °C at the city boundary. For different LULC classes, the highest mean LST increase during 1988–2018 was 7.11 °C for IL with the lowest being 3.18 °C for VL although there were some fluctuations during this time period. While open land only occupies a small proportion of the landscape, it usually had higher mean LST than all other LULC classes. There was a negative relationship both between LST and Normal Difference Vegetation Index (NDVI) and LST and Normal Difference Moisture Index (NDMI), respectively, and a positive relationship between LST and Normal Difference Built-up Index (NDBI). The result of an urban–rural gradient analysis showed there was sharp decrease of mean LST from the city center outwards to about 15 kms because the NDVI also sharply increased, especially in 2008 and 2018, which clearly shows a surface urban heat island effect. Further from the city center, around 20–25 kms, mean LST increased due to increased agriculture activity. The population of Kathmandu Valley was 2.88 million in 2016 and if the growth trend continues then it is predicted to reach 3.85 million by 2035. Consequently, to avoid the critical effects of increasing SUHI in Kathmandu it is essential to improve urban planning including the implementation of green city technologies.

Groundwater level simulation and forecasting using interior search algorithm-least square support vector regression (ISA-LSSVR)

Least square support vector regression (LSSVR) is a powerful data-driven method for simulation and forecasting, with two parameters to tune. In this study, these parameters were automatically tuned using the interior search algorithm (ISA) and genetic algorithm (GA). The main purpose is in situ simulation and forecast of monthly groundwater level in Karaj plain, Iran, using historical groundwater level, precipitation, and evaporation data. The results of the interior search algorithm-least support vector regression (ISA-LSSVR) and genetic algorithm-least support vector regression (GA-LSSVR) compared with genetic programming (GP) and adaptive neural fuzzy inference system (ANFIS). Based on average Nash-Sutcliffe criterion, the results revealed that the ISA-LSSVR improves the simulation and forecasting accuracy compared to other methods. Also, the results of the different model structure selection indicate that including precipitation and evaporation does not necessarily improve simulation and forecasting accuracy, but it would increase uncertainty. This increase suggests that groundwater level in the case study is affected by groundwater flow, recharge from leaky urban water infrastructure, and reduced recharge from precipitation due to impervious surfaces in urban areas rather than being solely governed by precipitation and evaporation. Finally, a sensitivity analysis was performed to assess the impacts of optimization algorithm parameters on the simulation and forecasting accuracy. The results indicate high and low sensitivity associated with GA and ISA, respectively. In conclusion, ISA-LSSVR was suggested as the best model due to computational efficiency, low sensitivity to its parameters, and high accuracy compared to other methods.

Extracting impervious surfaces from full polarimetric SAR images in different urban areas

ABSTRACTAccurate mapping of impervious surface in urban areas is of great demand in environmental and socio-economic studies since impervious surface growth is recognized as an indicator of urbanization. To demonstrate the potential of full polarimetric Synthetic Aperture Radar (SAR) in impervious surface detection in different urban areas, this study focused on the exploitation of only SAR data. Three cities with different levels of urbanization – Tehran, Kordkuy, and Arak – have been selected to reduce the effect of input data on achieved results. Advanced Land Observing Satellite/Phased Array L-band Synthetic Aperture Radar (ALOS/PALSAR) images have been classified by support vector machine (SVM) with the help of training data from high-resolution satellite images. Quantitative assessment of classification accuracy revealed that Kordkuy, a not fully developed city (i.e. 84.2%) has the lowest accuracy and Arak, a medium urbanized city, has the highest accuracy (i.e. 90.0%). To further explore the efficiency of full polarimetric SAR, grey level co-occurrence matrix (GLCM) texture of polarized bands has been extracted and put into the classification procedure. The texture information of SAR data provided positive contribution to the impervious surface estimation in three study cases. The improvement is especially noted in dark impervious surface class. All three study areas show an increase of about 6–8% in classification accuracy. The results prove that single use of full polarimetric SAR images holds high potential in identifying impervious surfaces in urban areas. The findings are of great importance in frequent urban impervious surface mapping and monitoring especially in cloud-prone area, where the use of optical data as well as the fusion of optic and SAR data are limited.

A Comparative Study of various Indices for extraction urban impervious surface of Landsat 8 OLI

Impervious surface is one of the major land cover types of urban and suburban environment. Conversion of rural landscapes and vegetation area to urban and suburban land use is directly related to the increase of the impervious surface area. The impervious surface expansion is straight-lined with decreasing green spaces in urban areas. Impervious surface is one of indicator for detecting urban heat islands. This study compares various indices for mapping impervious surfaces using Landsat 8 OLI imagery by optimizing the different spectral characteristics of Landsat 8 OLI imagery. The research objectives are (1) to apply various indices for impervious surface mapping and (2) identifies impervious surfaces in urban areas based on multiple indices and provide recommendations and find the best index for mapping impervious surface in urban areas. In addition to utilizing the index, land use supervised classification method, maximum likelihood classification used for extracting built-up, and non-built-up areas. Accuracy assessment of this research used field data collection as primary data for calculating kappa coefficient, producer accuracy, and user accuracy. The study can also be extended to find the land surface temperature and correlate the impervious surface extraction data with urban heat islands.

Greening up stormwater infrastructure: Measuring vegetation to establish context and promote cobenefits in a diverse set of US cities

Urban greening practices are often adopted to mitigate the negative impacts of increasing impervious surfaces in urban areas. In the United States, green approaches are prevalent in the field of stormwater management as some cities are required to install green stormwater infrastructure (GSI) to meet regulatory requirements. While the primary function of GSI is to address stormwater quality and quantity issues, stormwater managers often tout the ancillary social and environmental benefits, or co-benefits, when promoting their green approach. Co-benefits are difficult to quantify because they span a diverse set of categories and cannot be easily measured using any single metric. Drawing from existing techniques in the field of ecosystem services, this study uses the normalized difference vegetation index (NDVI) to establish trends in ecosystem services in ten US cities with GSI programs and to evaluate the impacts of GSI interventions on urban greenness. Results show that only two of the ten study cities (Seattle, WA and Milwaukee, WI) are getting greener, likely due to maturing vegetation. A case study for one of the 10 cities, Philadelphia, utilizes a stormwater control measure (SCM) inventory of GSI installations, and shows decreasing greenness at the city-wide scale. This case study demonstrates that 62% of GSI project area is composed of non-vegetated SCMs. High-resolution imagery and spatial GSI data identifies densification trends in Philadelphia where non-vegetated SCMs are installed to control post-development stormwater, resulting in a decrease in NDVI. Smaller, vegetated SCMs contribute to greenness and related co-benefits when installed in series, especially when near larger vegetated vacant lots. Moving forward, decision makers are encouraged to incorporate NDVI into their planning processes to move beyond water quality and quantity control measures and directly incorporate and incentivize co-benefits into GSI goals.

Mechanical Properties, Permeability, and Freeze–Thaw Resistance of Pervious Concrete Modified by Waste Crumb Rubbers

Due to the negative effects that derive from large impervious surfaces in urban areas, pervious concrete has been developed, and has become an environmentally friendly pavement material. As a porous and permeable material, pervious concrete presents an overwhelming advantage in solving urban problems, such as flooding, groundwater decline, urban heat island phenomena, etc. Waste crumb rubber has been verified as a feasible modifier for pavement material. The objective of this paper is to explore the effects of rubber particle size and incorporation level on the permeability, mechanical properties, and freeze–thaw resistance of pervious concrete. Two kinds of rubbers (fine and coarse) with four incorporation levels (2%, 4%, 6%, and 8%) are used in the experiment. Permeability, compressive strength, flexural strength, flexural strain, and freeze–thaw resistance are tested. The results indicate that the addition of rubber slightly decreases strength and permeability, but significantly enhances ductility and freeze-thaw resistance. Fine crumb rubber with a suitable incorporation level could remarkably improve the ductility and freeze–thaw resistance of pervious concrete without sacrificing excessively strength and permeability.

Multi-hydro hydrological modelling of a complex peri-urban catchment with storage basins comparing C-band and X-band radar rainfall data

ABSTRACTThe spread of impervious surfaces in urban areas combined with the rise in the intensity of rainfall events as a result of climate change has led to dangerous increases in storm water flows. This paper discusses a new implementation of the fully distributed hydrological model Multi-Hydro (developed at École des Ponts ParisTech), when operating storage basins, and its ability to deal with high-resolution radar rainfall data. The peri-urban area of Massy (south of Paris, France) was selected as a case study for having six of these drainage facilities, used extensively in flood control. Two radar rainfall datasets with different spatiotemporal resolutions were used: Météo-France’s PANTHER rainfall product (C-band) and ENPC’s X-band DPSRI. The rainfall spatiotemporal variability was analysed statistically using Universal Multifractals (UM). Finally, to validate the application, the water level simulations were compared with local measurements in the Cora storage basin located next to the catchment’s single outlet.

Domestic gardens as favorable pollinator habitats in impervious landscapes

Urban expansion is correlated to negative biodiversity trends. The amount of impervious surfaces in urban areas is a determinant of pollinator species assemblages. While the increase in urbanization and impervious surfaces negatively impacts pollinators, cities also encompass urban green spaces, which have a significant capacity to support biodiversity. Among them, domestic gardens that represent a non-negligible fraction of green spaces have been shown to benefit pollinators. Domestic gardens may form habitat clusters in residential areas, although their value at a landscape scale is still unknown. Here, we investigate the combined effects of impervious surfaces and domestic garden areas on pollinator richness. Due to the difficulty of accessing privately owned domestic gardens, we chose to use citizen science data from a well-established French citizen science program known as SPIPOLL. Using regression tree analysis on buffers located from 50 m to 1000 m around the data points, we show the importance of pollinators being in close proximity to domestic gardens as locally favorable habitats that are embedded within a landscape, in which impervious surfaces represent unfavorable areas. We highlight the inter-connection between local and landscape scales, the potential for patches of domestic gardens in residential areas, and the need to consider the potential of gardeners' coordinated management decisions within a landscape context.

The role of heterogeneous lithology in a glaciofluvial deposit on unsaturated preferential flow – a numerical study

Abstract An understanding of preferential flow in the vadose zone is crucial for the prediction of the fate of pollutants. Infiltration basins, developed to mitigate the adverse effects of impervious surfaces in urban areas, are established above strongly heterogeneous and highly permeable deposits and thus are prone to preferential flow and enhanced pollutant transport. This study numerically investigates the establishment of preferential flow in an infiltration basin in the Lyon suburbs (France) established over a highly heterogeneous glaciofluvial deposit covering much of the Lyon region. An investigation of the soil transect (13.5 m long and 2.5 m deep) provided full characterization of lithology and hydraulic properties of present lithofacies. Numerical modeling with the HYDRUS-2D model of water flow in the transect was used to identify the effects of individual lithofacies that constitute the deposit. Multiple scenarios that considered different levels of heterogeneity were evaluated. Preferential flow was studied for several values of infiltration rates applied after a long dry period. The numerical study shows that the high contrast in hydraulic properties of different lithofacies triggers the establishment of preferential flow (capillary barriers and funneled flow). Preferential flow develops mainly for low water fluxes imposed at the surface. The role of individual lithofacies in triggering preferential flow depends on their shapes (layering versus inclusions) and their sizes. While lenses and inclusions produce preferential flow pathways, the presence of the surface layer has no effect on the development of preferential flow and it only affects the effective hydraulic conductivity of the heterogeneous transect.

Modelling infiltration enhancement in a tropical urban catchment for improved stormwater management

AbstractTo mitigate the impacts of impervious surfaces in urban areas, structures such as bioretention systems and permeable pavements have been installed to enhance infiltration in many countries. However, relatively little knowledge is available regarding the performance of such infiltration‐based structures in humid tropical and highly urbanized areas. This study investigates the feasibility of enhancing the infiltration of stormwater in tropical urbanized areas using Singapore as a case study. It first shows that the rainfall depth and intensity are both high, but the time interval between consecutive rainfall event is long in Singapore. It then numerically simulates single‐event local infiltration and finds that the fraction of infiltrated rainfall is actually high. It finally performs catchment‐scale simulations and finds that bioretention systems can enhance infiltration and groundwater recharge particularly during wet periods. However, local mounding of groundwater can be significant and can hinder the performance of those structures. Furthermore, with 5% of catchment area being converted to such structures, the infiltration of the entire catchment is enhanced but still not yet up to the natural level. To increase the overall effectiveness, future studies can look into bioretention systems with underdrain systems. Copyright © 2016 John Wiley & Sons, Ltd.

Smart adaptation activities and measures against urban flood disasters

Frequent inundation has become a serious problem in urban areas. It is necessary to improve rainwater retention/infiltration in the urban watershed. The purpose of this study is to report how private rainwater-retention/harvesting facilities can be spread gradually but steadily in the city by citizen-initiated activities. Rainwater harvesting tanks were installed intensively and a rainwater harvesting house was constructed in the city of Fukuoka, Japan after the city experienced a flood disaster. The former enhanced users’ daily preparedness for emergency, and the latter inspired construction of a rainwater-harvesting housing complex. A public elementary school is in use from April 2016, which is inspired by these facilities. The school premises are located on the land reclaimed from an old irrigation pond. Thus the school needs to be adapted to this condition. 3000m3 of rainwater can be retained within the premises. The amounts of retention and discharge are monitored, and the data is utilized for science education. In big cities, people tend to depend too much on the top-down, mega-system, which invites more impervious surfaces in urban areas. Bottom-up, individual/collaborative approaches should be adopted in order to achieve multiple purposes of preventing/mitigating disasters, preserving/conserving ecosystems and nurturing/rebuilding communities in the city.

Influences of Land Use Change on Baseflow in Mountainous Watersheds

It is crucial for effective water resource management in a watershed that the relationship between land use changes and baseflow. This study quantifies the influence of land use changes on the baseflow dynamics using a hydrological model and partial least-squares (PLS) regression in the Upper Du Watershed (8961 km2), China. Our study suggests that forest can be a major factor with a negative impact on the baseflow. Additionally, farmland and urban land have second-order negative effects on the baseflow dynamics. Baseflow increases when forest is replaced by farmland because the evapotranspiration (ET), associated with baseflow recession, is weaker and shorter in duration in the farmland than in the forest. The conversion of forest to urban land increases baseflow owing to the presence of non-contributing impervious surfaces in urban areas, which prevents the urban land from intercepting the baseflow discharge. These results indicate that the baseflow dynamics are closely associated with varying land use types within a watershed. Thus, this study is intended to provide a deeper understanding of the baseflow processes and useful quantitative information on land use factors in watersheds, enabling more informed decision-making in forest and watershed management.