High Temporal Resolution Rainfall Research Articles

Characteristics of debris-flow-prone watersheds and debris-flow-triggering rainstorms following the Tadpole Fire, New Mexico, USA

Abstract. Moderate- or high-severity fires promote increases in runoff and erosion, leading to a greater likelihood of extreme geomorphic responses, including debris flows. In the first several years following fire, the majority of debris flows initiate when runoff rapidly entrains sediment on steep slopes. From a hazard perspective, it is important to be able to anticipate when and where watershed responses will be dominated by debris flows rather than flood flows. Rainfall intensity averaged over a 15 min duration, I15, in particular, has been identified as a key predictor of debris flow likelihood. Developing effective warning systems and predictive models for post-fire debris flow hazards therefore relies on high-temporal resolution rainfall data at the time debris flows initiate. In this study, we documented the geomorphic response of a series of watersheds following a wildfire in western New Mexico, USA, with an emphasis on constraining debris flow timing within rainstorms to better characterize debris-flow-triggering rainfall intensities. We estimated temporal changes in soil hydraulic properties and ground cover in areas burned at different severities over >2 years to offer explanations for observed differences in spatial and temporal patterns in debris flow activity. We observed 16 debris flows, all of which initiated during the first several months following the fire. The average recurrence interval of the debris-flow-triggering I15 is 1.3 years, which highlights the susceptibility of recently burned watersheds to runoff-generated debris flows in this region. All but one of the debris flows initiated in watersheds burned primarily at moderate or high soil burn severity. Since soil hydraulic properties appeared to be relatively resilient to burning, we attribute reduced debris flow activity at later times to decreases in the fraction of bare ground. Results provide additional constraints on the rainfall characteristics that promote post-fire debris flow initiation in a region where fire size and severity have been increasing.

A validation study of citizen science-based rainfall observation

Citizen science-based data collection approaches offer new opportunities to produce high-quality rainfall products. One of the most promising options, personal rain gauges (PRGs), allows for high spatial and temporal resolution rainfall observation and has received much attention in recent years. Doubts about the accuracy and stability of PRGs, however, have led many researchers to remain hesitant about using PRG-based rainfall datasets. It is, therefore, of great importance to investigate the effectiveness of the PRG rainfall observation network. In this paper, 294 daily (24 hr) rainfall data between June 2022 and June 2023 was collected in the Netherlands using the PRG network and compared with ground rain gauge and radar estimations. The experimental results indicate that: (1) during the large-scale rainfall events, PRG achieved a Pearson correlation performance of 0.498, 0.488, and 0.324 compared to the corrected/uncorrected rain gauge network and radar observations, respectively, which decreased to 0.11, 0.101, and 0.108 for small-scale rainfall. It can be concluded that the PRG network shows a certain degree of agreement with ground rainfall gauges and radar measurements and is more suitable for large-scale rainfall observation tasks; (2) the relative errors between the PRGs and compared rainfall products indicate that the accuracy of the PRG network still needs to be improved. This study could offer a useful complement to the existing rainfall observation system while providing a helpful supplement to the development of citizen science.

Rainfall erosivity across Austria's main agricultural areas: Identification of rainfall characteristics and spatiotemporal patterns

Study regionTarget area of this study are the main agricultural production zones of Austria. Most important croplands cover the flat to pre-alpine areas concentrated in the north, east, and southeast of Austria. Study focusThe novelty of our study is the spatiotemporal assessment of rainfall characteristics that drive erosivity at the event level as well as the identification of erosive rainfall distribution patterns within the events. Our assessment approach allows the definition of both typical and extreme erosivity. Long-term and high temporal resolution rainfall datasets were used to apply a clustering approach, seasonal and spatial analyses, and rainfall distributions assessment (isopleths) of the identified rainfall types (clusters). New hydrological insights for the regionThree dominant erosive event-types (clusters) were identified that strongly relate with Austria’s seasonality and complex topography. The most erosive rainfall events (cluster C1) are characterized by a high intensity and short duration. C1 events have the largest occurrence frequency in pre-alpine southern Austria and occur from May to September. Unlike the less erosive and more evenly distributed event types (C2 and C3) the highly erosive C1 events have a pronounced maximum rainfall intensity at the onset of the event.

Development of an integrated model for assessing landslide susceptibility on vegetated slopes under random rainfall scenarios

Physically-based landslide susceptibility analysis has been extensively employed to predict the likelihood of landslides owing to its remarkable predictive capability. However, due to an insufficiently comprehensive analysis of the vegetation-disaster connection, this approach may face limitations in its application to regions where there is significant variation in vegetation diversity and topography. An integrated model was thus proposed for predicting landslide susceptibility with the consideration of the positive and negative effects of vegetation on slope stability. However, to accurately implement the slope failure process under the influence of vegetation, it is important to have a high temporal resolution rainfall scenario for simulating the interception process of vegetation crowns on rainfall. Analysis of vegetation hydrological processes is associated with difficulties in a regional area because of limited data availability. The stochastic simulation method for extreme rainfall scenarios, adopted in this study, is known to be effective in dealing with uncertainty caused by insufficient data. The method, which comprises the Gumbel model and Bounded Random Cascade Model (BRCM), was used to overcome the difficulties in integrating the comprehensive effects of vegetation, the results of which provide refined rainfall scenarios for landslide susceptibility prediction from the perspective of the failure mechanism of vegetated slopes. The application of the proposed method for landslide susceptibility zoning was validated using a confusion matrix and ROC curve analysis in a practical case. Results showed that the method performed effectively and reliably. Furthermore, this study investigated the influence of different extreme rainfall scenarios on slope stability. Results revealed that as the return period of extreme rainfall increased from 50 to 150 years, the overall proportion of susceptible areas for different rainfall durations exhibited an upward trend. Additionally, the study found that increasing vegetation biomass had a positive impact on slope stability in the study area. The findings of this study are significant for disaster prevention and mitigation in mountainous regions, providing scientific support for risk management strategies.

Quantitative estimation and fusion optimization of radar rainfall in the Duanzhuang watershed at the eastern foot of the Taihang Mountains

Abstract The temporal and spatial resolutions of rainfall data directly affect the accuracy of hydrological simulation. Weather radar has been used in business in China, but the uncertainty of data is large. At present, research on radar data and fusion in small and medium-sized basins in China is very weak. In this paper, taking the Duanzhuang watershed as an example, based on station data, Shijiazhuang's radar data are preprocessed, optimized and fused. Eleven rainfall events are selected for fusion by three methods and quality evaluation, and three flood simulations are used to test their effect. The results show that preprocessing and initial optimization have poor effects on radar data improvement. The rainfall proportional coefficient fusion method performs best in rainfall spatial estimation, where the R2 values of the three inspection stations are increased to 0.51, 0.78 and 0.82. Three fusion datasets in the peak flow and flood volume of flood simulation perform better than station data. For example, in the No.20210721 flood, the NSE of the three fusion data increased by 39, 30 and 48%. This shows that the fusion method can effectively improve the data accuracy of radar and can obtain high temporal and spatial resolution rainfall data.

Rainfall erosivity estimation models for the Tibetan Plateau

Many models have been developed internationally to estimate rainfall erosivity. However, the effectiveness of these pre-existing models has not been verified for the Tibetan Plateau with large spatiotemporal heterogeneity of rainfall due to lack of high-temporal resolution rainfall data. The purpose of this study was to develop models to accurately estimate daily and semi-monthly rainfall erosivity values in the Tibetan Plateau. We used one-minute resolution rainfall data from 1,182 weather stations to develop rainfall erosivity models over the Tibetan Plateau for the first time. The particle swarm optimization (PSO) method was performed to determine the newly developed model parameters. The newly developed models were compared with three internationally common existing models (exponentially seasonal1Exponentially seasonal model denoted Richardson model (1983) that was revised as R =αP1.7265 by Xie et al. (2016).1, cosine2Cosine model denoted Yu and Rosewell model (1996) that was revised as R=0.26861+0.5412cosπ6M-7π6P1.7265 by Xie et al. (2016).2, and exponentially semi-month3Exponentially semi-month model denoted Zhang model (2002) that was expressed as R=α∑j=1mPjβ,α= 21.586β- 7.1891 and β=0.8363+18.177Pd12+24.455Py12.3) to evaluate their effectiveness. Our results showed that (1) the three existing models all overestimated the values of mean annual rainfall erosivity in the Tibetan Plateau because limited rainfall data in the area were used to develop these models; (2) the new models greatly reduced computational error values of mean annual rainfall erosivity (the symmetric mean absolute percentage error (SMAPE) of ≤ 23%), relative to the three existing models (SMAPE of ≥ 40%); (3) the new semi-monthly models (SMAPE of 17.7 – 29.2%) generally outperformed the new daily models (SMAPE of 17.9 – 30.7%) because of their better ability to estimate the values of extreme rainfall erosivity. It is advisable to use the semi-monthly model rather than daily model to compute rainfall erosivity when using universal soil loss equation (USLE) and its derived models to calculate soil erosion. That will benefit to reduce the time-scale conversion errors between event-based and daily data when lacking high-temporal resolution data and using daily rainfall data to analyze the influence of rainfall on soil erosion.

Rooftop water harvesting for managed aquifer recharge and flood mitigation in tropical cities: Towards a strategy of co-benefit evaluations in João Pessoa, northeast Brazil

Intense urbanisation in many coastal areas has led to intensification of groundwater consumption, while reducing permeable areas and increasing the frequency and magnitude of flooding. Among the potential strategies to compensate for these adverse effects, which are expected to become worse as a result of climate change, rooftop rainwater harvesting (RWH) in combination with managed aquifer recharge (MAR), may be indicated. This work investigated the performance of different configurations of such a system, tested as a twofold sustainable stormwater and domestic water management tool in a tropical metropole (João Pessoa, Brazil). This area located over a sedimentary aquifer system illustrates the water security challenges of densely urbanised areas in southern cities. To that end, several configurations of rooftop catchments and storage volumes were evaluated, by simulating a MAR-RWH system connected to the regional unconfined aquifer (Barreiras Formation) through a 6″ diameter injection well. Rainfall-runoff-recharge processes and water balances were simulated using monitored high-temporal resolution rainfall data. The results showed that catchments ranging from 180 to 810 m2, connected to tanks from 0.5 to 30.0 m³, are the optimal solutions in terms of efficient rainwater retention and peak flow reduction. These solutions provided mean annual estimates of aquifer recharge between 57 and 255 m³/yr from 2004 to 2019. The results of this study highlight the opportunity for MAR schemes to reconcile stormwater management and water supply goals.

Keynote lecture. Forecasting the inundation of postfire debris flows

In the semi-arid regions of the western United States, postfire debris flows are typically runoff generated. The U.S. Geological Survey has been studying the mechanisms of postfire debris-flow initiation for multiple decades to generate operational models for forecasting the timing, location, and magnitude of postfire debris flows. Here we discuss challenges and progress for extending operational capabilities to include modeling postfire debris-flow inundation extent. Analysis of volume and impacted area scaling relationships indicated that postfire debris flows do not conform to assumptions of geometric self-similarity. We documented sensitivity of impacted areas to rainfall intensity using a candidate methodology for generating inundation hazard assessments. Our results emphasize the importance of direct measurements of debris-flow volume, inundated area, and high temporal resolution rainfall intensity.

Exploring the possible role of satellite-based rainfall data in estimating inter- and intra-annual global rainfall erosivity

Abstract. Despite recent developments in modeling global soil erosion by water, to date, no substantial progress has been made towards more dynamic inter- and intra-annual assessments. In this regard, the main challenge is still represented by the limited availability of high temporal resolution rainfall data needed to estimate rainfall erosivity. As the availability of high temporal resolution rainfall data will most likely not increase in future decades since the monitoring networks have been declining since the 1980s, the suitability of alternative approaches to estimate global rainfall erosivity using satellite-based rainfall data was explored in this study. For this purpose, we used the high spatial and temporal resolution global precipitation estimates obtained with the National Oceanic and Atmospheric Administration (NOAA) Climate Data Record (CDR) Climate Prediction Center MORPHing (CMORPH) technique. Such high spatial and temporal (30 min) resolution data have not yet been used for the estimation of rainfall erosivity on a global scale. Alternatively, the erosivity density (ED) concept was also used to estimate global rainfall erosivity. The obtained global estimates of rainfall erosivity were validated against the pluviograph data included in the Global Rainfall Erosivity Database (GloREDa). Overall, results indicated that the CMORPH estimates have a marked tendency to underestimate rainfall erosivity when compared to the GloREDa estimates. The most substantial underestimations were observed in areas with the highest rainfall erosivity values. At the continental level, the best agreement between annual CMORPH and interpolated GloREDa rainfall erosivity maps was observed in Europe, while the worst agreement was detected in Africa and South America. Further analyses conducted at the monthly scale for Europe revealed seasonal misalignments, with the occurrence of underestimation of the CMORPH estimates in the summer period and overestimation in the winter period compared to GloREDa. The best agreement between the two approaches to estimate rainfall erosivity was found for fall, especially in central and eastern Europe. Conducted analysis suggested that satellite-based approaches for estimation of rainfall erosivity appear to be more suitable for low-erosivity regions, while in high-erosivity regions (> 1000–2000 MJ mm ha−1 h−1 yr−1) and seasons (> 150–250 MJ mm ha−1 h−1 month−1), the agreement with estimates obtained from pluviographs (GloREDa) is lower. Concerning the ED estimates, this second approach to estimate rainfall erosivity yielded better agreement with GloREDa estimates compared to CMORPH, which could be regarded as an expected result since this approach indirectly uses the GloREDa data. The application of a simple-linear function correction of the CMORPH data was applied to provide a better fit to GloREDa and correct systematic underestimation. This correction improved the performance of CMORPH, but in areas with the highest rainfall erosivity rates, the underestimation was still observed. A preliminary trend analysis of the CMORPH rainfall erosivity estimates was also performed for the 1998–2019 period to investigate possible changes in the rainfall erosivity at a global scale, which has not yet been conducted using high-frequency data such as CMORPH. According to this trend analysis, an increasing and statistically significant trend was more frequently observed than a decreasing trend.

Climatological features of high temporal resolution rainfall over the Hyderabad city, India

High spatial variability and continuous evolution of a city's land use and land cover makes the city's rainfall mechanism more complex and dynamic, thus climatological rainfall features have both scientific and engineering relevance. A high-resolution hourly rainfall data from 37 Automatic Weather Stations (AWSs) of the Hyderabad city (an area of 650 km2) was considered. Rainfall analysis was carried out on an event-based approach from a total of 118 rainfall events. Rainfall frequency, depths and intensities were higher in the oldest region of the city for all seasons with exception of rainfall depth in summer. Diurnal variations in rainfall amount suggest large monsoon rainfall in the evening to midnight in fewer 1-hour spells. An increasing trend in rainfall depths and intensities highlights non-stationarity. In addition, decreased association among stations with increasing distance and increased spatial extent of precipitation with decreased spatial variability were observed. Further, enhanced rainfall activity in the city's region was observed as compared to its surrounding suburban region (has 33 AWSs). Results suggest significant rainfall variability across space- and time- scales, which may be associated with urbanization, and have great implications in a city's planning and development particularly under climate change and exponential urbanization context.

Estimation of rainfall erosivity factor in Italy and Switzerland using Bayesian optimization based machine learning models

This study aimed to evaluate the estimation accuracy of rainfall erosivity (R-factor) in Italy and Switzerland through five Machine learning (ML) models (Decision Tree (DT), K-Nearest Neighbors (KNN), Random Forest (RF), Gradient Boosting (GB), and eXtreme Gradient Boost (XGB)) tuned with optimal hyperparameters. To build the ML model, high temporal resolution (HTR) rainfall data were collected from 297 rain-gauge stations located in the study area. To estimate the RUSLE-based R-factor through the models, the rainfall amount for each rainfall event, the rainfall duration, and the maximum 60-min intensity were used as input data. The datasets for training/validation and testing consisted of rainfall data from 287 and 10 stations, respectively. In a second phase, each ML model was trained through 10-fold cross validation based on training and validation data. For hyperparameter adjustment, the models were optimized using the Bayesian optimization algorithm (BOA). The R-factor estimation performance of each ML model through cross validation improved from 6.1% to 62.8% as hyperparameters were optimized through BOA. In particular, ensemble models such as RF, GB, and XGB were superior to other models with an accuracy performance of 0.9 or even more. And the RF showed an excellent estimation performance (R2 = 0.965, NSE = 0.958, RMSE = 44.993 MJ mm ha−1h−1, and MAE = 13.901 MJ mm ha−1h−1) for test stations, followed by GB and XGB with similar performance. However, the R-factor for the extremely intense rainfall event estimated by the ML models showed a significant difference from the RUSLE-based R-factor. This result implies that although the ML model built in this study can reasonably estimate the R-factor in the general rainfall event, additional training and validation through securing various rainfall event data is required to improve estimation accuracy on an extreme rainfall event.

Spatial and seasonal patterns of rainfall erosivity in the Lake Kivu region: Insights from a meteorological observatory network

In the Lake Kivu region, water erosion is the main driver for soil degradation, but observational data to quantify the extent and to assess the spatial-temporal dynamics of the controlling factors are hardly available. In particular, high spatial and temporal resolution rainfall data are essential as precipitation is the driving force of soil erosion. In this study, we evaluated to what extent high temporal resolution data from the TAHMO network (with poor spatial and long-term coverage) can be combined with low temporal resolution data (with a high spatial density covering long periods of time) to improve rainfall erosivity assessments. To this end, 5 minute rainfall data from TAHMO stations in the Lake Kivu region, representing ca. 37 observation-years, were analyzed. The analysis of the TAHMO data showed that rainfall erosivity was mainly controlled by rainfall amount and elevation and that this relation was different for the dry and wet season. By combining high and low temporal resolution databases and a set of spatial covariates, an environmental regression approach (GAM) was used to assess the spatiotemporal patterns of rainfall erosivity for the whole region. A validation procedure showed relatively good predictions for most months (R2 between 0.50 and 0.80), while the model was less performant for the wettest (April) and two driest months (July and August) (R2 between 0.24 and 0.38). The predicted annual erosivity was highly variable with a range between 2000 and 9000 MJ mm ha−1 h−1 yr−1 and showed a pronounced east–west gradient which is strongly influenced by local topography. This study showed that the combination of high and low temporal resolution rainfall data and spatial prediction models can be used to improve the assessments of monthly and annual rainfall erosivity patterns that are grounded in locally calibrated and validated data.

An analytical solution for rapidly predicting post‐fire peak streamflow for small watersheds in southern California

AbstractFollowing wildfires, the probability of flooding and debris flows increase, posing risks to human lives, downstream communities, infrastructure, and ecosystems. In southern California (USA), the Rowe, Countryman, and Storey (RCS) 1949 methodology is an empirical method that is used to rapidly estimate post‐fire peak streamflow. We re‐evaluated the accuracy of RCS for 33 watersheds under current conditions. Pre‐fire peak streamflow prediction performance was low, where the average R2was 0.29 and average RMSE was 1.10 cms/km2for the 2‐ and 10‐year recurrence interval events, respectively. Post‐fire, RCS performance was also low, with an average R2of 0.26 and RMSE of 15.77 cms/km2for the 2‐ and 10‐year events. We demonstrated that RCS overgeneralizes watershed processes and does not adequately represent the spatial and temporal variability in systems affected by wildfire and extreme weather events and often underpredicted peak streamflow without sediment bulking factors. A novel application of machine learning was used to identify critical watershed characteristics including local physiography, land cover, geology, slope, aspect, rainfall intensity, and soil burn severity, resulting in two random forest models with 45 and five parameters (RF‐45 and RF‐5, respectively) to predict post‐fire peak streamflow. RF‐45 and RF‐5 performed better than the RCS method; however, they demonstrated the importance and reliance on data availability. The important parameters identified by the machine learning techniques were used to create a three‐dimensional polynomial function to calculate post‐fire peak streamflow in small catchments in southern California during the first year after fire (R2= 0.82; RMSE = 6.59 cms/km2) which can be used as an interim tool by post‐fire risk assessment teams. We conclude that a significant increase in data collection of high temporal and spatial resolution rainfall intensity, streamflow, and sediment loading in channels will help to guide future model development to quantify post‐fire flood risk.

High temporal resolution rainfall–runoff modeling using long-short-term-memory (LSTM) networks

Accurate and efficient models for rainfall runoff (RR) simulations are crucial for flood risk management. Most rainfall models in use today are process-driven; i.e. they solve either simplified empirical formulas or some variation of the St. Venant (shallow water) equations. With the development of machine-learning techniques, we may now be able to emulate rainfall models using, for example, neural networks. In this study, a data-driven RR model using a sequence-to-sequence Long-short-Term-Memory (LSTM) network was constructed. The model was tested for a watershed in Houston, TX, known for severe flood events. The LSTM network's capability in learning long-term dependencies between the input and output of the network allowed modeling RR with high resolution in time (15 minutes). Using 10-years precipitation from 153 rainfall gages and river channel discharge data (more than 5.3 million data points), and by designing several numerical tests the developed model performance in predicting river discharge was tested. The model results were also compared with the output of a process-driven model Gridded Surface Subsurface Hydrologic Analysis (GSSHA). Moreover, physical consistency of the LSTM model was explored. The model results showed that the LSTM model was able to efficiently predict discharge and achieve good model performance. When compared to GSSHA, the data-driven model was more efficient and robust in terms of prediction and calibration. Interestingly, the performance of the LSTM model improved (test Nash-Sutcliffe model efficiency from 0.666 to 0.942) when a selected subset of rainfall gages based on the model performance, were used as input instead of all rainfall gages.

Modelling of Intensity-Duration Frequency curves for Upper Cauvery Karnataka through Normal Distribution

The IDF Curves accessible are for the most part done by fitting arrangement of yearly greatest precipitation force to parametric dispersions. Intensity-duration-frequency (IDF) curves represent the relationship between storm intensity, storm duration and return period. Environmental change is relied upon to intensify the boundaries in the atmosphere factors. Being prone to harsh climate impacts, it is very crucial to study extreme rainfall-induced flooding for short durations over regions that are rapidly growing. One way to approach the extremes is by the application of the Intensity-Duration-Frequency (IDF) curves. The annual maximum rainfall intensity (AMRI) characteristics are often used to construct these IDF curves that are being used in several infrastructure designs for urban areas. Thus, there is a necessity to obtain high temporal and spatial resolution rainfall information. Many urban areas of developing countries lack long records of short-duration rainfall. The shortest duration obtained is normally at a daily scale/24 h. Thus, it is very crucial to find a methodology to construct IDF curves for short-duration rainfall (sub-daily) for these urban areas. The fast extension of urban area that does not have adequate preparedness to cope with climate change is certainly a big risk to life and economy. The study region lies in Karnataka India. The sub-daily IDF curves for current and future climate for the region were constructed from 1 to 24 h based on the Normal Distribution approach. Rainfall data of 23 (Twenty three) hydrological years of all stations were used. Maximum rainfall frequency analysis was made by Normal Distribution method. Finally Equations were developed for different return periods.

Rainfall intensity bursts and the erosion of soils: an analysis highlighting the need for high temporal resolution rainfall data for research under current and future climates

Abstract. Many land surface processes, including splash dislodgment and downslope transport of soil materials, are influenced strongly by short-lived peaks in rainfall intensity but are less well accounted for by longer-term average rates. Specifically, rainfall intensities reached over periods of 10–30 min appear to have more explanatory power than hourly or longer-period data. However, most analyses of rainfall, and particularly scenarios of possible future rainfall extremes under climate change, rely on hourly data. Using two Australian pluviograph records with 1 s resolution, one from an arid and one from a wet tropical climate, the nature of short-lived “intensity bursts” is analysed from the raw inter-tip times of the tipping bucket gauges. Hourly apparent rainfall intensities average just 1.43 mm h−1 at the wet tropical site and 2.12 mm h−1 at the arid site. At the wet tropical site, intensity bursts of extreme intensity occur frequently, those exceeding 30 mm h−1 occurring on average at intervals of <1 d and those of >60 mm h−1 occurring on average at intervals of <2 d. These bursts include falls of 13.2 mm in 4.4 min, the equivalent of 180 mm h−1, and 29 mm in 12.6 min, equivalent to 138 mm h−1. Intensity bursts at the arid site are much less frequent, those of 50–60 mm h−1 occurring at intervals of ∼1 month; moreover, the bursts have a much shorter duration. The aggregation of rainfall data to hourly level conceals the occurrence of many of these short-intensity bursts, which are potentially highly erosive. A short review examines some of the mechanisms through which intensity bursts affect infiltration, overland flow, and soil dislodgment. It is proposed that more attention to resolving these short-lived but important aspects of rainfall climatology is warranted, especially in light of possible changes in rainfall extremes under climate change.

High temporal resolution rainfall rate estimation from rain gauge measurements

Abstract Rainfall rates derived from tipping bucket rain gauges generally ignore the detailed variation at a finer temporal scale that particularly occurs in light rainfall events. This study extends the exploration of using artificial neural networks (ANNs), in comparison with the conventional linear interpolation method (LIM) and the cubic spline algorithm (CSA) for rainfall rate estimation at fine temporal resolution using rain gauge data based on a case study at Chilbolton and Sparsholt observatories, UK. A supervised feed-forward neural network integrated with the backpropagation algorithm is used to identify the complex nonlinear relationships between input and target variables. The results indicate that the ANN considerably outperforms the CSA and LIM with higher Nash–Sutcliffe efficiency, lower root mean square error and lower rainfall amount differences when compared to the disdrometer observations when the model is trained within a broad span of input values. Consistent stability in accurately estimating rainfall rate in different sites shows the intrinsic advantage of ANNs in learning and self-adaptive abilities in modelling complex nonlinear relationships between the inputs and target variables.

Global rainfall erosivity assessment based on high-temporal resolution rainfall records

The exposure of the Earth’s surface to the energetic input of rainfall is one of the key factors controlling water erosion. While water erosion is identified as the most serious cause of soil degradation globally, global patterns of rainfall erosivity remain poorly quantified and estimates have large uncertainties. This hampers the implementation of effective soil degradation mitigation and restoration strategies. Quantifying rainfall erosivity is challenging as it requires high temporal resolution(<30 min) and high fidelity rainfall recordings. We present the results of an extensive global data collection effort whereby we estimated rainfall erosivity for 3,625 stations covering 63 countries. This first ever Global Rainfall Erosivity Database was used to develop a global erosivity map at 30 arc-seconds(~1 km) based on a Gaussian Process Regression(GPR). Globally, the mean rainfall erosivity was estimated to be 2,190 MJ mm ha−1 h−1 yr−1, with the highest values in South America and the Caribbean countries, Central east Africa and South east Asia. The lowest values are mainly found in Canada, the Russian Federation, Northern Europe, Northern Africa and the Middle East. The tropical climate zone has the highest mean rainfall erosivity followed by the temperate whereas the lowest mean was estimated in the cold climate zone.

Rainfall variability in the Himalayan orogen and its relevance to erosion processes

AbstractRainfall is an important driver of erosion processes. The mean rainfall rate is often used to account for the erosive impact of a particular climate. However, for some erosion processes, erosion rate is a nonlinear function of rainfall, e.g., due to a threshold for erosion. When this is the case, it is important to take into account the full distribution of rainfall, instead of just the mean. In light of this, we have characterized the variability of daily rainfall over the Himalayan orogen using high spatial and temporal resolution rainfall data sets. We find significant variations in rainfall variability over the Himalayan orogen, with increasing rainfall variability to the west and north of the orogen. By taking into account variability of rainfall in addition to mean rainfall rate, we find a pattern of rainfall that, from a geomorphological perspective, is significantly different from mean rainfall rate alone. Using these findings, we argue that short‐term rainfall variability may help explain observed short and long‐term erosion rates in the Himalayan orogen.

Obtaining Homogeneous Regions by Determining the Generalized Fractal Dimensions of Validated Daily Rainfall Data Sets

Extreme rainfall data are widely used in several hydrological models and civil engineering design. Despite high temporal resolution rainfall data are not commonly available, daily rainfall data series are easily found. When these available data series are short in length the Regional Frequency Analysis (RFA) is a good tool to enlarge them by joining stations into homogeneous regions. This is by far, the most complicated step in RFA. This work presents a new method to form homogeneous regions of extreme annual daily rainfall data series. Daily rainfall data series from 53 weather stations in the Maule Region (Chile) have been used. Their fractal dimensions spectra have been obtained by applying the box counting method. Each station has been characterized by the fractal dimensions D1 and D2. A cluster analysis has been carried out based on these at-site characteristics and three regions have been obtained. After performing a RFA of extreme daily annual rainfall data series within each region they have shown as homogeneous. Only one of the available stations has not been possible to be included into any homogeneous regions, being the local frequency analysis the only suitable method to be applied at this location.