Rapid Urbanization Impact on the Hydrological Processes in Zhengzhou, China

Research on machine learning hybrid framework by coupling grid-based runoff generation model and runoff process vectorization for flood forecasting

One of the important non-engineering measures for flood forecasting and disaster reduction in watersheds is the application of machine learning flood prediction models, with Long Short-Term Memory (LSTM) being one of the most representative time series prediction models. However, the LSTM model has issues of underestimating peak flows and poor robustness in flood forecasting applications. Therefore, based on a thorough analysis of complex underlying surface attributes, this study proposes a framework for distinguishing runoff models and integrates a Grid-based Runoff Generation Model (GRGM). Simultaneously considering the time series characteristics of runoff processes, including rising, peak, and recession, a runoff process vectorization (RPV) method is proposed. In this study, a hybrid deep learning flood forecasting framework, GRGM-RPV-LSTM, is constructed by coupling the GRGM, RPV, and LSTM neural network models. Taking the Jialu River in the Zhongmu station control basin as an example, the model is validated using 18 instances of measured floods and compared with the LSTM and GRGM-LSTM models. The study shows that the GRGM model has a relative error and average coefficient of determination for simulating runoff of 8.41% and 0.976, respectively, indicating that considering the spatial distribution of runoff patterns leads to more accurate runoff calculations. Under the same lead time conditions, the GRGM-RPV-LSTM hybrid forecasting model has a Nash efficiency coefficient greater than 0.9, demonstrating better simulation performance compared to the GRGM-LSTM and LSTM models. As the lead time increases, the GRGM-RPV-LSTM model provides more accurate peak flow predictions and exhibits better robustness. The research findings can provide scientific basis for coordinated management of flood control and disaster reduction in watersheds.

Possibility of using the STORAGE rainfall generator model in the flood analyses in urban areas

In this investigation, we evaluated the applicability of the Stochastic Rainfall Generator (STORAGE) as a data source for deriving design hydrographs in urban catchments. This assessment involved a comparison with design rainfall calculated using Intensity-Duration-Frequency (IDF) curves derived from observed time-series data. The resulting design rainfall values from both methods were incorporated into a hydrodynamic model of the storm sewer network. To simulate peak discharge and flood areas, the Storm Water Management Model (SWMM) program was employed in conjunction with SCALGO. Our findings indicate that design rainfall values obtained from the STORAGE model exceeded those derived from the observed time-series, with a more pronounced difference for shorter rainfall durations. Simulations further revealed that peak runoff disparities between the two approaches were most evident at a 0.10 probability of exceedance compared to a 0.01 probability. Hydrodynamic simulations demonstrated that the flooding volume induced by design rainfall based on the STORAGE model surpassed that resulting from observed rainfall. Across all events, both the flooding volume and area from STORAGE were consistently greater than those derived from IDF curves. The integration of the SWMM model with the SCALGO application introduced a novel functionality for dynamic visualization of flooding, offering valuable insights for effective flood management in urban areas.

BK-SWMM flood simulation framework is being proposed for urban storm flood modeling based on uncertainty parameter crowdsourcing data from a single functional region

In recent years, urban flood disasters caused by sudden heavy rains have become increasingly severe, posing a serious threat to urban public infrastructure and the life and property safety of residents. Rapid simulation and prediction of urban rain-flood events can provide timely decision-making reference for urban flood control and disaster reduction. The complex and arduous calibration process of urban rain-flood models has been identified as a major obstacle affecting the efficiency and accuracy of simulation and prediction. This study proposes a multi-scale urban rain-flood model rapid construction method framework, BK-SWMM, focusing on urban rain-flood model parameters and based on the basic architecture of Storm Water Management Model (SWMM). The framework comprises two main components: 1) constructing a SWMM uncertainty parameter sample crowdsourcing dataset and coupling Bayesian Information Criterion (BIC) and K-means clustering machine learning algorithm to discover clustering patterns of SWMM model uncertainty parameters in urban functional areas; 2) coupling BIC and K-means with SWMM model to form BK-SWMM flood simulation framework. The applicability of the proposed framework is validated by modeling three different spatial scales in the study regions based on observed rainfall-runoff data. The research findings indicate that the distribution pattern of uncertainty parameters, such as depression storage, surface Manning coefficient, infiltration rate, and attenuation coefficient. The distribution patterns of these seven parameters in urban functional zones indicate that the values are highest in the Industrial and Commercial Areas (ICA), followed by Residential Areas (RA), and lowest in Public Areas (PA). All three spatial scales' REQ, NSEQ, and RD2 indices were superior to the SWMM and less than 10%, greater than 0.80, and greater than 0.85, respectively. However, when the study area's geographical scale expands, the simulation's accuracy will decline. Further research is required on the scale dependency of urban storm flood models.

How to simulate future scenarios of urban stormwater management? A novel framework coupling climate change, urbanization, and green stormwater infrastructure development

Climate change, urbanization, and green stormwater infrastructure (GSI) planning policies lead to uncertainties in future urban sustainability. Coupling multiple influencing factors such as climate change, urbanization, and GSI development, this study proposes a novel framework for simulating future scenarios of urban stormwater. Subsequently, the changes in annual surface runoff and runoff pollutants in Shanghai's new and old urban areas were compared and analyzed based on 35 typical future and seven baseline scenarios. The following results were obtained: 1) The runoff control rate of the new urban area was significantly higher than that of the old urban area before GSI construction. After GSI construction, both areas could control stormwater runoff and pollutants, while the decline in efficiency in GSI facilities enormously impacted the old area. 2) Surface runoff in the new urban area was mainly affected by urbanization, while climate change was a major factor in the old urban area; runoff pollutants in new and old urban areas were mainly affected by urbanization, and the change in pollutants in new areas was more pronounced. 3) GSI facilities were unlikely to guarantee the quantity and quality of water resources, especially in scenarios where the efficiency of GSI facilities decreases. In old urban areas, the more extreme climate change and urbanization were, the more significant the effect of improving stormwater management facilities. Our findings showed that future studies on stormwater management should specifically consider the different characteristics of new and old urban regions, pay attention to the maintenance and management of GSI facilities, and build adaptive strategies to cope with climate change, urbanization, and GSI facility destruction.

Individual and combined impacts of urbanization and climate change on catchment runoff in Southeast Queensland, Australia

Assessing the impacts of climate change and land-use change is of critical importance, particularly for urbanized catchments. In this study, a novel framework was used to examine and quantify these impacts on the runoff in six catchments in Southeast Queensland, Australia. For each catchment, temporal variations in impervious areas were derived from six satellite images using a sub-pixel classification technique and incorporated into the SIMHYD hydrological model. This model was satisfactorily calibrated and validated with daily runoff observations (0.63 ≤ Nash-Sutcliffe efficiency coefficient ≤ 0.94, percent bias ≤ ±18 %) and was used to produce baseline runoff for 1986-2005 in these six catchments. The projected population increase was used to predict future imperviousness based on the linear relationship between the two. The projected rainfall and evapotranspiration were derived from the ensemble means of the eight general circulation models. Catchment runoff was projected under two climate change scenarios (RCP4.5 and 8.5), three urbanization scenarios (low, medium, and high), and six combined scenarios for two future periods (2026-2045 and 2046-2065). Comparing with the baseline, it was found that (1) climate change alone would lead to a -3.8 % to -17.6 % reduction in runoff among the six catchments, for all scenarios and both future periods; (2) a 11.8 % to 78 % increase in runoff was projected under the three urbanization scenarios, and (3) a decrease in runoff due to climate change would moderate the increase in runoff caused by urbanization. For example, the combined effect would be a 54 % increase in runoff, with a -17.2 % decrease due to climate change and 78 % increase due to urbanization. Overall, runoff in the six catchments may be significantly affected by urban expansion. From this study, decision makers could gain a better understanding of the relative importance of the effects of climate and land-use change, which can be applied when developing future long-term water management plans at the catchment scale.

Evaluation of climate change, urbanization, and low-impact development practices on urban flooding

The Personal Computer Storm Water Management Model was used in this study to evaluate the potential impacts of climate change, urbanization, and low-impact developments (LIDs) on urban flooding in Robe town, Ethiopia. To achieve the objective, four scenarios were developed in order to simulate changes in peak runoff, inundated volume, and the performance of existing drainage systems. The findings revealed that as urbanization increased from 10% to 70%, the inundated volume of nodes and peak runoff increased from 35,418 to 52,118 × 10³ m³ and 89.4-111.96 m³/s, respectively. Furthermore, the peak runoff in response to climate change is increased by 46.9%, 34.8%, and 37.5%, respectively, as a result of the Rossby Centre Regional Climate Model version 4 (RCA4), Regional Atmospheric Climate Model (RACMO22T), and the hydrostatic version of the regional model (REMO2009). Overall, the findings showed that existing drainage systems were unable to collect and convey the amplified inundation from different simulated scenarios, and the Welmel sub-city to roundabout was threatened by increased flooding, causing significant damage to properties and infrastructure. The implemented LIDs are capable of reducing the expected peak runoff, flooding magnitude, and flooded junctions in climate change and urbanization scenarios; however, combining both mitigation measures can further reduce the study area. The implementation of a mitigation strategy with adequate drainage systems will be required to mitigate the flooding risks in Robe town.

Development of Flood Hazard Index (FHI) of the Kelantan River Catchment Using Geographic Information System (GIS) Based Analytical Hierarchy Process (AHP)

Kelantan has been facing several cases of catastrophic flooding, causing significant damage to this area. Heavy monsoon rainfall is believed to trigger those floods. This study aims to identify and classify the flood occurrence using the Kelantan River catchment’s flood hazard index (FHI) based on the analytical hierarchy process (AHP). This study developed the FHI using the AHP based on spatial analysis in the geographic information system (GIS) environment. Six physical parameters were selected: annual rainfall, slope, river density, land use and land cover (LULC); elevation; and soil permeability. According to the AHP model, the annual rainfall was the first ranked parameter in terms of importance weight score. Moreover, Tanah Merah and Jeli were the high-risk areas for floods. The present study suggests that the GIS-based AHP method can be highly effective for mapping flood hazards and benefit flood management decision-making.

Spatio-Temporal Responses of Precipitation to Urbanization with Google Earth Engine: A Case Study for Lagos, Nigeria

Lagos, Nigeria, is considered a rapidly growing urban hub. This study focuses on an urban development characterization with remote sensing-based variables for Lagos as well as understanding spatio-temporal precipitation responses to the changing intensity of urban development. Initially, a harmonic analysis showed an increase in yearly precipitation of about 3 mm from 1992 to 2018 for the lower bound of the fitted curve and about 2 mm for the upper bound. The yearly total precipitation revealed no significant trend based on the Mann–Kendall trend test. Subsequent analyses first involved characterizing urbanization based on nighttime light and population density data and then combined them together for the final analysis. Each time, the study area was subdivided into four zones: Zone 0, Zone 1, Zone 2, and Zone 3, which refer to non-urbanized, low-urbanized, mid-urbanized, and highly urbanized regions, respectively. The results from the Google Earth Engine-based analysis uncovered that only Zone 1 has a statistical monotonic increasing precipitation trend (Tau 0.29) with a 0.03 significance level when the combined criteria were applied. There is about a 200 mm precipitation increase in Zone 1. Insignificant patterns for the other three zones (Zone 2, Zone 3, and Zone 4) indicate that these trends are not consistent, they might change over time, and fluctuate heavily.

Statistical and Hydrological Evaluations of Water Dynamics in the Lower Sai Gon-Dong Nai River, Vietnam

The water levels downstream of the Sai Gon and Dong Nai river in Southern Vietnam have been significantly changed over the last three decades, leading to severe impacts on urban flooding and salinity intrusion and threating the socio-economic development of the region and lives of many local people. In this study, the Mann-Kendall (MK) and trend-free prewhitening (TFPW) tests were applied to detect the water level trends and changepoints based on a water level time series at six gauging stations that were located along the main rivers to the sea over 1980–2019. The results indicated that the water level has rapidly increased by about 0.17 to 1.8 cm/year at most gauge stations surrounding Ho Chi Minh City, strongly relating to urbanization and the dike polder system’s impacts that eliminates the water storage space. In addition, the operation of upstream reservoirs has contributed to water level changes with significant consequences since the high-water level at Tri An station on the Dong Nai river occurs 1000–1500 times compared to 300–500 times before the operation. Although the effects of the flows from the sea are less than the two other factors, the local government should seriously consider water level changes, especially in the coastal regions. Our study contributes empirical evidence to evaluate the water level trends in the planning and development of infrastructure, which is necessary to adapt to future changes in Southern Vietnam’s hydrologic system.