Improving soil moisture and runoff simulations at 3 km over Europe using land surface data assimilation

Abstract

Accurate and reliable hydrologic simulations are important for many applications such as water resources management, future water availability projections and predictions of extreme events. However, the accuracy of water balance estimates is limited by the lack of large-scale observations, model simulation uncertainties and biases related to errors in model structure and uncertain inputs (e.g., hydrologic parameters and atmospheric forcings). The availability of long-term and global remotely sensed soil moisture offers the opportunity to improve model estimates through data assimilation with complete spatiotemporal coverage. In this study, we assimilated the European Space Agency (ESA) Climate Change Initiative (CCI) derived soil moisture (SM) information to improve the estimation of continental-scale soil moisture and runoff. The assimilation experiment was conducted over a time period 2000-2006 with the Community Land Model, version 3.5 (CLM3.5), integrated with the Parallel Data Assimilation Framework (PDAF) at a spatial resolution of 0.0275 • (∼ 3 km) over Europe. The model was forced with the high-resolution reanalysis COSMO-REA6 from the Hans Ertel Centre for Weather Research (HErZ). The performance of assimilation was assessed against openloop model simulations and cross-validated with independent ESA CCI-derived soil moisture (CCI-SM) and gridded runoff observations. Our results showed improved estimates of soil moisture, particularly in the summer and au-tumn seasons when cross-validated with independent CCI-SM observations. The assimilation experiment results also showed overall improvements in runoff, although some regions were degraded, especially in central Europe. The results demonstrated the potential of assimilating satellite soil moisture observations to produce downscaled and improved high-resolution soil moisture and runoff simulations at the continental scale, which is useful for water resources assessment and monitoring.

A 3 km spatially and temporally consistent European daily soil moisture reanalysis from 2000 to 2015

“…To generate ensembles of forecast states, we perturbed the precipitation and the soil parameters (sand and clay percentage) by applying log normally distributed multiplicative perturbations (with a mean of 1 and standard deviation of 0.15) to the precipitation field and random noise drawn from spatially uniform distribution (±10%) to the sand and clay content, respectively. In the present study, we only updated the soil moisture state variable and kept the soil texture constant for individual ensemble members throughout our simulations instead of joint state and parameter updates of soil moisture and soil texture in the DA approach as used in our previous study 31 . The ensemble size was set to 20 in our assimilation experiment using similar methodology as used in Naz et al 31 .…”

“…In the present study, we only updated the soil moisture state variable and kept the soil texture constant for individual ensemble members throughout our simulations instead of joint state and parameter updates of soil moisture and soil texture in the DA approach as used in our previous study 31 . The ensemble size was set to 20 in our assimilation experiment using similar methodology as used in Naz et al 31 . Our initial study found slightly improved SM estimates when ensemble size was increased from 12 to 20 31 .…”

“…The ensemble size was set to 20 in our assimilation experiment using similar methodology as used in Naz et al 31 . Our initial study found slightly improved SM estimates when ensemble size was increased from 12 to 20 31 . However, increasing ensemble size is quite challenging for such a large-scale high-resolution model because of the memory and storage requirements.…”

“…In the future, the local ensemble transform Kalman filter (LETKF) could be used to avoid this limitation. The strength and limitations associated with our methods are also discussed in detail in Naz et al 31 .…”

Application of soil moisture information for operational water management

“…Pre-storm soil moisture conditions partly determine whether precipitation will either in ltrate to deeper soil layers or lead to standing water or overland ow. The accuracy of ( ash) ood forecasting systems greatly bene t if up-to-date information on soil moisture is included (Crow and Ryu, 2009;Brocca et al, 2009Brocca et al, , 2010Tramblay et al, 2010;Sutanudjaja et al, 2014;Naz et al, 2019). In particular, soil moisture information improves the understanding of ood peak heights (Wanders et al, 2014b).…”