Rainfall Events Research Articles

Electric inter-row control of lupin plants does not adversely affect the neighbouring non-target lupin plants

Abstract Inter-row weed control is used in a wide range of crops, traditionally applied via physical cultivation or banded herbicide application. However, these methods may result in crop damage, herbicide resistance development, or off-target environmental impacts. Electric inter-row weed control presents an alternative, though its potential impact on crop yield requires further investigation. One of the modes of action of electric weed control is the continuous electrode-plant contact method, which passes a current through the weed and into the roots. As the current passes into the roots, it can potentially disperse through the soil to neighbouring root systems. Such off-target current dispersion, particularly in moist topsoil with low resistance, poses potential concern for neighbouring crops when electric inter-row weed control is applied. This research evaluated the continuous electrode-plant contact method, using a ZassoTM XPower machine, in comparison with mowing, across three trials conducted in 2022 and 2023. Both treatments were used to remove target lupin plants adjacent to a row of non-target lupin. Electric weed control was applied to plants in dry soil or following a simulated rainfall event. The trials demonstrated that electric weed control and mowing did not reduce density and biomass of neighbouring non-target lupin plants compared to the untreated control. Likewise, pod and seed production, grain size and protein, as well as grain germinability and vigour of the resulting seedlings was not reduced by these weed control tactics. This research used technology that was not fit for purpose in broad scale grain crops but concludes that electric weed control via the continuous electrode-plant contact method or mowing did not result in crop damage. Therefore, it is unlikely that damage will occur using commercial-grade electric weed control or mowing technology designed for large acreage inter-row weed control, thus offering non-chemical weed management options.

Effect of Progressive Wetting on Permanent Deformation of Fouled Ballast under Cyclic Loading

Climate change in recent decades has increased the frequency and intensity of extreme rainfall events, causing varied moisture contents in the ballasted track, which greatly challenges railway operation and track maintenance. Currently, most research about permanent deformation of fouled ballast are under dry condition or with a moisture content in the individual tested sample, which could not fully represent varied moisture conditions in the field induced by varying rainfall intensities. In this paper, permanent deformation of field-sourced fouled ballast under progressive wetting condition was investigated using large-scale triaxial cyclic tests. The results indicate that, with progressively rising water contents (0% to 12%), the fouled ballast sample maintained their stability; however, the rate of permanent strain increases to a peak value before experiencing a slight decline, aligning with classified shakedown ranges. This observed phenomenon can be attributed to the interplay between wetting, densification, and interlocking under conditions of cyclic loading and progressive wetting, an aspect that existing research have not elaborated. To encapsulate the identified deformation characteristics, this study proposes and validates a new predictive model for the permanent deformation of fouled ballast, demonstrating high prediction accuracy for fouled ballast with varying water contents.

Optimize Stormwater Management for Green Roofs Based on IoT Technology and Sensor Networks

The intensification of global climate change and the significant increase of extreme rainfall events have made traditional urban drainage systems face great adjustments. As a key component of green infrastructure to avoid urban flood risk, green roofs are receiving increasing attention due to their unique capabilities in stormwater management. This paper explores the potential of Internet of Things (IoT) technology and sensor networks to enhance green roof stormwater management. Integrated real-time monitoring of key environmental parameters, including soil moisture, temperature and rainfall, enables the system to dynamically adjust roof drainage and irrigation strategies. This greatly improves the rainwater retention capacity and reduces the pressure on the municipal sewage system. The results show that the combination of IoT technology and sensor networks not only improves the efficiency of green roof management in combating climate change, but also promotes the efficient use of water resources and enhances the adaptability and sustainability of the system. This research provides the necessary technical support for the wider adoption of green roofs in urban environments and offers viable solutions to future challenges posed by climate change.

Monitoring and Modelling Fluxes of Water and Nutrients to Surface Drainage Network From Irrigated Agricultural Fields in a Hydraulically Reclaimed Coastal Area

ABSTRACTThis paper describes the application of a physically based agrohydrological model (named FLOWS), coupled with a kinematic wave approach model (named KWV) for water and solute runoff routing, for interpreting the fate of water and nutrients coming from cultivated fields to surface drainage network located in ‘Piana del Sassu’ in the Arborea plain, a hydraulically reclaimed area with shallow groundwater. Modelling was supported by a large complex database on soil, groundwater and surface drainage water, which was used for establishing the boundary conditions for simulations, as well as for calibrating and validating the model. The model FLOWS provided the water and nutrient fluxes to the surface water, which were passed to the KWV model for their routing along the elementary fields in the experimental area and from these to the ditches and finally to the drainage channel. The modelling approach effectively predicted the water and solute distribution along the soil profile, as well as the losses of water and nutrients to the surface water. The results showed a significant amount of water and dissolved nutrients to flow quickly from the soil uppermost layer to the surface drainage network during both the irrigation season and during rainfall events. During irrigation applications, losses were mostly due to rainfall intensity exceeding the maximum infiltration velocity of the shallow soil layer in the case of sprinkler irrigation and to subsurface lateral drainage in the case of exceeding irrigation water provided by drip irrigation. This makes the Sassu plain a significant contributor of nutrients (nitrate and phosphorus) to the surface water. Consequently, even though the agricultural activities might not be an important issue for the groundwater vulnerability, the management of water and nutrients should be significantly improved to avoid ecohydrological threats to the important coastal water bodies present in the area.

Bayesian inference of forest road failure frequency depending on rainfall intensity for every prefecture in Japan

ABSTRACT To make a quantitative prediction on forest road failures in the future under the influence of climate change, forest road failure frequency depending on rainfall intensity needs to be clarified. The forest road failure frequency at a certain rainfall intensity should vary regionally because of the variability in susceptibility. In the current situation of a lack of nationwide detailed forest road failure inventory data and GIS-based forest road data, this study analyzed the relationship between rainfall intensity and forest road failure frequency based on Bayesian inference using data available for all the 47 prefectures in Japan; the annual number of forest road failures, forest road length, analyzed precipitation data and polygon data of forest areas. The predicted forest road failure frequency varied broadly by prefecture; 10−4–10−2 km−1 at a rainfall event of 100 mm/24 h, while 10−2–100 km−1 at that of 400 mm/24 h. The frequency at the same rainfall intensity tended to be lower in conventionally rainy prefectures. The results imply that the number of forest road failures in currently less rainy regions may show a larger increase in the future as intense rainfall events are predicted all over Japan due to climate change.

Comparison of Different Quantitative Precipitation Estimation Methods Based on a Severe Rainfall Event in Tuscany, Italy, November 2023

Accurate quantitative precipitation estimation (QPE) is fundamental for a large number of hydrometeorological applications, especially when addressing extreme rainfall phenomena. This paper presents a comprehensive comparison of various rainfall estimation methods, specifically those relying on weather radar data, rain gauge data, and their fusion. The study evaluates the accuracy and reliability of each method in estimating rainfall for a severe event that occurred in Tuscany, Italy. The results obtained confirm that merging radar and rain gauge data outperforms both individual approaches by reducing errors and improving the overall reliability of precipitation estimates. This study highlights the importance of data fusion in enhancing the accuracy of QPE and also supports its application in operational contexts, providing further evidence for the greater reliability of merging methods.

Spatial and Temporal Patterns of Rainfall Erosivity in Southern Africa in Extreme Wet and Dry Years

Soil erosivity is a key indicator of the effectiveness of precipitation acting on the land’s surface and is mainly controlled by event-scale and seasonal weather and climatic factors but is also influenced by the nature of the land’s surface, including relief and vegetation cover. The aim of this study is to examine spatial and temporal variations in soil erosivity across southern Africa using rainfall data for the period 2000–2023 and a gridded raster spatial modelling approach. The two wettest and driest years in the record (±>1.5 standard deviation of rainfall values) were identified, which were 2000 and 2006, and 2003 and 2019, respectively. Monthly rainfall values in these extreme wet/dry years were then analyzed for four rainfall regions (arid, semiarid, subhumid, humid), identified according to their annual rainfall totals. These data were then used to calculate Precipitation Concentration Index (PCI) values as an expression of rainfall seasonality, and the modified Fournier index (MFI) was used to quantify rainfall erosivity. The results show that there are significant differences in erosivity between the different climate regions based on rainfall seasonality and also their distinctive environmental settings. In turn, these reflect the synoptic climatic conditions in these regions, their different precipitation sources, and rainfall totals. The results of this study show that calculated MFI values at the national scale, which is the approach taken in most previous studies, cannot effectively describe or account for erosivity values that characterize different climatic regions at the sub-national scale. Furthermore, the mismatch between PCI and MFI spatial patterns across the region highlights that, under semiarid, and highly seasonal rainfall regimes, episodic rainfall events interspersed with periods of dryness result in significant variability in erosivity values that are unaccounted for by rainfall totals or seasonality alone. In these environments, flash floods and wind erosion result in regional-scale soil erosion and land degradation, but these processes and outcomes are not clear when considering MFI values alone. Fully evaluating spatial and temporal patterns of erosivity in their climatic and environmental contexts, as developed in this study, has implications for sediment and carbon exports, as well as identifying the major regions in which land degradation is an environmental and agricultural issue.

Adapting to Climate Change with Machine Learning: The Robustness of Downscaled Precipitation in Local Impact Analysis

The skill, assumptions, and uncertainty of machine learning techniques (MLTs) for downscaling global climate model’s precipitation to the local level in Bolivia were assessed. For that, an ensemble of 20 global climate models (GCMs) from CMIP6, with random forest (RF) and support vector machine (SVM) techniques, was used on four zones (highlands, Andean slopes, Amazon lowlands, and Chaco lowlands). The downscaled series’ skill was evaluated in terms of relative errors. The uncertainty was analyzed through variance decomposition. In most cases, MLTs’ skill was adequate, with relative errors less than 50%. Moreover, RF tended to outperform SVM. Robust (weak) stationary (perfect prognosis) assumptions were found in the highlands and Andean slopes. The weakness was attributed to topographical complexity. The downscaling methods were shown to be the dominant source of uncertainties. This analysis allowed the derivation of robust future projections, showing higher annual rainfall, shorter dry spell duration, and more frequent but less intense high rainfall events in the highlands. Apart from the dry spell’s duration, a similar pattern was found for the Andean slopes. A decrease in annual rainfall was projected in the Amazon lowlands and an increase in the Chaco lowlands.

Pattern-based assessment of the influence of catchment characteristics on urban stormwater quality

ABSTRACT Use of traditional catchment characteristics and lack of effective separation of catchment characteristics from rainfall characteristics limits the adequate understanding of the actual influences of catchment characteristics on stormwater quality. In this context, this study adopted a pattern-based separation approach to identifying common events from three rainfall clusters of study catchments, where rainfall characteristics for identified common 8, 9 and 36 events are similar, but catchment characteristics are different. This study identified that the locations of pervious surfaces and urban forms are also important catchment characteristics. The contribution of the pervious area to runoff is significant for long durational (cluster 1) and high-intensity (cluster 3) rainfall events associated with low antecedent dry days and initiated at around 35 mm rainfall depth. A catchment having a large impervious area and pervious area located at far distances from the drainage system can contribute more pollutant load at first 10% runoff volume. High socio-economic developed urban form can contribute a high fraction wash-off corresponding to 10–70% runoff volume due to traffic-related behaviour and various anthropogenic activities. The developed knowledge will overcome the shortcomings of lumped characteristics-based treatment design and improve the efficiency of current stormwater treatment system design practices.

Climate change is intensifying rainfall erosivity and soil erosion in West Africa

Soil erosion is a critical environmental challenge with significant implications for agriculture, water quality, and ecosystem stability. Understanding its dynamics is essential for sustainable environmental management and societal welfare. Here, we analyze rainfall erosivity and erosion patterns across West Africa (WAF) during the historical (1982–2014), near future (2028–2060), and far future (2068–2100) periods under Shared Socioeconomic Pathways (SSPs 370 and 585). Using bias-corrected-downscaled (BCD) climate models validated against reference data, we ensure an accurate representation of rainfall—a key driver of erosivity (R-factor) and soil erosion. We compare Renard's approach and the Modified Fournier Index (MFI) to calculate the R-factor and note a strong correlation. However, Renard's method shows slightly lower accuracy in Sierra Leone, Guinea, and The Gambia, likely due to its inability to capture high-intensity, short-duration rainfall events. In contrast, the MFI, utilizing continuous rain gauge data, proves more reliable for these regions. We also attribute fluctuations in erosivity, such as those seen during the 2003 West Africa floods, to synoptic weather patterns influenced by multiple climate processes.Furthermore, our analysis reveals regions where future soil erosion could exceed 20 t/ha/yr due to climate change. Under the SSP 370 scenario, soil erosion in WAF is projected to rise by 14.84 % in the near future and 18.65 % in the far future, increasing further under SSP 585 to 19.86 % and 23.49 %, respectively. The most severe increases are expected in Benin and Nigeria, with Nigeria potentially facing a 66.41 % rise in erosion by the far future under SSP 585. These findings highlight the region's exposure to intensified climatic conditions and underscore the urgent need for targeted soil management and climate adaptation strategies to mitigate erosion's ecological and socioeconomic impacts.

Optimizing urban flood management: enhancing urban drainage system efficiency under extreme rainfall events

ABSTRACT Extreme rainfall events, particularly those induced by tropical cyclones, pose a heightened risk to the urban drainage system (UDS). Existing UDSs, having been established long ago, often fail to account for the extreme rainfall caused by cyclones. To address this issue, this study designs a multi-objective intelligent scheduling model within a simulation -optimization framework, aiming to optimize the operation of urban drainage infrastructure and hydraulic structures. This is achieved by integrating the Storm Water Management Model (SWMM) with the multi-objective particle swarm optimization algorithm (MOPSO) and distinctly evaluating typhoons and torrential rains for their impact on extreme rainfall. The study results show that the multi-objective intelligent scheduling model can effectively devise operation strategies for pumping stations and weirs in the study area, thereby optimizing their use for urban drainage. The model was successful in reducing the total flood volume (TFV) and the water level fluctuation (WLF) by 3.11%–57.77% and 26.32%–65.48%, respectively. This not only mitigates urban flooding but also enhances the infrastructure stability of the UDS. The model outperformed the local adaptation strategy in most scenarios for the two selected objectives, suggesting that the efficiency can be significantly improved by optimizing UDSs without expansion of existing infrastructure or additional costs.

Tropical intraseasonal oscillations as key driver and source of predictability for the 2022 Pakistan record-breaking rainfall event

In August 2022, Pakistan experienced unprecedented monsoon rains, leading to devastating floods and landslides affecting millions. While previous research has mainly focused on the contributions of seasonal and synoptic anomalies, this study elucidates the dominant influences of tropical and extratropical intraseasonal oscillations on both the occurrence and subseasonal prediction of this extreme rainfall event. Our scale-decomposed moisture budget analysis revealed that intense rainfall in Pakistan was triggered and sustained by enhanced vertical moisture transport anomalies, primarily driven by interactions between intraseasonal circulation anomalies and the prevailing background moisture field when tropical and mid-latitude systems coincided over Pakistan. Evaluation of subseasonal-to-seasonal prediction models further highlighted the critical role of tropical intraseasonal modes in causing this extreme rainfall event in Pakistan. Models that accurately predicted northward-propagating intraseasonal convection with a forecast lead time of 8–22 days demonstrated good skill in predicting the extreme event over Pakistan.

Enhancing Sustainability in Watershed Management: Spatiotemporal Assessment of Baseflow Alpha Factor in SWAT

The increasing frequency of extreme rainfall events poses significant challenges to sustainable water resource management, leading to severe natural disasters. To mitigate these challenges, understanding the hydrological characteristics of watersheds, especially baseflow, is critical for enhancing watershed resilience and supporting sustainable water quality and resource management. However, conventional watershed models often neglect the accurate simulation of baseflow recession. This study proposes a method for calculating and applying the alpha factor for each hydrologic response unit (HRU) in the Soil and Water Assessment Tool (SWAT), considering both temporal and spatial variability in baseflow. The study watershed has undergone significant development, increasing the need for effective water management strategies that promote long-term sustainability. The alpha factor was computed using BFlow2021, and its effectiveness was evaluated by comparing recession and baseflow estimates under different methods. The results indicate that incorporating monthly HRU-specific alpha factors significantly improves model predictions of recession characteristics, highlighting the need for a more spatially and temporally detailed approach in hydrological modeling. The proposed methodology can help clarify the connection between recession and baseflow and can be applied to ungauged stations, offering a valuable tool for sustainable watershed and water quality management.

Modeling the effect of land-use change on runoff water quality in the urban watershed: a case study of Settat city, Morocco

ABSTRACT The purpose of this study is to create a model of the quantity and quality of runoff from the urban watershed. The modeling was performed using a personal computer stormwater management model (PCSWMM) tool and was based on washoff and buildup for total suspended solids (TSS), total nitrogen (TN), and total phosphorus (TP) measured in the field. In addition, low impact development (LID) was used under four different scenarios to improve runoff control of the urban hydrologic cycle and reduce pollutant concentrations. Model performance was analyzed using the R2 and NSE goodness-of-fit indices. Thus, three typical rainfall events, as well as field measurement parameters (TSS, TN, and TP), were used for the model's calibration and validation. The base scenario quantified the runoff volume and pollutant load without any intervention in the runoff process control. Scenarios S2 (BR: bioretention) and S3 (PP: permeable pavement) contributed, separately, in reducing runoff and pollutant load but with relatively small percentages. In addition, the results show that scenario S4 (BR and PP) has the most significant impact on flow reduction, by about 10% compared with the base scenario, as well as on the pollutant load reduction, by about 31, 29, and 40% for the parameters TSS, TN, and TP, respectively.

Spatially seamless and temporally continuous assessment on compound flood risk in Hong Kong

Compound flooding results from the simultaneous occurrence of extreme storm surges, sea level rise, and heavy rainfall. These events often lead to impacts significantly more severe than those caused by any individual flood-inducing factor alone. However, the limited and sparse data from tidal gauges hampers precise risk assessment at ungauged sites in coastal cities. Our study addresses this gap by integrating ensemble machine learning with Bayesian inference, offering a comprehensive spatial–temporal analysis of compound flood risk from 1979 to 2022 in Hong Kong. We developed an ensemble machine learning approach within the Bayesian hierarchical modeling framework to achieve spatial–temporal continuity in the estimation of extreme storm surges and mean sea level at sites without tidal gauge stations. Results show a significant yearly increase in maximum storm surge levels by 3 mm and a significant rise in mean sea level of 25 mm per decade in Hong Kong. Our analysis also indicates a significant increase in daily heavy rainfall intensity. Furthermore, in 14.54 % of cases, extreme storm surges coincided with heavy rainfall, while 13.69 % of heavy rainfall events occurred alongside extreme sea level conditions. The copula-based joint analysis reveals significant positive correlations among these extreme events. Our findings further reveal that the return level for a 100-year heavy rainfall event increases dramatically from 126.36 mm in the univariate case to 261.16 mm in the trivariate scenario, underlining the escalated risk associated with compound flooding. Similarly, for storm surge extremes, trivariate analysis reveals elevated risk during compound flood events, with the return level rising from 1.18 m (univariate scenario) to 1.40 m (trivariate scenario) for a 100-year return period. These spatial–temporal maps and comprehensive compound flood risk assessments offer crucial insights for addressing the multi-hazard flood risk in coastal urban areas.

Study on the Sand Reduction Effect of Slope Vegetation Combination in Loess Areas

Slope erosion in the Loess Plateau region has long been a concern, and vegetation plays an important role in slowing down erosion and controlling sedimentation. However, a single vegetation model shows some limitations when facing complex natural conditions and variable rainfall events. Therefore, this study investigated the influence mechanism of vegetation configuration on slope sand production at different slopes through theoretical analyses and indoor experiments. The results of the study showed that certain factors, such as vegetation configuration mode, flow rate, runoff power, runoff velocity, and runoff shear, are closely related to slope runoff sand production. The specific findings are as follows: (1) Under the condition of slope gradient of 2°, the sand reduction effect of the rigid–flexible single-row staggered configuration is the most significant, and the sediment production is reduced by 29.89%. (2) With the increase in the slope gradient and flow rate, the sand production on the slope surface rises significantly, and when the slope gradient is increased from 2° to 6°, the average sand production is increased from 1.43 kg to 2.51 kg.(3) The erosion reduction effects of different vegetation configurations were in the order of rigid–flexible single-row staggered combination > flexible vegetation single combination > rigid–flexible double-row staggered combination > rigid vegetation single combination > upstream rigid downstream flexible combination > bare slope. This study provides a theoretical basis for optimizing the vegetation configuration for effective sand reduction and provides an important reference for the sustainable development of the Yellow River Basin.

Numerical Simulations of a Permeability Test on Non-Cohesive Soil Under an Increasing Water Level

With the intensification of global climate change, extreme rainfall events are occurring more frequently. Continuous rainfall causes the debris flow gully to collect a large amount of rainwater. Under the continuous increase in the water level, the water flow has enough power to carry plenty of loose solids, thus causing debris flow disasters. The intensity of the soil is reduced with the infiltration of rainwater, which is one of the key causes of the disaster. The rise in the water level affects the infiltration behavior. There have been few previous studies on infiltration under variable head. In order to understand the infiltration behavior of soils under the action of water level rises, this paper conducted an indoor permeability test on non-cohesive soil under the condition of an increasing water level. A numerical model was established using the finite element analysis software, Abaqus 6.14, and the pore pressure was increased intermittently to simulate the intermittent increase in the water level. Thereafter, the permeability coefficient and seepage length were changed to interpret the changes in the flow velocity and rate in the permeability test of the non-cohesive soil. The results showed that the finite element numerical simulation method could not reflect the particle movement process in the soil. The test could better reflect the through passage and void plugging phenomenon in soil; when the permeability coefficient alone changed, the velocity of the measuring point with higher velocity changed more violently with the permeability coefficient; when the length of soil seepage diameter was uniformly shortened, the velocity of water flow increased faster and faster.

Ground-Based MAX-DOAS Observations for Spatiotemporal Distribution and Transport of Atmospheric Water Vapor in Beijing

Understanding the spatiotemporal distribution and transport of atmospheric water vapor in urban areas is crucial for improving mesoscale models and weather and climate predictions. This study employs Multi-Axis Differential Optical Absorption Spectroscopy to monitor the dynamic distribution and transport flux of water vapor in Beijing within the tropospheric layer (0–4 km) from June 2021 to May 2022. The seasonal peaks in precipitable water occur in August, reaching 39.13 mm, with noticeable declines in winter. Water vapor was primarily distributed below 2.0 km and generally decreases with increasing altitude. The largest water vapor transport flux occurs in the southeast–northwest direction, whereas the smallest occurs in the southwest–northeast direction. The maximum flux, observed at about 1.2 km in the southeast–northwest direction during summer, reaches 31.77 g/m2/s (transported towards the southeast). Before continuous rainfall events, water vapor transport, originating primarily from the southeast, concentrates below 1 km. Backward trajectory analysis indicates that during the rainy months, there was a higher proportion of southeasterly winds, especially at lower altitudes, with air masses from the southeast at 500 m accounting for 69.11%. This study shows the capabilities of MAX-DOAS for remote sensing water vapor and offers data support for enhancing weather forecasting and understanding urban climatic dynamics.

Response time of fast flowing hydrologic pathways controls sediment hysteresis in a low-gradient watershed, as evidenced from tracer results and machine learning models

Hydrologic controls on the timing of sediment transport and sediment hysteresis patterns remain an open area of investigation in hydrology, especially for low-gradient watersheds with substantial instream sediment deposition. Sediment hysteresis, which describes the mismatch between hydrograph peak and sedigraph peak, aids with elucidation of the mechanisms of sediment transport in watersheds. Most frequently, the controls of hysteresis are attributed to the proximity of sediment sources to monitoring locations in a watershed. However, this assumption, while widely applied, is infrequently verified. We investigated the controls of sediment hysteresis in a low gradient system located in the Bluegrass Region of central Kentucky, USA. Turbidity and conductivity sensors installed at the basin outlet provided data to quantify sediment hysteresis and separate hydrologic flow pathways (i.e., by describing the source of water delivered to the watershed’s outlet) using a tracer-based approach. Predictive hydrologic parameters, including hydrologic pathways, event magnitude, and antecedent conditions, were estimated and grouped based on hydrologic similitude. Thereafter, we identified parameters required to predict sediment hysteresis using a tailored ensemble feature selection approach coupled with three machine learning algorithms—Random Forest, K-Nearest Neighbors, and Gradient Boosted Trees. Results from the analysis of 68 storm events occurring over a two-year period showed that clockwise events accounted for 85 % of the total sediment yield despite comprising only 53 % of the events. The hysteresis index (HI) can be predicted (r = 0.8, RMSE = 0.12) using three, out of the thirty-nine hydrologic parameters considered. The most important predictors of HI reflect the volume of event rainfall and the relative proportions of new water (i.e., water derived from precipitation during the storm event) and old water (i.e., water previously stored in the watershed) comprising the hydrograph. Further analyses reveal that new water timing—which changes with the rainfall volume—and sediment timing are closely linked, suggesting that variations in the hysteresis patterns are controlled by changes in the response time of fast flowing water pathways. This implies that hydrologic pathways, as opposed to sediment proximity to the watershed outlet, control sediment hysteresis in this watershed. These results have important implications for better understanding the mechanisms controlling sediment transport at the watershed scale.

Community structure of tropics emerging from spatio-temporal variations in the Intertropical Convergence Zone dynamics

The Intertropical Convergence Zone (ITCZ) is a narrow tropical belt of deep convective clouds, intense precipitation, and monsoon circulations encircling the Earth. Complex interactions between the ITCZ and local geophysical dynamics result in high climate variability, making weather forecasting and prediction of extreme rainfall or drought events challenging. We unravel the complex spatio-temporal dynamics of the ITCZ and the resulting teleconnection patterns via a novel tropical climate classification achieved using complex network analysis and community detection. We reduce the high-dimensional complex ITCZ dynamics into a simple yet insightful community structure that classifies the tropics into seven regions representing distinct ITCZ dynamics. The two largest communities, encompassing landmasses over the Northern and Southern hemispheres, are associated with coherent seasonal ITCZ dynamics and have significant long-range connections. Temporal analysis of the community structure highlights that the tropical Pacific and Atlantic Oceans communities exhibit substantial variation on multidecadal scales. Further, these communities exhibit incoherent dynamics due to atmosphere-ocean interactions driven by equatorial and coastal oceanic upwelling.