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The quest for hydrological hyper-resolution modelling has been on-going for more than a decade. While global hydrological models (GHMs) have seen a reduction in grid size, they have thus far never been consistently applied at a hyper-resolution (<=1 km) at the large scale. Here, we present the first application of the GHM PCR-GLOBWB at 1 km over Europe. We thoroughly evaluated simulated discharge, evaporation, soil moisture, and terrestrial water storage anomalies against long-term observations and subsequently compared results with the established 10 and 50 km resolutions of PCR-GLOBWB. Subsequently, we could assess the added value of this first hyper-resolution version of PCR-GLOBWB and assess the scale dependencies of model and forcing resolution. Eventually, these insights can help us in understanding the current challenges and opportunities from hyper-resolution models and in formulating the model and data requirements for future improvements. We found that, for most variables, epistemic uncertainty is still large, and issues with scale commensurability exist with respect to the long-term yet coarse observations used. Merely for simulated discharge, we can confidently state that model output at hyper-resolution improves over coarser resolutions due to better representation of the river network at 1 km. However, currently available observations are not yet widely available at hyper-resolution or lack a sufficiently long time series, which makes it difficult to assess the performance of the model for other variables at hyper-resolution. Here, additional model validation efforts are needed. On the model side, hyper-resolution applications require careful revisiting of model parameterization and possibly the implementation of more physical processes to be able to resemble the dynamics and spatial heterogeneity at 1 km. With this first application of PCR-GLOBWB at 1 km, we contribute to meeting the grand challenge of hyper-resolution modelling. Even though the model was only assessed at the continental scale, valuable insights could be gained which have global validity. As such, it should be seen as a modest milestone on a longer journey towards locally relevant model output. This, however, requires a community effort from domain experts, model developers, research software engineers, and data providers.

The climate of High Mountain Asia (HMA) has changed in recent decades. While the temperature is consistently increasing at a higher rate than the global warming rate, precipitation changes are inconsistent, with substantial temporal and spatial variation. Climate warming will have enormous consequences for hydroclimatic extremes. For the higher altitudes of the HMA, which are a significant source of water for the large rivers in Asia, often trends are calculated using a limited number of in situ observations mainly observed in valleys. This study explores the changes in mean, extreme, and compound-extreme climate variables and their seasonality along the full altitudinal range in HMA using daily ERA5 reanalysis data (1979–2018). Our results show that winter warming and summer wetting dominate the interior part of HMA. The results indicate a coherent significant increasing trend in the occurrence of heatwaves across all regions in HMA. The number of days with heavy precipitation shows more significant trends in southern and eastern basins than in other areas of HMA. The dry period occurrence shows a distinct demarcation between lower- and higher-altitude regions and is increasing for most basins. Although precipitation and temperature show variable tendencies, their compound occurrence is coherent in the monsoon-dominated basins. These changes in indicators of climatic extremes may imply substantial increases in the future occurrence of hazards such as floods, landslides, and droughts, which in turn impact economic production and infrastructure.

The original version of this Article contained errors. The fourth sentence of the first paragraph in the section titled “Halving the proportionof untreated wastewater (SDG6.3)” incorrectly read “The largest expansions required before 2030 are in China (40 million m3), the USA (16 million m3) and India (15 million m3), with these three countries alone accounting for ~45% of the required expansions.” The correct version states ‘billion m3 yr-1’ in place of ‘million m3’. In addition, the first sentence of the caption for Figure 2 incorrectly read “a Expansions in wastewater treatment capacity (106 m3) required by 2030 to achieve SDG6.3 for the top 30 countries”. The correct version states “(109 m3 yr-1)” in place of “(106 m3)”. These errors have been corrected in both the PDF and HTML versions of the Article.

The Hindu Kush–Karakoram–Himalayan system, named the Third Pole because it is the largest global store of frozen water after the polar regions, provides a reliable water supply to almost 2 billion people. Marked atmospheric warming has changed the balance of this so-called Asian water tower and altered water resources in downstream countries. In this Review, we synthesize observational evidence and model projections that describe an imbalance in the Asian water tower caused by accelerated transformation of ice and snow into liquid water. This phase change is associated with a south–north disparity due to the spatio-temporal interaction between the westerlies and the Indian monsoon. A corresponding spatial imbalance is exhibited by alterations in freshwater resources in endorheic or exorheic basins. Global warming is expected to amplify this imbalance, alleviating water scarcity in the Yellow and Yangtze River basins and increasing scarcity in the Indus and Amu Darya River basins. However, the future of the Asian water tower remains highly uncertain. Accurate predictions of future water supply require the establishment of comprehensive monitoring stations in data-scarce regions and the development of advanced coupled atmosphere–cryosphere–hydrology models. Such models are needed to inform the development of actionable policies for sustainable water resource management.

Contains fulltext : 292585.pdf (Publisher’s version ) (Open Access) Global maps of agricultural expansion potential at a 300 m resolution This repository contains data from “Global maps of agricultural expansion potential at a 300 m resolution” study. Abstract: The global expansion of agricultural land is a leading driver of climate change and biodiversity loss. However, the spatial resolution of current global land change models is relatively coarse, which limits environmental impact assessments. To address this issue, we developed global maps representing the potential for conversion into agricultural land at a resolution of 10 arc-seconds (approximately 300 m at the equator). We created the maps using Artificial Neural Network (ANN) models relating locations of recent past conversions (2007-2020) into one of three cropland categories (cropland only, mosaics with >50% crops, and mosaics with 50% crops, and (iii) mosaics with 50% crops, and mosaics with 50% crops, and 40 - mosaics with 50% crops category at the spatial resolution of 10 arc-seconds. - “Agri_potential_mosaic_40.tif” is the global raster map for mosaics with 50% crops, and category 40 is mosaics with

Funding Information: This open-access publication was funded by the University of Freiburg. Funding Information: Financial support. The project is supported by the Wassernetzwerk Baden-Württemberg (Water Research Network of the State Baden-Württemberg), which is funded by the Ministerium für Wissenschaft, Forschung und Kunst Baden-Württemberg (Ministry of Science, Research and the Arts of the State Baden-Württemberg) (grant no. AZ. 7532.21/2.1.6) and Maa-ja vesitekniikan tuki ry foundation. Doris E. Wendt acknowledges her support as part of the NERC-funded Groundwater Drought Initiative (NE/R004994/1). Lucy J. Barker was supported by the Natural Environment Research Council (NE/R016429/1) as part of the UK-SCAPE programme delivering National Capability. The contributions of Mirek Trnka, Lenka Bartošová, and Jaak Jaagus have been supported by SustES – Adaptation strategies for sustainable ecosystem services and food security under adverse environmental conditions (CZ.02.1.01/0.0/0.0/16_019/0000797). Publisher Copyright: © 2022 Veit Blauhut et al. Drought events and their impacts vary spatially and temporally due to diverse pedo-climatic and hydrologic conditions, as well as variations in exposure and vulnerability, such as demographics and response actions. While hazard severity and frequency of past drought events have been studied in detail, little is known about the effect of drought management strategies on the actual impacts and how the hazard is perceived by relevant stakeholders. In a continental study, we characterised and assessed the impacts and the perceptions of two recent drought events (2018 and 2019) in Europe and examined the relationship between management strategies and drought perception, hazard, and impact. The study was based on a pan-European survey involving national representatives from 28 countries and relevant stakeholders responding to a standard questionnaire. The survey focused on collecting information on stakeholders' perceptions of drought, impacts on water resources and beyond, water availability, and current drought management strategies on national and regional scales. The survey results were compared with the actual drought hazard information registered by the European Drought Observatory (EDO) for 2018 and 2019. The results highlighted high diversity in drought perception across different countries and in values of the implemented drought management strategies to alleviate impacts by increasing national and sub-national awareness and resilience. The study identifies an urgent need to further reduce drought impacts by constructing and implementing a European macro-level drought governance approach, such as a directive, which would strengthen national drought management and mitigate damage to human and natural assets.

Agriculture is the largest user of water in the United States. Yet, we do not understand the spatially resolved sources of irrigation water use (IWU) by crop. The goal of this study is to estimate crop-specific IWU from surface water withdrawals (SWW), total groundwater withdrawals (GWW), and nonrenewable groundwater depletion (GWD). To do this, we employ the PCR-GLOBWB 2 global hydrology model to partition irrigation information from the U.S. Geological Survey Water Use Database to specific crops across the Continental United States (CONUS). We incorporate high-resolution input data on agricultural production and climate within the CONUS to obtain crop-specific irrigation estimates for SWW, GWW, and GWD for 20 crops and crop groups from 2008 to 2020 at county spatial resolution. Over the study period, SWW decreased by 20%, while both GWW and GWD increased by 3%. On average, animal feed (alfalfa/hay) uses the most irrigation water across all water sources: 33 from SWW, 13 from GWW, and 10 km3/yr from GWD. Produce used less SWW (43%), but more GWW (57%), and GWD (27%) over the study time-period. The largest changes in IWU for each water source between the years 2008 and 2020 are: rice (SWW decreased by 71%), sugar beets (GWW increased by 232%), and rapeseed (GWD increased by 405%). These results present the first national-scale assessment of irrigation by crop, water source, and year. In total, we contribute nearly 2.5 million data points to the literature (3,142 counties; 13 years; 3 water sources; and 20 crops).

Hydrological forecasts are important for operational water management and near-future planning, even more so in light of the increased occurrences of extreme events such as floods and droughts. Having a forecasting framework, which is flexible in terms of input forcings and forecasting locations (local, regional, or national) that can deliver this information in fast and computational efficient manner, is critical. In this study, the suitability of a hybrid forecasting framework, combining data-driven approaches and seasonal (re)forecasting information from dynamical models, to predict hydrological variables was explored. Target variables include discharge and surface water levels for various stations at a national scale, with the Netherlands as the focus. Five different machine learning (ML) models, ranging from simple to more complex and trained on historical observations of discharge, precipitation, evaporation, and seawater levels, were run with seasonal (re)forecast data, including the European Flood Awareness System (EFAS) and ECMWF seasonal forecast system (SEAS5), of these driver variables in a hindcast setting. The results were evaluated using the evaluation metrics, i.e. anomaly correlation coefficient (ACC), continuous ranked probability (skill) score (CRPS and CRPSS), and Brier skill score (BSS), in comparison to a climatological reference hindcast. Aggregating the results of all stations and ML models revealed that the hindcasting framework outperformed the climatological reference forecasts by roughly 60 % for discharge predictions (80 % for surface water level predictions). Skilful prediction for the first lead month, independently of the initialization month, can be made for discharge. The skill extends up to 2–3 months for spring months due to snowmelt dynamic captured in the training phase of the model. Surface water level hindcasts showed similar skill and skilful lead times. While the different ML models showed differences in performance during a testing and training phase using historical observations, running the ML framework in a hindcast setting showed only minor differences between the models, which is attributed to the uncertainty in seasonal forecasts. However, despite being trained on historical observations, the hybrid framework used in this study shows similar skilful predictions to previous large-scale forecasting systems. With our study, we show that a hybrid framework is able to bring location-specific skilful seasonal forecast information with global seasonal forecast inputs. At the same time, our hybrid approach is flexible and fast, and as such, a hybrid framework could be adapted to make it even more interesting to water managers and their needs, for instance, as part of a fast model-predictive control framework.

Dikes often have a long history of reinforcement, with each reinforcement adding new material resulting in a heterogeneous dike. As data on the dike internal heterogeneity is sparse, it is generally overlooked in the stability assessment of dikes. We present an object-based and process-based model simulating dike construction history on archeological dike cross, yielding similar patterns of heterogeneity as observed in real dikes, and apply it in a dike safety assessment. Model predictions improve when being based on more accurate statistics of dike buildup, or when being conditioned to ground truth data. When incorporated in a dike stability assessment, multiple model runs can be coupled to hydrological simulations and dike slope stability calculations, resulting in a probabilistic stability assessment considering internal dike heterogeneity. While high-resolution observations are still sparse, good model accuracies can be reached by combining regional information on dike buildup with local point observations and this model provides a parsimonious basis to include information of internal dike heterogeneity in safety assessments.

Assessment of long-term human exposure to spatiotemporally highly variable air pollution requires accounting for human space–time activity. Individual exposure and space–time track data are not available over large populations and for long periods and a modelling approach is required. However, activity-based exposure models face here challenges in setting up the model and overly-large computations. Aiming for long-term and large-population simulations, we propose an activity model which integrates statistical and agent-based modelling by treating mobility-related variables as random variables. Probability distributions for these variables are estimated or derived from mobility datasets containing observed activities. On top of the activity model, we implemented an exposure model. A case-study of exposure assessment was developed using hourly air pollution maps. The activity model can potentially integrate any mobility data and is thus applicable when limited time activity data is available at the individual level.