Basin-scale runoff prediction: an Ensemble Kalman Filter framework based on global hydrometeorological datasets

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Maurice Haynes
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1 Basin-scale runoff prediction: an Ensemble Kalman Filter framework based on global hydrometeorological datasets Christof Lorenz 1, Mohammad J. Tourian 2, Balaji Devaraju 3, Nico Sneeuw 2, Harald Kunstmann 1,4 INSTITUTE OF METEOROLOGY AND CLIMATE RESEARCH, ATMOSPHERIC ENVIRONMENTAL RESEARCH, IMK-IFU REGIONAL CLIMATE AND HYDROLOGY 1 Karlsruhe Institute of Technology, Institute for Meteorology and Climate Research, Garmisch-Partenkirchen, Germany 2. University of Stuttgart, Institute of Geodesy, Stuttgart, Germany 3. University of Hannover, Institute of Geodesy, Hannover, Germany 4. University of Augsburg, Institute of Geography,, Augsburg, Germany KIT The Research University in the Helmholtz Association

Decrease in the number of in situ observations Catchments with limited (< 5 yrs) runoff observations after 2002 cover an area of more than 11,500,000 km2!

2 Decrease in the number of in situ observations Catchments with limited (< 5 yrs) runoff observations after 2002 cover an area of more than 11,500,000 km2! freshwater discharge of more than 125,000 m3/s! gauged ungauged Dai & Trenberth (2002): dischargeless Annual runoff rate over unmonitored areas equals annual runoff rate over monitored areas! Lorenz et al. (2014), JHM

3 Estimation through water budgets? Terrestrial R = P ET ds dt Atmospheric-terrestrial R = Q ds dt PROS: Can be applied globally Does not require, e.g., in situ runoff Not restricted to the basin scale Anthropogenic changes do not matter! Biases might (!!!) cancel out CONS: Only as good as the worst input Error propagation Temporal/spatial resolution

4 Hydrometeorological datasets Variable Dataset Version Resolution Time-period Spatial Temporal P GPCC x month GPCP x month present CRU x month DEL x month CPC x month 1979 present* ET ERA Interim x month, 1day, 6h present GLDAS NOAH x month, 3h present GLEAM v1b 0.25 x day MOD16 A2 0.5 x year, 1 month, 8 days Fluxnet MTE x month present MERRA Land - 1/2 x 2/3 ds/dt GRACE CSR R5-1 month 2002 present* GRACE GFZ R5-1 month 2002 present* MERRA Land 1.0 1/2 x 2/3 1 month, 1 day, 1h present GLDAS NOAH x month, 3h present WGHM NOUSE 0.5 x month R obs GRDC

5 Water budgets are not closed P (GPCC) ET (MODIS) ds/dt (GRACE) R (GRDC)

6 Large residuals in the long-term water budgets Evaluation of the basinscale water budget closure from 90 combinations of state-of-the-art datasets for precipitation, evapotranspiration, and water storage changes

7 Number of catchments Some agreement with observations... Correlation w.r.t. GRDC Prof. Dr. Harald Kunstmann Lorenz et al. (2014), JHM

8 Number of catchments...but not enough for reasonable predictions NSE w.r.t. GRDC Can we improve these combinations? Lorenz et al. (2014), JHM Prof. Dr. Harald Kunstmann

9 Cornerstones of the approach Development of a data-merging approach for the consistent combination, correction, and prediction of basin-scale water cycle variables: Simple and straightforward maths Framework based on an EnKF Purely data-driven Exploit the joint inter- and intra-catchment auto and cross covariances between the four major water cycle variables through LS-prediction Application of a constrained EnKF for ensuring water budget closure Empirical hydrological model which is based on hydrometeorological data and their statistical dependencies

10 Derivation of the observation equation State X t with water cycle variables of the study Observations and uncertainties from global datasets Tourian et al. (2013), WRR

11 Unconstrained vs. Constrained correction Unconstrained observation equation Constrained observation equation Y t = H t X t + ν t Y t 0 = H t G X t + ν t ω t with G = I I I I 0 = P t ET t M t R t + ω t 1. Hard constraints: ω t = 0 water budgets are closed 2. Soft constraints: ω t ~ N 0, Q wb small imbalances are allowed

12 Derivation of the prediction equation Anomalies at time-step t r t = X t X t with X t being the long-term mean annual cycle. Auto- and cross-covariance of the water cycle variables Σ = D r t, r t, Σ Δ = D r t, r t 1 Prediction of the anomalies from t 1 to t Prediction of the full signal r t = Ar t 1 + ε t with A = Σ Δ Σ 1 X t = AX t 1 + A I X t 1 X t + ε t

13 Overview of the study regions Study comprises 29 large river-basins like, e.g., Amazon, Mississippi, Ob, Mackenzie,... Prediction of monthly runoff for 16 basins (blue) Compare runoff predictions against monthly observations from the GRDCdatabase

14 Performance of the EnKF-approach

15 Performance of the EnKF-approach

16 Performance of the different configurations Ensemble Kalman Filter (EnKF), hard and soft Constrained Ensemble Kalman Filter (CEnKF h, CEnKF s ), Ensemble Kalman Smoother (EnKS), and hard and soft Constrained Ensemble Kalman Smoother (CEnKS h, CEnKS s ) Lorenz et al. (2015), WRR

17 Exemplary time-series Full Signal Anomalies (w.r.t. MAC) Very good agreement of both the full signal and the runoff anomalies Shorter and longer term deviations from the mean annual cycle of runoff are well represented in the predicted time-series However: Problems in the representation of extremes

18 Conclusions Decrease in the number of rain- and river-gauges Large imbalances in the catchment-scale water budgets Analysis of an intensification of the water cycle or water budget studies not possible Urgent need for alternative approaches for estimating/predicting/correcting our data sources for the water cycle variables EnKF-based framework for predicting basin-scale runoff Very good agreement with monthly runoff observations Prof. Dr. Harald Kunstmann

19 Outlook Representation of Extremes? Application to climate models (CMIP5-ensemble) Prediction for other catchments Thank you for your attention

Project overview & geodetic mass estimation

Direct Water Balance 1 Project overview & geodetic mass estimation Nico Sneeuw, Balaji Devaraju Geodätisches Institut, Universität Stuttgart Johannes Riegger, Henry Kindt, András Bárdossy Institut für