scholarly journals Evaluation of a near-real time NEXRAD DSP product in evolution of heavy rain events on the Upper Guadalupe River Basin, Texas

2012 ◽  
Vol 15 (2) ◽  
pp. 464-485 ◽  
Author(s):  
Xianwei Wang ◽  
Hongjie Xie ◽  
Newfel Mazari ◽  
Jon Zeitler ◽  
Hatim Sharif ◽  
...  
Keyword(s):  
Real Time ◽  
River Basin ◽  
Flash Flood ◽  
Heavy Rain ◽  
Accurate Information ◽  
Total Rainfall ◽  
Light Rain ◽  
Guadalupe River ◽  
Rain Events ◽  
Resource Budget

This study evaluates the Next Generation Weather Radar (NEXRAD) Digital Storm-Total Precipitation product (DSP) by analyzing 30 rain events on the Upper Guadalupe River Basin, Texas, from September 2006 to May 2007. The DSP product provides relatively accurate information on the evolution of rain events at high spatial and temporal resolutions in near-real time. This is particularly important for rainfall estimation of heavy rain events and flash flood forecasting. The DSP's accuracy is comparable to the other NEXRAD product MPE (multisensor precipitation estimator, at hourly resolution and 4 km grid spacing) at both hourly and event total scales for some heavy rain events, although the DSP is inferior to the MPE product for total rainfall of all 30 rain events analyzed, especially for light rain events. The DSP product shows the best agreement with gauges at ranges of 50–150 km from the radar (with mean absolute estimation bias (MAEB) of +15–22% for total rainfall of 30 rain events), while underestimating precipitation at both close ranges (<30 km) and far ranges (>180 km). The DSP product also tends to underestimate (overestimate) precipitation during event growth (dissipation). However, the total rainfall estimate for all rain events over a long period from DSP shows range dependence and is not recommended for calculation of water resource budget.

scholarly journals Statistical Evaluation of the Latest GPM-Era IMERG and GSMaP Satellite Precipitation Products in the Yellow River Source Region

Water ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 1006 ◽  
Author(s):  
Jiayong Shi ◽  
Fei Yuan ◽  
Chunxiang Shi ◽  
Chongxu Zhao ◽  
Limin Zhang ◽  
...  
Keyword(s):  
Real Time ◽  
Yellow River ◽  
Source Region ◽  
Tropical Rainfall Measuring Mission ◽  
Heavy Rain ◽  
The Yellow River ◽  
Light Rain ◽  
Yellow River Source ◽  
Rain Events

As the successor of Tropical Rainfall Measuring Mission, Global Precipitation Measurement (GPM) has released a range of satellite-based precipitation products (SPPs). This study conducts a comparative analysis on the quality of the integrated multisatellite retrievals for GPM (IMERG) and global satellite mapping of precipitation (GSMaP) SPPs in the Yellow River source region (YRSR). This research includes the eight latest GPM-era SPPs, namely, IMERG “Early,” “Late,” and “Final” run SPPs (IMERG-E, IMERG-L, and IMERG-F) and GSMaP gauge-adjusted product (GSMaP-Gauge), microwave-infrared reanalyzed product (GSMaP-MVK), near-real-time product (GSMaP-NRT), near-real-time product with gauge-based adjustment (GSMaP-Gauge-NRT), and real-time product (GSMaP-NOW). In addition, the IMERG SPPs were compared with GSMaP SPPs at multiple spatiotemporal scales. Results indicate that among the three IMERG SPPs, IMERG-F exhibited the lowest systematic errors and the best quality, followed by IMERG-E and IMERG-L. IMERG-E and IMERG-L underestimated the occurrences of light-rain events but overestimated the moderate and heavy rain events. For GSMaP SPPs, GSMaP-Gauge presented the best performance in terms of various statistical metrics, followed by GSMaP-Gauge-NRT. GSMaP-MVK and GSMaP-NRT remarkably overestimated total precipitation, and GSMaP-NOW showed an evident underestimation. By comparing the performances of IMERG and GSMaP SPPs, GSMaP-Gauge-NRT provided the best precipitation estimates among all real-time and near-real-time SPPs. For post-real-time SPPs, GSMaP-Gauge presented the highest capability at the daily scale, and IMERG-F slightly outperformed the other SPPs at the monthly scale. This study is one of the earliest studies focusing on the quality of the latest IMERG and GSMaP SPPs. The findings of this study provide SPP developers with valuable information on the quality of the latest GPM-era SPPs in YRSR and help SPP researchers to refine the precipitation retrieving algorithms to improve the applicability of SPPs.

scholarly journals Microphysical Characteristics of Rainfall Observed by a 2DVD Disdrometer during Different Seasons in Beijing, China

2021 ◽  
Vol 13 (12) ◽  
pp. 2303
Author(s):  
Li Luo ◽  
Jia Guo ◽  
Haonan Chen ◽  
Meilin Yang ◽  
Mingxuan Chen ◽  
...  
Keyword(s):  
Rain Rate ◽  
Heavy Rain ◽  
Occurrence Frequency ◽  
Total Rainfall ◽  
Axis Ratio ◽  
Seasonal Differences ◽  
Mean Values ◽  
Light Rain ◽  
Beijing China ◽  
Maximum Occurrence

The seasonal variations of raindrop size distribution (DSD) and rainfall are investigated using three-year (2016–2018) observations from a two-dimensional video disdrometer (2DVD) located at a suburban station (40.13°N, 116.62°E, ~30 m AMSL) in Beijing, China. The annual distribution of rainfall presents a unimodal distribution with a peak in summer with total rainfall of 966.6 mm, followed by fall. Rain rate (R), mass-weighted mean diameter (Dm), and raindrop concentration (Nt) are stratified into six regimes to study their seasonal variation and relative rainfall contribution to the total seasonal rainfall. Heavy drizzle/light rain (R2: 0.2~2.5 mm h−1) has the maximum occurrence frequency throughout the year, while the total rainfall in summer is primarily from heavy rain (R4: 10~50 mm h−1). The rainfall for all seasons is contributed primarily from small raindrops (Dm2: 1.0~2.0 mm). The distribution of occurrence frequency of Nt and the relative rainfall contribution exhibit similar behavior during four seasons with Nt of 10~1000 m−3 registering the maximum occurrence and rainfall contributions. Rainfall in Beijing is dominated by stratiform rain (SR) throughout the year. There is no convective rainfall (CR) in winter, i.e., it occurs most often during summer. DSD of SR has minor seasonal differences, but varies significantly in CR. The mean values of log10Nw (Nw: mm−1m−3, the generalized intercept parameter) and Dm of CR indicate that the CR during spring and fall in Beijing is neither continental nor maritime, at the same time, the CR in summer is close to the maritime-like cluster. The radar reflectivity (Z) and rain rate (?) relationship (Z = ?R?) showed seasonal differences, but were close to the standard NEXRAD Z-R relationship in summer. The shape of raindrops observed from 2DVD was more spherical than the shape obtained from previous experiments, and the effect of different axis ratio relations on polarimetric radar measurements was investigated through T-matrix-based scattering simulations.

scholarly journals River discharge prediction for ungauged mountainous river basins during heavy rain events based on seismic noise data

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Shakti P.C. ◽  
Kaoru Sawazaki
Keyword(s):  
River Basin ◽  
Seismic Noise ◽  
River Discharge ◽  
Heavy Rain ◽  
River Basins ◽  
Peak Discharge ◽  
Discharge Data ◽  
Noise Data ◽  
Rain Events ◽  
Mountainous River

AbstractSeveral mountainous river basins in Japan do not have a consistent hydrological record due to their complex environment and remoteness, as discharge measurements are not economically feasible. However, understanding the flow rate of rivers during extreme events is essential for preventing flood disasters around river basins. In this study, we used the high-sensitivity seismograph network (Hi-net) of Japan to identify the time and peak discharge of heavy rain events. Hi-net seismograph stations are distributed almost uniformly at distance intervals of approximately 20 km, while being available even in mountainous regions. The Mogami River Basin in Northeastern Japan was selected as a target area to compare the seismic noise data of two Hi-net stations with the hydrological response of a nearby river. These stations are not located near hydrological stations; therefore, direct comparison of seismic noise and observed discharge was not possible. Therefore, discharge data simulated using a hydrological model were first validated with gauging station data for two previous rain events (10–23 July 2004 and 7–16 September 2015). Then, the simulated river discharge was compared with Hi-net seismic noise data for three recent events (10–23 July 2004, 7–16 September 2015, and 10–15 October 2019). The seismic noise data exhibited a similar trend to the time series of simulated discharge in a frequency range of 1–2 Hz for the selected events. Discharge values predicted from the noise data effectively replicate the simulated discharge values in many cases, especially the timing and amount of peak discharge.Simulated and predicted discharge near NIED Hi-net seismic stations in the Mogami River Basin for the event of October 2019 (Typhoon Hagibis).

Spatial controls of microbial pesticide degradation in soils – A model-based scenario analysis

2021 ◽  
Author(s):  
Erik Schwarz ◽  
Swamini Khurana ◽  
Luciana Chavez Rodriguez ◽  
Johannes Wirsching ◽  
Christian Poll ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Scenario Analysis ◽  
Soil Column ◽  
Heavy Rain ◽  
Spatial Aggregation ◽  
Pesticide Degradation ◽  
Natural Soils ◽  
Light Rain ◽  
Rain Events ◽  
Precipitation Events

<p>Despite all legislative efforts, pesticides persist in soils at low concentrations and are leached to groundwater. This environmental issue has previously been associated with control factors relevant in natural soils but elusive in lab experiments and standard modeling approaches. One such factor is the small-scale spatial distribution of pesticide-degrading microorganisms in soil. Microbes are distributed heterogeneously in natural soils. They are aggregated in biogeochemical “hotspots” at the centimeter scale. The aim of our study is to investigate the relevance of such aggregation for pesticide degradation. For this, we upscaled the effect of the heterogeneity-induced accessibility limitations to degradation to the soil-column scale and analyzed kinetic constraints and amplifying factors under contrasting unsaturated flow regimes.</p><p>We performed a 2D spatially explicit, site-specific model-based scenario analysis for bioreactive transport of the model pesticide 4-chloro-2-methylphenoxyacetic acid (MCPA) in an arable soil (Luvisol). Stochastic centimeter-scale spatial distributions of microbial degraders were simulated with a spatial statistical model (log Gaussian Cox process), parametrized to meet experimentally observed spatial distribution metrics. Three heterogeneity levels were considered, representing homogenized soil conditions, and the lower and upper limit of expected microbial spatial aggregation in natural soils. Additionally, two contrasting precipitation scenarios (continuous light rain vs. heavy rain events directly following MCPA application) were assessed. A reactive transport model was set up to simulate a 0.3 m x 0.9 m soil column based on hydraulic and bioreactive measurements from a soil monitoring station (Germany, SM#3/ DFG CRC 1253 CAMPOS).</p><p>Our simulations revealed that heavy precipitation events were the main driver of pesticide leaching. Leached amounts from the topsoil increased by two to five orders of magnitude compared to the light rain scenario and at max. ca. 20 ng was leached from 90 cm after one year. With the increasing spatial aggregation of microbial degraders, upscaled pesticide degradation rates decreased, and considerable differences emerged between homogeneous and highly aggregated scenarios. In the latter, leaching from the plow layer into the subsoil was more pronounced and MCPA was detectable (LOD = 4 µg/kg) 5-6 times longer. In heterogeneous scenarios, degradation in microbial hotspots was mainly diffusion-limited during “hot moments” (times of high substrate availability), with a fraction of MCPA simultaneously “locked in” in coldspots with low microbial abundance. During intense precipitation events MCPA was remobilised from these coldspots by advective-dispersive transport, thereby increasing pesticide accessibility.</p><p>Our results indicate that predicted environmental concentrations and detectability of pesticides might be underestimated if spatial heterogeneity of microbial degraders is neglected, and they highlight the importance of heavy rain events as drivers of leaching and substrate accessibility.</p>

scholarly journals Small-scale (flash) flood early warning in the light of operational requirements: opportunities and limits with regard to user demands, driving data, and hydrologic modeling techniques

2016 ◽  
Vol 373 ◽  
pp. 201-208 ◽  
Author(s):  
Andy Philipp ◽  
Florian Kerl ◽  
Uwe Büttner ◽  
Christine Metzkes ◽  
Thomas Singer ◽  
...  
Keyword(s):  
Early Warning ◽  
Hydrological Modeling ◽  
Flash Flood ◽  
Input Parameter ◽  
Heavy Rain ◽  
Small Scale ◽  
Pilot Studies ◽  
Potential Benefits ◽  
Rain Events ◽  
Small Catchments

Abstract. In recent years, the Free State of Saxony (Eastern Germany) was repeatedly hit by both extensive riverine flooding, as well as flash flood events, emerging foremost from convective heavy rainfall. Especially after a couple of small-scale, yet disastrous events in 2010, preconditions, drivers, and methods for deriving flash flood related early warning products are investigated. This is to clarify the feasibility and the limits of envisaged early warning procedures for small catchments, hit by flashy heavy rain events. Early warning about potentially flash flood prone situations (i.e., with a suitable lead time with regard to required reaction-time needs of the stakeholders involved in flood risk management) needs to take into account not only hydrological, but also meteorological, as well as communication issues. Therefore, we propose a threefold methodology to identify potential benefits and limitations in a real-world warning/reaction context. First, the user demands (with respect to desired/required warning products, preparation times, etc.) are investigated. Second, focusing on small catchments of some hundred square kilometers, two quantitative precipitation forecasts are verified. Third, considering the user needs, as well as the input parameter uncertainty (i.e., foremost emerging from an uncertain QPF), a feasible, yet robust hydrological modeling approach is proposed on the basis of pilot studies, employing deterministic, data-driven, and simple scoring methods.

scholarly journals An observational and numerical study of a flash-flood event over south-eastern Italy

2008 ◽  
Vol 8 (6) ◽  
pp. 1417-1430 ◽  
Author(s):  
M. M. Miglietta ◽  
A. Regano
Keyword(s):  
Flash Flood ◽  
Numerical Study ◽  
Remote Sensing Data ◽  
Heavy Rain ◽  
Rain Event ◽  
Sensitivity Experiment ◽  
Low Level ◽  
South Eastern ◽  
Unstable Environment ◽  
Rain Events

Abstract. A flash-flood episode affecting a small area in Apulia (south-eastern Italy) on 22 October 2005 is documented. A rainfall amount of 160 mm was recorded in a 6 h interval in the central part of the region, producing severe damage and causing six fatalities. Synoptic maps, observations from surface stations and remote-sensing data are used here to describe the evolution of the rainfall system. The vertical profiles show features similar to those observed in other orographic heavy-rain events, such as a low-level jet, a conditionally unstable environment, and a nearly saturated warm low-level air mass. The low hills in the centre of the region play an important role in the release of the instability and the localisation of the rainfall, providing the uplift necessary to the air parcels to reach the level of free convection. Numerical simulations are performed in order to understand the mechanisms responsible for the heavy rain event. The Weather Research and Forecasting model (WRF) is setup in a 2-way nesting configuration including two domains. The model is able to realistically simulate the evolution of the precipitation system and to capture fairly well the localisation, the amount and the timing of the rainfall. The simulations suggest the important synergy of low and upper-tropospheric features which act as the triggering mechanism for the development of convection. A sensitivity experiment confirms the importance of the orography for the development of convective cells.

scholarly journals A Probabilistic Rain Diagnostic Model Based on Cyclone Statistical Analysis

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
V. Iordanidou ◽  
A. G. Koutroulis ◽  
I. K. Tsanis
Keyword(s):  
Eastern Mediterranean ◽  
Middle Eastern ◽  
Region Of Interest ◽  
Heavy Rain ◽  
Diagnostic Model ◽  
Light Rain ◽  
Statistical Measures ◽  
Probability Maps ◽  
Ms Scheme ◽  
Rain Events

Data from a dense network of 69 daily precipitation gauges over the island of Crete and cyclone climatological analysis over middle-eastern Mediterranean are combined in a statistical approach to develop a rain diagnostic model. Regarding the dataset, 0.5 × 0.5, 33-year (1979–2011) European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA-Interim) is used. The cyclone tracks and their characteristics are identified with the aid of Melbourne University algorithm (MS scheme). The region of interest is divided into a grid mesh and for each grid the probability of rain occurrence from passing cyclones is estimated. Such probability maps are estimated for three rain intensity categories. The probability maps are evaluated for random partitions of the data as well as for selected rain periods. Cyclones passing south of Italy are found to have greater probability of producing light rain events in Crete in contrast to medium and heavy rain events which are mostly triggered by cyclones of southern trajectories. The performance of the probability maps is very satisfactory, recognizing the majority of “affecting” cyclones and rejecting most cyclones that do not trigger rain events. Statistical measures of sensitivity and specificity range between 0.5 and 0.8 resulting in effective forecasting potential.

Evaluation and Application of Quantitative Precipitation Forecast Products for Mainland China Based on TIGGE Multimodel Data

2021 ◽  
Vol 22 (5) ◽  
pp. 1199-1219
Author(s):  
Zhangkang Shu ◽  
Jianyun Zhang ◽  
Junliang Jin ◽  
Lin Wang ◽  
Guoqing Wang ◽  
...  
Keyword(s):  
High Reliability ◽  
Mainland China ◽  
Heavy Rain ◽  
Precipitation Forecast ◽  
Hydrological Models ◽  
Light Rain ◽  
Jma Model ◽  
Quantitative Precipitation Forecast ◽  
Rain Events ◽  
Precipitation Events

AbstractWe evaluated 24-h control forecast products from The International Grand Global Ensemble center over the 10 first-class water resource regions of Mainland China in 2013–18 from the perspective of precipitation processes (continuous) and precipitation events (discrete). We evaluated the forecasts from the China Meteorological Administration (CMA), the Centro de Previsão de Tempo e Estudos Climáticos (CPTEC), the Canadian Meteorological Centre (CMC), the European Centre for Medium-Range Weather Forecasts (ECMWF), the Japan Meteorological Agency (JMA), the Korea Meteorological Administration (KMA), the United Kingdom Met Office (UKMO), and the National Centers for Environmental Prediction (NCEP). We analyzed the differences among the numerical weather prediction (NWP) models in predicting various types of precipitation events and showed the spatial variations in the quantitative precipitation forecast efficiency of the NWP models over Mainland China. Meanwhile, we also combined four hydrological models to conduct meteo-hydrological runoff forecasting in three typical basins and used the Bayesian model averaging (BMA) method to perform the ensemble forecast of different scenarios. Our results showed that the models generally underestimate and overestimate precipitation in northwestern China and southwestern China, respectively. This tendency became increasingly clear as the lead time rose. Each model has a high reliability for the forecast of no-rain and light rain in the next 10 days, whereas the NWP model only has high reliability on the next day for moderate and heavy rain events. In general, each model showed different capabilities of capturing various precipitation events. For example, the CMA and CMC forecasts had a better prediction performance for heavy rain but greater errors for other events. The CPTEC forecast performed well for long lead times for no-rain and light rain but had poor predictability for moderate and heavy rains. The KMA, UKMO, and NCEP forecasts performed better for no-rain and light rain. However, their forecasting ability was average for moderate and heavy rain. Although the JMA model performed better in terms of errors and accuracy, it seriously underestimated heavy rain events. The extreme rainstorm and flood forecast results of the coupled JMA model should be treated with caution. Overall, the ECMWF had the most robust performance. Discrepancies in the forecasting effects of various models on different precipitation events vary with the lead time and region. When coupled with hydrological models, NWP models not only control the accuracy of runoff prediction directly but also increase the difference among the prediction results of different hydrological models with the increase in NWP error significantly. Among all the single models, ECMWF, JMA, and NCEP have better effects than the other models. Moreover, the ensemble forecast based on BMA is more robust than the single model, which can improve the quality of runoff prediction in terms of accuracy and reliability.

scholarly journals Trends in total rainfall, heavy rain events, and number of dry days in San Juan, Puerto Rico, 1955-2009

2014 ◽  
Vol 19 (2) ◽  
Author(s):  
Pablo A. Méndez-Lázaro ◽  
Alejandro Nieves-Santiango ◽  
Julieanne Miranda-Bermúdez
Keyword(s):  
Puerto Rico ◽  
Heavy Rain ◽  
San Juan ◽  
Total Rainfall ◽  
Rain Events
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