Abstract

Abstract. This study evaluates the impact of assimilating surface soil moisture (SSM) and leaf area index (LAI) observations into a land surface model using the SAFRAN–ISBA–MODCOU (SIM) hydrological suite. SIM consists of three stages: (1) an atmospheric reanalysis (SAFRAN) over France, which forces (2) the three-layer ISBA land surface model, which then provides drainage and runoff inputs to (3) the MODCOU hydro-geological model. The drainage and runoff outputs from ISBA are validated by comparing the simulated river discharge from MODCOU with over 500 river-gauge observations over France and with a subset of stations with low-anthropogenic influence, over several years. This study makes use of the A-gs version of ISBA that allows for physiological processes. The atmospheric forcing for the ISBA-A-gs model underestimates direct shortwave and long-wave radiation by approximately 5 % averaged over France. The ISBA-A-gs model also substantially underestimates the grassland LAI compared with satellite retrievals during winter dormancy. These differences result in an underestimation (overestimation) of evapotranspiration (drainage and runoff). The excess runoff flowing into the rivers and aquifers contributes to an overestimation of the SIM river discharge. Two experiments attempted to resolve these problems: (i) a correction of the minimum LAI model parameter for grasslands and (ii) a bias-correction of the model radiative forcing. Two data assimilation experiments were also performed, which are designed to correct random errors in the initial conditions: (iii) the assimilation of LAI observations and (iv) the assimilation of SSM and LAI observations. The data assimilation for (iii) and (iv) was done with a simplified extended Kalman filter (SEKF), which uses finite differences in the observation operator Jacobians to relate the observations to the model variables. Experiments (i) and (ii) improved the median SIM Nash scores by about 9 % and 18 % respectively. Experiment (iii) reduced the LAI phase errors in ISBA-A-gs but had little impact on the discharge Nash efficiency of SIM. In contrast, experiment (iv) resulted in spurious increases in drainage and runoff, which degraded the median discharge Nash efficiency by about 7 %. The poor performance of the SEKF originates from the observation operator Jacobians. These Jacobians are dampened when the soil is saturated and when the vegetation is dormant, which leads to positive biases in drainage and/or runoff and to insufficient corrections during winter, respectively. Possible ways to improve the model are discussed, including a new multi-layer diffusion model and a more realistic response of photosynthesis to temperature in mountainous regions. The data assimilation should be advanced by accounting for model and forcing uncertainties.

Highlights

  • Soil moisture influences the flow of water to rivers and aquifers on weekly to monthly timescales, which makes it an important factor in hydrological models

  • To begin with we examine the influence of the different model simulations (NIT, NITm and NITbc) on the leaf area index (LAI) evolution for the four dominant vegetation patches

  • We investigate the influence of data assimilation (DA) on the drainage and runoff fluxes in Fig. 7f–j, which is equivalent to Fig. 7a–e except that LDAS1 and LDAS2 are compared with NIT

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Summary

Introduction

Soil moisture influences the flow of water to rivers and aquifers on weekly to monthly timescales, which makes it an important factor in hydrological models. D. Fairbairn et al.: The effect of land data assimilation on streamflow simulations over France eter (ASCAT) instrument on board the METOP satellites (Wagner et al, 2007), the Soil Moisture and Ocean Salinity (SMOS) Mission (Kerr et al, 2001) and the Soil Moisture Active Passive (SMAP) Mission (Entekhabi et al, 2010), amongst others. Fairbairn et al.: The effect of land data assimilation on streamflow simulations over France eter (ASCAT) instrument on board the METOP satellites (Wagner et al, 2007), the Soil Moisture and Ocean Salinity (SMOS) Mission (Kerr et al, 2001) and the Soil Moisture Active Passive (SMAP) Mission (Entekhabi et al, 2010), amongst others These instruments can only indirectly observe the top 1–3 cm of soil moisture and the data are subject to retrieval errors. DA methods are necessary to account for the errors in the observations and the model, and to spread the information through space and time

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