Significance of spatial variability in precipitation for process-oriented modelling: results from two nested catchments using radar and ground station data

International audienceThe importance of considering the spatial distribution of rainfall for process-oriented hydrological modelling is well-known. However, the application of rainfall radar data to provide such detailed spatial resolution is still under debate. In this study the process-oriented TACD (Tracer Aided Catchment model, Distributed) model had been used to investigate the effects of different spatially distributed rainfall input on simulated discharge and runoff components on an event base. TACD is fully distributed (50x50m2 raster cells) and was applied on an hourly base. As model input rainfall data from up to 7 ground stations and high resolution rainfall radar data from operational C-band radar were used. For seven rainfall events the discharge simulations were investigated in further detail for the mountainous Brugga catchment (40km2) and the St. Wilhelmer Talbach (15.2km2) sub-basin, which are located in the Southern Black Forest Mountains, south-west Germany. The significance of spatial variable precipitation data was clearly demonstrated. Dependent on event characteristics, localized rain cells were occasionally poorly captured even by a dense ground station network, and this resulted in inadequate model results. For such events, radar data can provide better input data. However, an extensive data adjustment using ground station data is required. For this purpose a method was developed that considers the temporal variability in rainfall intensity in high temporal resolution in combination with the total rainfall amount of both data sets. The use of the distributed catchment model allowed further insights into spatially variable impacts of different rainfall estimates. Impacts for discharge predictions are the largest in areas that are dominated by the production of fast runoff components. The improvements for distributed runoff simulation using high resolution rainfall radar input data are strongly dependent on the investigated scale, the event characteristics and the existing monitoring network

CEH-GEAR: 1 km resolution daily and monthly areal rainfall estimates for the UK for hydrological and other applications

The Centre for Ecology & Hydrology – Gridded Estimates of Areal Rainfall (CEH-GEAR) data set was developed to provide reliable 1 km gridded estimates of daily and monthly rainfall for Great Britain (GB) and Northern Ireland (NI) (together with approximately 3500 km2 of catchment in the Republic of Ireland) from 1890 onwards. The data set was primarily required to support hydrological modelling. The rainfall estimates are derived from the Met Office collated historical weather observations for the UK which include a national database of rain gauge observations. The natural neighbour interpolation methodology, including a normalisation step based on average annual rainfall (AAR), was used to generate the daily and monthly rainfall grids. To derive the monthly estimates, rainfall totals from monthly and daily (when complete month available) rain gauges were used in order to obtain maximum information from the rain gauge network. The daily grids were adjusted so that the monthly grids are fully consistent with the daily grids. The CEH-GEAR data set was developed according to the guidance provided by the British Standards Institution. The CEH-GEAR data set contains 1 km grids of daily and monthly rainfall estimates for GB and NI for the period 1890–2012. For each day and month, CEH-GEAR includes a secondary grid of distance to the nearest operational rain gauge. This may be used as an indicator of the quality of the estimates. When this distance is greater than 100 km, the estimates are not calculated due to high uncertainty

High-resolution QPF Uncertainty And Its Implications For Flood Prediction: A Case Study For The Eastern Iowa Flood Of 2016

This study addresses the uncertainty of High-Resolution Rapid Refresh (HRRR) quantitative precipitation forecasts (QPFs), which were recently appended to the operational hydrologic forecasting framework. In this study, we examine the uncertainty features of HRRR QPFs for an Iowa flooding event that occurred in September 2016. Our evaluation of HRRR QPFs is based on the conventional approach of QPF verification and the analysis of mean areal precipitation (MAP) with respect to forecast lead time. The QPF verification results show that the precipitation forecast skill of HRRR significantly drops during short lead times and then gradually decreases for further lead times. The MAP analysis also demonstrates that the QPF error sharply increases during short lead times and starts decreasing slightly beyond 4-h lead time. We found that the variability of QPF error measured in terms of MAP decreases as basin scale and lead time become larger and longer, respectively. The effects of QPF uncertainty on hydrologic prediction are quantified through the hillslope-link model (HLM) simulations using hydrologic performance metrics (e.g., Kling-Gupta efficiency). The simulation results agree to some degree with those from the MAP analysis, finding that the performance achieved from the QPF forcing decreases during 1-3-h lead times and starts increasing with 4-6-h lead times. The best performance acquired at the 1-h lead time does not seem acceptable because of the large overestimation of the flood peak, along with an erroneous early peak that is not observed in streamflow observations. This study provides further evidence that HRRR contains a well-known weakness at short lead times, and the QPF uncertainty (e.g., bias) described as a function of forecast lead times should be corrected before its use in hydrologic prediction

Uncertainty In Radar-rainfall Composite And Its Impact On Hydrologic Prediction For The Eastern Iowa Flood Of 2008

Key Points A significant potential source of error exists in mosaicked radar-rainfall maps. Different radar calibration offsets lead to misestimation of rainfall amounts. Systematic error in rainfall significantly affects hydrologic predictions. This study addresses a significant potential source of error that exists in radar-rainfall maps that are combined using data from multiple WSR-88D radars of the Next Generation Radar (NEXRAD) national network in the United States. This error stems from different radar calibration offsets that create a border within discontinuous rainfall fields at the equidistance zone among radars. The discontinuity in rainfall fields could lead to misestimation of rainfall over basins and subsequently, to significant errors in streamflow predictions through a hydrologic model. In this study, we produce enhanced radar-rainfall estimates (HN3) based on a novel approach that allows us to reduce the effects of the relative radar calibration bias. We use the relative bias information previously presented in a radar reflectivity comparison study. To investigate the effects of the relative bias adjustment, we evaluate the HN3 and Stage IV radar-rainfall by comparing them with rain gauge data and analyzing their ability to simulate streamflow for an extreme flood case. While the HN3 estimates are statistically comparable to the Stage IV estimates in the rain gauge data comparison, the borderline that identifies discontinuous rainfall fields disappears in the HN3 estimates. We performed hydrological simulations using a physically based, data-intensive, calibration-free, hillslope-link hydrologic model called CUENCAS and demonstrated CUENCAS\u27s ability to accurately simulate flows by comparing results with observed and predicted streamflow generated by the Sacramento (SAC) model. SAC is the operational flood forecast model that has been used by the National Weather Service since 1969, and it was extensively calibrated based on historical data. The simulation results show that the adjustment improves streamflow predictions in the regions where the misestimation of rainfall quantity is considerable. We conclude that systematic error arising from different calibration offsets in rainfall fields can significantly affect hydrologic predictions. ©2013. American Geophysical Union. All Rights Reserved

Scale Dependence Of Radar Rainfall Uncertainty: Initial Evaluation Of NEXRAD\u27s New Super-resolution Data For Hydrologic Applications

This study explores the scale effects of radar rainfall accumulation fields generated using the new super-resolution level II radar reflectivity data acquired by the Next Generation Weather Radar (NEXRAD) network of the Weather Surveillance Radar-1988 Doppler (WSR-88D) weather radars. Eleven months (May 2008-August 2009, exclusive of winter months) of high-density rain gauge network data are used to describe the uncertainty structure of radar rainfall and rain gauge representativeness with respect to five spatial scales (0.5, 1, 2, 4, and 8 km). While both uncertainties of gauge representativeness and radar rainfall show simple scaling behavior, the uncertainty of radar rainfall is characterized by an almost 3 times greater standard error at higher temporal and spatial resolutions (15 min and 0.5 km) than at lower resolutions (1 h and 8 km). These results may have implications for error propagation through distributed hydrologic models that require high-resolution rainfall input. Another interesting result of the study is that uncertainty obtained by averaging rainfall products produced from the super-resolution reflectivity data is slightly lower at smaller scales than the uncertainty of the corresponding resolution products produced using averaged (recombined) reflectivity data. © 2010 American Meteorological Society

Evaluation of SWAT model - subdaily runoff prediction in Texas watersheds

Spatial variability of rainfall is a significant factor in hydrologic and water quality modeling. In recent years, characterizing and analyzing the effect of spatial variability of rainfall in hydrologic applications has become vital with the advent of remotely sensed precipitation estimates that have high spatial resolution. In this study, the effect of spatial variability of rainfall in hourly runoff generation was analyzed using the Soil and Water Assessment Tool (SWAT) for Big Sandy Creek and Walnut Creek Watersheds in North Central Texas. The area of the study catchments was 808 km2 and 196 km2 for Big Sandy Creek and Walnut Creek Watersheds respectively. Hourly rainfall measurements obtained from raingauges and weather radars were used to estimate runoff for the years 1999 to 2003. Results from the study indicated that generated runoff from SWAT showed enormous volume bias when compared against observed runoff. The magnitude of bias increased as the area of the watershed increased and the spatial variability of rainfall diminished. Regardless of high spatial variability, rainfall estimates from weather radars resulted in increased volume of simulated runoff. Therefore, weather radar estimates were corrected for various systematic, range-dependent biases using three different interpolation methods: Inverse Distance Weighting (IDW), Spline, and Thiessen polygon. Runoff simulated using these bias adjusted radar rainfall estimates showed less volume bias compared to simulations using uncorrected radar rainfall. In addition to spatial variability of rainfall, SWAT model structures, such as overland flow, groundwater flow routing, and hourly evapotranspiration distribution, played vital roles in the accuracy of simulated runoff

Assessing the socio-economic impacts of flash floods for early warning at regional, national, and continental scales

Flash floods are one of the most devastating natural hazards, claiming numerous lives and tremendous economic losses. One of the main reasons for their catastrophic potential is the limited time available for precautionary measures, such as warnings or evacuations. Early warning systems (EWSs) play a key role for emergency managers to react in a timely manner to upcoming floods and effectively mitigate the impacts. This thesis explores possibilities to enhance the methods available for flash flood early warning and thus improve the operational decision support. While a variety of existing methods aims at the prediction of the hazard component of flash floods (e.g. the peak streamflow), an increasing number of EWS developers and end-users have recognised the potential of tools that automatically translate the flash flood hazard forecasts into the expected socio-economic impacts (e.g. the population affected). These so-called impact forecasts enable more objective and rapid decisions, ultimately leading to a more effective flood response. While for fluvial floods, impact forecasts have been available for several years and over various spatial scales, the existing approaches for flash floods have been limited to a small number of prototypes focusing on individual catchments or relatively small regions. These small-scale approaches can be useful for the coordination of local emergency measures, but their potential is limited for supporting the decisions of authorities operating over larger domains (e.g. regional, national, or international civil protection mechanisms). The main goal of this thesis has been to extend the available decision support by applying the concept of flash flood impact forecasting over large spatial scales. Two methods have been developed for estimating the impacts in real time, named ReAFFIRM and ReAFFINE. The two methods take into account that emergency services operating at different spatial scales require different kinds of real-time information to make informed decisions: ReAFFIRM provides detailed impact estimates in high resolution to support regional or national authorities in the coordination of location-specific emergency measures (e.g. evacuations), whereas ReAFFINE generates order-of-magnitude impact estimates with pan-European coverage that can be useful for end-users operating across regions or countries. The application of ReAFFIRM and ReAFFINE for a number of past flood events has demonstrated their capabilities to identify flash flood impacts in real time over the different spatial scales. The developed algorithms have a moderate computational cost and require only datasets that are available throughout the EU, which facilitates the real-time implementation of the methods and their integration into the operational procedures of end-users across Europe. An additional objective of this thesis has been to explore a more integrated perspective of flood early warning. Traditionally, EWSs are designed separately for the different physical processes that lead to flooding (i.e. individual systems for fluvial, pluvial, coastal, and flash floods). This means that the end-users need to monitor a number of separate flood forecasts with potentially even contradicting outputs. Especially during events in which different flood types coincide (so-called compound floods), this can be time-consuming and confusing. The decision support could be significantly simplified by automatically integrating the forecasts of different flood types into an overall compound flood forecast. This idea has been explored through the analysis of a recent catastrophic compound flood, for which the impact estimates from ReAFFIRM have been combined with those from a system designed for fluvial floods. The combined performance of the methods has shown to be superior to the individual performances, clearly demonstrating the potential of such integrated approaches for improving the decision support.Las avenidas torrenciales son una de las amenazas naturales más devastadoras, causando numerosas víctimas y enormes pérdidas económicas. Los sistemas de alerta temprana (SAT) juegan un papel clave para que los servicios de emergencia puedan reaccionar de manera oportuna y mitigar con eficacia los impactos. Esta tesis explora diferentes posibilidades de ampliar los métodos disponibles para la alerta temprana de avenidas torrenciales, con el objetivo de mejorar la toma de decisiones de los servicios de emergencia. Una variedad de métodos se dedica a la predicción del componente de amenaza de las avenidas repentinas (e.g. los caudales máximos instantáneos). No obstante, un número creciente de desarrolladores de SAT y usuarios finales han reconocido el potencial de herramientas que traducen automáticamente estos pronósticos de amenaza en impactos socioeconómicos (e.g. la cantidad de población afectada). Estas predicciones de impacto permiten tomar decisiones más objetivas y rápidas, que conducen a una respuesta más eficaz ante las avenidas y sus consecuencias. Los estudios realizados para la predicción del impacto de avenidas torrenciales han sido limitados a unos pocos prototipos que se enfocan en cuencas individuales o regiones relativamente pequeñas que pueden resultar útiles para la coordinación de medidas de emergencia locales, pero su potencial es limitado para apoyar las decisiones de las autoridades que actúan en dominios más amplios (e.g. autoridades de protección civil regionales, nacionales o europeas). El objetivo principal de esta tesis ha sido extender el apoyo a la toma de decisiones disponible mediante la aplicación del concepto de previsión del impacto de avenidas torrenciales en grandes escalas espaciales. Para ello, se desarrollaron dos métodos para estimar los impactos en tiempo real: ReAFFIRM y ReAFFINE. ReAFFIRM proporciona estimaciones de impacto detalladas y en alta resolución para dar apoyo a las autoridades regionales o nacionales en la coordinación de medidas de emergencia específicas (e.g. evacuaciones), mientras que ReAFFINE genera estimaciones de impacto en órdenes de magnitud con cobertura paneuropea que resultan útiles para los usuarios finales que actúan en grandes dominios espaciales. El uso de ReAFFIRM y ReAFFINE para una serie de inundaciones pasadas ha demostrado su capacidad para identificar los impactos de las avenidas torrenciales en tiempo real y en diferentes escalas espaciales. Los algoritmos desarrollados tienen un coste computacional moderado y solo requieren datos que están disponibles en toda la UE, permitiendo su implementación e integración en los procedimientos operativos de varios usuarios finales en toda Europa. Un objetivo adicional de esta tesis ha sido explorar una perspectiva más integrada de la alerta temprana de inundaciones. Tradicionalmente, los SAT son diseñados por separado para los diferentes procesos físicos que pueden resultar en inundaciones. Esto significa que los usuarios finales deben monitorear una serie de pronósticos de inundaciones por separado con resultados que podrían resultar potencialmente contradictorios, especialmente durante eventos en los que coincidan diferentes tipos de inundaciones (también llamadas inundaciones compuestas). Lo anterior puede alargar los tiempos de respuesta, generar confusión y, en última instancia, impedir una respuesta de emergencia eficaz. El apoyo a la toma de decisiones podría ser simplificada significativamente y de manera automática mediante la integración de los SAT de diferentes tipos de inundaciones en un único pronóstico que las englobe. Esta idea se explora a través de la combinación de las estimaciones de impacto de ReAFFIRM con las de un sistema diseñado para inundaciones fluviales. El rendimiento de ambos métodos combinados ha demostrado ser superior al de cada uno de manera individual, indicando el potencial de combinar el pronóstico de impacto por inundacionesPostprint (published version

A Hydrometeorological And Geospatial Analysis Of Precipitation Within The Glacial Ridge Wildlife Refuge Using The R2ain-Gis Tool

Weather radar (radio detection and ranging) is a specialized meteorological tool used to sample and track meteorological objects. This tool is critical for meteorologists and public decision-makers to inform and provide for their constituents in a timely manner, often with the protection of lives and property on the line. With the application of using meteorological and geospatial data in the realm of geographic information systems (G.I.S.), the task of blending the two sciences to inhibit further research and dissemination of information occurs. This study focuses on the creation and implementation of a new geospatial tool, the Radar and Rainfall Analyzed in GIS (R2AIn-GIS) tool. The R2AIn-GIS tool was built upon the initial concepts from Zhang and Srinivasan’s (2010) NEXRAD validation and calibration (NEXRAD-VC) tool for G.I.S. R2AIn-GIS is updated to support the latest software features present in the geospatial world as well as analyze dual-polarization radar products. To test the R2AIn-GIS tool, a warm seasonal precipitation study along with statistical analysis was performed over the Glacial Ridge National Wildlife Refuge in Minnesota, the largest prairie and wetland restoration site. Utilizing rain gauges operated by the United States Geological Survey, warm season precipitation events from 24 May 2012 to 31 August 2013 were analyzed using the R2AIn-GIS tool. The R2AIn-GIS tool calculates the values from various dual-polarization radar products in conjunction with the recorded precipitation gauges to provide a detailed depiction of the weather event. Statistical tests including several iterations of multiple-linear regression of various combinations of dual-polarization radar variables allowed determination of rainfall rate prediction equations over the study area. This contributes to the body of radar literature regarding the best prediction equations for other locations. Unlike treatments in prior literature, most of the various assumptions in multiple linear regression are considered herein. Based off the findings of the various statistical tests that adhere to the linear regression assumptions, regression models utilizing both reflectivity and correlation coefficient were the best models found during this study. These two variables had statistical significant p-values and their Durbin-Watson scores were among the highest even compared with the other radar variables of differential reflectivity and specific differential phase. Models including the radar variables reflectivity and correlation coefficient were found to be heteroscedastic along with the highest R Squared values. While the overall rainfall amounts were too small in terms of effective precipitation sampling, the results still positively contribute to the literature and provides the opportunity for future work