ALBERT

Description: The present study provides a novel approach to the challenge of identifying behavioural parameters in the context of parameter sensitivity and related hydrologic similarity classification. A methodical framework is presented wherein global sensitivity analysis of a spatially distributed conceptual hydrologic model within 14 different mesoscale headwater catchments is combined with a parameter estimation scheme based upon both classification by (1) physiographic and climate and (2) related dynamic response characteristics represented by hydrologic signatures (fingerprints) creating an interface between hydrologic variables of observed and simulated origin. Changing ranks in (3) partial parameter sensitivities within the catchments indicate that hydrologic dynamics might be governed by different hydrologic processes. Model simulated and the respective observed response fingerprints are found to cluster within typical sample regions. These findings show a general model adequacy to represent mesoscale streamflow response processes that relate temporally dominant parameters and allow a reasonable constraint on the parameter space. The senstivity-nested approach may be useful to calibrate hydrologic models sequentially on streamflow sections as well as on constraining (observable) single or combined hydrologic fingerprints and also to transfer results to similar sites, ungauged or anthropogenically altered.

Description: The lack of comprehensive groundwater observations at regional and global scales has promoted the use of alternative proxies and indices to quantify and predict groundwater droughts. Among them, the Standardized Precipitation Index (SPI) is commonly used to characterize droughts in different compartments of the hydro-meteorological system. In this study, we explore the suitability of the SPI to characterize local- and regional-scale groundwater droughts using observations at more than 2000 groundwater wells in geologically different areas in Germany and the Netherlands. A multiscale evaluation of the SPI is performed using the station data and their corresponding 0.5° gridded estimates to analyze the local and regional behavior of groundwater droughts, respectively. The standardized anomalies in the groundwater heads (SGI) were correlated against SPIs obtained using different accumulation periods. The accumulation periods to achieve maximum correlation exhibited high spatial variability (ranges 3–36 months) at both scales, leading to the conclusion that an a priori selection of the accumulation period (for computing the SPI) would result in inadequate characterization of groundwater droughts. The application of the uniform accumulation periods over the entire domain significantly reduced the correlation between the SPI and SGI (≈ 21–66 %), indicating the limited applicability of the SPI as a proxy for groundwater droughts even at long accumulation times. Furthermore, the low scores of the hit rate (0.3–0.6) and a high false alarm ratio (0.4–0.7) at the majority of the wells and grid cells demonstrated the low reliability of groundwater drought predictions using the SPI. The findings of this study highlight the pitfalls of using the SPI as a groundwater drought indicator at both local and regional scales, and stress the need for more groundwater observations and accounting for regional hydrogeological characteristics in groundwater drought monitoring.

Description: Heterogeneity of landscape features like terrain, soil, and vegetation properties affects the partitioning of water and energy. However, it remains unclear to what extent an explicit representation of this heterogeneity at the sub-grid scale of distributed hydrological models can improve the hydrological consistency and the robustness of such models. In this study, hydrological process complexity arising from sub-grid topography heterogeneity was incorporated into the distributed mesoscale Hydrologic Model (mHM). Seven study catchments across Europe were used to test whether (1) the incorporation of additional sub-grid variability on the basis of landscape-derived response units improves model internal dynamics, (2) the application of semi-quantitative, expert-knowledge-based model constraints reduces model uncertainty, and whether (3) the combined use of sub-grid response units and model constraints improves the spatial transferability of the model. Unconstrained and constrained versions of both the original mHM and mHMtopo, which allows for topography-based sub-grid heterogeneity, were calibrated for each catchment individually following a multi-objective calibration strategy. In addition, four of the study catchments were simultaneously calibrated and their feasible parameter sets were transferred to the remaining three receiver catchments. In a post-calibration evaluation procedure the probabilities of model and transferability improvement, when accounting for sub-grid variability and/or applying expert-knowledge-based model constraints, were assessed on the basis of a set of hydrological signatures. In terms of the Euclidian distance to the optimal model, used as an overall measure of model performance with respect to the individual signatures, the model improvement achieved by introducing sub-grid heterogeneity to mHM in mHMtopo was on average 13 %. The addition of semi-quantitative constraints to mHM and mHMtopo resulted in improvements of 13 and 19 %, respectively, compared to the base case of the unconstrained mHM. Most significant improvements in signature representations were, in particular, achieved for low flow statistics. The application of prior semi-quantitative constraints further improved the partitioning between runoff and evaporative fluxes. In addition, it was shown that suitable semi-quantitative prior constraints in combination with the transfer-function-based regularization approach of mHM can be beneficial for spatial model transferability as the Euclidian distances for the signatures improved on average by 2 %. The effect of semi-quantitative prior constraints combined with topography-guided sub-grid heterogeneity on transferability showed a more variable picture of improvements and deteriorations, but most improvements were observed for low flow statistics.

Description: Most of the current large scale hydrological models do not contain a physically-based groundwater flow component. The main difficulties in large-scale groundwater modeling include the efficient representation of unsaturated zone flow, the characterization of dynamic groundwater-surface water interaction and the numerical stability while preserving complex physical processes and high resolution. To address these problems, we propose a highly-scalable coupled hydrologic and groundwater model (mHM#OGS) based on the integration of two open-source modeling codes: the mesoscale hydrologic Model (mHM) and the finite element simulator OpenGeoSys (OGS). mHM#OGS is coupled using a boundary condition-based coupling scheme that dynamically links the surface and subsurface parts. Nested time stepping allows smaller time steps for typically faster surface runoff routing in mHM and larger time steps for slower subsurface flow in OGS. mHM#OGS features the coupling interface which can transfer the groundwater recharge and river baseflow rate between mHM and OpenGeoSys. Verification of the coupled model was conducted using the time-series of observed streamflow and groundwater levels. Moreover, we force the transient model using groundwater recharge in two scenarios: (1) spatially variable recharge based on the mHM simulations, and (2) spatially homogeneous groundwater recharge. The modeling result in first scenario has a slightly higher correlation with groundwater head time-series, which further validates the plausibility of spatial groundwater recharge distribution calculated by mHM in the mesocale. The statistical analysis of model predictions shows a promising prediction ability of the model. The offline coupling method implemented here can reproduce reasonable groundwater head time series while keep a desired level of detail in the subsurface model structure with little surplus in computational cost. Our exemplary calculations show that the coupled model mHM#OGS can be a valuable tool to assess the effects of variability in land surface heterogeneity, meteorological, topographical forces and geological zonation on the groundwater flow dynamics.

Description: Groundwater is the biggest single source of high-quality fresh water worldwide, which is also continuously threatened by the changing climate. This paper is designed to investigate the response of regional groundwater system to the climate change under three global warming levels (1.5, 2, and 3 °C) in a central German basin (Nägelstedt). This investigation is conducted by deploying an integrated modeling workflow that consists of a mesoscale Hydrologic Model (mHM) and a fully-distributed groundwater model OpenGeoSys (OGS). mHM is forced by five general circulation models under three representative concentration pathways. The diffuse recharges estimated by mHM are used as outer forcings of the OGS groundwater model to compute changes in groundwater levels and travel time distributions. Simulation results indicate that under future climate scenarios, groundwater recharges and levels are expected to increase slightly. Meanwhile, the mean travel time is expected to decrease compared to the historical average. However, the ensemble simulations do not all agree on the sign of relative change. The ensemble simulations do not show a systematic relationship between the predicted change and the warming level, but they indicate an increased variability in predicted changes with the enhanced warming level from 1.5 to 3 °C. This study indicates that a higher warming level may introduce more uncertain and extreme events for the studied regional groundwater system.

Description: There is growing evidence that climate change will alter water availability in Europe. Here, we investigate how hydrological low flows are affected under different levels of future global warming (i.e. 1.5, 2, and 3 K with respect to the pre-industrial period) in rivers with a contributing area of more than 1000 km2. The analysis is based on a multi-model ensemble of 45 hydrological simulations based on three representative concentration pathways (RCP2.6, RCP6.0, RCP8.5), five Coupled Model Intercomparison Project Phase 5 (CMIP5) general circulation models (GCMs: GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-LR, MIROC-ESM-CHEM, NorESM1-M) and three state-of-the-art hydrological models (HMs: mHM, Noah-MP, and PCR-GLOBWB). High-resolution model results are available at a spatial resolution of 5 km across the pan-European domain at a daily temporal resolution. Low river flow is described as the percentile of daily streamflow that is exceeded 90 % of the time. It is determined separately for each GCM/HM combination and warming scenario. The results show that the low-flow change signal amplifies with increasing warming levels. Low flows decrease in the Mediterranean region, while they increase in the Alpine and Northern regions. In the Mediterranean, the level of warming amplifies the signal from −12 % under 1.5 K, compared to the baseline period 1971–2000, to −35 % under global warming of 3 K, largely due to the projected decreases in annual precipitation. In contrast, the signal is amplified from +22 (1.5 K) to +45 % (3 K) in the Alpine region due to changes in snow accumulation. The changes in low flows are significant for regions with relatively large change signals and under higher levels of warming. However, it is not possible to distinguish climate-induced differences in low flows between 1.5 and 2 K warming because of (1) the large inter-annual variability which prevents distinguishing statistical estimates of period-averaged changes for a given GCM/HM combination, and (2) the uncertainty in the multi-model ensemble expressed by the signal-to-noise ratio. The contribution by the GCMs to the uncertainty in the model results is generally higher than the one by the HMs. However, the uncertainty due to HMs cannot be neglected. In the Alpine, Northern, and Mediterranean regions, the uncertainty contribution by the HMs is partly higher than those by the GCMs due to different representations of processes such as snow, soil moisture and evapotranspiration. Based on the analysis results, it is recommended (1) to use multiple HMs in climate impact studies and (2) to embrace uncertainty information on the multi-model ensemble as well as its single members in the adaptation process.

Description: Satellite-based earth observations offer great opportunities to improve spatial model predictions by means of spatial-pattern-oriented model evaluations. In this study, observed spatial patterns of actual evapotranspiration (AET) are utilised for spatial model calibration tailored to target the pattern performance of the model. The proposed calibration framework combines temporally aggregated observed spatial patterns with a new spatial performance metric and a flexible spatial parameterisation scheme. The mesoscale hydrologic model (mHM) is used to simulate streamflow and AET and has been selected due to its soil parameter distribution approach based on pedo-transfer functions and the build in multi-scale parameter regionalisation. In addition two new spatial parameter distribution options have been incorporated in the model in order to increase the flexibility of root fraction coefficient and potential evapotranspiration correction parameterisations, based on soil type and vegetation density. These parameterisations are utilised as they are most relevant for simulated AET patterns from the hydrologic model. Due to the fundamental challenges encountered when evaluating spatial pattern performance using standard metrics, we developed a simple but highly discriminative spatial metric, i.e. one comprised of three easily interpretable components measuring co-location, variation and distribution of the spatial data. The study shows that with flexible spatial model parameterisation used in combination with the appropriate objective functions, the simulated spatial patterns of actual evapotranspiration become substantially more similar to the satellite-based estimates. Overall 26 parameters are identified for calibration through a sequential screening approach based on a combination of streamflow and spatial pattern metrics. The robustness of the calibrations is tested using an ensemble of nine calibrations based on different seed numbers using the shuffled complex evolution optimiser. The calibration results reveal a limited trade-off between streamflow dynamics and spatial patterns illustrating the benefit of combining separate observation types and objective functions. At the same time, the simulated spatial patterns of AET significantly improved when an objective function based on observed AET patterns and a novel spatial performance metric compared to traditional streamflow-only calibration were included. Since the overall water balance is usually a crucial goal in hydrologic modelling, spatial-pattern-oriented optimisation should always be accompanied by traditional discharge measurements. In such a multi-objective framework, the current study promotes the use of a novel bias-insensitive spatial pattern metric, which exploits the key information contained in the observed patterns while allowing the water balance to be informed by discharge observations.

Description: Diagnostics of hydrological models is pivotal for a better understanding of catchment functioning. The analysis of dominating parameters for the simulation of streamflow plays a key role for region specific model diagnostics, model calibration or parameter transfer. A major challenge in this analysis of parameter sensitivity is the assessment of both temporal and spatial differences of parameter influences on simulated streamflow response. A methodical approach is presented, wherein a two-tiered global sensitivity analysis on a spatially distributed hydrological model is applied to 14 mesoscale headwater catchments of the river Ruhr in western Germany. The analysis of parameter sensitivity is geared towards two complementary forms of streamflow response targets. The analysis of the temporal dynamics of parameter sensitivity (TEDPAS) is contrasted with sensitivity analysis directed to hydrological fingerprints, i.e. temporally independent and temporally aggregated characteristics of streamflow (INDPAS). The two-tiered approach allows to discern a clarified sensitivity pattern pinpointed to diverse response characteristics, to detect regional differences and to reveal the regional relevance of the response target. Small local differences in the hydroclimatic and topographic setting of the headwaters lead to slight differences in the hydrological functioning, which was revealed by gradual differences in TEDPAS and INDPAS.

Description: In this synthesis paper addressing hydrologic scaling and similarity, we posit that roadblocks in the search for universal laws of hydrology are hindered by our focus on computational simulation (the third paradigm) and assert that it is time for hydrology to embrace a fourth paradigm of data-intensive science. Advances in information-based hydrologic science, coupled with an explosion of hydrologic data and advances in parameter estimation and modeling, have laid the foundation for a data-driven framework for scrutinizing hydrological scaling and similarity hypotheses. We summarize important scaling and similarity concepts (hypotheses) that require testing; describe a mutual information framework for testing these hypotheses; describe boundary condition, state, flux, and parameter data requirements across scales to support testing these hypotheses; and discuss some challenges to overcome while pursuing the fourth hydrological paradigm. We call upon the hydrologic sciences community to develop a focused effort towards adopting the fourth paradigm and apply this to outstanding challenges in scaling and similarity.

Description: Routing streamflow through a river network is a fundamental requirement to verify lateral water fluxes simulated by hydrologic and land surface models. River routing is performed at diverse resolutions ranging from few kilometers to around 1°. The presented multiscale Routing Model mRM calculates streamflow at diverse spatial and temporal resolutions. mRM solves the kinematic wave equation using a finite difference scheme. An adaptive time stepping scheme fulfilling a numerical stability criteria is introduced in this study and compared against the original parametrization of mRM that has been developed within the mesoscale Hydrologic Model (mHM). mRM requires a high-resolution river network, which is upscaled internally to the desired spatial resolution. The user can change the spatial resolution by simply changing one number in the configuration file without any further adjustments of the input data. The performance of mRM is investigated on two datasets: a high-resolution German dataset and a slightly lower resolution European dataset. The adaptive time step scheme within mRM shows a remarkable scalability compared to its predecessor. Median Kling-Gupta efficiencies change less than 3 percent when the model parametrization is transferred from 3 to 48 km resolution. mRM also exhibits seamless scalability in time, providing identical results when forced with hourly and daily runoff. The streamflow calculated over the Danube catchment by the Regional Climate Model REMO coupled to mRM is comparable at 25 and 50 km resolution. The mRM source code is freely available and highly modular facilitating an easy internal coupling in existing Earth System Models.