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

Uncertainty in geological and hydrogeological data

Uncertainty in conceptual model structure and in environmental data is of essential interest when dealing with uncertainty in water resources management. To make quantification of uncertainty possible is it necessary to identify and characterise the uncertainty in geological and hydrogeological data. This paper discusses a range of available techniques to describe the uncertainty related to geological model structure and scale of support. Literature examples on uncertainty in hydrogeological variables such as saturated hydraulic conductivity, specific yield, specific storage, effective porosity and dispersivity are given. Field data usually have a spatial and temporal scale of support that is different from the one on which numerical models for water resources management operate. Uncertainty in hydrogeological data variables is characterised and assessed within the methodological framework of the HarmoniRiB classification

Current Results of the EC-sponsored Catchment Modelling (CatchMod) Cluster

To support the Water Framework Directive implementation, much research has been commissioned at both national and European levels. CatchMod is a cluster of these projects, which is focusing on the development of computational catchment models and related tools. This paper presents an overview of the results of the CatchMod cluster to dat

Uncertainties in river basin data at various support scales ? Example from Odense Pilot River Basin

International audienceIn environmental modelling studies field data usually have a spatial and temporal scale of support that is different from the one at which models operate. This calls for a methodology for rescaling data uncertainty from one support scale to another. In this paper data uncertainty is assessed for various environmental data types collected for monitoring purposes from the Odense river basin in Denmark by use of literature information, expert judgement and simple data analyses. It is demonstrated how such methodologies can be applied to data that vary in space or time such as precipitation, climate variables, discharge, surface water quality, soil parameters, groundwater abstraction, heads and groundwater quality variables. Data uncertainty is categorised and assessed in terms of probability density functions and temporal or spatial autocorrelation functions. The autocorrelation length scales are crucial when support scale is changing and it is demonstrated how the assumption used when estimating the autocorrelation parameters may limit the applicability of these autocorrelation functions

Local control on precipitation in a fully coupled climate-hydrology model

The ability to simulate regional precipitation realistically by climate models is essential to understand and adapt to climate change. Due to the complexity of associated processes, particularly at unresolved temporal and spatial scales this continues to be a major challenge. As a result, climate simulations of precipitation often exhibit substantial biases that affect the reliability of future projections. Here we demonstrate how a regional climate model (RCM) coupled to a distributed hydrological catchment model that fully integrates water and energy fluxes between the subsurface, land surface, plant cover and the atmosphere, enables a realistic representation of local precipitation. Substantial improvements in simulated precipitation dynamics on seasonal and longer time scales is seen for a simulation period of six years and can be attributed to a more complete treatment of hydrological sub-surface processes including groundwater and moisture feedback. A high degree of local influence on the atmosphere suggests that coupled climate-hydrology models have a potential for improving climate projections and the results further indicate a diminished need for bias correction in climate-hydrology impact studies

Integrated hydrological modeling of the North China Plain and implications for sustainable water management

Groundwater overdraft has caused fast water level decline in the North China Plain (NCP) since the 1980s. Although many hydrological models have been developed for the NCP in the past few decades, most of them deal only with the groundwater component or only at local scales. In the present study, a coupled surface water–groundwater model using the MIKE SHE code has been developed for the entire alluvial plain of the NCP. All the major processes in the land phase of the hydrological cycle are considered in the integrated modeling approach. The most important parameters of the model are first identified by a sensitivity analysis process and then calibrated for the period 2000–2005. The calibrated model is validated for the period 2006–2008 against daily observations of groundwater heads. The simulation results compare well with the observations where acceptable values of root mean square error (RMSE) (most values lie below 4 m) and correlation coefficient (<i>R</i>) (0.36–0.97) are obtained. The simulated evapotranspiration (ET) is then compared with the remote sensing (RS)-based ET data to further validate the model simulation. The comparison result with a <i>R</i>² value of 0.93 between the monthly averaged values of simulated actual evapotranspiration (AET) and RS AET for the entire NCP shows a good performance of the model. The water balance results indicate that more than 70% of water leaving the flow system is attributed to the ET component, of which about 0.25% is taken from the saturated zone (SZ); about 29% comes from pumping, including irrigation pumping and non-irrigation pumping (net pumping). Sustainable water management analysis of the NCP is conducted using the simulation results obtained from the integrated model. An effective approach to improve water use efficiency in the NCP is by reducing the actual ET, e.g. by introducing water-saving technologies and changes in cropping

An automated method to build groundwater model hydrostratigraphy from airborne electromagnetic data and lithological borehole logs

Abstract. Large-scale integrated hydrological models are important decision support tools in water resources management. The largest source of uncertainty in such models is the hydrostratigraphic model. Geometry and configuration of hydrogeological units are often poorly determined from hydrogeological data alone. Due to sparse sampling in space, lithological borehole logs may overlook structures that are important for groundwater flow at larger scales. Good spatial coverage along with high spatial resolution makes airborne time-domain electromagnetic (AEM) data valuable for the structural input to large-scale groundwater models. We present a novel method to automatically integrate large AEM data-sets and lithological information into large-scale hydrological models. Clay-fraction maps are produced by translating geophysical resistivity into clay-fraction values using lithological borehole information. Voxel models of electrical resistivity and clay fraction are classified into hydrostratigraphic zones using k-means clustering. Hydraulic conductivity values of the zones are estimated by hydrological calibration using hydraulic head and stream discharge observations. The method is applied to a Danish case study. Benchmarking hydrological performance by comparison of simulated hydrological state variables, the cluster model performed competitively. Calibrations of 11 hydrostratigraphic cluster models with 1–11 hydraulic conductivity zones showed improved hydrological performance with increasing number of clusters. Beyond the 5-cluster model hydrological performance did not improve. Due to reproducibility and possibility of method standardization and automation, we believe that hydrostratigraphic model generation with the proposed method has important prospects for groundwater models used in water resources management.</jats:p