Low-intensity Rainfall Research Articles

Analysis of short- and long-term controls on the variability of event-based runoff coefficient

Study regionThis study focuses on the Ohio Region, which spans 11 states in the eastern United States. Study focusRunoff coefficients are crucial in hydrology, indicating the relationship between rainfall and runoff. Understanding their controls and variability is essential for water resource assessment, management strategies, and land use planning. This research examines factors influencing runoff coefficients and their trends in the Ohio region using data from the North American Land Data Assimilation System phase-2 (NLDAS-2) Mosaic Land Surface Model, covering 2000–2020. The analysis considers short-term controls, such as climatic features (rainfall intensity, amount, and duration), hydrological factors (antecedent soil moisture, drainage density, and curve number), topographic factors (drainage area, land use, slope, elevation), and watershed shape. Additionally, the study investigates trends in runoff coefficients and their long-term controls, including climatic factors and land use changes. New hydrological insights for the regionThe findings indicate that runoff coefficients increase with antecedent soil moisture and rainfall intensity. Higher elevations show lower runoff coefficients due to forested land use. Larger watersheds have lower runoff coefficients at low rainfall intensity but higher ones when intensity is high. Long-term trends reveal soil moisture as the primary control, with land cover changes as a secondary factor. This research deepens understanding of runoff coefficient dynamics and controls in the Ohio region. Future studies could explore the impacts of urbanization, reservoirs, evapotranspiration, and snowmelt on runoff coefficients.

Effectiveness of wetlands as reservoirs for integrated water resource management in the Ruzizi plain based on water evaluation and planning (WEAP) approach for a climate-resilient future in eastern D.R. Congo

It is widely predicted that climate change’s adverse effects will intensify in the future, and along with inadequate agricultural practices, settlement development, and other anthropic activities, could contribute to rapid wetland degradation and thus exert significant negative effects on local communities. This study sought to develop an approach based on the Integrated Water Resource Management (IWRM) in the Ruzizi Plain, eastern Democratic Republic of Congo (DRC), where adverse effects of the climate change are increasingly recurrent. Initially, we analyzed the trends of climate data for the last three decades (1990–2022). Subsequently, the Water Evaluation and Planning (WEAP) approach was employed on two contrasting watersheds to estimate current and future water demands in the region and how local wetlands could serve as reservoirs to meeting water demands. Results indicate that the Ruzizi Plain is facing escalating water challenges owing to climate change, rapid population growth, and evolving land-use patterns. These factors are expected to affect water quality and quantity, and thus, increase pressure on wetland ecosystems. The analysis of past data shows recurrence of dry years (SPI ≤ − 1.5), reduced daily low-intensity rainfall (Pmm < 10 mm), and a significant increase in extreme rainfall events (Pmm ≥ 25 mm). The WEAP outcomes revealed significant variations in future water availability, demand, and potential stressors across watersheds. Cropland and livestock are the main water consumers in rural wetlands, while households, cropland (at a lesser extent), and other urban uses exert significant water demands on wetlands located in urban environments. Of three test scenarios, the one presenting wetlands as water reservoirs seemed promising than those considered optimal (based on policies regulating water use) and rational (stationary inputs but with a decrease in daily allocation). These findings highlight the impact of climate change in the Ruzizi plain, emphasizing the urgency of implementing adaptive measures. This study advocates for the necessity of the IWRM approach to enhance water resilience, fostering sustainable development and wetland preservation under changing climate.

The interaction of microplastic and heavy metal in bioretention cell: Contributions of water-soil-plant system

The effectiveness of bioretention cells for heavy metals (HMs) and microplastics (MPs) removal from stormwater runoff has been demonstrated. Knowledge of the mechanisms that dictate the interactions between MPs and HMs would be helpful in pollution control. In this study, the performances of different water-soil-plant bioretention cells for HMs removal through the interception of polyethylene MPs (PE-MPs) were investigated. The results showed that PE-MPs bound to HMs and preferentially tended to bind to Pb (32%–44%) in the complex HMs (Cu, Zn, Cd, and Pb). This could be the reason that the concentration of Pb significantly increased in the effluent under low-intensity simulated rainfall events over a long duration. The accumulation of 1.49 g/kg PE-MPs caused a significant soil pH value decrease and a notable soil zeta potential increase in the bioretention cell, while the low sand/silt ratio media buffered this process. The retention of PE-MPs increased 138.5% in the 0–10 cm soil surface layer when the sand/silt ratio reduced from 2:1 to 1:1 and planted with Canna indica. Meanwhile, PE-MPs amplified the instability of Zn removal in bioretention cells under low-intensity rainfall events in long-duration, high silt percentage substrate and vegetation coverage. The study would contribute to developing a long-term management program for PE-MPs and HMs trapped in bioretention cells to reduce the risk of pollution transport.

Effect of groundwater dynamics in rain-induced landslides: centrifuge and numerical study

Landslides are a multifaceted phenomenon triggered by rainfall infiltration as a consequence of the decrease in effective stress upon the development of porewater pressure. Although many studies concentrated only on rainfall infiltration as the source of the primary hydrological regime, the impact of groundwater dynamics has been relatively underexplored owing to its elusive nature. Field investigations after the landslide incidents provide insight into the influence of groundwater dynamics and speculate its effect as a secondary hydrological regime is immense. Therefore, this paper uses centrifuge modeling and numerical simulations to study groundwater dynamics in rain-induced landslides. Instrumented model slopes made of silty sand were tested to examine the hypothesis of pre-existing groundwater flow levels and surcharged groundwater flow conditions in rain-induced landslides. It was observed that swiftly rising porewater pressure along the soil–bedrock interface triggered landslides more rapidly under high groundwater flow and immediate surcharged groundwater flow conditions. Deformation analysis confirmed that a voluminous landslide could be expected if the role of groundwater dynamics is higher. A two–dimensional coupled hydromechanical finite element simulation was performed to back–analyze the experimental results and to discuss the failure mechanism. Upon validation, numerical simulation emphasized how the failure was accelerated under low-intensity rainfall if high groundwater flow exists. Furthermore, the study identified that surcharged flow profoundly affects landslide initiation if the slope has a low pre-existing groundwater flow. The outcomes highlighted that groundwater dynamics should be an integral part of the temporal predictability of landslides as they can also govern the magnitude of landslides.

Functional connectivity related to road linear erosion at rainfall event scale in an agricultural watershed on the Loess Plateau

Intersections of road and channel networks assist in better understanding of runoff and sediment transportation process feedback using the hydrological connectivity concept. However, quantifying functional connectivity and exploring the complex relationship between hydrological connectivity and the processes of runoff and sediment associated with road networks have largely been unexamined by field work. Therefore, our team conducted a 3-year monitoring of hydrological connectivity and road erosion from selected road segments in an agricultural watershed on the Loess Plateau. Rainfall events occurring during 2020–2022 were categorized into three types using K-means clustering analysis: small amounts of low-intensity rainfall (SL-R), large amounts of low-intensity rainfall (LL-R), and large amounts of high-intensity rainfall (LH-R). The monitored roads primarily consist of paved roads and unpaved roads (agricultural roads, abandoned roads, and petroleum roads). In our study, the intersections extracted by the road and channel networks were defined as hydrological connections, and sticks wrapped with clean gauze at the hydrological connections were monitored under variable rainfall conditions for functional connectivity assessment. The runoff heights and sediment traces attached to the sticks are alternative hydrological connectivity indicators. The results revealed an obvious increasing trend in the activation frequency and degree of hydrological connectivity as rainfall types transitioned from SL-R to LL-R and LH-R. Agricultural roads had a more intense response to hydrological connectivity between upslope drainage areas and road surfaces, and petroleum roads performed well in response to hydrological connectivity between road surfaces and down hillslopes. Random forest models indicate that PI30 is the most dominant influencing factor of road erosion and hydrological connectivity between road upslope drainage areas and road surfaces. Road surface erosion rates are the primary determinants of hydrological connectivity between road surfaces and down hillslopes. These findings suggest a close interaction between road erosion and hydrological connectivity driven by these off-site effects. They offer additional evidence concerning runoff and sediment patterns and dynamics in relation to road networks and identify hydrological connectivity and road erosion hotspots. Coordinating measures for hydrological connectivity regulation and road erosion mitigation are essential for minimizing hydrological connectivity and road erosion off-site effects within watersheds.

Effects of gravel-sand and plastic film mulching on soil water and temperature retention in cold and arid regions without irrigation

Gravel-sand mulch (GSM) and plastic film mulch (PFM) are important ways of farming in cold and arid regions without irrigation. Nevertheless, there has been a lack of studies of the system response to live weather conditions. To quantify the effects of GSM and PFM on soil moisture and temperature retention, in-situ monitoring experiments were carried out in the arid belt of central Ningxia, China, using continuous monitoring of the field soil water and meteorological conditions at a 30-mimute time-step under three treatments: a bare soil (CK), soil covered by a layer of GSM, and soil covered by GSM and a layer of plastic film (i.e., GSM + PFM). Results show that: (1) With a limited precipitation of 221 mm during the growing season, the average volumetric soil water content (SWC) in the top 30-cm soil layer was lowest for CK, medium high for GSM, and highest for GSM + PFM. Compared to CK, the soil water storage increased by 54 % under GSM and 75.2 % under GSM + PFM; (2) The most frequently occurring low-intensity rainfalls are more efficiently stored in soil under GSM + PFM; (3) Similarly, the soil temperature was significantly increased under GSM and GSM + PFM conditions. Compared to CK, the average soil temperature in the top 5-cm layer increased by 2.5 °C under GSM and 4.8 °C under GSM + PFM during the germination period, which had effectively extended the growing season for about 30 and 50 days, respectively; (4) Although dewfall is only 4 % of rainfall, the total number of dew day was more than twice that of rain day. Thus, dewfall is a more frequent and dependable source of water for native plants and animals. Our results demonstrate that the benefits of GSM and PFM can be applied globally where either insufficient rainfall or low temperatures are limiting factors.

Towards transient connectivity of river networks during rainfall events: Insight from hydrological observation and functional data analysis

River connectivity, which is critical for river networks to perform their functions, is deteriorating in highly urbanized areas. While it is well known about the structural and functional connectivity of river networks using traditional indicators, much less is known about the process connectivity and its influence factor. To this end, we propose a new approach based on hydrological observations and functional data analysis that has the advantage of capturing transient changes in process connectivity and responses to rainfall events. Applying the method to a typical river-dense plain in China, the results show that 1) the highest and the lowest values of process connectivity were concentrated in 0.6–0.95 and 0.04–0.61 during 2015–2020, respectively. The average values of process connectivity were lower in 2015, 2016, and 2020 with 0.49, 0.44, and 0.46, respectively, and higher in 2017 to 2019 with 0.52, 0.57, and 0.58, respectively. 2) There is a significant negative correlation between rainfall intensity and process connectivity, especially when heavy rainfall occurs. Under the same rainfall intensity, the process connectivity was weaker in the river network with lower structural connectivity, lower overflow capacity of main streams, strong concentration of rivers, more sluices and larger unit areas. 3) The main influencing factors on process connectivity were structural connectivity and rainfall, with contributions of 34.67% and 22.67%, respectively. Following closely are unit features and human regulation, contributing 14.07% and 12.61% to process connectivity, respectively. 4) Structural connectivity and unit features are the primary factors influencing process connectivity during low-intensity rainfall events. During high-intensity rainfall events, rainfall and unit features become the predominant factors affecting process connectivity. Our results provide a new insight for quantifying process connectivity in the plain and are expected to support the study of the changing mechanism of hydrological connectivity.

Wildfire susceptibility mapping by incorporating damage proxy maps, differenced normalized burn Ratio, and deep learning algorithms based on sentinel-1/2 data: a case study on Maui Island, Hawaii

ABSTRACT Climate change has contributed to the recent increase in wildfire occurrences, vegetation failures, human health risks, physical damage, and economic losses. Wildfire susceptibility mapping is an essential technique for assessing areas prone to wildfires. In this study, we proposed the combination of the damage proxy map (DPM) and differenced normalized burn ratio (dNBR) method to generate a precise wildfire inventory map and used it to predict areas susceptible to wildfire. The wildfire susceptibility maps were produced using frequency ratio (FR), convolutional neural network (CNN), and long short-term memory (LSTM)-based deep learning and their performances were compared. We implemented the proposed method on Maui Island, Hawaii, where wildfires frequently occur. We started the process by generating a wildfire inventory map from 2019 to 2023 based on the DPM method applied to Sentinel-1 synthetic aperture radar (SAR) data combined with a dNBR map retrieved from Sentinel-2 data. The wildfire inventory was randomly divided into a training dataset (70%) and a testing dataset (30%). Fifteen wildfire-related factors, including topographical, meteorological, land use, environmental, and anthropological factors, were selected to predict wildfires. The wildfire-related factors were selected by conducting study literature and considering spatial correlation analysis based on the FR method, information gain ratio analysis (IGR), and multicollinearity assessment using tolerance (TOL) and variance inflation factor (VIF) metrics. The level of susceptibility of an area to wildfire is divided into five, namely very high, high, moderate, low, and very low. The FR, CNN, and LSTM produced wildfire susceptibility maps with similar patterns, significantly influenced by land use and rainfall factors. The highly susceptible areas are located on gentle slopes covered by agricultural land and unhealthy vegetation, and these areas have low rainfall intensity but receive high levels of solar radiation. Meanwhile, areas with relatively low susceptibility occur in forests with high levels of wet canopy evaporation. The prediction results were evaluated using the area under the receiver operating characteristic (ROC) curve (AUC), and the CNN performed slightly better than the FR and LSTM, with AUC values of 0.879, 0.877, and 0.870, respectively. Hence, the use of the CNN algorithm in the proposed method is appropriate, specifically for the study area. In addition, the susceptibility map provides information on susceptible areas on Maui Island, Hawaii, to increase public awareness.

Sugarcane leaf return and mixing significantly reduce slope runoff and soil loss

AbstractImproper management of sugarcane cultivation may cause severe soil erosion, especially in lateritic red soil hilly areas. Crop straw return to the field can effectively improve the soil structure, but there is a lack of evaluation studies of the soil and water conservation benefits of sugarcane leaf mixing and return to the field, and the underlying regulatory mechanism has rarely been described. Thus, we conducted a series of simulated rainfall experiments to investigate the impact of returning different mixed sugarcane leaf weights (0, 3, 6, 9, 12 and 15 t/ha) to the field on erodible lateritic red soil slopes that support sugarcane production under various slopes (5°, 10°, 15° and 20°) in different rainfall events (60 and 120 mm/h). The results indicated that returning sugarcane leaves into farmland could effectively regulate the process of runoff and sediment production on lateritic red soil slopes, and the optimal mixing amount was 9 t/ha. Under a low rainfall intensity, the erosion process was mainly affected by the slope, and the benefits of applying the sugarcane leaf mixing treatment to the slope to reduce runoff and sediment could reach 13.37–53.04% and 23.86–77.55%, respectively. However, under a high rainfall intensity, the sugarcane leaf mixing amount was the main influencing factor, the benefits for reducing runoff and sediment could reach 4.64–38.72% and 30.87–93.14%, respectively. The sugarcane leaf mixing amount exhibited a linear relationship with the Reynolds number (R2 &gt; 0.63), whereas the runoff velocity (R2 &gt; 0.96), Froude number (R2 &gt; 0.94) and drag coefficient (R2 &gt; 0.85) exhibited well‐fitting exponential function relationship with it. This study provides a reference for future endeavours regarding the inhibition mechanisms of sugarcane leaves mixed and returned to the field for decreasing slope erosion.

Tracking the nitrogen leaching from different sources in bioretention systems with a process-based model

Bioretention systems are a typical stormwater management technology intended to reduce runoff and non-point source pollution. Multiple sources can contribute to nitrogen (N) leaching from bioretention systems, including the current rainfall-runoff (i.e., immediate leaching), recent rainfall-runoffs (i.e., fast leaching) and long-ago legacy N (i.e., slow leaching), however, there is a lack of methods to quantify these contributions. Here, a process-based Bioretention System (BRS) model for N leaching simulations was verified with artificially-labeled 15N data besides N data to reduce predictive uncertainties. The BRS model was then coupled with an Nitrogen Source Apportionment (NSA) module for N leaching tracking to quantify the contributions from different sources. Results from a bioretention system showed that additional 15N data reduced the predictive uncertainty of total N (TN) concentrations by 23 %. Immediate leaching, fast leaching and slow leaching increased, decreased and remained steady during the leaching process and accounted for 79.54 ± 5.08 %, 2.80 ± 1.74 % and 17.66 ± 4.11 % of N leaching, respectively. The dominant immediate leaching can be significantly weakened by a submerged zone, a shorter antecedent dry period (ADP), a lower rainfall intensity or influent concentration. This study provides a framework to quantify N leaching from multiple sources with implications for long-term N leaching management.

Instability evolution of expansive soil slope due to short duration-varying intensities of rainfall

This study examines the evolution of instability in induced expansive soil slopes under varying rainfall intensities. The destabilization evolution of expansive soil slopes and their stability under three different rainfall intensities were revealed through indoor modelling and numerical simulation. The findings indicate that slopes are not destabilized by short duration and low rainfall intensity alone. Slope failure under strong rainfall intensity follows a three-stage evolutionary process. As rainfall intensity increases, the slope's water content, soil pressure, and pore water pressure increase. However, the stress at each monitoring point of the slope affected by rainfall follows a different pattern. Under low rainfall intensity, the water content of the slope surface is higher than that of the slope foot. Under strong rainfall conditions, the water content of the slope foot is higher than that of the slope surface. Therefore, it is important to implement seepage prevention measures on the slope surface and drainage measures on the slope foot to ensure slope stability. Rainfall affects the soil pressure at each monitoring point of the slope, causing unloading on the slope surface and fluctuation at the foot of the slope. This phenomenon becomes more pronounced with increasing rainfall intensity. Numerical simulation confirms that the overall horizontal displacement distance of the slope increases with rainfall intensity, but the horizontal displacement range becomes shallower. The slope's maximum horizontal displacement occurs at its foot, with values of 0.29 m and 0.34 m under moderate and heavy rainfall, respectively, representing a growth rate of 17 %. Settlement of the slope under different rainfall intensities was greatest at the top of the slope area. The lateral stresses generated by the settlement resulted in soil uplift at the leading edge of the slope.

The Mechanism of Rainfall-Induced Landslide Around Railway Tracks in Central Java Province, Indonesia

Landslide is one of the most disastrous natural hazards in Indonesia, causing significant fatalities and economic losses. Landslides can be triggered by several factors, such as rainfall, earthquakes, soil conditions, and others, where each landslide event has its own triggering and controlling factors. A progressive landslide occurred on the Central Java railway line which resulted in damage to the double-track railway as a transportation infrastructure. The objective of this paper is to understand the process and triggering factors of the landslide. Information was collected through field investigations and measurements based on drilling results at 3 points, geophysical surveys at 5 lines, and laboratory testing of several soil samples. Geological and geotechnical settings, topography, lithology, hydrogeology, and rainfall data of the area were analyzed. Aerial photographs and other remote sensing data were used to evaluate and discuss the information. Landslides in the study area occurred in stages, starting with the formation of a tension crack, followed by two landslides over five months. The results show that the clay material that dominates the study area is the dominant controlling factor of a landslide, triggered by long-duration, low-intensity rainfall. Rainwater entering through tension cracks increases moisture content, adding load to the slope and triggering landslides. Furthermore, the train's external load on the slope also contributes to the occurrence of landslides. The static and cyclic load of the train causes changes in the slope's pressure balance, generating a force that drives the downslope soil. Further analysis was performed using back analysis method with the limit equilibrium method to enhance understanding of slope stability parameters at the time of slope failure. The analysis was performed considering the rising groundwater level. A factor of safety (FS) value of 0.989 was obtained at the end of the simulation, indicating that the slope had failed.

Effects of multiple factors on particle size selectivity under artificial extreme rainfall events on simulated Gobi surface

Understanding multiple-factor effects on particle size selectivity by extreme rainfall events in Ala-Shan Gobi desert is of great significance for better estimation of potential Asian dust emission sources. Artificial rainfall simulation experiments were used to investigate the particle size selectivity characteristics by extreme rainfall events under different rainfall intensities (20 mm h−1 and 40 mm h−1), slope gradients (3° and 15°) and gravel coverages (0, 30%, and 60%). Moreover, the relations of clay content (Clc), silt content (Sic), fine particle (< 50 μm) content (Fic) and enrichment ratio of fine particles (ER<50) with multiple factors were regressed and validated. Results show that rainfall intensity significantly (P < 0.05) affect runoff and sediment yield processes, but slope gradient was a dominant factor that changed particle size distribution (PSD). The selectivity of fine particles was higher at low rainfall intensity (20 mm h−1), gentle slope (3°) and moderate gravel coverage (30%), with ER<50 reaching 6.14, which dominate the potential Asian dust emission sources. The interaction were discussed and classified into ‘Synergy’ and ‘Trade-off’. Clc and Fic showed negative exponential relationship with rainfall intensity and slope gradient, but positive exponential relationship with gravel coverage. While Sic and ER<50 showed negative power function relationship with rainfall intensity, slope gradient and gravel coverage. These findings could help to understand the effects of multiple factors on potential sources of Asian dust emission under extreme rainfall events in Gobi region of northwestern China and provide basic science reference for the prediction of dust emission in this region.

Inter-event and intra-event dynamics of microplastic emissions in an urban river during rainfall episodes

Urban rivers represent the major conduits for land-sourced microplastics in the global oceans, yet the real-time dynamics of their emissions in rivers during rainfall (and runoff) events are poorly understood. Herein, we report the results of high-frequency sampling of microplastic particles (MPs) and fibers (MPFs) in the surface water of an urban river in Japan over the course of three rainfall events (i.e., light, moderate, and heavy rainfalls). The event mean concentrations (EMCs) of MPs amounted to 35,000 items/m3, 929,000 items/m3, and 331,000 items/m3; and the corresponding total loads were 0.5 kg, 19.8 kg, and 35.0 kg for light, moderate and heavy rainfalls, respectively. The inter-event total loads of MPs correlate well with the total rainfall, while the concentrations were linked with the number of antecedent dry days. The dynamic trends show that <2000 μm MPs displayed first flush effects during light to moderate rainfall events (>50% mass discharged with the initial 20–40% of flow). Small-sized MPs (10–40 μm) mobilized rapidly at lower rainfall intensities, whereas MPs over 2000 μm discharged immediately after the peak rainfall intensity. Moreover, <70 μm MPs depicted a surge following heavy rainfall events due to turbulent flow conditions reverting the deposited MPs into suspension. Overall, the three events increased the loads by 4–110 folds, and EMCs by 10–350 folds compared to the concentrations during dry weather while portraying a significant impact on 300–1000 μm MPs. The dynamics of MPs were correlated with those of suspended solids in river water, and the characteristics were comparable to the same of road dust sampled in Japan. Although the dynamic trends between MPs and MPFs in river water were comparable, MPFs were relatively less impacted by rain, likely due to the intervention of separate sewer systems in the study area.

Integrating the Spatial Configurations of Green and Gray Infrastructure in Urban Stormwater Networks

AbstractGreen infrastructure (GI) practices improve stormwater quality and reduce urban flooding, but as urban hydrology is highly controlled by its associated gray infrastructure (e.g., stormwater pipe network), GI's watershed‐scale performance depends on its siting within its associated watershed. Although many stormwater practitioners have begun considering GI's spatial configuration within a larger watershed, few approaches allow for flexible scenario exploration, which can untangle GI's interaction with gray infrastructure network and assess its effects on watershed hydrology. To address the gap in integrated gray‐green infrastructure planning, we used an exploratory model to examine gray‐green infrastructure performance using synthetic stormwater networks with varying degrees of flow path meandering, informed by analysis on stormwater networks from the Minneapolis‐St. Paul Metropolitan Area, MN, USA. Superimposed with different coverage and placements of GI (e.g., bioretention cells), these gray‐green stormwater networks are then subjected to different rainfall intensities within Environmental Protection Agency's Storm Water Management Model to simulate their hydrological benefits (e.g., peak flow reduction, flood reduction). Although only limited choices of green and gray infrastructure were explored, the results show that the gray infrastructure's spatial configuration can introduce tradeoffs between increased peak flow and increased flooding, and further interacts with GI coverage and placement to reduce peak flow and flooding at low rainfall intensity. However, as rainfall intensifies, GI ceases to reduce peak flow. For integrated gray‐green infrastructure planning, our results suggest that physical constraints of the stormwater networks and the range of rainfall intensities must be considered when implementing GI.

Hydrological Modeling of Urban Flooding in Nandigama: Land Use Dynamics and Climate Influence

Urban flooding affects many areas in the world, caused by heavy rainfall and global warming. Our focus is on Nandigama, a highly urbanized region in Andhra Pradesh, India. Flood maps created based on urbanisation in the study area. TREX model and spatial data used to evaluate the impact of land use changes on runoff. Our analysis showed that urban expansion has increased significantly over the past thirty years, increasing surface area and runoff, leading to more flooding events with lower-intensity rainfall. Climate change has also contributed to this problem, with shorter rainfall durations but higher intensities leading to more frequent small floods. This has resulted in a 21% rise in discharge, from 2.39 m3 /s in 2000 to 2.91 m3 /s in 2020. This study provides essential information for land use planning and flood control management projects.

Exploring biochar addition impacts on soil erosion under natural rainfall: A study based on four years of field observations on the Loess Plateau

Biochar’s potential impact on soil erosion has gained attention, but remained inconsistent, particularly lacks the results from continuous field monitoring under diverse rainfall conditions. This study aimed to explore the long-term variation in soil erosion induced by biochar addition under distinct natural rainfalls. Six runoff plots, treated with different amounts of biochar derived from apple tree branches (0, 1%, 2.5%, 4%, 5.5% and 7%), were established. Over a four-year period from 2017 to 2020, runoff and soil loss from these plots were continuously monitored and analyzed. Among 215 recorded rainfall events, 27 events resulted in runoff and sediment production were classified into three rainfall patterns (RI, RII and RIII) using K-means clustering. RIII characterized by high mean rainfall depth (138.80 mm), duration (3443 min), and erosivity (879 MJ.mm (ha. h)−1), but infrequent occurrence, followed by RII and RI with moderate and low mean depth, duration and erosivity, but higher frequency. Biochar addition, relative to control, resulted in an average annual runoff reduction, ranging from 9% to 36%, with a mean reduction of 28%, and reduced runoff for each rainfall, ranging from 4% to 28%, with a mean reduction of 12%. Similarly, biochar addition also reduced average annual soil loss, ranging from 43% to 79%, with a mean reduction of 61%, and reduced soil loss for each rainfall, ranging from 29% to 74%, with a mean reduction of 52%. The reduction of runoff and soil loss was positively correlated with biochar addition rates. Notably, the soil loss reduction was exceeded that of runoff. Soil erosion was negatively linked to surface area of soil particles (p < 0.05), total carbon, and total organic carbon (p < 0.01), but positively correlated with sand content (p < 0.01) and bulk density (BD) (p < 0.05) under biochar addition. Comparison of the mean reduction of runoff and soil loss among three rainfall patterns, the order was as follows: RII>RI>RIII. The optimal controlling of soil erosion was observed in 7% biochar addition under RII. Runoff-soil loss relationships well-fitted by power equation, showed soil incorporated with biochar treatments exhibited heightened susceptibility to erosion under RIII with high intensity compared to RII and RI with moderate or low rainfall intensity. Thus, the findings indicate biochar is a potential soil amendment to improve a degraded or degrading sloping farmland, however, the rainfall characteristics need to be considered when it is applied to the sloping farmland prone to erosion.

The Characteristics of Rainfall-Runoff Generation and Its Influencing Factors in a Desert Steppe, China

Understanding the effect of land surface characteristics on rainfall-runoff generation is crucial in mastering the mechanisms of soil and water conservation. To characterize rainfall-runoff generation in desert steppes and to quantify the contribution of different influencing factors, a field-simulated experiment with three land degradation levels and three rainfall intensities (RIs) was conducted in the Inner Mongolia Desert Steppe. The results revealed that rainfall-runoff generation in different degraded plots at various RIs differed significantly. The runoff was generated faster and accumulated larger volumes under high RIs and heavy degradation levels (HDs) in comparison with generation under moderate/light degradation levels (MDs/LDs) and moderate/low RIs. The accumulated runoff over 40 min under a high RI on the HD plot is 153.37 L, which is much larger (0.77 L) than that under a low RI on the LD plot. The result from the structural equation model (SEM) indicated that RI is the most important factor directly driving rainfall-runoff generation, and its standardized path coefficient reached a value of 0.52. The vegetation condition is the second direct factor, with a standardized path coefficient of −0.51. However, the soil water content (SWC) has an indirect impact on rainfall-runoff generation through affecting vegetation conditions. So, interactions also exist between variables such as vegetation and soil. Therefore, the rainfall-runoff generation in the desert steppe may be mitigated through an enhancement of the vegetation and soil properties or through optimizing the interaction relationship between soil and vegetation.

A novel approximate solution to slope rainfall infiltration

Slope rainfall infiltration is a critical component of the Earth's hydrologic processes. Although various infiltration models have been proposed to simulate rainfall infiltration, simple and accurate ones are still scarce. This work developed a non-integral infiltration model for sloping surfaces, called modified approximate solutions under constant pressure conditions (MASCP). New expressions were proposed to improve slope infiltration simulation by introducing a developing saturated zone in soil water content (SWC) profile to quantify the role of ponding water during infiltration. The MASCP was then extended to simulate the infiltration with uniform and non-uniform initial SWC distributions under complex rainfall patterns. The model performance was evaluated by comparing with Hydrus-1D and the sloping Green-Ampt model for four distinct textured soils. The results demonstrated that the ponding time and infiltration rate simulated by the MASCP agreed well with those by Hydrus-1D at all given conditions. Besides, the MASCP yielded higher simulation accuracy than the sloping Green-Ampt model, especially for coarse-textured soils, low rainfall intensity, and long rainfall hiatus. In conclusion, the MASCP is a simple and accurate computation tool for slope rainfall infiltration modeling.

Optimized strategies of green and grey infrastructures for integrated control objectives of runoff, waterlogging and WWDP in old storm drainages

Frequent waterlogging occurs in old high-density urban areas where the sewage is inappropriately connected to storm drainages, resulting in serious wet weather discharge pollution (WWDP). To address urban waterlogging and runoff, the optimization of green infrastructures (GIs) and grey infrastructures (GRs) has been proposed to improve rainwater management efficiency. However, most strategies neglect WWDP and fail to achieve integrated control of runoff, waterlogging, and discharge pollution. In the present study, a new optimization method was introduced to identify optimal solutions for renovating outdated storm drainage systems, considering the management of discharge pollution in wet weather. A case study in Shanghai, China was conducted to demonstrate the application of the method. The cost-benefit index (CBI) of optimized GIs (0.06) was lower than that of optimized GRs (2.78) under 22.2 mm rainfall (no runoff and WWDP), but the costs of the former were only half those of the latter. In a 5-year return period storm (no waterlogging), optimized GIs had a significantly higher CBI (2.85 times) compared to optimized GRs, costing only 44 % of the latter. When WWDP reached the control objective (COD≤70 mg/L), the optimized GIs needed to be further optimized with GRs. The CBI of optimized GI-GRs was higher than GRs by 2.50, and the cost was 58% of the latter. In areas with frequent low-intensity rainfall, optimized GIs and GRs should be selected based on local cost or benefit requirements for drainage reconstruction. In high-intensity storm-prone areas, the optimized GI-GR combination should be selected for drainage reconstruction. The proposed method can compensate for the shortcomings of existing optimization methods in controlling WWDP for the reconstruction of old storm drainages.