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The evapotranspiration / potential evapotranspiration (AET / PET) ratio is traditionally termed as the crop coefficient (Kc) and has been generally used as ecosystem evaporative stress index. In the current hydrology literature, Kc has been widely used as a parameter to estimate crop water demand by water managers but has not been well examined for other types of ecosystems such as forests and other perennial vegetation. Understanding the seasonal dynamics of this variable for all ecosystems is important for projecting the ecohydrological responses to climate change and accurately quantifying water use at watershed to global scales. This study aimed at deriving monthly Kc for multiple vegetation cover types and understanding its environmental controls by analyzing the accumulated global eddy flux (FLUXNET) data. We examined monthly Kc data for seven vegetation covers, including open shrubland (OS), cropland (CRO), grassland (GRA), deciduous broad leaf forest (DBF), evergreen needle leaf forest (ENF), evergreen broad leaf forest (EBF), and mixed forest (MF), across 81 sites. We found that, except for evergreen forests (EBF and ENF), Kc values had large seasonal variation across all land covers. The spatial variability of Kc was well explained by latitude, suggesting site factors are a major control on Kc. Seasonally, Kc increased significantly with precipitation in the summer months, except in EBF. Moreover, leaf area index (LAI) significantly influenced monthly Kc in all land covers, except in EBF. During the peak growing season, forests had the highest Kc values, while croplands (CRO) had the lowest. We developed a series of multivariate linear monthly regression models for Kc by land cover type and season using LAI, site latitude, and monthly precipitation as independent variables. The Kc models are useful for understanding water stress in different ecosystems under climate change and variability as well as for estimating seasonal ET for large areas with mixed land covers.

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.

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.

Nonlinear relations between rain input and hillslope outflow are common observations in hillslope hydrology field studies. In this paper we use percolation theory to model the threshold relationship between rainfall amount and outflow and show that this nonlinear relationship may arise from simple linear processes at the smaller scale. When the rainfall amount exceeds a threshold value, the underlying elements become connected and water flows out of the base of the hillslope. The percolation approach shows how random variations in storage capacity and connectivity at the small spatial scale cause a threshold relationship between rainstorm amount and hillslope outflow. As a test case, we applied percolation theory to the well characterized experimental hillslope at the Panola Mountain Research Watershed. Analysing the measured rainstorm events and the subsurface stormflow with percolation theory, we could determine the effect of bedrock permeability, spatial distribution of soil properties and initial water content within the hillslope. The measured variation in the relationship between rainstorm amount and subsurface flow could be reproduced by modelling the initial moisture deficit, the loss of free water to the bedrock, the limited size of the system and the connectivity that is a function of bedrock topography and existence of macropores. The values of the model parameters were in agreement with measured values of soil depth distribution and water saturation. Hydrology and Earth System Sciences, 11 (2) ISSN:1027-5606 ISSN:1607-7938

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.

Abstract. Groundwater contaminated with nitrate poses a serious health risk to infants when this contaminated water is used for culinary purposes. To avoid this health risk, people need to know whether their culinary water is contaminated or not. Therefore, there is a need to design an effective groundwater monitoring network, acquire information on groundwater conditions, and use acquired information to inform management options. These actions require time, money, and effort. This paper presents a method to estimate the value of information (VOI) provided by a groundwater quality monitoring network located in an aquifer whose water poses a spatially heterogeneous and uncertain health risk. A decision tree model describes the structure of the decision alternatives facing the decision-maker and the expected outcomes from these alternatives. The alternatives include (i) ignore the health risk of nitrate-contaminated water, (ii) switch to alternative water sources such as bottled water, or (iii) implement a previously designed groundwater quality monitoring network that takes into account uncertainties in aquifer properties, contaminant transport processes, and climate (Khader, 2012). The VOI is estimated as the difference between the expected costs of implementing the monitoring network and the lowest-cost uninformed alternative. We illustrate the method for the Eocene Aquifer, West Bank, Palestine, where methemoglobinemia (blue baby syndrome) is the main health problem associated with the principal contaminant nitrate. The expected cost of each alternative is estimated as the weighted sum of the costs and probabilities (likelihoods) associated with the uncertain outcomes resulting from the alternative. Uncertain outcomes include actual nitrate concentrations in the aquifer, concentrations reported by the monitoring system, whether people abide by manager recommendations to use/not use aquifer water, and whether people get sick from drinking contaminated water. Outcome costs include healthcare for methemoglobinemia, purchase of bottled water, and installation and maintenance of the groundwater monitoring system. At current methemoglobinemia and bottled water costs of $ 150/person and $ 0.6/baby/day, the decision tree results show that the expected cost of establishing the proposed groundwater quality monitoring network exceeds the expected costs of the uninformed alternatives and there is no value to the information the monitoring system provides. However, the monitoring system will be preferred to ignoring the health risk or using alternative sources if the methemoglobinemia cost rises to $ 300/person or the bottled water cost increases to $ 2.3/baby/day. Similarly, the monitoring system has value if the system can more accurately report actual aquifer concentrations and the public more fully abides by manager recommendations to use/not use the aquifer. The system also has value if it will serve a larger population or if its installation costs can be reduced, for example using a smaller number of monitoring wells. The VOI analysis shows how monitoring system design, accuracy, installation and operating costs, public awareness of health risks, costs of alternatives, and demographics together affect the value of implementing a system to monitor groundwater quality.

Abstract. The snowpack over the Mediterranean mountains constitutes a key water resource for the downstream populations. However, its dynamics have not been studied in detail yet in many areas, mostly because of the scarcity of snowpack observations. In this work, we present a characterization of the snowpack over the two mountain ranges of Lebanon. To obtain the necessary snowpack information, we have developed a 1 km regional scale snow reanalysis (ICAR_assim) covering the period 2010–2017. ICAR_assim was developed by means of ensemble-based data assimilation of MODIS fractional snow-covered area (fSCA) through the energy and mass balance model the Flexible Snow Model (FSM2), using the Particle Batch Smoother (PBS). The meteorological forcing data was obtained by a regional atmospheric simulation developed through the Intermediate Complexity Atmospheric Research model (ICAR) nested inside a coarser regional simulation developed by the Weather Research and Forecasting model (WRF). The boundary and initial conditions of WRF were provided by the ERA5 atmospheric reanalysis. ICAR_assim showed very good agreement with MODIS gap-filled snow products, with a spatial correlation of R = 0.98 in the snow probability (P(snow)), and a temporal correlation of R = 0.88 in the day of peak snow water equivalent (SWE)Similarly, ICAR_assim has shown a correlation with the seasonal mean SWE of R = 0.75 compared with in-situ observations from Automatic Weather Stations (AWS). The results highlight the high temporal variability of the snowpack in the Lebanon ranges, with differences between Mount Lebanon and Anti-Lebanon that cannot be only explained by its hypsography been Anti-Lebanon in the rain shadow of Mount Lebanon. The maximum fresh water stored in the snowpack is in the middle elevations approximately between 2200 and 2500 m. a.s.l. Thus, the resilience to further warming is low for the snow water resources of Lebanon due to the proximity of the snowpack to the zero isotherm.

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.

The study of the relationship between water storage and runoff generation has long been a focus of the hydrological sciences. NASA's Gravity Recovery and Climate Experiment (GRACE) mission provides monthly depth-integrated information on terrestrial water storage anomalies derived from time-variable gravity observations. As the first basin-scale storage measurement technique, these data offer potentially novel insight into the storage–discharge relationship. Here, we apply GRACE data in a streamflow recession analysis with river discharge measurements across several subdomains of the Mississippi River basin. Nonlinear regression analysis was used for 12 watersheds to determine that the fraction of baseflow in streams during non-winter months varies from 52 % to 75 % regionally. Additionally, the first quantitative estimate of absolute drainable water storage was estimated. For the 2002–2014 period, the drainable storage in the Mississippi River basin ranged from 2900±400 to 3600±400 km3.

Abstract. Acquiring in-situ data of tidal flooding is key for the successful restoration planning of intertidal wetlands such as salt marshes and mangroves. However, monitoring spatially explicit inundation time series and tidal currents can be costly and technically challenging. With the increasing availability of low-cost sensors and data loggers, customized solutions can now be designed to monitor intertidal hydrodynamics with direct applications for restoration and management. In this study, we present the design, calibration, and application of the Mini Buoy, a low-cost bottom-mounted float containing an acceleration data logger for monitoring tidal inundation characteristics and current velocities derived from single-axis equilibrium acceleration (i.e. logger tilt). The acceleration output of the Mini Buoys was calibrated against water-level and current velocity data in the hypertidal Bay of Fundy, Canada, and in a tidally reconnected former aquaculture pond complex in North Sumatra, Indonesia. Key parameters, such as submersion time and current velocities during submergence can be determined over several months using the Mini Buoy. An open-source application was developed to generate ecologically meaningful hydrological information from the Mini Buoy data for mangrove restoration planning. We present this specific SE Asian mangrove restoration application alongside a flexible concept design for the Mini Buoy to be customized for research and management of intertidal wetlands worldwide.