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The interception of rainfall by plant canopies alters the depth and spatial distribution of water arriving at the soil surface, and thus the location, volume, and depth of infiltration. Mechanisms like stemflow are known to concentrate rainfall and route it deep into the soil, yet other mechanisms of flow concentration are poorly understood. This study characterizes pour points, formed by the detachment of water flowing under a branch, using a combination of field observations in Western Australian banksia woodlands and rainfall simulation experiments on Banksia menziesii branches. We aim to establish the hydrological significance of pour points in a water‐limited woodland ecosystem, along with the features of the canopy structure and rainfall that influence pour point formation and fluxes. Pour points were common in the woodland and could be identified by visually inspecting trees. Throughfall depths at pour points were up to 15 times greater than rainfall and generally comparable to or greater than stemflow. Soil water content beneath pour points was greater than in adjacent controls, with 20%–30% of the seasonal rainfall volume infiltrated into the top 1 m of soil beneath pour points, compared to 5% in controls. Rainfall simulations showed that pour points amplified the spatial heterogeneity of throughfall, violating assumptions used to close the water balance. The simulation experiments demonstrated that pour point fluxes depend on the interaction of branch angle and foliation for a given branch architecture. Pour points can play a significant part in the water balance, depending on their density and rainfall concentration ability. Plain Language Summary When rain hits a tree canopy, it either wets the canopy, falls off, or flows along the tree's surfaces (leaves, branches, and trunk). This interaction changes the amount and location of water arriving at the ground. The water flowing underneath branches is may eventually reach the ground by flowing along the tree trunk as stemflow. Using a combination of field observations in seasonally dry Banksia woodlands and rainfall simulation experiments on tree branches, we show that this water may, alternatively, peel off the branch and reach the ground at a “pour point.” Rain gauges placed under pour points recorded 1.5–15 times the water recorded at rain gauges under the open sky. We showed that the quantity of water arriving at the pour points varies with the rain volume, and with branch properties including the upstream leaf area, angle, and shape of the branch. The changes in the distribution of water received beneath tree canopies and deeper infiltration into the soil due to pour points proved their hydrological significance. Understanding pour points represents one path toward an improved characterization of the complex processes occurring when rain hits a tree canopy. Key Points Pour points occur when intercepted rain flowing under tree branches detach and their depths were 1.5–15 times the rainfall Pour points increase spatial heterogeneity of throughfall and enhance infiltration into the soil Rainfall simulation showed branch structure, foliation, and angle impose unclear controls on the volume of water received at the pour point

Synthetic rainfall scenarios at high temporal resolutions are pivotal in numerous environmental applications. Despite the abundance of available simulation methods, their practical utilization among practitioners remains limited, often due to challenges in model calibration stemming from sample size constraints. We introduce a novel parsimonious approach for estimating parameters of multifractal disaggregation models, based solely on available Intensity-Duration-Frequency curves, which are widely and readily accessible within the practitioner community. The performance of the proposed approach is assessed using three case studies, wherein detailed statistical properties of the simulated time series are compared against observed benchmarks. Our results indicate the potential of our approach to facilitate the straightforward application of complex models.

Floor litter can reduce the amount of water reaching the soil layer through rainfall interception. The rainfall interception capacity of floor litter varies with the physical features of the litter and rainfall characteristics. This study aimed to define the maximum and minimum interception storages (Cmx, Cmn) of litter layers using rainfall simulation experiments, and examine the effects of litter type and rainfall characteristics on rainfall retention and drainage processes that occur in the litter layer. Different types of needle-leaf and broadleaf litters were used: Abies holophylla, Pinus strobus, Pinus rigida, Quercus acutissima, Quercus variabilis, and Sorbus alnifolia. Our results indicate a wide variation in interception storage values of needle leaf litter, regardless of the rainfall intensity and duration. The A. holophylla needle-leaf litter showed the highest Cmx and Cmn values owing to its short length and low porosity. Conversely, the lowest interception storage values were determined for the P. strobus needle leaf litter. No significant differences in interception storage were established for the broadleaf litter. Moreover, except for A. holophylla litter, the broadleaf litter retained more water than the needle leaf litter. An increase in the intensity or duration of rainfall events leads to an increase in the water retention storage of litter. However, these factors do not influence the litter’s drainage capacity, which depends primarily on the force of gravity.

The Dunhuang Mogao Grotto is a famous Buddhist monument and was inscribed in the list of world cultural heritage sites by UNESCO in 1987. Water poses a major threat to the preservation of this heritage even though it is located in an arid region. This study was conducted to investigate the effect of rainfall on rock erosion. Specifically, the formation mechanism of slope runoff and the erosion threshold of rainfall were analyzed, and erosion-prone areas of the site were identified. This was carried out using field artificial rainfall simulation testing, and the results inform methods of preventing rainfall-induced cliff erosion. In addition, the rainfall threshold and erosion-prone areas obtained from the experiment were further validated and optimized using monitoring data for natural rainfall and historic documentation. The threshold value of erosive rainfall obtained by empirical statistical analysis method was found to be similar to that obtained by the runoff generation mechanism. The areas identified as prone to erosion using field tests coincided with areas of historic erosion as recorded in site documentation. Furthermore, the forecast grade of cliff slope erosion and its erosion-prone areas are determined after comprehensive analysis of the results obtained by these two methods. The research results are critical for the monitoring, early warning, and prevention of cliff slope erosion. The research methods can also be used as reference in areas for which rainfall data are missing.

Very few studies have focused on diatomaceous earth slopes along high-speed railways, and the special properties of diatomaceous earth under alternating dry and wet conditions are unknown. This paper studies diatomaceous earth in the Shengzhou area, through which the newly built Hangzhou–Taizhou high-speed railway passes, and the basic physical and hydraulic properties of diatomaceous earth are analyzed by indoor test methods. A convenient, efficient, and controllable high-speed railway slope artificial rainfall simulation system is designed, and in situ comprehensive monitoring and fissure observation are performed on site to analyze the changes in various diatomaceous soil slope parameters under rainfall infiltration, and to explore the cracking mechanisms of diatomaceous earth under alternating dry and wet conditions. The results indicate extremely poor hydrophysical properties of diatomaceous earth in the Shengzhou area; the disintegration resistance index values of natural diatomaceous earth samples subjected to dry and wet cycles are 1.8–5.6%, and the disintegration is strong. Comprehensive indoor tests and water content monitoring show that natural diatomaceous earth has no obvious influence when it contacts water, but it disintegrates and cracks under alternating dry and wet conditions. The horizontal displacement of both slope types mainly occurs within 0.75–2.75 m of the surface layer, indicating shallow surface sliding; after testing, natural slope crack widths of diatomaceous earth reach 10–25 mm, and their depths reach 40–60 cm. To guarantee safety during high-speed railway engineering construction, implementing proper protection for diatomaceous earth slopes is recommended.

The classical way of studying the rainfall‐runoff processes in the water cycle relies on conceptual or physically‐based hydrologic models. Deep learning (DL) has recently emerged as an alternative and blossomed in the hydrology community for rainfall‐runoff simulations. However, the decades‐old Long Short‐Term Memory (LSTM) network remains the benchmark for this task, outperforming newer architectures like Transformers. In this work, we propose a State Space Model (SSM), specifically the Frequency Tuned Diagonal State Space Sequence (S4D‐FT) model, for rainfall‐runoff simulations. The proposed S4D‐FT is benchmarked against the established LSTM and a physically‐based Sacramento Soil Moisture Accounting model under in‐sample and out‐of‐sample simulation setups across 531 watersheds in the contiguous United States (CONUS). Results show that S4D‐FT is able to outperform the LSTM model across diverse regions under both simulation setups, especially for regions that feature snowmelt‐driven or intermittent flow regimes. In contrast, S4D‐FT tends to underperform in flashier, high‐magnitude flow regimes, likely due to its global state‐space convolution computation that emphasizes slow, storage‐driven dynamics, which makes it less effective at picking up short bursts and noisy spikes in the data. In summary, our pioneering introduction of the S4D‐FT for rainfall‐runoff simulations challenges the dominance of LSTM in the hydrology community and expands the arsenal of DL tools available for hydrological modeling.

Recurrent neural networks (RNNs) are a class of artificial neural networks capable of learning complicated nonlinear relationships and functions from a set of data. Catchment scale daily rainfall–runoff relationship is a nonlinear and sequential process that can potentially benefit from these intelligent algorithms. However, RNNs are perceived as being difficult to parameterize, thus translating into significant epistemic (lack of knowledge about a physical system) and aleatory (inherent randomness in a physical system) uncertainties in modeling. The current study investigates a variational Bayesian dropout (or Monte Carlo dropout (MC-dropout)) as a diagnostic approach to the RNNs evaluation that is able to learn a mapping function and account for data and model uncertainty. MC-dropout uncertainty technique is coupled with three different RNN networks, i.e. vanilla RNN, long short-term memory (LSTM), and gated recurrent unit (GRU) to approximate Bayesian inference in a deep Gaussian noise process and quantify both epistemic and aleatory uncertainties in daily rainfall–runoff simulation across a mixed urban and rural coastal catchment in North Carolina, USA. The variational Bayesian outcomes were then compared with the observed data as well as with a well-known Sacramento soil moisture accounting (SAC-SMA) model simulation results. Analysis suggested a considerable improvement in predictive log-likelihood using the MC-dropout technique with an inherent input data Gaussian noise term applied to the RNN layers to implicitly mitigate overfitting and simulate daily streamflow records. Our experiments on the three different RNN models across a broad range of simulation strategies demonstrated the superiority of LSTM and GRU approaches relative to the SAC-SMA conceptual hydrologic model.

Global efforts to upgrade water, drainage, and sanitation services are hampered by hydrometeorological data-scarcity plus uncertainty about climate change. Intensity–duration–frequency (IDF) tables are used routinely to design water infrastructure so offer an entry point for adapting engineering standards. This paper begins with a novel procedure for guiding downscaling predictor variable selection for heavy rainfall simulation using media reports of pluvial flooding. We then present a three-step workflow to: (1) spatially downscale daily rainfall from grid-to-point resolutions; (2) temporally scale from daily series to sub-daily extreme rainfalls and; (3) test methods of temporal scaling of extreme rainfalls within Regional Climate Model (RCM) simulations under changed climate conditions. Critically, we compare the methods of moments and of parameters for temporal scaling annual maximum series of daily rainfall into sub-daily extreme rainfalls, whilst accounting for rainfall intermittency. The methods are applied to Kampala, Uganda and Kisumu, Kenya using the Statistical Downscaling Model (SDSM), two RCM simulations covering East Africa (CP4 and P25), and in hybrid form (RCM-SDSM). We demonstrate that Gumbel parameters (and IDF tables) can be reliably scaled to durations of 3 h within observations and RCMs. Our hybrid RCM-SDSM scaling reduces errors in IDF estimates for the present climate when compared with direct RCM output. Credible parameter scaling relationships are also found within RCM simulations under changed climate conditions. We then discuss the practical aspects of applying such workflows to other city-regions.

As a new generation of global climate models, monsoon simulation in Coupled Model Intercomparison Project (CMIP) Phase 6 models is of great concern to climate modeling community. Using 21 CMIP3 models, 28 CMIP5 models and 38 CMIP6 models, we show evidence that the long-standing dry biases in South Asia (SA) are resulted from less rainfall with both less frequency and intensity in a shortened monsoon season. By evaluating several key metrics, we identify that the monsoon rainfall simulation in CMIP6 models has improved in both of the multimodel ensemble mean (MME) and individual models, consistent with the improvements in monsoon annual cycle and rainfall characteristics. Further analyses and sensitivity experiments show that the cold SST biases all year round over the northern Indian Ocean (NIO) are important sources for the persistent dry biases in the CMIPs’ models. The cooling effect of SST biases on the tropospheric temperature becomes increasingly prominent since the boreal spring, weakening the baroclinity of monsoon circulation via the thermal wind relationship and eventually resulting in insufficient monsoon rainfall. Comparison across the three generation CMIP models also confirms that the improvement of SA summer rainfall simulation in CMIP6 MME benefits from the reduction of NIO SST biases. This study highlights the importance of improving SST simulation in reducing the monsoon rainfall biases.

This paper introduces for the first time the concept of Bayesian model averaging (BMA) with multiple prior structures, for rainfall‐runoff modeling applications. The original BMA model proposed by Raftery et al. (2005, https://doi.org.10.1175/MWR2906.1) assumes that the prior probability density function (pdf) is adequately described by a mixture of Gamma and Gaussian distributions. Here we discuss the advantages of using BMA with fixed and flexible prior distributions. Uniform, Binomial, Binomial‐Beta, Benchmark, and Global Empirical Bayes priors along with Informative Prior Inclusion and Combined Prior Probabilities were applied to calibrate daily streamflow records of a coastal plain watershed in the southeast United States. Various specifications for Zellner's g prior including Hyper, Fixed, and Empirical Bayes Local (EBL) g priors were also employed to account for the sensitivity of BMA and derive the conditional pdf of each constituent ensemble member. These priors were examined using the simulation results of conceptual and semidistributed rainfall‐runoff models. The hydrologic simulations were first coupled with a new sensitivity analysis model and a parameter uncertainty algorithm to assess the sensitivity and uncertainty associated with each model. BMA was then used to subsequently combine the simulations of the posterior pdf of each constituent hydrological model. Analysis suggests that a BMA based on combined fixed and flexible priors provides a coherent mechanism and promising results for calculating a weighted posterior probability compared to individual model calibration. Furthermore, the probability of Uniform and Informative Prior Inclusion priors received significantly lower predictive error, whereas more uncertainty resulted from a fixed g prior (i.e., EBL). Plain Language Summary This study presents a two‐step procedure that includes model calibration of a range of hydrological models using DREAM (zs) algorithm, followed by ensemble prediction of streamflow using Bayesian model averaging (BMA) with various prior structures. The hydrological modeling simulations were first coupled with a new sensitivity analysis model and a parameter uncertainty algorithm to assess the sensitivity and uncertainty associated with each hydrologic model simulation. BMA was then used to subsequently combine the simulations on the most important parts of the posterior probabilities of each constituent hydrological model. Analysis suggests a BMA with fixed and flexible priors provides a coherent mechanism and promising results for calibrating a weighted posterior probability compared to individual model calibration. The hierarchy of prior distributions used in this study increased the flexibility of BMA fitting for daily streamflow simulation and reduced the dependence of posterior and predictive uncertainty (including model probabilities) on prior assumptions of hydrological modeling simulation. Key Points Bayesian model averaging with fixed and flexible prior structures were applied to combine the posterior probability distribution of four hydrological models Custom prior inclusion and uniform prior induced a much sharper posterior median Putting a prior on both θ and g makes the analysis naturally adaptive and avoids the information paradox