Operational Hydrology Research Articles

Snowfall Limit Forecasts and Hydrological Modeling

Abstract Hydrological flood forecasting in mountainous areas requires accurate partitioning between rain and snowfall to properly estimate the extent of runoff contributing areas. Here a method to make use of snowfall limit information—a standard output of limited-area models (LAMs)—for catchment-scale hydrological modeling is proposed. LAMs consider the vertical, humid, atmospheric structure in their snowfall limit calculations. The proposed approach is thus more physically based than inferring snowfall limit estimates based on (dry) ground temperature measurements, which is the standard procedure in most hydrological models. The presented case study uses forecast reanalyses from the Consortium for Small-Scale Modeling (COSMO) limited-area model as input for discharge simulation in a topographically complex catchment in the Swiss Alps. Results suggest that the use of COSMO snowfall limits during spring snowmelt periods can provide more accurate runoff simulations than routine procedures, with practical implications for operational hydrology in Alpine regions.

Fuzzy Model Used for the Prediction of a State of Emergency for a River Basin in the Case of a Flash Flood - PART 2

Fuzzy Model Used for the Prediction of a State of Emergency for a River Basin in the Case of a Flash Flood - PART 2This article is a continuation of a previous one named Fuzzy model use for prediction of the state of emergency of river basin in the case of flash flood (Janál&Starý, 2009), where the potential applications of fuzzy logic in the field of flash flood forecasting were described. Flash flood forecasting needs a specific approach because of the character of torrential rainfall. Storms are very difficult to forecast in space and time. The hydrological models designed for flash flood prediction have to be able to work with very uncertain input data. Moreover, the models have to be capable of evaluating the level of danger in as short a time as possible because of the highly dynamic character of the modeled process. The fuzzy model described in the previous article was modified into a form usable in operational hydrology and a simulation of its operational application was run using this model. The selected time period for the simulation was the summer of 2009, when numerous flash floods occurred in Czech Republic. The topic of this article is the preparation of the model for practical use and the results of the simulation of its operation.

What Drives the Variability of Evaporative Demand across the Conterminous United States?

AbstractTo understand the sources of temporal and spatial variability of atmospheric evaporative demand across the conterminous United States (CONUS), a mean-value, second-moment uncertainty analysis is applied to a spatially distributed dataset of daily synthetic pan evaporation for 1980–2009. This evaporative demand measure is from the “PenPan” model, which is a combination equation calibrated to mimic observations from U.S. class-A evaporation pans and here driven by six North American Land Data Assimilation System variables: temperature, specific humidity, station pressure, wind speed, and downwelling shortwave and longwave radiation. The variability of evaporative demand is decomposed across various time scales into contributions from these drivers. Contrary to popular expectation and much hydrologic practice, temperature is not always the most significant driver of temporal variability in evaporative demand, particularly at subannual time scales. Instead, depending on the season, one of four drivers (temperature, specific humidity, downwelling shortwave radiation, and wind speed) dominates across different regions of CONUS. Temperature generally dominates in the northern continental interior. This analysis assists land surface modelers in balancing parameter parsimony and physical representativeness. Patterns of dominant drivers are shown to cycle seasonally, with clear implications for modeling evaporative demand in operational hydrology or as a metric of climate change and variability. Depending on the region and season, temperature, specific humidity, downwelling shortwave radiation, and wind speed must together be examined, with downwelling longwave radiation as a secondary input. If any variable may be ignored, it is atmospheric pressure. Parameterizations of evaporative demand based solely on temperature should be avoided at all time scales.

Geomorphology-based Time-Lagged Recurrent Neural Networks for runoff forecasting

Artificial Neural Networks have been widely used to develop effective runoff-forecasting models. An overwhelming majority of networks are static in nature and also developed without incorporating geomorphologic information of the watershed. The objective of this study is to develop an efficient dynamic neural network model which also accounts for morphometric characteristics of the catchment. The model developed using Time-Lagged Recurrent Neural Networks (TLRNs) is used to estimate runoff for river Dikrong, a tributary of river Brahmaputra in India. Comparisons with traditional static models, with and without integration of geomorphologic data, reveal the proposed model to be a promising tool in operational hydrology.

Subgrid variability of snow water equivalent at operational snow stations in the western USA

AbstractThe spatial distribution of snow water equivalent (SWE) is a key variable in many regional‐scale land surface models. Currently, the assimilation of point‐scale snow sensor data into these models is commonly performed without consideration of the spatial representativeness of the point data with respect to the model grid‐scale SWE. To improve the understanding of the relationship between point‐scale snow measurements and surrounding areas, we characterized the spatial distribution of snow depth and SWE within 1‐, 4‐ and 16‐km2 grids surrounding 15 snow stations (snowpack telemetry and California snow sensors) in California, Colorado, Wyoming, Idaho and Oregon during the 2008 and 2009 snow seasons. More than 30 000 field observations of snowpack properties were used with binary regression tree models to relate SWE at the sensor site to the surrounding area SWE to evaluate the sensor representativeness of larger‐scale conditions. Unlike previous research, we did not find consistent high biases in snow sensor depth values as biases over all sites ranged from 74% overestimates to 77% underestimates. Of the 53 assessments, 27 surveys indicated snow station biases of less than 10% of the surrounding mean observed snow depth. Depth biases were largely dictated by the physiographic relationship between the snow sensor locations and the mean characteristics of the surrounding grid, in particular, elevation, solar radiation index and vegetation density. These scaling relationships may improve snow sensor data assimilation; an example application is illustrated for the National Operational Hydrologic Remote Sensing Center National Snow Analysis SWE product. The snow sensor bias information indicated that the assimilation of point data into the National Operational Hydrologic Remote Sensing Center model was often unnecessary and reduced model accuracy. Copyright © 2012 John Wiley & Sons, Ltd.

A preliminary appraisal of Thurnham dual polarisation radar in the context of hydrological modelling structure

The Thurnham radar is a prototype of a potential operational C-Band dual-polarisation weather radar designed specifically for the measurement of rainfall. It is also designed to increase the radar coverage over London when operating as a conventional C-Band radar as a direct consequence of the Lewes floods of October 2000. Dual-polarisation processing is expected to provide improved estimation of rainfall rates, especially at higher intensities, in terms of clutter removal, attenuation correction and rainfall estimation. In this study, three hydrological models with different mathematical structures were selected to evaluate the impact that dual-polarisation technology could have on operational hydrology and recommendations provided on the further development of the dual-polarisation algorithms in the short term. The preliminary appraisal was focused on the Upper Medway Catchment (south of London, UK) using different precipitation inputs, including raingauge measurements, radar rainfall estimates from single-polarised algorithms (cartesian format) and five different dual-polarisation algorithms (polar format). The influence of the different rainfall inputs on the various hydrological models were compared using a extreme flood event to provide an initial evaluation of the performance of the Thurnham radar. Recommendations for applying dual-polarisation radar to real-time flood forecasting are discussed in detail.

Multitemporal analysis of hydrological soil surface characteristics using aerial photos: A case study on a Mediterranean vineyard

Soil surface characteristics (SSC) constitute an important land surface property that drives the partitioning between infiltration and runoff. Therefore, knowledge of SSC is crucial for runoff-forecasting in hydrology. However, the difficulties in measuring spatial variabilities and temporal dynamics of SSC have limited the use of this property in operational hydrology at the catchment extent. Recent progresses have permitted to characterize hydrological SSC classes (H-SSC) with distinct infiltration rates, by implementing monotemporal classifications along with aerial photos. However, when dealing with Mediterranean vineyards, some classes still are difficult to discriminate on the basis of remotely sensed spectral and spatial information only.The objective of the current study was to propose a multitemporal classification that integrates a priori information about possible H-SSC evolutions, such as it is possible improving their characterization. H-SSC evolutions could be either natural, depending on rainfall events, or anthropogenic, driven by soil management practices. Knowledge of possible H-SSC evolutions was translated in the form of decision rules. It was applied to a time series of H-SSC class maps derived from a monotemporal classification of monthly aerial photos. As compared to the monotemporal classification, the multitemporal classification had two advantages for the identification of H-SSC classes. First, it allowed improving the discrimination of classes related to crusting processes, with increased performances between 35 and 48% relative. Second, it was able to detect H-SSC temporal evolutions in relation to soil management practices.

Assessing the high frequency quality of long rainfall series

High resolution, long and reliable rainfall time series are extremely important to assess reliable statistics, e.g. the Depth–Duration–Frequency curves that have been widely used to define design rainfalls and rainfall drainage network dimensioning. The potential consequences of changes in measuring and recording techniques have been somewhat discussed in the literature with respect to a possible corresponding introduction of artificial inhomogeneities in time series. In this paper, we show how to detect another artificiality: most of the rainfall time series have a lower recording frequency than that is assumed, furthermore the effective high-frequency limit often depends on the recording year due to algorithm changes. This question is particularly important for operational hydrology, because we show that an error on the effective recording high frequency introduces biases in the corresponding statistics. We developed a simple automatic procedure to assess this frequency period by period and station by station on a large database. The scaling analysis of these time series also shows the influence of high frequency limitations on the scaling behaviour, leading to possible misinterpretation of the significance of characteristic scales and scale-dependent hydrological quantities.

International Meteorological and Hydrological Training and Its Evaluation at WMO RTC Nanjing

Abstract This paper deals with the international meteorological and hydrological training issue and its evaluation approach based on the teaching practice and experience during last twenty years at World Meteorological Organization Regional Training Centre (WMO RTC) in Nanjing. The success of the training courses which are organized by the RTC Nanjing in the framework of Guidelines for the Education and Training of Personnel in Meteorology and Operational Hydrology (WMO- No. 258) has been widely recognized by those countries which benefit from the training programs as well as the WMO and other related international organizations. The design of the training syllabuses fulfils the Member's training requirements of WMO. Some new training courses which reflect the advances in meteorology and hydrology have also been offered in order to enhance the capabilities of trainees in the use of the up-to-date numerical products. In addition to face-to-face teaching, blended learning has been introduced for some training courses to enhance the training efficiency. Based on the “CIPP model” and “Kirkpatrick Four Levels Model”, a more efficient evaluation system for the international meteorological and hydrological training has been established. Results show that this evaluation system is capable of assessing the training target, the training results as well as training itself. The evaluation index system consists of six indices which focus on the training subjects and trainees, respectively. The application of this evaluation system in the international meteorological and hydrological training has significantly improved the training efficiency and training itself at RTC Nanjing.

Peramalan Aliran Masukan Waduk Mrica Menggunakan ModelThomas-Fieringdan Jaringan Syaraf Tiruan ANFIS

<p>Inflow forecasting in hydrology processes is important tool in water resources management,planning, and utilization. The fulfillment of this operational hydrology isvery applicable, especially where onlyan insufficient amount of data collected over an insufficient length of time is available. The Thomas-Fiering Method is one of the most useful and widely used synthetic flow models. In last year’s, ArtificialNeural Network (ANN)method and Fuzzy Logic have introduced in hydrological processes. Mrica hydropower reservoir in Central Java, Indonesia, has suffered water sustainability andenergy sustainability problems since the reservoir management used simple-operator judged waterinflow forecasting method. In this paper, an ANN and Fuzzy Logic hybrid algorithm calledAdaptive Neuro-Fuzzy Inference System(ANFIS) and Thomas-Fiering model are employed to estimate water inflow to the Mrica reservoir. ANFIS performs better for long-range inflow forecasting, while Thomas-Fiering model was better for short-range forecasting.</p>

No monsters, no miracles: in nonlinear sciences hydrology is not an outlier!

The end users of hydrological models may be justified for being tired of the excessive uncertainty of these models, not to mention their simplistic approximations and crude modelling. The ever-increasing sophistication of model parameter fitting is simply a smoke-screen that hides the models' lack of physical basis, their scale dependence, and their inability to fit widely diverse behaviours. More generally, we have to admit a lack of qualitative improvement in hydrological modelling in recent times. In fact, operational hydrology may have suffered for some time from ignoring the advances in theoretical hydrology, which have, in contrast, greatly stimulated the nonlinear sciences. For instance, more than a century ago fractals were considered as geometrical monsters, whereas decades ago river networks became classical fractal objects, and rainfall and discharges are now classical examples of multifractal fields. These hydrological characteristics are still often ignored by operational hydrology, whereas they explain not only its current limitations, but also how to overcome them. To illustrate these problems, this paper focuses on the fact that hydrological fields are most likely singular with respect to measures of time and volume. This would not only explain the ubiquitous scale dependence of hydrological observations, but would also give the possibility to transform them into scale-independent quantities. The upscaling of a rainfall time series from an hour to a year is therefore discussed in detail, and enables us to quickly introduce other examples. Citation Schertzer, D., Tchiguirinskaia, I., Lovejoy, S. & Hubert, P. (2010) No monsters, no miracles: in nonlinear sciences hydrology is not an outlier! Hydrol. Sci. J. 55(6), 965–979.

Communicating uncertainty in hydro‐meteorological forecasts: mission impossible?

AbstractThere is a common agreement in the scientific community that communicating uncertain hydro‐meteorological forecasts to water managers, civil protection authorities and other stakeholders is far from being a resolved issue. This paper focuses on the communication of uncertain hydrological forecasts to decision‐makers such as operational hydrologists and water managers in charge of flood warning and scenario‐based reservoir operation. Results from case studies conducted together with flood forecasting experts in Europe and operational forecasters from the hydroelectric sector in France are presented. They illustrate some key issues on dealing with probabilistic hydro‐meteorological forecasts and communicating uncertainty in operational flood forecasting. Copyright © 2010 Royal Meteorological Society

Comparing Sigmoid Transfer Functions for Neural Network Multistep Ahead Streamflow Forecasting

One of the main problems of neural networks is the lack of consensus on how to best implement them. This work targets the question of the transfer function selection—a vital part of neural network providing nonlinear mapping potential. Three nonlinear transfer functions bounded by 1 and 1 are selected for testing, based on a literature review: the Elliott sigmoid, the bipolar sigmoid, and the tangent sigmoid. They are used to design multilayer perceptron neural networks for multistep ahead streamflow forecasting over five diverse watersheds and lead times from 1 to 5 days. All multilayer perceptrons have shown a good performance on the account of the four selected criteria, which confirms that the selected multilayer perceptron implementation procedure was adequate, namely, the data set length, the Kohonen network clustering method to create the training and testing sets, and the Levenberg-Marquardt back-propagation training procedure with Bayesian regularization. Specifically, results endorsed the tangent sigmoid as the most pertinent transfer function for streamflow forecasting, over the bipolar logistic and Elliott sigmoids, but the latter requires less computing time and as such may be a valuable option for operational hydrology. Also, results averaged over five lead times confirmed the universal approximation theorem that a linear transfer function is suitable for the output layer—a nonlinear transfer function in the output layer failed to improve performance values. DOI: 10.1061/ASCEHE.1943-5584.0000188 CE Database subject headings: Neural networks; Streamflow; Forecasting; Transfer functions; Computation. Author keywords: Neural networks; Streamflow forecasting; Transfer function; Performance evaluation; Computing time.

Assessment of the NASA AMSR-E SWE Product

Since the launch of the Scanning Multichannel Microwave Radiometer (SMMR) in 1978, several studies have demonstrated the capability of spaceborne passive microwave sensors for mapping global snow water equivalent (SWE). Currently, SWE values are estimated operationally from microwave brightness temperatures measured by the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) and distributed through the National Snow and Ice Data Center (NSIDC). In this study, we report results regarding the comparison between AMSR-E SWE and SWE/snow depth values distributed by the Snow Data Assimilation System (SNODAS) product of the NOAA's National Operational Hydrologic Remote Sensing Center and snow depth measured by automatic weather stations of the World Meteorological Organization. Generally, we found poor correlation between the AMSR-E and SNODAS SWE/snow depth values. The algorithm performance improves when considering WMO data, though the number of samples used for the analysis might play a role in this sense. We discuss algorithm-related sources of error and uncertainties, such as vegetation and grain size. Moreover, we report results aimed at evaluating whether replacing the linear approach with a nonlinear one and not using the brightness temperatures and ancillary data sets combined as in the current approach but taken separately as inputs to the algorithm might improve the performance of the algorithm.

Application of Forecast Verification Science to Operational River Forecasting in the U.S. National Weather Service

Forecast verification in operational hydrology has been very limited to date, mainly due to the complexity of verifying both forcing input forecasts and hydrologic forecasts on multiple space-time scales. However, forecast verification needs to be the driver in both hydrologic research and operations to help advance the understanding of predictability and help the diverse users better utilize the river forecasts. Therefore, in NOAA's National Weather Service, the Hydrologic Services Program is developing a comprehensive river forecast verification service to routinely and systematically verify all hydrometeorological and hydrologic forecasts. This verification service will include capabilities for archiving forecast and observed data, evaluating logistical properties of the forecast services, computing a variety of verification metrics to evaluate the different aspects of forecast quality, displaying and disseminating verification data and metrics, and analyzing the sources of forecast skill and uncertainty through the use of multiple forecast and hindcast scenarios. This paper describes ongoing and planned verification activities for enhancing the collaboration between the meteorological and hydrologic research and operational communities to quantify forecast improvements based on rigorous forecast verification.

Scale‐recursive estimation for merging precipitation data from radar and microwave cross‐track scanners

This paper presents an application of scale‐recursive estimation (SRE) used to assimilate rainfall rates within a storm, estimated from the data of two remote sensing devices. These are a ground‐based weather radar and a spaceborne microwave cross‐track scanner. The rain rate products corresponding to the latter were provided by the EUMETSAT Satellite Application Facility on Support to Operational Hydrology and Water Management. In our approach, we operate directly on the data so that it is not necessary to consider a predefined multiscale model structure. We introduce a simple and computationally efficient procedure to model the variability of the rain rate process in scales. The measurement noise of the radar is estimated by comparing a large number of data sets with rain gauge data. The noise in the microwave measurements is roughly estimated by using upscaled radar data as a reference. Special emphasis is placed on the specification of the multiscale structure of precipitation under sparse or noisy data. The new methodology is compared with the latest SRE method for data fusion of multisensor precipitation estimates. Applications to the Belgian region show the relevance of the new methodology.

On quality indicators for radar-based river flow forecasts

Although there has been considerable progress in improving the quality of radar estimates of precipitation, including developments in radar technology, the unpredictable nature of radar errors continues to discourage many operational hydrologists from using radar data quantitatively as input to models. Indeed, the tendency has been to wait for all errors to be removed. Unfortunately it is unlikely that all errors will be removed all of the time, even with new promising radar technologies such as dual polarisation systems. In this paper a procedure is proposed and partially tested for deriving a quality index from single polarisation radar based on the use of a ‘peaks-over-threshold’ (POT) parameter for gauged catchments. The procedure is tested for several different catchments and for different rainfall types. For ungauged catchments a different approach is proposed based on the fitting and analysis of an autoregressive model to rainfall time series. This procedure is tested using the radar data distributed to users in real time during major flood events in the Midlands, UK. The practicality of applying these techniques to radar data in an operational environment is discussed.

Automatic state updating for operational streamflow forecasting via variational data assimilation

Summary In operational hydrologic forecasting, to account for errors in the initial and boundary conditions, and in parameters and structures of the hydrologic models, the forecasters routinely make adjustments in real-time to the hydrometeorological input, hydrologic model states and, in certain cases, model parameters based on streamflow observations. Though a great deal of effort has been made in recent years to automate such “run-time modifications” (MOD) by human forecasters to a possible extent, automatic state updating of hydrologic models is yet to be widely accepted or routinely practiced in operational hydrology for a range of reasons. In this paper, we describe a state updating procedure intended specifically for operational streamflow forecasting for gauged headwater basins, and compare its performance with human forecaster MOD through a real-time forecasting experiment. The procedure is based on variational assimilation (VAR) of streamflow, precipitation and potential evaporation (PE) data into lumped soil moisture accounting and routing models operating at a 1-h timestep. The procedure has been in experimental operation since 2003 at the National Weather Service’s (NWS) West Gulf River Forecast Center (WGRFC) in Fort Worth, TX. Also described is a novel parameter estimation and optimization tool, the Adjoint-Based OPTimizer (AB_OPT), used for lumped hydrologic modeling at a 1-h timestep necessary for VAR.

Real-Time Visualization in Operational Hydrology through Web-based Cartography

Early warning and monitoring activities in the field of natural hazard management are crucial in order to limit damage to life and property from extreme natural events. Decision makers in these fields, such as operational hydrologists, must be enabled to quickly and comprehensively assess an on-going situation, examine its details, and then take further actions based on the most current information available. The cartographic challenge is to deliver automated, real-time visualizations from distributed raw measurement data while applying sound cartographic rules and standards. In this research, a web-based, cartographic, real-time information system is presented. True real-time data of Swiss hydro-meteorological measuring networks were used, and a prototype application was tested that integrates the acquisition, processing, and visualizations in different ways, depending on the chosen timeframe and level of detail. Besides monitoring visualizations, data are retraceable using visualizations that clearly stress the course of a hydrological event and the data's spatio-temporal character. Further visualizations and functionalities are provided that allow for relating real-time data to a broader historical context, facilitating learning from former experiences. Data for past events are immediately retrievable from a data archive, then classified and visualized for comparisons.

EFFECT OF DATA LENGTH ON WATER RESOURCES ASSESSMENT

ABSTRACT A robust hydrological information system is a pre-requisite for efficient water resources assessment of a region. Decision makers tend to make low use of external depositories of problem-solving water data if conditions exist by way of distance, cost, etc. which dissuade or inhibit them from seeking or using it. This is true even when users are aware of the potential benefits that accrue from long term data in decision making. Planners and researchers need to be cautious about the uncertainties in using data of short length for water resources assessment. In this context, the paper examines the hydrological information system in the country, and studies the effect of data length on water resources assessment with real life examples. Use of operational hydrology techniques for assessment of water resources yield and flood potential is illustrated, in the context of a study on effect of data length water resources assessment. The results show that, in general, lower the data length, higher is the likelihood of overestimating water resources availability in a region. Uncertainties in respect of flood potential assessment also increase as the examples show.