Exploiting ASCAT-derived Soil Moisture Products for improving Flash Floods Forecast in Mediterranean Catchments via Data Assimilation

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1 Exploiting ASCAT-derived Soil Moisture Products for improving Flash Floods Forecast in Mediterranean Catchments via Data Assimilation Luca Cenci, Paola Laiolo, Simone Gabellani, Lorenzo Campo, Francesco Silvestro, Fabio Delogu, Giorgio Boni, Roberto Rudari, Luca Pulvirenti, Giuseppe Squicciarino

2 Introduction Flash Floods (FF): floods occurring in a short period of time FF are difficult to forecast FF can have severe consequences FF genesis is strictly linked to the rainfall-runoff process Soil Moisture (SM): governs the non-linear partitioning of the precipitation into infiltration and runoff The exploitation of Soil Moisture - Data Assimilation (SM-DA) techniques in hydrological modelling can: Produce more accurate SM estimates Reduce the uncertainty of streamflow simulations Improve FF early warning systems (EWS)

3 Research Objectives In small catchments, such as those characterizing the North-West Mediterranean area, FF risk can be high. Given the small sizes these basins, it is interesting to evaluate the performances of SM-DA systems based on observations retrieved by using coarse spatial resolution instruments (e.g. scatterometers like ASCAT), in a FF risk mitigation framework. ASCAT-DERIVED SM PRODUCTS: H07 H08 H14 H-SAF Products NUDGING-BASED DA ALGORITHMS: Nudging Model Scale Nudging Satellite Scale Ensemble Nudging Model Scale STUDY AREAS: NW MEDITERRANEAN CATCHMENTS Orba Catchment Casentino Catchment Magra Catchment MULTY YEAR PERIOD: July June 2014

4 Study Areas: North-West Mediterranean Catchments 1. Small/medium size basins with steep morphology and short time of concentration 2. Rivers with torrential regime 3. Occurrence of orographic-induced heavy precipitations 4. Runoff strongly influenced by the seasonal distribution of precipitation. Lower flows dry summer Higher flows FF hazard autumn Study Area Catchment Area [km 2 ] River Length [Km] ORBA (OB) CASENTINO (CS) MAGRA (MG) Merheb et al., 2016; Thornes et al., 2009

5 ASCAT-Derived Soil Moisture Products Period of Analysis: July 2012 to June 2014 Name Retrieval Algorithm Temp. Res. Sp. Res. Reference Soil Layer Notes H07 (SM-OBS-1) Change Detection [Wagner et al., 1999] 36 h 25 km Near-Surface Only morning acquisitions were assimilated H08 (SM-OBS-2 ) 1 km disagregation of H07 [Wagner et al., 2013] 36 h 1 km Near-Surface Only morning acquisitions were assimilated H14 (SM-DAS-2) Assimilation of H07 within the ECMWF Land DA System [De Rosnay et al., 2013] 24 h 25 km 4 layers (0-7, 7-28, , cm) In the SM-DA experiments H14 error was assumed to be not correlated with the model error PRODUCTS DERIVED FROM SATELLITE IMAGES OF THE ASCAT SENSOR: Scatterometer - C-Band (ƒ = GHz) Spatial resolution: 50 km Carried by MetOp-A and MetOp-B

6 Hydrological Model: Continuum CONTINUOUS, PHYSICALLY BASED AND DISTRIBUTED Soil Moisture Root Zone Saturation Degree (RZ-SD) SD V ( t) V max Spatial Resolution: 100 m Temporal Step: 1 h V(t)= Actual water volume [mm] (modeled by Continuum) V max = Max soil retention capacity [mm] (related to soil type and land use through the CN) Silvestro et al.,

7 Hydrological Model: Continuum CONTINUOUS, PHYSICALLY BASED AND DISTRIBUTED Soil Moisture Root Zone Saturation Degree (RZ-SD) SD V ( t) V max Spatial Resolution: 100 m Temporal Step: 1 h V(t)= Actual water volume [mm] (modeled by Continuum) V max = Max soil retention capacity [mm] (related to soil type and land use through the CN) Silvestro et al.,

8 Soil Moisture Data Assimilation: Challenges Significant differences between modelled and observed data must be removed before the assimilation Different spatial resolutions: Satellite: 25 km / 1 km Model: 100 m Seneviratne et al., 2010 Different reference soil layers Satellite Surface layer: 0-5 cm Model Root zone (RZ): cm Presence of systematic bias Different climatology

9 Laiolo, Laiolo et al., 2015 Data Preprocessing: ASCAT-derived SM Products 1. Data Resampling to model spatial resolution: Nearest neighbour 2. Estimation of the RZ: Exponential Filter (Soil Water Index - SWI ) (Wagner et al., 1999) H07/H08 Surface SM H14 SWI n = SWI n1 + Kn(SSM(t n ) SWI n1 ) 3. Bias Removal H07/H08: Minimum Maximum Correction H SWI * SWI max minswi SWI minswi Albergel et al., 2008 H14: Linear Rescaling H14 Mean H14 StDev H14 maxsd minsd minsd mod K n = mod K Brocca et al., 2013 n1 K + e n-1 tn t T mod n1 SD Mean * 14 StDev mod SDmod Resampling RZ Estimation (SWI) Bias Removal (SWI * ) Assimilation To avoid poor quality data, a selection was made discarding SM products with an overall quality flag higher than 20 (flag 100 = worst quality). Resampling Weighted mean of the first two levels Bias Removal (H14 * )

10 Assimilation Algorithm: Nudging at Model Scale (NudMS) X mod t X t G X t X t mod obs mod NO ASSIMILATION OVER URBAN AREAS, RIVERS AND IN FROZEN SOIL CONDITIONS Updated State Model Forecast Gain Innovation: [Observation Model Forecast] G Gain RMSDmod RMSDmod RMSD obs RMSD mod = Root Mean Square Difference of X - mod = 0.1 (Obtained comparing Continuum data with in situ stations present in a different study area) RMSD H14 : 0.22 [-] (Albergel et al., 2012) RMSD obs = Root Mean Square Difference of X obs RMSD SWI,H07-H08 : 0.12 [-] (Brocca et al., 2011) Laiolo, 2015 Laiolo et al., 2015

11 Assimilation Algorithm: Nudging at Satellite Scale (NudSS) t X t S R G H X t H X t * * * * mod mod obs * Updated State Model Forecast Gain RMSD mod = Root Mean Square Difference of X - mod = 0.1 mod Innovation: [Observation Model Forecast] NO ASSIMILATION OVER URBAN AREAS, RIVERS AND IN FROZEN SOIL CONDITIONS G Gain RMSD H14 : 0.22 [-] (Albergel et al., 2012) RMSDmod RMSDmod RMSD obs (Obtained comparing Continuum data with in situ stations present in a different study area) RMSD obs = Root Mean Square Difference of X obs RMSD SWI,H07-H08 : 0.12 [-] (Brocca et al., 2011) H = Observation operator (allow to obtain the map at satellite resolution from the map at model resolution) Laiolo, 2015 Laiolo et al., 2015 R = Regrid operator (allow to obtain the map at model resolution from the map at satellite resolution) S = Spatialization operator (allow to redistribute the correction on the model grid. The correction depends on the ratio between the value of X-mod at each model pixel and the mean soil moisture value at the corresponding satellite pixel)

12 Assimilation Algorithm: Ensemble Nudging (EnsNud) at Model Scale X t X t G t X t X t ( mod mod ) obs mod NO ASSIMILATION OVER URBAN AREAS, RIVERS AND IN FROZEN SOIL CONDITIONS Updated State Model Forecast Gain Innovation: [Observation Model Forecast] Laiolo, ensemble members obtained by perturbing the model calibration parameters that regulate infiltration G varies in time Variance( X mod( t)) G( t) Gain Variance(X mod )= Variance of the ensemble Variance( X ( t)) Variance( X )* Variance (X obs ) = RMSD OBS 2 X n obs introduced for modulating the error associated to the observations to assign a different weight to Variance(OBS) n = 2 mod obs X n obs ( t)

13 Evaluation Metrics Computed on (hourly) Discharge Predictions Qo VS Qs Nash Sutcliffe (NS) model efficiency coefficient [-] NS 1 n t1 t t n Qot Qo t1 Qo Qs 2 2 Q DA VS Q OL Efficiency of assimilation (Eff) [%] Eff n t1 n t1 Q Q DA OL t t Q Q O O t t 2 2 Where: Qo= Observed discharge; Qs = Generic simulated disharge; Q DA = Discharge after DA; Q OL = OL discharge Meaning: NS ranges from - to 1 (perfect model). NS=0 model does not add any information to the climatology. NS<0 model is performing worse than using the climatology. Eff ranges from - to 100 (best SM-DA performances). Percentage of improvements (Eff>0) or worsening (Eff<0) of the assimilated results with respect to the OL.

14 Results: Multiyear Period (July 2012 to June 2014) Higher improvement of the model in OB e CS Lower improvement (H14) in MG NudMS and NudSS similar results, generally better than EnsNud Sometimes, H08 & H14 provided worse performances than H07 Improvement Eff 75% No Effect Worsening ORBA - NS: OL Det = 0.87; OL Ens = 0.87 DA Alg. Eff Eff Eff NudMS NudSS EnsNud CASENTINO - NS: OL Det = 0.47; OL Ens = 0.59 DA Alg. Eff Eff Eff NudMS NudSS EnsNud MAGRA - NS: OL Det = 0.86; OL Ens = 0.86 DA Alg. Eff Eff Eff NudMS < 0 NudSS < 0 EnsNud H-SAF H07 H08 H14 50 Eff > 75% 25 Eff > 50 % 0 Eff > 25%

15 Seasonal Analysis: Autumn (Higher FF Risk) Orba Casentino Magra H07 H08 H14

16 Results: Analysis on Higher Flows (Q>Threshold) General improvement on higher flows predictions for NudMS/NudSS and H07 EnsNud worsening in OB & CS Threshold=200 m 3 /s Threshold=200 m 3 /s Threshold=400 m 3 /s

17 Results: Analysis on Higher Flows (Q>Threshold) General improvement on higher flows predictions for NudMS/NudSS and H07 EnsNud worsening in OB & CS H08 worsened the model performances in CS & MG H14 worsened the model Performances in MG Threshold=200 m 3 /s Threshold=200 m 3 /s Threshold=400 m 3 /s

18 Analysis of Permanent Catchment Conditions 1. Different topographic complexity: OB lower, MG and CS higher 2. MG located close to the sea 3. Different spatial distribution of Agricultural areas and Forest and seminatural areas

19 Conclusions 1. SM-DA of ASCAT-derived SM products using simple assimilation algorithms like NudMS and NudSS (computationally efficient) improved Continuum discharge predictions 2. Improvement affected the higher flows. 3. Despite H08 and H14 are added-value products, they do not always outperform H07 An added value of this study is the future perspective of practical implementation for civil protection activities. However, before the methods presented in this talk could be applied in operational applications for civil protection purposes, further analyses should be undertaken.

20 Further details on this research can be found in: Cenci et al., 2016(JSTARS)

21 References Albergel, C., de Rosnay, P., Gruhier, C., Muñoz-Sabater, J., Hasenauer, S., Isaksen, L., Kerr, Y., et al. [2012] Evaluation of remotely sensed and modelled soil moisture products using global ground-based in situ observations, Remote Sensing of Environment, Elsevier Inc., Vol. 118, pp Albergel, C., Rüdiger, C., Pellarin, T., Calvet, J.-C., Fritz, N., Froissard, F., Suquia, D., et al. [2008] From near-surface to root-zone soil moisture using an exponential filter: an assessment of the method based on in-situ observations and model simulations, Hydrology and Earth System Sciences, Vol. 12, No. 6, pp Brocca, L., Melone, F., Moramarco, T., Wagner, W., Albergel, C. [2013] Scaling and Filtering Approaches for the Use of Satellite Soil Moisture Observations, in Petropoulos, G.P. (Ed.), Remote Sensing of Energy Fluxes and Soil Moisture Content, CRC Press, pp Brocca, L., Hasenauer, S., Lacava, T., Melone, F., Moramarco, T., Wagner, W., Dorigo, W., Matgen, P., Martínez-Fernández, J., Llorens, P., Latron, J., Martin, C., Bittelli, M., [2011] Soil moisture estimation through ASCAT and AMSR-E sensors: An intercomparison and validation study across Europe, Remote Sens. Environ., vol. 115, no. 12, pp De Rosnay, P., Drusch, M., Vasiljevic, D., Balsamo, G., Albergel, C., Isaksen, L. [2013] A simplified extended kalman filter for the global operational soil moisture analysis at ECMWF, Quarterly Journal of the Royal Meteorological Society, Vol. 139, No. 674, pp Laiolo, P., Gabellani, S., Campo, L., Silvestro, F., Delogu, F., Rudari, R., Pulvirenti, L., et al. [2015] Impact of different satellite soil moisture products on the predictions of a continuous distributed hydrological model, International Journal of Applied Earth Observation and Geoinformation, Elsevier B.V., Vol. 48, pp Laiolo, P. [2015] Combining soil moisture from observations and models, PhD Thesis, Thesis, University of Genoa. Merheb, M., Moussa, R., Abdallah, C., Colin, F., Perrin, C., Baghdadi, N. [2016] Hydrological response characteristics of Mediterranean catchments at different time scales: a meta-analysis, Hydrological Sciences Journal, Vol. 61, No. 14, pp Seneviratne, S.I., Corti, T., Davin, E.L., Hirschi, M., Jaeger, E.B., Lehner, I., Orlowsky, B., et al. [2010] Investigating soil moisture-climate interactions in a changing climate: A review, Earth-Science Reviews, Elsevier B.V., Vol. 99, No. 3 4, pp Silvestro, F., Gabellani, S., Delogu, F., Rudari, R., Boni, G. [2013] Exploiting remote sensing land surface temperature in distributed hydrological modelling: the example of the Continuum model, Hydrology and Earth System Sciences, Vol. 17, No. 1, pp Thornes, J., Lopez-Bermudez, F., Woodward, J. [2009] The Physical Geography of the Mediterranean, in Woodward, J. (Ed.), Oxford Regional Environments, p Wagner, W., Lemoine, G., Rott, H. [1999] A method for estimating soil moisture from ERS Scatterometer and soil data, Remote Sensing of Environment, Vol. 70, No. 2, pp Wagner, W., Hahn, S., Kidd, R., Melzer, T., Bartalis, Z., Hasenauer, S., Figa-Saldaña, J., et al. [2013] The ASCAT soil moisture product: A review of its specifications, validation results, and emerging applications, Meteorologische Zeitschrift, Vol. 22, No. 1, pp

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