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How is public engagement perceived to contribute to open science? This commentary highlights common reflections on this question from interviews with 12 public engagement fellows in Utrecht University’s Open Science Programme in the Netherlands. We identify four reasons why public engagement is an essential enabler of open science. Interaction between academics and society can: (1) better align science with the needs of society; (2) secure a relationship of trust between science and society; (3) increase the quality and impact of science; and (4) support the impact of open access and FAIR data practices (data which meet principles of findability, accessibility, interoperability and reusability). To be successful and sustainable, such public engagement requires support in skills training and a form of institutionalisation in a university-wide system, but, most of all, the fellows express the importance of a formal and informal recognition and rewards system. Our findings suggest that in order to make public engagement an integral part of open science, universities should invest in institutional support, create awareness, and stimulate dialogue among staff members on how to ‘do’ good public engagement.

Human actions and climate change have drastically altered river flows across the world, resulting in adverse effects on riverine ecosystems. Environmental flows (EFs) have emerged as a prominent tool for safeguarding the riverine ecosystems, but at the global scale, the assessment of EFs is associated with high uncertainty related to the hydrological data and EF methods employed. Here, we present a novel, in-depth global EF assessment using environmental flow envelopes (EFEs). Sub-basin-specific EFEs are determined for approximately 4400 sub-basins at a monthly time resolution, and their derivation considers the methodological uncertainties related to global-scale EF studies. In addition to a lower bound of discharge based on existing EF methods, we introduce an upper bound of discharge in the EFE. This upper bound enables areas to be identified where streamflow has substantially increased above natural levels. Further, instead of only showing whether EFs are violated over a time period, we quantify, for the first time, the frequency, severity, and trends of EFE violations during the recent historical period. Discharge was derived from global hydrological model outputs from the ISIMIP 2b ensemble. We use pre-industrial (1801-1860) quasi-natural discharge together with a suite of hydrological EF methods to estimate the EFEs. We then compare the EFEs with recent historical (1976-2005) discharge to assess the violations of the EFE. These violations most commonly manifest as insufficient streamflow during the low-flow season, with fewer violations during the intermediate-flow season, and only a few violations during the high-flow season. The EFE violations are widespread and occur in half of the sub-basins of the world during more than 5% of the months between 1976 and 2005, which is double compared with the pre-industrial period. The trends in EFE violations have mainly been increasing, which will likely continue in the future with the projected hydroclimatic changes and increases in anthropogenic water use. Indications of increased upper extreme streamflow through EFE upper bound violations are relatively scarce and dispersed. Although local fine-tuning is necessary for practical applications, and further research on the coupling between quantitative discharge and riverine ecosystem responses at the global scale is required, the EFEs provide a quick and globally robust way of determining environmental flow allocations at the sub-basin scale to inform global research and policies on water resources management.

Soil moisture is highly variable in space, and its deficits (i.e. droughts) plays an important role in modulating crop yields and its variability across landscapes. Limited hydroclimate and yield data, however, hampers drought impact monitoring and assessment at the farmer field-scale. This study demonstrates the potential of field-scale soil moisture simulations to advance high-resolution agricultural yield prediction and drought monitoring at the smallholder farm field-scale. We present a multi-scale modeling approach that combines HydroBlocks, a physically-based hyper-resolution Land Surface Model (LSM), and machine learning. We applied HydroBlocks to simulate root zone soil moisture and soil temperature in Zambia at 3-hourly 30-m resolution. These simulations along with remotely sensed vegetation indices, meteorological conditions, and data describing the physical properties of the landscape (topography, land cover, soil properties) were combined with district-level maize data to train a random forest model (RF) to predict maize yields at the district- and field-scale (250-m) levels. Our model predicted yields with a coefficient of variation (R2) of 0.61, Mean Absolute Error (MAE) of 349 kg ha−1, and mean normalized error of 22 %. We captured maize losses due to the 2015/2016 El Niño drought at similar levels to losses reported by the Food and Agriculture Organization (FAO). Our results revealed that soil moisture is the strongest and most reliable predictor of maize yield, driving its spatial and temporal variability. Consequently, soil moisture was also the most effective indicator of drought impacts in crops when compared with precipitation, soil and air temperatures, and remotely-sensed NDVI-based drought indices. By combining field-scale root zone soil moisture estimates with observed maize yield data, this research demonstrates how field-scale modeling can help bridge the spatial scale discontinuity gap between drought monitoring and agricultural impacts.

In the past decade, several efforts have been made to project armed conflict risk into the future. This study broadens current approaches by presenting a first-of-its-kind application of machine learning (ML) methods to project sub-national armed conflict risk over the African continent along three Shared Socioeconomic Pathway (SSP) scenarios and three Representative Concentration Pathways towards 2050. Results of the open-source ML framework CoPro are consistent with the underlying socioeconomic storylines of the SSPs, and the resulting out-of-sample armed conflict projections obtained with Random Forest classifiers agree with the patterns observed in comparable studies. In SSP1-RCP2.6, conflict risk is low in most regions although the Horn of Africa and parts of East Africa continue to be conflict-prone. Conflict risk increases in the more adverse SSP3-RCP6.0 scenario, especially in Central Africa and large parts of Western Africa. We specifically assessed the role of hydro-climatic indicators as drivers of armed conflict. Overall, their importance is limited compared to main conflict predictors but results suggest that changing climatic conditions may both increase and decrease conflict risk, depending on the location: in Northern Africa and large parts of Eastern Africa climate change increases projected conflict risk whereas for areas in the West and northern part of the Sahel shifting climatic conditions may reduce conflict risk. With our study being at the forefront of ML applications for conflict risk projections, we identify various challenges for this arising scientific field. A major concern is the limited selection of relevant quantified indicators for the SSPs at present. Nevertheless, ML models such as the one presented here are a viable and scalable way forward in the field of armed conflict risk projections, and can help to inform the policy-making process with respect to climate security.

Future flood and drought risks have been predicted to transition from moderate to high levels at global warmings of 1.5 °C and 2.0 °C above pre-industrial levels, respectively. However, these results were obtained by approximating the equilibrium climate using transient simulations with steadily warming. This approach was recently criticised due to the warmer global land temperature and higher mean precipitation intensities of the transient climate in comparison with the equilibrium climate. Therefore, it is unclear whether floods and droughts projected under a transient climate can be systematically substituted for those occurring in an equilibrated climate. Here, by employing a large ensemble of global hydrological models (HMs) forced by global climate models, we assess the validity of estimating flood and drought characteristics under equilibrium climates from transient simulations. Differences in flood characteristics under transient and equilibrium climates could be largely ascribed to natural variability, indicating that the floods derived from a transient climate reasonably approximate the floods expected in an equally warm, equilibrated climate. By contrast, significant differences in drought intensity between transient and equilibrium climates were detected over a larger global land area than expected from natural variability. Despite the large differences among HMs in representing the low streamflow regime, we found that the drought intensities occurring under a transient climate may not validly represent the intensities in an equally warm equilibrated climate for approximately 6.7% of the global land area. Environment Research and Technology Development Fund Ministry of Education, Culture, Sports and Technology/Japan Society for the Promotion of Science National Science Foundation

Skillful seasonal climate forecasts can support decision making in water resources management and agricultural planning. In arid and semi-arid regions, tailoring reliable forecasts has the potential to improve water management by using key hydroclimate variables months in advance. This article analyses and compares the performance of two common approaches (empirical and hybrid dynamical-statistical) in seasonal climate forecasting over a drought-prone area located in Southwest Asia including Iran. Empirical models are framed as a baseline skill that hybrid models need to outperform. Both approaches provide probabilistic forecasts of precipitation and temperature using canonical correlation analysis to provide forecasts at 0.25° resolution. Empirical models are developed based on the large-scale observed atmosphere–ocean patterns for forecasting using antecedent climate anomalies as predictors, while the hybrid approach makes use of model output statistics to correct systematic errors in dynamical climate model forecast outputs. Eight state-of-the-art dynamical models from the North American Multi-Model Ensemble project are analysed. Individual models with the highest goodness index are weighted to develop seven different hybrid dynamical-statistical Multi-model Ensembles. In this study, (October–December) and (January–February) are considered as target seasons which are the most important periods within the water year for water resource allocation to the agriculture sector. The results show that the hybrid approach has improved performance compared to the raw general circulation models and purely empirical models, and that the performance of the hybrid models is season-dependent. Seasonal forecasts of precipitation (temperature) have a higher skill in OND (JFM). In addition, in most cases, Multi-model Ensemble (MME) is more skillful than the empirical models and outperforms individual dynamical models. However, the best individual model might be as skillful as the MME given the target season and region of interest.

Recent droughts in Europe have shown that national water systems are facing increasing challenges when dealing with drought impacts. Especially the Netherlands has seen an increasing need to adapt their water management to improve preparedness for future drought events. Ideally, the necessary information needed for operational water management decisions should be readily available ahead of time and/or computed flexibly and efficiently to ensure sufficient time to evaluate the various management actions. In this study, we show that in addition to physically based hydrological models, the upcoming and promising trend of incorporating machine learning (ML) in hydrology can provide the basis for future efforts in supporting national operational water management by providing the needed information efficiently and with the required accuracy. As a precursor for their use in a forecasting system, we assessed the ability of five different data driven methods to simulate hydrological variables at a national-scale. We developed a unified workflow where we use limited information on hydro-meteorological variables and general water management policies to simulate historic timeseries of discharge, groundwater levels, surface water levels and surface water temperatures. We find that all ML methods, ranging from very simple to more complex ones, showed a generally good performance for stations and target levels which are closely linked to the input data and location (e.g. stations along main river network). For downstream stations and small rivers, the Random Forest method outperforms the other methods both for discharge and surface water levels. For surface water temperature no location dependency was observed and for groundwater levels, all methods were performing comparable with most stations ranging in nRMSE 0.2-0.3. Generally, the best performances were reached by the more advanced Random Forest and LSTM methods, which was also seen when simulating high and low flow events. High flow events were slightly better captured than low flow events but overall simulating extreme events based on a simple input data set remains challenging. Specific training sets, including event related information and additional input variables, could like improve future assessments. Including the feature importance of the methods allowed us to detect how and where water management influence played an important role. The addition of information on water management in the ML routines increases overall performance, although limited. We conclude that ML and other data driven approaches have potential in predicting different hydrological variables. We were able to capture and incorporate water management aspects in our analysis, creating a base for future experiments where scenario analysis might reveal ML based mitigation strategies. The combination of limited input data requirement and short computation times makes this new framework suitable for forecasting purposes.