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Information about the raindrop size distribution (RSD) is vital for comprehending the precipitation microphysics, improving the rainfall estimation algorithms, and appraising the rainfall erosivity. Previous research has revealed that the RSD exhibits diversity with geographical location and weather type, which leads to the assessment of the region and weather-specific RSDs. Based on long-term (2004 to 2016) disdrometer measurements in northern Taiwan, this study attempts to demonstrate the RSD aspects of summer seasons that were bifurcated into two weather conditions, namely typhoon (TY) and non-typhoon (NTY) rainfall. The results show a higher concentration of small drops and a lower concentration of large-sized drops in TY compared to NTY rainfall, and this behavior persisted even after characterizing the RSDs into different rainfall rate classes. RSDs expressed in gamma parameters show higher mass-weighted mean diameter (Dm) and lower normalized intercept parameter (Nw) values in NTY than TY rainfall. Moreover, sorting these two weather conditions (TY and NTY rainfall) into stratiform and convective regimes revealed a larger Dm in NTY than in TY rainfall. The RSD empirical relations used in the valuation of rainfall rate (Z–R, Dm–R, and Nw–R) and rainfall kinetic energy (KE–R and KE–Dm) were enumerated for TY and NTY rainfall, and they exhibited profound diversity between these two weather conditions. Attributions of RSD variability between the TY and NTY rainfall to the thermodynamical and microphysical processes are elucidated with the aid of reanalysis, remote sensing, and ground-based data sets.

The stable isotope composition of lacustrine sediments is routinely used to infer Late Holocene changes in precipitation over Scandinavia and, ultimately, atmospheric circulation dynamics in the North Atlantic realm. However, such archives only provide a low temporal resolution (ca. 15 years), precluding the ability to identify changes on inter-annual and quasi-decadal timescales. Here, we present a new, high-resolution reconstruction using shells of freshwater pearl mussels, Margaritifera margaritifera, from three streams in northern Sweden. We present seasonally to annually resolved, calendar-aligned stable oxygen and carbon isotope data from 10 specimens, covering the time interval from 1819 to 1998. The bivalves studied formed their shells near equilibrium with the oxygen isotope signature of ambient water and, thus, reflect hydrological processes in the catchment as well as changes, albeit damped, in the isotope signature of local atmospheric precipitation. The shell oxygen isotopes were significantly correlated with the North Atlantic Oscillation index (up to 56 % explained variability), suggesting that the moisture that winter precipitation formed from originated predominantly in the North Atlantic during NAO+ years but in the Arctic during NAO− years. The isotope signature of winter precipitation was attenuated in the stream water, and this damping effect was eventually recorded by the shells. Shell stable carbon isotope values did not show consistent ontogenetic trends, but rather oscillated around an average that ranged from ca. −12.00 to −13.00 ‰ among the streams studied. Results of this study contribute to an improved understanding of climate dynamics in Scandinavia and the North Atlantic sector and can help to constrain eco-hydrological changes in riverine ecosystems. Moreover, long isotope records of precipitation and streamflow are pivotal to improve our understanding and modeling of hydrological, ecological, biogeochemical and atmospheric processes. Our new approach offers a much higher temporal resolution and superior dating control than data from existing archives.

Evapotranspiration (ET) is a critical component in global water cycle and links terrestrial water, carbon and energy cycles. Accurate estimate of terrestrial ET is important for hydrological, meteorological, and agricultural research and applications, such as quantifying surface energy and water budgets, weather forecasting, and scheduling of irrigation. However, direct measurement of global terrestrial ET is not feasible. Here, we first gave a retrospective introduction to the basic theory and recent developments of state-of-the-art approaches for estimating global terrestrial ET, including remote sensing-based physical models, machine learning algorithms and land surface models (LSMs). Then, we utilized six remote sensing-based models (including four physical models and two machine learning algorithms) and fourteen LSMs to analyze the spatial and temporal variations in global terrestrial ET. The results showed that the mean annual global terrestrial ET ranged from 50.7 × 103 km3 yr−1（454 mm yr−1）to 75.7 × 103 km3 yr−1 (6977 mm yr−1), with the average being 65.5 × 103 km3 yr−1 (588 mm yr−1), during 1982–2011. LSMs had significant uncertainty in the ET magnitude in tropical regions especially the Amazon Basin, while remote sensing-based ET products showed larger inter-model range in arid and semi-arid regions than LSMs. LSMs and remote sensing-based physical models presented much larger inter-annual variability (IAV) of ET than machine learning algorithms in southwestern U.S. and the Southern Hemisphere, particularly in Australia. LSMs suggested stronger control of precipitation on ET IAV than remote sensing-based models. The ensemble remote sensing-based physical models and machine-learning algorithm suggested significant increasing trends in global terrestrial ET at the rate of 0.62 mm yr−2 (p  0.05), even though most of the individual LSMs reproduced the increasing trend. Moreover, all models suggested a positive effect of vegetation greening on ET intensification. Spatially, all methods showed that ET significantly increased in western and southern Africa, western India and northeastern Australia, but decreased severely in southwestern U.S., southern South America and Mongolia. Discrepancies in ET trend mainly appeared in tropical regions like the Amazon Basin. The ensemble means of the three ET categories showed generally good consistency, however, considerable uncertainties still exist in both the temporal and spatial variations in global ET estimates. The uncertainties were induced by multiple factors, including parameterization of land processes, meteorological forcing, lack of in situ measurements, remote sensing acquisition and scaling effects. Improvements in the representation of water stress and canopy dynamics are essentially needed to reduce uncertainty in LSM-simulated ET. Utilization of latest satellite sensors and deep learning methods, theoretical advancements in nonequilibrium thermodynamics, and application of integrated methods that fuse different ET estimates or relevant key biophysical variables will improve the accuracy of remote sensing-based models.

For more than two decades, research groups in hydrology, ecology, soil science, and biogeochemistry have performed cryogenic water extractions (CWEs) for the analysis of δ2H and δ18O of soil water. Recent studies have shown that extraction conditions (time, temperature, and vacuum) along with physicochemical soil properties may affect extracted soil water isotope composition. Here we present results from the first worldwide round robin laboratory intercomparison. We test the null hypothesis that, with identical soils, standards, extraction protocols, and isotope analyses, cryogenic extractions across all laboratories are identical. Two standard soils with different physicochemical characteristics along with deionized (DI) reference water of known isotopic composition were shipped to 16 participating laboratories. Participants oven-dried and rewetted the soils to 8 and 20 % gravimetric water content (WC), using the deionized reference water. One batch of soil samples was extracted via predefined extraction conditions (time, temperature, and vacuum) identical to all laboratories; the second batch was extracted via conditions considered routine in the respective laboratory. All extracted water samples were analyzed for δ18O and δ2H by the lead laboratory (Global Institute for Water Security, GIWS, Saskatoon, Canada) using both a laser and an isotope ratio mass spectrometer (OA-ICOS and IRMS, respectively). We rejected the null hypothesis. Our results showed large differences in retrieved isotopic signatures among participating laboratories linked to soil type and soil water content with mean differences compared to the reference water ranging from +18.1 to −108.4 ‰ for δ2H and +11.8 to −14.9 ‰ for δ18O across all laboratories. In addition, differences were observed between OA-ICOS and IRMS isotope data. These were related to spectral interferences during OA-ICOS analysis that are especially problematic for the clayey loam soils used. While the types of cryogenic extraction lab construction varied from manifold systems to single chambers, no clear trends between system construction, applied extraction conditions, and extraction results were found. Rather, observed differences in the isotope data were influenced by interactions between multiple factors (soil type and properties, soil water content, system setup, extraction efficiency, extraction system leaks, and each lab's internal accuracy). Our results question the usefulness of cryogenic extraction as a standard for water extraction since results are not comparable across laboratories. This suggests that defining any sort of standard extraction procedure applicable across laboratories is challenging. Laboratories might have to establish calibration functions for their specific extraction system for each natural soil type, individually.

In 2009, the International Soil Moisture Network (ISMN) was initiated as a community effort, funded by the European Space Agency, to serve as a centralised data hosting facility for globally available in situ soil moisture measurements (Dorigo et al., 2011b, a). The ISMN brings together in situ soil moisture measurements collected and freely shared by a multitude of organisations, harmonises them in terms of units and sampling rates, applies advanced quality control, and stores them in a database. Users can freely retrieve the data from this database through an online web portal (https://ismn.earth/en/, last access: 28 October 2021). Meanwhile, the ISMN has evolved into the primary in situ soil moisture reference database worldwide, as evidenced by more than 3000 active users and over 1000 scientific publications referencing the data sets provided by the network. As of July 2021, the ISMN now contains the data of 71 networks and 2842 stations located all over the globe, with a time period spanning from 1952 to the present. The number of networks and stations covered by the ISMN is still growing, and approximately 70 % of the data sets contained in the database continue to be updated on a regular or irregular basis. The main scope of this paper is to inform readers about the evolution of the ISMN over the past decade, including a description of network and data set updates and quality control procedures. A comprehensive review of the existing literature making use of ISMN data is also provided in order to identify current limitations in functionality and data usage and to shape priorities for the next decade of operations of this unique community-based data repository.

Abstract. This work explores the potential of the distributed GEM-Hydro runoff modeling platform, developed at Environment and Climate Change Canada (ECCC) over the last decade. More precisely, the aim is to develop a robust implementation methodology to perform reliable streamflow simulations with a distributed model over large and partly ungauged basins, in an efficient manner. The latest version of GEM-Hydro combines the SVS (Soil, Vegetation and Snow) land-surface scheme and the WATROUTE routing scheme. SVS has never been evaluated from a hydrological point of view, which is done here for all major rivers flowing into Lake Ontario. Two established hydrological models are confronted to GEM-Hydro, namely MESH and WATFLOOD, which share the same routing scheme (WATROUTE) but rely on different land-surface schemes. All models are calibrated using the same meteorological forcings, objective function, calibration algorithm, and basin delineation. GEM-Hydro is shown to be competitive with MESH and WATFLOOD: the NSE √ (Nash–Sutcliffe criterion computed on the square root of the flows) is for example equal to 0.83 for MESH and GEM-Hydro in validation on the Moira River basin, and to 0.68 for WATFLOOD. A computationally efficient strategy is proposed to calibrate SVS: a simple unit hydrograph is used for routing instead of WATROUTE. Global and local calibration strategies are compared in order to estimate runoff for ungauged portions of the Lake Ontario basin. Overall, streamflow predictions obtained using a global calibration strategy, in which a single parameter set is identified for the whole basin of Lake Ontario, show accuracy comparable to the predictions based on local calibration: the average NSE √ in validation and over seven subbasins is 0.73 and 0.61, respectively for local and global calibrations. Hence, global calibration provides spatially consistent parameter values, robust performance at gauged locations, and reduces the complexity and computation burden of the calibration procedure. This work contributes to the Great Lakes Runoff Inter-comparison Project for Lake Ontario (GRIP-O), which aims at improving Lake Ontario basin runoff simulations by comparing different models using the same input forcings. The main outcome of this study consists in a new generalizable methodology for implementing a distributed hydrologic model with a high computation cost in an efficient and reliable manner, over a large area with ungauged portions, using global calibration and a unit hydrograph to replace the routing component.

Irrigation intensifies land use by increasing crop yield but also impacts water resources. It affects water and energy balances and consequently the microclimate in irrigated regions. Therefore, knowledge of the extent of irrigated land is important for hydrological and crop modelling, global change research, and assessments of resource use and management. Information on the historical evolution of irrigated lands is limited. The new global historical irrigation data set (HID) provides estimates of the temporal development of the area equipped for irrigation (AEI) between 1900 and 2005 at 5 arcmin resolution. We collected sub-national irrigation statistics from various sources and found that the global extent of AEI increased from 63 million ha (Mha) in 1900 to 111 Mha in 1950 and 306 Mha in 2005. We developed eight gridded versions of time series of AEI by combining sub-national irrigation statistics with different data sets on the historical extent of cropland and pasture. Different rules were applied to maximize consistency of the gridded products to sub-national irrigation statistics or to historical cropland and pasture data sets. The HID reflects very well the spatial patterns of irrigated land as shown on historical maps for the western United States (around year 1900) and on a global map (around year 1960). Mean aridity on irrigated land increased and mean natural river discharge on irrigated land decreased from 1900 to 1950 whereas aridity decreased and river discharge remained approximately constant from 1950 to 2005. The data set and its documentation are made available in an open-data repository at https://mygeohub.org/publications/8 (doi:10.13019/M20599).

Abstract. Continental- to global-scale hydrologic and land surface models increasingly include representations of the groundwater system, driven by crucial Earth science and sustainability problems. These models are essential for examining, communicating, and understanding the dynamic interactions between the Earth System above and below the land surface as well as the opportunities and limits of groundwater resources. A key question for this nascent and rapidly developing field is how to evaluate the realism and performance of such large-scale groundwater models given limitations in data availability and commensurability. Our objective is to provide clear recommendations for improving the evaluation of groundwater representation in continental- to global-scale models. We identify three evaluation approaches, including comparing model outputs with available observations of groundwater levels or other state or flux variables (observation-based evaluation); comparing several models with each other with or without reference to actual observations (model-based evaluation); and comparing model behavior with expert expectations of hydrologic behaviors that we expect to see in particular regions or at particular times (expert-based evaluation). Based on current and evolving practices in model evaluation as well as innovations in observations, machine learning and expert elicitation, we argue that combining observation-, model-, and expert-based model evaluation approaches may significantly improve the realism of groundwater representation in large-scale models, and thus our quantification, understanding, and prediction of crucial Earth science and sustainability problems. We encourage greater community-level communication and cooperation on these challenges, including among global hydrology and land surface modelers, local to regional hydrogeologists, and hydrologists focused on model development and evaluation.

Streamflow in south-central Chile (SCC, ∼ 37–42∘ S) is vital for agriculture, forestry production, hydroelectricity, and human consumption. Recent drought episodes have generated hydrological deficits with damaging effects on these activities. This region is projected to undergo major reductions in water availability, concomitant with projected increases in water demand. However, the lack of long-term records hampers the development of accurate estimations of natural variability and trends. In order to provide more information on long-term streamflow variability and trends in SCC, here we report findings of an analysis of instrumental records and a tree-ring reconstruction of the summer streamflow of the Río Imperial (∼ 37∘ 40′ S–38∘ 50′ S). This is the first reconstruction in Chile targeted at this season. Results from the instrumental streamflow record (∼ 1940 onwards) indicated that the hydrological regime is fundamentally pluvial with a small snowmelt contribution during spring, and evidenced a decreasing trend, both for the summer and the full annual record. The reconstruction showed that streamflow below the average characterized the post-1980 period, with more frequent, but not more intense, drought episodes. We additionally found that the recent positive phase of the Southern Annular Mode has significantly influenced streamflow. These findings agree with previous studies, suggesting a robust regional signal and a shift to a new hydrological scenario. In this paper, we also discuss implications of these results for water managers and stakeholders; we provide rationale and examples that support the need for the incorporation of tree-ring reconstructions into water resources management.

Abstract. In many regions with seasonal snow cover, summer streamflow is primarily sustained by groundwater that is recharged during the snowmelt period. Therefore, below-normal snowpack (snow drought) may lead to below-normal summer streamflow (streamflow drought). Summer streamflow is important for supplying human needs and sustaining ecosystems. Climate change impacts on snow have been widely studied, but the relationship between snow drought and streamflow drought is not well understood. In this study, a combined investigation of climate change impacts on snow drought and streamflow drought was completed using generic groundwater – surface water models for four headwater catchments in different ecoregions of British Columbia. Results show that, in response to increased precipitation and temperature, the snow drought regime changes substantially for all four catchments. Warm snow droughts, which are caused by above-normal winter temperatures, increase in frequency, and dry snow droughts, which are caused by below-normal winter precipitation, decrease in frequency. The shift toward more frequent and severe temperature-related snow droughts leads to decreased summer runoff, decreased summer groundwater storage, and more extreme low flows in summer. Moreover, snow droughts propagate into summer streamflow droughts more frequently in the future time periods (2050s, 2080s) as compared to the baseline 1980s period. Thus, warm snow droughts not only become more frequent and severe in the future but also more likely to result in summer streamflow drought conditions.