A comparative study on urban waterlogging susceptibility assessment based on multiple data-driven models.

Accurate identification of urban waterlogging areas and assessing waterlogging susceptibility are crucial for preventing and controlling hazards. Data-driven models are utilized to forecast waterlogging areas by establishing intricate relationships between explanatory variables and waterlogging states. This approach tackles the constraints of mechanistic models, which are frequently complex and unable to incorporate socio-economic factors. Previous research predominantly employed single-type data-driven models to predict waterlogging locations and evaluation of their effectiveness. There is a scarcity of comprehensive performance comparisons and uncertainty analyses of different types of models, as well as a lack of interpretability analysis. The chosen study area was the central area of Beijing, which is prone to waterlogging. Given the high manpower, time, and economic costs associated with collecting waterlogging information, the waterlogging point distribution map released by the Beijing Water Affairs Bureau was selected as labeled samples. Twelve factors affecting waterlogging susceptibility were chosen as explanatory variables to construct Random Forest (RF), Support Vector Machine with Radial Basis Function (SVM-RBF), Particle Swarm Optimization-Weakly Labeled Support Vector Machine (PSO-WELLSVM), and Maximum Entropy (MaxEnt). The utilization of diverse single evaluation indicators (such as F-score, Kappa, AUC, etc.) to assess the model performance may yield conflicting results. The Distance between Indices of Simulation and Observation (DISO) was chosen as a comprehensive measure to assess the model's performance in predicting waterlogging points. PSO-WELLSVM exhibited the highest performance with a DISOtest value of 0.63, outperforming MaxEnt (0.78), which excelled in identifying areas highly susceptible to waterlogging, including extremely high susceptibility zones. The SVM-RBF and RF models demonstrated suboptimal performance and exhibited overfitting. The examination of waterlogging susceptibility distribution maps predicted by the four models revealed significant spatial differences due to variations in computational principles and input parameter complexities. The integration of four WSAMs based on logistic regression has been shown to significantly decrease the uncertainty of a single data-driven model and identify the most flood-prone areas. To improve the interpretability of the data model, a geographical detector was incorporated to demonstrate the explanatory capacity of 12 variables and the process of waterlogging. Building Density (BD) exhibits the highest explanatory power in relation to explain waterlogging susceptibility (Q value = 0.202), followed by Distance to Road, Frequency of Heavy Rainstorms (FHR), DEM, etc. The interaction between BD and FHR results in a nonlinear increase in the explanatory power of waterlogging susceptibility. The presence of waterlogging susceptibility risk in the research area can be attributed to the interactions of multiple factors.

What drives the changing characteristics of phytoplankton in urban lakes: Climate, hydrology, or human disturbance?

Phytoplankton in shallow urban lakes are influenced by various environmental factors. However, the long-term coupling effects and impact pathways of these environmental variables on phytoplankton remain unclear. This is an emerging issue due to high urbanization and the resultant complex climate, lake hydrology and morphology, human interference, and water quality parameter changes. This study used Tangxun Lake, the largest urban lake in the Yangtze River Economic Belt, as an example to assess for the first time the individual contributions and coupled effects of four environmental variables and fourteen indicators on chlorophyll-a (Chla) concentrations under two scenarios from 2000 to 2019. Additionally, the influence pathways between the environmental variables and Chla concentration were quantified. The results indicated that the Chla concentration was most affected by lake hydrology and morphology, as were the total nitrogen, total phosphorus, and transparency. Especially after urbanization (2015-2019), the coupling effect of human interference, lake hydrology and morphology, and water quality parameters was strongest (18%). This is mainly due to fluctuations in the lake water level and an increase in the shape index of lake morphology, large amounts of nutrients were input, which reduced lake transparency and indirectly changed the Chla content. In addition, due to the rapid development of Wuhan city, the expansion of construction land has led to an increase in impervious surface area and a decrease in lake area. During periods of intense summer rainfall, a substantial amount of pollutants entered the lakes through surface runoff, resulting in decreased lake transparency, and elevated concentrations of nitrogen and phosphorus, indirectly increasing the Chla content. This study provides a scientific basis for aquatic ecological assessment and pollution control in urban shallow lakes.

Study on spatiotemporal dynamic characteristics of precipitation and causes of waterlogging based on a data-driven framework.

The discernible alterations in regional precipitation patterns, influenced by the intersecting factors of urbanization and climate change, exert a substantial impact on urban flood disasters. Based on multi-source precipitation data, a data-driven model fusion framework was constructed to analyze the spatial and temporal dynamic distribution characteristics of precipitation in Beijing. Wavelet analysis method was used to reveal the periodic variation characteristics and multi-scale effects of precipitation, and the machine learning method was used to characterize the spatiotemporal dynamic change pattern of precipitation. Finally, geographical detector was used to explore the causes of waterlogging in Beijing. The research outcomes reveal a disparate distribution of precipitation across the year, with 78 % of the total precipitation occurring during the flood season. The principal periodic cycles observed in annual cumulative precipitation (ACP) were identified at 21, 13, and 9-year intervals. Spatially, while a decreasing trend in precipitation was observed in most areas of Beijing, 63.4 % of the region exhibited an escalating concentration trend, thereby heightening the risk of urban waterlogging. Machine learning model clustering elucidated three predominant spatial dynamic distribution patterns of precipitation in Beijing. The utilization of web crawler technology to acquire water accumulation data addressed challenges in obtaining urban waterlogging data, and validation through Landsat8 images enhanced data reliability and authenticity. Factor detection shows that road network density, topography, and precipitation were the main factors affecting urban waterlogging. These findings hold significant implications for informing flood control strategies and emergency management protocols in urban areas across China.

Impacts of groundwater level fluctuation on soil microbial community, alkane degradation efficiency and alkane-degrading gene diversity in the critical zone: Evidence from an accelerated water table fluctuation simulation.

Petroleum hydrocarbons are hazardous to ecosystems and human health, commonly containing n-alkanes and polycyclic aromatic hydrocarbons. Previous researches have studied alkane degraders and degrading genes under aerobic or anaerobic conditions, but seldom discussed them in the intermittent saturation zone which is a connective area between the vadose zone and the groundwater aquifer with periodic alteration of oxygen and moisture. The present study investigated the difference in alkane degradation efficiency, bacterial community, and alkane degrading gene diversity in aerobic, anaerobic, and aerobic-anaerobic fluctuated treatments. All biotic treatments achieved over 90% of n-alkane removal after 120 days of incubation. The removal efficiencies of n-alkanes with a carbon chain length from 16 to 25 were much higher in anaerobic scenarios than those in aerobic scenarios, explained by different dominant microbes between aerobic and anaerobic conditions. The highest removal efficiency was found in fluctuation treatments, indicating an accelerated n-alkane biodegradation under aerobic-anaerobic alternation. In addition, the copy numbers of the 16S rRNA gene and two alkB genes (alkB-P and alkB-R) declined dramatically when switched from aerobic to anaerobic scenarios and oppositely from anaerobic to aerobic conditions. This suggested that water level fluctuation could notably change the presence of aerobic alkane degrading genes. Our results suggested that alkane degradation efficiency, soil microbial community, and alkane-degrading genes were all driven by water level fluctuation in the intermittent saturation zone, helping better understand the effects of seasonal water table fluctuation on the biodegradation of petroleum hydrocarbons in the subsurface environment.

The divergence between potential and actual evapotranspiration: An insight from climate, water, and vegetation change.

Recently, unprecedented extreme drought has appeared around the world. As the most direct signal of drought, evapotranspiration deserves a more systematic and comprehensive study. Further depicting their divergence of potential (ETp) and actual evapotranspiration (ETa) will help to explore the limitation of evapotranspiration. In this paper, the multi-source remote sensing datasets from the Climate Research Unit (CRU), Gravity Recovery and Climate Experiment (GRACE) and its follow-on experiment (GRACE-FO), the Global Land Data Assimilation System (GLDAS), and the Moderate Resolution Imaging Spectroradiometer (MODIS) during 2002 to 2020 were employed to explore the influence of meteorological, hydrological and botanical factors on ETp, ETa and their divergence - reduction of evapotranspiration (Er) which represents regional vegetation and water limitations. According to the Pearson correlation analysis and the Boruta Algorithm based on Random Forest, the temperature is the first decisive promoter of evapotranspiration in the most area while the sparse vegetation is the primary or second determinant limiting the evapotranspiration in 61.84% of the world. In addition, the Coupled Model Intercomparison Project (CMIP6) data from 2030 to 2090 and the support vector machine regression (SVMR) model were applied to predict the future global ETp, ETa and Er on the pixel scale. Predicted results of the model considering the water change not only can highly improve the model performance (with higher R2), but also can simulate the drought in Europe and the more intense ETa in Africa. Thus, Er proposed in this study provide a good reference for regional ETa except for ETp. The future evapotranspiration value derived by introducing the water storage changes into the machine learning model in this study is also valuable for climate change adaptation and drought warning.

Integrating satellite observations and human water use data to estimate changes in key components of terrestrial water storage in a semi-arid region of North China.

Sustainable management strategies for water resources rely on accurate knowledge about the dynamics of hydrological processes, especially in drylands, where freshwater is the limiting factor for the development of human society and ecosystems. The populated Loess Plateau (LP) in North China is a typical semi-arid region where competition for water between people and nature is worth noting because of afforestation promoted by the Grain to Green Program. In this study, changes in key components of terrestrial water storage (TWS) in the LP were explored using a multi-satellite approach, including Gravity Recovery and Climate Experiment (GRACE) observations and Earth observations of precipitation, evapotranspiration and soil moisture. By integrating data on human water use from different sources with satellite observations, we were able to examine the mechanisms driving these changes. The results demonstrated that, according to an evaluation based on reproducing TWS computed from the regional water balance in the LP, the mascon solution of the Center for Space Research (CSR) at University of Texas at Austin performed best out of the commonly used GRACE products. Regional TWS derived from the CSR mascon solution in the LP decreased significantly for the period 2003-2015. Significant decreases were also detected for regional ground water storage (GWS) estimated by decomposing the GRACE TWS using multi-sources remote sensing data. GWS made the greatest contribution to the changes in TWS. Increased plant transpiration was one reason for the decreasing trend of GWS. Because changes in precipitation, soil moisture and water consumed by irrigation were minor at regional scales, we concluded that the increase of transpiration is driven by deep-rooted trees planted, which use the part of precipitation that previously recharged groundwater. The findings from this study are valuable for water resource management and ecological restoration in semi-arid regions with high populations.

Anthropogenic influences on the water quality of the Baiyangdian Lake in North China over the last decade.

Baiyangdian Lake, the largest shallow lake in the North China Plain, is essential for maintaining ecosystem functioning in this highly populated region. To explore the influences of human activities on the lake's water quality, an improved Water Quality Index (WQI) method and multivariate statistical techniques were adopted to assess the temporal and spatial variations of the lake's water quality and explore the dominant factors of these variations. Datasets for 11 water quality parameters from six monitoring stations were used to evaluate the period spanning from 2006 to 2016. Assessment of the annual WQI showed that the water quality of the lake has generally improved over the past decade. Cluster analysis divided 12 months into the dry and wet periods and the six monitoring stations into those located in the western and eastern parts of the lake. Discriminant analysis demonstrated that with only two parameters (water temperature and fluoride) and six parameters (dissolved oxygen, ammonia nitrogen, total nitrogen, total phosphorus, anionic surfactant, and fecal coliform), 96.0% and 93.8% of the water quality data can be classified into the correct spatial and temporal clusters, respectively. For the principal component analysis and factor analysis, the varifactors detected for the two temporal clusters were similar, and varifactors related to pollution explained more variance in the water quality variation than the ones representing natural factors. For the two spatial clusters, the varifactors were different, indicating they are influenced by different types of anthropogenic activities. Correlation analysis between lake water level and water quality indicated that environmental water allocation to the lake generally improve water quality. These findings provide a more thorough understanding of driving mechanism of water quality and may be helpful for making environmental management decisions in Baiyangdian Lake and other large, shallow lakes in highly populated dryland regions.

Estimating the Bed-Load Layer Thickness in Open Channels by Tsallis Entropy.

In the research field of river dynamics, the thickness of bed-load is an important parameter in determining sediment discharge in open channels. Some studies have estimated the bed-load thickness from theoretical and/or experimental perspectives. This study attempts to propose the mathematical formula for the bed-load thickness by using the Tsallis entropy theory. Assuming the bed-load thickness is a random variable and using the method for the maximization of the entropy function, the present study derives an explicit expression for the thickness of the bed-load layer as a function with non-dimensional shear stress, by adopting a hypothesis regarding the cumulative distribution function of the bed-load thickness. This expression is verified against six experimental datasets and are also compared with existing deterministic models and the Shannon entropy-based expression. It has been found that there is good agreement between the derived expression and the experimental data, and the derived expression has a better fitting accuracy than some existing deterministic models. It has been also found that the derived Tsallis entropy-based expression has a comparable prediction ability for experimental data to the Shannon entropy-based expression. Finally, the impacts of the mass density of the particle and particle diameter on the bed-load thickness in open channels are also discussed based on this derived expression.

An Expression for Velocity Lag in Sediment-Laden Open-Channel Flows Based on Tsallis Entropy Together with the Principle of Maximum Entropy.

In the context of river dynamics, some experimental results have shown that particle velocity is different from fluid velocity along the stream-wise direction for uniform sediment-laden open-channel flows; this velocity difference has been termed velocity lag in the literature. In this study, an analytical expression for estimating the velocity lag in open-channel flows was derived based on the Tsallis entropy theory together with the principle of maximum entropy. The derived expression represents the velocity lag as a function of a non-dimensional entropy parameter depending on the average and maximum values of velocity lag from experimental measurements. The derived expression was tested against twenty-two experimental datasets collected from the literature with three deterministic models and the developed Shannon entropy-based model. The Tsallis entropy-based model agreed better with the experimental datasets than the deterministic models for eighteen out of the twenty-two total real cases, and the prediction accuracy for the eighteen experimental datasets was comparable to that of the developed Shannon entropy-based model (the Tsallis entropy-based expression agreed slightly better than the Shannon entropy-based model for twelve out of eighteen test cases, whereas for the other six test cases, the Shannon entropy-based model had a slightly higher prediction accuracy). Finally, the effects of the friction velocity of the flow, the particle diameter, and the particles' specific gravity on the velocity lag were analyzed based on the Tsallis entropy-based model. This study shows the potential of the Tsallis entropy theory together with the principle of maximum entropy to predict the stream-wise velocity lag between a particle and the surrounding fluid in sediment-laden open-channel flows.

On the Kaolinite Floc Size at the Steady State of Flocculation in a Turbulent Flow.

The flocculation of cohesive fine-grained sediment plays an important role in the transport characteristics of pollutants and nutrients absorbed on the surface of sediment in estuarine and coastal waters through the complex processes of sediment transport, deposition, resuspension and consolidation. Many laboratory experiments have been carried out to investigate the influence of different flow shear conditions on the floc size at the steady state of flocculation in the shear flow. Most of these experiments reported that the floc size decreases with increasing shear stresses and used a power law to express this dependence. In this study, we performed a Couette-flow experiment to measure the size of the kaolinite floc through sampling observation and an image analysis system at the steady state of flocculation under six flow shear conditions. The results show that the negative correlation of the floc size on the flow shear occurs only at high shear conditions, whereas at low shear conditions, the floc size increases with increasing turbulent shear stresses regardless of electrolyte conditions. Increasing electrolyte conditions and the initial particle concentration could lead to a larger steady-state floc size.

Estimating daily time series of streamflow using hydrological model calibrated based on satellite observations of river water surface width: Toward real world applications.

Lacking observation data for calibration constrains applications of hydrological models to estimate daily time series of streamflow. Recent improvements in remote sensing enable detection of river water-surface width from satellite observations, making possible the tracking of streamflow from space. In this study, a method calibrating hydrological models using river width derived from remote sensing is demonstrated through application to the ungauged Irrawaddy Basin in Myanmar. Generalized likelihood uncertainty estimation (GLUE) is selected as a tool for automatic calibration and uncertainty analysis. Of 50,000 randomly generated parameter sets, 997 are identified as behavioral, based on comparing model simulation with satellite observations. The uncertainty band of streamflow simulation can span most of 10-year average monthly observed streamflow for moderate and high flow conditions. Nash-Sutcliffe efficiency is 95.7% for the simulated streamflow at the 50% quantile. These results indicate that application to the target basin is generally successful. Beyond evaluating the method in a basin lacking streamflow data, difficulties and possible solutions for applications in the real world are addressed to promote future use of the proposed method in more ungauged basins.

Evaluation of gridded precipitation data for driving SWAT model in area upstream of Three Gorges Reservoir.

Gridded precipitation data are becoming an important source for driving hydrologic models to achieve stable and valid simulation results in different regions. Thus, evaluating different sources of precipitation data is important for improving the applicability of gridded data. In this study, we used three gridded rainfall datasets: 1) National Centers for Environmental Prediction-Climate Forecast System Reanalysis (NCEP-CFSR); 2) Asian Precipitation-Highly-Resolved Observational Data Integration Towards Evaluation (APHRODITE); and 3) China trend-surface reanalysis (trend surface) data. These are compared with monitoring precipitation data for driving the Soil and Water Assessment Tool in two basins upstream of Three Gorges Reservoir (TGR) in China. The results of one test basin with significant topographic influence indicates that all the gridded data have poor abilities in reproducing hydrologic processes with the topographic influence on precipitation quantity and distribution. However, in a relatively flat test basin, the APHRODITE and trend surface data can give stable and desirable results. The results of this study suggest that precipitation data for future applications should be considered comprehensively in the TGR area, including the influence of data density and topography.

Influences of climate change on water resources availability in Jinjiang Basin, China.

The influences of climate change on water resources availability in Jinjiang Basin, China, were assessed using the Block-wise use of the TOPmodel with the Muskingum-Cunge routing method (BTOPMC) distributed hydrological model. The ensemble average of downscaled output from sixteen GCMs (General Circulation Models) for A1B emission scenario (medium CO2 emission) in the 2050s was adopted to build regional climate change scenario. The projected precipitation and temperature data were used to drive BTOPMC for predicting hydrological changes in the 2050s. Results show that evapotranspiration will increase in most time of a year. Runoff in summer to early autumn exhibits an increasing trend, while in the rest period of a year it shows a decreasing trend, especially in spring season. From the viewpoint of water resource availability, it is indicated that it has the possibility that water resources may not be sufficient to fulfill irrigation water demand in the spring season and one possible solution is to store more water in the reservoir in previous summer.

Estimating the occurrence of wind-driven coastal upwelling associated with "Aoshio" on the northeast shore of Tokyo Bay, Japan: an analytical model.

"Aoshio" in Tokyo Bay is a hydroenvironmental phenomenon in which seawater appears milky blue due to reflection of sunshine off surface water which contains lots of sulfur particles. Its appearance is due to coastal upwelling of bottom oxygen-depleted water, which causes many deaths of shellfish and other aquatic animals around the bay. In this study, we derived some analytical solutions in the context of a two-layered fluid and used them to make a simple analytical model to estimate the occurrence of "Aoshio" phenomenon on the northeast shore of Tokyo Bay. Comparison with observation data suggested that this model was valid to a certain degree.

Parameter determination to calculate water environmental capacity in Zhangweinan canal sub-basin in China.

Zhangweinan canal sub-basin (ZWN) has the most serious water resource shortage and water pollution problems in north of China. To calculate the water environmental capacity in ZWN, determination methods for design flow rates and degradation coefficients were discussed in this study. Results showed that 90% and 50% hydrological guarantee flow rates were suitable to be the design flow rates for rainy and dry seasons, respectively. Degradation coefficients of COD(Mn) and NH3-N were 0.25 day(-1) and 0.15 day(-1) for branch streams and 0.5 day(-1) and 0.25 day(-1) for mainstreams, respectively in ZWN. With one-dimensional water quality simulation model, water environmental capacities were calculated to be 82,139 tons/yr for COD(Mn) and 2394 tons/yr for NH3-N in ZWN.

A waste load allocation method based on unfairness factors and its application in the Zhangweinan Watershed, northern China.

A waste load allocation method was developed for industrial wastewater management based on unfairness factors, an industrial allocation factor and pollution reduction discounts. Three unfairness factors were defined to assess the relative efficiencies of energy consumption, pollution discharge and waste treatment costs for different industries. The overall effect of these factors was described by an industrial allocation factor. Based on the values of these factors, industries were classified into three types, after which waste load allocation proportions among different industries were determined using different pollution reduction discounts. This waste load allocation method was then applied in the Zhangweinan Watershed, which is one of the most seriously polluted watersheds in northern China. The results revealed that extractive, mechanical and food industries comprise the type I industries, which had the lowest pollution reduction discounts of 0, 0.25 and 0.5, respectively. The metallurgical industry and other industries were characterized as type II and discounts of 0.5 and 0.6 were given to their primary reductions. Textile, pharmaceutical, oil and pyrogenic, chemical and paper industries were classified as type III industries and had a waste load reduction of more than 80% of the pollution discharge in 2004.