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

Multivariate hydrological data assimilation of soil moisture and groundwater head

Observed groundwater head and soil moisture profiles are assimilated into an integrated hydrological model. The study uses the ensemble transform Kalman filter (ETKF) data assimilation method with the MIKE SHE hydrological model code. The method was firstly tested on synthetic data in a catchment of less complexity (the Karup catchment in Denmark), and later implemented using data from real observations in a larger and more complex catchment (the Ahlergaarde catchment in Denmark). In the Karup model, several experiments were designed with respect to different observation types, ensemble sizes and localization schemes, to investigate the assimilation performance. The results showed the necessity of using localization, especially when assimilating both groundwater head and soil moisture. The proposed scheme with both distance localization and variable localization was shown to be more robust and provide better results. Using the same assimilation scheme in the Ahlergaarde model, groundwater head and soil moisture were successfully assimilated into the model. The hydrological model with assimilation showed an overall improved performance compared to the model without assimilation

Future socioeconomic conditions may have a larger impact than climate change on nutrient loads to the Baltic Sea

The Baltic Sea is suffering from eutrophication caused by nutrient discharges from land to sea, and these loads might change in a changing climate. We show that the impact from climate change by mid-century is probably less than the direct impact of changing socioeconomic factors such as land use, agricultural practices, atmospheric deposition, and wastewater emissions. We compare results from dynamic modelling of nutrient loads to the Baltic Sea under projections of climate change and scenarios for shared socioeconomic pathways. Average nutrient loads are projected to increase by 8% and 14% for nitrogen and phosphorus, respectively, in response to climate change scenarios. In contrast, changes in the socioeconomic drivers can lead to a decrease of 13% and 6% or an increase of 11% and 9% in nitrogen and phosphorus loads, respectively, depending on the pathway. This indicates that policy decisions still play a major role in climate adaptation and in managing eutrophication in the Baltic Sea region.Peer reviewe