High-resolution Rainfall Data Research Articles

Assessing rainfall threshold for shallow landslides triggering: a case study in the Alpes Maritimes region, France

AbstractIn this work, we use a statistical approach for modeling shallow landslide rainfall thresholds (Caine 1980) with a case study for the Alpes-Maritimes region (France). Cumulated rainfall / duration (ED) thresholds are obtained with the CTRL-T algorithm (Melillo and al. 2018) for different non-exceedance probabilities from a landslide and two climatic datasets. This tool allows to automatically define rainfall events that might trigger landslides, ensuring robustness and objectivity in this process. The first climate dataset stores high resolution gridded rainfall data (1km resolution, hourly), which provides rainfall data with high temporal and spatial accuracy. This dataset, coming from radar data, is calibrated with rainfall gauges, ensuring a higher accuracy of the rainfall measurements. It provides the rainfall records directly used in the threshold construction The second dataset contains lower resolution gridded rainfall, snow, temperature, and evapotranspiration data (8km resolution, daily); it enables to assess the region’s climate through parameters imported in CTRL-T. The thresholds are then validated using a method designed by Gariano and et al. (2015). Several improvements are made to the initial method. First, evapotranspiration values approximated in the process are replaced by values from the second climate dataset, the result accounting best for the regional climate. Then, computing duration values used for isolating events and sub-events for each mesh point allows to consider the heterogeneity of the Alpes-Maritimes climate. Rainfall thresholds are eventually obtained, successively from a set of probable conditions (MRC) and a set of highly probable conditions (MPRC). The validation process strengthens the analysis as well as enables to identify best performing thresholds. This work represents novel scientific progress towards landslide reliable warning systems by (a) making a case study of empirical rainfall thresholds for Alpes-Maritimes, (b) using high-resolution rainfall data and (c) adapting the method to climatically heterogeneous zones.

Estimating sediment delivery ratio using the RUSLE-IC-SDR approach at a complex landscape: A case study at the Lower Snowy River area, Australia

Understanding the dynamics of sediment transport and deposition in natural landscapes is critical to developing cost-effective mitigation measures to control soil erosion and protect ecosystems. However, none of a single existing model can quantify sediment delivery ratio (SDR) and the impact factors such as vegetation and geomorphology, especially in a complex landscape. In this case study, we applied an integrated approach including the revised universal soil loss equation (RUSLE) and the index of connectivity (IC) to assess hillslope erosion and SDR, namely RUSLE-IC-SDR, across a complex landscape in the Lower Snowy River area, Australia. The RUSLE factors were derived from a high-resolution (2 m) digital elevation model (DEM), digital soil maps, high-resolution rainfall data and remotely sensed fractional vegetation cover. A seven-class landform classification was delineated from the high-resolution DEM using a fuzzy logic landform model (FLAG). We further examined the impacts of rainfall, vegetation cover and geomorphology on sediment dynamics and distribution across the study area. Field and laboratory data from 10 plot sites across the study area were collected and used for model validation. This case study showed that the RUSLE-IC-SDR approach can assess the overall sediment budget and the impacts of rainfall, vegetation cover and geomorphology across a complex landscape. Findings from this study can identify and track the areas likely to generate high sediment yield for developing ecological restoration, feral animal management and other catchment management measures.

Characterizing streamflow regimes using a distributed model for sustainable resource management in the humid tropics

Estimating streamflow regimes on small tropical islands is challenging due to limited observational data, poor accuracy of remote sensing, and the limitations of coarse-scale climate models, restricting our understanding of hydrological processes and the management of water resources. The inadequacy of data has also affected the development of instream flow standards (i.e., environmental flows, e-flows) needed to protect ecological and cultural values central to island communities. Estimates of water availability for agriculture, hydropower, and drinking water supply are also critical for informed investments in water resource planning. We utilized high-resolution (250 m) gridded daily rainfall data to parameterize the Soil and Water Assessment Tool (SWAT) to characterize streamflow regimes at ungauged locations in a watershed on Kaua‘i Island. The resultant flow statistics were then used to describe the daily, seasonal, and annual availability of surface water to evaluate the implications of potential e-flow scenarios on water available for run-of-river hydropower. Compared to rainfall data derived from a single station within the watershed, the gridded daily rainfall product increased the accuracy of model output from not satisfactory to good. Model techniques using high resolution rainfall data may substitute for long-term hydrological data collection across complex tropical environments and provide data for making sustainable management decisions at ungauged locations.

Evaluation of climate change impacts on urban flooding using high-resolution rainfall data

Evaluation of climate change impacts on urban flooding using high-resolution rainfall data

PRINCIPLES OF CALCULATIONS AND ARRANGEMENT OF LOCAL DRAINAGE SYSTEMS IN PRIVATE BUILDING TERRITORIES

Abnormally heavy rains in the first two spring months of 2023 revealed the unpreparedness and lack of protection of many settlements in the Kyiv region from excessive moisture and inundation. Among them, is Novi Petrivtsi village, where the natural conditions for surface runoff and precipitation infiltration (lack of visible surface slopes and poorly permeable cover sediments) are unfavourable and significantly complicated by buildings, and a network of highways. The long-term retention of water on the surface, the rise of groundwater levels, and the layered structure of the upper part of the geological section provide grounds for the use of combined local drainage systems with compliance with drainage standards of at least 3,0 m. Since the high density of buildings often does not allow for contour drainage around residential buildings, it is necessary to lay single-line horizontal drainage to a greater depth than for a conventional contour drainage of 3,5 meters or more. However, the lack of roadside ditches and other water intakes and means of orderly drainage do not allow homestead drainage systems to work as efficiently as possible. This requires the creation of an orderly system of water intakes (trenches and closed collectors) on the scale of the village. Foreign experience convinces that the rational planning of such systems is possible under the conditions of establishing the character of rainfall distribution with a resolution of 1–5 minutes in time and a step of 500 m across the area. Meteorological radar is used to record radar images of rain and study its intensity. An effective solution to the water drainage problem is impossible without detailed engineering and geological investigations. Due to them, litho-facies inhomogeneities in the aeration zone and water-saturated stratum, which lead to the retention and support of groundwater, were discovered in the local area. Taking into account the spatial boundaries of these engineering and geological elements allow drainage more efficiently. Drainage capacity is substantiated by forecasts of changes in the maximum amount of precipitation per day and two days in a row. Due calculating the drainage capacity, it should be taken into account that the maximum amount of precipitation in the future period will have a guarantee of 0,5-2,0% less than the actual maximum values. In the calculation part, the main attention is paid to the selection of equations for determining the width of influence of a single horizontal drain. Five formulas have been selected that can be used to solve similar problems. The time of onset of the established mode of operation of a single drain was calculated. Future research should focus on the collection of high-resolution rainfall and local urban runoff data, as well as the implementation of urban drainage models.

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.

Extraction of Spatiotemporal Distribution Characteristics and Spatiotemporal Reconstruction of Rainfall Data by PCA Algorithm

Scientific analyses of urban flood risks are essential for evaluating urban flood insurance and designing drainage projects. Although the current rainfall monitoring system in China has a dense station network and high-precision rainfall data, the time series is short. In contrast, historical rainfall data have a longer sample time series but lower precision. This study introduced a PCA algorithm to reconstruct historical rainfall data. Based on the temporal and spatial characteristics of rainfall extracted from high-resolution rainfall data over the past decade, historical (6 h intervals) rainfall spatial data were reconstructed into high-resolution (1 h intervals) spatial data to satisfy the requirements of the urban flood risk analysis. The results showed that the average error between the reconstructed data and measured values in the high-value area was within 15% and in the low-value area was within 20%, representing decreases of approximately 65% and 40%, respectively, compared to traditional interpolation data. The reconstructed historical spatial rainfall data conformed to the temporal and spatial distribution characteristics of rainfall, improved the granularity of rainfall spatial data, and enabled the effective and reasonable extraction and summary of the fine temporal and spatial distribution characteristics of rainfall.

Exploring the relationship between flood insurance claims, crowdsourced rainfall, and tide levels for coastal urban communities: Case study for the mid-Atlantic United States

The Federal Emergency Management Agency (FEMA) recently released a large dataset (∼2.5 million records) of flood insurance claim transactions from the National Flood Insurance Program (NFIP) that are geolocated by census tract. In this study, we present a methodology for using these NFIP flood claims along with agency rainfall and tide level data, combined with high-resolution rainfall data collected from growing networks of crowdsourced Personal Weather Stations (PWSs), to investigate the characteristics of storm events that result in flood insurance claims. Using 25 cities/counties in five metropolitan areas in the Mid-Atlantic region as case studies for testing the methodology, we find that the majority of flood claim events in this region were associated with rainfall below a 1-year return period and a tide level below the NOAA minor flood risk tide level. When storm events exceeded these rainfall and tide thresholds, the probability of a flood claim occurring could reach 100% for rainfall only events and 33% for tidal only events, depending on the region. While compound events of both high rainfall and high tide were rare, when seen in Virginia Beach, the probability of insurance claims being made during these events far exceeded the probability that would be expected if rainfall and tide were independent, showing how compound events in this region exacerbate flood impacts. Additionally, analysis of 110 heavy rainfall events showed that crowdsourced PWSs, can better capture rainfall extremes associated with insurance claims due to their higher spatial density compared to federal agency rainfall networks. This suggests that the PWS networks, once quality controlled, can offer greater insights into which rainfall events are likely to contribute to insurance claims. These region-specific findings and the general methodology presented in this study can benefit multiple stakeholders including researchers, risk management officials, insurers as well as the general public in understanding the impacts of rainfall, tidal, and compound flooding, along with the value of crowdsourced data in assessing flood risk.

Surveillance audio-based rainfall observation: An enhanced strategy for extreme rainfall observation

The increased frequency of extreme rainfall events (EREs) causing disastrous effects on society has become an indisputable fact in recent years. The weak performance of the current observation network in terms of accuracy and spatiotemporal resolution makes the development of new rainfall estimation techniques essential to reduce the impact of ERE-driven disasters. The rainfall sounds collected by widespread surveillance cameras provide an opportunity to produce high-resolution, all-weather rainfall data. However, the complex structure of the ground surface and the random background noise make building an effective surveillance audio-based ERE estimation system challenging. In this study, from the viewpoint of surveillance sound space, a 3D printer was used to create a shelter for the surveillance camera to define the underlying surface of falling raindrops artificially. Combining the knowledge of meteorology, micro-physics, and acoustics of rainfall, the shelter structure was designed to standardize the acoustical behavior while enhancing the consistency and specificity of raindrop sound, especially in complex scenarios such as those disturbed by different levels of wind. After that, convolutional neural network-based deep learning algorithms were used to classify ERE levels, and an audio-based ERE classification system was built. The experimental results show that the shelter facilitates audio-based rainfall representation; moreover, with the help of shelter, our proposed system achieved performance with about 93.4% accuracy in complex rainfall scenarios. Our study supports high-resolution rainfall data production on existing surveillance resources, developing a novel and reliable alternative for the perception of ERE and the calibration of observations from current rainfall networks.

A Neural Network-Based Hydrological Model for Very High-Resolution Forecasting Using Weather Radar Data

Many hydro-meteorological disasters in small and steep watersheds develop quickly and significantly impact human lives and infrastructures. High-resolution rainfall data and machine learning methods have been used as modeling frameworks to predict those events, such as flash floods. However, a critical question remains: How long must the rainfall input data be for an empirical-based hydrological forecast? The present article employed an artificial neural network (ANN)hydrological model to address this issue to predict river levels and investigate its dependency on antecedent rainfall conditions. The tests were performed using observed water level data and high-resolution weather radar rainfall estimation over a small watershed in the mountainous region of Rio de Janeiro, Brazil. As a result, the forecast water level time series only archived a successful performance (i.e., Nash–Sutcliffe model efficiency coefficient (NSE) > 0.6) when data inputs considered at least 2 h of accumulated rainfall, suggesting a strong physical association to the watershed time of concentration. Under extended periods of accumulated rainfall (>12 h), the framework reached considerably higher performance levels (i.e., NSE > 0.85), which may be related to the ability of the ANN to capture the subsurface response as well as past soil moisture states in the watershed. Additionally, we investigated the model’s robustness, considering different seeds for random number generating, and spacial applicability, looking at maps of weights.

Enhanced Hydrological Simulations in Paddy-Dominated Watersheds Using the Hourly SWAT-MODFLOW-PADDY Modeling Approach

Accurate hydrological simulations are crucial for managing water resources and promoting sustainable agriculture in submerged paddy agricultural watersheds. The SWAT-MODFLOW, which couples the Soil and Water Assessment Tool (SWAT) and the Modular Groundwater Flow (MODFLOW) model, is a widely used tool for hydrologic simulations that consider surface water and groundwater (SW-GW) interactions. However, it falls short of effectively simulating the hydrological processes of submerged rice paddy field areas. To address this, we developed the hourly SWAT-MODFLOW-PADDY model, which enables integrated surface and groundwater simulations and effectively represents the hydrological responses of submerged paddy fields to high-resolution rainfall data. Our findings demonstrated that the hourly SWAT-MODFLOW-PADDY model could dynamically simulate soil moisture and runoff patterns in submerged paddy fields. Notably, the developed model showed enhanced performance throughout the entire period for hourly flow in the watershed, with an average coefficient of determination (R2) of 0.75, Nash and Sutcliffe efficiency (NSE) of 0.76, and percent bias (PBIAS) of 13.22 compared to the original model (R2 = 0.62, NSE = 0.70, PBIAS = 48.21). The model’s performance in predicting water quality was improved, and it highlighted the significant impact of complex hydrological mechanisms within submerged paddy fields on the spatial distribution of groundwater recharge and stream water volumes exchanged through SW-GW interactions. Given these promising results, the SWAT-MODFLOW-PADDY model could be a valuable resource for managing submerged paddy-dominated agricultural watersheds across various climates and regions.

Climate change indications in short-period rainfall records from coastal India using trend analysis: a proposed framework

Climate change has reportedly resulted in higher magnitude and frequency of extreme weather variables in many parts of India. Such studies pertaining to rainfall were based on long-period records, gridded rainfall data, or climate model projections, which have inherent uncertainties. As long-period records might not be available for many locations, gridded data or climate model analysis could be supplemented with the examination of short-period observations (< 20 years) for climate change effects. Detecting climate change effect from short-period high-resolution data has not yet been reported in the literature. For such exercise, this study proposes a framework based on trend analysis along with a demonstration case at a coastal site in India. Using the proposed analysis, indications of climate change are identified in short-period (January 1997 to December 2013: 17 years) high-resolution (hourly) rainfall data, which are subsequently corroborated with results from long-period (60-year) annual data from the site. This proof-of-concept study opens up an intriguing possibility of detecting climate change indications from short-period data, subject to validation on a larger scale with observations from more weather stations. The results could aid judicious decision-making regarding safety margins required in hydraulic design, to account for climate change effects. The methodology, if corroborated with more studies, might prove useful for detecting climate change effects from other short-period hydro-meteorological data as well.

Post-wildfire erosion rates and triggering of debris flows: A case study in Susa Valley (Bussoleno)

Post-wildfires geological hazards are an emerging problem in many places, including areas not typically associated with these events such as the Alpine Region. Hazards connected with post-fire processes such as debris-flows and flood-type events threatens people, infrastructures, services and economical activities. Apart from a few examples, there is a lack of models available to quantify the increase in susceptibility as a result of the modification induced by the wildfires. In this work we test the application of a modified version of the RUSLE, on GIS, to quantify the post-fire erosive phenomena for a case study in the north-western Italian Alps. The results of its application, taking advantage of high-resolution rainfall series and data deriving from field surveys, highlight the marked increase (more than 20 times) in erosion rates, quantified by expressing both the EI (erodibility index), the A (monthly soil loss) and the SL (monthly sediment loss) rise. The months of April, May and June represents the larger share of the total quantities. This is a consequence of the noticeable increase of the Erodibility Index EI, which for the post-fire scenario is more than one order of magnitude higher than the pre-fire one.

Weather shocks and food price seasonality in Sub-Saharan Africa: Evidence from Niger

Semi-Arid regions of Sub-Saharan Africa are vulnerable to weather shocks, especially rural areas where households depend on rainfed agriculture and access to stable food markets are limited by high transaction costs. Understanding how weather shocks affect rural Sub-Saharan African households through both production and market price changes is crucial in designing sustainable and effective programs to increase resilience. Weather shock impacts on agricultural production and market prices are well documented in Africa. But little is known about the impacts of weather shocks on market price seasonality, which is an important determinant of food security. We investigate the distinct impacts of positive and negative rainfall shocks on millet production and millet price seasonality in Niger using district-level longitudinal production and price data, along with high-resolution rainfall data. We find that a one standard deviation decrease in seasonal rainfall from historical averages is associated with declines in millet market price initially after harvest, but strong upward pressure on market prices 6 months after harvest. As a result, drought exacerbates existing price seasonality, which in turn can amplify negative impacts on households. Social protection programs need to account for potential increases in seasonal price variability in the design of programs to enhance household resilience to weather shocks.

Combined high-resolution rainfall and wind data collected for 3 months on a wind farm 110 km southeast of Paris (France)

Abstract. The Hydrology Meteorology and Complexity laboratory of École des Ponts ParisTech (http://hmco.enpc.fr, last access: 16 August 2022) has made a data set of high-resolution atmospheric measurements available, which is of interest for the atmospheric science community. It comes from a campaign carried out in the framework of the Rainfall Wind Turbine or Turbulence project (RW-Turb; supported by the French National Research Agency, grant no. ANR-19-CE05-0022) on a meteorological mast installed at a wind farm located approx. 110 km southeast of Paris in France. In total, 3 months of data, covering the spring period from 1 March to 1 June 2021, are made available. We used six devices, namely two 3D sonic anemometers (manufactured by Thies), two mini meteorological stations (manufactured by Thies), and two disdrometers (Parsivel2, manufactured by OTT). They are installed at two heights (approx. 45 and 80 m), which enables us to monitor potential effects of altitude. The temporal resolution is of 100 Hz for the 3D sonic anemometers, 1 Hz for the meteorological stations, and 30 s for the disdrometers. A multifractal analysis is implemented to assess the effective resolution of the devices, and it is suggested that the anemometers and stations are able to measure expected variability only down to 1 and 16 s, respectively. A link to the data set can be found at https://doi.org/10.5281/zenodo.5801900 (Gires et al., 2021)

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 (&gt; 1000–2000 MJ mm ha−1 h−1 yr−1) and seasons (&gt; 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.

Spatiotemporal characterization of meteorological drought: a global approach using the Drought Exceedance Probability Index (DEPI)

We present a global spatiotemporal characterization of meteorological droughts using historical precipitation data through the Drought Exceedance Probability Index (DEPI). The relationship between meteorological drought characteristics and monthly precipitation is explored at a global level. This study contributes to our understanding of the drought features observed in different areas of the planet, which can help predict the behavior of future droughts. The DEPI was applied to the Climate Research Unit global gridded high-resolution rainfall data set covering the period 1901-2019. Monthly drought index series were examined to extract the number of droughts experienced in each pixel (0.50° × 0.50°) of the globe, as well as their durations, intensities and severities. Results show agreement with other global drought characterization efforts, revealing areas with a greater drought occurrence. This paper demonstrates that regions with less seasonality and less intra- and inter-annual rainfall variability report fewer drought episodes. Duration and severity of droughts are also related to these rainfall features. The last part of the study describes the temporal distribution of droughts throughout the world. We conclude that regions with many events show stable, even distributions over time, but many pixels in the intertropical regions, the Middle East and smaller patches in Mongolia, China, Siberia and Canada currently show higher-intensity and longer-duration drought events than at the beginning of the twentieth century, while the opposite occurs in parts of Scandinavia, Russia, Argentina and Tanzania. The analysis demonstrates that DEPI is easy to use, is applicable to different climates and is effective in detecting the onset, end and intensity of droughts.

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.

A novel quality control model of rainfall estimation with videos – A survey based on multi-surveillance cameras

The widespread use of surveillance cameras has become an emerging means for rainfall observations. With the advantages of high spatial–temporal resolution, rainfall information obtained from surveillance videos is highly suitable for meteorological-related research and has bright prospects. However, due to the complex and variable monitoring scenarios, the quality of the rainfall data estimated by each camera is always inconsistent, resulting in low practical value. Dense ground-level surveillance cameras have temporal and spatial correlations that can be used to improve the accuracy of rainfall estimation through mutual verification. In this study, we first introduce camera parameters to refine the spatial volume of rainfall (SVoR)11Most acronyms used in this study appear in the Appendix 1. perceived by cameras to improve the accuracy of rainfall intensity (RI) estimation. Next, a novel quality control (QC) model of rainfall estimation with multi-surveillance camera collaboration that takes the rainfall observations of all cameras as input is proposed. (i) We build a reliability evaluation (RE) model for the estimation of RI in accordance with raindrop imagery features to provide a reference for the subsequent correction of RI estimation; (ii) inspired by the first law of geography (Tobler, 1970), we then construct a spatial–temporal consistency filter and a situation consistency filter by using the spatial–temporal constraints between cameras to coarsely evaluate the RI values; and (iii) the correlation between cameras is calculated based on the fuzzy method to further build a correlation filter for the fine-grained correction of RI values. Experiments show that our method can effectively eliminate RI outliers and improve the accuracy and reliability of rainfall estimation results. Moreover, our method is highly suitable for heavy and violent rainfall application scenarios and can provide high-resolution rainfall data support for flooding warnings and simulations in urban areas.

Comparative Evaluation of the Rainfall Erosivity in the Rieti Province, Central Italy, Using Empirical Formulas and a Stochastic Rainfall Generator

Soil erosion caused by intense rainfall events is one of the major problems affecting agricultural and forest ecosystems. The Universal Soil Loss Equation (USLE) is probably the most adopted approach for rainfall erosivity estimation, but in order to be properly employed it needs high resolution rainfall data which are often unavailable. In this case, empirical formulas, employing aggregated rainfall data, are commonly used. In this work, we select 12 empirical formulas for the estimation of the USLE rainfall erosivity in order to assess their reliability. Moreover, we used a Stochastic Rainfall Generator (SRG) to simulate a long and high-resolution rainfall time series with the aim of assessing its application to rainfall erosivity estimations. From the analysis, performed in the Rieti province of Central Italy, we identified three equations which seem to provide better results. Moreover, the use of the selected SRG seems promising and it could help in solving the problem of hydrological data scarcity and consequently guarantee major accuracy in soil erosion estimation.