ALBERT

Description: Soil moisture and soil temperature are tightly coupled variables in land surface models. The objective of this study was to evaluate the impact of the joint assimilation of soil moisture and land surface temperature data in a land surface model on soil moisture and soil temperature characterization. Three synthetic tests evaluated the joint assimilation of surface temperature (measured by MODIS) and brightness temperature (from L-band) into the Community Land Model using the local ensemble transform Kalman filter (LETKF). The following three tests were performed for dry and wet conditions: (i) assimilating surface temperature observations only; (ii) assimilating brightness temperature observations only; and (iii) assimilating both surface temperature and brightness temperature observations. The results show that the joint assimilation of surface temperature and brightness temperature results in the best characterization of soil moisture and soil temperature profiles under dry conditions. The assimilation of surface temperature contributed to an improved characterization of soil moisture profiles under dry conditions. For the dry period, brightness temperature assimilation resulted in improved prediction of sensible and latent heat fluxes, whereas surface temperature assimilation improved only the prediction of latent heat flux. Under wet conditions, the joint assimilation scheme cannot outperform the single brightness temperature assimilation. Neither the estimation of soil moisture and soil temperature profiles nor the estimates of the turbulent fluxes were improved by joint assimilation (compared with assimilation of brightness temperature only) under wet conditions.

Description: Several studies have claimed to have found significant weekly cycles of meteorological variables appearing over large domains, which can hardly be related to urban effects exclusively. Nevertheless, there is still an ongoing scientific debate whether these large-scale weekly cycles exist or not, and some other studies fail to reproduce them with statistical significance. In addition to the lack of the positive proof for the existence of these cycles, their possible physical explanations have been controversially discussed during the last years. In this work we review the main results about this topic published during the recent two decades, including a summary of the existence or non-existence of significant weekly weather cycles across different regions of the world, mainly over the US, Europe and Asia. In addition, some shortcomings of common statistical methods for analyzing weekly cycles are listed. Finally, a brief summary of supposed causes of the weekly cycles, focusing on the aerosol-cloud-radiation interactions and their impact on meteorological variables as a result of the weekly cycles of anthropogenic activities, and possible directions for future research, is presented.

Description: Remote sensing images deliver important information about soil moisture, but often cover only part of an area, for example due to the presence of clouds or vegetation. This paper examines the potential of incorporating the spatial horizontal correlation characteristics of surface soil moisture observations in land data assimilation in order to obtain improved estimates of soil moisture at uncovered grid cells (i.e. grid cells without observations). Observing system simulation experiments were carried out to assimilate the synthetic surface soil moisture observations into the Community Land Model for the Babaohe River Basin in northwestern China. The estimation of soil moisture at the uncovered grid cells was improved when information about surrounding observations and their spatial correlation structure was included. Including an increasing number of observations for covered and uncovered grid cells in the assimilation procedure led to a better prediction of soil moisture with an upper limit of five observations. A further increase of the number of observations did not further improve the results for this specific case. High observational coverage resulted in a better assimilation performance, depending also on the spatial distribution of observation data. In summary, the spatial horizontal correlation structure of soil moisture was found to be helpful for improving the surface soil moisture data characterization, especially for uncovered grid cells.

Description: The normal-score ensemble Kalman filter (NS-EnKF) is tested on a synthetic aquifer characterized by the presence of channels with a bimodal distribution of its hydraulic conductivities. This is a clear example of an aquifer that cannot be characterized by a multiGaussian distribution. Fourteen scenarios are analyzed which differ among them in one or various of the following aspects: the prior random function model, the boundary conditions of the flow problem, the number of piezometers used in the assimilation process, or the use of covariance localization in the implementation of the Kalman filter. The performance of the NS-EnKF is evaluated through the ensemble mean and variance maps, the connectivity patterns of the individual conductivity realizations and the degree of reproduction of the piezometric heads. The results show that (i) the localized NS-EnKF can characterize the non-multiGaussian underlying hydraulic distribution even when an erroneous prior random function model is used, (ii) localization plays an important role to prevent filter inbreeding and results in a better logconductivity characterization, and (iii) the NS-EnKF works equally well under very different flow configurations.

Description: In this paper, we present a two-stage hybrid Kalman filter to estimate both observation and forecast bias in hydrologic models, in addition to state variables. The biases are estimated using the discrete Kalman filter, and the state variables using the ensemble Kalman filter. A key issue in this multi-component assimilation scheme is the exact partitioning of the difference between observation and forecasts into state, forecast bias and observation bias updates. Here, the error covariances of the forecast bias and the unbiased states are calculated as constant fractions of the biased state error covariance, and the observation bias error covariance is a function of the observation prediction error covariance. In a series of synthetic experiments, focusing on the assimilation of discharge into a rainfall-runoff model, it is shown that both static and dynamic observation and forecast biases can be successfully estimated. The results indicate a strong improvement in the estimation of the state variables and resulting discharge as opposed to the use of a bias-unaware ensemble Kalman filter. Furthermore, minimal code modification in existing data assimilation software is needed to implement the method. The results suggest that a better performance of data assimilation methods should be possible if both forecast and observation biases are taken into account.

Description: Future risks for groundwater resources, due to global change are usually analyzed by driving hydrological models with the outputs of climate models. However, this model chain is subject to considerable uncertainties. Given the high uncertainties it is essential to identify the processes governing the groundwater dynamics, as these processes are likely to affect groundwater resources in the future, too. Information about the dominant mechanisms can be achieved by the analysis of long-term data, which are assumed to provide insight in the reaction of groundwater resources to changing conditions (weather, land use, water demand). Referring to this, a dataset of 30 long-term time series of precipitation dominated groundwater systems in northern Switzerland and southern Germany is collected. In order to receive additional information the analysis of the data is carried out together with hydrological model simulations. High spatio-temporal correlations, even over large distances could be detected and are assumed to be related to large-scale atmospheric circulation patterns. As a result it is suggested to prefer innovative weather-type-based downscaling methods to other stochastic downscaling approaches. In addition, with the help of a qualitative procedure to distinguish between meteorological and anthropogenic causes it was possible to identify processes which dominated the groundwater dynamics in the past. It could be shown that besides the meteorological conditions, land use changes, pumping activity and feedback mechanisms governed the groundwater dynamics. Based on these findings, recommendations to improve climate change impact studies are suggested.

Description: Climate change related modifications in the spatio-temporal distribution of precipitation and evapotranspiration will have an impact on groundwater resources. This study presents a modelling approach exploiting the advantages of integrated hydrological modelling and a broad climate model basis. We applied the integrated MIKE SHE model on a perialpine, small catchment in northern Switzerland near Zurich. To examine the impact of climate change we forced the hydrological model with data from eight GCM-RCM combinations showing systematic biases which are corrected by three different statistical downscaling methods, not only for precipitation but also for the variables that govern potential evapotranspiration. The downscaling methods are evaluated in a split sample test and the sensitivity of the downscaling procedure on the hydrological fluxes is analyzed. The RCMs resulted in very different projections of potential evapotranspiration and, especially, precipitation. All three downscaling methods reduced the differences between the predictions of the RCMs and all corrected predictions showed no future groundwater stress which can be related to an expected increase in precipitation during winter. It turned out that especially the timing of the precipitation and thus recharge is very important for the future development of the groundwater levels. However, the simulation experiments revealed the weaknesses of the downscaling methods which directly influence the predicted hydrological fluxes, and thus also the predicted groundwater levels. The downscaling process is identified as an important source of uncertainty in hydrological impact studies, which has to be accounted for. Therefore it is strongly recommended to test different downscaling methods by using verification data before applying them to climate model data.

Description: Data assimilation (DA) holds considerable potential for improving hydrologic predictions as demonstrated in numerous research studies. However, advances in hydrologic DA research have not been adequately or timely implemented in operational forecast systems to improve the skill of forecasts for better informed real-world decision making. This is due in part to a lack of mechanisms to properly quantify the uncertainty in observations and forecast models in real-time forecasting situations and to conduct the merging of data and models in a way that is adequately efficient and transparent to operational forecasters. The need for effective DA of useful hydrologic data into the forecast process has become increasingly recognized in recent years. This motivated a hydrologic DA workshop in Delft, the Netherlands in November 2010, which focused on advancing DA in operational hydrologic forecasting and water resources management. As an outcome of the workshop, this paper reviews, in relevant detail, the current status of DA applications in both hydrologic research and operational practices, and discusses the existing or potential hurdles and challenges in transitioning hydrologic DA research into cost-effective operational forecasting tools, as well as the potential pathways and newly emerging opportunities for overcoming these challenges. Several related aspects are discussed, including (1) theoretical or mathematical aspects in DA algorithms, (2) the estimation of different types of uncertainty, (3) new observations and their objective use in hydrologic DA, (4) the use of DA for real-time control of water resources systems, and (5) the development of community-based, generic DA tools for hydrologic applications. It is recommended that cost-effective transition of hydrologic DA from research to operations should be helped by developing community-based, generic modeling and DA tools or frameworks, and through fostering collaborative efforts among hydrologic modellers, DA developers, and operational forecasters.

Description: River–aquifer exchange fluxes influence local and regional water balances and affect groundwater and river water quality and quantity. Unfortunately, river–aquifer exchange fluxes tend to be strongly spatially variable, and it is an open research question to which degree river bed heterogeneity has to be represented in a model in order to achieve reliable estimates of river–aquifer exchange fluxes. This research question is addressed in this paper with the help of synthetic simulation experiments, which mimic the Limmat aquifer in Zurich (Switzerland), where river–aquifer exchange fluxes and groundwater management activities play an important role. The solution of the unsaturated–saturated subsurface hydrological flow problem including river–aquifer interaction is calculated for ten different synthetic realities where the strongly heterogeneous river bed hydraulic conductivities (L) are perfectly known. Hydraulic head data (100 in the default scenario) are sampled from the synthetic realities. In subsequent data assimilation experiments, where L is unknown now, the hydraulic head data are used as conditioning information, with the help of the ensemble Kalman filter (EnKF). For each of the ten synthetic realities, four different ensembles of L are tested in the experiments with EnKF; one ensemble estimates high-resolution L fields with different L values for each element, and the other three ensembles estimate effective L values for 5, 3 or 2 zones. The calibration of higher-resolution L fields (i.e. fully heterogeneous or 5 zones) gives better results than the calibration of L for only 3 or 2 zones in terms of reproduction of states, stream–aquifer exchange fluxes and parameters. Effective L for a limited number of zones cannot always reproduce the true states and fluxes well and results in biased estimates of net exchange fluxes between aquifer and stream. Also in case only 10 head data are used for conditioning, the high-resolution characterization of L fields with EnKF is still feasible. For less heterogeneous river bed hydraulic conductivities, a high-resolution characterization of L is less important. When uncertainties in the hydraulic parameters of the aquifer are also regarded in the assimilation, the errors in state and flux predictions increase, but the ensemble with a high spatial resolution for L still outperforms the ensembles with effective L values. We conclude that for strongly heterogeneous river beds the commonly applied simplified representation of the streambed, with spatially homogeneous parameters or constant parameters for a few zones, might yield significant biases in the characterization of the water balance. For strongly heterogeneous river beds, we suggest adopting a stochastic field approach to model the spatially heterogeneous river beds geostatistically. The paper illustrates that EnKF is able to calibrate such heterogeneous streambeds on the basis of hydraulic head measurements, outperforming zonation approaches.

Description: Remote sensing images deliver important information about soil moisture and soil temperature, but often cover only part of an area, for example due to the presence of clouds or vegetation. This paper examines the potential of incorporating the spatial horizontal correlation characteristics of observations of surface soil moisture and surface temperature in data assimilation to get improved estimations of soil moisture and soil temperature at such uncovered grid cells. Observing system simulation experiments were carried out to assimilate the synthetic surface soil moisture and surface temperature observations into the community land model (CLM) for the Babaohe River Basin in northwestern China. The estimations of soil moisture at the uncovered grids (i.e., grid cells without observations) were improved when information from surrounding observations was included considering the spatial correlation. An increasing number of observations led to a better prediction of soil moisture with an upper limit of nine observations. A further increase of the number of observations did not improve much the results in this case. Even the estimation for the covered grid cells was improved when multiple observations were included in the LETKF. The results of surface temperature assimilation show that for the covered grids (i.e., grid cells with observations), more observations will not improve the estimations, and one nearest observation is enough to obtain the best estimation. For the uncovered grids, more observations will be better. The estimations at the uncovered grids could be improved when the surrounding correlated observations were used. In summary, the spatial horizontal correlations of soil moisture and surface temperature were found to be helpful to the surface soil moisture and surface temperature data assimilation through this study, especially for uncovered grid cells.