The notion of convergent and transdisciplinary integration, which is about braiding together different knowledge systems, is becoming the mantra of numerous initiatives aimed at tackling pressing water challenges. Yet, the transition from rhetoric to actual implementation is impeded by incongruence in semantics, methodologies, and discourse among disciplinary scientists and societal actors. This paper confronts these disciplinary barriers by advocating a synthesis of existing and missing links across the frontiers distinguishing hydrology from engineering, the social sciences and economics, Indigenous and place-based knowledge, and studies of other interconnected natural systems such as the atmosphere, cryosphere, and ecosphere. Specifically, we embrace ‘integrated modeling’, in both quantitative and qualitative senses, as a vital exploratory instrument to advance such integration, providing a means to navigate complexity and manage the uncertainty associated with understanding, diagnosing, predicting, and governing human-water systems. While there are, arguably, no bounds to the pursuit of inclusivity in representing the spectrum of natural and human processes around water resources, we advocate that integrated modeling can provide a focused approach to delineating the scope of integration, through the lens of three fundamental questions: a) What is the modeling ‘purpose’? b) What constitutes a sound ‘boundary judgment’? and c) What are the ‘critical uncertainties’ and how do they propagate through interconnected subsystems? More broadly, we call for investigating what constitutes warranted ‘systems complexity’, as opposed to unjustified ‘computational complexity’ when representing complex natural and human-natural systems, with particular attention to interdependencies and feedbacks, nonlinear dynamics and thresholds, hysteresis, time lags, and legacy effects.

Knowing where and when rivers cease to flow provides an important basis for evaluating riverine biodiversity, biogeochemistry and ecosystem services. We present a novel modeling approach to estimate monthly time series of streamflow intermittence at high spatial resolution at the continental scale. Streamflow intermittence is quantified at more than 1.5 million river reaches in Europe as the number of no-flow days grouped into five classes (0, 1-5, 6-15, 16-29, 30-31 no-flow days) for each month from 1981 to 2019. Daily time series of observed streamflow at 3706 gauging stations were used to train and validate a two-step Random Forest modeling approach. Important predictors were derived from time series of monthly streamflow at 73 million 15 arc-sec (~500 m) grid cells that were computed by downscaling the 0.5 arc-deg (~55 km) output of the global hydrological model WaterGAP, which accounts for human water use. Of the observed perennial and intermittent station-months, 97.8% and 86.4%, respectively, are correctly predicted. Interannual variations of the number of intermittent months at intermittent reaches are satisfactorily simulated, with a median Pearson correlation of 0.5. While the spatial prevalence of intermittent reaches is underestimated, the number of intermittent months is overestimated in dry regions of Europe where artificial storage abounds. Our model estimates that 3.8% of all European reach-months and 17.2% of all reaches were intermittent during 1981-2019, predominantly with 30-31 no-flow days. Although estimation uncertainty is high, our study provides, for the first time, information on the continent-wide dynamics of intermittent rivers and streams.

Continental and global-scale groundwater models have been proposed recently to complete and improve the simulation of the hydrologic cycle. This development is still impeded by the resolution of these models either due to data availability and/or computational demands. One of the major challenges is determining the location of surface water bodies on the global scale based on land surface elevation data, as surface water elevation directly influences the model results. Closely connected to this problem is the comparison of simulated model results to observations of depth to groundwater. The models calculate hydraulic head so depth to groundwater is heavily influenced by how the variation in topography is reflected in one computational cell. This presentation demonstrates by means of the newly developed global groundwater model G³M that depth to groundwater observations need to be contemplated in the context of the elevation of the surface water bodies to draw proper conclusions on the model performance. The impact of uncertainty in surface water body elevation is illustrated based on multiple grid size experiments (5 arcmin, 30 arcsec, and 3 arcsec resolution) for New Zealand.

Electricity production by hydropower is negatively affected by drought. To understand, monitor and manage risks of less than normal streamflow for hydroelectricity production (HP) at the global scale, we developed an HP model that simulates time series of monthly HP worldwide and thus enables analyzing and monitoring the impact of drought on HP. The HP model is based on a new global hydropower database (GHD), containing 8748 geo-localized plant records, and on monthly streamflow values computed by the global hydrological model WaterGAP. The GHD includes 43 attributes and covers 91.8% of the globally installed capacity. The HP model can capture the interannual variability of country-scale HP that was caused by both (de)commissioning of hydropower plants and streamflow variability. It can also simulate the streamflow drought and its impact on HP reasonably well. A drought risk analysis for period 1975−2016 revealed the reduction of HP that is exceeded in 1 out of 10 years. 71 out of 134 countries with hydropower suffer from a reduction of more than 20% of average HP, and 20 countries from a reduction of more than 40%. We suggest four indices for monitoring the drought impact on HP in grid cells and on total electricity production in countries. These indices quantify the impact in terms of either relative reduction or anomaly. Applying the developed HP model, these indices can be included in global drought monitoring systems and inform stakeholders such as hydropower producer and national energy agencies about the reduced energy production due to streamflow drought.