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Makkah region is one of the most flash flood-prone areas of Saudi Arabia due to terrain characteristics and the synoptic-scale weather conditions that intensify through interaction with the local topography causing high convective short-lived rainfall events, although these conditions are quite infrequent. Most of these events last for less than two hours. This study aims to assess the performance of five satellite precipitation products over a 1725 km2 sparsely gauged, arid basin. A fully distributed, physically based hydrologic model was forced by the five satellite precipitation products, and the evaluation included the hydrographs and runoff maps predicted by the model. Moreover, the propagation of the satellite rainfall errors into runoff predictions was quantified. Large variations and significant biases were found in satellites precipitation estimates compared to the available ground rainfall measurements. The Early IMERG product showed the best agreement with the reported total rainfall accumulations followed by Late IMERG while the other products significantly underestimated precipitation accumulations. Comparison with estimated runoff peaks showed that the Early IMERG product has the lowest errors in runoff peaks. Therefore, the hydrographs produced by the Early IMERG product were used as a reference to quantify the propagation of satellite precipitation errors into runoff predictions over the Makkah watershed. The results clearly indicated that both systematic and random rainfall errors were significantly amplified in runoff predictions. [ABSTRACT FROM AUTHOR]

In 2018-2021, several natural phenomena occurred that caused climate change in Indonesia. This climate change is estimated to have affected the dynamics of rainfall and temperature in Indonesia. This study aims to analyze the incidence of climate change, rainfall dynamics, and temperature dynamics, and find the correlation between rainfall versus temperature during the 2018-2021 dry season. The location of this research is on peatlands in Central Kalimantan and West Kalimantan where both locations have installed automatic measurement stations for rainfall and temperature. Peatlands were chosen because the dynamics of rainfall and temperature greatly affect the condition of peatlands that are prone to fires and floods. The results of this study indicate that very minimal rainfall occurs in the dry season in 2019, especially from July to September 2019. Rainfall in the 2019 dry season is much lower when compared to the dry season in 2018, 2020, and 2021. This happens because in 2019 two natural phenomena occurred simultaneously, namely: moderate IOD+ and weak El Niño. These two phenomena reinforce each other in reducing rainfall in Indonesia. The temperature during the dry season for 4 years did not show a significant difference, but in general, the temperature in Central Kalimantan was lower than the temperature in West Kalimantan. It was also found that there was a trend in the relationship between rainfall and temperature where the higher the rainfall, the lower the temperature. Based on the results of the statistical method of linear regression and t-test, it has been found that the correlation between rainfall and temperature is significant. [ABSTRACT FROM AUTHOR]

Droughts in winter and spring are one of the most prominent natural disasters in the Yunnan Province in China. They occur frequently, with long durations and have a wide range of damage, which has a serious impact on social and economic development, as well as agricultural production and, therefore, strongly impacts the lives of the people living in the region. The traditional drought monitoring model does not take terrain into consideration, thereby affecting the comparative nature of results, as baseline conditions are not the same. Therefore, this study proposed a comprehensive drought monitoring model considering the influence of terrain factors to improve the evaluation effect. Firstly, based on NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) and Tropical Rainfall Measurement Mission (TRMM 3B43) data, vegetation condition index (VCI), temperature condition index (TCI), precipitation condition index (TRCI), and three terrain factors ground elevation (DEM), slope (SLOPE), aspect (ASPECT) were selected as model parameters. Then, a comprehensive drought monitoring model without considering terrain factors (Model A) and a comprehensive drought monitoring model of considering terrain factors (Model B) were constructed by using multiple linear regression models. Finally, the effects of the two models were evaluated by using standardized precipitation evapotranspiration index (SPEI) in southwest Yunnan Province, China, and model B was used to analyze the drought in winter and spring in the study area from 2008 to 2019. The results showed that (1) the correlation coefficient of model B was higher than that of model A in winter and spring and the standard error of model B was lower than that of model A. (2) The grade consistency rate of Model A and SPEI was 0.92 in winter and 0.33 in spring; the grade consistency between model B and SPEI was 0.83 in winter and 0.75 in spring, and therefore the monitoring effect of model B was more stable. (3) There were periodic droughts during the study period, and the degree of drought in spring was less than in winter. Medium and severe droughts were observed in winter. Thus, this study concluded that the effect of terrain has an important influence on the evaluation of droughts. The comprehensive drought monitoring model which considers topographic factors can effectively identify the occurrence of drought, and therefore provide significant input with regards to disaster prevention and mitigation policies in southwest Yunnan. [ABSTRACT FROM AUTHOR]

Contemporary challenges in the management of stormwater and modelling of rainfall--runoff processes (in urban areas in particular) require the use of rainfall-estimation devices more advanced than rain gauges. One such device is the laser disdrometer, which allows (alongside radar reflectivity) for measurement of the intensity of rainfall of high temporal resolution and an accuracy greater than that available using rain gauges. On the other hand, meteorological radar makes it possible to estimate rainfall with a high degree of spatial resolution. The disadvantage of radar observations is the inaccuracy of the rainfall data obtained. Measurements of atmospheric precipitation conducted at the WULS-SGGW Meteorological Station in Warsaw in the years 2012-2014 and 2019-2020, using a tipping-bucket rain gauge and the laser disdrometer (Parsivel), were combined with data obtained from the meteorological radar in Legionowo (in the C-band), with this allowing data to be collected to allow for assessment of the usefulness of the disdrometer and radar where the estimation of rainfall is concerned. The two instruments have independent systems by which to record precipitation data, ensuring a mutual time shift. This made temporary synchronisation a necessity. The data for the entire study period were used in analysing correlations between 24-hour rainfall depths estimated on the basis of the rain gauge, on the one hand; and the disdrometer on the other. The correlation coefficient R obtained was equal to 0.87. However, the total amount of rainfall calculated on the basis of the data from the disdrometer was about 40% greater than the corresponding value from the rain gauge. From the dataset for the years 2012-2014, 21 individual events were selected for further analysis, with these being ones for which radar-estimated rainfall data in the form of a PAC hydrological product generated by the system belonging to Poland's Institute of Meteorology and Water Management (IMGW-PIB) were also available. The data measured using the rain gauge and the disdrometer were characterised by a high time resolution, of 1 min. The rainfall-intensity values obtained from the PAC product had a temporal resolution of 10 min and a spatial resolution of 1 km. The rainfall data from the disdrometer and radar were then used in analysing the correlations between these and corresponding measurements made by rain-gauge. The mean and median values of the R correlation coefficient, obtained in these analyses on the basis of rainfall-intensity values averaged over 10-min time intervals (though observed at the basic 1-min resolution) were, when estimated using the disdrometer, of 0.98 and 0.99 respectively. Correlations based around rainfall-intensity values at the 1-min level of resolution only assumed lower values. The adequate values of the R coefficient, as determined for the radar data, were of 0.68 and 0.77 respectively. The study also extended to include comparison of total values estimated for 21 individual rainfall events using the disdrometer and radar (the PAC hydrological product), as compared with data measured using the rain gauge. The analysed values from the disdrometer were greater than the corresponding rain-gauge values for almost all events, while those obtained using radar were lower in most cases. The mean and median values of the relative error, obtained in relation to the values of rainfall totals measured using the disdrometer, were 35.2 and 38.3% respectively. The relative error values, obtained in adequate analysis based around data from the PAC radar product, proved to be much higher, and amounted to 49.1 and 59.1% respectively. This analysis therefore made it clear how disdrometer- and radar-based data require prior correction before any potential use can be made of them, e.g. in hydrological analyses. This paper's simple method of adjusting the heights of rainfall estimated on the basis of the disdrometer at specific (assumed 10-min) time intervals during the event was able to achieve a significant reduction of differences in the total rainfall values for single events, as supplied by data from the disdrometer and the rain gauge. In regard to the adjusted data from the disdrometer, the mean and median values for relative error were of 15.5 and 17.6% respectively, in respect of the 12 rainfall events used to verify the method. [ABSTRACT FROM AUTHOR]

This study of the Quantitative Estimation Precipitation (QEP) of rainfall, detected by two Meteorology Radars over Chi Basin, North-east Thailand, used data from the Thai Meteorological Department (TMD). The rainfall data from 129 rain gauge stations in the Chi Basin area, covering a period of two years, was also used. The study methodology consists of: firstly, deriving the QPE between radar and rainfall based on meteorological observations using the Marshall Palmer Stratiform, the Summer Deep Convection, and Regression Model and calibrating with rain gauge station data; secondly, Bias Correction using statistical method; thirdly, determining spatial variation using three methods, namely Kriging, Inverse Distance Weight (IDW), and the Minimum Curvature Method. The results of the study demonstrated the accuracy of estimating precipitation using meteorological radar. Estimated precipitation compared against an equivalent of 2 years of rain station measurement had a probability of detection (POD) of 0.927, where a value of 1 indicated perfect agreement, demonstrating the effectiveness of the method used to calibrate the radar data. The bias correction method gave high accuracy compared with measured rainfall. Furthermore, of the spatial estimation of rainfall methods, the Kriging methodology showed the best fit between estimation of rainfall distribution and measured rainfall distribution. Therefore, the results of this study showed that the rainfall estimation, using data from a meteorology radar, has good accuracy and can be useful, especially in areas where it is not possible to install and operate rainfall measurement stations, such as in heavily forested areas and/or in steep terrain. Additionally, good accuracy rainfall data derived from radar data can be integrated with other data used for water management and natural disasters for applications to reduce economic losses, as well as losses of life and property. [ABSTRACT FROM AUTHOR]

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Accurate spatiotemporal precipitation quantification is a crucial prerequisite for hydrological analyses. The optimal reconstruction of the spatial distribution, that is, the rainfall patterns, is particularly challenging. In this study, we reconstructed spatial rainfall on a countrywide scale for Germany by combining commercial microwave link and rain gauge observations for a better representation of the variability and spatial structure of rainfall. We further developed and applied the Random‐Mixing‐Whittaker‐Shannon method, enabling the stochastic reconstruction of ensembles of spatial fields via linear combinations of unconditional random fields. The pattern of rainfall objects is evaluated by three performance characteristics, that is, ensemble Structure‐, Amplitude‐, and Location‐error. Precipitation estimates obtained are in good agreement with the gauge‐adjusted weather radar product RADOLAN‐RW of the German Weather Service (DWD) which was used as a reference. Compared to reconstructions by Ordinary Kriging, Random Mixing showed clear advantages in the pattern representation via a five times smaller median structure error. Plain Language Summary: Rainfall is commonly measured by dedicated sensors such as rain gauges or weather radars. Commercial microwave links (CMLs), which have the primary purpose of signal forwarding within cellular networks, can be used for rainfall measurements too. The signal, which is transmitted from one antenna to another, is being attenuated if it rains along the path. From the amount of attenuation an average rain rate can be retrieved. For many hydrological applications, it is of major interest to estimate area‐wide rainfall (i.e., rainfall maps) while observations provide only scattered information. In this study, we used the local information from almost 1,000 rain gauges and the information along the paths of 3,900 CMLs distributed over Germany to reconstruct rainfall maps. We did this by applying a method of stochastic simulation (called Random Mixing) which we compared to a more common method of estimation (Ordinary Kriging). To evaluate the quality of the obtained maps, we compared them to rainfall information from weather radars. We found that the general agreement is high, and that maps reconstructed by Random Mixing have particular advantages in representing the spatial structure, that is, the shape of rainfall cells. Key Points: Geostatistical Random Mixing simulation now capable of countrywide spatial rainfall interpolationVariability assessment via commercial microwave link path consideration and ensemble estimationRealistic rainfall pattern representation quantified by ensemble Structure‐, Amplitude‐, and Location‐error metrics [ABSTRACT FROM AUTHOR]

Wildfire is a global phenomenon that has dramatic effects on erosion and flood potential. On steep slopes, burned areas are more likely to experience significant overland flow during heavy rainfall leading to post fire debris flows (PFDFs). Previous work establishes methods for PFDF hazard assessment, often relying on regional‐scale parameterizations with in‐situ rainfall measurements to categorize hazard as a function of meteorological and surface properties. We present a globally scalable approach to extend the benefit these models provide to new areas. Our new model relies on publicly available satellite‐based inputs with a global extent to provide first order hazard assessments of recently burned areas. Our results show it is possible to identify the conditions relevant for PFDF‐initiation processes across a variety of physiographic settings. Improvements to satellite‐borne rainfall intensity data and increased availability of PFDF occurrence data worldwide are expected to enhance model skill and applicability further. Plain Language Summary: Wildfires are destructive hazards whose subsequent effects can be long lasting. This includes the increased risk of flooding and post fire debris flows (PFDFs). We present a framework that uses freely available satellite‐based data to inform where PFDF activity may be elevated. We use a standardized set of resources available globally such that it is easier to make direct comparisons between regions. This work lays the groundwork to more thoroughly investigate why PFDFs are more common in some regions over others, as well as demonstrate the use of satellite data for timely assessments of the hazards that follow wildfire. Key Points: We introduce a framework for post fire debris flow (PFDF) hazard assessments using publicly available, global‐scale inputsOur methods include the first study of satellite derived rainfall data for estimating PFDF initiation thresholdsModel results demonstrate a promising approach for providing situational awareness of PFDF hazards in near real‐time at the global scale [ABSTRACT FROM AUTHOR]

Differentiating local‐scale precipitation (LP) and nonlocal‐scale precipitation (NLP) and understanding their corresponding prestorm environment are of importance for accurate severe weather analysis and forecasting. However, the difference in the transient prestorm environments of LP and NLP is largely undetermined, thereby fundamentally limiting the predictability of convective storms. The present study focuses on the precursor signals of LP and their differences from those of NLP based on explicit observational analyses using high‐resolution radiosonde measurements from the China radiosonde network with a vertical resolution of 5‐m, combined with 1‐min rain gauge data from the China rain gauge network during 2013–2020. LP and NLP can be recognized by most proximity sounding parameters. For example, the upcoming LP is characterized by larger most‐unstable convective available potential energy (MUCAPE), K index, and total precipitable water (TPW), and a lower lifting condensation level (LCL), compared to the NLP scenario. By taking proximity sounding parameters as inputs and precipitation types as learning targets, a nu‐support vector machine algorithm can effectively predict LP or NLP events, achieving an overall precision of 97%. However, the precision significantly drops by approximately 20% after removing the variable of low‐level wind shear, indicating the crucial role of wind observation when predicting the LP event. Furthermore, more intensive LPs are characterized by larger MUCAPE, K index, TPW, and moist static energy, and lower LCL. Among others, MUCAPE is the most important feature, according to the gradient boosting machine algorithm. Plain Language Summary: The prestorm environment of local‐scale precipitation (LP) and nonlocal‐scale precipitation (NLP) can be substantially different. However, the difference is currently not well understood or constrained by observation. This study uses high‐resolution radiosonde measurements from the China radiosonde network with a vertical resolution of 5‐m, combined with 1‐min rain gauge data from China rain gauge network during 2013–2020, to quantify the difference. Radiosonde balloons were released at 1315 Beijing time (BJT) and provided 12 parameters that represent the prestorm environment of LP or NLP, and the matched rainfall measurements were taken from the 1320–1520 BJT period. LP and NLP can be clearly distinguished by most radiosonde parameters, confirming the fact that LP is likely triggered by local thermal convection whereas NLP could be related to medium or large‐scale circulation. Moreover, LP and NLP events can be precisely classified by the nu‐support vector machine (NuSVM) algorithm. However, the precision of NuSVM significantly drops by 20% when removing the variable of low‐level wind shear. Furthermore, based on the training result from the gradient boosting machine algorithm, the most‐unstable convective available potential energy is the most important feature for the intensity of LP. Key Points: Most proximity sounding parameters of the forthcoming local‐scale precipitation (LP) and nonlocal‐scale precipitation are strikingly differentLow‐level wind measurements are indispensable for the classification of precipitation typesThe intensity and frequency of LP largely rely on the prestorm environment of the atmosphere, particularly the convective available potential energy value [ABSTRACT FROM AUTHOR]

The declining state of municipal wastewater treatment is one of the major contributors to the many pollution challenges faced in most parts of South Africa. Escherichia coli and Chemical Oxygen Demand are used as indicators for the performance of wastewater treatment plants. Wastewater treatment plant (WWTP) efficiency challenges are associated with susceptibility to seasonal variations that alter microbial density in wastewater. This study sought to investigate the effect of rainfall on E. coli and COD in the effluent wastewater discharged from the Crocodile River, Mpumalanga Province, South Africa. To cover the spatial distribution of the pollutant in the Crocodile River, water samples were collected from 2016 to 2021 at three strategic sites. The rainfall data was acquired from the South African Weather Services from 2016 to 2021, which contains daily rainfall measurements for each sampling site. Data analysis was carried out using Microsoft Excel 2019, Seaborn package, and Python Spyder (version 3.8). The White River, which is located on the upper stream, recorded the highest COD levels of 97.941 mg/L and 120.588 mg/L in autumn and spring, respectively. Matsulu WWTP was found to have the highest E. coli concentration per milliliter (72.47 cfu/100 mL) in the spring compared to any other location or time of year. The results also indicated that each of the sampling sites recorded above 60 (cfu)/100 mL of E. coli in Kanyamazane (spring), Matsulu (summer), and White River (winter). It was noted that the rainfall is a significant predictor (p < 0.004) of E. coli. Additionally, it was discovered during the data analysis that the rainfall parameter did not significantly affect COD prediction (p > 0.634), implying that rain was not a reliable predictor of COD. [ABSTRACT FROM AUTHOR]

An effective filtering technique is required for rainfall rate measurement by weather radar. A Jensen–Shannon distance (JSD)-based thresholding filter is proposed to mitigate nonmeteorological signals, either in clear air or rain situations. This algorithm classifies range-Doppler bins into two classes, hydrometeors and nonhydrometeors, based on spectral polarimetric variable features. The result is a mask to be applied on the spectrograms. The variable selected here is the spectral co-polar correlation coefficient, available in dual-polarization and full polarimetric radars. The algorithm first does global thresholding by finding an optimized threshold value based on the averaged clear-air spectral polarimetric variable distribution. Next, classical filtering steps are carried out like a ground clutter notch filter around 0 ms−1, a mathematical morphology to fill gaps in the hydrometeor areas, and a removal of narrow Doppler power spectra. The second part of this article is the assessment of filtering techniques without ground truth. An assessment without ground truth is useful to select optimal algorithm configurations from a large solution space. Criteria of good filtering are defined both in the spectral and time domain. Based on those criteria, subjective and objective unsupervised evaluation metrics are derived, with a focus on the objective ones. Data, including clear air and rain collected from a full polarimetric Doppler X-band radar in the urban area, are used. With the proposed unsupervised evaluation metrics, the JSD-based thresholding filter is compared to two spectral polarimetric filters. Overall, the JSD-based filter performs very well considering both the subjective and the objective evaluation metrics. [ABSTRACT FROM AUTHOR]

Extreme weather events, such as typhoons, have occurred more frequently in the last few decades in the Philippines. The heavy precipitation caused by typhoons is difficult to measure with traditional instruments, such as rain gauges and ground‐based radar because these instruments have an uneven distribution in remote areas. Satellite precipitation data sets (SPDs) provide integrated spatial coverage of rainfall measurements, even for remote areas. However, the speed and direction of the wind has the interaction with terrain, which leads the uncertainty of the SPDs. This study performed sub‐daily assessments of near‐real‐time and high resolution SPDs (i.e., IMERG, GSMaP, and PERSIANN data sets) during five typhoon‐related heavy precipitation events in the Philippines, with the analysis under the impact due to wind and terrain effect. The aforementioned assessments were performed through a point‐to‐grid comparison by using continuous and volumetric statistical validation indices for the 34‐knot wind radii of the typhoons, rainfall intensity, terrain, and wind velocity effects. The results revealed that the IMERG exhibited good agreement with rain gauge measurements and exhibited high performance in detecting rainfall. The GSMaP data set overestimated the gauge observations during peak rainfall, while the IMERG and PERSIANN data sets considerably underestimated rainfall. The GSMaP exhibited the best performance for detecting heavy rainfall at high elevations, whereas IMERG exhibited the best performance for rainfall detection at low elevations. The IMERG exhibited a strong ability to detect heavy rainfall under various wind speeds. Key Points: Satellite precipitation data sets (SPDs) were evaluated during typhoon‐related heavy precipitation events in the Philippines for the first timeThe high spatial resolution SPDs (0.04° and 0.1°) were assessed in this studyThe performance of SPDs varies clearly with terrain complexity [ABSTRACT FROM AUTHOR]

We investigate long-term changes in winter rainfall patterns across the Arabian Peninsula (AP) through an analysis of the Climate Research Unit (CRU) gridded rainfall dataset, and long-term rainfall measurements collected at 39 stations distributed across the AP over the period 1951–2010. We reveal a long-term increase in winter rainfall of about 25–30% over the eastern AP and a long-term decrease of about 10–20% in the southern and northeastern AP. A partial correlation analysis suggests that canonical El Niños are associated with significant negative winter rainfall anomalies in the southern and southwest AP during the 1951–1980 period. However, the extent of the El Niño-induced rainfall deficit decreased in subsequent decades. In fact, a significant above-average rainfall occurs in recent decades over Ethiopia, southwest Yemen and central AP during canonical El Niños. Furthermore, positive phases of the Indian Ocean basin mode (IOBM), which lags the canonical ENSO signal by 3–4 months, are linked with significant below-average winter rainfall over the central and northern AP, but only until the 1970 s. We investigated the teleconnections between the variability of AP winter rainfall and various atmospheric parameters from the European Centre for Medium Range Weather Forecasting (ECMWF) twentieth century (ERA-20C) reanalysis. Notably, sub-tropical westerly jet (STJ) shifted southward and intensified over the AP during recent decades. This shift of the STJ favoured an increase in the frequent passage of transients, which contributed to increased winter rainfall over AP. These events anomalously strengthen the upper level westerlies during El Niño Modokis, adding to the recently-strengthened STJ over the AP, thereby further intensifying the transient activity. This large-scale background change likely weakened the impact of canonical El Niño and the IOBM events. [ABSTRACT FROM AUTHOR]

Studies of tree water source partitioning have primarily focused on the growing season. However, little is yet known about the source of transpiration before, during, and after snowmelt when trees rehydrate and recommence transpiration in the spring. This study investigates tree water use during spring snowmelt following tree's winter stem shrinkage. We document the source of transpiration of three boreal forest tree species—Pinus banksiana, Picea mariana, and Larix laricina—by combining observations of weekly isotopic signatures (δ18O and δ2H) of xylem, soil water, rainfall and snowmelt with measurements of soil moisture dynamics, snow depth and high‐resolution temporal measurements of stem radius changes and sap flow. Our data shows that the onset of stem rehydration and transpiration overlaps with snowmelt for evergreens. During rehydration and transpiration onset, xylem water at the canopy reflected a constant pre‐melt isotopic signature likely showing late fall conditions. As snowmelt infiltrates the soil and recharges the soil matrix, soil water shows a rapid isotopic shift to depleted‐snowmelt water values. While there was an overlap between snowmelt and transpiration timing, xylem and soil water isotopic values did not overlap during transpiration onset. Our data showed 1–2‐week delay in the shift in xylem water from pre‐melt to clear snowmelt‐depleted water signatures in evergreen species. This delay appears to be controlled by tree water transit time that was in the order of 9–18 days. Our study shows that snowmelt is a key source for stem rehydration and transpiration in the boreal forest during spring onset. Plain Language Summary: Are trees thirsty for snowmelt when they wake up in the spring? Most studies have investigated the source of transpiration in the summer. Here we investigate the water source for transpiration prior, during and after snowmelt in the boreal forest. Our data shows that when the snow melts and recharges soil water storage, trees also refill internal water stores and rehydrate. Evergreen species start to transpire during snowmelt. While there is a clear overlap between snowmelt and when trees begin to transpire, the tracer data does not show the same. The tracer signature in the canopy did not reflect the snowmelt soil‐water signature until one or 2 weeks after the onset of transpiration. Our transpiration age estimates suggest that this mismatch in tracer signature between trees and soil results from the time it takes for water to travel inside the trees, from roots to canopy. We conclude that trees use snowmelt water to rehydrate and start transpiring in the spring. Our findings shed light on ecohydrological investigations and highlight the importance of snowmelt water input to boreal forest spring onset and carbon uptake. Key Points: Both the onset of stem rehydration and transpiration in evergreen species overlap the spring snowmelt, while only rehydration for deciduousTrees used snowmelt water during stem rehydration and the onset of transpiration. Evergreen snowmelt water use was detected earlierTree water transit time explained the time lag between the xylem water shift (at canopy‐level) to snowmelt water signatures from soil water [ABSTRACT FROM AUTHOR]

Numerous early warning systems based on rainfall measurements have been designed to forecast the onset of rainfall-induced shallow landslides. However, their use over large areas poses challenges due to uncertainties related to the interaction among various controlling factors. We propose a hybrid stochastic-mechanical approach to quantify the role of the hydro-mechanical factors influencing slope stability and rank their importance. The proposed methodology relies on a physically based model of landslide triggering and a stochastic approach treating selected model parameters as correlated aleatory variables. The features of the methodology are illustrated by referencing data for Campania, an Italian region characterized by landslide-prone volcanic deposits. Synthetic regional intensity-duration (ID) thresholds are computed through Monte Carlo simulations. Several key variables are treated as aleatoric, constraining their statistical properties through available measurements. The variabilities of topographic features (e.g., slope angle), physical and hydrological properties (e.g., porosity, dry unit weight γd, and saturated hydraulic conductivity, Ks), and pre-rainstorm suction are evaluated to inspect its role on the resulting scatter of ID thresholds. We find that: (1) Ks is most significant for high-intensity storms; (2) in steep slopes, changes in pressure head greatly reduce the timescale of landslide triggering, making the system heavily reliant on initial conditions; (3) for events occurring at long failure times (gentle slopes and/or low-intensity storms), the significance of the evolving stress level (through γd) is highest. The proposed approach can be translated to other regions, expanded to encompass new aleatory variables, and combined with other hydro-mechanical triggering models. [ABSTRACT FROM AUTHOR]

The use of satellite sensors to infer rainfall measurements has become a widely used practice in recent years, but their spatial resolution usually exceeds 10 km, due to technological limitations. This poses an important constraint on its use for applications such as water resource management, index insurance evaluation or hydrological models, which require more and more detailed information. In this work, the algorithm SM2RAIN (Soil Moisture to Rain) for rainfall estimation is applied to two soil moisture products over the Po River basin: a high-resolution soil moisture product derived from Sentinel-1, named S1-RT1, characterized by 1 km spatial resolution (500 m spacing), and a 25 (12.5 km spacing) product derived from ASCAT, resampled to the same grid as S1-RT1. In order to overcome the need for calibration and to allow for its global application, a parameterized version of SM2RAIN algorithm was adopted along with the standard one. The capabilities in estimating rainfall of each obtained product were then compared, to assess both the parameterized SM2RAIN performances and the added value of Sentinel-1 high spatial resolution. The results show that good estimates of rainfall are obtainable from Sentinel-1 when considering aggregation time steps greater than 1 d, since the low temporal resolution of this sensor (from 1.5 to 4 d over Europe) prevents its application for infer daily rainfall. On average, the ASCAT-derived rainfall product performs better than S1-RT1, even if the performances are equally good when 30 d accumulated rainfall is considered (resulting in a mean Pearson correlation for the parameterized SM2RAIN product of 0.74 and 0.73, respectively). Notwithstanding this, the products obtained from Sentinel-1 outperform those from ASCAT in specific areas, like in valleys inside mountain regions and most of the plains, confirming the added value of the high-spatial-resolution information in obtaining spatially detailed rainfall. Finally, the performances of the parameterized products are similar to those obtained with the calibrated SM2RAIN algorithm, confirming the reliability of the parameterized algorithm for rainfall estimation in this area and fostering the possibility to apply SM2RAIN worldwide, even without the availability of a rainfall benchmark product. [ABSTRACT FROM AUTHOR]

There is limited understanding of how vegetation responds to intraseasonal modes of rainfall variability despite their importance in many tropical regions. We use observations of precipitation and X‐band Vegetation Optical Depth (VOD) from 2000 to 2018 to assess the relationships between rainfall and vegetation water content on 25–60‐day timescales. Cross‐spectral analysis identifies coherent intraseasonal relationships between precipitation and VOD, mostly in arid or semi‐arid regions where vegetation is water‐limited. Changes in VOD tend to lag anomalous rainfall, usually within 7 days. The fastest vegetation response is observed in sparsely vegetated areas (median 3 days). Following strong intraseasonal wet events, anomalously high VOD can persist for 2 months after the rainfall peak. This vegetation response can feed back onto the atmosphere, so improved representation of vegetation responses in models has the potential to improve subseasonal‐to‐seasonal forecasts. Plain Language Summary: It is difficult to predict temperature and rainfall more than 2 weeks in advance. Predictions at this timescale are helped by some common patterns of rainfall which cause wet and dry spells lasting from a few weeks to 2 months. Here, we use satellite measurements of rainfall and a vegetation metric that is strongly related to vegetation water content to study the relationships between these rainfall patterns and vegetation across the world. We find many regions where increased rainfall is followed by an increase in vegetation water content, usually with a delay of less than a week. This is most commonly seen in drier locations, where vegetation has a limited supply of water. After a wet spell, vegetation water content can persist above normal levels for over 2 months. This means that changes in vegetation caused by a wet or dry spell have the potential to affect rainfall in the next wet or dry spell, via changes in the energy and water transfers between the land and the atmosphere. Therefore, representing these vegetation responses correctly in models may lead to improvements in temperature and rainfall prediction weeks ahead. Key Points: Vegetation Optical Depth responds to intraseasonal precipitation variability in arid and semi‐arid regionsThe phase difference of this response is usually less than 7 days, and is shortest for sparse vegetationAfter a wet intraseasonal period, the vegetation response can persist for over 2 months [ABSTRACT FROM AUTHOR]

Changes in the frequency and morphology of convection in a warming climate have major implications for the hydrologic cycle and continue to be a large source of variability in climate models. Recent studies of model output indicate a narrowing and intensification of the ascending branch of the Hadley circulation (i.e., the Intertropical Convergence Zone). The present work uses tropical ascent area fraction (Au) and Tropical Rainfall Measurement Mission precipitation features to study global tropical variability in convection with ascent area changes in the current climate. Ascent region convection becomes more intense and organized when Au is low. Overall increases in mean precipitation as the tropical ascent regions shrink are primarily driven by an increase in the areal extent of convective systems, rather than intensity of the convective system rainfall, suggesting an increase in the organization and aggregation of convection as the climate continues to warm. Plain Language Summary: The effects of human‐caused climate change are becoming more apparent, with weather related natural disasters occurring more frequently each year. In tropical regions, these changes can have far reaching consequences through changes to a large‐scale circulation that transports heat and moisture from the equator to higher latitudes. This circulation has a region of rising air near the equator that contains storms with some of the most intense rainfall in the world, and sinking branches around 30° latitude that control the location of deserts. Climate models indicate that the region of rising air will shrink and the rainfall in this region will become more intense as the climate continues to warm. Using information from satellite observations, the present study finds that storms are more intense and organized as this region of rising air shrinks, suggesting that rain will become less frequent, but more extreme. Such changes in rainfall may lead to more extreme flooding events, which will be detrimental to agriculture that relies on consistent rainfall patterns. Understanding the behavior of storms and rainfall in this circulation can ultimately help improve prediction of future changes, which is vital to the planning of effective mitigation strategies. Key Points: Convective features become more organized and intense as tropical ascent area fraction decreasesPrecipitation increases with shrinking ascent regions are primarily due to increases in storm area, rather than frequency or rainfall ratesShort‐term ascent area‐precipitation relationship is consistent with longer records, suggesting findings may apply at longer timescales [ABSTRACT FROM AUTHOR]

There has been little systematic analysis of the influence of the three‐step staircase topography on the characteristics of precipitation along latitude 30°N in East Asia. After the distribution of atmospheric climate states over East Asia was analysed, in this study, the climatological characteristics of summer convective and stratiform precipitation on horizontal distribution, vertical structure, and diurnal variation in East Asia were investigated by using a merged dataset derived from the measurements of the Tropical Rainfall Measuring Mission Precipitation Radar and Visible and Infrared Scanner from 1998 to 2012 reanalysis datasets. Results indicate that the rain frequency, the mean rain rate, and the maximum echo reflectivity factor show clear differences among the three‐step staircase topography, especially between the first staircase and its eastern region. There are small changes in the echo‐top height, the cloud‐top height, the maximum echo reflectivity factor and its corresponding height from the East China Sea westwards to the eastern part of the first staircase, passing the third staircase and the second staircase along 30°N. However, the rain rate decreased obviously twice westwards along this latitude, which responds to aerosol indirect effect and humidity effect, respectively. The diurnal variation of the convective precipitation reflectivity factor indicated that the development of convection in the eastern part of the first staircase gets at the maximum from the early afternoon to sunset, whereas the strongest echo reflectivity factor of precipitation in the second and third staircases including the East China Sea appears in the early morning, which is consistent with the diurnal variations of the atmospheric circulation. [ABSTRACT FROM AUTHOR]

Ku-band have a larger attenuation during heavy rain in the equatorial region. Despite that, Ku-band has been identified as a frequency band in high-throughput satellite systems (HTS) for broadband satellite communication. The available rain fade prediction models are still not able to accurately predict rain attenuation in the equatorial region. The models depend on the specific attenuation parameters produced based on the international telecommunication union radiocommunication sector (ITU-R) instead of the measured value. Direct measurement of specific attenuation is more accurate but difficult to obtain because the correlation of rainfall rate and satellite signal loss due to rain must be obtained simultaneously. This paper aims to derive new specific attenuation frequency-dependent coefficients for Ku-band using the semi-empirical method. The Malaysia East Asia Satellite 3 (MEASAT-3) satellite data was collected using a 13 m antenna located at Cyberjaya, Selangor while the rainfall data was collected by the nearby hydrological station. The specific attenuation was obtained from correlations of the direct measurement of rain attenuation and rainfall rate. The new frequency-dependent coefficients for Ku-band specific attenuation values are k = 4.6690 and α = 0.1941. The newly acquired specific attenuation coefficients have improved the rain attenuation model prediction for the equatorial region. [ABSTRACT FROM AUTHOR]

• Signal attenuation due to orographic rain is analyzed at 20.2 GHz over hills. • Measured attenuation statistics are compared with ITU-R and Changsheng Model. • Ka band signal attenuation prediction model over hill region is Proposed and validated. Experimental measurement of satellite signal at 20.2 GHz using GSAT-14 satellite beacon has been carried out over the hilly tropical location Umiam, Shillong during 2017–2019. Collocated rainfall measurements have been done using a laser precipitation monitor for the same duration. The data have been analyzed to understand the signature of orographic rainfall on the Ka-band signal and to achieve the highest possible reliability of signal reception for a high rainfall tropical location. Complementary cumulative distribution of experimentally obtained rain attenuation is compared with that of conventional ITU-R model calculated rain attenuation and recently developed Chang Sheng model predicted rain attenuation values. The result shows severe overestimation of the actual measurement by both the ITU-R model and Chang Sheng model. The Chang Sheng model is further modified with the orographic adjustment factor for predicting orographic rain attenuation. The model was developed based on the data of 2017–18 and validated using the measured attenuation of the year 2019. The performance of the proposed model is evaluated in terms of RMSE and correlation coefficient. The value of the correlation coefficient has been found to be 0.988, and the RMSE value has been found to be around 3.0986, which indicates the acceptable performance of the proposed model. The proposed model is further optimized for operational elevation angle range, rain rate range, and frequency range. The proposed model will help in optimizing the power utilization in the Uplink Power Control technique for rain attenuation mitigation over high rainfall hilly regions. [ABSTRACT FROM AUTHOR]

This study aimed to analyze the available amount of water in the Dragaçina River to meet the different water needs in the Municipality of Suhareka. The water problems in this city are more pronounced, especially in the vegetation period of July-September, where the area is significantly affected by drought. The Dragacina River carries about 10 hm³ of water per year, and affected neither by urbanism nor massive deforestation of the basin. However, there are no multi-year measurements of inflows for this river, whether they are average, maximum or minimum ones. Therefore, the study is based on several multi-annual monthly rainfall measurements and some characteristics of the Dragaçina River Basin. Knowing the average annual flow coefficient η = Peff / Pbruto it is possible to convert these precipitations to Peff [mm] flow and then to monthly flow. The inputs for other years from 1983/84 onwards are obtained by simulating time series. Then, for such inflows, the probability distribution functions of small waters are assigned and the usable volume balance is carried out. Assuming an average annual withdrawal from the reservoir QAminmes. = 0.63 × Qmes. which should be constant throughout the years, then the length of the critical period will be 0.13 years or approximately 48 days, for PH = 95%. Starting from the initial acquired volume of 1 hm³ it is possible to achieve 95% < PH < 99%. Therefore, it follows from this analysis that this river can provide a significant amount of water for the needs of the Municipality of Suhareka. [ABSTRACT FROM AUTHOR]

Copyright of Hydrogeology Journal is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Climate change is bringing drastic changes to the food availability, accessibility, quality, and stability in the world. Pakistan heavily relies on production of wheat for food security. This study investigates the role of climate change on food security over Punjab regions, Pakistan, from 1979–2020. The study utilized the total production of wheat to measure food security. Moreover, the study utilized mean, maximum, minimum temperature, wind speed, and rainfall as a measurement of climate change. This study utilized auto-regressive distributed lag (ARDL) bound test cointegration approach to test the long-run cointegration, while ARDL model is applied to investigate the short-run relationship among modeled variables. This study adopted Augmented Dicky Fuller (ADF) and Phillips Parron (PP) test to check the stationarity of the data. Moreover, the study ensures reliability and validity of the model by utilizing Breusch-Godfrey serial correlation, Breusch-Pagan-Godfry HSK, and Ramsey RESET test. This study found that wheat cultivated area (0.46*), total irrigated area (2.67***), total un-irrigated area (1.93***), and total area sown ([1.0809**) have a positive and significant long-run impact on food production in all regions of Punjab Pakistan. However, while average, the maximum and minimum temperature is negatively and significantly associated with food production in all regions of Punjab (− 1.07**; − 3.33***; − 1.84**), except northern Punjab, where maximum temperature affects positively food production Punjab. Rainfall negatively and significantly affects food production in northern (− 0.34**) and central and southern (− 0.13***) Punjab. Furthermore, wind speed negatively affects food production in all regions of Punjab (− 0.22**; − 0.21*; − 0.11**), except northern Punjab. This study implies that government should develop policies to increase irrigation facilities and loan facilities to increase the total area sown that will help to increase wheat yield and ensure food security. Moreover, the government should devise policies for large-scale plantations to minimize climate change impacts. The study also suggests new improved varieties of the wheat crop that can survive and flourish in the presence of adverse climatic changes, high temperature, and high wind speed. [ABSTRACT FROM AUTHOR]

The current study presents the first attempt to investigate the November 2019 catastrophic flash flood in Olympiada (North Greece) under an operational forecasting and monitoring context, based on the mesoscale weather and research forecasting (WRF) model and the integrated multi‐satellite retrievals for global precipitation measurement (GPM‐IMERG) algorithm. When evaluated against ground‐based rainfall measurements, WRF showed an adequate predictive capability concerning the severity of the observed rainfall, even though the model's performance suffered from phase and spatial displacement errors. The GPM‐IMERG algorithm yielded the best performance in capturing the timing of the observed excessive rainfall. Concerning the hydrological outcome using the Hydrologic Engineering Center‐Hydrologic Modelling System (HEC‐HMS), a strong indication of the forthcoming flash flood could have been provided at least 2 days in advance based on the WRF‐based HEC‐HMS‐simulated flood peak (139.3 m3 s−1), as it was close to the drainage capacity of a constructed bridge in the plain stream bed, and to the 100‐year return period flood discharge. [ABSTRACT FROM AUTHOR]

As one of the most critical elements in the hydrological cycle, real-time and accurate rainfall measurement is of great significance to flood and drought disaster risk assessment and early warning. Using commercial microwave links (CMLs) to conduct rainfall measure is a promising solution due to the advantages of high spatial resolution, low implementation cost, near-surface measurement, and so on. However, because of the temporal and spatial dynamics of rainfall and the atmospheric influence, it is necessary to go through complicated signal processing steps from signal attenuation analysis of a CML to rainfall map. This article first introduces the basic principle and the revolution of CML-based rainfall measurement. Then, the article illustrates different steps of signal process in CML-based rainfall measurement, reviewing the state of the art solutions in each step. In addition, uncertainties and errors involved in each step of signal process as well as their impacts on the accuracy of rainfall measurement are analyzed. Moreover, the article also discusses how machine learning technologies facilitate CML-based rainfall measurement. Additionally, the applications of CML in monitoring phenomena other than rain and the hydrological simulation are summarized. Finally, the challenges and future directions are discussed. [ABSTRACT FROM AUTHOR]

Reliable precipitation is crucial for hydrological studies over Tibetan Plateau (TP) basins with sparsely distributed rainfall gauges. In this study, four widely used precipitation products, including the Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation of the water resources (APHRODITE), the High Asia Reanalysis (HAR), and the satellite-based precipitation estimates from Global Precipitation Measurement (GPM) and Tropical Rainfall Measurement Mission (TRMM), were comprehensively evaluated by combining statistical analysis and hydrological simulation over the Upper Brahmaputra (UB) River Basin of TP during 2001–2013. In respect to the statistical assessment, the overall performances of GPM and HAR are comparable to each other, and both are superior to the other two datasets. For hydrological assessment, both daily and monthly GPM-based streamflow simulations perform the best not only at the UB outlet with very good results, but they also illustrate satisfactory results at Yangcun and Lhasa hydrological stations within the UB. Runoff simulation using HAR only performs well at the UB outlet, whereas it shows poor results at both Yangcun and Lhasa stations. The simulated results based on APHRODITE and TRMM show poor performances at UB. Generally, the GPM shows an encouraging potential for hydro-meteorological investigation over UB, although with some bias in flood simulation. [ABSTRACT FROM AUTHOR]

More than half of the rainfall brought to the Indian subcontinent by the summer monsoon is associated with lowpressure systems (LPSs). Yet their relationship with the Boreal Summer Intraseasonal Oscillation (BSISO) - the dominant intraseasonal forcing on the monsoon - is only superficially understood. Using reanalysis data, we explore the relationship between the BSISO and LPS intensity, propagation, and precipitation, and associated underlying mechanisms. The BSISO has a large impact on mean monsoon vorticity and rainfall as it moves northward - maximising both in phases 2-3 over southern India and phases 5-6 over northern India - but a much weaker relationship with total column water vapour. We present evidence that LPS genesis also preferentially follows these phases of the BSISO. We identify significant relationships between BSISO phase and LPS precipitation and propagation: for example, during BSISO phase 5, LPSs over north India produce 51% heavier rainfall and propagate northwestward 20% more quickly. Using a combination of moisture flux 10 linearisation and quasigeostrophic theory, we show that these relationships are driven by changes to the underlying dynamics, rather than the moisture content or thermodynamic structure, of the monsoon. Using the example of LPSs over northern India during BSISO phase 5, we show that the vertical structure of anomalous vorticity can be split into contributions from the BSISO and the nonlinear response of the LPS to anomalous BSISO circulation. Complementary hypotheses emerge about the source of this nonlinear vorticity response: nonlinear frictional convergence and secondary barotropic growth. We show that both are important. The BSISO imparts greater meridional shear on the background state, supporting LPS intensification. The BSISO background and nonlinear LPS response both contribute significantly to anomalous boundary layer convergence, and we show through vortex budget arguments that the former supports additional LPS intensification in boundary layer while the latter supports faster westward propagation. This work therefore yields important insights into the scale interactions controlling one of the dominant synoptic systems contributing to rainfall during the monsoon. [ABSTRACT FROM AUTHOR]

This study aimed to determine the relationship between weather conditions as to wet and dry as recorded in the Department of Science and Technology (DOST) -- Philippine Atmospheric, Geophysical and Astronomical Services Administration (PAGASA) Weather Forecast Station and the crimes committed in Pagadian City in calendar years 2014-2018. Weather conditions as to wet and dry were correlated to crimes against person and property that was committed in Pagadain City. It employed a quantitative descriptive correlational method of research using the data of crime occurrences recorded in the Philippine National Police Pagadian City Station and the rainfall data from Department of Science and Technology (DOST) -- Philippine Atmospheric, Geophysical and Astronomical Services Administration (PAGASA) Weather Forecast Station in the city statistically treated using frequency count, mean, paired sample t-test, and chi-square test. Findings of the study revealed that physical injuries were profiled the highest in crimes against persons, followed by murder, homicide, and rape. At the same time, theft cases were dominantly registered for crimes against properties, followed by robbery, carnapping, and cattle rustling. The average distribution of murder, homicide, and physical injuries during the wet season outnumbered the cases during the dry season, except for rape cases that were higher during the dry season. Likewise, robbery, theft, carnapping, and cattle rustling were higher during the wet season than the dry season. The average distribution differences of crimes committed between the wet and the dry seasons tested at a 0.05 level of significance were not significant. The relationship between crimes against persons and the weather conditions was not significant. The relationship between crimes against properties and the weather conditions was also not significant. Moreover, crimes against persons and properties in the city do not depend on weather conditions. Murder, homicide, physical injuries, rape, robbery, theft, carnapping, cattle rustling, and the like could happen in the city regardless of whether it is wet or dry weather conditions, and there is a need to implement the crime prevention programs designed by the researcher. [ABSTRACT FROM AUTHOR]

Accurate hydrological simulation plays an important role in the research of hydrological problems; the accuracy of the watershed hydrological model is seriously affected by model-parameter uncertainty and model-input uncertainty. Thus, in this study, different calibration methods and rainfall inputs were introduced into the SWAT (Soil and Water Assessment Tool) model for watershed hydrological simulation. The Chengbi River basin, a typical karst basin in Southwest China, was selected as the target basin. The indicators of the NSE (Nash efficiency coefficient), Re (relative error) and R2 (coefficient of determination) were adopted to evaluate the model performance. The results showed that: on the monthly and daily scales, the simulated runoff with the single-site method calibrated model had the lowest NSE value of 0.681 and highest NSE value of 0.900, the simulated runoff with the multi-site method calibrated model had the lowest NSE value of 0.743 and highest NSE value of 0.953, increased correspondingly, indicating that adopting the multi-site method could reduce the parameter uncertainty and improve the simulation accuracy. Moreover, the NSE values with IMERG (Integrated Multisatellite Retrievals for Global Rainfall Measurement) satellite rainfall data were the lowest, 0.660 on the monthly scale and 0.534 on the daily scale, whereas the NSE values with fusion rainfall data processed by the GWR (geographical weighted regression) method greatly increased to 0.854 and 0.717, respectively, and the NSE values with the measured rainfall data were the highest, 0.933 and 0.740, respectively, demonstrating that the latter two rainfall inputs were more suitable sources for hydrological simulation. [ABSTRACT FROM AUTHOR]

The Tibetan Plateau (TP) vortex (TPV), one of the crucial weather systems triggering rainfall, plays a key role in modulating precipitation over TP and downstream regions. The role of atmospheric heat source in TPV development is explored by a case study in June 2016, using high-resolution ERA5 reanalysis, black-body temperature (TBB) obtained from the Fengyun-2E (FY-2E) satellite, and precipitation amount from the Tropical Rainfall Measurement Mission (TRMM). The evolutions of TPV can be split into three stages, i.e., generation, development, and pre-moving-off stage. The intensity of TPV increases with fluctuations, with weaker and shallower TPV in the generation stage, strongest in the development stage and deepest in the pre-moving-off stage. Importantly, the genesis of TPV is related to the surface warming center driven by surface sensible heating while its development is primarily dependent on the latent heat of condensation. The main contributor of the latent heat of condensation is further analyzed as a vertical transport of the water vapor that promotes TPV development. [ABSTRACT FROM AUTHOR]

Despite great advances in remote sensing technologies, accurate satellite information is sometimes challenged in tropical regions where dense vegetation prevents the instruments from retrieving reliable readings. In this work, we introduce a satellite-based landslide rainfall threshold for the country of Colombia by studying 4 years of rainfall measurements from The Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) for 346 rainfall-triggered landslide events (the dataset). We isolate the two successive rainy/dry periods leading to each landslide to create variables that simulate the dynamics of antecedent wetness and dryness. We test the performance of the derived variables (Rainfall Period 1 (PR1), Rainfall Sum 1 (RS1), Rainfall Period 2 (PR2), Rainfall Sum 2 (RS2), and Dry Period (DT)) in a logistic regression that includes three (3) static parameters (Soil Type (ST), Landcover (LC), and Slope angle). Results from the logistic model describe the influence of each variable in landslide occurrence with an accuracy of 73%. Subsequently, we use these dynamic variables to model a landslide threshold that, in the absence of satellite antecedent soil moisture data, helps describe the interactions between the dynamic variables and the slope angle. We name it the Landslide Triggering Factor—LTF. Subsequently, with a training dataset (65%) and one for testing (35%) we evaluate the LTF threshold performance and compare it to the well-known event duration (E-D) threshold. Results demonstrate that The LTF performs better than the E-D threshold for the training and testing datasets at 71% and 81% respectively. [ABSTRACT FROM AUTHOR]

High-precision retrieval of rainfall over large areas is of great importance for the research of atmospheric detection and the social life. With the rapid development of communication satellite constellations and 5G communication networks, the use of widely distributed networks of earth–space links (ESLs) and horizontal microwave links (HMLs) to retrieve rainfall over large areas has great potential for obtaining high-precision rainfall fields and complementing traditional instruments of rainfall measurement. In this paper, we carry out the research of combining multiple ESLs with HMLs to retrieve rainfall fields. Firstly, a rainfall detection network for retrieving rainfall fields is built based on the atmospheric propagation model of ESL and HML. Then, the ordinary Kriging interpolation (OK) and radial basis function (RBF) neural network are applied to the reconstruction of rainfall fields. Finally, the performance of the joint network of ESLs and HMLs to retrieve rainfall fields in the area is validated. The results show that the joint network of ESLs and HMLs based on OK algorithm and RBF neural network is capable of retrieving the distribution of rain rates in different rain cells with high accuracy, and the root mean square error (RMSE) of retrieving the rain rates of real rainfall fields is lower than 0.56 mm/h, and the correlation coefficient (CC) is higher than 0.996. In addition, the CC for retrieving stratiform rainfall and convective rainfall by the joint network of ESLs and HMLs is higher than 0.949, indicating that the characteristics of the two different types of rainfall events can be accurately monitored. [ABSTRACT FROM AUTHOR]

Selection of a best suited satellite-based gridded rainfall product (SGRP) is challenging due to their significant variations at spatial and temporal scale. The present study comprehensively evaluated the SGRPs (Climate Hazards Group Infrared Precipitation with Stations (CHIRPS), Tropical Rainfall Measuring Mission (TRMM), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), and SM2RAIN algorithm-based product) at daily scale for the period 1998–2013, with reference to gauge-based gridded rainfall product generated by Indian Meteorological Department (IMD) over the Mahanadi river basin in eastern India. Spatio-temporal evaluation of SGRPs was carried out intra-seasonally using both descriptive (event detection) and value-based approaches (coherence in measurement values). Studies that require accurate representation of rainfall event occurrence (descriptive parameter approach) results indicated that TRMM, CHIRPS, SM2RAIN, and CHIRPS gridded datasets performed better during winter, pre-monsoon, south-west monsoon, and north-east monsoon, respectively. On the contrary, studies that require precise measurements of rainfall values on a daily scale (value-based parameter approach) indicated that CHIRPS (during winter and pre-monsoon) and SM2RAIN (during south-west monsoon and north-east monsoon) may be used. Further, best performing rainfall products at seasonal scale were identified through ensembling approach. CHIRPS datasets prominently identified rainfall events across the study region during the winter, pre-monsoon, and the north-east monsoon periods. However, during the southwest monsoon period, SM2RAIN gridded dataset best represented the rainfall pattern of the study region. An ensembled gridded precipitation product is also generated incorporating the grid-level performance of various SRGPs. [ABSTRACT FROM AUTHOR]

Small urban catchments pose challenges in applying performance metrics when comparing measured and simulated hydrographs. Indeed, results are hampered by the short peak flows, due to rainfall variability and measurement synchronization errors, and it can be both difficult and inconvenient to remove base flows from the analysis, given their influence on combined sewer overflow (CSO) performance. A new approach, based on the application of metrics to peak flows for a selected set of different durations, is proposed and tested to support model quality assessment and calibration. Its advantages are: avoiding inconveniences arising from lags in peak flows and subjectivity of possible adjustments; favouring the assessment of the influence of base flow variability and flow lamination by CSOs; promoting integrated analysis for a wide range of rainfall events; facilitating bias identification and also guiding calibration. However, this new approach tends to provide results (e.g., for NSE, r2 and PBIAS) closer to optimal values than when applying metrics to compare the measured and simulated values of hydrographs, so the comparison of results with thresholds widely used in the literature should be done with caution. The various case study examples highlight the importance of using a judicious set of different metrics and graphical analyses. [ABSTRACT FROM AUTHOR]

Copyright of Hydrogeology Journal is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The availability of accurate spatiotemporal rainfall data is of utmost importance for reliable predictions from hydroclimatological studies. Challenges and limitations faced due to the absence of dense rain gauge (RG) networks are seen especially in the developing countries. Therefore, alternative rainfall measurements such as satellite rainfall products (SRPs) are used when RG networks are scarce or completely do not exist. Noteworthy, rainfall data retrieved from satellites also possess several uncertainties. Hence, these SRPs should essentially be validated beforehand. The Mahaweli River Basin (MRB), the largest river basin in Sri Lanka, is the heart of the country's water resources contributing to a significant share of the hydropower production and agricultural sector. Given the importance of the MRB, this study explored the suitability of SRPs as an alternative for RG data for the basin. Daily rainfall data of six types of SRPs were extracted at 14 locations within the MRB. Thereafter, statistical analysis was carried out using continuous and categorical evaluation indices to evaluate the accuracy of SRPs. Nonparametric tests, including the Mann-Kendall and Sen's slope estimator tests, were used to detect the possibility of trends and the magnitude, respectively. Integrated MultisatellitE Retrievals for Global Precipitation Measurement (IMERG) outperformed among all SRPs, while Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) products showed dire performances. However, IMERG also demonstrated underestimations when compared to RG data. Trend analysis results showcased that the IMERG product agreed more with RG data on monthly and annual time scales while Tropical Rainfall Measurement Mission Multisatellite Precipitation Analysis–3B42 (TRMM-3B42) agreed more on the seasonal scale. Overall, IMERG turned out to be the best alternative among the SRPs analyzed for MRB. However, it was clear that these products possess significant errors which cannot be ignored when using them in hydrological applications. The results of the study will be valuable for many parties including river basin authorities, agriculturists, meteorologists, hydrologists, and many other stakeholders. [ABSTRACT FROM AUTHOR]

Meteorological phenomena is an area in which a large amount of data is generated and where it is more difficult to make predictions about events that will occur due to the high number of variables on which they depend. In general, for this, probabilistic models are used that offer predictions with a margin of error, so that in many cases they are not very good. Due to the aforementioned conditions, the use of machine learning algorithms can serve to improve predictions. This article describes an exploratory study of the use of machine learning to make predictions about the phenomenon of rain. To do this, a set of data was taken as an example that describes the measurements gathered on rainfall in the main cities of Australia in the last 10 years, and some of the main machine learning algorithms were applied (knn, decision tree, random forest, and neural networks). The results show that the best model is based on neural networks. [ABSTRACT FROM AUTHOR]

Changes in rainfall patterns can have a profound impact on water availability, and therefore, examining the variability of rainfall is critical in addressing water resources and regional climate change. Tripura state of northeast India is known to have received a high amount of rainfall in the past few decades, but currently the region suffers from water shortage as soon as the rainy season retreats. Here, we use the surface, satellite and reanalysis data of Tripura in 1986–2019 to examine the seasonal and annual trends in rainfall. The satellite and reanalysis data agree very well with the surface rainfall measurements for Tripura and reproduce the seasonal and inter-annual variability. We find a decreasing trend in the seasonal and annual averaged rainfall at most stations for the period 1986–2019. The monsoon rainfall is declining at a rate of 0.025–0.216 mm/year, with the highest rate of −0.216 mm/year at the Sabroom station during the period. The correlation analysis of rainfall with land use and land cover (LULC) suggests the impact of LULC on rainfall; indicating the anthropogenic influence in rainfall changes there. Our analysis also shows a substantial increase in the Arabian Sea and Bay of Bengal sea surface temperature (SST) after 2000, and the changes in SST with respect to the El Niño, La Niña and Indian Ocean Dipole events contribute significantly to the rainfall variability in the region; suggesting plausible causes of recent changes in regional climate in the northeast. [ABSTRACT FROM AUTHOR]

For ungauged rivers, when there are no hydrological measurements and there is a lack of data on perennial flow rates, the latter one to be determined based on other hydrological data. The river Suhareka catchment represents a similar case. Since there is no data on Suhareka's flow rates, the authors of this study aimed for the flow rate determination based on rainfall measurements. From the available data on annual precipitation (monthly sums) provided by the Kosovo Hydrometeorological Institute for the Suhareka hydrometric station, the observed monthly rainfall data for 30 years were analysed. Those gaps were initially filled by connecting the hydrometric station in Suhareka with those of Prishtina, Prizren and Ferizaj, and as a result a fairly good fit was ensured. Moreover, the intensity-duration-frequency curves were formed using the expression of Sokolovsky, as a mathematical model of the dependence I (T, P). For a transformation of rainfall into flow, the American method SCS was used. As a result, the equation for the Suhareka River basin was derived, which enabled the determination of maximum inflows, for different return periods. The results obtained through this paper, indicates that even for ungauged river basins the peak flows can be determined from available rainfall data. [ABSTRACT FROM AUTHOR]

Several millimeter Wave (mmWave) bands, which suffer from rain attenuation, were identified in the World Radiocommunication Conference 2019 (WRC‐19) for fifth generation (5G) radio networks. In this paper, long‐term attenuation is measured over typical building to building radio links in the built environment, which constitute two 36 m links along a direct link and an indirect side link at 25.84 and 77.54 GHz and a 200 m link at 77.125 GHz. The attenuation was also estimated using precipitation data from a high end accurate disdrometer weather station using the drop size distribution and the International Telecommunication Union (ITU) models. The results indicate that attenuation using Mie theory is in agreement with the ITU model for most of the rainfall events; with higher attenuation being measured than predicted when snow grains and raindrops mix. Raindrops with diameter between 0.1 and 4 mm indicate that the dominant raindrops have considerable influence on the measured attenuation, especially at light and moderate rainfall events. The maximum distance factor restriction of 2.5 in ITU‐R P.530‐17 is shown not to be suitable for short‐range fixed links as it excessively underestimates attenuation. Plain Language Summary: Next generation mobile radio networks, 5G, are assigned several frequencies in the millimeter wave band, which are likely to suffer from rain attenuation. This can lead to outage in the communication link. This paper presents results of measurements and prediction models in the millimeter wave band using weather statistical data and radio data. Key Points: Rain attenuation prediction in the mmWave band using rain statistics and fixed links radio dataLong‐term study through measured and predicted rain attenuationAttenuation measurement as a function of rainfall intensity, raindrop distribution, dominant drops, and drops diameter [ABSTRACT FROM AUTHOR]

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• Rainfall measurement is always inconclusive if the structure of cloud is tilted. • Vertical wind shear a solely factor for cloud tilt is rarely examined. • Globally 60.68% of deep convective clouds are developed in strong wind shear. • These deep convective clouds produced intense rain > 50 mm/hr. • Need to develop correction procedures for rain estimation from tilted clouds. The percentage occurrence of titled deep convective clouds (DCCs) is rarely examined but it is crucial to overcome the challenges of precise measurement of rainfall intensity and location during dreadful weather events. Present study is carried out with this objective and analysed 2451 globally distributed DCCs identified by Ku-band (13.6 GHz) radar reflectivity profiles of Dual-frequency Precipitation Radar aboard Global Precipitation Measurement mission under varying conditions of environmental vertical wind shear (EVWS) during 2018. Globally (tropics) 60.68% (55.29%) of DCCs that produced intense rain (>50 mm/hr) are strengthened under strong EVWS and thus may have possibility of vertically tilted structure. Substantial 60.49% of DCCs that developed under strong EVWS grow deeper (up to 12 km) in atmosphere. Statistics on the global distribution of DCCs developed in strong EVWS with different categories of rainfall and cloud height would be an important source of information for precise assessment of rainfall distribution, especially during extreme weather. [ABSTRACT FROM AUTHOR]

Realistic runoff estimates are crucial for the accurate design of stormwater drainage systems, particularly in developing urban catchments which are prone to overland flow and street inundation following extreme rainstorms. This paper derives new intensity–duration–frequency (IDF) curves for the Yalamlam area in the Kingdom of Saudi Arabia. These curves were obtained based on daily rainfall measurements and, in some short durations, across the entire study area over 30 years. The study is based on applying two distributions—the Log-Pearson type III and Gumbel—to estimate the average rainfall for the different return periods. The results show that there are slight differences between the Log-Pearson type III distribution and the Gumbel distribution, so the average parameters were used to construct the IDF curve in the Yalamlam area. The maximum daily rainfall was converted into sub-daily intervals using two methods and compared with the observed value. The new ratios were calculated using the converting rainfall from daily to sub-daily. These ratios are recommended for application in the Yalamlam area if there are no short-time-interval data available. The following ratios for 1-day rainfall were proposed: 0.37, 0.40, 0.46, 0.53, 0.61, 0.66, 0.70, 0.76, 0.80, and 0.87 for 10 min, 15 min, 30 min, 1 h, 2 h, 3 h, 4 h, 6 h, 8 h, and 12 h rainfall, respectively. The developed IDF curve for the Yalamlam district was built based on the daily and sub-daily observed data. [ABSTRACT FROM AUTHOR]

Rainfall is a natural phenomenon and its observations are frequently essential for flood warning and its monitoring. The conventional techniques which are used for rain measurement is rain gauge, radar and satellite, precipitation estimation with each technique has its own asset with restriction and the methods are supportive to some extent, providing information at different spatial and terrestrial scales. In this article, a new approach is developed to estimate rainfall from droplet images data using proposed signal processing algorithm. This proposed method uses multi-sensor image data captured by two orthogonal placed cameras. After selection of appropriate data image, it is enhanced for noise free and smooth edges then. Fusion of multi sensor image data gets more informative image followed by segmentation for rainfall parameter analysis. Finally, drop size distribution, rainfall rate and direction of precipitation with its intensity are computed using the proposed algorithm. The real time image data in the form of images / videos were collected at Indian Meterology Centre, Coloba Mumbai, during the day time from 24th July to 6th August 2016 and experiment results of the method are compared with rain gauge observation techniques at the same place. We found that mathematical computations are only involved in this method and most convenient alternative by portable (cellular) device over conventional tedious and laborious methods. It would be additional helpful to the disaster administrators the city, water dam administrators, traveler, piscatorial and farmers to know rainy condition in every side in advance. [ABSTRACT FROM AUTHOR]

Purpose: In recent decades, soil erosion has been recognized as a serious environmental problem in many countries of the world and the impacts of climate change have focused attention on potential changes in erosion rates that could further increase such problem. These impacts are documented by a general decrease of annual precipitation and a corresponding increase in the number of heavy rainfall events, intensity and frequency that accelerate the loss of fertile soil material. Materials and methods: Direct observations of soil loss obtained during the period 2006–2016 on five experimental plots in Southern Italy were preliminarily presented and discussed. These measurements were used to calibrate the RUSLE model that, coupled with independent long-term measurements of rainfall erosivity, allowed calculation of soil erosion from 1954 to date. Results and discussion: The plot measurements showed annual values of soil erosion generally higher than the long-term estimates provided by the RUSLE (1954–2019) suggesting an increasing trend of soil erosion rates during the last 20–25 years. On the contrary, a decreasing trend of the annual rainfall measurements can be observed for the same period. Conclusions: The overall results demonstrated that models like the RUSLE can be considered a useful tool to individuate changes of erosion rates and to isolate the effect of climate change on soil loss. Also, the opposite trend obtained for the annual rainfall measurements suggests that these should not be used to explore the effects of climate change in Mediterranean areas but measurements of rainfall at shorter time intervals (≤ 30 min) are necessary. [ABSTRACT FROM AUTHOR]

The curve-number method is reviewed and assessed using rainfall and runoff measurements at 80 well-instrumented experimental watersheds in the United States. Key results of the study show that the applicable curve number (CN) in design applications is not always independent of the rainfall amount, the uncertainty factors associated with CN can vary between watersheds, CN dependence on both rainfall duration and rainfall amount is detected for some catchments, and the characteristic infiltration rate of a catchment can be expressed as a function of CN. When the curve-number equation is used in estimating the runoff hydrograph, it is shown that implicit infiltration rates can sometimes exceed the realistic infiltration capacity of the catchment, and that not representing CN as a function of the rainfall amount can lead to substantial underestimation of the peak-runoff rate. [ABSTRACT FROM AUTHOR]