Abstract. Extreme Rainfall Events (EREs) and resulting flash floods in Saudi Arabia pose major threats, frequently causing fatalities and significant economic losses. Accurate ERE simulations are crucial for weather forecasting, climate change assessment, and disaster management. This study evaluates planetary boundary layer (PBL) and cloud microphysics (MP) schemes to simulate EREs in the Arabian Peninsula (AP) using the Advanced Research version of the Weather Research and Forecasting (WRF-ARW) model V4.4. Thirty-six combinations of four PBL and nine MP schemes were tested across 17 EREs at a convection-permitting 3 km resolution and compared with IMERG gridded satellite data for rainfall and station observations for temperature, humidity, and wind speed. The Kling–Gupta Efficiency (KGE), which incorporates correlation, variability, and bias, was used as performance metric. We found a good agreement between observed and simulated rainfall patterns, though some over- and underestimations were present. Among the PBL schemes, Yonsei University (YSU; BL1) tended to perform best in terms of rainfall, while Thompson (MP8) ranked the highest among the MP schemes. Goddard (MP7) also delivered strong results. Among all 36 combinations, the Thompson-YSU (MP8_BL1) combination produced the highest mean KGE across the 17 EREs for rainfall, performing statistically significantly better than 21 other combinations. While MP8_BL1 also performed best for the other three meteorological variables, performance rankings varied across variables, likely because different physical processes govern the simulation of different variables. This study highlights the complexity of scheme evaluation and the importance of analyzing multiple EREs with high-quality reference data. The results offer practical guidance for scheme selection and lay the foundation for improving ERE forecasting and regional climate modeling over the AP.

Runoff and soil erosion on woodland-encroached sagebrush rangeland under simulated rainfall and concentrated flow conditions

Sediment and runoff losses from rainfall simulation: Effects of elevated atmospheric CO2 and tillage practice

ABSTRACT Both freeze–thaw cycles and vegetation cover changes significantly influence slope runoff and sediment yield in permafrost regions. Nevertheless, their synergistic mechanisms remain inadequately quantified and poorly understood. Through simulated rainfall experiments conducted on slopes in the source region of the Yangtze River, this study investigated the impacts of vegetation cover variation combined with soil freeze–thaw processes on runoff and sediment yield from typical alpine meadows and alpine steppes. The results indicate that: (1) The three factors of vegetation type and coverage, as well as rainfall intensity, jointly shape the relationship between precipitation runoff and sediment. Alpine meadows showed stronger erosion resistance than alpine steppes. (2) The freeze–thaw process of soil dominated the runoff and sediment generation: Runoff volume across varying vegetation coverage followed the order: autumn freezing period > spring thawing period > summer thawed period. However, sediment yield was highest during the spring thawing period, followed by the autumn freezing period and summer thawed period. (3) For higher vegetation coverage, freeze–thaw effects had a greater impact on runoff than on sediment yield; on the contrary, under low‐coverage vegetation, the freeze–thaw process influenced sediment yield more than runoff; These findings provide theoretical guidance for achieving integrated soil erosion regulation goals in alpine grassland ecosystems within the Qinghai–Tibet Plateau under climate change.

The impact of amendments on the microbial community response in green roof substrate layers.

Influence of interlayer fissures on runoff characteristics of karst slopes: insights from simulated rainfall experiments

Liquid-gas responses during rainfall test in MSW landfill.

Abstract Tropical cyclone (TC) plays a critical role in driving hydrological extremes. However, current generation climate models often fail to capture TC inner‐core dynamical structures, leading to substantial underestimation (>50%) of TC rainfall (TCR). Here, using a set of eddy‐resolving high‐resolution (HR) simulations from the Community Earth System Model (CESM), we show that simulated TCR closely aligns with observations, primarily due to the improved TC upward motion. Under the Representative Concentration Pathway 8.5 warming scenario, projected TCR increases in non‐eddy‐resolving models reach only 15%–50% of those in HR CESM, likely reflecting their large historical TCR biases. Owing to suppressed TC upward motion, the projected TCR fractional increase by non‐eddy‐resolving models remain comparable to or below the Clausius‐Clapeyron (C‐C) scaling. In contrast, HR CESM projects a much larger TCR increase rate (∼12.0% K −1 ), exceeding the C‐C rate and driven by a strengthened TC upward motion.

<span lang="EN-US">Climate change has increased rainfall variability and unpredictability, significantly impacted agricultural productivity, and raised the risk of crop failure, particularly in rain-fed rice farming systems. This study models rainfall data from Tabanan, Bali, using a continuous-time Hidden Markov Model (HMM) to identify latent weather states and assess the associated risk of rice crop failure. The model assumes four hidden states, each generating rainfall observations following a Gamma distribution. Simulation results produced Mean Absolute Percentage Error (MAPE) values below 5% for training and testing sets, indicating strong model performance in replicating rainfall patterns. Risk analysis compared simulated rainfall with rice crop water requirements across three planting periods. The second planting period (July–October) exhibited the highest risk at 3.75%. Compared to other predictive models, HMM offers superior capability in capturing temporal rainfall structure and identifying critical transition phases, making it highly suitable for agricultural risk assessment and climate-adaptive planning.</span>

Abstract Background The volumetric water content, which remains temporarily constant (i.e., quasi-saturated) during the downward infiltration of rainwater into the soil, can serve as a useful indicator for evaluating slope stability under rainfall. To apply a volumetric-water-content-based failure prediction method to real slopes, it is necessary to establish a quantitative evaluation approach that accounts for variations in rainfall intensity, soil permeability, and water retention characteristics. We performed a series of vertical rainfall infiltration experiments on a uniform, horizontal sand deposit using a finely tuned rainfall simulator capable of reproducing specified rainfall intensities in a centrifuge. Results Two-fluid type spray nozzles with adjustable pneumatic pressure, arranged in series to overlap spray distributions, enabled reproduction of three constant rainfall intensities in the centrifuge with small droplet sizes and low injection velocities. Sprayed rainwater infiltrated the soil, increasing its volumetric water content, and the process from the initial state through the quasi-saturated state to the final saturated state was captured. Higher rainfall intensities or higher viscosity of the sprayed liquid led to greater quasi-saturated volumetric water contents. Conclusions The relationship between the volumetric water content at a quasi-saturated state and relative hydraulic conductivity was generally consistent with that obtained from the Mualem-van Genuchten model. As the model is derived from the soil water characteristic curve and the saturated hydraulic conductivity, it has been demonstrated that the volumetric water content in the quasi-saturated state can be estimated from the soil water characteristic curve, the saturated hydraulic conductivity, and the rainfall intensity.

This study evaluated the applicability of the dual drainage model, Hyper Connected–Solution for Urban Flood (HC-SURF), for real-time urban flood forecasting. The model was applied to the extreme rainfall event of August 2022 in the Sillim and Daerim drainage basins in Seoul. Its accuracy and computational efficiency were quantitatively compared with those of two widely used commercial models, the Personal Computer Storm Water Management Model (PC-SWMM) and InfoWorks Integrated Catchment Modelling (ICM). Accuracy was assessed by measuring spatial agreement with observed inundation trace maps using binary indicators, including the Critical Success Index (CSI), Probability of Detection (POD), and False Alarm Ratio (FAR). Computational efficiency was evaluated by comparing simulation times under identical conditions. In terms of accuracy against observations, HC-SURF achieved CSI values ranging from 0.26 to 0.45, with POD values from 0.37 to 0.81 and FAR values from 0.49 to 0.53 across the two basins. In inter-model comparisons, the model showed high hydraulic consistency, demonstrating CSI values between 0.72 and 0.88, POD between 0.82 and 0.99, and FAR between 0.08 and 0.15. In terms of computational efficiency, HC-SURF reduced calculation times by approximately 9% and 44% compared with InfoWorks ICM and PC-SWMM, respectively, for a 48 h simulation. The model also completed a 6 h rainfall simulation in approximately 8 min, meeting the lead time requirements for rapid urban flood forecasting. Overall, these findings show that HC-SURF effectively balances simulation accuracy with computational efficiency, demonstrating its suitability for real-time urban flood forecasting.

Microtopography regulates soil erosion by shaping surface heterogeneity, but the mechanism of loess slope soil loss remains insufficiently quantified. This study combined laboratory rainfall simulations and machine learning to investigate how tillage-induced microtopography modulates soil loss through surface heterogeneity and hydrodynamic processes. Simulations used loess soil (silty loam) with a 5° slope, 60 mm/h rainfall intensity, and 5–30 min rainfall durations (RD). Results indicated that the mean weight diameter (MWD) and aggregate stability index (ASI) of structural, transition, and depositional crusts under micro-terrain decreased by 36~65% and 41~60%, respectively, while the fractal dimension (D) increased by 10~19%. Negative relationships were observed between ASI/MWD and D (R2 = 0.83~0.98). Horizontal cultivation (THC, surface roughness [SR] = 1.76, average depression storage [ADS] = 2.34 × 10−2 m3) delayed runoff connectivity and reduced cumulative soil loss (LS) by 42–58% compared to hoeing cultivation (THE, SR = 1.47, ADS = 3.23 × 10−4 m3). Abrupt hydrodynamic transitions occurred at 10 min RD (THE) and 15 min RD (artificial digging [TAD]), driven by trench connectivity and depression overflow. LS exhibited a significant positive correlation with D and RD and was inversely correlated with ASI, MWD, and SR. A three-hidden-layer BPNN exhibited high predictive accuracy for LS (mean square error = 0.07), verifying applicability in complex scenarios with significant microtopographic heterogeneity and multi-factor coupling. This study demonstrated that surface roughness and depression storage were the dominant microtopographic controls on loess slope soil loss. BPNN provided a reliable tool for soil loss prediction in heterogeneous microtopographic systems. The findings provide critical insights into optimizing tillage-based soil conservation strategies for sloping loess farmlands.

The pervasive presence of nanoplastics (NPs) in the soil environment has been widely documented. However, the mechanisms governing their transport through soil remain poorly understood. This study investigated the migration and vertical distribution of NPs under simulated rainfall, examining the effects of NP properties (concentration, polymer type, aging) and rainfall conditions (duration, pH). The results demonstrated that rainfall facilitated the entry and retention of NPs in soil, with long-term rainfall promoting gradual migration to deeper layers or groundwater. NP mobility was inversely related to their contamination levels. Lower concentrations enhanced downward transport, while higher concentrations led to preferential retention in the topsoil. Due to its hydrophilicity, polyamide (PA) exhibits greater mobility in soil than hydrophobic polystyrene (PS). Both UV aging and acidic rainfall conditions inhibited the migration of NPs, which increased their long-term retention in soil, thereby elevating ecological risk. These results highlight the need for increased attention to the risk of groundwater contamination posed by hydrophilic NPs following long-term rainfall, as well as the threat posed by hydrophobic NPs, particularly after aging and under acidic rainfall conditions, to soil organisms and food safety. Our findings provide critical insights for assessing NP risks in soil environments.

Objective: To calculate the runoff ratio, sediment yield, and infiltration capacity in an open bunchgrass grassland invaded by Lehmann lovegrass in northern Mexico. Design/methodology/approach: The runoff-rainfall ratio, infiltration capacity, and the sediment yield were calculated using the simulated rainfall approach. Each simulation (n = 20) lasted 30 min and involved two runs (i.e., dry and wet). Results: Differences were non-significant for the analyzed variables. The dry run showed a runoff-rainfall ratio of 0.32, and an infiltration capacity of 112 mm h⁻¹, whereas the wet run recorded a runoff-rainfall ratio of 0.39 and an infiltration capacity of 96.9 mm h⁻¹. Regarding sediment yield, a mean of 59.8 g m-² and 53.8 g m-² were recorded for the dry and the wet runs, respectively. Limitations on study/implications: Our rainfall simulator use a single nozzle connected to a water pump. where is difficult to control the two rainfall properties of a natural rainfall event (rainfall intensity and drop-size characteristics). Therefore, the high application rate does not necessarily correspond to high-intensity storm characteristics if the drop size and velocity are reduced. Findings/conclusions: Soil moisture content, soil bulk density, slope, and depth were determining factors in the hydrological response of this grassland. The runoff ratio was relatively low, even for the wet runs, suggesting that the sandy loam texture of the soil favors infiltration rates and that this effect is much higher than the ground cover effect in an invaded grassland.

Urbanization has increased the incidence of floods due to impermeable surfaces that prevent rainwater absorption, underscoring the necessity for effective stormwater management. Using green roofs, which include growth and other material layers, is an alternative solution. This research evaluates the hydraulic performance of green roofs incorporating banana trunk waste, a highly effective material due to its water absorption and retention properties in a drainage and filter layer, with the added support of PET bottle caps for lightweight structural applications. The study looks at the fibre model’s peak flow reduction ranging from 61% to 77% and retention rate ranging from 64.75% to 79.47%, with the rainfall simulation carried out in low, moderate, and severe intensities, which are 10 mm/h, 30 mm/h, 60 mm/h, 180 mm/h, 350 mm/h and 550 mm/h. The research highlights the effectiveness of banana trunk waste and recycled materials, such as PET bottle caps, in enhancing the performance of green roofs to address waste disposal issues, while promoting circular economy systems.

Evaluation of CMIP6 models for rainfall simulation in Central Eastern Africa using extreme precipitation indices

Cyphomandra betacea, a valuable understory crop in southwestern China, exhibits high sensitivity to water availability. Under global climate change with increasingly erratic precipitation, understanding how Cyphomandra betacea, seedlings respond to rainfall variations is crucial for sustaining this distinctive industry. Through controlled experiments, this work systematically investigates how different rainfall patterns affect seedling growth and physiology, providing a theoretical basis for science-based management under future climate scenarios. Seedlings were subjected to a four-month simulated rainfall experiment with two rainfall intervals (T: 3-day; T+: 6-day) and three rainfall amounts (W: control; W+: +40%; W-: -40%). Biomass, non-structural carbohydrates (NSC), and carbon, nitrogen, phosphorus stoichiometric characteristics were analysed. Seedling growth is more sensitive to variations in rainfall amount, and appropriate increases in rainfall can promote seedling growth and development. Under changes in rainfall patterns, seedlings prioritize the storage of NSC in stems, followed by leaves, with the lowest allocation to roots. Nitrogen content within organs is pivotal for the composition of NSC and can regulate the sugar-starch conversion process. The July W+T treatment resulted in optimal performance for the majority of growth indicators and demonstrated the highest nutrient accumulation efficiency. We identified a stem-preferential carbon allocation strategy and systemic N limitation, offering key insights for conservation and cultivation under changing climates.

Rainfall simulators are crucial devices in erosion research, enabling the controlled reproduction of precipitation characteristics for both laboratory and field investigations. This study presents a comprehensive characterization of a rainfall simulator originally designed to assess the erosive effects of precipitation on heritage surfaces. The simulator, installed at the University of León, was evaluated using volumetric methods and disdrometric techniques, employing a Parsivel2 optical disdrometer. Simulations were conducted with a falling height of 10 m and high-intensity rainfalls. Spatial uniformity was assessed through thematic mapping and the Christiansen Uniformity (CU) coefficient, revealing limited uniformity across the full wetted area, but an improved performance within the central zone (CU up to 80%). Disdrometric data provided detailed insights into drop size and velocity distributions, enabling the estimation of rainfall intensity, kinetic energy, and momentum, as well as the spatial uniformity of the energetic parameters. Empirical models to estimate the raindrop’s fall velocity were tested against disdrometric measurements, confirming the simulator’s ability to generate rainfall with velocity characteristics comparable to those of natural precipitation. Moreover, the findings underscore the importance of integrating multiple measurement approaches to enhance the reliability and accuracy of rainfall simulator characterization.

Abstract Assessing the performance of a limited-area model (LAM) based on an emerging dynamical core such as the Finite-Volume Cubed-Sphere Dynamical Core (FV3) in regional forecasting is essential to advancing existing operational systems. In this study, we configure an FV3-LAM using the Short-Range Weather Application and evaluate its performance in forecasting extreme precipitation over Taiwan, referred to as the SRW-TW experiment. We conduct the simulation of SRW-TW over 10 selected cases of different rainfall types between 2019 and 2022 and then compare the simulated rainfall with the quantitative precipitation estimation (QPE) and segregation using multiple sensors (QPESUMS) data and WRF-based forecasts as the ground reference and baseline product, respectively. By examining the simulated rainfall patterns and various performance metrics, we find that SRW-TW exhibits slightly higher correlation coefficient and critical success index values relative to the WRF baseline, although the differences are not statistically significant. However, SRW-TW performs slightly worse than the WRF baseline in terms of bias and frequency bias metrics. Our findings indicate that SRW-TW generates more realistic rainfall patterns but tends to underestimate precipitation, particularly in southwesterly flow-related events. Additionally, forecasting typhoon rainfall and hourly scale precipitation remains challenging, requiring further model refinement and the potential development of a bias-correction scheme.

Variability in rill morphometry, surface runoff and erosion with soil conservation techniques (application of polyacrylamide and rice husk biochar and hydromulching) in deforested hillslopes under simulated rainfall