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Advances in Water Resources 32 (2009) 969–974
Contents lists available at ScienceDirect
Advances in Water Resources
journal homepage: www.elsevier.com/locate/advwatres
Preface
Weather radar and hydrology
1. Introduction This article is a preface to a special issue journal that has been
initiated in parallel to the International Symposium Weather Radar
The growing concerns about environmental and climate change and Hydrology, held in Grenoble and Autrans, France, in March
issues, as well as the emergence of the concept of sustainable 2008. This event continues a series of symposia originally called
development enshrined in the European Water Framework Direc- Hydrological Applications of Weather Radar that started in 1989
tive (WFD) legislation, has modified the requirements for future at the University of Salford, UK. This first symposium was followed
hydrological observation and modelling initiatives and techniques. by the Hannover (1992), Sao Paulo (1995), San Diego (1998), Kyoto
Originally, the focus at larger catchment scales was on the water (2001) and Melbourne (2004) meetings. The main objective of the
stream flow at a limited number of locations, essentially for oper- 2008 symposium was to promote the use of quantitative precipita-
ational purposes such as flood prediction, dam regulation and tion estimates (QPE) derived from weather radar technology in
hydropower production as well as hydrological process under- hydrological sciences and their applications. The symposium was
standing. Water is now considered under its multiple functions composed of two events:
such as water supply, hydrological risk, hydropower, irrigation,
tourism, ecological value, to name a few. The demand for better (i) A 3-day conference held on 10–12 March 2008 in Grenoble.
hydrological understanding and techniques has thus moved to a The programme was composed of 5 keynote speeches, 52
more integrated prediction of the water balance components (rain- oral and 80 poster presentations covering the multiple
fall, runoff, water storage, transpiration, evaporation, groundwater aspects of radar physics, radar QPE and distributed hydro-
levels, etc.) at every point within a region [21]. In addition, consid- logical modelling. This scientific material (abstracts,
eration of land-use and human-induced landscape modifications is extended abstracts, oral presentations) can still be accessed
now a major concern for water management problems (i.e. for on the symposium web site at the following address: http://
flood forecasting) and impact studies of land use change on stream www.wrah-2008.com.
flow, pollutants and/or sediments transport. For most of these (ii) A 2-day workshop held on 13–14 March 2008 in Autrans
questions, knowledge of the general characteristics of water bal- (Vercors). The workshop was dedicated to thematic discus-
ance components is not sufficient, more detailed process under- sions aimed at summarizing the symposium results and
standing (i.e. storm responses) throughout the landscape are investigating future research directions.
required. The development of distributed and/or semi-distributed
hydrological models, where there is a greater representation of In the following, a presentation of the most promising tech-
the land-surface heterogeneities and the characterization of dis- niques for radar rainfall QPE is proposed in Section 2 prior to a dis-
tributed inputs, are thus increasingly necessary. The usefulness of cussion about some of the challenges of distributed hydrological
distributed hydrological models has been questioned, mainly due modelling (Section 3). A set of recommendations is made in Section
to overparameterization problems, parameter estimation and vali- 4 to foster the use of weather radar in hydrologic sciences. Section
dation limitations [9,43,8]. Such difficulties cannot be ignored but 5 briefly introduces the articles of the special issue.
the continuous development of hydrological observation and land-
scape characterization techniques are likely to offer renewed pos- 2. The most promising techniques for radar rainfall estimation
sibilities in this field. We must recognise there is an inevitable
compromise between the complexity of the model proposed, and Real-time rainfall detection and estimation as well as rainfall
the spatial data that is available to drive the model (inputs) and ro- nowcasting have been the main motivations for the deployment
bustly evaluate its predictions. of operational weather radar networks over the past six decades.
We suggest weather radar technology offers a unique means for Although the qualitative value of radar networks is unquestioned,
characterizing the rainfall variability over the range of scales and the complex technology of weather radar systems combined with
with the space-time resolutions required for a large variety of the variety of uncertainty sources and the strong variability of pre-
hydrological problems. This is especially true for risk management cipitation at all scales [30] has limited the quality of radar precip-
in urban and mountain watersheds characterized by fast dynamics itation estimates [37] and therefore the use of radar QPE in
and for which rainfall monitoring and nowcasting are critical operational hydrology.
[21,37]. For the largest scales, continental rainfields derived from Measurement artefacts (e.g., clear air echoes, ground and sea
radar networks may also provide valuable information in terms clutter, anthropogenic targets such as wind mills, airplanes, etc.)
of water budget in complement with climatologic networks and are a first very important limitation for quantitative use. In spite of
satellite imagery. automated identification techniques now being routinely
0309-1708/$ - see front matter Ó 2009 Elsevier Ltd. All rights reserved.
doi:10.1016/j.advwatres.2009.03.006970 Preface / Advances in Water Resources 32 (2009) 969–974
implemented for conventional radars (i.e. single-polarization non- Although much less established, microwave links rainfall mea-
coherent Doppler radar in the following), residual artefacts may sig- surement also offers interesting potential with the deployment
nificantly alter rain reflectivity values and the subsequent radar data of wireless communication networks all over the world [56,49].
processing steps. An agreement exists now about the main radar er- A major advantage is related to the fact that such measure-
ror sources related to both instrumental and sampling properties ments are made close to the ground. First studies indicate that
[75,45,37]: calibration, attenuation due to wet radome, ground clut- effects of rainfall variability are limited if the link length and
ter and beam blockages, attenuation due to gas and rain, vertical het- frequency are chosen correctly [6,50]. For such settings, the
erogeneity of the hydrometeors (referred to as the vertical profile of main error sources are related to the wetting of antenna covers
reflectivity –VPR- in the following), reflectivity-rain rate conversion and to the resolution of power measurements.
(Z–R relationship), rainfall advection. Volume-scanning protocols
and physically-based data processing are now generally imple-
mented operationally to cope with such error sources. Although 3. Distributed hydrological modelling
some noticeable progress has thus been obtained [39,68,69,26]
some inherent limitations will continue to exist due for instance to Recent and ongoing research initiatives such as DMIP (Distrib-
the difficulty in performing an absolute calibration, to the sampling uted Model Intercomparison Project; [66]) and PUB (Prediction in
problems (radar resolution varies as a function of range, non-uni- ungauged basins; [34]) are focused on distributed hydrological
form beam filling, non-observation of the low-level altitudes above modelling. For PUB the concept is to reduce the uncertainties in
ground level beyond 60–80 km), to entangled errors (e.g., combina- the modelling process by improving our understanding and repre-
tion of calibration, attenuation and VPR errors at C-band) and to the sentation in models. We briefly review hereafter some of the re-
high intra-field variability of the Z–R relationship [72]. lated challenges.
A major advent in the recent years is the implementation of One of the difficulties in hydrological modelling is that the spa-
dual polarization and Doppler techniques in a pre-operational tial and temporal scales of hydrological processes span several or-
mode for several weather radar networks [76,62,42]. In addition ders of magnitude: from a few m2 and minutes for soil infiltration
to the reflectivity Z, the differential reflectivity ZDR, the cross corre- to thousands of km2 and several years for groundwater [13]. Fur-
lation qhv and the differential phase shift /DP between the horizon- thermore, depending on the climate and the characteristics of the
tal and vertical polarizations can be measured for each radar catchment (topography, land use, pedology, etc.), only one or sev-
resolution volume. It is worth noting that the potential for dual- eral of these processes may be dominant. For instance, it is usually
polarisation techniques to eliminate non-meteorological echoes recognized that runoff (both surface and sub-surface runoff) is
[42] has actually been the main reason why several countries have dominant in the case of flash-floods, but that evapotranspiration
decided to deploy a network of polarimetric radars. Classification must be accounted for in long-term simulations. According to the
algorithms are used to distinguish several classes of liquid and fro- scale of interest, the dominant processes might also change. For
zen hydrometeors [67] prior to the application of specific rainrate example while soil hydraulic characteristics are influential for run-
estimators using various combinations of the radar measurables off generation through infiltration excess and ponding at the plot
[62]. Polarimetry also offers some interesting possibilities such as scale, the presence of macropores and preferential flow might be
self-calibration of reflectivity, attenuation correction [70,41] and the dominant factor when studying the runoff generated at the
DSD retrieval [16,57]. Of lesser direct value for radar QPE, the hillslope scale. Finally, there are several thresholds in hydrology
Doppler capability allows for a better description of the rain sys- (e.g., appearance of ponding, snow melting, saturated areas, flood-
tem dynamics [15]. Refractivity measurements from the phase var- ing, etc.) which make the modelling more complex due to the asso-
iation of ground targets [31] also offer potential for the ciated high level of non-linearities in hydrological responses [65].
characterization of low-level humidity fields. A second type of difficulty arises from the combined heteroge-
There is an agreement about the critical importance of calibra- neities of the land surface and the sub-soil. The large spatial heter-
tion and maintenance operations for quantitative applications. The ogeneity of the land surface is driven by topography, land use,
parameters of the radar systems need to be monitored continu- geology and pedology and increasingly by human actions. To sim-
ously and alarms have to be triggered when predefined thresholds plify the problem, hydrologists try to define so-called homoge-
are exceeded. Data from the monitoring system are critical meta- neous zones, i.e. areas where the hydrological behaviour can be
data and need to be archived along with the radar observations considered as homogeneous. This has led to the definition of
and products. Polarization means more measured variables and Hydrological Representative Units (HRU) [33,10], Representative
thus more information, but also increased technical complexity Elementary Areas (REA) [74], Representative Elementary Water-
and workload in terms of system maintenance. sheds [61]. In terms of modelling, this range of concepts has led
Despite the promises of polarimetry, there is evidence that the to various landscape discretizations. Models are mostly based on
density of existing operational radar networks is insufficient for a regular meshes that are usually derived from Digital Terrain Mod-
proper hydrological coverage at the national/continental scale if els (e.g. [1,11,60,18]. However, the continental surface heterogene-
one considers the 100-km range as a maximum practical limit. ity is not well described by regular grids and other discretizations
Two complementary solutions are currently being investigated: have been proposed such as triangular irregular networks [44],
hillslopes [71] and hydrolandscapes [24].
The X-band frequency offers attractive design characteristics For process representations, two approaches can be distin-
compared to the S-band and C-band frequencies (reduced trans- guished [64]. The first one is an upward approach where processes
mitted power and antenna size; reduced sensitivity to ground are modelled using equations established at very small scales (such
clutter) that are counterbalanced by a dramatic sensitivity to as the Richards equation for water transfer within the saturated/
rain attenuation. In the recent years, a breakthrough has been unsaturated zone, the St-Venant equation for water flow within a
achieved in radar QPE at X-band through the use of polarimetry river channel, or the Boussinesq equation for water transfer within
and range-profiling algorithms [70,2,40,51,53]. Several indus- an aquifer) and extended to larger scales. Effective parameters, rel-
trial (e.g. (http://www.casa.umass.edu/) and research projects evant to the scale of discretization must therefore be specified if
have been initiated to further develop this concept which has not calibrated [13]. This approach is difficult to verify due to limi-
certainly a great future for radar QPE in rugged topography tations with current hydrological observation techniques (e.g., for
and/or in urban areas. subsurface flow processes). Given the non-linearity in hydrologicalPreface / Advances in Water Resources 32 (2009) 969–974 971
processes, it is also difficult to derive theoretical approaches consisting in gathering large data sets, performing extensive qual-
describing how the model parameters might change across scales. ity checks and combining them to produce rainfall estimates, is a
The second approach is a downward approach where process con- very demanding task and requires a community wide international
ceptualization is derived from the available data, with a minimum effort.
of parameters, without representing what happens at smaller A first issue that needs to be addressed concerns the data that
scales. The simplest approach to this is data based modelling tech- are available to perform re-analyses. Concerning rain gauge data,
niques; other examples of differing complexity of representation rain gauge networks date back to the 1950s. However, the diversity
can be found in [46,29]. But the establishment of such models re- in data quality, type, consistency and accessibility is a serious is-
quires that data are available at the modelling scale, which is not sue. The effort and cost required to develop and maintain rain
always the case, and the extrapolation to ungauged catchments gauge networks is significant and there seems to be a trend of
and/or potentially more extreme observational events that are reducing the density of rain gauge networks as the radar network
not yet characterised within the data is difficult. is improved. Concerning radar data, there has been a strong evolu-
In terms of modelling practice, a change in modelling paradigm tion of the radar networks during the previous two decades both in
is certainly required as well as accounting for the uncertainties in terms of density and operating protocols. Moreover radar systems
model prediction. Modellers were trying to answer the following collect huge amounts of numerical data, which raises the issue of
question: ‘‘Which model is suited to my problem?”. The question efficient, safe and fast access archiving solutions. This issue will be-
should move to ‘‘Which combination of processes is relevant to come even more acute as polarimetry becomes a standard for oper-
this problem?” [48]. Adopting this paradigm has two conse- ational weather radar networks. Although radar networks date
quences; firstly the spatial and temporal discretization of the mod- back to the 1980s, exploitable radar data will probably cover a
el should be adjusted so as to meet the main objectives of the much shorter period due to unreliable numerical archiving. In or-
modelling study and the data availability, and secondly the com- der to produce data sets that are consistent at the continental scale,
plexity and spatial heterogeneity of model process representation the question of the homogeneity of different radar networks is
should be adapted to the model spatial and temporal discretiza- important. In Europe, the OPERA project (http://www.knmi.nl/op-
tion. Addressing these challenges is one way to reconcile the era), which aims at harmonizing radar data from 28 countries and
downward and upward approaches of model conceptualisation at promoting data exchange, is an important initiative in this
that provides pragmatic solutions to modelling problems [66]. respect.
Some authors have described this process as a learning framework The second issue concerns the choice of methodologies to apply
for hydrological modelling [28]. Progress in computer science, such when performing a rainfall re-analyses from radar and other
as object-oriented modelling, and the appearance of modular mod- sources of rainfall data.
elling platforms [3], which allow the user to couple various models
in an efficient way, makes the exploration of different model rep-
resentations more possible now and for the future. Depending on the archived data (raw volume data versus
This new perspective of pragmatic model development to meet already processed QPE products), the possibility to apply phys-
the needs of the application effectively in terms of spatial discret- ically-based processing algorithms may vary significantly. Prag-
ization and process conceptualization will benefit from the avail- matic and standardised approaches should be implemented to
ability of new observational techniques that describe landscape control the stability of the radar signal, to determine the radar
heterogeneity and provide hydrological observations at various detection domain, to identify and remove residual artefacts, to
scales. Weather radar rainfall fields will contribute to progress in quantify and correct for attenuation effects, and to characterize
this direction. Understanding the variability of storm event rain- the vertical profile of reflectivity. Radar–radar merging tech-
falls over catchment areas will improve the quantification of the niques should receive more attention especially for relatively
uncertainties involved in the modelling process by better defining dense networks operating in rugged topography: an early-
the input uncertainties to models, potentially moving away from merging of volume data may be preferable to merging the
the necessarily simplified representation of these estimates in cur- QPE products of various radars.
rent techniques [36]. Continuous increase in computing power As already mentioned, conventional radar technology does
makes uncertainty assessments possible now even for the more not guarantee an accurate electronic calibration and the Z–R
complex physically-based models [12]. This should be an impor- relationship is highly variable. Therefore recourse to other
tant consideration in such studies, especially when using diverse sources of rainfall data is needed to optimize the radar
data to assess model structures spatially [35]. parameters and/or to control the radar QPE quality. Rather
than a simple ‘‘radar calibration”, such an operation should
4. Recommendations be seen as a multi-sensor merging requiring a detailed
knowledge of the space-time structure of rainfall and of the
Besides the necessary development of operational radar net- instrumental/sampling errors associated with the various sen-
works for rainfall detection and nowcasting purposes, two main sors used [37,5,73]. The lack of absolute rainfall reference is
recommendations can be formulated to foster the use of weather often invoked; rain gauge networks with adequate densities
radar data in hydrological sciences (see also [37]). remain the only practical solution for obtaining such refer-
ence values for past data. The issue of representativeness
4.1. Rainfall re-analyses for hydrological sciences and discrepancies in sampling scales is significant; time inte-
gration is a possible way to limit this influence. Other types
Huge amounts of radar data have been collected for the last 30 of rainfall data such as disdrometers, microwave links, and
years in almost all types of climate and for many regions all over satellite data (in particular TRMM and GPM that offer suitable
the world. This wealth of data has not yet been fully exploited. In- resolutions) could be progressively incorporated in such
spired by what has been done for atmospheric data sets (e.g., NCAR merging procedures.
or ECMWF meteorological re-analyses), efforts should be encour-
aged for using archived radar and rain gauge data to produce A variety of applications can benefit from the possibility of
long-term rainfall space-time data sets with relevant quantifica- accessing a standardized long-term rainfall re-analyses (in a simi-
tion of the associated errors. However, rainfall re-analysis, mainly lar way to the extensive use of atmospheric re-analyses for many972 Preface / Advances in Water Resources 32 (2009) 969–974
research or operational applications). For example, rainfall re-anal- uted hydrological models beyond their common calibration limits
yses will be useful for research and engineering applications like [38,20,14]. The availability of accurate radar QPE is again a key ele-
the analysis of extremes, the derivation of space-time ‘‘design rain- ment for successful post-flood surveys.
fall”, the detection of climatic trends, the evaluation of meteorolog- In the next years, one challenge for distributed hydrological
ical numerical models, the compilation of multiscale water models is thus to be able to integrate all these new sources of data
budgets, the forcing of distributed hydrological models. Critical together with their error characteristics in a consistent way and to
information that has to be provided in conjunction with the re- progress in the knowledge of water pathways and fluxes. A second
analyses is the quantification of the uncertainties associated with challenge is to tackle the problem of process conceptualisation at
precipitation estimates to enable ensemble and probabilistic various scales (change of scale problem). This must be based on
approaches. the recognition that when moving to larger scales, the form of
the equations should perhaps be modified [47]. McDonnell et al.
[54] go one step further in this direction by promoting the adapta-
4.2. Hydrometeorologic observatories tion of methodologies borrowed from ecology, focusing on the
hydrological functions which can be associated to patterns of
A synergy between various disciplines including hydrology and heterogeneity.
meteorology, but also neighbouring disciplines such as geochemis- In situ measurements and models will benefit from a mutual
try, ecology, geography, applied mathematics is required to enrichment. One of the promising ways to reach this goal is by
achieve progress in process understanding and modelling in extending the technique of sensitivity analysis and data assimila-
hydrology. Better techniques to regionalise the most appropriate tion, used in meteorology and oceanography to hydrology. Recent
model structures and to define in the landscape what drives the results [17] are very promising and show that these techniques
dominant modes of hydrological behaviour are critically needed. can be useful in improving measurement networks by identifying
Hydrometeorologic observatories (CUASHI initiative in the USA – the places and time where additional observations can be most
http://www.cuashi.org; HYDRATE project in Europe – http:// informative.
www.hydrate.tesaf.unipd.it/; [25]) are now proposed worldwide
to provide the required datasets. International scientific experi- 5. Overview of the special issue
ments like AMMA (http://amma-international.org/) and HyMeX
(http://www.cnrm.meteo.fr/hymex) offer additional opportunities This special issue of Advances in Water Resources contains
for coupled studies on the water cycle including its oceanic, atmo- twelve articles, which represent only a partial sample of the scien-
spheric and continental components. tific material presented during the International Symposium
Operational and research radars, together with the other instru- Weather Radar and Hydrology (http://www.wrah-2008.com/).
ments evoked in Section 2, have the potential to dramatically im- About radar QPE, Diss et al. [27] offer a convincing assessment
prove the description of rainfall patterns. Dense raingauge of a dual-polarization Doppler X-band radar in mountainous ter-
networks are required to further validate radar QPE and character- rain with reference to raingauge measurements and the estimates
ize their error structure. In addition, hydrologic experimental de- of an operational S-band radar. With regard to merging techniques,
sign should be based on nested instrumented catchments, to Velasco-Forero et al. [73] propose a non-parametric blending
address process understanding and model evaluation at various methodology intended to be used in real-time operation for the ra-
scales as well as understanding the uncertainties in our observa- dar-raingauge estimation while Cummings et al. [23] utilize micro-
tional data: wave link measurements for the mean field bias adjustment of an
operational radar.
Detailed hillslope instrumentation is required to advance the Three papers address more fundamental aspects related to the
understanding of factors controlling soil moisture redistribution rainfall spatial variability. With X-band polarimetric Doppler mea-
and for upscaling from local to the hillslope scales. Besides con- surements, Moreau et al. [58] analyze the rainfall spatial correla-
ventional instrumentation, hydrogeophysics offers now a vari- tion function in order to assess the representativeness error of
ety of new instruments to characterize the soil profiles and rain gauges with respect to areal rainfall and to more precisely
the water dynamics. characterize the radar instrumental error. In the same line, Man-
Small- and medium-scale catchments (1–100 km2) instrumen- dakapa et al. [52] present a theoretical framework for estimating
tation should include the distributed monitoring of soil mois- the spatial correlation of the radar rainfall error. Berne et al. [7] de-
ture, evapotranspiration and the other terms of the water and velop a method based on indicator variograms to automatically
energy budget. Distributed hydrometry could be achieved using determine the anisotropic rainfall spatial structure from radar
new remote sensing techniques such as large-scale particle data.
imagery velocimetry [22]. Very high resolution satellite images Two articles are about quantitative precipitation forecast (QPF).
and lidar also offer new opportunities for a better description of This important subject is not specifically addressed in the present
surface water pathways. preface and the interested readers are referred to [55] for a recent
For regional scale catchments (100–10,000 km2), the collection review. Herein, Fabry and Seed [32] analyze the accuracy of rainfall
and organisation of all the existing operational data sets is nowcasts from the US 3D radar mosaic and consider weather-
required. Improving the accuracy of flood discharge measure- dependent predictors to try improving the forecast and/or reducing
ments using 1D/2D hydraulic modelling and image analysis is its uncertainty. Barillec and Cornford [4] introduce a new varia-
necessary. Satellite data providing estimate of soil moisture tional Bayesian assimilation method for precipitation nowcasting
should also be acquired and processed in order to provide a using a spatial rainfall model based on radar data.
regional view of soil moisture variability. Four articles are about hydrologic modelling with radar rainfall
input. Focus is given to extremes for which weather radar offers
As a complement to detailed instrumentation, post-flood event unprecedented observations. Morin et al. [59] present a robust
field campaigns have to be further developed to gather and analy- hydrological model utilizing radar data intended to be used for
sis the large number of evidence left by extreme flood events even flash-flood warning in a semi-arid region. Bonnifait et al. [14] dis-
on ungauged catchments. Such surveys appear to be an efficient cuss the hydrologic and hydraulic modelling exercise that was con-
way of progressing the hydrology of extremes and testing distrib- ducted to put in coherence the various sources of data (includingPreface / Advances in Water Resources 32 (2009) 969–974 973
operational data and post-event field estimates) collected for an [21] Creutin JD, Borga M. Radar hydrology modifies the monitoring of flash flood
hazard. Hydrol Proc 2003;17:1453–6.
extreme event. Sangati et al. [63] utilize high resolution rainfall
[22] Creutin JD, Muste M, Bradley AA, Kim SC, Kruger A. River gauging using PIV
fields and a distributed hydrologic model to test the sensitivity of technique: proof of concept experiment on the Iowa river. J Hydrol
flash-flood simulations to spatial aggregation of rainfall and soil 2003;277:182–94.
properties over a range of scales. Finally, Cole and Moore [19] per- [23] Cummings RJ, Upton GJG, Holt AR, Kitchen M. Using microwave links to adjust
the radar rainfall field. Adv Water Resour 2009;32:1003–10. [Corrected proof,
form an ungauged modelling investigation using a distributed Available online 31 August 2008, ISSN: 0309-1708].
hydrologic model and radar rainfall inputs for instrumented nested [24] Dehotin J, Braud I. Which spatial discretization for distributed hydrological
catchments. models? Proposition of a methodology and illustration for medium to large
scale catchments. Hydrol Earth Sys Sci 2008;12:769–96.
[25] Delrieu G, Ducrocq V, Gaume E, Nicol J, Payrastre O, Yates E, et al. The
Acknowledgements catastrophic flash-flood event of 8–9 September 2002 in the Gard region,
France: a first case study for the Cévennes–Vivarais mediterranean hydro-
meteorological observatory. J Hydrometeorol 2005;6:34–52.
First author would like to thank the members of the scientific [26] Delrieu G, Boudevillain B, Nicol J, Chapon B, Kirstetter PE, Andrieu H, et al.
and organizing committees for their invaluable help for the organi- Bollène 2002 experiment: radar rainfall estimation in the Cévennes–Vivarais
region, France. J Appl Meteorol Climatol, in press.
zation of the WRaH 2008 international symposium. This event has [27] Diss S, Testud J, Lavabre J, Ribstein P, Moreau E, Parent du Chatelet J. Ability of
been sponsored by the Centre National de la Recherche Scientifique, dual polarized X-band radar to estimate rainfall. Adv Water Resour
the Institut National des Sciences de l’Univers, Météo France and the 2009;32:975–85.
[28] Dunn SM, Freer J, Weiler M, Kirkby MJ, Seibert J, Quinn PF, et al.
Cluster Environnement of the Rhône-Alpes region. A contribution
Conceptualization in catchment modelling: simply learning? Hydrol Proc
in time was funded by the UK Natural Environment Research 2008;22(13):2389–93.
Council (NERC), Flood Risk from Extreme Events (FREE) pro- [29] Eder G, Sivapalan M, Nachtnebel HP. Modelling water balances in an Alpine
catchment through exploitation of emergent properties over changing time
gramme with the grant number NE/E002242/1. The guest editors
scales. Hydrol Proc 2003;17(11):2125–49. doi:10.1002/hyp.1325.
wish to recognize the scientific contributions of the anonymous [30] Fabry F. On the determination of scale ranges for precipitation fields. J Geophys
reviewers to the special issue. Res 1996;101(D8):12819–26.
[31] Fabry F. Meteorological value of ground targets measurements by radar. J
Atmos Oceanic Technol 2004;21:560–73.
References [32] Fabry F, Seed AW. Quantifying and predicting the accuracy of radar-based
quantitative precipitation forecasts. Adv Water Resour 2009;32:1043–49.
[1] Abbott MB, Bathurst JC, Cunge JA, O’Connell PE, Rasmussen J. An introduction [Corrected proof, Available online 31 October 2008, ISSN: 0309-1708].
to the European hydrological system – système hydrologique European, (SHE), [33] Flügel WA. Delineating hydrological response units by geographical
1: history and philosophy of a physically-based distributed modelling system. J information system analyses for regional hydrological modelling using
Hydrol 1986;87:45–59. PRMS/MMS in the drainage basin of the river Bröl in Germany. Hydrol Proc
[2] Anagnostou EM, Anagnostou MN, Krajewski WF, Kruger A, Miriovsky BJ. High- 1995;9(3–4):423–36.
resolution rainfall estimation from X-band polarimetric radar measurements. J [34] Franks SW, Sivapalan M, Takeuchi K, Tachikawa Y. Predictions in ungauged
Hydrometeorol 2004;5(1):110–28. basins: international perspectives on the state of the art and pathways
[3] Argent RM. An overview of model integration for environmental applications- forward, book series, vol. 301. IAHS Publication; 2005. p. 1–348.
components, frameworks and semantics. Environ Model Soft 2004;19:219–34. [35] Freer JE, McMillan H, McDonnell JJ, Beven KJ. Constraining dynamic
[4] Barillec R, Cornford D. Data assimilation for precipitation nowcasting using TOPMODEL responses for imprecise water table information using fuzzy rule
Bayesian inference, Adv Water Resour 2009;32:1050–65. [Corrected proof, based performance measures. J Hydrol 2004;291(3-4):254–77.
Available online 8 October 2008, ISSN: 0309-1708]. [36] Kavetski D, Franks S, Kuczera G. In: Duan Q. et al., editor. Confronting input
[5] Berenguer M, I Zawadzki. A study of the error covariance matrix of radar uncertainty in environmental modelling, in calibration of watershed models.
rainfall estimates in stratiform rain. Weather Forecast 2008;23:1085–101. AGU; 2002. p. 49–68.
[6] Berne A, Uijlenhoet R. Path-averaged rainfall estimation using microwave [37] Krajewski W, Smith JA. Radar hydrology – rainfall estimation. Adv Water Res
links: uncertainty due to spatial rainfall variability. Geophys Res Lett 2002;25:1387–94.
2007;34:L07403. doi:10.1029/2007GL029409. [38] Gaume E, Borga M. Post-flood field investigations in upland catchments after
[7] Berne A, Delrieu G, Boudevillain B. Variability of the spatial structure of intense major flash floods: proposal of a methodology and illustrations. J Flood Risk
mediterranean precipitation. Adv Water Resour 2009;32:1031–42. [Corrected Manage 2009;1(4):175–89.
proof, Available online 3 December 2008, ISSN: 0309-1708]. [39] Germann U, Galli G, Boscacci M, Bolliger M. Radar precipitation
[8] Beven K. How far can we go in distributed hydrological modelling. Hydrol measurement in a mountainous region. Q J R Meteorol Soc 2006;132:
Earth Syst Sci 2000;5:1–12. 1669–92.
[9] Beven K, Binley A. The future of distributed models: model calibration and [40] Gorgucci E, Chandrasekar V, Baldini L. Correction of X-band radar observation
uncertainty prediction. Hydro Proc 1992;6:279–98. for propagation effects based on the self-consistency principle. J Atmos
[10] Beven KJ, Freer J. A dynamic TOPMODEL. Hydro Proc 2001;15(10):1993–2011. Oceanic Technol 2006;23(12):1668–81.
[11] Beven K, Kirkby MJ. A physically-based variable contributing area model of [41] Gorgucci E, Baldini L. Attenuation and differential attenuation correction of C-
basin hydrology. Hydrol Sci Bull 1979;24:43–69. band radar observations using a fully self-consistent methodology. IEEE Geosci
[12] Blasone RS, Madsen H, Rosbjerg D. Uncertainty assessment of integrated Remote Sensing 2007;4(2):326–30.
distributed hydrological models using GLUE with Markov chain Monte Carlo [42] Gourley JJ, Tabary P, Parent du Chatelet J. A fuzzy logic algorithm for the
sampling. J Hydrol 2008;353(1-2):18–32. separation of precipitating from nonprecipitating echoes using polarimetric
[13] Blöschl G, Sivapalan M. Scales issues in hydrological modelling – a review. radar observations. J Atmos Oceanic Technol 2007;24:1439–51.
Hydrol Proc 1995;9:251–90. [43] Grayson RB, Moore ID, McMahon TA. Physically based hydrologic modelling 2.
[14] Bonnifait L, Delrieu G, Le Lay M, Boudevillain B, Masson A, Belleudy P, et al. Is the concept realistic. Wat Resour Res 1992;28:2659–66.
Hydrologic and hydraulic distributed modelling with radar rainfall input – [44] Ivanov VY, Vivoni ER, Bras RL, Entekhabi D. Catchment hydrologic response
reconstruction of the 8–9 September 2002 catastrophic flood event in the Gard with a fully distributed triangulated irregular network model. Wat Resour Res
region, France. Adv Water Resour 2009;32:1077–89. 2004;40:W11102.
[15] Bousquet O, Tabary P, Parent du Châtelet J. On the value of operationally [45] Joss J, Waldvogel A. Precipitation measurement and hydrology. In: Atlas D.
synthesized multiple-Doppler wind fields. Geophys Res Lett 2007;34:L22813. editor. Radar in meteorology: Battan memorial and 40th anniversary radar
doi:10.1029/2007GL030464. meteorology conference. Am Meteor Soc; 1990. p. 577–606.
[16] Bringi V, Chandrasekar V, Hubbert J, Gorgucci E, Randeu WL, Schoenhuber M. [46] Jothityangkoon C, Sivapalan M, Farmer DL. Process controls of water balance
Raindrop size distribution in different climatic regimes from disdrometer and variability in a large semi-arid catchment: downward approach to
dual-polarized radar analysis. J Atmos Sci 2003;60(2):354–65. hydrological model development. J Hydrol 2001;254:174–98.
[17] Castaings W, Dartus D, Le Dimet FX, Saulnier GM. Sensitivity analysis and [47] Kirchner JW. Getting the right answer for the right reasons: linking
parameter estimation for the distributed modelling of infiltration excess measurements, analyses, and models to advance the science of hydrology.
overland flow. Hydrol Earth Sys Sci 2007;4:363–405. Wat Resour Res 2006;42:W03S04.
[18] Ciarapica L, Todini E. TOPKAPI: a model for the representation of the rainfall- [48] Leavesley GH, Markstrom SL, Restrepo PJ, Viger RJ. A modular approach to
runoff process at different scales. Hydrol Proc 2002;16:207–29. addressing model design, scale, and parameter estimation issues in distributed
[19] Cole SJ, Moore RJ. Distributed hydrological modelling using weather radar in hydrological modelling. Hydrol Proc 2002;16:173–87.
gauged and ungauged basins. Adv Water Resour 2009;32:1107–20. [49] Leijnse H, Uijlenhoet R, Stricker JNM. Rainfall measurement using radio links
[20] Costa JE, Jarrett RD. An evaluation of selected extraordinary floods in the from cellular communication networks. Water Resour Res 2007;43:W03201.
United States reported by the U.S. geological survey and implications for doi:10.1029/2006WR005631.
future advancement of flood science. USGS Scientific Investigations Report [50] Leijnse H, Uijlenhoet R, Stricker JNM. Microwave link rainfall estimation:
2008–5164, Reston, Virginia; 2008. effects of link length and frequency, temporal sampling, power resolution, and974 Preface / Advances in Water Resources 32 (2009) 969–974
wet antenna attenuation. Adv Water Resour 2008;31:1481–93. doi:10.1016/ formulation and characteristic response. Water Resour Res 2003;39:1316.
j.advwaterres.2008.03.004. doi:10.1029/2002WR001728.
[51] Maki M, Iwanami K, Misumi R, Park SG, Moriwaki H, Maruyama K, et al. Semi- [72] Uijlenhoet R, Steiner M, Smith JA. Variability of raindrop size distributions in a
operational rainfall observations with X-band multi-parameter radar. Atmos squall line and implications for radar rainfall estimation. J Hydrometeor
Sci Lett 2005;6:12–8. 2003;4:43–61.
[52] Mandapaka PV, Krajewski WF, Ciach GJ, Villarini G, Smith JA. Estimation [73] Velasco-Forero CA, Sempere-Torres D, Cassiraga EF, Gomez-Hernandez JJ. A
of radar-rainfall error spatial correlation. Adv Water Resour 2009;32:1020–30. non-parametric automatic blending methodology to estimate rainfall fields
[Corrected proof, Available online 11 September 2008, ISSN: 0309-1708]. from rain gauge and radar data. Adv Water Resour 2009;32:986–1002.
[53] Matrosov SY, Clark KA, Martner BE, Tokay A. X-band polarimetric radar [Corrected proof, Available online 18 October 2008, ISSN: 0309-1708].
measurements of rainfall. J Appl Meteor 2002;41(9):941–52. [74] Wood EF, Sivapalan M, Beven K, Band L. Effects of spatial variability and scale
[54] McDonnell JJ, Sivapalan M, Vaché K, Dunn S, Grant G, Haggerty R, et al. Moving with implication to hydrologic modelling. J Hydrol 1988;102:29–47.
beyond heterogeneity and process complexity: a new vision for watershed [75] Zawadzki I. Factors affecting the precision of radar measurements of rain. In:
hydrology. Wat Resour Res 2007;43:W07301. Preprints 22nd conference on radar meteorology, Am Meteor Soc. Zurich,
[55] Mecklenburg S, Jurczyk A, Szturc J, Osrodka K. Use of radar observations in Switzerland; 1984. p. 251–6.
hydrological and NWP models. COST action 717: topic WG1-8: quantitative [76] Zrnic DS, Ryzhkov AV. Polarimetry for weather surveillance radar. Bull Am
precipitation forecasts (QPF) based on radar data for hydrological models. Meteor Soc 1999;80:389–406.
2002; Available on-line at: .
[56] Messer H, Zinevich A, Alpert P. Environmental monitoring by wireless
Guy Delrieu
communication networks. Science 2006;312:713. Laboratoire d’étude des Transferts en Hydrologie et Environnement,
[57] Moisseev DN, Chandrasekar V. Nonparametric estimation of raindrop size UMR 5564 (CNRS, INPG, IRD, UJF),
distributions from dual-polarization radar spectral observations. J Atmos
BP 53, F-38041 Grenoble Cedex 9, France
Oceanic Technol 2007;24(6):1008–18.
[58] Moreau E, Testud J, Le Bouar E. Rainfall spatial variability observed by X-band E-mail address: Guy.Delrieu@hmg.inpg.fr
weather radar and its implication for the accuracy of rainfall estimates. Adv
Water Resour 2009;32:1011–19. [Corrected proof, Available online 3 Isabelle Braud
December 2008, ISSN: 0309-1708]. Cemagref, UR HHLY, Lyon, France
[59] Morin E, Y Jacoby, Navon S, Bet-Halachmi E. Towards flash-flood prediction in
the dry Dead Sea region utilizing radar rainfall information. Adv Water Resour Alexis Berne
2009;32:1066–76. [Corrected proof, Available online 7 December 2008, ISSN: Ecole Polytechnique Fédérale de Lausanne, Switzerland
0309-1708].
[60] Motovilov YG, Gottschalk L, Engeland K, Rodhe A. Validation of a distributed Marco Borga
hydrological model against spatial observations. Agr Forest Meteorol 1999;98– Department of Land and Agroforest Environment, University of Padova,
99:257–77.
[61] Reggiani P, Sivapalan M, Hassanizadeh SM. A unifying framework for Legnaro, Italy
watershed thermodynamics: balance equations for mass, momentum,
energy and entropy, and the second law of thermodynamics. Adv Water
Brice Boudevillain
Resour 1998;22:367–598. Laboratoire d’étude des Transferts en Hydrologie et Environnement,
[62] Ryzhkov AV, Schuur TJ, Burgess DW, Heinselman PL, Giangrande SE, UMR 5564 (CNRS, INPG, IRD, UJF), BP 53, F-38041 Grenoble Cedex 9,
Zrnic DS. The joint polarimetric experiment: polarimetric rainfall
France
measurements and hydrometeor classification. Bull Am Meteor Soc
2005;86:809–24. Frédéric Fabry
[63] Sangati M, Borga M, Rabuffetti D, Bechini R. Influence of rainfall and soil
properties spatial aggregation on extreme flash flood response modelling: an McGill University, Montreal, Canada
evaluation based on the Sesia river basin, North Western Italy. Adv Water
Resour 2009;32:1090–1106. [Accepted manuscript, Available online 1 January
Jim Freer
2009, ISSN: 0309-1708]. School of Geographical Sciences, University of Bristol, UK
[64] Sivapalan M. Prediction in ungauged basins: a grand challenge for theoretical
hydrology. Hydrol Proc 2003;17:3163–70. Eric Gaume
[65] Sivapalan M. Process complexity at hillslope scale, process simplicity at the Laboratoire Central des Ponts et Chaussées, Bouguenais, France
watershed scale: is there a connection? Hydrol Proc 2003;17:1037–41.
[66] Smith MB, Georgakakos KP, Liang X, editors. The distributed model Eiichi Nakakita
intercomparison project (DMIP). J Hydrol 2004;298(1–4):1–334 [Special Disaster Prevention Research Institute, University of Kyoto, Japan
issue].
[67] Straka JM, Zrnić DS, Ryzhkov AV. Bulk hydrometeor classification and Alan Seed
quantification using polarimetric radar data: synthesis of relations. J Appl
Center for Australian Weather and Climate Research, Melbourne,
Meteor 2000;39:1341–72.
[68] Tabary P. The new French operational radar rainfall product Part 1: Australia
Methodology. Weather Forecast 2007;22(3):393–408.
[69] Tabary P, Desplats J, Do Khac K, Eideliman F, Guégen C, Heinrich JC. The new
Pierre Tabary
French operational radar rainfall product Part 2: Validation. Weather Forecast Centre de Météorologie Radar, Météo-France, Toulouse, France
2007;22(3):409–27.
[70] Testud J, Le Bouar E, Obligis, Ali-Mehenni A. The rain profiling algorithm Remko Uijlenhoet
applied to polarimetric weather radar. J Atmos Oceanic Technol Wagenigen University, The Netherlands
2000;17:332–56.
[71] Troch PA, van Loon E, Paniconi C. Hillslope-storage Boussinesq model for
subsurface flow and variable source areas along complex hillslopes 1: Available online 29 March 2009You can also read
