Harnessing the runoff reduction potential of urban bioswales as an adaptation response to climate change

Nature-based solutions (NbS), including China's Sponge City Program (SCP), can address the challenges urban communities face due to surface runoff and flooding. The current capacity of SCP facilities in urban environments falls short of meeting the demands placed on communities by climate change. Bioswales are a form of SCP facility that plays an important role in reducing surface runoff by promoting infiltration. This study assesses the potential of SCP facilities to reduce runoff in urban communities under climate change using the storm water management model. The study site in Ningbo, China, was used to evaluate the potential role of bioswales in reducing runoff risks from climate change. We found that bioswales were most effective in scenarios when rainfall peaks occurred early and were less effective in right-skewed rainfall events. The overall performance of SCP facilities was similar across all climate scenarios. To maintain the current protection level of SCP facilities, bioswales would need to cover at least 4% of the catchment area. These findings from Ningbo provide a useful method for assessing NbS in other regions and indicative values for the increase in the bioswale coverage needed to adapt to climate change.

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.

A new alpha logarithmic-generated class to model precipitation data with theory and inference

Precipitation, or rainfall, is a central feature of the weather cycle and plays a vital role in sustaining life on Earth. However, existing models such as the Poisson, exponential, normal, log-normal, generalized Pareto, gamma, generalized extreme value, lognormal, beta, Gumbel, Weibull, and Pearson type III distributions used for predicting precipitation are often inadequate for precisely representing the complex pattern of rainfall. This study proposes a novel and innovative approach to address these limitations through the new alpha logarithmic-generated (NAL-G) class of distributions. The study authors thoroughly examine the NAL-G class and a unique model, the NAL-Exponential (NAL-Exp) distribution, with a focus on analyzing mathematical properties such as moments, quantile function, entropy, order statistics, and more. Six recognized classical estimation methods are employed, and extensive simulations are conducted to determine the best one. The NAL-Exp distribution demonstrates its high adaptability and value through its superior performance in modeling four distinct rainfall data sets. The results show that the NAL-Exp distribution outperforms other commonly used distribution models, highlighting its potential as a valuable tool in hydrological modeling and analysis. The increased versatility and flexibility of this new approach hold great potential for enhancing the accuracy and reliability of future rainfall predictions.

Event-Based Time Distribution Patterns, Return Levels, and Their Trends of Extreme Precipitation across Indus Basin

This study presented the spatio-temporal characteristics of extreme precipitation events in the Northern Highlands of Pakistan (NHPK). Daily precipitation observations of 30 in situ meteorological stations from 1961 to 2014 were used to estimate the 11 extreme precipitation indices. Additionally, trends in time distribution patterns (TDPs) and return periods were also investigated for event based extreme precipitations (EEP). Results found that the precipitation events with an amount of 160–320 mm and with a concentration ratio of 0.8–1.0 and a duration of 4–7 consecutive days were dominant. The frequency of heavy, very heavy and extremely heavy precipitation days decreased, whereas the frequency of wet, very wet and extremely wet days increased. Most of the indices, generally, showed an increasing trend from the northeast to middle parts. The extreme precipitation events of the 20 and 50-year return period were more common in the western and central areas of NHPK. Moreover, the 20 and 50-year return levels depicted higher values (up to 420 mm) for an event duration with all daily precipitation extremes dispersed in the first half (TDP1) in the Chitral, Panjkora and Jhelum Rivers basins, whilst the maximum values (up to 700 mm) for an event duration with all daily precipitation extremes dispersed in the second half (TDP2) were observed in the eastern part of the NHPK for 20-year and eastern and south-west for 50-year, respectively.

Generating Regional Models for Estimating the Peak Flows and Environmental Flows Magnitude for the Bulgarian-Greek Rhodope Mountain Range Torrential Watersheds

The flood magnitudes with 25, 50, and 100 years return periods and the environmental flows (Qenv) are of outmost importance in the context of hydraulic and hydrologic design. In this study, 25 watershed characteristics were linked with the aforementioned recurrence intervals, peak discharge values, as well as Qenv for 15 pristine torrential watersheds with more than 10 years of streamflow records in the Rhodopi mountain range with a view to generating regional relationships for the assessment of discharge annual peaks and environmental flows regarding the ungauged torrential watersheds in the region. The Log-Pearson Type III probability distribution was fitted in the discharge annual peaks time series, so as to predict Q25, Q50, and Q100, whereas the Tennant method was utilised so as to estimate the environmental flows magnitude. Similarly, the Kolmogorov–Smirnov and the Anderson–Darling tests were performed to verify the distribution fitting. The Principal Components Analysis method reduced the explanatory variables number to 14, whilst the stepwise multiple regression analysis indicated that the exponential model is suitable for predicting the Q25, the power model best forecasted the Q50 and Q100, whereas the linear model is appropriate for Qenv prognosis. In addition, the reliability of the obtained regression models was evaluated by employing the R2, the Nash–Sutcliffe efficiency, and the Index of Agreement Statistical Criteria, which were found to range from 0.91–0.96, 0.88–0.95 and 0.97–0.99, respectively, thereby denoting very strong and accurate forecasts by the generated equations. Thus, the developed equations could successfully predict the peak discharge values and environmental flows within the region’s ungauged watersheds with the drainage size not exceeding 330 km2.

Precipitation Variations under a Changing Climate from 1961–2015 in the Source Region of the Indus River

The source region of the Indus River (SRIR), which is located in the Hindukush, Karakoram and Himalayan (HKH) mountainous range and on the Third Pole (TP), is very sensitive to climate change, especially precipitation changes, because of its multifarious orography and fragile ecosystem. Climate changes in the SRIR also have important impacts on social and economic development, as well as on the ecosystems of the downstream irrigation areas in Pakistan. This paper investigates the changes in precipitation characteristics by dividing the daily precipitation rate into different classes, such as light (0–10 mm), moderate (10.1–25 mm) and heavy precipitation (>25 mm). Daily precipitation data from gauging and non-gauging stations from 1961–2015 are used. The results of the analysis of the annual precipitation and rainy day trends show significant (p < 0.05) increases and decreases, respectively, while light and heavy precipitation show significant decreasing and increasing trends, respectively. The analysis of the precipitation characteristics shows that light precipitation has the highest number of rainy days compared to moderate or heavy precipitation. The analysis of the seasonal precipitation trends shows that only 18 stations have significant increasing trends in winter precipitation, while 27 stations have significant increasing trends in summer precipitation. Both short and long droughts exhibit increasing trends, which indicates that the Indus Basin will suffer from water shortages for agriculture. The results of this study could help policymakers cope with floods and droughts and sustain eco-environmental resources in the study area.

Estimating Maximum Daily Precipitation in the Upper Vistula Basin, Poland

The aim of this study was to determine the best probability distributions for calculating the maximum annual daily precipitation with the specific probability of exceedance (Pmaxp%). The novelty of this study lies in using the peak-weighted root mean square error (PWRMSE), the root mean square error (RMSE), and the coefficient of determination (R2) for assessing the fit of empirical and theoretical distributions. The input data included maximum daily precipitation records collected in the years 1971–2014 at 51 rainfall stations from the Upper Vistula Basin, Southern Poland. The value of Pmaxp% was determined based on the following probability distributions of random variables: Pearson’s type III (PIII), Weibull’s (W), log-normal, generalized extreme value (GEV), and Gumbel’s (G). Our outcomes showed a lack of significant trends in the observation series of the investigated random variables for a majority of the rainfall stations in the Upper Vistula Basin. We found that the peak-weighted root mean square error (PWRMSE) method, a commonly used metric for quality assessment of rainfall-runoff models, is useful for identifying the statistical distributions of the best fit. In fact, our findings demonstrated the consistency of this approach with the RMSE goodness-of-fit metrics. We also identified the GEV distribution as recommended for calculating the maximum daily precipitation with the specific probability of exceedance in the catchments of the Upper Vistula Basin.