Flow regimes profoundly influence river organisms and ecosystem functions, but regulatory approaches often lack the scientific basis to support sustainable water allocation. In part, this reflects the challenge of understanding the ecological effects of flow variability over different temporal and spatial domains. Here, we use a process-based distributed hydrological model to simulate 23 years of natural flow regime in 100 target bioassessment sites across the Adige River network (NE Italy), and to identify typical nivo-glacial, nivo-pluvial, and pluvial reaches. We then applied spatial stream-network models (SSN) to investigate the relationships between hydrologic and macroinvertebrate metrics while accounting for network spatial autocorrelation and local habitat conditions. Macroinvertebrate metrics correlated most strongly with maximum, minimum and temporal variation in streamflow, but effects varied across flow regime types. For example: i) taxon richness appeared limited by high summer flows and high winter flows in nivo- glacial and pluvial streams, respectively; ii) invertebrate grazers increased proportionally with the annual coefficient of flow variation in nivo-glacial streams but tended to decline with flow variation in pluvial streams. SSN models revealed that most variation in macroinvertebrate metrics was accounted for by spatial autocorrelation, although local land use and water quality also affected benthic invertebrate communities, particularly at lower elevations. These findings highlight the importance of developing environmental flow management policies in ways that reflect specific hydro-ecological and land use contexts. Our analyses also illustrate the importance of spatially-explicit approaches that account for auto-correlation when quantifying flow-ecology relationships.

Environmental hot spots and hot moments (HSHMs) represent rare locations and events that exert disproportionate influence over the environment. While several mechanistic models have been used to characterize HSHMs behavior at specific sites, a critical missing component of research on HSHMs has been the development of clear, conventional statistical models. In this paper, we introduced a novel stochastic framework for analyzing HSHMs and the uncertainties. This framework can easily incorporate heterogeneous features in the spatiotemporal domain and can offer inexpensive solutions for testing future scenarios. The proposed approach utilizes indicator random variables (RVs) to construct a statistical model for HSHMs. The HSHMs indicator RVs are comprised of spatial and temporal components, which can be used to represent the unique characteristics of HSHMs. We identified three categories of HSHMs and demonstrated how our statistical framework are adjusted for each category. The three categories are (1) HSHMs defined only by spatial (static) components, (2) HSHMs defined by both spatial and temporal (dynamic) components, and (3) HSHMs defined by multiple dynamic components. The representation of an HSHM through its spatial and temporal components allows researchers to relate the HSHM’s uncertainty to the uncertainty of its components. We illustrated the proposed statistical framework through several HSHM case studies covering a variety of surface, subsurface, and coupled systems.

Six conceptually different models of steady groundwater flow and conservative transport are applied to the heterogeneous MADE aquifer. Their predictive capability is assessed by comparing the modelled and observed longitudinal mass distributions at different times of the plume in the MADE-1 experiment, as well as at a later time. The models differ in their conceptualization of the heterogeneous aquifer structure, computational complexity, and use of permeability data obtained from various observation methods (DPIL, Grain Size Analysis, Pumping Tests and Flowmeter). Models depend solely on aquifer structural and flow data, without calibration by transport observations. Comparison of model results by various measures, i.e. peak location, bulk mass and leading tail, reveals that the predictions of the solute plume agree reasonably well with observations if the models are underlined by a few parameters of close values: mean velocity, a parameter reflecting log-conductivity variability and a horizontal length scale related to conductivity spatial correlation. From practitioners perspective the robustness of the models is an important and useful property. The model comparison provides insight into relevant features of transport in heterogeneous aquifers. After further validation by additional field experiments or by numerical simulations, the results can be used to provide guidelines for users in selecting conceptual aquifer models, characterization strategies, quantitative models and implementation for particular goals.