ALBERT

Description: Here we present “CO-RIP”, a novel spatial dataset delineating riparian corridors and riparian vegetation along large streams and rivers in the United States (US) portion of the Colorado River Basin. The consistent delineation of riparian areas across large areas using remote sensing has been a historically complicated process partially due to differing definitions in the scientific and management communities regarding what a “riparian corridor” or “riparian vegetation” represents. We use valley-bottoms to define the riparian corridor and establish a riparian vegetation definition interpretable from aerial imagery for efficient, consistent, and broad-scale mapping. Riparian vegetation presence and absence data were collected using a systematic, flexible image interpretation process applicable wherever high resolution imagery is available. We implemented a two-step approach using existing valley bottom delineation methods and random forests classification models that integrate Landsat spectral information to delineate riparian corridors and vegetation across the 12 ecoregions of the Colorado River Basin. Riparian vegetation model accuracy was generally strong (median kappa of 0.80), however it varied across ecoregions (kappa range of 0.42–0.90). We offer suggestions for improvement in our current image interpretation and modelling frameworks, particularly encouraging additional research in mapping riparian vegetation in moist coniferous forest and deep canyon environments. The CO-RIP dataset created through this research is publicly available and can be utilized in a wide range of ecological applications.

Description: Heterogeneity of landscape features like terrain, soil, and vegetation properties affect the partitioning of water and energy. However, it remains unclear to which 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 in 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 (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 overall measure for 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. Besides, 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: 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: Media such as television, newspapers and social media play a key role in the communication between scientists and the general public. Communicating your science via the media can be positive and rewarding by providing the inherent joy of sharing your knowledge with a broader audience, promoting science as a fundamental part of culture and society, impacting decision and policy makers, and giving you a greater recognition by institutions, colleagues and funders. However, the interaction between scientists and journalists is not always straightforward. For instance, scientists may not always be able to translate their work into a compelling story, and journalists may sometimes misinterpret scientific output. In this paper, we present insights from hydrologists and journalists discussing the advantages and benefits as well as the potential pitfalls and aftermath of science-media interaction. As we perceive interacting with the media as a rewarding and essential part of our work, we aim to encourage scientists to participate in the diverse and evolving media landscape. With this paper, we call on the scientific community to support scientists who actively contribute to a fruitful science-media relationship.

Description: Media such as television, newspapers and social media play a key role in the communication between scientists and the general public. Communicating your science via the media can be positive and rewarding by providing the inherent joy of sharing your knowledge with a broader audience, promoting science as a fundamental part of culture and society, impacting decision- and policy-makers, and giving you a greater recognition by institutions, colleagues and funders. However, the interaction between scientists and journalists is not always straightforward. For instance, scientists may not always be able to translate their work into a compelling story, and journalists may sometimes misinterpret scientific output. In this paper, we present insights from hydrologists and journalists discussing the advantages and benefits as well as the potential pitfalls and aftermath of science–media interaction. As we perceive interacting with the media as a rewarding and essential part of our work, we aim to encourage scientists to participate in the diverse and evolving media landscape. With this paper, we call on the scientific community to support scientists who actively contribute to a fruitful science–media relationship.

Description: Most large-scale hydrologic models fall short in reproducing groundwater head dynamics and simulating transport process due to their oversimplified representation of groundwater flow. In this study, we aim to extend the applicability of the mesoscale Hydrologic Model (mHM v5.7) to subsurface hydrology by coupling it with the porous media simulator OpenGeoSys (OGS). The two models are one-way coupled through model interfaces GIS2FEM and RIV2FEM, by which the grid-based fluxes of groundwater recharge and the river–groundwater exchange generated by mHM are converted to fixed-flux boundary conditions of the groundwater model OGS. Specifically, the grid-based vertical reservoirs in mHM are completely preserved for the estimation of land-surface fluxes, while OGS acts as a plug-in to the original mHM modeling framework for groundwater flow and transport modeling. The applicability of the coupled model (mHM–OGS v1.0) is evaluated by a case study in the central European mesoscale river basin – Nägelstedt. Different time steps, i.e., daily in mHM and monthly in OGS, are used to account for fast surface flow and slow groundwater flow. Model calibration is conducted following a two-step procedure using discharge for mHM and long-term mean of groundwater head measurements for OGS. Based on the model summary statistics, namely the Nash–Sutcliffe model efficiency (NSE), the mean absolute error (MAE), and the interquartile range error (QRE), the coupled model is able to satisfactorily represent the dynamics of discharge and groundwater heads at several locations across the study basin. Our exemplary calculations show that the one-way coupled model can take advantage of the spatially explicit modeling capabilities of surface and groundwater hydrologic models and provide an adequate representation of the spatiotemporal behaviors of groundwater storage and heads, thus making it a valuable tool for addressing water resources and management problems.