Hydrometeorological Analysis and Remote Sensing of Extremes: Was the July 2012 Beijing Flood Event Detectable and Predictable by Global Satellite Observing and Global Weather Modeling Systems?

2015 ◽  
Vol 16 (1) ◽  
pp. 381-395 ◽  
Author(s):  
Yu Zhang ◽  
Yang Hong ◽  
Xuguang Wang ◽  
Jonathan J. Gourley ◽  
Xianwu Xue ◽  
...  
Keyword(s):  
Real Time ◽  
Extreme Event ◽  
Tropical Rainfall Measuring Mission ◽  
Extreme Rainfall ◽  
Rain Gauge ◽  
Precipitation Forecast ◽  
Economic Losses ◽  
Lead Times ◽  
Additional Information ◽  
Streamflow Forecasts

Abstract Prediction, and thus preparedness, in advance of flood events is crucial for proactively reducing their impacts. In the summer of 2012, Beijing, China, experienced extreme rainfall and flooding that caused 79 fatalities and economic losses of $1.6 billion. Using rain gauge networks as a benchmark, this study investigated the detectability and predictability of the 2012 Beijing event via the Global Hydrological Prediction System (GHPS), forced by the NASA Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis at near–real time and by the deterministic and ensemble precipitation forecast products from the NOAA Global Forecast System (GFS) at several lead times. The results indicate that the disastrous flooding event was detectable by the satellite-based global precipitation observing system and predictable by the GHPS forced by the GFS 4 days in advance. However, the GFS demonstrated inconsistencies from run to run, limiting the confidence in predicting the extreme event. The GFS ensemble precipitation forecast products from NOAA for streamflow forecasts provided additional information useful for estimating the probability of the extreme event. Given the global availability of satellite-based precipitation in near–real time and GFS precipitation forecast products at varying lead times, this study demonstrates the opportunities and challenges that exist for an integrated application of GHPS. This system is particularly useful for the vast ungauged regions of the globe.

scholarly journals Evaluation of IMERG Level-3 Products in Depicting the July to October Rainfall over Taiwan: Typhoon Versus Non-Typhoon

2021 ◽  
Vol 13 (4) ◽  
pp. 622
Author(s):  
Wan-Ru Huang ◽  
Pin-Yi Liu ◽  
Ya-Hui Chang ◽  
Cheng-An Lee
Keyword(s):  
Real Time ◽  
Tropical Rainfall Measuring Mission ◽  
Temporal Variations ◽  
Rain Gauge ◽  
Advantages And Disadvantages ◽  
Level 3 ◽  
Spatio Temporal ◽  
Typhoon Season ◽  
Early Late ◽  
Better Than

This study assesses the performance of satellite precipitation products (SPPs) from the latest version, V06B, Integrated Multi-satellitE Retrievals for Global Precipitation Mission (IMERG) Level-3 (including early, late, and final runs), in depicting the characteristics of typhoon season (July to October) rainfall over Taiwan within the period of 2000–2018. The early and late runs are near-real-time SPPs, while final run is post-real-time SPP adjusted by monthly rain gauge data. The latency of early, late, and final runs is approximately 4 h, 14 h, and 3.5 months, respectively, after the observation. Analyses focus on the seasonal mean, daily variation, and interannual variation of typhoon-related (TC) and non-typhoon-related (non-TC) rainfall. Using local rain-gauge observations as a reference for evaluation, our results show that all IMERG products capture the spatio-temporal variations of TC rainfall better than those of non-TC rainfall. Among SPPs, the final run performs better than the late run, which is slightly better than the early run for most of the features assessed for both TC and non-TC rainfall. Despite these differences, all IMERG products outperform the frequently used Tropical Rainfall Measuring Mission 3B42 v7 (TRMM7) for the illustration of the spatio-temporal characteristics of TC rainfall in Taiwan. In contrast, for the non-TC rainfall, the final run performs notably better relative to TRMM7, while the early and late runs showed only slight improvement. These findings highlight the advantages and disadvantages of using IMERG products for studying or monitoring typhoon season rainfall in Taiwan.

scholarly journals A Multiscale, Hydrometeorological Forecast Evaluation of National Water Model Forecasts of the May 2018 Ellicott City, Maryland, Flood

2020 ◽  
Vol 21 (3) ◽  
pp. 475-499 ◽  
Author(s):  
Francesca Viterbo ◽  
Kelly Mahoney ◽  
Laura Read ◽  
Fernando Salas ◽  
Bradford Bates ◽  
...  
Keyword(s):  
Flash Flood ◽  
Model Development ◽  
Extreme Rainfall ◽  
Precipitation Forecast ◽  
Water Model ◽  
Extreme Rainfall Event ◽  
Flood Prediction ◽  
Streamflow Forecasts ◽  
Atmospheric Forcings ◽  
National Water

AbstractThe NOAA National Water Model (NWM) became operational in August 2016, producing the first ever real-time, distributed, continuous set of hydrologic forecasts over the continental United States (CONUS). This project uses integrated hydrometeorological assessment methods to investigate the utility of the NWM to predict catastrophic flooding associated with an extreme rainfall event that occurred in Ellicott City, Maryland, on 27–28 May 2018. Short-range forecasts (0–18-h lead time) from the NWM version 1.2 are explored, focusing on the quantitative precipitation forecast (QPF) forcing from the High-Resolution Rapid Refresh (HRRR) model and the corresponding NWM streamflow forecast. A comprehensive assessment of multiscale hydrometeorological processes are considered using a combination of object-based, grid-based, and hydrologic point-based verification. Results highlight the benefits and risks of using a distributed hydrologic modeling tool such as the NWM to connect operational CONUS-scale atmospheric forcings to local impact predictions. For the Ellicott City event, reasonably skillful QPF in several HRRR model forecast cycles produced NWM streamflow forecasts in the small Ellicott City basin that were suggestive of flash flood potential. In larger surrounding basins, the NWM streamflow response was more complex, and errors were found to be governed by both hydrologic process representation, as well as forcing errors. The integrated, hydrometeorological multiscale analysis method demonstrated here guides both research and ongoing model development efforts, along with providing user education and engagement to ultimately engender improved flash flood prediction.

scholarly journals Evaluation of the TRMM product for monitoring drought over Paraíba State, northeastern Brazil: a trend analysis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Reginaldo Moura Brasil Neto ◽  
Celso Augusto Guimarães Santos ◽  
Jorge Flávio Casé Braga da Costa Silva ◽  
Richarde Marques da Silva ◽  
Carlos Antonio Costa dos Santos ◽  
...  
Keyword(s):  
Standardized Precipitation Index ◽  
Tropical Rainfall Measuring Mission ◽  
Extreme Rainfall ◽  
Rain Gauge ◽  
Rainfall Data ◽  
Northeastern Brazil ◽  
Rainfall Time Series ◽  
Extreme Rainfall Events ◽  
Spatiotemporal Trends ◽  
The Standardized Precipitation Index

AbstractDroughts are complex natural phenomena that influence society's development in different aspects; therefore, monitoring their behavior and future trends is a useful task to assist the management of natural resources. In addition, the use of satellite-estimated rainfall data emerges as a promising tool to monitor these phenomena in large spatial domains. The Tropical Rainfall Measuring Mission (TRMM) products have been validated in several studies and stand out among the available products. Therefore, this work seeks to evaluate TRMM-estimated rainfall data's performance for monitoring the behavior and spatiotemporal trends of meteorological droughts over Paraíba State, based on the standardized precipitation index (SPI) from 1998 to 2017. Then, 78 rain gauge-measured and 187 TRMM-estimated rainfall time series were used, and trends of drought behavior, duration, and severity at eight time scales were evaluated using the Mann–Kendall and Sen tests. The results show that the TRMM-estimated rainfall data accurately captured the pattern of recent extreme rainfall events that occurred over Paraíba State. Drought events tend to be drier, longer-lasting, and more severe in most of the state. The greatest inconsistencies between the results obtained from rain gauge-measured and TRMM-estimated rainfall data are concentrated in the area closest to the coast. Furthermore, long-term drought trends are more pronounced than short-term drought, and the TRMM-estimated rainfall data correctly identified this pattern. Thus, TRMM-estimated rainfall data are a valuable source of data for identifying drought behavior and trends over much of the region.

scholarly journals Bias correction of daily satellite-based rainfall estimates for hydrologic forecasting in the Upper Zambezi, Africa

2016 ◽  
Author(s):  
Rodrigo Valdés-Pineda ◽  
Eleonora M. C. Demaría ◽  
Juan B. Valdés ◽  
Sungwook Wi ◽  
Aleix Serrat-Capdevilla
Keyword(s):  
Real Time ◽  
Bias Correction ◽  
Principal Component ◽  
Tropical Rainfall Measuring Mission ◽  
Rain Gauges ◽  
Precipitation Estimation ◽  
Streamflow Forecasts ◽  
Semi Arid Region ◽  
Rainfall Estimates ◽  
Zambezi River

Abstract. The Zambezi Basin is located in the semi-arid region of southern Africa and is one of the largest basins in Africa. The Upper Zambezi River Basin (UZRB) is sparsely gauged (only 11 rain gauges are currently accessible), and real-time rainfall estimates are not readily available. However, Satellite Precipitation Products (SPPs) may complement that information, thereby allowing for improved real-time forecasting of streamflows. In this study, three SPPs for the UZRB are bias-corrected and evaluated for use in real-time forecasting of daily streamflows: (1) CMORPH (Climate Prediction Center’s morphing technique), (2) PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks), and (3) TRMM-3B42RT (Tropical Rainfall Measuring Mission). Two approaches for bias correction (Quantile Mapping and a Principal Component-based technique) are used to perform Bias Correction (BC) for the daily SPPs; for reference data, the Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) was used. The two BC approaches were evaluated for the period 2001–2016. The bias-corrected SPPs were then used for real-time forecasting of streamflows at Katima Mulilo in the UZRB. Both BC approaches significantly improve the accuracy of the streamflow forecasts in the UZRB.

scholarly journals Evaluation of Multiple Satellite-Based Precipitation Products over Complex Topography

2014 ◽  
Vol 15 (4) ◽  
pp. 1498-1516 ◽  
Author(s):  
Yagmur Derin ◽  
Koray K. Yilmaz
Keyword(s):  
Real Time ◽  
Tropical Rainfall Measuring Mission ◽  
Cold Season ◽  
Rain Gauge ◽  
Research Quality ◽  
Complex Topography ◽  
Precipitation Regime ◽  
Black Sea Region ◽  
Main Challenge ◽  
Precipitation Estimation

Abstract This study evaluates the performance of four satellite-based precipitation (SBP) products over the western Black Sea region of Turkey, a region characterized by complex topography that exerts strong controls on the precipitation regime. The four SBP products include the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis version 7 experimental near-real-time product (TMPA-7RT) and post-real-time research-quality product (TMPA-7A), the Climate Prediction Center morphing technique (CMORPH), and the Multisensor Precipitation Estimate (MPE) of the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT). Evaluation is performed at various spatial (point and grid) and temporal (daily, monthly, seasonal, and annual) scales over the period 2007–11. For the grid-scale evaluation, a rain gauge–based gridded precipitation dataset was constructed using a knowledge-based system in which “physiographic descriptors” are incorporated in the precipitation estimation through an optimization framework. The results indicated that evaluated SBP products generally had difficulty in representing the precipitation gradient normal to the orography. TMPA-7RT, TMPA-7A, and MPE products underestimated precipitation along the windward region and overestimated the precipitation on the leeward region, more significantly during the cold season. The CMORPH product underestimated the precipitation on both windward and leeward regions regardless of the season. Further investigation of the datasets used in the development of these SBP products revealed that, although both infrared (IR) and microwave (MW) datasets contain potential problems, the inability of MW sensors to detect precipitation especially in the cold season was the main challenge over this region with complex topography.

scholarly journals The Precipitation Inferred from Soil Moisture (PrISM) Near Real-Time Rainfall Product: Evaluation and Comparison

2020 ◽  
Vol 12 (3) ◽  
pp. 481 ◽  
Author(s):  
Thierry Pellarin ◽  
Carlos Román-Cascón ◽  
Christian Baron ◽  
Rajat Bindlish ◽  
Luca Brocca ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Real Time ◽  
Central Africa ◽  
Tropical Rainfall Measuring Mission ◽  
Product Evaluation ◽  
Rain Gauge ◽  
Satellite Precipitation ◽  
Weather Radars ◽  
Global Precipitation Climatology Centre ◽  
Almost All

Near real-time precipitation is essential to many applications. In Africa, the lack of dense rain-gauge networks and ground weather radars makes the use of satellite precipitation products unavoidable. Despite major progresses in estimating precipitation rate from remote sensing measurements over the past decades, satellite precipitation products still suffer from quantitative uncertainties and biases compared to ground data. Consequently, almost all precipitation products are provided in two modes: a real-time mode (also called early-run or raw product) and a corrected mode (also called final-run, adjusted or post-processed product) in which ground precipitation measurements are integrated in algorithms to correct for bias, generally at a monthly timescale. This paper describes a new methodology to provide a near-real-time precipitation product based on satellite precipitation and soil moisture measurements. Recent studies have shown that soil moisture intrinsically contains information on past precipitation and can be used to correct precipitation uncertainties. The PrISM (Precipitation inferred from Soil Moisture) methodology is presented and its performance is assessed for five in situ rainfall measurement networks located in Africa in semi-arid to wet areas: Niger, Benin, Burkina Faso, Central Africa, and East Africa. Results show that the use of SMOS (Soil Moisture and Ocean Salinity) satellite soil moisture measurements in the PrISM algorithm most often improves the real-time satellite precipitation products, and provides results comparable to existing adjusted products, such as TRMM (Tropical Rainfall Measuring Mission), GPCC (Global Precipitation Climatology Centre) and IMERG (Integrated Multi-satellitE Retrievals for GPM), which are available a few weeks or months after their detection.

scholarly journals Applications of TRMM- and GPM-Era Multiple-Satellite Precipitation Products for Flood Simulations at Sub-Daily Scales in a Sparsely Gauged Watershed in Myanmar

2019 ◽  
Vol 11 (2) ◽  
pp. 140 ◽  
Author(s):  
Fei Yuan ◽  
Limin Zhang ◽  
Khin Soe ◽  
Liliang Ren ◽  
Chongxu Zhao ◽  
...  
Keyword(s):  
Real Time ◽  
Flood Control ◽  
Tropical Rainfall Measuring Mission ◽  
Rain Gauge ◽  
Disaster Mitigation ◽  
Efficiency Coefficient ◽  
Precipitation Data ◽  
Ungauged Basins ◽  
Satellite Precipitation ◽  
Standard Version

Tropical Rainfall Measuring Mission (TRMM) and its successor, Global Precipitation Measurement (GPM), have provided hydrologists with important precipitation data sources for hydrological applications in sparsely gauged or ungauged basins. This study proposes a framework for statistical and hydrological assessment of the TRMM- and GPM-era satellite-based precipitation products (SPPs) in both near- and post-real-time versions at sub-daily temporal scales in a poorly gauged watershed in Myanmar. It evaluates six of the latest GPM-era SPPs: Integrated Multi-satellite Retrievals for GPM (IMERG) “Early”, “Late”, and “Final” run SPPs (IMERG-E, IMERG-L, and IMERG-F, respectively), and Global Satellite Mapping of Precipitation (GSMaP) near-real-time (GSMaP-NRT), standard version (GSMaP-MVK), and standard version with gauge-adjustment (GSMaP-GAUGE) SPPs, and two TRMM Multi-satellite Precipitation Analysis SPPs (3B42RT and 3B42V7). Statistical assessment at grid and basin scales shows that 3B42RT generally presents higher quality, followed by IMERG-F and 3B42V7. IMERG-E, IMERG-L, GSMaP-NRT, GSMaP-MVK, and GSMaP-GAUGE largely underestimate total precipitation, and the three GSMaP SPPs have the lowest accuracy. Given that 3B42RT demonstrates the best quality among the evaluated four near-real-time SPPs, 3B42RT obtains satisfactory hydrological performance in 3-hourly flood simulation, with a Nash–Sutcliffe model efficiency coefficient (NSE) of 0.868, and it is comparable with the rain-gauge-based precipitation data (NSE = 0.895). In terms of post-real-time SPPs, IMERG-F and 3B42V7 demonstrate acceptable hydrological utility, and IMERG-F (NSE = 0.840) slightly outperforms 3B42V7 (NSE = 0.828). This study found that IMERG-F demonstrates comparable or even slightly better accuracy in statistical and hydrological evaluations in comparison with its predecessor, 3B42V7, indicating that GPM-era IMERG-F is the reliable replacement for TRMM-era 3B42V7 in the study area. The GPM scientific community still needs to further refine precipitation retrieving algorithms and improve the accuracy of SPPs, particularly IMERG-E, IMERG-L, and GSMaP SPPs, because ungauged basins urgently require accurate and timely precipitation data for flood control and disaster mitigation.

scholarly journals Potential impact of climate change and extreme events on slope land hazard – a case study of Xindian watershed in Taiwan

2018 ◽  
Vol 18 (12) ◽  
pp. 3283-3296
Author(s):  
Shih-Chao Wei ◽  
Hsin-Chi Li ◽  
Hung-Ju Shih ◽  
Ko-Fei Liu
Keyword(s):  
Climate Change ◽  
Water Supply ◽  
Extreme Event ◽  
Extreme Rainfall ◽  
Economic Losses ◽  
Loss Assessment ◽  
Extreme Rainfall Events ◽  
Sediment Volume ◽  
Slope Land ◽  
Water Supply Shortage

Abstract. The production and transportation of sediment in mountainous areas caused by extreme rainfall events that are triggered by climate change is a challenging problem, especially in watersheds. To investigate this issue, the present study adopted the scenario approach coupled with simulations using various models. Upon careful model selection, the simulation of projected rainfall, landslide, debris flow, and loss assessment was integrated by connecting the models' input and output. The Xindian watershed upstream from Taipei, Taiwan, was identified and two extreme rainfall scenarios from the late 20th and 21st centuries were selected to compare the effects of climate change. Using sequence simulations, the chain reaction and compounded disaster were analysed. Moreover, the potential effects of slope land hazards were compared for the present and future, and the likely impacts in the selected watershed areas were discussed with respect to extreme climate. The results established that the unstable sediment volume would increase by 28.81 % in terms of the projected extreme event. The total economic losses caused by the chain impacts of slope land disasters under climate change would be increased to USD 358.25 million. Owing to the geographical environment of the Taipei metropolitan area, the indirect losses of a water supply shortage caused by slope land disasters would be more serious than direct losses. In particular, avenues to ensure the availability of the water supply will be the most critical disaster prevention topic in the event of a future slope land disaster. The results obtained from this study are expected to be beneficial because they provide critical information for devising long-term strategies to combat the impacts of slope land disasters.

scholarly journals Extreme Rainfall over Complex Terrain: An Application of the Linear Model of Orographic Precipitation to a Case Study in the Italian Pre-Alps

Geosciences ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 18
Author(s):  
Andrea Abbate ◽  
Monica Papini ◽  
Laura Longoni
Keyword(s):  
Complex Terrain ◽  
Extreme Event ◽  
Extreme Rainfall ◽  
Rain Gauge ◽  
Orographic Precipitation ◽  
Mountain Areas ◽  
Rainfall Extremes ◽  
Realistic Description ◽  
Terrain Elevation

Intense meteorological events are the primary cause of geohazard phenomena in mountain areas. In this paper, we present a study of the intense rainfall event that occurred in the provinces of Lecco and Sondrio from 11 to 12 June 2019. The aim of our work is to understand the effect of local topography on the spatial distribution of rainfall and to attempt the reconstruction of a realistic rainfall field relative to that extreme event. This task represents a challenge in the context of complex orography. Classical rain-gauge interpolation techniques, such as Kriging, may be too approximate, while meteorological models can be complex and often unable to accurately predict rainfall extremes. For these reasons, we tested the linear upslope model (LUM) designed for estimating rainfall records in orographic precipitation. This model explicitly addresses the dependence of rainfall intensification caused by the terrain elevation. In our case study, the available radio sounding data identified the convective nature of the event with a sustained and moist southern flow directed northward across the Pre-Alps, resulting in an orographic uplift. The simulation was conducted along a smoothed elevation profile of the local orography. The result was a reliable reconstruction of the rainfall field, validated with the ground-based rain gauge data. The error analysis revealed a good performance of the LUM with a realistic description of the interaction between the airflow and local orography. The areas subjected to rainfall extremes were correctly identified, confirming the determinant role of complex terrain in precipitation intensification.

Landslide Hazard and Exposure Modeling for Situational Awareness and Response in Rio de Janeiro

2021 ◽  
Author(s):  
Dalia Kirschbaum ◽  
Felipe Mandarino ◽  
Raquel Fonseca ◽  
Ricardo D'Orsi ◽  
Robert Emberson ◽  
...  
Keyword(s):  
Real Time ◽  
Landslide Susceptibility ◽  
Rio De Janeiro ◽  
Situational Awareness ◽  
Extreme Rainfall ◽  
Early Warning Systems ◽  
Landslide Hazard ◽  
Precipitation Forecast ◽  
Rainfall Thresholds ◽  
The City

<p>The city of Rio de Janeiro is situated within a coastal region with steep slopes, intense seasonal rainfall, and vulnerable populations located on marginal slopes. Landslides are a seasonal challenge within the city and proximate regions and increasing real-time awareness of the hazard and exposure is paramount to saving lives and mitigating damage. A local alerting system has been developed for the city that leverages a global landslide hazard assessment for situational awareness (LHASA) framework, developed by NASA, with local rainfall thresholds and landslide susceptibility information. The LHASA-Rio system uses a decision tree approach to first identify extreme rainfall based on a series of rainfall thresholds established by Geo-Rio (the City’s agency responsible for landslide hazards) for 1 hour, 1 day or 1 hour and 4 day thresholds. This is then coupled with information on landslide susceptibility also developed by the Geo-Rio team. The LHASA-Rio system has been running operationally since 2017 within the city to provide real-time, high resolution estimates of areas within the city at higher hazard at 15-minute intervals consistent with the rainfall gauge network distributed throughout the city. Results of the LHASA-Rio system indicate excellent performance for several case studies where extreme rainfall triggered landslides within the city over areas identified as high hazard zones by LHASA-Rio. The model has recently been updated to accommodate additional rainfall thresholds to differentiate moderate to very high and critical intensities. The modeling effort is also incorporating information on landslide exposure by connecting the hazard estimates to city-wide data on population, road networks and other infrastructure. The goal of this system is ultimately to provide key tools to emergency response teams, civil protection and other hazard monitoring organizations within Rio’s City Government in real-time and provide  actionable information for key communities, city management and planning. Future work of this system is the application of a regional precipitation forecast to improve the lead time.</p><p>This work has been done in partnership through an agreement established between NASA and the City of Rio de Janeiro in 2015 that was recently extended in 2020. This agreement seeks to support innovative efforts to better understand, anticipate, and monitor hazards and environmental issues, including heavy rainfall and landslides, urban flooding, air quality and water quality in and around the city. This collaboration leverages the unique attributes of NASA's satellite data and modeling frameworks and Rio de Janeiro's management and monitoring capabilities to improve awareness of how the city of Rio may be impacted by hazards and affected by climate change. If the success of this technology is demonstrated, other cities in the world with physiographic and socioeconomic characteristics similar to Rio de Janeiro may benefit by implementing, or strengthening, their own Early Warning Systems for landslides triggered by heavy rains using LHASA's open source algorithms and the experience gathered by the use of LHASA-Rio. This presentation highlights the achievements and advancements of the LHASA-Rio system and discusses lessons learned regarding the applications of the landslide modeling systems to advance decision-relevant science at the city level.</p>

Sign in / Sign up

Export Citation Format

Share Document