Wetlands are often vital physical and social components of a country’s natural capital, as well as providers of ecosystem services to local and national communities. We performed a network analysis to prioritize Sustainable Development Goal (SDG) targets for sustainable development in iconic wetlands and wetlandscapes around the world. The analysis was based on the information and perceptions on 45 wetlandscapes worldwide by 49 wetland researchers of the Global Wetland Ecohydrological Network (GWEN). We identified three 2030 Agenda targets of high priority across the wetlandscapes needed to achieve sustainable development: Target 6.3—“Improve water quality”; 2.4—“Sustainable food production”; and 12.2—“Sustainable management of resources”. Moreover, we found specific feedback mechanisms and synergies between SDG targets in the context of wetlands. The most consistent reinforcing interactions were the influence of Target 12.2 on 8.4—“Efficient resource consumption”; and that of Target 6.3 on 12.2. The wetlandscapes could be differentiated in four bundles of distinctive priority SDG-targets: “Basic human needs”, “Sustainable tourism”, “Environmental impact in urban wetlands”, and “Improving and conserving environment”. In general, we find that the SDG groups, targets, and interactions stress that maintaining good water quality and a “wise use” of wetlandscapes are vital to attaining sustainable development within these sensitive ecosystems.

Wetlands provide multiple ecosystem services of local and global importance, but currently there exists no comprehensive, high‐quality wetland map for the Arctic region. Improved information about Arctic wetland extents and their vulnerability to climate change is essential for adaptation and mitigation efforts, including for indigenous people dependent on the ecosystem services that wetlands provide, as inadequate planning could result in dire consequences for societies and ecosystems alike. Synthesizing high‐resolution wetland databases and datasets on soil wetness and soil types from multiple sources, we created the first high‐resolution map with full coverage of Arctic wetlands. We assess the vulnerability of Arctic wetlands for the years 2050, 2075, and 2100, using datasets on permafrost extent, soil types, and projected mean annual air temperature from the HadGEM2‐ES climate model for three change scenarios (RCP2.6, RCP4.5, and RCP8.5). Our mapping shows that wetlands cover approximately 3.5 million km² or roughly 25% of Arctic landmass and 99% of these wetlands are in permafrost areas, indicating considerable vulnerability to future climate change. Unless global warming is limited to scenario RCP2.6, robust results show that large areas of Arctic wetlands are vulnerable to ecosystem regime shifts. If scenario RCP8.5 becomes a reality, at least 50% of the Arctic wetland area would be highly vulnerable to regime shifts with considerable adverse impacts on human health, infrastructure, economics, ecosystems, and biodiversity. The developed wetland and vulnerability maps can aid planning and prioritization of the most vulnerable areas for protection and mitigation of change.

Population growth is increasing the pressure on water resource availability. For useful assessment and planning for societal water availability impacts, it is imperative to disentangle the direct influences of human activities in the landscape from external climate‐driven influences on water flows and their variation and change. In this study we used the water balance model, a gridded global hydrological model, to quantify and distinguish human‐driven change components, modified by interventions such as dams, reservoirs, and water withdrawals for irrigation, industry, and households, from climate‐driven change components on four key water balance variables in the terrestrial hydrological system (evapotranspiration, runoff, soil moisture, storage change). We also analyzed emergent effect patterns in and across different parts of the world, facilitating exploration of spatial variability and regional patterns on multiple spatial scales, from pixel to global, including previously uninvestigated parts of the world. Our results show that human activities drive changes in all hydrological variables, with different magnitudes and directions depending on geographical location. The differences between model scenarios with and without human activities were largest in regions with the highest population densities. In such regions, which also have relatively large numbers of dams for irrigation, water largely tends to be removed from storage and go to feed increased runoff and evapotranspiration fluxes. Our analysis considers a more complete set of hydrological variables than previous studies and can guide further research and management planning for future hydrological and water availability trends, including in relatively data‐poor parts of the world.

Soil moisture is an important variable for land-climate and hydrological interactions. To investigate emergent large-scale, long-term interactions between soil moisture and other key hydro-climatic variables (precipitation, actual evapotranspiration, runoff, temperature), we analyze monthly values and anomalies of these variables in 1378 hydrological catchments across Europe over the period 1980–2010. The study distinguishes results for the main European climate regions, and tests how sensitive or robust they are to the use of three alternative observational and re-analysis datasets. Robustly across the European climates and datasets, monthly soil moisture anomalies correlate well with runoff anomalies, and extreme soil moisture and runoff values also largely co-occur. For precipitation, evapotranspiration, and temperature, anomaly correlation and extreme value co-occurrence with soil moisture are overall lower than for runoff. The runoff results indicate a possible new approach to assessing variability and change of large-scale soil moisture conditions by use of long-term time series of monitored catchment-integrating stream discharges.

Extreme precipitation events such as floods and droughts have occurred with higher frequency over the recent decades as a result of the climate change and anthropogenic activities. To understand and mitigate such events, it is crucial to investigate their spatio-temporal variations globally or regionally. Global precipitation products provide an alternative way to the in situ observations over such a region. In this study, we have evaluated the performance of the latest version of the Global Precipitation Measurement-Integrated Multi-satellitE Retrievals (GPM-IMERGV6.0 Final Run (GPM-IMERGF)). To this end, we have employed ten most common extreme precipitation indices, including maximum indices (Rx1day, Rx5day, CDD, and CWD), percentile indices (R95pTOT and R99pTOT), and absolute threshold indices (R10mm, R20mm, SDII, and PRCPTOT). Overall, the spatial distribution results for error metrics showed that the highest and lowest accuracy for GPM-IMERGF were reported for the absolute threshold indices and percentile indices, respectively. Considering the spatial distribution of the results, the highest accuracy of GPM-IMERGF in capturing extreme precipitations was observed over the western highlands, while the worst results were obtained along the Caspian Sea regions. Our analysis can significantly contribute to various hydro-metrological applications for the study region, including identifying drought and flood-prone areas and water resources planning.

Large‐scale covariations of freshwater fluxes and storages on land can critically regulate the balance of green (evapotranspiration) and blue (runoff) water fluxes, and related land‐atmosphere interactions and hydroclimatic hazards. Such large‐scale covariation patterns are not evident from smaller‐scale hydrological studies that have been most common so far, and remain largely unknown for various regions and climates around the world. To contribute to bridging the large‐scale knowledge gaps, we synthesize and decipher hydroclimatic data time series over the period 1980–2010 for 6,405 catchments around the world. From observation‐based data, we identify dominant large‐scale linear covariation patterns between monthly freshwater fluxes and soil moisture (SM) for different world parts and climates. These covariation patterns are also compared with those obtained from reanalysis products and Earth System Models (ESMs). The observation‐based data sets robustly show the strongest large‐scale hydrological relationship to be that between SM and runoff (R), consistently across the study catchments and their different climate characteristics. This predominantly strongest covariation between monthly SM and R is also the most misrepresented by ESMs and reanalysis products, followed by that between monthly precipitation and R. Comparison of observation‐based and ESM results also shows that an ESM may perform well for individual monthly variables, but fail in representing the patterns of large‐scale linear covariations between them. Observation‐based quantification of these patterns, and ESM and reanalysis improvements for their representation are essential for fundamental understanding, and more accurate and reliable modeling and projection of large‐scale hydrological conditions and changes under ongoing global and regional change.