Data assimilation in integrated hydrological modeling using ensemble Kalman filtering:evaluating the effect of ensemble size and localization on filter performance

Groundwater head and stream discharge is assimilated using the ensemble transform Kalman filter in an integrated hydrological model with the aim of studying the relationship between the filter performance and the ensemble size. In an attempt to reduce the required number of ensemble members, an adaptive localization method is used. The performance of the adaptive localization method is compared to the more common distance-based localization. The relationship between filter performance in terms of hydraulic head and discharge error and the number of ensemble members is investigated for varying numbers and spatial distributions of groundwater head observations and with or without discharge assimilation and parameter estimation. The study shows that (1) more ensemble members are needed when fewer groundwater head observations are assimilated, and (2) assimilating discharge observations and estimating parameters requires a much larger ensemble size than just assimilating groundwater head observations. However, the required ensemble size can be greatly reduced with the use of adaptive localization, which by far outperforms distance-based localization. The study is conducted using synthetic data only

Data assimilation in integrated hydrological modelling in the presence of observation bias

The use of bias-aware Kalman filters for estimating and correcting observation bias in groundwater head observations is evaluated using both synthetic and real observations. In the synthetic test, groundwater head observations with a constant bias and unbiased stream discharge observations are assimilated in a catchment-scale integrated hydrological model with the aim of updating stream discharge and groundwater head, as well as several model parameters relating to both streamflow and groundwater modelling. The coloured noise Kalman filter (ColKF) and the separate-bias Kalman filter (SepKF) are tested and evaluated for correcting the observation biases. The study found that both methods were able to estimate most of the biases and that using any of the two bias estimation methods resulted in significant improvements over using a bias-unaware Kalman filter. While the convergence of the ColKF was significantly faster than the convergence of the SepKF, a much larger ensemble size was required as the estimation of biases would otherwise fail. Real observations of groundwater head and stream discharge were also assimilated, resulting in improved streamflow modelling in terms of an increased Nash–Sutcliffe coefficient while no clear improvement in groundwater head modelling was observed. Both the ColKF and the SepKF tended to underestimate the biases, which resulted in drifting model behaviour and sub-optimal parameter estimation, but both methods provided better state updating and parameter estimation than using a bias-unaware filter

Governance Struggles and Policy Processes in Disaster Risk Reduction: A Case Study from Nepal

In the neo-liberal climate of reduced responsibility for the state, alongside global platforms established to implement the Hyogo Framework for Action, a new arena opens for a multitude of stakeholders to engage in disaster risk reduction (DRR). The key role that the state can play in instituting effective DRR tends to receive little attention, yet in situations where the state apparatus is weak, such as in Nepal, it becomes evident that integrating DRR into development is a particularly challenging task. Due to the political situation in Nepal, progress has been stalled in providing a legislative context conducive to effective DRR. This paper traces the evolution of key DRR initiatives that have been developed in spite of the challenging governance context, such as the National Strategy for Disaster Risk Management and the Nepal Risk Reduction Consortium. Informed by in-depth interviews with key informants, the argument is made that the dedicated efforts of national and international non-governmental organisations, multilateral agencies and donors in mainstreaming DRR demonstrate that considerable progress can be made even where government departments are protective of their own interests and are slow to enact policies to support DRR. The paper suggests however, that without stronger engagement of key political actors the prospects for further progress in DRR may be limited. The findings have implications for other post-conflict countries or weak states engaging in DRR

Comparison of evapotranspiration estimates using the water balance and the eddy covariance methods

Abstract The eddy covariance method estimates the energy flux of latent heat for evapotranspiration. However, imbalance between the land surface energy output and input is a well‐known fact. Energy balance closure is most commonly not achieved, and therefore the eddy covariance method potentially underestimates actual evapotranspiration. Notwithstanding, the method is one of the most established measurement techniques for estimating evapotranspiration. Here, evapotranspiration from eddy covariance (ETEC) is cross‐checked with evapotranspiration calculated as the residual of the water balance (ETwb). The water balance closure using ETEC is simultaneously validated. Over a 6‐yr period, all major terms of the water balance are measured including precipitation, recharge from percolation lysimeters, and soil moisture content from a cosmic‐ray neutron sensor, a capacitance sensor network, and time domain reflectometry (TDR), respectively. In addition, we estimate their respective uncertainties. The study demonstrates that both monthly and yearly ETEC and ETwb compare well and that the water balance is closed when ETEC is used. Concurrently, incoming available energy (net radiation minus ground heat flux) on average exceeds the turbulent energy fluxes (latent heat flux and sensible heat flux) by 31%, exposing the energy–surface imbalance. Consequently, the imbalance in the energy balance using the eddy covariance method must, to a lesser degree, be caused by errors in the latent heat estimates but can mainly be attributed to errors in the other energy flux components

Monitoring CO2 migration in a shallow sand aquifer using 3D crosshole electrical resistivity tomography

AbstractThree-dimensional (3D) crosshole electrical resistivity tomography (ERT) was used to monitor a pilot CO2 injection experiment at Vrøgum, western Denmark. The purpose was to evaluate the effectiveness of the ERT method for detection of small electrical conductivity (EC) changes during the first 2 days of CO2 injection in a shallow siliciclastic aquifer and to study the early-time behavior of a controlled small gaseous CO2 release. 45kg of CO2 was injected over a 50-h period at 9.85m depth. ERT data were collected using horizontal bipole-bipole (HBB) and vertical bipole-bipole (VBB) arrays. The combined HBB and VBB data sets were inverted using a difference inversion algorithm for cancellation of coherent noises and enhanced resolution of small changes. ERT detected the small bulk EC changes (<10%) from conductive dissolved CO2 and resistive gaseous CO2. The primary factors that control the migration of a CO2 plume consist of buoyancy of gaseous CO2, local heterogeneity, groundwater flow and external pressure exerted by the injector. The CO2 plume at the Vrøgum site migrated mostly upward due to buoyancy and it also skewed toward northeastern region by overcoming local groundwater flow. The conductive eastern part is more porous and becomes the preferential pathway for the CO2 plume, which was trapped within the slightly more porous glacial sand layer between 5m and 10m depths. The gaseous and dissolved CO2 plumes are collocated and grow in tandem for the first 24h and their opposite effects resulted in a small bulk EC increase. After raising the injection rate from 10g/min to 20g/min at the 24-h mark, the CO2 plume grew quickly. The bulk EC changes from ERT agreed partially with water sample EC and GPR data. The apparent disagreement between high CO2 gas saturation and prevailing positive bulk EC changes may be caused by limited and variable ERT resolution, low ERT sensitivity to resistive anomalies and uncalibrated CO2 gas saturation. ERT data show a broader CO2 plume while water sample EC had higher fine-scale variability. Our ERT electrode configuration can be optimized for more efficient data acquisition and better spatial resolution