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Floods are often caused by small-scale weather and hydrological phenomena that require very high-resolution models to adequate resolve and simulate. Unfortunately, high-resolution models are expensive to run continuously in real-time, which makes the case for only running these specialized systems “on-demand” when it is deemed necessary. The aim of this study is to investigate a high-resolution digital twin designed for on-demand use and test it on the case of Vejle, Denmark. The city of Vejle is of special interest because it is prone to frequent floods from long-term winter precipitation, convective cloudburst events in summer as well as storm surges from the sea. On top of this, several fast and slow responding rivers meet inside the cityHere, we present results from a hydrological forecasting setup based around the conceptual HYPE model, which is developed by the Swedish Meteorological and Hydrological Institute. The model is developed with high-resolution soil and land use data, forced with high-resolution meteorological observations, and its predictions are evaluated at several gauges along the Vejle and Grejs rivers. The final aim of the research is to assess the benefits of utilizing subkilometer-scale HARMONIE weather predictions, which especially is expected to improve the resolution of local rainfall fields. The full forecasting chain will be put into operation in the coming year.

Integrated hydrological models are usually calibrated against observations of river discharge and piezometric head in groundwater aquifers. Calibration of such models against spatially distributed observations of river water level can potentially improve their reliability and predictive skill. However, traditional river gauging stations are normally spaced too far apart to capture spatial patterns in the water surface, while spaceborne observations have limited spatial and temporal resolution. UAVs (Unmanned Aerial Vehicles) can retrieve river water level measurements, providing: i) high spatial resolution; ii) spatially continuous profiles along or across the water body; iii) flexible timing of sampling. A semi-synthetic study was conducted to analyse the value of the new UAV-borne datatype for improving hydrological models, in particular estimates of GW (Groundwater)- SW (Surface Water) interaction. Mølleåen River (Denmark) and its catchment were simulated using an integrated hydrological model (MIKE 11-MIKE SHE). Calibration against distributed surface water levels using the DiffeRential Evolution Adaptive Metropolis (DREAM) algorithm demonstrated a significant improvement in estimating spatial patterns and time series of GW-SW interaction. After water level calibration, the sharpness of the estimates of GW-SW time series improves of ca. 50% and RMSE (Root Mean Square Error) decreases by ca. 75% compared to a model calibrated against discharge only.

This paper assesses how various sources of uncertainty propagate through the uncertainty cascade from emission scenarios through climate models and hydrological models to impacts, with a particular focus on groundwater aspects from a number of coordinated studies in Denmark. Our results are similar to those from surface water studies showing that climate model uncertainty dominates the results for projections of climate change impacts on streamflow and groundwater heads. However, we found uncertainties related to geological conceptualization and hydrological model discretization to be dominant for projections of well field capture zones, while the climate model uncertainty here is of minor importance. How to reduce the uncertainties on climate change impact projections related to groundwater is discussed, with an emphasis on the potential for reducing climate model biases through the use of fully coupled climate–hydrology models.Editor D. Koutsoyiannis; Associate editor not assigned

In this study we develop and test a calibration approach on a spatially distributed groundwater–surface water catchment model (MIKE SHE) coupled to a land surface model component with particular focus on the water and energy fluxes. The model is calibrated against time series of eddy flux measurements from three sites of different land surface type (agriculture, forest and meadow) and river discharge data from the 2500 km 2 Skjern River catchment in Denmark. The approach includes initial calibrations of three one-dimensional models representing the three land surface types using the flux measurements for calibration. This step provides initial values for the subsequent modelling and calibration at catchment scale. To test the validity of the approach, two additional catchment scale distributed simulations were performed with no calibration and only calibration of the one-dimensional models, respectively. In addition, a subsequent validation period was simulated. A mean energy closure imbalance of 20% was seen for the three sites. For the distributed simulations, the energy imbalance was accounted for by two energy balance closure hypotheses ascribing the error to either energy fluxes or net radiation. In general, the distributed calibration approach improved model results substantially compared to using default values (no calibration) or calibration of the one-dimensional models only. For the distributed model simulations, the assumption regarding the energy balance closure had a substantial impact on the parameter sensitivities and on the simulated discharge and energy balance. During calibration, the simulation with corrected energy fluxes showed better performance on discharge than the simulation with corrected net radiation whereas the reverse was true for the validation period. Regarding energy fluxes, the simulation with corrected net radiation was superior in both the calibration and validation period.

Water Surface Elevation (WSE) is an important hydrometric observation, useful to calibrate hydrological models, predict floods, and assess climate change. However, the number of in-situ gauging stations is in decline worldwide. Satellite altimetry, including the recently launched satellite missions (e.g. the radar altimetry missions Cryosat 2, Jason 3, Sentinel 3A/B and the LIDAR mission ICESat-2), can determine WSE only in rivers which are more than ca. 100 m wide. WSE measurements in small streams currently remain limited to the few existing in-situ stations or to time-consuming in-situ surveys. Unmanned Aerial Systems (UAS) can acquire real-time WSE observations during periods of hydrological interest (but with flight limitations in extreme weather conditions), within short survey times and with automatic or semi-automatic flight operations. UAS-borne photogrammetry is a well-known technique that can estimate land elevation with an accuracy as high as a few cm, similarly UAS-borne LIDAR can estimate land elevation but without requiring Ground Control Points (GCPs). However, both techniques face limitations in estimating WSE: water transparency and lack of stable visual key points on the Water Surface (WS) complicate the UAS-borne photogrammetric estimates of WSE, while the LIDAR reflection from the water surface is generally not strong enough to be captured by most of the UAS-borne LIDAR systems currently available on the market. Thus, LIDAR and photogrammetry generally require extraction of the elevation of the “water-edge” points, i.e. points at the interface between land and water, for identifying the WSE. We demonstrate highly accurate WSE observations with a new radar altimetry solution, which comprises a 77 GHz radar chip with full waveform analysis and an accurate dual frequency differential Global Navigation Satellite System (GNSS) system. The radar altimetry solution shows the lowest standard deviation (σ) and RMSE on WSE estimates, ca. 1.5 cm and ca. 3 cm respectively, whilst photogrammetry and LIDAR show a σ and an RMSE at decimetre level. Radar altimetry also requires a significantly shorter survey and processing time compared to LIDAR and especially to photogrammetry.

To improve our understanding of the impacts of feedback between the atmosphere and the terrestrial water cycle including groundwater and to improve the integration of water resource management modelling for climate adaption we have developed a dynamically coupled climate–hydrological modelling system. The OpenMI modelling interface is used to couple a comprehensive hydrological modelling system, MIKE SHE running on personal computers, and a regional climate modelling system, HIRHAM running on a high performance computing platform. The coupled model enables two-way interaction between the atmosphere and the groundwater via the land surface and can represent the lateral movement of water in both the surface and subsurface and their interactions, not normally accounted for in climate models. Meso-scale processes are important for climate in general and rainfall in particular. Hydrological impacts are assessed at the catchment scale, the most important scale for water management. Feedback between groundwater, the land surface and the atmosphere occurs across a range of scales. Recognising this, the coupling was developed to allow dynamic exchange of water and energy at the catchment scale embedded within a larger meso-scale modelling domain. We present the coupling methodology used and describe the challenges in representing the exchanges between models and across scales. The coupled model is applied to one-way and two-way coupled simulations for a managed groundwater-dominated catchment, the Skjern River, Denmark. These coupled model simulations are evaluated against field observations and then compared with uncoupled climate and hydrological model simulations. Exploratory simulations show significant differences, particularly in the summer for precipitation and evapotranspiration the coupled model including groundwater and the RCM where groundwater is neglected. However, the resulting differences in the net precipitation and the catchment runoff in this groundwater dominated catchment were small. The need for further decadal scale simulations to understand the differences and insensitivity is highlighted.

Summary Changes in natural stream temperature levels can be detrimental to the health of aquatic ecosystems. Water use and land management directly affect the distribution of diffuse heat sources and thermal loads to streams, while riparian vegetation and geomorphology play a critical role in how thermal loads are buffered. In many areas, groundwater flow is a significant contribution to river flow, particularly during low flows and therefore has a strong influence on stream temperature levels and dynamics. However, previous stream temperature models do not properly simulate how surface water–groundwater dynamics affect stream temperature. A coupled surface water–groundwater and temperature model has therefore been developed to quantify the impacts of land management and water use on stream flow and temperatures. The model is applied to the simulation of stream temperature levels in a spring-fed stream, the Silver Creek Basin in Idaho, where stream temperature affects the populations of fish and other aquatic organisms. The model calibration highlights the importance of spatially distributed flow dynamics in the catchment to accurately predict stream temperatures. The results also show the value of including temperature data in an integrated flow model calibration because temperature data provide additional constraints on the flow sources and volumes. Simulations show that a reduction of 10% in the groundwater flow to the Silver Creek Basin can cause average and maximum temperature increases in Silver Creek over 0.3 °C and 1.5 °C, respectively. In spring-fed systems like Silver Creek, it is clearly not feasible to separate river habitat restoration from upstream catchment and groundwater management.

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.

Summary A series of numerical experiments has been designed to investigate how effective satellite estimates of radiometric surface temperatures and soil surface moisture are for calibrating a Soil–Vegetation–Atmosphere Transfer (SVAT) model. Multi–objective calibration based on error minimization of temperature and soil moisture model outputs is performed in a semi–arid environment. Model accuracy when calibrated using in situ versus satellite objectives is explored in detail. Observational meteorological datasets from the African Monsoon Multidisciplinary Analysis (AMMA) were used to force a column model during a growing season in Mali. Fourier Amplitude Sensitivity Test (FAST) revealed the most sensitive parameters to model outputs. Parameters found sensitive were subsequently optimized in a series of model calibrations to reveal trade-offs between model objectives. Our main findings are (1) the SVAT model performs well in the semi–arid environment, but underestimates peak growing season evapotranspiration and overestimates soil moisture, (2) most of the parameters important for flux estimates can be constrained using surface temperature and soil surface moisture with the three exceptions: root depth, the extinction coefficient and unstressed stomatal resistance, (3) flux simulations are improved when the model is calibrated using in situ surface temperature and soil surface moisture versus satellite estimates.

Summary The use of high-resolution regional climate models (RCM) to examine the hydrological impacts of climate change has grown significantly in recent years due to the improved representation of the local climate. However, the application is not straightforward because most RCMs are subject to considerable systematic errors. In this study, projected climate change data from the RCM HIRHAM4 are used to generate climate scenario time series of precipitation, temperature, and reference evapotranspiration for the period 2071–2100 for hydrological impact assessments in Denmark. RCM output for the present-day period (1961–1990) are compared to an observational data set, with precipitation corrected for undercatch and wetting losses, to quantify systematic model errors. A delta change method is applied to cope with these biases. A question arises as to how variable the climate change signals are, as they are influenced by the forcing GCM, the emissions scenario, and the choice of RCM. Here, we focus on the choice of RCM and the effect of increasing horizontal resolution. A comparison of HIRHAM4 output to seven similar RCMs showed that for precipitation the HIRHAM4 model is as representative as any of the RCMs in the ensemble. For temperature HIRHAM4 at 50 km resolution simulates higher temperatures than the other RCMs, while HIRHAM4 at 12 km resolution is within the range of the other models. This study highlights some of the uncertainties related to the use of RCM data in hydrological studies, which contribute to a range in hydrological effects of climate change, mainly originating from simulated precipitation amounts.

Distributed hydrologic modeling holds significant promise for improved estimates of streamflow with high spatial resolution. However, uncertainty in model structure and parameters, which are distributed in space, and in operational weather radar rainfall estimates, which comprise the main input to the models, contributes to significant uncertainty in distributed model streamflow simulations over a wide range of space and time scales. Using the simulations produced for the Distributed Model Intercomparison Project (DMIP), this paper develops and applies sample-path methods to characterize streamflow simulation uncertainty by diverse distributed hydrologic models. The emphasis in this paper is on the model parameter and structure uncertainty given radar rainfall forcing. Multimodel ensembles are analyzed for six application catchments in the Central US to characterize model structure uncertainty within the sample of models (both calibrated and uncalibrated) participating in DMIP. Ensembles from single distributed and lumped models are also used for one of the catchments to provide a basis to characterize the impact of parametric uncertainty versus model structure uncertainty in flow simulation statistics. Two main science questions are addressed: (a) what is the value of multimodel streamflow ensembles in terms of the probabilistic characterization of simulation uncertainty? And (b) how do probabilistic skill measures of multimodel versus single-model ensembles compare? Discussed also are implications for the operational use of streamflow ensembles generated by distributed hydrologic models. The results support the serious consideration of ensemble simulations and predictions created by diverse models in real time flow prediction.

In recent years research on the coupling of existing regional climate models and hydrology/land surface models has emerged. A major challenge in this emerging research field is the computational interaction between the models. In this study we present results from a full two-way coupling of the HIRHAM regional climate model over a 4000 km x 2800 km domain in 11 km resolution and the combined MIKE SHE-SWET hydrology and land surface models over the 2500 km2 Skjern river catchment. A total of 26 one-year runs were performed to assess the influence of the data transfer interval (DTI) between the two models and the internal HIRHAM model variability of ten variables. In general, the coupled model simulations exhibit less accurate performance than the uncoupled simulations which is to be expected as both models prior to this study have been individually refined or calibrated to reproduce observations. Four of six output variables from HIRHAM, precipitation, relative humidity, wind speed and air temperature, showed statistically significant improvements in RMSE with a reduced DTI as evaluated in the range of 12–120 min. For these four variables the perturbation induced HIRHAM variability was shown to correspond to 47% of the RMSE improvement when using a DTI of 120 min compared to a DTI of 12 min and the variability resulted in large ranges in simulated precipitation. Also, the DTI was shown to substantially affect computation time. The MIKE SHE energy flux and discharge output variables experienced little impact from the DTI.

Two-dimensional laboratory investigations of flow and transport in a fractured permeable medium are presented. Matrix blocks of a manufactured consolidated permeable medium were arranged together to create fractures in the spaces between the blocks. Experiments examined flow and transport in four different configurations: (1) matrix only, (2) and (3) matrix blocks containing single fractures of different mean apertures, and (4) a brickwork pattern setup simulating a tortuous multiple fracture network. The observed partitioning of flow and solute concentrations suggested mass exchange between the fractures and the matrix was occurring. An analysis of the experimental results using a discrete fracture model and a range of constant aperture models showed that this approach did not capture the correct flow mechanisms. Subsequent simulations including spatial variations of the fracture aperture were better able to describe the experimental observations and to show that these variations cause mass exchange between the fracture and matrix.

An experimental study of the migration of denser-than-water nonaqueous-phase organic contaminants through heterogeneous porous media was carried out. The purpose of the study was to observe the flow and record the migration of the contaminant to gain a fundamental insight into the way aquifer heterogeneities influence the movement and subsequent distribution of immiscible contaminants after a spill. In addition, the experiments were designed to gather quantitative data to validate multiphase flow models. The experimental results demonstrate the importance of layering in the soil in determining the flow and entrapment behavior of dense nonaqueous-phase contaminants. The findings are of use for model conceptualization and developing field characterization strategies in aquifer remediation.

Roskilde University (Denmark) hosted a November 2015 workshop, Environmental Risk—Assessing and Managing Multiple Risks in a Changing World. This Focus article presents the consensus recommendations of 30 attendees from 9 countries regarding implementation of a common currency (ecosystem services) for holistic environmental risk assessment and management; improvements to risk assessment and management in a complex, human-modified, and changing world; appropriate development of protection goals in a 2-stage process; dealing with societal issues; risk-management information needs; conducting risk assessment of risk management; and development of adaptive and flexible regulatory systems. The authors encourage both cross-disciplinary and interdisciplinary approaches to address their 10 recommendations: 1) adopt ecosystem services as a common currency for risk assessment and management; 2) consider cumulative stressors (chemical and nonchemical) and determine which dominate to best manage and restore ecosystem services; 3) fully integrate risk managers and communities of interest into the risk-assessment process; 4) fully integrate risk assessors and communities of interest into the risk-management process; 5) consider socioeconomics and increased transparency in both risk assessment and risk management; 6) recognize the ethical rights of humans and ecosystems to an adequate level of protection; 7) determine relevant reference conditions and the proper ecological context for assessments in human-modified systems; 8) assess risks and benefits to humans and the ecosystem and consider unintended consequences of management actions; 9) avoid excessive conservatism or possible underprotection resulting from sole reliance on binary, numerical benchmarks; and 10) develop adaptive risk-management and regulatory goals based on ranges of uncertainty. © 2016 SETAC Funding: Roskilde University and their Environmental Risk Research Initiative. S. Loureiro acknowledges the financial support of CESAM (UID/AMB/50017), to FCT/MEC through national funds, and the cofunding by FEDER (POCI-01-0145-FEDER-007638), within the PT2020 Partnership Agreement and Compete 2020.