High Intensity Rainfall Events Research Articles

Assessment of nitrogen and phosphorus losses due to erosion in compost-treated and non-treated vineyard soils: effect of rainfall intensity

Vineyards in Mediterranean areas suffer from significant soil degradation through erosion, due to rainfall and soil characteristics, as well as soil management practices. Previous studies pointed out the nutrient losses produced by erosion and the benefits that some management practices could have on reducing erosion. This research tried to evaluate the effect of events of different intensities and to assess whether the beneficial effect of compost amendment may pose a potential risk of nutrient loss and environmental pollution in particular under high-intensity events. The study compared soil and nutrient losses in compost-treated and non-treated vineyard soils after rainfall events of different intensities analyzed over 2 years in two vineyards. Runoff samples were collected by triplicate in treated and non-treated soils. Sediment and nitrogen and phosphorous concentrations in the runoff samples were analyzed. The results reveal a reduction in runoff rates and an increase in soil water content in compost-treated soils, which represents a benefit for rainfed vineyards. Both nitrogen and phosphorus losses depended on rainfall characteristics. Although for low intensities there were no significant differences in the amount of nutrient lost by runoff in both treated and non-treated soils, nitrogen and phosphorus losses were higher after high-intensity rainfall events in compost-treated soils. With the expected increase in high-intensity rainfall events associated with climate change in the Mediterranean region, organic amendments should be applied in several splits or incorporated into the soil to avoid increased nutrient loss to water bodies.

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.

Evaluation of Minnesota Phosphorus Loss Index performance.

Supported by the National Phosphorus (P) Research Project led by Dr. Andrew Sharpley, Minnesota developed its statewide P-Index, the Minnesota P Loss Index (MNPI), to manage critical source areas of agricultural P. The MNPI has remained unchanged since its last revision in 2006. The overall goal of this study was to critically evaluate the MNPI to determine, in the parlance of Sharpley, if the MNPI remains "directionally and magnitudinally correct." Observed P loss from 67 site-years of annual edge-of-field data was compared with MNPI-predicted P loss. Our assessment indicates that MNPI performance is directionally correct: it correctly ranks fields that are more at risk than others. The MNPI performed better in years with high-intensity rainfall events. Averaging MNPI assessment across multiple years of data input, along with minor adjustments to the calculation algorithm, improved the robustness of MNPI estimates. Continued re-evaluation of the MNPI will ensure that this important tool for nutrient management is properly evaluating P loss potential. This study reflects Dr. Sharpley's decades-long effort to improve and revise P indices so that they reflect advances in the science and management of agricultural P.

The impact of dissolved organic nitrogen (DON) retention in the vadose zone on nitrogen leaching losses

Leaching of dissolved organic nitrogen (DON) is a significant pathway for nitrogen (N) loss in agricultural ecosystems. The excessive application of N for enhanceing agricultural productivity often results in the leaching of N into groundwater. Yet not well understood, the extent of retention in the vadose zone has critical implications for risk management and remediation strategies. This study aims to advance simulation techniques for modelling the transport process of reactive DON within a heterogeneous vadose zone. Through a combination of laboratory experiments and numerical simulations, the study firstly examines the extent of DON retention in the vadose zone and quantitatively analyse groundwater contamination risk from this kind of accumulation. Our findings indicate that heavy N fertilizer application and high-intensity rainfall events led to elevated contents of DON in the vadose zone and increased DON leaching fluxes into groundwater. Besides, intensifier rainfall reduced the N storage more quickly in scenarios devoid of DON application with higher mineralization rate, while DON slowly mineralized to other forms, largely accumulated in the top layer and migrated deeper with intensifier rainfall after input of urea. In our scenarios, DON accounted for a substantial portion (33–68%) of the total dissolved nitrogen (TDN) leaching fluxes, with exogenous DON content contributing significantly (25–85%) to the overall DON leaching into the aquifer. These results underscore the need for effective strategies to mitigate groundwater contamination risks associated with agricultural N use.

Estimating Rainfall Intensity Using an Image-Based Convolutional Neural Network Inversion Technique for Potential Crowdsourcing Applications in Urban Areas

High-quality rainfall data are essential in many water management problems, including stormwater management, water resources management, and more. Due to the high spatial–temporal variations, rainfall measurement could be challenging and costly, especially in urban areas. This could be even more challenging in tropical regions with their typical short-duration and high-intensity rainfall events, as some of the undeveloped or developing countries in those regions lack a dense rain gauge network and have limited resources to use radar and satellite readings. Thus, exploring alternative rainfall estimation methods could be helpful to back up some shortcomings. Recently, a few studies have examined the utilisation of citizen science methods to collect rainfall data as a complement to the existing rain gauge networks. However, these attempts are in the early stages, and limited works have been published on improving the quality of such data. Therefore, this study focuses on image-based rainfall estimation with potential usage in citizen science. For this, a novel convolutional neural network (CNN) model is developed to predict rainfall intensity by processing the images captured by citizens (e.g., by smartphones or security cameras) in an urban area. The developed model is merely a complementary sensing tool (e.g., better spatial coverage) to the existing rain gauge network in an urban area and is not meant to replace it. This study also presents one of the most extensive datasets of rain image data ever published in the literature. The estimated rainfall data by the proposed CNN model of this study using images captured by surveillance cameras and smartphone cameras are compared with observed rainfall by a weather station and exhibit strong R2 values of 0.955 and 0.840, respectively.

Interpretation of Soil Characteristics and Preferential Water Flow in Different Forest Covers of Karst Areas of China

Soil hydrology seriously affects the prevention of desertification in karst areas. However, water infiltration in the different soil layers of secondary forests and artificial forests in karst areas remains uncertain. This lack of clarity is also the factor that constrains local vegetation restoration. Therefore, monitoring and simulating the priority transport of soil moisture will help us understand the shallow soil moisture transport patterns after artificial vegetation restoration in the local area, providing a reference for more scientific restoration of the ecological environment and enhancement of carbon storage in karst areas. The integration of soil physical property assessments, computed tomography (CT) scanning, dye tracing studies, and HYDRUS-2D modeling was utilized to evaluate and contrast the attributes of soil macropores and the phenomenon of preferential flow across various forestland categories. This approach allowed for a comprehensive analysis of how the soil structure and water movement are influenced by different forest ecosystems and infiltration head simulations (5 mm, 15 mm, 35 mm, and 55 mm) to elucidate the dynamics of water movement across diverse soil types within karst regions, to identify the causes of water leakage due to preferential flow in secondary forests, and to understand the mechanisms of water conservation and reduction in artificial forests adopting a multifaceted approach. This study demonstrated that (1) the soil hydrological capacity of a plantation forest was 20% higher than a natural forest, which may be promoted by the clay content and distribution. (2) Afforestation-enhanced soils in karst regions demonstrate a significant capacity to mitigate the loss of clay particles during episodes of preferential flow and then improve the soil erosion resistance by about 5 times, which can effectively control desertification in karst area. (3) The uniform distribution of macropores in plantation forest soil was conducive to prevent water leakage more effectively than the secondary forest but was incapable of hindering the occurrence of preferential flow. The secondary forest had a very developed preferential flow phenomenon, and soil clay deposition occurred with an increase in depth. (4) Moreover, the results for preferential flow showed that the matrix flow depth did not increase with the increase in water quantity. Short-term and high-intensity heavy rainfall events facilitated the occurrence of preferential flow. Infiltration along the horizontal and vertical directions occurred simultaneously. These results could facilitate a further understanding of the contribution of the plantation to soil amelioration and the prevention of desertification in karst areas, and provide some suggestions for the sustainable development of forestry in karst areas where plantation restoration is an important ingredient.

Effects of a landslide on the geochemistry of dissolved major and trace elements in a granite-gneiss forest catchment of Southeast Brazil

This study investigated the hydrogeochemical variations in a scenario following a landslide event in the Soberbo pristine rainforest small catchment (13 km2). The selected catchment is underlain by granite gneiss and located at the Serra dos Órgãos a mountainous region in the Rio de Janeiro, Brazil, that faces frequent high-intensity rainfall events. Stream water sampling, water discharge, and pH and electrical conductivity measurements were conducted monthly from May 2018 to October 2019. In January 2019, a landslide occurred upstream. Concentrations of dissolved organic carbon (DOC), HCO3−, major elements (Ca, K, Mg, Na, and Si), and trace elements (Al, Ba, Ce, Fe, Mn, Nd, Sr, Rb, La, Pr and Sm) were determined. The Spearman correlation and the multivariate analysis (PCA) indicated that during baseflow, rock weathering hydrolysis reactions are the main source of Si, Na, Mg, Ba and Sr. On the other hand, the forest plays a significant role during the rainy months by remobilizing colloidal litter material (represented by the DOC concentration) closely associated with colloidal Al. The landslide promoted a change in local redox conditions, resulting in the early release of Mn-scavenged elements, and about a month later, Fe and LREE, following a deepening in reduced conditions. The Ce/La molar ratio appeared to be a reliable hydrogeochemical redox proxy. The landslide effects persisted for at least nine months. In this case, the traceability of natural geochemical impacts of landslides was achieved through the river hydrogeochemistry in a mountainous small catchment model area.

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.

Impact of Hillslope Agriculture on Soil Compaction and Seasonal Water Dynamics in a Temperate Vineyard

Major losses of agricultural production and soils are caused by erosion, which is especially pronounced on hillslopes due to specific hydrological processes and heterogeneity. Therefore, the aim of this study was to assess the impact of agricultural management on the compaction, infiltration, and seasonal water content dynamics of the hillslope. Measurements were made at the hilltop and footslope, i.e., soil water content and potential were measured using sensors, wick lysimeters were used to quantify water flux, while a mini-disk infiltrometer was used to measure the infiltration rate and calculate the unsaturated hydraulic conductivity (K_unsat). Soil texture showed differences between hillslope positions, i.e., at the hilltop after 50 cm depth, the soil is classified as silty clay loam, and from 75 cm onward, the soil is silty clay, while at the footslope, the soil is silt loam even at the deeper depths. The results show a higher K_unsat at the footslope as well as higher average water volumes collected in wick lysimeters compared to the hilltop. Average water volumes showed a statistically significant difference at p < 0.01 between the hilltop and the footslope. The soil water content and water potential sensors showed higher values at the footslope at all depths, i.e., 8.0% at 15 cm, 8.4% at 30 cm, and 27.3% at 45 cm. The results show that, even though the vineyard is located in a relatively small area, soil heterogeneity is present, affecting the water flow along the hillslope. This suggests the importance of observing water movement in the soil, especially today when facing extreme weather (e.g., short-term high-intensity rainfall events) in order to protect soil and water resources.

Modelling antecedent soil hydrological conditions to improve the prediction of landslide susceptibility in typhoon-prone regions

Most regional landslide susceptibility models do not consider the evolving soil hydrological conditions leading up to a multiple occurrence regional landslide event. This results in inaccurate predictions due to the non-linear behaviour of the terrain. To address this, we have developed a simple and efficient model that incorporates the mid-term evolution of soil hydrological conditions. The model combines a water balance model and a geotechnical model based on infinite slope theory. The analysis of 561 high-intensity rainfall events in a typhoon-prone region of the Philippines revealed that the percolation of water during the 5-month wet season is crucial in determining landslide susceptibility. Consequently, high-intensity rainfall events at the start of the wet season are less likely to trigger landslides, while later events are more hazardous. We analysed the change in landslide susceptibility during the 2018 rainy season by comparing the probability of failure (PoF) before and after three high-intensity rainfall events (July, August and September). Only the event in September caused a significant increase in the probability of failure (PoF). The model showed an accuracy of 0.63, with stable cells better represented than unstable cells. The antecedent hydrological conditions on the lower soil layers are responsible for changes in landslide susceptibility. Our findings support the hypothesis that new approaches to developing hydro-meteorological thresholds for landslide early warning systems should be evaluated, especially in regions with strong seasonality.

Evaluation of ERA5 and CHIRPS rainfall estimates against observations across Ethiopia

Satellite-based precipitation estimates and global reanalysis products bear the promise of supporting the development of accurate and timely climate information for end users in sub-Sharan Africa. The accuracy of these global models, however, may be reduced in data-scarce regions and should be carefully evaluated. This study evaluates the performance of ERA5 reanalysis data and CHIRPS precipitation data against ground-based measurements from 167 rain gauges in Ethiopia, a region with complex topography and diverse climates. Focusing over a 38-year period (1981–2018), our study utilizes a point-to-pixel analysis to compare daily, monthly, seasonal, and annual precipitation data, conducting an evaluation based on continuous and categorical metrics. Our findings indicate that over Ethiopia CHIRPS generally outperforms ERA5, particularly in high-altitude areas, demonstrating a better capability in detecting high-intensity rainfall events. Both datasets, however, exhibit lower performance in Ethiopia's lowland regions, possibly the influence of sparse rain gauge networks informing gridded datasets. Notably, both CHIRPS and ERA5 were found to underestimate rainfall variability, with CHIRPS displaying a slight advantage in representing the erratic nature of Ethiopian rainfall. The study’s results highlight considerable performance differences between CHIRPS and ERA5 across varying Ethiopian landscapes and climatic conditions. CHIRPS’ effectiveness in high-altitude regions, especially for daily rainfall estimation, emphasizes its suitability in similar geographic contexts. Conversely, the lesser performance of ERA5 in these areas suggests a need for refined calibration and validation processes, particularly for complex terrains. These insights are essential for the application of satellite-based and reanalysis of rainfall data in meteorological, agricultural, and hydrological contexts, particularly in topographically and climatically diverse regions.

Spatial and Temporal Characteristics of Channel Disturbance Zones in Big River, Southeast Missouri (1937–2018)

abstract: Ozarks watersheds have responded to land clearing and settlement disturbances by transporting large amounts of gravel and sediment to main valleys of medium-large river systems. Historical gravel and sediment accumulate in disturbance zones, which are areas of excessive channel activity that can be detected over time using historical aerial photographs. This project uses a series of aerial photos from 1937–2018 to identify disturbance zones in the Big River of southeast Missouri, which has a history of lead mining and ore processing that has caused widespread contamination of the channel and floodplain deposits. Variations in active areas of the historical meander belt show that disturbance zones account for 24 percent of the channel length at an interval of one per 2.5 km of channel. Megabars and extensions are the most prominent types of disturbance zones, but translations and cutoffs are larger with higher potential for sediment storage. From 1937 to 1976, areas of channel activity have cycled between expansion and contraction. However, after a period of recovery prior to 1976, disturbance zone areas have been expanding over the last 40 years likely in response to an increase in flood magnitude and frequency from more high intensity rainfall events since 1973.

CHIRPS rainfall product application for analyzing rainfall concentration and seasonality in Johor river basin, Malaysia

It is crucial to carry out a spatiotemporal evaluation of rainfall concentration in the Johor river basin (JRB) given that erratic rainfall can lead to floods. The study utilized the CHIRPS rainfall dataset from 1983 to 2018 to determine rainfall concentration using the Concentration Index (CI). To further define the rainfall regime, the Seasonality Index (SI) and Precipitation Concentration Index (PCI) were examined. The Mann-Kendall (MK) test was utilized to examine the trend at a 95% level of significance. The results showed the better accuracy of SI than PCI in classifying the rainfall regime in the JRB, defined as “equable but with a definite wetter season” type. The CI values ranged from 0.61 to 0.72 and were typically high all over the basin, with the northwestern and western regions having the highest values. At 27 grid points on the eastern border of the upper and middle parts of the basin, CI has significantly increased as a result of an upsurge in rainfall concentration brought on by climate change. More than 67% and up to 83% of the yearly rainfall for all grid locations was accounted for by the North East monsoon (NEM), with the 25% wettest days caused by high-intensity rainfall events.

Geo-spatial assessment of pluvial floods in city district Lahore, Pakistan.

This study is an effort of geo-spatial assessment of pluvial floods in District Lahore, Pakistan, caused by urban expansion and the growing frequency and intensity of high-intensity rainfall events. The use of geospatial techniques such as watershed modeling, maximum likelihood image classification, and weighted overlay analysis based on secondary data has enabled the researchers to assess the extent and severity of pluvial floods in the study area. The study's findings highlight the high risk of pluvial floods in the central part of the study area, which is dominated by built-up land and concrete roads. The increase in the area of built-up land from 34.913 km2 in 2018 to 37.442 km2 in 2022 has further intensified the risk of pluvial floods. The findings of this study can assist policymakers in developing effective strategies to reduce the risks associated with pluvial floods. Alongside, it also highlights the importance of geospatial techniques to better understand and address the complex challenges of urbanization and climate change. Flood risk zone-specific strategies are recommended to reduce the risk of pluvial floods.

Comparing Different Coupling and Modeling Strategies in Hydromechanical Models for Slope Stability Assessment

The dynamic interaction between subsurface flow and soil mechanics is often simplified in the stability assessment of variably saturated landslide-prone hillslopes. The aim of this study is to analyze the impact of conventional simplifications in coupling and modeling strategies on stability assessment of such hillslopes in response to precipitation using the local factor of safety (LFS) concept. More specifically, it investigates (1) the impact of neglecting poroelasticity, (2) transitioning from full coupling between hydrological and mechanical models to sequential coupling, and (3) reducing the two-phase flow system to a one-phase flow system (Richards’ equation). Two rainfall scenarios, with the same total amount of rainfall but two different relatively high (4 mm h−1) and low (1 mm h−1) intensities are considered. The simulation results of the simplified approaches are compared to a comprehensive, fully coupled poroelastic hydromechanical model with a two-phase flow system. It was found that the most significant difference from the comprehensive model occurs in areas experiencing the most transient changes due to rainfall infiltration in all three simplified models. Among these simplifications, the transformation of the two-phase flow system to a one-phase flow system showed the most pronounced impact on the simulated local factor of safety (LFS), with a maximum increase of +21.5% observed at the end of the high-intensity rainfall event. Conversely, using a rigid soil without poroelasticity or employing a sequential coupling approach with no iteration between hydromechanical parameters has a relatively minor effect on the simulated LFS, resulting in maximum increases of +2.0% and +1.9%, respectively. In summary, all three simplified models yield LFS results that are reasonably consistent with the comprehensive poroelastic fully coupled model with two-phase flow, but simulations are more computationally efficient when utilizing a rigid porous media and one-phase flow based on Richards’ equation.

Flood susceptibility mapping using a novel integration of multi-temporal sentinel-1 data and eXtreme deep learning model

Flash floods (FFs) are amongst the most devastating hazards in arid regions in response to climate change and can cause the loss of agricultural land, human lives and infrastructure. One of the major challenges is the high-intensity rainfall events affecting low-lying areas that are vulnerable to FF. Several works in this field have been conducted using ensemble machine learning models and geohydrological models. However, the current advancement of eXtreme deep learning, which is named eXtreme deep factorisation machine (xDeepFM), for FF susceptibility mapping (FSM) is lacking in the literature. The current study introduces a new model and employs a previously unapplied approach to enhance FSM for capturing the severity of floods. The proposed approach has three main objectives: (i) During- and after-flood effects are assessed through flood detection techniques using Sentinel-1 data. (ii) Flood inventory is updated using remote sensing-based methods. The derived flood effects are implemented in the next step. (iii) An FSM map is generated using an xDeepFM model. Therefore, this study aims to apply xDeepFM to estimate susceptible areas using 13 factors in the emirates of Fujairah, UAE. The performance metrics show a recall of 0.9488), an F1-score of 0.9107), precision of (0.8756) and an overall accuracy of 90.41%. The accuracy of the applied xDeepFM model is compared with that of traditional machine learning models, specifically the deep neural network (78%), support vector machine (85.4%) and random forest (88.75%). Random forest achieves high accuracy, which is due to its strong performance that depends on factors contribution, dataset size and quality, and available computational resources. Comparatively, the xDeepFM model works efficiently for complicated prediction problems having high non-collinearity and huge datasets. The obtained map denotes that the narrow basins, lowland coastal areas and riverbank areas up to 5 km (Fujairah) are highly prone to FF, whilst the alluvial plains in Al Dhaid and hilly regions in Fujairah show low probability. The coastal city areas are bounded by high-rise steep hills and the Gulf of Oman, which can elevate the water levels during heavy rainfall. Four major synchronised influencing factors, namely, rainfall, elevation, drainage density, distance from drainage and geomorphology, account for nearly 50% of the total factors contributing to a very high flood susceptibility. This study offers a platform for planners and decision makers to take timely actions on potential areas in mitigating the effects of FF.

Spatial and Temporal Characteristics of Channel Disturbance Zones in Big River, Southeast Missouri (1937-2018)

Ozarks watersheds have responded to land clearing and settlement disturbances by transporting large amounts of gravel and sediment to main valleys of medium-large river systems. Historical gravel and sediment accumulate in disturbance zones, which are areas of excessive channel activity that can be detected over time using historical aerial photographs. This project uses a series of aerial photos from 1937-2018 to identify disturbance zones in the Big River of southeast Missouri, which has a history of lead mining and ore processing that has caused widespread contamination of the channel and floodplain deposits. Variations in active areas of the historical meander belt show that disturbance zones account for 24 percent of the channel length at an interval of one per 2.5 km of channel. Megabars and extensions are the most prominent types of disturbance zones, but translations and cutoffs are larger with higher potential for sediment storage. From 1937 to 1976, areas of channel activity have cycled between expansion and contraction. However, after a period of recovery prior to 1976, disturbance zone areas have been expanding over the last 40 years likely in response to an increase in flood magnitude and frequency from more high intensity rainfall events since 1973.

Accuracy of satellite and reanalysis rainfall estimates over Africa: A multi-scale assessment of eight products for continental applications

Study RegionContinental Africa Study FocusThis study evaluates the accuracy of eight gauge-corrected rainfall products across Africa through direct comparisons with in situ observations for the period 2001–2020. The effect of validation datasets on the performance of the rainfall products was also quantified in ten African countries. Four categorical and five continuous metrics were estimated at multiple spatial and temporal scales as part of the evaluation. New hydrological insights for the RegionResults indicate that the performance of the rainfall products varied in space and time. Evaluation at temporal scales revealed that, on average, most rainfall products showed poor results (KGE < 0.35) at the daily timescale. In contrast, RFE v2.0, ARC v2.0, and MSWEP v2.8 were reliable (KGE > 0.75) at the monthly and annual timescales. Among the rainfall products, the performance of TAMSATv3.1, PERSIANN-CDR, and ERA 5 was relatively poor in capturing in situ observations. Evaluation at various spatial scales revealed mixed results. The ARC v2.0 and CHIRPS v2.0 rainfall products were reliable in detecting no rains (< 1 mm/day) for all 19 spatial scales, indicating a high level of confidence for drought studies. IMERG-F v6B and RFE v2.0 were reliable in detecting heavy and high-intensity rainfall events for all spatial scales. Using the KGE performance metrics at the regional level, MSWEP v2.8 in the Northern Africa region, RFE v2.0 in the Western and Southern Africa regions, ARC v2.0 in Central Africa, and CHIRPS v2.0 in the Eastern Africa region showed better performances at monthly timescale. Moreover, the performance of the gauge-corrected rainfall datasets was reduced when compared with independent validation data (gauge data not used by rainfall products) than dependent validation data. This study provides several new insights into choosing a rainfall product for continental to regional applications and identifies the need for bias correction.

Dynamic N transport and N2O emission during rainfall events in the coastal river

The coastal zone exhibited a high population density with highly impacted by anthropogenic activities, such as river impoundment to prevent saline intrusion, which resulted in weak hydrological conditions. Rainfall events can result in dramatic changes in hydrological and nutrient transportation conditions, especially in rivers with weak hydrological conditions. However, how the nitrogen transport and N2O emissions or biogeochemistry responds to the different types of rainfall events in the weak hydrodynamics rivers is poorly understood. In this study, the hydrological, nitrogenous characteristic, as well as N2O dynamics, were studied by high-frequency water sampling during two distinct rainfall events, high-intensity with short duration (E1) and low-intensity with long duration (E2). The results displayed that the hydrologic condition in E1 with a wider range of d-excess values (from −9.50 to 32.1 ‰), were more dynamic than those observed in E2. The N2O concentrations (0.01–3.33 μmol/L) were higher during E1 compared to E2 (0.03–1.11 μmol/L), which indicated that high-intensity rainfall has a greater potential for N2O emission. On the contrary, the concentrations of nitrogen (e.g., TN and NO3−-N) were lower during E1 compared to E2. Additionally, hysteresis was observed in both water and nitrogen components, resulting in a prolonged recovery time for pre-rainfall levels during the long-duration event. Moreover, the results showed that the higher average N2O flux (78.3 μmol/m2/h) in the rainfall event period was much larger than that in the non-rainfall period (1.63 μmol/m2/h). The frequency dam regulation resulted in the water level fluctuation, which could enhance wet-dry alternation and simulated N2O emissions. This study highlighted the characteristic of N dynamic and hydrological responses to diverse rainfall events occurrences in the coastal river. Rainfall could increase the N2O emission, especially during high-intensity rainfall events, which cannot be ignored in the context of annual N2O release.

Rainfall intensity affects the recharge mechanisms of groundwater in a headwater basin of the North China plain

Understanding the origins of groundwater and its movement from mountain to plain during different high intensity rainfall events is critical for conserving water supplies, determining water-use policies and controlling pollution. These factors are also the keys for understanding the dominant processes in hydrological models. In this study, groundwater resources and recharge processes during heavy precipitation were explored by using stable isotope tracers in the hilly area of Taihang Mountain. It was found that the δ2H and δ18O values of precipitation exhibited obvious precipitation amount effect during different precipitation intensity events. The stable isotopic values in groundwater and river water showed significantly varied during the single extreme heavy precipitation and the continuous heavy precipitation events. In the rainy season, precipitation amounts greater than 40 mm/d could effectively recharge the shallow groundwater in the study area. By comparing the spatial isotopic distribution of groundwater, soil water, and river water with precipitation, we showed distinct groundwater recharge patterns in terms of their water resources, timing, and the degree of river water and groundwater interaction during the single extreme heavy precipitation and the continuous heavy precipitation. After the single extreme heavy precipitation, the δ2H and δ18O values of groundwater, soil water, and river water showed stable over time and had the same similar variations, suggested that the groundwater recharge was mainly dominated by precipitation with preferential flow or bypass flow. While after the continuous heavy precipitation, the variation of δ2H and δ18O in all water is consistent with the previous precipitation, which shown a mixing effect of previous enrich precipitation and depleted heavy precipitation, suggested that groundwater source was dominated by a continuous recharge of previous heavier precipitation with translatory flow. The groundwater main recharge mechanism is not constant, but changes with rainfall intensity. The rainfall intensity play an important role in groundwater recharge change affecting runoff process. Overall, this paper presents a new insight to understand the effect of rainfall intensity on hydrological process, which could be used to provide vital information in the semi-humid and semi-arid regions where water resources are critical in climate change adaptation strategies.