It is important to evaluate the effectiveness of permeable pavements on flood mitigation at different spatial scales for their effective application, for example, sponge city construction in China. This study evaluated the effectiveness of three types of permeable pavements (i.e., permeable asphalts (PA), permeable concretes (PC), and permeable interlocking concrete pavers (PICP)) on flood mitigation at a community scale in China using a hydrological model. In addition, the effects of clogging and initial water content in permeable pavements on flood mitigation performance were assessed. The results indicated that in 12 scenarios, permeable pavements reduced total surface runoff by 1–40% and peak flow by 7–43%, respectively. The hydrological performance of permeable pavements was limited by clogging and initial water content. Clogging resulted in the effectiveness on total surface runoff reduction and peak flow reduction being decreased by 62–92% and 37–65%, respectively. By increasing initial water content at the beginning of the simulation, the effectiveness of total runoff reduction and peak flow reduction decreased by 57–85% and 37–67%, respectively. Overall, among the three types of permeable pavements, PC without clogging had the best performance in terms of flood mitigation, and PICP was the least prone to being clogged. Our findings demonstrate that both the type and the maintenance of permeable pavements have significant effects on their performance in the flood mitigation.

Precipitation, as a primary hydrological variable in the water cycle plays an important role in hydrological modeling. The reliability of hydrological modeling is highly related to the quality of precipitation data. Accurate long-term gauged precipitation in the Mekong River Basin, however, is limited. Therefore, the main objective of this study is to assess the performances of various gridded precipitation datasets in rainfall-runoff and flood-inundation modeling of the whole basin. Firstly, the performance of the Rainfall-Runoff-Inundation (RRI) model in this basin was evaluated using the gauged rainfall. The calibration (2000–2003) and validation (2004–2007) results indicated that the RRI model had acceptable performance in the Mekong River Basin. In addition, five gridded precipitation datasets including APHRODITE, GPCC, PERSIANN-CDR, GSMaP (RNL), and TRMM (3B42V7) from 2000 to 2007 were applied as the input to the calibrated model. The results of the simulated river discharge indicated that TRMM, GPCC, and APHRODITE performed better than other datasets. The statistical index of the annual maximum inundated area indicated similar conclusions. Thus, APHRODITE, TRMM, and GPCC precipitation datasets were considered suitable for rainfall-runoff and flood inundation modeling in the Mekong River Basin. This study provides useful guidance for the application of gridded precipitation in hydrological modeling in the Mekong River basin.

The pressures on water system are increasing in cities. Rapid urbanisation caused by booming population leads to more impervious area and less infiltration, with the consequence of larger runoff volume and higher flood risk. Launched in 2014, the low impact development (LID), an important part of Sponge City in China initiative, invests in projects that aim to restore the water cycle in the urban area. A comprehensive understanding of the performance of LID measures at watershed scale under different rainfall scenarios and life cycle costs is necessary. The objectives of this study are to assess the hydrological performance and to identify the optimal LID design by using SWMM model and life cycle cost (LCC) method. This study found that LID practices, including bioretention, grass swale, and permeable pavement, showed good performance on urban storm mitigation at watershed scale under different rainfall scenarios. Furthermore, the rates of surface runoff reduction were largely insusceptible to the change of rainfall volume and duration. Regarding the cost‐effectiveness, the priority was grass swale > bioretention > permeable pavement in the study area. The optimal LID scenario was the combination of these three types of LID. The proposed approach can help the decision‐makers to determine the preferable LID plan suitable for the local communities.

As a result of urbanization and climate change, urban areas are increasingly vulnerable to flooding, which can have devastating effects on the loss of life and property. Remote sensing technology can provide practical help for urban flood disaster management. This research presents a review of urban flood-related remote sensing to identify research trends and gaps, and reveal new research opportunities. Based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), the systematic literature search resulted in 347 documents classified as geography, disaster management application, and remote sensing data utilization. The main results include 1. most of the studies are located in high-income countries and territories and inland areas; 2. remote sensing for observing the environment was more popular than observing the building; 3. the most often applied disaster management activities were vulnerability assessment and risk modeling (mitigation) and rapid damage assessment (response); 4. DEM is often applied to simulate urban floods as software inputs. We suggest that future research directions include 1. coastal urban study areas in non-high-income countries/territories to help vulnerable populations; 2. understudied disaster management activities, which often need to observe the buildings in more urban areas; 3. data standardization will facilitate integration with international standard methods for assessing urban floods.

Analyzing the spatiotemporal characteristics and causes of landscape pattern changes in watersheds around big cities is essential for understanding the ecological consequence of urbanization and provides a basic reference for the watershed management. This study used a land-use transition matrix and landscape indices to explore the spatiotemporal change of land use and landscape pattern over Liuxihe River basin of Guangzhou in the southeast of China from 1980 to 2015 with multitemporal Landsat satellite data in response to the rapid urbanization process. Primary temporal and spatial influencing factors were first quantitatively identified through grey relation analysis (calculating correlation degree between land use changes and influencing factors) and Geodetector (detecting landscape spatial heterogeneity and its driving factors), respectively. Considerable spatial and temporal differences in land use and landscape pattern changes were observed herein, thus determining the influencing factors of these differences in the Liuxihe River basin. These changes were characterized by a large increase in construction land converted from cropland, particularly in the middle and lower reaches of the basin from 2000 to 2010, causing dramatic fragmentation and homogenization of the landscape pattern there. Meanwhile, the landscape pattern gradually transitioned from an agricultural land use dominant landscape to a construction land use dominant landscape in these regions. Furthermore, the rapid growth of a nonagricultural population and the transformation of industry primarily caused the temporal changes of landscape pattern, and the landscape spatial heterogeneity was mainly caused by the interaction of complicated geomorphology and anthropogenic activities in different spatial locations, particularly after 2000. This study not only provides an improved approach to quantifying the main spatiotemporal influencing factors of landscape pattern changes during different time periods, but also offers a reference for decision-makers to formulate optimal strategies on ecological protection and urban sustainable development of different regions in this study area.