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Ground weather observations are scarce in many parts of the globe, hampering effective climate monitoring and disaster management. In the Amazon basin, this occurs due to its remoteness and the challenging measurement of rainfall within the forest. Innovative rainfall estimation methods are thus requested to fill this gap. Here we present an approach to estimate rainfall based on sound measurements. We identified the best frequency range to estimate rainfall occurrence and intensity, trained classification and regression models with sound and rain gauge data collected in the Central Amazon during 9 months. By training a random forest classifier/regression model based on power spectrum values it was possible to identify and satisfactorily estimate hourly rainfall rates in two vegetation environments distinct from the training site, located 30 km from it. The proposed method is a promising approach for future weather monitoring in remote tropical areas. Plain Language Summary: Understanding and predicting rainfall is a complex task, especially in areas where the availability of data from surface stations is limited, a common feature in many developing regions with insufficient rain gauge coverage. Recently, new opportunistic methods of rainfall measurement have emerged. Among them, is the use of the relationship between rainfall intensity and the sound produced by droplets hitting a surface. Sound recorders offer a low‐cost solution and could provide an interesting means to increase spatial coverage of rainfall measurements, but also to fill information gaps under dense forests where conventional devices do not work. Our study developed a new technique and applied it to the Central Amazon region, by training a supervised machine learning model applied to sound recordings obtained in a tropical rainforest. To our knowledge, for the first time, such techniques are validated in locations far from the calibration site. We showed that reasonable results can be obtained for sites with distinct vegetation types and up to 30 km of distance from where the training data was acquired. Our findings demonstrate a strong capability for estimating hourly rainfall rates. Key Points: Rainfall intensity estimated from sound measurements in the Amazon rainforest, tipping bucket rain gauge, and machine learning modelsThe best model successfully detects rainfall in 88% of the cases, with R2 > 0.87 for hourly rainfall rates on the training siteModel validated over two sites in the Amazon, 97% accuracy identifying rainfall events, R2 of 0.69 and 0.93 for hourly rainfall rate [ABSTRACT FROM AUTHOR]

A circular capacitive rainfall sensor can operate from non-touch mode to touch mode; that is, under the action of enough rainwater, its movable electrode plate can form a circular contact area with its fixed electrode plate. Therefore, the weight of rainwater is borne by only its movable electrode plate in non-touch mode operation but by both its movable and fixed electrode plates in touch mode operation, and the total capacitance of its touch mode operation is much larger than that of its non-touch mode operation. Essential to its numerical design and calibration is the ability to predict the deflection shape of its moveable electrode plate to determine its total capacitance. This requires the analytical solution to the fluid–structure interaction problem of its movable electrode plate under rainwater. In our previous work, only the analytical solution for the fluid–structure interaction problem before its movable electrode plate touches its fixed electrode plate was obtained, and how to numerically design and calibrate a circular non-touch mode capacitive rainfall sensor was illustrated. In this paper, the analytical solution for the fluid–structure interaction problem after its movable electrode plate touches its fixed electrode plate is obtained, and how to numerically design and calibrate a circular touch mode capacitive rainfall sensor is illustrated for the first time. The numerical results show that the total capacitance and rainwater volume when the circular capacitive rainfall sensor operates in touch mode is indeed much larger than that when the same circular capacitive rainfall sensor operates in non-touch mode, and that the average increase in the maximum membrane stress per unit rainwater volume when the circular capacitive rainfall sensor operates in touch mode can be about 20 times smaller than that when the same circular capacitive rainfall sensor operates in non-touch mode. This is where the circular touch mode capacitive rainfall sensor excels. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The wind‐induced bias of rainfall measurements obtained from non‐catching instruments is addressed in this work with reference to the Laser Precipitation Monitor (LPM) optical disdrometer manufactured by Thies CLIMA. A numerical simulation approach is adopted to quantify the expected bias, involving three different models with increasing complexity. Computational Fluid‐Dynamics simulation of the airflow field around the instrument with an embedded Lagrangian particle‐tracking module to obtain raindrop trajectories are performed by solving the Unsteady Reynolds Averaged Navier‐Stokes (URANS) equations and a Large Eddy Simulation (LES) model. URANS‐uncoupled, LES‐uncoupled, and LES‐coupled approaches are tested to assess the impact of modeling the airflow turbulent fluctuations in detail. Due to the non‐radially symmetric external shape of the instrument, various combinations of the wind speed and direction are considered. Catch ratios for monodisperse rain are obtained as a function of the particle Reynolds number and the wind direction and fitted to obtain site‐independent curves to support application of the simulation results. Based on literature expressions to link the drop size distribution of real rainfall events with the rainfall intensity (which instead depend on the local rainfall climatology at the measurement site), sample collection efficiency curves are obtained from the catch ratios of monodisperse rain. The resulting adjustment curves allow rainfall measurements to be corrected using either a real‐time or post‐processing approach. However, at high wind speed and assuming that the wind blows parallel to the instrument sensing area, the instrument may fail to report precipitation altogether. Key Points: The wind‐induced bias for the Thies CLIMA LPM is investigated using time dependent and time independent numerical simulationsCollection efficiency is derived from the catch ratios of monodisperse rain by linking the drop size distribution with rainfall intensityAdjustment curves for field measurements are provided as a function of wind speed, wind direction and rainfall intensity for practical use [ABSTRACT FROM AUTHOR]

A method is proposed and demonstrated for extracting the spatial and temporal characteristics of individual rainfall events using hourly rainfall measurements at an array of rain gauges. The method is based on identifying the probabilities of overlapping rainfall events at the rain gauge locations. The method is demonstrated in a 47,000 km2 area covered by 150 rain gauges with 17 years of unbroken hourly data. The results show that the length scales of individual events in the wet season are in the range of 1.2–18.3 km depending on the event magnitude, and dry season events with corresponding magnitudes have length scales in the range of 1.5–35.9 km. The timescales of wet and dry season events generally increase with event magnitude in both seasons. The shapes and locations of cluster areas that tend to have overlapping rainfall events are substantially different between seasons, being smaller and more dispersed in the wet season and larger and lesser in number in the dry season. [ABSTRACT FROM AUTHOR]

Stormwater control measures (SCMs) are designed to treat the runoff from a water-quality rainfall (WQR). The design of SCMs also requires specification of a drawdown time which is limited by the characteristic interevent time, and it commonly is overlooked that the WQR is not independent of the characteristic interevent time. A procedure is proposed for extracting the relation between the WQR and the characteristic interevent time so that consistent specifications can be developed for WQR and design drawdown time. The proposed procedure was demonstrated for South Florida, where it is shown that, for a given minimum interevent time of 6 h, the limiting design drawdown times of SCMs decrease with increasing percentile rainfall amounts, and that design drawdown times of less than 20 h might be needed to provide a reliability of 50% that water quality basins will fully drain the water-quality volume prior to the next event. It also is shown that both the WQR and the limiting design drawdown time are sensitive to the minimum interevent time that is used to separate rainfall measurements into discrete rainfall events. [ABSTRACT FROM AUTHOR]

A common and largely unresolved problem of national-scale landslide early warning systems is their independent evaluation. In a recent paper, Mondini et al. (Nat Commun 14:2466, 2023) proposed a deep-learning system for short-term forecasting of rain-induced shallow landslides in Italy. Here, we independently evaluate the performance of this national-scale system by demonstrating its application between 1 January and 31 May 2021. For the purpose, we use hourly rainfall measurements from the same rain gauge network and different and independent information on the timing and location of 163 rain-induced landslides obtained from the FraneItalia catalogue that occurred in Italy in a period non considered in the construction of the system (https://zenodo.org/records/7923683). Independent demonstration confirmed the good predictive performance of the forecasting system and revealed no geographical or temporal bias in the forecasts. The analysis also showed that the system was more effective at predicting multiple landslides in the same general area than single landslides. This was a good result as multiple landslides are inherently more dangerous than single failures. Analysis of the few misclassified landslides showed that approximately one-third of the landslides were rockfalls, and for approximately another third there was uncertainty about when or where the landslides occurred. We conclude that, despite the inevitable misclassifications inherent in any probabilistically based national-scale landslide forecasting system, the deep-learning system analysed is well suited for short-term operational forecasting of rain-induced shallow landslides in Italy. [ABSTRACT FROM AUTHOR]

Groundwater-level and rainfall measurements from 37 borewells in the Visakhapatnam district, Andhra Pradesh, India, from 2002 to 2021 were analyzed using Bayesian Neural Networks (BNNs) to comprehend the predictability of groundwater levels. We found chaotic dynamics in the groundwater and rainfall data, but a dominant trend component was seen in the groundwater-level data from phase plots. Dynamics suggest the presence of self-organized criticality/chaos in the groundwater dynamics over decadal time scales. We used BNN prediction models, (i) nonlinear autoregressive (NAR), (ii) nonlinear input–output (NIO) and (iii) nonlinear autoregressive exogenic Input (NARX), to predict the groundwater levels with rainfall and temperature as exogenic inputs. We noticed ~ 94 to 95% prediction accuracy with the NAR model with optimal inputs and ~ 1% improvement with added exogenic input. Interestingly, the study indicates that (i) the dynamics of the groundwater differ significantly from rainfall and temperature in the region, (ii) the nonlinear autoregressive model based on the self-organized dynamics of groundwater-level changes is robust in providing prediction accuracy up to ~ 95%, and (iii) the dynamics of remaining ~ 5% groundwater-level changes may be due to the presence of randomly varying extreme weather events and man-made/anthropogenic changes. [ABSTRACT FROM AUTHOR]

Rainfall serves as a lifeline for crop cultivation in many agriculture-dependent countries including India. Being spatio-temporal data, the forecasting of rainfall becomes a more complex and tedious process. Application of conventional time series models and machine learning techniques will not be a suitable choice as they may not adequately account for the complex spatial and temporal dependencies integrated within the data. This demands some data-driven techniques that can handle the intrinsic patterns such as non-linearity, non-stationarity, and non-normality. Space–Time Autoregressive Moving Average (STARMA) models were highly known for its ability to capture both spatial and temporal dependencies, offering a comprehensive framework for analyzing complex datasets. Spatial Weight Matrix (SWM) developed by the STARMA model helps in integrating the spatial effects of the neighboring sites. The study employed a novel dataset consisting of annual rainfall measurements spanning over 50 (1970–2019) years from 119 different locations (grid of 0.25 × 0.25 degree resolution) of West Bengal, a state of India. These extensive datasets were split into testing and training groups that enable the better understanding of the rainfall patterns at a granular level. The study findings demonstrated a notable improvement in forecasting accuracy by the STARMA model that can exhibit promising implications for agricultural management and planning, particularly in regions vulnerable to climate variability. [ABSTRACT FROM AUTHOR]

Large spatio‐temporal gradients in the Congo basin vegetation and rainfall are observed. However, its water‐balance (evapotranspiration minus precipitation, or ET − P) is typically measured at basin‐scales, limited primarily by river‐discharge data, spatial resolution of terrestrial water storage measurements, and poorly constrained ET. We use observations of the isotopic composition of water vapor to quantify the spatio‐temporal variability of net surface water fluxes across the Congo Basin between 2003 and 2018. These data are calibrated at basin scale using satellite gravity and total Congo river discharge measurements and then used to estimate time‐varying ET − P over four quadrants representing the Congo Basin, providing first estimates of this kind for the region. We find that the multi‐year record, seasonality, and interannual variability of ET − P from both the isotopes and the gravity/river discharge based estimates are consistent. Additionally, we use precipitation and gravity‐based estimates with our water vapor isotope‐based ET − P to calculate time and space averaged ET and net river discharge within the Congo Basin. These quadrant‐scale moisture flux estimates indicate (a) substantial recycling of moisture in the Congo Basin (temporally and spatially averaged ET/P > 70%), consistent with models and visible light‐based ET estimates, and (b) net river outflow is largest in the Western Congo where there are more rivers and higher flow rates. Our results confirm the importance of ET in modulating the Congo water cycle relative to other water sources. Plain Language Summary: Rainfall and vegetation vary substantially across the Congo Basin. However, the spatial variations, seasonality, and interannual variability of the net water balance, (the difference between evapotranspiration and rainfall) is not well quantified. Atmospheric observations of the isotopic composition of water vapor are sensitive to the balance of evapotranspiration (ET) and precipitation (P). We calibrate new observations of the isotopic composition of water vapor to ET − P that is based on satellite gravity measurements and ground‐based river discharge measurements to quantify ET − P across four quadrants of the Congo basin. When combined with satellite measurements of rainfall, we show that ET is the largest source of Congo basin water vapor. As ET is about 70% of observed rainfall, vegetation therefore plays an outsized role on the Congo water cycle. Additionally, when combined with satellite measurements of gravity, we show that river discharge is higher in the western part of the basin, where there are more rivers and stronger flows. Key Points: Water vapor isotopes provide a reliable proxy for evapotranspiration (ET) minus precipitation (P) in the Congo BasinET contributes over 70% to P within the four quadrants of the Congo BasinRiver discharge is highest in the western part of the basin, where there are more rivers and higher flow [ABSTRACT FROM AUTHOR]

Earth – satellite microwave links such as TV-SAT can help for rainfall monitoring and could be a complement or an alternative to ground-based weather radars, rain gauges or satellites dedicated to Earth observation. Rain induced attenuation which is harmful for telecommunication is exploited here as an opportunistic way to estimate rain rate along the path link. This technique makes it possible to obtain rain measurements at a fine temporal resolution (a few tens of seconds) and with a spatial resolution of few kilometers, which is a good compromise for human activities such as civil security (watershed monitoring, flash flood), agriculture or transport. Among the advantages of this technique, one can note the low cost of the hardware used (which can be commercial) as well as that of its maintenance on site. However, measured attenuation does not directly provide rain intensity and some parameters have to be estimated. Among these, it is necessary to take into account the contribution of the natural radiation of the atmosphere. In this paper, we detail a theoretical framework allowing to estimate rainfall from the measurements of a low-cost sensor operating simultaneously over two parts of the Ku-frenquency band. Then this framework is assessed in a densely instrumented area in south of France, where it is shown that very good results are obtained when compared to rain gauges measurements, both in terms of overall rain accumulation and in terms of rainfall rate distribution. Then we apply this dual channel method in Ivory Coast, in the metropolitan area of Abidjan, where such an approach is very promising. It is shown that this technique when compared to rain gauge measurements give results far better than a single-channel naive approach neglecting natural radiation of atmosphere, but that there are still significant errors in the rain assessment, leading to a persistent underestimation of rain accumulation. Finally we discuss various effects that could lead to this remaining underestimation, opening the door to further studies. [ABSTRACT FROM AUTHOR]

A unipolar ground-based weather radar is a widely-used instrument for rainfall measurement. These radar measurements, however, need to be calibrated for more accurate rainfall estimation. This article introduces a new calibration approach using time-stepwise processing of reflectivity–rainfall rate () relationship. Based on the previous work utilizing the radar–rainfall relationship, this article hypothesizes that the rainfall measurement from a ground based radar are affected by the distance from radar, altitudes and rainfall time duration. Unipolar ground-based radar data sets for two consecutive years with 77 occurrences of rainfall from 39 stations in calibration window of three hours as well as the corresponding rainfall measured from registered rain gauges were used in this study. The results indicated that it is better to use the radar–rainfall relationship in view of the altitude effect () and empirical coefficient (), such that . The changes in the distance from the radar and duration of precipitation were evidently significant. For radar–rainfall relationship individually, the values of the determination coefficient made up from 0.88 to 0.97, and those for this relationship in view of the altitude effect () were from 0.70 to 0.97. It is therefore concluded that the use of other effective parameters (distance from radar, altitude and rainfall time duration) leads to optimum accuracy of the relationship. [ABSTRACT FROM AUTHOR]

Accurate rainfall measurement with a precise spatial and temporal resolution is essential for making informed decisions during disasters and conducting scientific studies, particularly in regions characterized by intricate terrain and limited coverage of automated weather stations. Retrieval of precipitation with satellite is currently the most effective means to obtain precipitation over large‐scale areas. The key to enhancing the accuracy of precipitation estimation and forecasting in regions with complex terrain lies in effectively integrating satellite data with topographic information. This paper introduces a deep learning approach called AttUnet_R_SFT that utilizes high temporal, spatial, and spectral resolution data obtained from the Fengyun 4A satellite, and incorporates the Deep Spatial Feature Transform (SFT) layer to incorporate geographical data for estimating half‐hourly precipitation in northeastern China. We assess it by compared to operational near‐real‐time satellite precipitation products demonstrated to be successful in estimating precipitation and baseline deep learning models. According to the experimental findings, the AttUnet_R_SFT model outperforms practical precipitation products and baseline deep learning models in both identifying and estimating precipitation. The main enhancement of the model performance is shown in the windward slope of the Greater Khingan Mountains as a result of the successful incorporation of geographical data. Hence, the suggested framework holds the capability to function as a superior and dependable satellite‐derived precipitation estimation solution in regions characterized by intricate terrain and infrequent rainfall. The findings of this study indicate that the utilization of deep learning algorithms for satellite precipitation estimation shows potential as a fruitful avenue for further research. Plain Language Summary: A deep learning model named AttUnet_R_SFT is proposed, which use high temporal, spatial and spectral resolution data from the Fengyun 4A satellite, and combines with the Deep Spatial Feature Transform (SFT) layer to input geographic information for half‐hourly precipitation estimation in the complex terrain region represented by northeast China. The model can provide a reference for improving the performance of precipitation estimation in areas with complex topography. Key Points: A deep‐learning model is proposed to effectively fuse satellite multispectral data of the Fengyun 4A satellite, with topographic informationHalf‐hourly precipitation is estimated with higher temporal resolution, which is closer to the operational needs of weather forecastingAs precipitation in the study area is a non‐high‐frequency event, data enhancement is attempted to use and obtain effective results [ABSTRACT FROM AUTHOR]

As wildfire increases in the western United States, so do postfire debris-flow hazards. The U.S. Geological Survey (USGS) has developed two separate models to estimate (1) rainfall intensity thresholds for postfire debris-flow initiation and (2) debris-flow volumes. However, the information necessary to test the accuracy of these models is seldom available. Here, we studied how well these models performed over a 2-year period in the 2020 Grizzly Creek Fire burn perimeter in Glenwood Canyon, Colorado, USA, through the development of a debris-flow response inventory. The study area had the advantage of a network of 11 rain gauges for rainfall intensity measurements and repeat lidar data for volume estimates. Our observations showed that 89 % of observed debris flows in the first year postfire were triggered by rainfall rates higher than the fire-wide rainfall threshold produced by the current USGS operational model (M1). No debris flows were observed in the second year postfire, despite eight rainstorms with intensities higher than the modeled rainfall threshold. We found that the operational model for debris-flow initiation rainfall thresholds works well in this region during the first year but may be too conservative in year 2 due to vegetation recovery and sediment depletion. However, rainfall thresholds in the second year can be improved by using updated remote sensing imagery to recalculate the debris-flow initiation likelihood with the M1 model. The current volume model overestimates for this region by a median value of 4.4 times. However, the offset between the model estimates and observations is approximately linear, and the volumes from the Grizzly Creek debris flows had a similar magnitude to historic postfire debris flows in the region. Consequently, the current volume model could be adjusted with a regional correction factor. [ABSTRACT FROM AUTHOR]

Slope instability due to tectonic, hydrological and anthropogenic activities cause severe landslides in Himalaya. Joshimath, a densely populated Himalayan town witnessed a catastrophic landslide event during December 2022 and January 2023 causing damages to ∼700 buildings. We use Interferometric synthetic aperture radar, Global Positioning System and rainfall measurements to probe the kinematics of the Joshimath landslide. We separate the seasonal and episodic deformation components using singular spectrum analysis. While the low amplitude annual landslide motions are modulated by seasonal precipitation, acceleration phases are triggered by extreme rain events. Our analysis revealed episodes of cascading motions triggered by extreme rain events resulting an overall increase in landslide velocity from −22 mm/yr during 2004–2010 to −325 mm/yr during 2022–2023. We estimate the landslide depth (∼30 m) and hydraulic diffusivity (∼3 × 10−5 m2/s) using a 1‐D pore‐water pressure diffusion model. Our study reveals the importance of systematic monitoring of ground deformation and weather parameters for landslide hazard mitigation. Plain Language Summary: The Himalayas, world's largest and youngest mountain range formed as a result of the India‐Eurasia continental collision hosts thousands of landslides each year related to tectonic and anthropogenic activities. It is important to characterize the landslide kinematics, especially the slow moving landslides at the fast‐developing Himalayan urban centers for hazard assessment. A recent catastrophic landslide event in the Joshimath town in NW Himalaya occurred between December 2022 and January 2023 caused damages to ∼700 buildings leading to mass evacuation. We map the spatio‐temporal evolution of land deformation at Joshimath using about two decades of geodetic measurements and singular spectrum analysis technique. While the decadal‐scale landslide motions match well with the overall increase in rainfall intensity, the episodic accelerations are triggered by extreme rain events. The extreme rainfall event on 19 October 2021, triggered the latest episode of landslide acceleration, which eventually led to the tragic disaster. The present study, for the first time, provides constraints on the landslide depth and hydraulic diffusivity. Our study also emphasis on the systematic monitoring of ground deformation and weather parameters at the Himalayas for an effective landslide hazard preparedness and mitigation. Key Points: Joshimath landslide displays episodic and seasonal kinematic changes; episodic accelerations are triggered by extreme rain eventsThe 19 October 2021 extreme rain event triggered the latest episode of landslide acceleration culminating to the 2022 disasterDepth and hydraulic diffusivity of the landslide is constrained using 1‐D pore‐water pressure diffusion model [ABSTRACT FROM AUTHOR]

This paper presents an open-access data set of reanalysed radar reflectivities and rainfall rates at sub-kilometre spatial and minute temporal scales. Variability at these scales is a blind spot for both operational rain gauge networks and operational radar networks. In the urban area of Hamburg, precipitation measurements of a single-polarized X-band weather radar operating at high temporal (30s), range (60m), and azimuthal sampling (1°) resolutions are made available for a period of more than 8 years. We describe in detail the reanalysis of the raw radar data, outline the radar performance for the years 2013 to 2021, and discuss open issues and limitations of the data set. Several sources of radar-based errors were adjusted gradually, affecting the radar reflectivity and rainfall measurements, e.g. noise, alignment, non-meteorological echoes, radar calibration, and attenuation. The deployment of additional vertically pointing micro rain radars yields drop size distributions at the radar beam height, which effectively reduces errors concerning the radar calibration and attenuation correction and monitors the radar data quality. A statistical evaluation revealed that X-band radar reflectivities and rainfall rates are in very good agreement with the micro rain radar measurements. Moreover, the analyses of rainfall patterns shown for an event and accumulated rainfall of several months prove the quality of the data set. The provided radar reflectivities facilitate studies on attenuation correction and the derivation of further weather radar products, like an improved rainfall rate. The rainfall rates themselves can be used for studies on the spatial and temporal scales of precipitation and hydrological research, e.g. input data for high-resolution modelling, in an urban area. The radar reflectivities and rainfall rates are available at https://doi.org/10.26050/WDCC/LAWR_UHH_HHG_v2. [ABSTRACT FROM AUTHOR]

This study employs copula functions to establish the dependency structure of the joint distribution among rainfall intensity, wind speed, and wind direction in Qatar. Based on a Vine Copula, the trivariate distribution between rainfall intensity, wind speed, and wind direction is found to exhibit a root-mean-square error (RMSE) of 0.0072 on the observed vs. modeled cumulative probabilities using ranked normalized observations. It is also found that the winter Shamal winds are most pronounced during rainfall. However, a secondary component of easterly winds known as the Kaus winds is also found to exert an important influence. This wind pattern is observable during rainfall at all the selected stations, albeit with minor variations. It is also found that rainfall stations where the rainfall is obstructed in any way from northwest to north and from east to southeast significantly influence the rainfall measurements. Specific rain gauges in Qatar are found to be situated in disrupted surroundings, such as meteorological stations close to passing traffic, where road spray could infiltrate the rain gauge funnel, impacting the accuracy of rainfall measurements. The study results necessitated the relocation of approximately half of these roadside gauges to mitigate wind-induced biases from road spray. An evaluation of operations is recommended for approximately 80 meteorological stations responsible for measuring rainfall in Qatar. The methodology devised in this study holds potential for application to other Middle Eastern countries and regions with similar climates. [ABSTRACT FROM AUTHOR]

The amount of rainfall in different regions is influenced by various factors, including time, place, climate, and geography. In the Lake Urmia basin, Mediterranean air masses significantly impact precipitation. This study aimed to model precipitation in the Lake Urmia basin using monthly rainfall data from 16 meteorological stations and five machine learning methods (RF, M5, SVR, GPR, and KNN). Eight input scenarios were considered, including the monthly index, longitude, latitude, altitude, distance from stations to Lake Urmia, and distance from the Mediterranean Sea. The results revealed that the random forest model consistently outperformed the other models, with a correlation rate of 0.968 and the lowest errors (RMSE = 5.66 mm and MAE = 4.03 mm). This indicates its high accuracy in modeling precipitation in this basin. This study's significant contribution is its ability to accurately model monthly precipitation using spatial variables and monthly indexes without measuring precipitation. Based on the findings, the random forest model can model monthly rainfall and create rainfall maps by interpolating the GIS environment for areas without rainfall measurements. [ABSTRACT FROM AUTHOR]

Slope has an important effect on the runoff production process. Slope correction of runoff curve number in the SCS-CN runoff production model is important to improve the model prediction accuracy, In order to conduct an investigation of the effect of average watershed slope in the runoff production process, in this paper, based on Huang's slope modification formula, the first 40 out of 70 rainfall runoff measurements in the study area from 1955 to 1981 were used as the validation data, and a1 and a2 parameters in Huang's slope modification formula were re-determined, so as to propose the slope modification formula of the CN value for the study area under three different antecedent moisture conditions, and then the SCS-WJG method, which is of good applicability to the region, was established and compared to the SCS-CN look-up table method, and the SCS-Huang method, which is a direct transfer of the Huang's slope modification formula. The conclusion is as follows: the λ-value of the initial rainfall loss rate determined using the ergodic method is 0.09, so the standard value of 0.2 is not applicable to the study area. After comparing the size of the CN values obtained by the three methods, it is found that the CN value obtained by the SCS-Huang method is the largest, followed by the SCS-CN look-up table method, and the smallest is obtained by the SCS-WJG method, so the CN value obtained by the SCS-WJG method is more suitable for the study area as shown in the simulation results. Comparison of the SCS-CN lookup table method and the SCS-Huang method reveals that the NSE of the SCS-Huang method is lower and the RMSE is higher; therefore, it is necessary to propose a slope correction formula for the CN value applicable to the study area and establish the SCS-WJG method. When λ = 0.09, the NSE of the SCS-CN lookup table method, and the SCS-Huang method are -2.85 and -3.25, respectively, while the NSE of the SCS-WJG method is increased to 0.86, and the RMSE of the SCS-WJG method is reduced by 77%, and 78%, respectively, compared with the first two methods. The simulation accuracy of the SCS-WJG method is significantly better than that of the remaining two methods, indicating that the SCS-WJG method has good regional applicability and can provide a reference for the simulation of runoff production in small watersheds. [ABSTRACT FROM AUTHOR]

Rainfall is one of the factors affecting soil erosion, and rainfall kinetic energy or momentum is used to represent the capability of the precipitation to erode soil, named rainfall erosivity. Accurate measurements of rainfall erosivity are useful for a reliable prediction of soil loss. Rainfall momentum and kinetic energy, both calculated per unit time and area, can be obtained using terminal raindrop velocity and the drop size distribution (DSD) measured by disdrometers, which are instruments expensive and not suitable for large scale use. An innovative patented method to measure the rainfall energy is based on the estimation of DSD by simultaneous detection, in a given time interval, of the rainfall intensity I and the number N of raindrops that hit a specific piezoelectric surface. In this paper, advances of this method are presented. In particular, two new theoretical procedures to estimate the parameters μ and Λ of Ulbrich's distribution, that allow for the calculation of the rainfall kinetic energy and momentum, are proposed. Both procedures (Scenario 1 and 2) are based on the frequency distribution of the momentum M(D) of raindrops detected in a sampling time interval. Specifically, in the Scenario 1, μ and Λ are estimated by using I, N and the standard deviation, s(D), of the drop diameters obtained from the measured momentum distribution. In the Scenario 2 the parameters are estimated using I, N and the mean value, m(D), of the drop diameters deriving from the momentum distribution. The reliability of the proposed procedures was tested using DSD measurements recorded in three different experimental sites. The developed analysis demonstrated that Scenario 2 is the best method to estimate μ and Λ, and to reproduce the DSD, accordingly. The proposed method, associated with a patented device, not yet build, will allow the direct measurement of the rainfall energy characteristics, which are usually roughly estimated from rainfall intensity. The possibility to easily measure these energy variables can support the development of research in the field of soil erosion and, in general, of hydrogeological instability. In particular, the proposed measurement method and the construction of the device could stimulate the scientific community to deepen the study of the effect of the rainfall energy on soil erosion for improving the predictive capability of water erosion models. [ABSTRACT FROM AUTHOR]

The ability of numerical weather prediction models to accurately predict extreme weather events, such as thunderstorms marked by heavy rainfall and lightning activities, has consistently been of great importance for human life. The objective of this study is to assess the long‐term reliability of the European Centre for Medium‐Range Weather Forecasts Reanalysis version 5 (ERA5) rainfall in comparison to the Indian gauge‐adjusted Global Satellite Mapping of Precipitation (GSMaP_ISRO) rainfall at the time of lightning flashes measured by the Lightning Imaging Sensor (LIS) onboard the Tropical Rainfall Measuring Mission satellite over the Indian region during the years 2001–2014. This analysis will provide valuable insights into the intricate relationship between lightning flashes and precipitation under various terrain conditions (low, mid, or high), across oceanic regions (Bay of Bengal, Arabian Sea), and during different monsoon phases (normal, active, or deficit). According to a prolonged examination of LIS data, April–June accounts for ~50% of the total flashes, with the largest number of flashes occurring over the Himalayan and the northeastern part of India. According to hourly GSMaP_ISRO rainfall, the most substantial lightning‐associated rainfall happens an hour prior to lightning flash (T − 1) and within three hours after (T + 3), indicating a robust correlation between heavy rainfall and lightning activity during this time frame. The rainfall in the ERA5 reanalysis misses the intensity as well as duration of the peak rainfall at the time of lightning flashes. Furthermore, the ERA5 reanalysis rainfall depicts under (over)‐estimation of rainfall in plain (orographic) regions. The underestimation of ERA5 rainfall is very pronounced over the Indian Ocean and Bay of Bengal regions, mainly between flash time (T) to two hours after the flash time (T + 2). The results indicate that there is a requirement for additional enhancements in the ERA5 reanalysis rainfall for lightning occurrences. [ABSTRACT FROM AUTHOR]

Satellite rainfall estimates (SRE) offer spatial-temporal rainfall representations in regions with limited ground-based gauge rainfall measurements. However, differences exist between SRE and gauge measured rainfall, which needs assessment and reduction. This study presents a method to correct errors in SRE to make their use in agro-hydrological applications and models meaningful. The main scientific objective is the determination of effective window sizes for SRE bias correction. To conclude on effective window sizes, the crop water requirement satisfaction index (WRSI) for gauged rainfall, uncorrected SRE and bias corrected SRE were estimated and propagation effects of SRE errors on respective WRSI estimates were assessed. WRSI indicates how much of the crop water needs are satisfied by rainfall. The Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) SRE was bias corrected using gauged rainfall data from 20 stations in the Lake Victoria basin of Kenya from 2012 to 2018. The results show that the error in WRSI can serve to determine effective window sizes for SRE bias correction rather than using SRE bias error itself. This proposed correction method resulted in improved estimates of WRSI. [ABSTRACT FROM AUTHOR]

The most reliable areal precipitation estimation is usually generated via combinations of different measurements. Path-averaged rainfall rates can be derived from commercial microwave links (CMLs), where attenuation of the emitted radiation is strongly related to rainfall rate. CMLs can be combined with data from other rainfall measurements or can be used individually. They are available almost worldwide and often represent the only opportunity for ground-based measurement in data-scarce regions. However, deriving rainfall estimates from CML data requires extensive data processing. The separation of the attenuation time series into rainy and dry periods (rain event detection) is the most important step in this processing and has a high impact on the resulting rainfall estimates. In this study, we investigate the suitability of Meteosat Second Generation Spinning Enhanced Visible and InfraRed Imager (MSG SEVIRI) satellite data as an auxiliary-data-based (ADB) rain event detection method. We compare this method with two time-series-based (TSB) rain event detection methods. We used data from 3748 CMLs in Germany for 4 months in the summer of 2021 and data from the two SEVIRI-derived products PC and PC-Ph. We analyzed all rain event detection methods for different rainfall intensities, differences between day and night, and their influence on the performance of rainfall estimates from individual CMLs. The radar product RADKLIM-YW was used for validation. The results showed that both SEVIRI products are promising candidates for ADB rainfall detection, yielding only slightly worse results than the TSB methods, with the main advantage that the ADB method does not rely on extensive validation for different CML datasets. The main uncertainty of all methods was found for light rain. Slightly better results were obtained during the day than at night due to the reduced availability of SEVIRI channels at night. In general, the ADB methods led to improvements for CMLs performing comparatively weakly using TSB methods. Based on these results, combinations of ADB and TSB methods were developed by emphasizing their specific advantages. Compared to basic and advanced TSB methods, these combinations improved the Matthews correlation coefficient of the rain event detection from 0.49 (or 0.51) to 0.59 during the day and from 0.41 (or 0.50) to 0.55 during the night. Additionally, these combinations increased the number of true-positive classifications, especially for light rainfall compared to the TSB methods, and reduced the number of false negatives while only leading to a slight increase in false-positive classifications. Our results show that utilizing MSG SEVIRI data in CML data processing significantly increases the quality of the rain event detection step, in particular for CMLs which are challenging to process with TSB methods. While the improvement is useful even for applications in Germany, we see the main potential of using ADB methods in data-scarce regions like West Africa where extensive validation is not possible. [ABSTRACT FROM AUTHOR]

One measurement modality for rainfall is a fixed location rain gauge. However, extreme rainfall, flooding, and other climate extremes often occur at larger spatial scales and affect more than one location in a community. For example, in 2017 Hurricane Harvey impacted all of Houston and the surrounding region causing widespread flooding. Flood risk modeling requires understanding of rainfall for hydrologic regions, which may contain one or more rain gauges. Further, policy changes to address the risks and damages of natural hazards such as severe flooding are usually made at the community/neighborhood level or higher geo-spatial scale. Therefore, spatial-temporal methods which convert results from one spatial scale to another are especially useful in applications for evolving environmental extremes. We develop a point-to-area random effects (PARE) modeling strategy for understanding spatial-temporal extreme values at the areal level, when the core information are time series at point locations distributed over the region. [ABSTRACT FROM AUTHOR]

Here we present FOCA (Italian FlOod and Catchment Atlas), the first systematic collection of data on Italian river catchments for which historical discharge time series are available. Hydrometric information, including the annual maximum peak discharge and average daily annual maximum discharge, is complemented by several geomorphological, climatological, extreme rainfall, land-cover and soil-related catchment attributes. All hydrological information derives from the most recently released datasets of discharge and rainfall measurements. To enhance the reproducibility and transferability of the analysis, this paper provides a description of all the raw data and the algorithms used to build the basin attribute dataset. We also describe the approaches adopted to solve problems encountered during the digital elevation model elaboration in areas characterized by a complex morphology. Details about the data quality-control procedure developed to detect and correct errors are also reported. One of the main novelties of FOCA with respect to other national-scale datasets is the inclusion of a rich set of geomorphological attributes and extreme rainfall features for a large set of basins covering a wide range of elevations and areas. Using this first nationwide data collection (available at 10.5281/zenodo.10446258, Claps et al., 2023), a wide range of environmental applications, with a particular focus on flood studies, can be undertaken within the Italian territory. [ABSTRACT FROM AUTHOR]

Summary: Since 2009, ONERA has been running Ka band propagation experiments in Toulouse (France, latitude 43.57°N, longitude 1.47°E). A rain gauge was also deployed on site to collect rainfall rate measurements concurrently to beacon data. Since April 2011, the beacon receiver has been recording the 20.2 GHz (vertical polarisation) Astra 3B beacon signal along a slant path of 35.1° of elevation angle. All years of the experiment had excellent data availability, hence giving 12 years of data at Ka band. First, the propagation experiment and the data processing methodology are described. Second, a statistical analysis of rain attenuation and rainfall rate is conducted. Comparisons are then performed with the prediction methods of Recommendations ITU‐R P.837‐7 (rainfall rate), P.618‐13 (rain attenuation) and P.678‐3 (variability of propagation phenomena). [ABSTRACT FROM AUTHOR]

The forest canopy affects the water entering the forest ecosystem by intercepting rainfall. This is especially pertinent in forests that depend on rainfall for their ecological water needs, quantifying and simulating interception losses provide critical insights into their ecological hydrological processes. In the semi‐arid areas of the Loess Plateau, afforestation has become an effective ecological restoration measure. However, the rainfall interception process of these plantations is still unclear. To quantify and model the canopy interception of these plantations, we conducted a two‐year rainfall redistribution measurement experiment in three typical plantations, including a deciduous broadleaf plantation (Robinia pseudoacacia) and two evergreen coniferous plantations (Platycladus orientalis and Pinus tabuliformis). Based on this, the revised Gash model was used to simulate their interception losses, and the model applicability across varying rainfall types was further compared and verified. The experiment clarified the rainfall redistribution in the three plantations, and the proportions of throughfall to gross rainfall in Robinia pseudoacacia, Platycladus orientalis, and Pinus tabuliformis were 84.8%, 70.4%, and 75.6%; corresponding, the stemflow proportions were 2.0%, 2.2%, and 1.8%; the interception losses were 13.2%, 27.4%, and 22.6%, respectively. The dominant rainfall pattern during the experiment was characterized by low‐amounts, moderate‐intensity, and short‐duration, during which the highest interception proportions across the three plantations were observed. We used the Penman‐Monteith equation and the regression method, respectively, to estimate the canopy average evaporation rate of the revised Gash model, finding that the latter provides a closer match to the measured cumulative interception (NSE >0.7). When simulating interception under the three rainfall patterns, the model with the regression method better simulated the cumulative interception and event‐scale interception for Platycladus orientalis and Pinus tabuliformis plantations under the dominant rainfall pattern. The results contribute valuable information to assess the impact of forest rainfall interception on regional hydrologic processes. [ABSTRACT FROM AUTHOR]

A circular capacitive rainfall sensor can operate from non-touch mode to touch mode; that is, under the action of enough rainwater, its movable electrode plate can form a circular contact area with its fixed electrode plate. Therefore, the weight of rainwater is borne by only its movable electrode plate in non-touch mode operation but by both its movable and fixed electrode plates in touch mode operation, and the total capacitance of its touch mode operation is much larger than that of its non-touch mode operation. Essential to its numerical design and calibration is the ability to predict the deflection shape of its moveable electrode plate to determine its total capacitance. This requires the analytical solution to the fluid–structure interaction problem of its movable electrode plate under rainwater. In our previous work, only the analytical solution for the fluid–structure interaction problem before its movable electrode plate touches its fixed electrode plate was obtained, and how to numerically design and calibrate a circular non-touch mode capacitive rainfall sensor was illustrated. In this paper, the analytical solution for the fluid–structure interaction problem after its movable electrode plate touches its fixed electrode plate is obtained, and how to numerically design and calibrate a circular touch mode capacitive rainfall sensor is illustrated for the first time. The numerical results show that the total capacitance and rainwater volume when the circular capacitive rainfall sensor operates in touch mode is indeed much larger than that when the same circular capacitive rainfall sensor operates in non-touch mode, and that the average increase in the maximum membrane stress per unit rainwater volume when the circular capacitive rainfall sensor operates in touch mode can be about 20 times smaller than that when the same circular capacitive rainfall sensor operates in non-touch mode. This is where the circular touch mode capacitive rainfall sensor excels.

Satellite precipitation products (SPPs) are undeniably subject to uncertainty due to retrieval algorithms and sampling issues. Many research efforts have concentrated on merging SPPs to create high-quality merged precipitation datasets (MPDs) in order to reduce these uncertainties. This study investigates the efficacy of dynamically weighted MPDs in contrast to those using static weights. The analysis focuses on comparing MPDs generated using the "dynamic clustered Bayesian averaging (DCBA)" approach with those utilizing the "regional principal component analysis (RPCA)" under fixed-weight conditions. These MPDs were merged from SPPs and reanalysis precipitation data, including TRMM (Tropical Rainfall Measurement Mission) Multi-satellite Precipitation Analysis (TMPA) 3B42V7, PERSIANN-CDR, CMORPH, and the ERA-Interim reanalysis precipitation data. The performance of these datasets was evaluated in Pakistan's diverse climatic zones—glacial, humid, arid, and hyper-arid—employing data from 102 rain gauge stations. The effectiveness of the DCBA model was quantified using Theil's U statistic, demonstrating its superiority over the RPCA model and other individual merging methods in the study area The comparative performances of DCBA and RPCA in these regions, as measured by Theil's U, are 0.49 to 0.53, 0.38 to 0.45, 0.37 to 0.42, and 0.36 to 0.43 in glacial, humid, arid, and hyper-arid zones, respectively. The evaluation of DCBA and RPCA compared with SPPs at different elevations showed poorer performance at high altitudes (>4000 m). The comparison of MPDs with the best performance of SPP (i.e., TMPA) showed significant improvement of DCBA even at altitudes above 4000 m. The improvements are reported as 49.83% for mean absolute error (MAE), 42.31% for root-mean-square error (RMSE), 27.94% for correlation coefficient (CC), 40.15% for standard deviation (SD), and 13.21% for Theil's U. Relatively smaller improvements are observed for RPCA at 13.04%, 1.56%, 10.91%, 1.67%, and 5.66% in the above indices, respectively. Overall, this study demonstrated the superiority of DCBA over RPCA with static weight. Therefore, it is strongly recommended to use dynamic variation of weights in the development of MPDs. [ABSTRACT FROM AUTHOR]

In July 2023, four devastating landslides occurred in Yecheon County, South Korea, due to heavy rainfall. These landslides resulted in 10 deaths, one injury, and the evacuation of around 500 people. The landslides occurred in different locations, including forest roads and natural slopes, and were caused by the liquefaction of collapsed soil into debris flows. The study emphasizes the vulnerability of South Korea to nocturnal heavy rains during the summer and recommends the establishment of early warning systems and check dams to mitigate the risks of sediment-related disasters in mountainous regions. The authors of the study, Seungjun Lee, Hyunuk An, Minseok Kim, and Song Eu, declare no conflicts of interest, ensuring the credibility of their findings. [Extracted from the article]

The measurement accuracy of an electroacoustic precipitation sensor, the Vaisala WXT520, is investigated to quantify the associated wind-induced bias. The device is widely used as a noncatching tool for measuring the integral features of liquid precipitation, specifically rainfall amount and intensity. A numerical simulation using computational fluid dynamics is used to determine the bluff-body behavior of the instrument when exposed to wind. The obtained airflow velocity patterns near the sensor are initially validated in a wind tunnel. Then, the wind-induced deviation and acceleration/deceleration of individual raindrop trajectories and the resulting impact on the measured precipitation are replicated using a Lagrangian particle tracking model. The sensor's specific measurement principle necessitates redefining catch ratios and the collection efficiency in terms of the resulting kinetic energy and quantifying them as a function of particle Reynolds number and precipitation intensity, respectively. Wind speed and direction and drop size distribution have been simulated across various combinations. The results show that the measured precipitation is overestimated by up to 400% under the influence of wind. The presented adjustment curves can be used to correct raw rainfall measurements taken by the Vaisala WXT520 in windy conditions, either in real time or as a postprocessing function. The magnitude of the adjustment at any operational aggregation level largely depends on the local rainfall and wind regimes at the site of measurement and may have a strong impact on applications in regions where wind is frequent during low- to medium-intensity precipitation. [ABSTRACT FROM AUTHOR]

In this study, the rainfall measurement characteristics of an optical particle size velocity (Parsivel) disdrometer, tipping-bucket rain gauge (TBG), and Pluvio weighing precipitation gauge (WPG) were analyzed and compared. Correlation analysis was performed between the 10-min and 1-h rainfall data observed with the Parsivel, TBG, and Pluvio from 2010 to 2019 at the Cloud Physics Observation Site which is located in northeast area of South Korea (N37.6869, E128.7586). At higher rainfall intensities, the Parsivel observed more rainfall; however, the TBG lost more rainfall during observation. The correlation between the Pluvio and Parsivel data was higher than that between the TBG and Parsivel data. Additionally, the Pluvio showed reduced loss in the rainfall observation than that by the TBG. The correlation between the Pluvio and TBG data was the highest, and the coefficient of determination increased by a maximum of 42.08% for 1-h rainfall compared to that for 10-min rainfall. Therefore, the Pluvio can generate relatively accurate rainfall data for water resource utilization. [ABSTRACT FROM AUTHOR]

Based on real-time field data from tropical outdoor microcellular networks (Akure, Nigeria), this study investigates the propagation of high-capacity millimeter waves (mmWave) in the 30-100 GHz frequency range. The data covers simultaneous measurements of rainfall rate and received signal strength (RSS) over a 300-meter path length between a Communication Research Lab (CRL) in FUTA Nigeria and the National Television Authority Tower at Iju Akure Nigeria at one-minute sampling time over a year (Jan 2021-Dec 2021). The study investigates the average network availability derived from the real-time data of rain rates. It compares the distributions of the measured raininduced attenuation at various times of signal unavailability for 30 GHz with the modified anticipated attenuation by the Synthetic Storm Techniques (SST) model. Using MATLAB software and the most recent NYUSIM channel model software package (Version 3.1), an investigation of important propagation channel characteristics such as route loss, fade margin, and received power during heavy rains was also conducted. Based on convective and stratiform rain types, the focus on the propagation characteristics of a typical network provider, a Mobile Telecommunication Network (MTN), was assessed power obtained at the receiving end, signal loss along the route, and the interval of signal fading. The results show that the modified SST model provides high consistency with the measured attenuation through the outdoor links. The signal loss of about 80 dB is needed to be compensated as the rain rates approach 140 mm/hr over the 300-meter path length. The study also demonstrates that for multidirectional and oriented directional antenna links, regardless of the fading due to rain, the NYUSIM platform model provides a better representation of the power delay profile. The research outcome will provide useful information for setting up fifth-generation (5G) and other future wireless networks for outdoor applications, especially in tropical areas. [ABSTRACT FROM AUTHOR]

Because of its high spatial and temporal variability, rainfall remains one of the most challenging meteorological variables to measure accurately. Obtaining high-quality rainfall products is essential for flood monitoring, disaster warning, and weather forecasting systems, but this is not always possible on the basis of current rainfall observation networks. Innovative alternatives draw inspiration from "citizen science" and "crowdsourcing," allowing for opportunistic sensing of rainfall from existing measurements at a low cost, which has become a popular topic and is beginning to play an important role in developing rainfall observation systems. This paper reviews the current state of new rainfall observation approaches and explores their opportunities to complement more traditional ways of rainfall data collection in a hydrological context. Furthermore, the challenges of each new approach are discussed. Although these new options show great potential in enhancing the current rainfall network, they still face problems in terms of their accuracy, real-time accessibility, and limited applicability when individually employed. In contrast, the fusion of new measurements with traditional observation networks is feasible and will be effective for regional rainfall monitoring. This study also serves as an important reference in developing monitoring techniques for other environmental factors. Significance Statement: New rainfall observation techniques provide a meaningful supplement to current rainfall networks in terms of spatiotemporal resolution and accuracy. In this paper, we present a comprehensive overview of the innovations in rainfall observation and their popularity in different regions around the world. Then, the application value and future opportunities that new techniques bring to hydrological research are analyzed. It is anticipated that this paper will be of value to researchers with an interest in improving the quality of rainfall data, thus paving the way to accelerate these studies, as well as the application and implementation of their findings, to the next stage. Furthermore, we expect to prompt a rethink on utilizing and exploiting these new rainfall products to enhance our understanding and optimization of current rainfall sensing systems. [ABSTRACT FROM AUTHOR]

This study aims to derive and evaluate new empirical rainfall thresholds as the basis for landslide early warning in Progo Catchment, Indonesia, using high-resolution rainfall datasets. Although attempts have been made to determine such thresholds for regions in Indonesia, they used coarse-resolution data and fixed rainfall duration that might not reflect the characteristics of rainfall events that induced the landslides. Therefore, we evaluated gauge-adjusted global satellite mapping of precipitation (GSMaP-GNRT) and bias-corrected climate prediction center morphing method (CMORPH-CRT) hourly rainfall estimates against measurements at rainfall stations. Based on this evaluation, a minimum rainfall of 0.2 mm/h was used to identify rain events, in addition to a minimum of 24 h of consecutive no-rain to separate two rainfall events. Rainfall thresholds were determined at various levels of non-exceedance probability, using accumulated and duration of rainfall events corresponding to 213 landslide occurrences from 2012 to 2021 compiled in this study. Receiver operating characteristics (ROC) analysis showed that thresholds based on rainfall station data, GSMaP-GNRT, and CMORPH-CRT resulted in area under ROC curve values of 0.72, 0.73, and 0.64, respectively. This result indicates that the performance of high-resolution satellite-derived data is comparable to that of ground observations in the Progo Catchment. However, GSMaP-GNRT outperformed CMORPH-CRT in discriminating the occurrence/non-occurrence of landslide-triggering rainfall events. For early warning purposes, the rainfall threshold is selected based on the probability exlevel at which the threshold maximizes the true skill score, i.e., at 10% if based on station data, or at 20% if based on GSMaP-GNRT. [ABSTRACT FROM AUTHOR]

In the recent years, science and technology in urban green space have largely focused on technologies that facilitate infiltration and reduce runoff (such as rain gardens and permeable sidewalks). Trees in urban green space reduce the net rainfall by interception, and on the other hand, their extensive root systems allow them to store and direct significant amounts of water into the soil. The present study investigates the effect of rainfall amount and tree species on rainfall interception in Hashtgerd city of Alborz province during two seasons of winter 2017 and spring 2018. For this purpose, during seven rainfall events, the amount of throughfall was measured by the number of five rain gauges installed under each tree. In order to record rainfall events, a rain gage container was installed in a location that was sufficiently distant from buildings and trees, and rainfall events ranging from 2.1 to 6.8 mm were recorded. The results showed that the percentages of rainfall interception for spruce, apricot, fig, willow, walnut, and oak species were 44.6, 42.6, 36.4, 35.1, 33.6 and 30.4 percent, respectively. The results of statistical analysis showed that there is a significant difference among the values of rainfall interception in different tree species (P <0.01). Also, there is a significant difference among the rainfall interception in the rainfall classes (low (lower than 4 mm), medium (4-6 mm), and high (higher than 6 mm)) (P <0.01). Among the studied species, sparrow and apricot species have the highest rainfall interception, which it is possible to make more use of these two types in the control of runoff with urban planning. [ABSTRACT FROM AUTHOR]

The potential of satellite precipitation products (SPPs) in monitoring and mitigating hydrometeorological disasters caused by extreme rainfall events has been extensively demonstrated. However, there is a lack of comprehensive assessment regarding the performance of SPPs over the Qinghai-Tibet Plateau (QTP). Therefore, this research aimed to evaluate the effectiveness of five SPPs, including CMORPH, IMERG-Final, PERSIANN-CDR, TRMM-3B42V7, and TRMM-3B42RT, in identifying variations in the occurrence and distribution of intense precipitation occurrences across the QTP during the period from 2001 to 2015. To evaluate the effectiveness of the SPPs, a reference dataset was generated by utilizing rainfall measurements collected from 104 rainfall stations distributed across the QTP. Ten standard extreme precipitation indices (SEPIs) were the main focus of the evaluation, which encompassed parameters such as precipitation duration, amount, frequency, and intensity. The findings revealed the following: (1) Geographically, the SPPs exhibited better retrieval capability in the eastern and southern areas over the QTP, while displaying lower detection accuracy in high-altitude and arid areas. Among the five SPPs, IMERG-Final outperformed the others, demonstrating the smallest inversion error and the highest correlation. (2) In terms of capturing annual and seasonal time series, IMERG-Final performs better than other products, followed by TRMM-3B42V7. All products performed better during summer and autumn compared to spring and winter. (3) The statistical analysis revealed that IMERG-Final demonstrates exceptional performance, especially concerning indices related to precipitation amount and precipitation intensity. Moreover, it demonstrates a slight advantage in detecting the daily rainfall occurrences and occurrences of intense precipitation. On the whole, IMERG-Final's ability to accurately detect extreme precipitation events on annual, seasonal, and daily scales is superior to other products for the QTP. It was also noted that all products overestimate precipitation events to some extent, with TRMM-3B42RT being the most overestimated. [ABSTRACT FROM AUTHOR]

A novel method is developed and demonstrated for identifying the dominant component of rainfall-driven infiltration and inflow (I&I) in sanitary sewers. The method uses synoptic measurements of rainfall, sewer flow rates, and water-table elevations to extract the ratio of infiltration to total I&I as a function of rainfall amount. The method is based on three quantifiable relations: the incremental flow rates resulting from rainfall events of different magnitudes, the responses of the water-table elevations to rainfall events of various magnitudes, and the responses of dry-weather flow rates to changes in water-table elevations. Application of the method is demonstrated at 10 locations in South Florida. The results show that infiltration is the dominant mechanism for small rainfall events at five of the 10 locations, and inflow is the dominant mechanism for large rainfall events at nine of the 10 locations. [ABSTRACT FROM AUTHOR]

Accurate and high spatiotemporal resolution rainfall observations are essential for hydrological forecasting and flood management, especially in urban hydrological applications. However, it is difficult for traditional rainfall gauges, weather radars, and satellites to accurately estimate rainfall while simultaneously capturing the spatial and temporal variability of rainfall well. In this context, video‐based rainfall measurement, a novel method, has the advantages of real‐time performance and low cost and may thus provide a new way to establish rainfall observation networks with high spatial and temporal resolution. In recent years, different algorithms have been developed to recognize raindrops and estimate rainfall from rainfall videos. It has been demonstrated that video‐based rainfall measurement methods can provide comprehensive rainfall information with fine spatial and temporal granularity. However, raindrop visibility and the depth of field effects are difficult to address. The motion blur effect of raindrops may result in substantial errors and uncertainties. A fundamental problem of video‐based rainfall measurements lies in locating raindrops and accurately calculating their actual size. Moreover, the effectiveness of deep learning‐based video rainfall measurement models is greatly influenced by the diversity of the training data. Therefore, enhancing the high robustness and accuracy of video‐based rainfall measurement algorithms and increasing the computational efficiency are paramount to further development, which are prerequisites for their application in practical rainfall monitoring and developing multicamera monitoring networks. This article is categorized under:Science of Water > MethodsScience of Water > Hydrological Processes [ABSTRACT FROM AUTHOR]

Using a comparison of three different major types, the best predictive model was determined. Statistical models and machine learning algorithms automatically learn and improve based on data. Deep learning uses neural networks to learn complex data patterns and relationships. A combination of satellite imagery, radar data, and ground-based observations are used and using aircraft or satellites, and remote sensing (RS) collects data on distant objects or locations. Satellites and radar are used to gather regional precipitation data for hybrid models. An algorithm trained on historical rainfall measurements would then process the data. Using remote monitoring instrument input features, the machine-learning model can predict precipitation. Evaluation of machine learning regression methods is based on the degree of agreement between predicted and observed values. The RMSE, R2, and MAE statistical measures check on the precision of a prediction or forecasting model. Machine learning excels at rainfall prediction regardless of climate or timescale. As one of the more popular models for predicting rainfall, the LSTM models demonstrate their superiority. Remote sensing and hybrid predictive models should be investigated further due to their scarcity. [ABSTRACT FROM AUTHOR]

Commercial microwave link (CML) has become one of the most widely used opportunistic sensors for rainfall monitoring. The high density and coverage of CMLs enable us to monitor near‐ground rainfall in high spatial resolution. The empirical power‐law (PL) model proposed in 1978 is the leading approach in relating CML attenuation with rain rate. However, while giving good rain rate estimates for CMLs longer than 1 km, in short CMLs, large errors are observed when the PL approach is applied. In this paper, we studied a statistical approach to convert CML attenuation measurement to rainfall intensity. The proposed approach calculates the exceedance probability, p, of rain‐induced attenuation and derives the corresponding rainfall intensity based on the rain rate cumulative distribution curve. Data from two cities in two countries were used to validate the approach. The results of the proposed approach were compared with that of the PL model. Results show that the models derived using our approach outperform PL for short CMLs, while showing similar performance for long ones. Since in the next generation mobile networks short CMLs will become more and more dominant, our work provides a way to derive retrieval models for the future generation CML networks. Plain Language Summary: Using commercial microwave link (CML) network for rainfall monitoring can provide us data in high spatial‐temporal resolution. However, the most widely used retrieval model for CML measurements may result in overestimation when applied to short links. To address this issue, this paper investigates an alternative approach to retrieve rainfall intensity from CML measurements, which also demonstrates favorable performance when applied to short links. As short links are expected to become increasingly prevalent in the next generation of mobile networks, their usage can greatly enhance the spatial resolution and accuracy of rainfall monitoring outcomes. Key Points: Investigating a statistical approach to retrieve rainfall intensity from commercial microwave link (CML) dataThe statistical models show better performance for short CMLsParameters of the statistical model can be transferred from other regions [ABSTRACT FROM AUTHOR]

The most reliable areal precipitation estimation is usually generated via combinations of different measurements and devices by merging their individual advantages. Path-averaged rain rate can be derived from Commercial Microwave Links (CML), where attenuation of the emitted radiation is strongly related with rainfall rate. CMLs can be combined with data from other rainfall measurements or used individually. They are available almost worldwide and often represent the only opportunity of ground-based measurement in data scarce regions. Deriving rainfall estimates from CML data requires extensive data processing, though. The separation of the attenuation time series in rainy and dry periods (rain event detection) is the most important step in this processing and largely determines the quality of the resulting rainfall estimates. In this study, we investigate the suitability of Meteosat Second Generation Spinning Enhanced Visible and InfraRed Imager (MSG SEVIRI) satellite data as an auxiliary-data-based (ADB) rain event detection method. We compare this method with two time series-based (TSB) rain event detection methods. The investigation uses data from 3901 CMLs in Germany for four months in summer 2021 and is carried out for the two SEVIRI-derived products PC and PC-Ph. We analyse all rain event detection methods for different precipitation intensity, differences between day and night, as well as their influence on the performance of rainfall estimates from individual CMLs. The radar product RADKLIM-YW is used for validation. The results show that both SEVIRI products are promising candidates for ADB rainfall detection methods and led to at least equivalent results as the TSB methods. The main uncertainty of all methods was found for light rain. Slightly better results were obtained during the day than at night, which is caused by dew formation on CML antennas and the reduced availability of SEVIRI channels at night. In general, the ADB methods lead to improvements for CMLs performing comparatively weakly using TSB methods. Based on these results, combinations of ADB and TSB methods were developed by emphasizing their specific advantages. Compared to basic and advanced TSB methods, these combinations were able to improve the Matthews Correlation Coefficient of the rain event detection from 0.53 (0.57 resp.) to 0.62 during the day and from 0.47 (0.55 resp.) to 0.6 during the night. Our results show that utilising MSG SEVIRI data in CML data processing significantly increases the quality of the rain event detection step, in particular for CMLs which are challenging to process with TSB methods. [ABSTRACT FROM AUTHOR]

Hydrometeorological monitoring and continuous data collection in ungauged mountainous regions are exciting and challenging for water resource planners compared to the measurement in plain areas. Lesser Himalayas in the mountainous areas face the insufficiency of continuous hydrometeorological data, hindering our understanding of hydrological processes and hampering integrated water resources management. This present study focuses on the setup of the field instruments for collecting hydrometeorological data and analyzing continuously collected data at Aglar watershed to assess hydrometeorological parameters' spatial and temporal distribution. The instrumentation includes monitoring one sub-surface flow, five stream flows, four rain gauges, and one automatic weather station. The relationship between the stage and the discharge was established based on the collected data for three streams. The analyzed seasonal rainfall revealed 726.7 mm of rain occurred during the monsoon with an intensity of less than 16 mm/day. The Paligaad sub-watershed displayed a flashy response towards the rainfall events, whereas the Upper Aglar exhibited a wide range of dampening runoff responses for different rainfall events. The monitored sub-surface flow varies annually, and during the monsoon season, interflow and baseflow hydrograph decayed more rapidly at the rate of 0.04 day−1 and 0.78 day−1, respectively. The installed AWS has been used to measure crop water requirements and plan for better strategies to cope with future food and water security. The high-frequency generated data will help answer the queries related to hydrological responses of different watershed characteristics. [ABSTRACT FROM AUTHOR]

Drought assessment is important to minimize its negative consequences and design appropriate management strategies. However, in situ rainfall measurement networks are scarce and unevenly distributed in the majority of watersheds in Ethiopia to assess drought. Performance-evaluated satellite rainfall estimates (SREs) could be an alternative data source in this regard. This study assessed the performance of SREs, namely Climate Hazards Group InfraRed Precipitation with Stations version 2 (CHIRPS), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System-Climate Data Record (PERSIANN-CCS-CDR) and Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) in Omo-Gibe basin. Point-to-pixel pairwise comparison using statistical indices such as Pearson correlation coefficient (r), mean error (ME), root mean square error (RMSE), and bias was used to evaluate the SREs for the 2000 to 2019 study periods. The best SRE was identified and Standardized Precipitation Index (SPI) was used to assess meteorological drought. The CHIRPS-based SPI was compared with station-based SPI and further compared with MODIS-derived vegetation drought indices. The results showed 'better' performance of CHIRPS with higher r and lower ME, and bias, and acceptable RMSE values at monthly, seasonally and annual timescales, and its meteorological drought detection capability. Consequently, the CHIRPS rainfall product was used as input data to assess the spatiotemporal meteorological drought for 3- and 12-month periods. The results showed that 2000, 2002, 2009, and 2015 were identified as drought years where the central and southern parts of the basin experienced severe to extreme drought. The 3-month SPI drought index was correlated with drought severity index (DSI) and Vegetation Condition Index (VCI) with R2 value of 0.4 and 0.5, respectively. In the next 100 years, 3-month drought occurrences will be expected in the basin with varying intensities. Henceforth, the CHIRPS rainfall product can be used as an alternative rainfall data source in developing grid-based drought monitoring tools for data-scarce basins, which could assist in creating early warning systems. [ABSTRACT FROM AUTHOR]

As wildfire increases in the western United States, so do postfire debris-flow hazards. The U.S. Geological Survey (USGS) has developed two separate models to estimate (1) rainfall intensity thresholds for postfire debris flow initiation and (2) debris-flow volumes. However, the information necessary to test the accuracy of these models is seldom available. Here, we studied how well these models performed over a two-year period in the 2020 Grizzly Creek Fire burn perimeter in Glenwood Canyon, Colorado, USA, through the development of a debris flow response inventory. The study area had the advantage of a network of 11 rain gauges for rainfall intensity measurements and repeat lidar data for volume estimates. Our observations showed that 89 % of observed debris flows in the first year postfire were triggered by rainfall rates higher than the fire-wide rainfall threshold produced by the current USGS operational model (M1). No debris flows were observed in the second year postfire, despite eight rainstorms with intensities higher than the modeled rainfall threshold. We found that the operational model for debris flow initiation rainfall thresholds works well in this region during the first year but may be too conservative in year 2 due to vegetation recovery and sediment exhaustion. However, rainfall thresholds in the second year can be improved by using updated remote sensing imagery to recalculate the debris-flow initiation probability with the M1 model. The current volume model overpredicts for this region by a median value of 4.4 times. However, the offset between the predictions and observations is linear, and the volumes from the Grizzly Creek debris flows had a similar magnitude to historic postfire debris flows in the region. Consequently, the current volume model could be adjusted with a regional correction factor. [ABSTRACT FROM AUTHOR]

Based on the hourly rainfall gauge data and ERA5 reanalysis for the period 1980–2020, typical synoptic patterns responsible for summer regional hourly extreme precipitation events (RHEPE) over the middle and lower Yangtze River basin have been objectively identified using a circulation clustering method. It is found that the Meiyu front with different locations and intensities imbedded in the East Asian summer monsoon, and landfalling typhoons are the leading contributors. As the dominant synoptic pattern, the Meiyu front pattern is associated with ∼92% of the total RHEPE occurrence and can be categorized into a southerly strong‐Meiyu type and a northerly weak‐Meiyu type. The RHEPE occurrence shows a predominant morning peak associated with the southerly strong‐Meiyu type and a secondary late afternoon peak related to the northerly weak‐Meiyu type, in which the Meiyu front is pushed northward by the strengthened western North Pacific subtropical high accompanied by accelerated low‐level southwesterly flow. Plain Language Summary: Using ERA5 reanalysis and hourly gauge rainfall measurements in summers of 1980–2020, this study investigates the driving mechanisms and temporal variation of summer regional hourly extreme rainfall events over the middle and lower Yangtze River basin. Typical synoptic patterns responsible for the summer regional hourly rainfall extremes are objectively identified using spectral clustering analysis. The Meiyu front with different locations and intensities imbedded in East Asian summer monsoon and the landfalling typhoons are the synoptic patterns leading to the regional hourly rainfall extremes. The diurnal twin peaks (morning and late afternoon) in the occurrence of regional hourly rainfall extremes are related to the Meiyu front with different locations and intensities. The results from this investigation may help improve the prediction and climate risk assessment of regional extreme rainfall events. Key Points: The Meiyu front imbedded in the East Asian summer monsoon and landfalling typhoons are the leading contributors of summer regional hourly extreme precipitation events (RHEPE) over middle and lower Yangtze River basinThe Meiyu front pattern can be sorted into a southerly type with strong Meiyu front and a northerly type with weak Meiyu frontThe northerly weak Meiyu front pattern with active convection contributes the most to the afternoon diurnal peak of RHEPE occurrence [ABSTRACT FROM AUTHOR]

Continuous measurements of rainfall parameters are required for radio planning and flood management among others. However, due to the dearth of requisite infrastructure and expertise within the region, it is usually necessary to use satellite and reanalysis data. In this study, the performance of ECMWF ERA5 data in providing fall velocity (W) and liquid water content (LWC) estimates at different altitudes was compared with measurements from a ground-based Micro Rain Radar in a tropical location. Results obtained showed correlation values of 0.60–0.94 and 0.16–0.93 for fall velocity and liquid water content, respectively. The performance of ERA5 was found to decrease with altitude. In general, the results show the sufficient reliability of the ERA-5 reanalysis data for the two parameters on vertical scales. Therefore, it can be inferred that the ERA-5 data are reliable in estimating the average LWC and W, especially on a vertical scale for both hydrological and radio wave propagation studies. [ABSTRACT FROM AUTHOR]

Asymbiotic nitrogen fixation (N‐fixation) is a microbial process that may result in the introduction of plant‐available N into the soil. Although the process of N mineralization in soils is mediated by many microorganisms and is related mostly to soil moisture, the soil factors which regulate asymbiotic N‐fixation are relatively unknown and the number of microorganisms with the ability to fix N is small relative to the whole soil microorganism pool. Soils under long‐term no‐till management have greater asymbiotic N‐fixing organism activity compared to no‐till. This study was conducted to determine the variance of N‐fixing activity over distance, and the temporal variability of asymbiotic N‐fixing organism activity at six sites with transitional no‐till soils in eastern North Dakota over three growing seasons. Sites were sampled at the same location each month of the growing season from 2019 to 2021. At one of the sampling dates, each year additional samples were obtained at distances from the central sampling location for use in statistical analysis. The sampling over distance indicated that to characterize a large field area, multiple samples should be taken and analyzed separately or mixed together for single analysis. Monthly sampling in all 3 years indicated that peak N‐fixation activity was favored by a moist, warm environment. Dry periods and excessively wet periods resulted in low activity. Heavy rains within 48 h of sampling resulted in extremely low N‐fixation activity at the subsequent sampling date. Models were constructed relating the accumulative rainfall from the 30 days prior to sampling, and the mean air temperature from the 30 days prior to sampling. These data indicate that the activity of asymbiotic N‐fixing organisms increased with temperature and cumulative rainfall prior to measurement. Core Ideas: Asymbiotic N‐fixing organism activity is variable over sampling locations.Asymbiotic N‐fixation was low in early spring after thaw (mid‐April to late April).Asymbiotic N‐fixation activity was favored by moist, warm soil.Asymbiotic N‐fixation activity was lowest during dry or excessively wet conditions. [ABSTRACT FROM AUTHOR]

Currently, the opportunistic method to estimate rainfall using commercial microwave links (CMLs) has been shown as an efficient way to complement traditional instruments in terms of spatial‐temporal resolution and coverage. In this paper, we collected data from 26 CMLs in Jiangyin City, Jiangsu Province, and conducted experiments on rainfall field reconstruction and nowcasting. First, the raw CML data were processed to invert the path‐averaged rainfall intensity. Second, the algorithms of inverse distance weighting (IDW) and ordinary kriging (OK) interpolation were employed to reconstruct the rainfall field. Then a 10‐min prediction of the rainfall field was achieved using a nowcasting model based on the long short‐term memory neural network and a setup window was introduced to improve the prediction performance of the first few minutes. The reconstruction results show that the average correlation coefficient (ACC) and the average root mean square error (ARMSE) between the IDW‐based results and daily cumulative rainfall from rain gauges (RGs) are 0.89 and 8.69 mm, respectively, while the ACC and ARMSE between the OK‐based results and RG data are 0.89 and 9.13 mm, respectively. The nowcasting results show that the ACC between the prediction results with a 5‐min setup window and the IDW‐retrieved rainfall fields can reach 0.91 at the first minute and gradually decrease to 0.20 within 10 min. Furthermore, the model has better nowcasting performance for stratiform precipitation and mixed precipitation compared to convective precipitation. Plain Language Summary: Accurate and real‐time rainfall monitoring and forecasting are of great significance for disaster prevention and control, agriculture, meteorological research, and related fields. However, traditional rainfall measurement methods are insufficient for meeting the demands of comprehensive precipitation observations because of poor spatial‐temporal resolution and limited coverage. Currently, measuring rainfall using additional microwave attenuation caused by raindrops has been shown as an efficient way to complement traditional instruments. Based on commercial microwave link (CML) rainfall measurement technology, this paper carries out rainfall field reconstruction and nowcasting experiments in Jiangyin City, China. The results show the CML network enables accurate rainfall field reconstruction and nowcasting. Key Points: Based on the commercial microwave link (CML) rainfall monitoring network in Jiangyin, China, accurate rainfall inversion by CMLs is achievedThe two‐dimensional rainfall fields are accurately reconstructed using the inverse distance weighting and ordinary kriging interpolation algorithmsA long short‐term memory‐based rainfall field nowcasting model is proposed to achieve 10‐min continuous predictions of rainfall fields [ABSTRACT FROM AUTHOR]