Hydrological Real-Time Modeling Using Remote Sensing Data P

Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Sciences Discussions Earth System Hydrology and erent sub-basins of ff process in the catchment. This time lag ff 8810 8809 cient and robust tool for a real-time modeling frame- ffi ¨ omelt, and W. Kinzelbach ¨ urich ¨ urich, Institute of Environmental Engineering (IfU), Wolfgang-Pauli Strasse 15, This discussion paper is/has beenSystem under Sciences review for (HESS). the Please journal refer to Hydrology the and corresponding Earth final paper in HESS if available. tem (e.g. induced bythe the other construction hand of simple irrigation conceptual schemes models or can land provide use a change). satisfactory On performance for weeks can be provided. However, thedecreasing quality prediction of time. the forecast In increasesto watersheds significantly with rainfall with events little the soil performance storage is and relatively a poor. quick response 1 Introduction In hydrological forecasting, fully distributed, physicallyto based account models both provide for the the ability heterogeneity of a watershed and physical changes of the sys- is the basis for theconceptual applicability model of the developed soil is moisturemodeled based data include on for evaporation hydrological two losses, forecasts. infiltration storage and The model compartments. percolation. in The a The application real-time processes of modelingsoil this framework storage is yields an good important results factor. in For watersheds the where largest the watershed a forecast over almost six can be characterized bythe satellite prediction based of measurements. dischargesthe is A Zambezi developed forecasting River and framework Basin. applieding for to The soil three model moisture di is andthe solely rainfall back-scattering based intensity estimates. on of remote a Theboard sensing radar soil the signal data moisture measured provid- ERS by product satellite. thedischarge used of radar is scatterometer the These corresponding on based soil watersheddependent on if moisture on the the data data size correlate are and shifted the well dominant by with runo a time the lag measured which is Reliable real-time forecasts ofmanagement the of discharge a river can basin provideKalman system. valuable Filter Sequential information data provides assimilation for a using the bothwork. the e Ensemble One key parameter in a hydrological system is the soil moisture which recently Abstract Hydrol. Earth Syst. Sci.www.hydrol-earth-syst-sci-discuss.net/7/8809/2010/ Discuss., 7, 8809–8835, 2010 doi:10.5194/hessd-7-8809-2010 © Author(s) 2010. CC Attribution 3.0 License. Received: 28 October 2010 – Accepted:Correspondence 2 to: November 2010 P. Meier – ([email protected]) Published: 10 NovemberPublished 2010 by Copernicus Publications on behalf of the European Geosciences Union. 8093 Z Hydrological real-time modeling using remote sensing data P. Meier, A. Fr ETH Z 5 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | and 2 Kitani- erence be- ). ff cient and ro- Wagner et al. ffi 2001 , erent ways. In real- ff ). , the Kafue River where 2 2009 , the potential evapotranspi- , -1 erent sub-basins within the Zam- Thiemann et al. ff ective water management in a river ff ). Piles et al. ; . -1 s 2009 3 , 8812 8811 2009 , predictions were greatly enhanced when measured ). These watersheds cover together more than one ers a unique opportunity to improve hydrological mod- ff mean annual discharge), the Kafue River discharges 2 ff -1 ) stated that the e s . 3 Naeimi et al. Crow and Ryu ). Sequential assimilation algorithms, also known as filter- -1 ; ; ). The three watersheds are the Upper Zambezi upstream 1 ciently sequential assimilation techniques are considered su- 1980a ( ). ffi b 2002 ). EnKF has, other than standard batch calibration, the advantage 2001 , 2003 , , , 1994 , 1980a , and the Luangwa River 700 m Kerr et al. -1 McLaughlin s ). It was shown that runo Aubert et al. at the mouth of the Zambezi, where the Upper Zambezi catchment contributes 3 Evensen ff The whole Zambezi River basin lies in the semi-arid zone of southern Africa. Rainfall The water resources in the Zambezi river basin are more and more developed. Fea- This modeling approach is evaluated in three di This study presents a prediction framework for river discharge based solely on re- As soil moisture is a key parameter in land surface hydrology, the availability of area The assimilation of remotely sensed soil moisture data becomes more and more This so called data assimilation problem can be solved in di Kitanidis and Bras 1999a sibility studies for several new hydro-power plants are carried out and new irrigation third of the wholeruno Zambezi watershed andthe contribute largest more than amount one (850300 m m half of the total is strongly seasonal andtotal occurs amount almost of exclusively rainfall between is Octoberration on around and average 2000 March. mm around yr 1000 The mm yr bezi River basinof (Fig. the gauging stationthe Senanga discharge with is an measuredthe area at Luangwa of the River Kafue 281 which 000 Hook km isthe Bridge gauged Zambezi with a River few an kilometers (142 area 070 upstream km of of 95 the 300 km confluence with motely sensed data ofassimilation soil techniques. moisture The and availability rainfall, ofto a the be simple input conceptual operated data in model indata real-time and real-time, are allows data retrieved. providing the When model a observationing prediction data sequential are for data available discharge the assimilation model each techniquesto state (EnKF). time is be The updated new relatively update us- simple. input step allows the model feasible. Several satellitenear missions future have equipped beenmissions with include launched instruments the or MetOp to Advanced willOcean retrieve Scatterometer Salinity be soil (ASCAT), Mission the launched moisture (SMOS) Soil(SMAP) in and using Moisture ( and NASA’s the radar. Soil Moisture These Active-Passive instrument of being able to incorporatedata, a model wide parameters range and of modeledbe uncertainties. observations incorporated The are in uncertainties considered the of separately same forcing but mathematical can scheme ( representative measurements o eling. The first( global dataset on soilsoil moisture moisture both has from been groundrated presented measurements ( and by from remote sensing were incorpo- states are propagated through timestep, using where a the model modeled and states forcingtween of data; the the second modeled system an are quantity update updated andproblems based its the on real-world Ensemble the observation. di Kalman Tobust Filter solve ( nonlinear (EnKF) filtering has proven to be both e dis and Bras time applications asolve new this assimilation problem problemperior e is ( formulated ating every algorithms, are time divided step. into two steps: To first a propagation step, where the system eter uncertainty. This leadsappropriate to to a use deficient observed knowledge system of outputs the to update system the states. states Hence of the it system is ( basin system needs a reliableof real-time the forecast. forecasts This based involves on amodel the continuous prediction is correction errors accompanied of by earlier several forecasts. sources The of application errors, of such a as model, input and param- relevant hydrological data this can be an advantage. short term forecasts. Especially in regions with limited facilities for the measurement of 5 5 25 15 20 10 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ). ). ) and t 2006 T − ). SAR , 1999a , 2007 , ) concluded that ). On larger scales 2005 ( erent properties of the ff ). The ERS radar scat- Verstraeten et al. Wagner et al. 2004 , Wagner et al. 1999b , Scipal et al. ects of vegetation and the surface rough- Western et al. ff ). 8814 8813 ). Wagner et al. 2005 2008 , , ). Therefore remotely sensed soil moisture data provides 2010 , Scipal et al. ) proposed a method to calculate a so called Soil Water Index ; Baghdadi et al. 2005 1999b , ( Brocca et al. Since the electromagnetic waves in the bandwidth only penetrate the top few cen- In this study the dataset of soil moisture derived from the radar scatterometerThe on measured back-scattering intensity is dependent on di There is a wide variety of techniques measuring the soil water content through re- Recent studies have shown the usefulness of radar scatterometer derived soil mois- Ceballos et al. bare soils. Despite the significantlyof lower scatterometers resolution, the can multiple facilitate antenna thetion configuration data on processing the signal to ( reduce the influence of vegeta- (SWI) based on aaverage simple of two-layer the moisture past balance measurements model. using an It exponential computes filter a of the weighted form exp( systems, providing data with a high spatial resolution, show a good performance over timeters of the soil, theWagner soil et column water al. content for large depths has to be estimated. is therefore acting asmeasurement has a declined low-pass by filter. onethis half study The provides is SWI globally time data set at aftereven to a this which 14 10-daily low time days. resolution the step. soil The weight moisture dataset for data used can a in be single applied in hydrological modeling. surface, mostly on the surface roughness,ally the wet soil vegetation results and in the higher soilment back-scattering moisture. intensity of than Gener- the a antennas dry allows soil.ness to Since rule can the out arrange- be the e consideredpixel to can be be determined constant using over a time, change detection the algorithm dry ( and the wet state of each data can betial applied variability successfully of for the( soil hydrological water modeling content since is small averaged within scaleboard the spa- scatterometer the footprint two ERSterometers are satellites operating in is the C-band usedlooking at antennas a ( frequency arranged of at 5.3 an GHz using angle three of sideways- 45 degrees. techniques are distinguished:synthetic Passive aperture radiometry radar and (SAR) the and two radar active scatterometer methods ( using ture data for hydrological applications. Even with the generally coarse resolution these Both active and passive methodscertain rely range on the of measurement wavelengths.band of the radiation For measurement intensities passive target in is systems a The the operating soil radiation surface in intensity temperature the measured ( by thermaltrolled systems infrared by operating the in the dielectricucts microwave constant based band of on is con- radar theinfluence satellite top of imagery soil cloud provide layer. coverthey an are and Especially attractive governed changing by source soil the atmospheric for moisture same conditions data physical prod- principles, since is generally the minimal. three types of Although microwave the temporal variation seems toconditions be ( very stable sincea it valuable is dataset mainly for influenced hydrological by monitoring climatic on a larger scale. mote sensing. However, the data can only be retrieved by indirect measurements. 2 Soil moisture The vadose zone isThrough one of the the coupling most ofand important global water components climate. and of However, the energy theand global fluxes variability time. water of soil cycle. Traditional soil moisture measurement moisturebut determines methods, is they such very the provide high as local information ininfeasible. TDR, on both are The space a reasonably typical very accurate tens correlation local of length scale. meters for such and Monitoring measurements a large lies few areas between hundred is a meters nearly few ( is growing, short termsmaller forecasts reservoirs of and the water dischargeriver abstraction can dependent schemes help ecosystems for optimizing as irrigation the a without third operation neglecting water of user. the schemes are developed all over the river basin. While the pressure on the resources 5 5 25 15 20 10 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | orts which include ). The variation of ff 2005 , ). The BWI is calculated by 3 Scipal et al. erent time lags. Not surprisingly the ff 8816 8815 ). All input data of the model, the soil moisture and the 4 model ). Since the soil moisture data used are available only up ff 1997 , ). In all three catchments the probability for a rainfall event to 2 ect can be observed if the correlation between the Basin Water Index ff Herman et al. erences can be explained by the geological and geomorphological settings of the Daily discharge data are available at the outlets of the three sub-basins. For the The rainfall dataset is provided by the Famine Early Warning Systems Network A long time lag entails a long potential forecast period. Therefore a real-time model The scatterometer based surface soil moisture data are strongly correlated to the A similar e The analysis of the correlation also shows that to obtain the best correlation ff considered. The basin-averaged soil moisture is equivalent to the Basin Water Index rainfall and soil moisture data areZambezi available sub-basin simultaneously. The is discharge measured of from the October Upper 1996 only. 3.2 Soil moisture – runo To model the dischargeveloped. at the The outlet modelsubsurface of water consists storage a of (Fig. basin tworainfall, a compartments: are simple averaged conceptual a over model surface the was water whole de- storage river and basin. a Hence, the spatial variability is not stations ( to January 2002 thelittle period more where than soil six years. moisture data and rainfallKafue data overlap and is the only Luangwa sub-basin the data available cover the whole period where 3.1 Data Besides the soil moisture data whichare are used described as in forcing the data previous and section, measured rainfall data discharge is applied(FEWS for NET) updating the and model. are can available be at downloaded aincorporate 10 free various days of satellite interval charge based starting from rainfall from estimates the July and 1995. internet. data FEWS measured The NET at rainfall data gauging data based on soil moisture data andin rainfall large is watersheds. more powerful in terms of the forecast period 3 Methodology occurrence of rainfallfall events events but (Fig. less correlated to the magnitude of these rain- have taken placemainly grows correlated for to higher soil moistureof soil saturation when moisture, (around the values soil whereas of has 0.8). the not amount yet reached of(BWI) a rainfall and certain is degree the measured discharge is analyzed (Fig. di watersheds. The Luangwa River flowstension through of the the Luangwa East Rift African Valley riftcarpment which valley. of is The the an tributaries rift ex- and of therefore theto have Luangwa the a drain very more the short gentle steep response slope es- time ofone to the large drainage rainfall. wetland area, In each the addition two whichused other retards is watersheds the feature not at flow very least of sensitivetional the to retardation water. the of the presence The discharge of soil which wetlands. moisture is product They mainly therefore formed cause outside an the addi- wetlands. the discharge haslargest watershed to shows a be long timewatershed shifted lag (Luangwa of by River) two the months, very whereas timeThe for optimal lag the di time second has lag largest to for be the Kafue set River to up zero to to Kafue obtain Hook Bridge an is optimal one fit. month. These averaging the SWI overthe the discharge whole is relatively riverto small basin 0.6 for ( low the soil variationextent moisture suddenly decoupled values. increases. from If This the theThis indicates soil values decoupling that moisture exceed is 0.5 the mainly as caused dischargerainfall the by data is soil rainfall. seem to approaches to Therefore some be modeling complete more e saturation. realistic. 5 5 20 15 10 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ff ). Q χ 5 (5) (1) (2) (3) (4) the is the GW are the τ R i ∆ k relates the are applied ) was used. 3 to the losses k and GW 1 ). τ S k 5 2005 τ ∆ ( ∆ and S τ ). Furthermore, they are ∆ Scipal et al. 1970 , ), a hydrometric scaling factor erent pairs of e 0 ff ff Q ). ). 4 . ) 1) ; 0 of the model was set to ten days. − ) 1963 erent time lags t t ff , ( 1) ∆ − 8818 8817 GW t ). Due to the discrete nature of the time lag, the ). The model is based on the following balance S ) 4 τ is the total area of the watershed and ) two di ,... k ∆ 3 Nash and Sutcli A BWI( , = ) 1) 2005 − GW ( Marquardt ) 2 − ) are mostly dependent on the size of the watershed. t − , t ) Q ( GW 1)). The sum of the rainfall routed to the subsurface t ( 1 t ( τ GW ff − S = max τ ∆ GW t S BWI( ∆ Q ciency ( 2 − BWI( − t k t, n ( ffi ) ( BWI ) 3 1) − ∆ t k and n max − are calibrated. The set of time lags with the minimal root mean GW e e t + )–BWI( Scipal et al. ff i S GW = ( Q t I τ are the surface storage volume and the subsurface storage volume, k τ + BWI( − . BWI GW ∆ are then chosen as optimal parameter set. Since the input data used is the direct infiltration of rainfall to the subsurface storage, ) GW I ∆ ) τ 1) and − i ( S t GW S ( k ∆ τ 1 4 − S ln ( GW k t ∆ I ) ( Q t AR max − ( S − χ 1 are the rates at which the linear storage compartments are depleted. These t S and k = + ( 2 4 AR 0 S S ) k = k 1 t S Q Q ( ) k = t t ) ( = = t = ) and the subsurface runo t ) ) S GW ∆ and ( t t S ∆ A physical interpretation of the parameters assigns the parameter To assess the overall quality of the model presented in this article it is run in de- For comparison the regression model developed by The storage compartments are considered to be single linear storages. Depending The time lags ( S S ( ( S 2 Q GW ∆ ∆ (through evaporation) of rainfall before it entersBWI a to storage. the The parameter totalk volume of water storedmodel in parameters are the calibrated subsurface by zone.Levenberg-Marquardt running algorithm the The ( model two in parameters deterministic mode using the Q terministic mode, withoutcompared the to data the assimilation measuredand step. ones the by calculating The Nash-Sutcli the simulatedcompared root to discharges mean the are squared discharges simulated error with (RMSE) the reference model Eq. ( This model uses a logarithmicIt regression uses between soil three moisture parameters and representingand discharge a a Eq. baseflow ( time ( lag The storage change insoil the moisture subsurface (BWI( isand modeled the through measured the changecompartment measured in which change soil is not moisture in ( allowed represent to the be recharge negative in toto this calculate the model. the subsurface total For discharge the according surface to runo Q Eq. ( In this model thesingle time time lags step are ( consideredparameter to identification be is an integral done multiplemodel in of two parameters the steps. length For ofsquare di a error (RMSE) betweencorresponding the observed and theare computed available flow every together ten days with the the time step model parameters. on the value ofwhereas the the remaining BWI, water volume a iswater part routed is to routed of the to the subsurface the storage. rainfall subsurface, If is if BWI BWI is routed 0 is to all 1 the all water surface is water routed storage to the surface storage. where respectively, average rainfall in the river basin, with Q equations: I (BWI) introduced by 5 5 15 20 20 10 15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | , (6) and )). The kθ ). = 6 ) k,θ . As obser- ( X Γ ( GW Herman et al. E S -line in adaptive ff and S S which leads to the con- 1 k is defined as X ). For the observed discharge data can be calculated. Based on these , θ ! is generated according to Eq. ( t 2 R 2005 ( t R σ and ; . 2 t 2 R t k 8820 8819 σ R

. The standard deviation is set to a fixed value at time t kθ ˜ = Γ show similar values for the Upper Zambezi basin and R -line) modeling mode. ) 4 √ ff k = k,θ Ceballos et al. ( X Γ σ . One can assume that the wetlands present in the Upper and 3 2 ∼ k k i t ˜ R erent watersheds as it was already observed for the correlation ff which mainly reflects the water losses through evapotranspiration not with ). The Upper Zambezi catchment has by far the longest response time 1 3 k erence between the measured observation and its predicted value. The , ff    . For the time lags one can see a similar dependency on the size and geo- is the measured rainfall at time i 1 t t 1 . . . t ˜ ˜ R R R 50 mm) which reflects the uncertainty of the rainfall data product ( . The Kafue River basin shows a very similar value of is twice as big. This supports the conclusion that the water is drained quickly from ). Using the measured rainfall amount as expected value of the perturbed rainfall    1 = 1 k = k The model parameters obtained by calibration in the deterministic mode are shown While the parameters To assess the possible accuracy of the forecast, the model is run o The only model parameter that shows a distinct dependency on the area of a water- Parameter For the uncertainty of the BWI the standard deviation is set to 0.025 according to For the real-time modeling framework an ensemble of randomly perturbed input and Observed discharge data are available on a daily basis. Since the temporal resolu- R t σ ˜ the Kafue river, they are much higher for the Luangwa river. This correlates well with The parameters for theanalyze developed the performance model of are the modelmode calibrated it and was for in assessed all the both in real-time three a (but watersheds. deterministic o modeling To in Table time in the catchment.spiration The can longer water be is expected. storedfor The in Upper a Zambezi watershed the basinclusion more shows that evapotran- indeed the the soil lowestthe properties value size in of a the watershedbasin two on is basins only the are one parameter similar half isof of and marginal. the that size While the of the the influencethe area Upper surface of of Zambezi to the watershed the Luangwa the river parameter and value therefore losses are low. mode for the historic time series from the years 1995 to 2002. 4 Results and discussion morphology of the di analysis (Fig. shed is the parameter thatthe correlates the subsurface BWI storage, to theZambezi total and volume the of water Kafue stored watersheds inside have a huge impact ononly the depends water on storage capacity. the soil properties of the watershed but also on the average retention whereas the Luangwa river basin shows a relatively quick response. where the standard error found by R Both, the BWI anddistribution. the discharge perturbations are considered to follow a Gaussian tion of the model is 10 days, data assimilation isobservation carried data out are in every generated.gamma time distribution. step. The The gamma rainfall distributionexpected data only value of needs ensemble a two gamma is parameters distributedthe ( generated random standard variable using deviation the as ( 1997 for each time step,parameters the the rainfall two ensemble parameters the variance ofdeviation the of added 0.05 times noise thecharge measured is measurements value, is as proportional generally the considered to absolute to measurement their be error dependent of magnitude on dis- the with discharge a itself. standard Ensemble Kalman Filtering updates theusing state the variables di of astate model variables which by are correcting updated them vation are data the the two measured storage volumes discharge is used. 3.3 Real-time modeling 5 5 25 15 20 10 25 10 20 15 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ff 5 cient ffi ). The 3 since with a and 1 2 k ect the amount of ff ) in the model. The τ ). While the predic- ∆ 3 ciency and the RMSE ffi and e e 2 ff ciency (Tables ciency are calculated (Tables 8822 8821 ffi ffi ). This statement is supported by the analysis e e e e 6 ff ff orts. ff ). ciency, the model prediction gets significantly more accurate for shorter forecast 6 ffi The application of the model provides a seamless integration of remote sensing prod- A higher spatial resolution of the data would allow a higher spatial resolution of the These results show clearly that the model presented is capable of providing useful A drawback for the testing of the method is the short time period over which data For the successful application of the model for real-time prediction it does not nec- Due to the parameter uncertainty the error band becomes large especially in the wet The performance of the model running in the deterministic mode is illustrated in Fig. model. Since the BWI and the rainfall are averaged over the whole area, the runo ucts. With only four parametersplicable and to a a simple class conceptualused formulation of are this watersheds developed model which to isundertake an comply ap- extensive operational with data standard. processing. certain This Therefore characteristics.time model modeling the framework. is All user especially More does data and suitedavailable more not for in data use have from real-time. in to the a A newer real- satelliteimprove higher systems modeling temporal will e and/or be a higher spatial resolution can greatly The Soil Water Index (SWI)soil thickness which everywhere. was The used actualinfluence to thickness on of calculate the the the model soil BWI results. ina a assumes The river spatial huge a basin variability influence uniform has of not on thecan a soil the big suddenly thickness results however, dominate has the because behavior certain of areas the system. with a relatively thin soil layer of e periods. Again theson. Whereas absolute the error relative error(Fig. of is especially the high prediction during ascending is and receding much flows higherdischarge in forecasts the in wet semi-aridevery sea- river river basins. since itswatersheds Yet with model this a relatively structure model low storage is can volume and not not a quick be designed response applied to to rainfall on events. reproduce the processes in ensemble of the forecastdence can interval. be As representederror time by generally approaches the the gets ensemble time mean smaller of and (Fig. prediction the fortion confi- a for certain the timestep maximum the forecast time shows the highest RMSE and lowest coe length of the forecast period is defined by the shortest time lag ( of the RMSE and the Nash-Sutcli When operating on-line in adaptive mode the quality of the forecast is of interest. The are available. The model reliesdischarge. on soil moisture The data, overlap onspan of rainfall data that these and can three on be measured datasets modeled.of dictates Eventually a the it bit is longest morereason only continuous a possible than time full to six validation test of years, theobtain the model a model for stable on was the calibration. a not possible. Upper period The Zambezi available data catchment were even usedessarily less. to have to For be this mechanistically correct but needs to reflect the correct tendency. does not allow to model the situation adequately. season. This uncertaintyhigh is rainfall mostly amount attached a slight towater which change the is in model routed the parameter to parameter the can system. greatly a model developed gives better results thanand the reference the method for Kafue theare Upper River Zambezi slightly basin higher whereare or both reproduced lower, the much respectively. better Nash-Sutcli rainfall than information It in is the is most reference clearlyand method. beneficial rainfall visible for Obviously deteriorates the that the for maxima inclusion largethe the as of precipitation model flood events. the performance maxima correlation However, was forIt between not the BWI even satisfactory Luangwa showed when river a running slightly in the poorer deterministic performance mode. than the reference method which also the flow is attenuated by the wetlands in the Kafue and Upper Zambezi basins. the generally steeper slopes in the Luangwa basin where water flows faster. Apparently and compared to thequite well reference with method. the measurederror The flows. (RMSE) modeled For and all discharge the the agrees Nash-Sutcli applied in models the general root mean squared 5 5 25 15 20 10 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | formation processes ff ff ) showed the 2007 ( erent runo ff Wagner et al. airs, Lusaka, Zambia. This research ff model, J. Hydrol., 280, 145–161, doi:10.1016/ 8824 8823 should be observed. The model however, will ff ff 8811 er promising opportunities to advance in this field. On one hand ff The soil moisture data were kindly provided by the Institute of Photogram- erational performance of current synthetic aperture radar sensors in mapping soil surface flow data inS0022-1694(03)00229-4, a 2003. conceptual rainfall-runo Running the model in real-time with a data assimilation procedure provides short If water management options for the distant future have to be assessed the described The prediction framework presented in this paper exploits the available data sets on Radar scatterometer data were found especially promising for the use in hydrological Baghdadi, N., Cerdan, O., Zribi, M., Auzet, V., Darboux, F., El Hajj, M., and Kheir, R. B.: Op- step flood forecasting isimproved also flash flood not forecasting possible.is could necessary If eventually be to the anhydrological quality improve models option. of the tailored to the quality But use input further remotely ofAcknowledgements. sensed data research the soil is moisture data data. greatly and to develop more sophisticated Aubert, D., Loumagne, C., and Oudin, L.: Sequential assimilation of soil moisture and stream- the model performance is not satisfactory. term forecasts which can bebasin used system for a such wide a varietyfew weeks forecast of can applications. is be To beneficial quantified. manageor a since Releases ecological river for the flood power discharge production, releases irrigation can expected water be for demands planned the based next onmodel this is information. not suitable because it is not physically based. Due to the relatively long time metry and Remote Sensingthree at rivers the were Vienna provided University by ofis the Technology. funded The Department by discharge of the data Water Swiss A of National the Science Foundation under project K-21K1-120266. References rainfall and soil moisture.the The surface relatively water simple and modelsoil consisting the moisture of subsurface two shows water, reservoirs, and goodof for an performance. water infiltration in Especially process the in basedLuangwa soil watersheds on river where is the basin the of which storage high is importance dominated the by model steep predictions slopes are and accurate. quick runo In the important driver of soil moisture a conceptual model should also utilize rainfall data. ture several satellite missionsnear have future. been deployed recently or will be launchedmodeling in where the the soilradar moisture is signal one penetrates ofinformation the only on most the the important surface top parameters. soilmodeled. moisture, few Since the The the centimeters water simple content two-layer of in modelare the the used appropriate soil to to soil, column generate be has hence the used to SWI be as only produces input giving data data which for a conceptual model. Since rainfall is one techniques for extracting informationdata have on advanced the in the hydrologicalwere past cycle designed few years. to from While gather some remote asentific years many sensing community ago types the with of satellite data data systems missions as that are possible can in designed be order for to exploited a provide in specific the several purpose. sci- ways, Especially nowadays satellite for the retrieval of soil mois- models to account for the usually high temporal variability of the soil5 water content. Conclusions Hydrological modeling undercent data developments o scarce conditionsreal-time remains modeling a techniques allow bigmodeled the challenge. system assimilation of state data every Re- in time a observation model, data updating are the available. On the other hand is completely neglected.high soil If moisture heavy aoverestimate convective peak the rainfall in infiltration is into the the runo occurringusefulness soil of in dramatically. high an resolutionhydrological area soil modeling. moisture with data a A from the higher Envisat temporal ASAR resolution instrument of in soil moisture data can allow processes are also averaged. The spatial variability of the di 5 5 25 15 10 20 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | predic- ects on ff ff 8834 8823 , , 8814 8819 8818 8813 , , 8814 8814 8811 relation at the catchment scale 8817 , ff 8812 8815 , 8814 8818 8812 8825 8826 8811 8818 8812 8811 8811 8811 8813 8819 , e, J.: River flow forecasting through conceptual models part I – A discussion ff er, C., and Wagner, W.: Soil moisture-runo 8813 8814 8811 ffl , 8816 8811 Model 2. 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V.: Objectively determined 10-day African Brocca, L., Melone, F., Moramarco, T., and Morbidelli, R.: Spatial-temporal variability of soil Crow, W. T. and Ryu, D.: A newEvensen, G.: data Sequential assimilation Data approach Assimilation With for A improving Nonlinear Quasi-Geostrophic runo Model Using 5 5 30 25 25 15 20 20 10 15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ). 1 09 46 58 68 − . . . . s 0 0 5 3 3 ± ± ± ± 35 68 06 44 . . . . 3203 18 0 09 0 99 10 . . . . 1 0 0 0 ± ± ± ± 61 13 29 00 . . . . 8828 8827 03 0 13 5 41 5 . . . 0.09 0 0 1 6 ± ± ± ± 40 d 20 d 10 d 100 d 70 d 50 d 15 22 23 . . . Upper Zambezi Kafue River Luangwa River 0 0.22 Upper Zambezi Kafue River Luangwa River ) 4 5 ) 32 3 − erent model runs for all the sub-basins (in m ff 10 10 40 d30 d20 d10 d0 d 281.5 265.1 238.8 199.1 131.4 70.5 59.8 46.5 483.6 412.6 × × ( ( S GW = = = = = t t 4 3 2 1 t t t t t k k k ∆ ∆ k DeterministicAdaptive mode ∆ 269.3∆ ∆ ∆ 99.5∆ Reference 513.7 285.0 103.8 502.6 Estimated parameters for the three sub-basins and the 95% confidence interval for RMSE of the di Table 2. Table 1. each parameter. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 40° Mozam- bique 35° Tanzania 3 30° Zimbabwe Longitude 8830 8829 Congo 25° erent model runs for all the sub-basins. Zambia ff 1 2 Botswana Upper Zambezi Kafue River Luangwa River 20° ). ciency of the di ffi Namibia Angola 15° e e 40 d30 d20 d10 d0 d 0.85 0.87 0.90 0.93 0.98 0.84 0.88 0.96 0.68 0.80 ff = = = = = -5°

t t t t t -25° -10° -15° -20° DeterministicAdaptive mode ∆ 0.90∆ ∆ ∆ ∆ 0.82Reference 0.74 0.88 0.80 0.75 Latitude Nash-Sutcli Overview of the Zambezi River Basin (light gray) and the three watersheds where the Fig. 1. model is applied.watersheds 1: are marked Upper ( Zambezi; 2: Kafue River; 3: Luangwa River. The outlets of the Table 3.

Probability (-) Probability (-) Probability (-) Probability

Rainfall (mm) Rainfall (mm) Rainfall (mm) Rainfall

0 3000 2000 1000 5000 4000 1 0.8 0.8 0.8 Kafue River: 0.8 (b) ) 0.6 -3 0.6 0.6 0.6 m (b) 3 60 d; BWI (-) 0.4 (c) (a) (b) = 8832 8831 t ∆ SWI (m 0.4 0.4 0.4 0.2 0 0

500 1500 1000 0.2 0.2 0.2 1 Average rainfall Probability ofrainfall 0.8 . 0 0 0 Upper Zambezi: 0 0 0 80 60 40 20 80 60 40 20 80 60 40 20 (c) 0.6 100 100 100 (a) (a) BWI (-) 0.4 0.2 0 d. 0 = t

Correlation between BWI and discharge shifted by the time lag (

The probability of a rainfall event given a Soil Water Index (SWI) class and the average

) s 3000 2000 1000 Discharge (m ∆

4 2

k k ielag time S 2000 2000 2000 τ GW τ ∆ ∆ 1999 1999 1999 ) BWI GW dt

dS (a) (c) S (b) Year 1998 1998 1998 dt dS 8834 8833 Soil moisture ( 1997 1997 1997 R R Modeled discharge Measured discharge Regression model × × ) 1996 1996 1996 0 0 0 BWI

. For comparison the results of the regression model by

1 500

Discharge (m Discharge Discharge (m Discharge ) s (m Discharge ) s )

s 1000 5000 4000 3000 2000 1000 4000 3000 2000 1000 1500

k BWI -1 3 -1 3 -1 3 (c) ) − R (1 1 BWI k ∝ Rainfall ( The modeled discharge (thin line) including the 95% conﬁdence interval compared to Structure of the conceptual hydrological model. ) are indicated (dashed line). and Luangwa River 2005 the measured discharge (thick line) for the three( watersheds Upper Zambezi Fig. 5. (b) Fig. 4.

(%) (%)

) s (m ) s (m

(%) (%) (%)

) s (m ) s (m ) s (m

(%) (%) (%) (%) (%)

) s (m ) s (m ) s (m ) s (m ) s (m

-1 3 -1 3

-1 3 -1 3 -1 3

-1 3 -1 3 -1 3 -1 3 -1 3

Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 100 50 0 -50 -100 100 50 0 -50 -100 2000 1999 1998 10 d assimilated 1997 0 0 500 500 -500 -500 1000 1000 -1000 -1000 -100 100 50 0 -50 -100 100 50 0 -50 -100 100 50 0 -50 2000 1999 8835 1998 erent forecast periods and the assimilation step. ﬀ 20 d 10 d assimilated absoulute error relative error 1997 0 0 0 500 500 500 -500 -500 -500 1000 1000 1000 -1000 -1000 -1000 100 50 0 -50 -100 100 50 0 -50 -100 100 50 0 -50 -100 100 50 0 -50 -100 100 50 0 -50 -100 2000 1999 1998 10 d assimilated 40 d 30 d 20 d Absolute and relative forecast error for the Upper Zambezi (left), the Kafue (center) and 1997 0 0 0 0 0 500 500 500 500 500 -500 -500 -500 -500 -500 1000 1000 1000 1000 1000 -1000 -1000 -1000 -1000 -1000 Fig. 6. the Luangwa watersheds (right) for the di