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The transport of bromide (Br) under matric heads of 0, −2, −5, and −10 cm using undisturbed soil columns was investigated for understanding the solute transport in arid soils. Undisturbed soil cores were collected at ground surface, directly below where tension infiltrometer measurements were made in the Amargosa Desert, Nevada, United States. Laboratory experiments were conducted by introducing water containing Br tracer into a soil column maintained at steady-state conditions. The observed data of breakthrough curves ( BTC) were well fitted to an one-region model, except for the cores at saturation, and a core at the matric head of −5 cm, from which the observed data were better fitted to a two-region model. Fitted pore water velocities with the one-region model ranged from 1.2 to 56.6 cm/h, and fitted dispersion coefficients ( D) ranged from 2.2 to 100 cm2/h. Results for the core analyzed with the two-region model indicated that D ranged from 27.6 to 70.9 cm2/h at saturation, and 25.7 cm2/h at the matric head of −5 cm; fraction of mobile water ( β) ranged from 0.18 to 0.65, and mass transfer coefficient ( ω) ranged from 0.006 to 0.03. In summary, the water fluxes and Br dispersion coefficients at investigated matric heads were very high due to the coarseness of the soils and possibly due to preferential flow pathways. These high water fluxes and Br dispersion coefficients would lead to a higher risk of deeper leaching accumulating nitrate nitrogen to the groundwater, and have significant effects on the desert ecosystem. [ABSTRACT FROM AUTHOR]

The impact on the hydrologic cycle of permafrost degradation under the influence of climate change has caused an inestimable threat to sustainable regulation of the ecosystem. This study quantified the responses of main hydrological elements, including soil moisture, groundwater, runoff components and discharge to totally degraded permafrost in eastern High Asia by establishing cases with and without thermodynamics using a cold region model combining hydrological processes and thermodynamics. The results showed that the model successfully simulated discharge in cold region basins. Totally degraded permafrost decreased soil moisture in the vadose zone (SMV) and increased the absolute depth to ground water (ADGW). In the daily scale, total permafrost degradation decreased the direct flow in autumn, slightly increased direct flow in spring and decreased interflow in summer. Total permafrost degradation also increased daily baseflow all year round and by >50% in spring, decreased daily discharge during autumn and increased daily discharge during spring. In the annual scale, total permafrost degradation increased direct flow, baseflow, and discharge, and decreased interflow. The magnitudes of these changes were positively related to the ratios of permafrost to the subbasin area. The responses of daily runoff components and discharge to totally degraded permafrost were significantly larger than the annual value. The groundwater level, direct flow and baseflow were far more sensitive to permafrost degradation than SMV, interflow and discharge. The responses of annual individual hydrological elements were more obvious than the annual discharge. These quantified results can be extensively used in lumped hydrology simulations, water resource assessments and eco-system management for partial permafrost degradation. Unlabelled Image • Responses of hydrologic elements to permafrost degradation are still un-quantified. • Two cases with and without thermodynamics using a fully coupled hydro-thermal model • Permafrost degradation decreases autumn and increases winter/spring discharge. • Smaller annual discharge increase in permafrost degradation than hydrologic elements [ABSTRACT FROM AUTHOR]

• SVMs and SVM-DA models forced with limited climate variables can forecast CGWLs up to 3 months lead time. • Performance of SVM model can be influenced by the local climate variables. • Addition of GRACE information can further improve the prediction of change in groundwater levels based on SVM-DA model. Groundwater (GW) is a vital resource for human consumption, agriculture, environmental sustainability, and socio-economic development in different parts of the world. Besides, GW plays a crucial role in minimizing the impacts during extreme drought events. However, there is a fast decline in groundwater resources due to increased sectoral water demand compounded by reduced rainfall and rising temperature. Therefore, it is essential to develop appropriate tools to study spatio-temporal dynamics and predict groundwater levels to improve water resource management, especially during drought events. In this study, we applied machine learning algorithms based on support vector machines (SVMs), combined with data assimilation (DA) technique to predict the change in groundwater levels (CGWLs) at 1 to 3-month time scales for 46 GW wells located at the northeast United States. The in-situ climate variables and the Gravity Recovery and Climate Experiment (GRACE) mission-informed groundwater anomalies data (GWA) are used to develop the models. The results suggest that SVMs (SVM-DA) models forced with limited climate variables (i.e., precipitation, solar radiation, air temperature, infrared surface temperature) can forecast the CGWLs up to 3-month lead times at most of the locations. The addition of GRACE data as a forcing variable can improve the performance of SVMs at most of the stations, where a strong relationship exists between the CGWLs and the GWA. The SVM-DA model comparatively performed better than SVMs at most of the stations. [ABSTRACT FROM AUTHOR]

Groundwater level fluctuations are affected by surface properties due to complex correlations of groundwater‐surface water interaction and/or other surface processes, which are usually hard to be accurately quantified. Previous studies have assessed the relationship between groundwater level fluctuations and specific controlling factors. However, few studies have been conducted to explore the impact of the combination of multiple factors on the groundwater system. Hence, this paper tries to explore the localized and scale‐specific multivariate relationships between the groundwater level and controlling factors (such as hydrologic and meteorological factors) using bivariate wavelet coherence and multiple wavelet coherence. The groundwater level fluctuations of two wells in areas covered by different plant densities (i.e., the riparian zone of the Colorado River, USA) are analyzed. Main findings include three parts. First, barometric pressure and river stage are the best factors to interpret the groundwater level fluctuations at small scales (<1 day) and large scales (>1 day) at the well of low‐density plants stand, respectively. Second, at the well of high‐density plants stand, the best predictors to control the groundwater level fluctuations include barometric pressure (<1 day), the combination of barometric pressure and temperature (1‐7 days), temperature (7‐30 days), and the combination of barometric pressure, temperature, and river stage (>30 days). The best predictor of groundwater head fluctuations depends on the variance of the vegetation coverage and hydrological processes. Third, these results provide a suite of factors to explain the groundwater level variations, which is an important topic in water‐resource prediction and management. Article impact statement: This method note provides a multivariate method to identify multiple factors that can best explain the groundwater level fluctuation. [ABSTRACT FROM AUTHOR]

Estimating groundwater recharge rate is essential for water sustainability of the arid region of the United Arab Emirates that receives most groundwater recharge from the bordering mountain ranges in Oman. Analyses of major ions, stable isotopes (δ2H and δ18O) and the radioactive isotope (14C) and the HydroGeoSphere (HGS) three-dimensional numerical model approach were applied here to investigate groundwater residence time in the aquifers and the recharge rates in the region. Estimate of groundwater recharge ranges from 1.03 to 36.6 mm/year with an average of 5.8 mm/year based on groundwater ages and ranges from 0.23 to 35.3 mm/year with an average of 6.5 mm/year based on HGS modelling. The data provide vital information for making sustainable plans for natural water resources with expected future reduction in rainfall in this region and other arid areas of the Middle East and the world. [ABSTRACT FROM AUTHOR]

On the Qinghai-Tibet Plateau, permafrost degradation and associated release of permafrost meltwater has undoubtedly modified the annual and seasonal streamflow patterns and influenced groundwater conditions. However, it is still unclear how extensively permafrost degradation has influenced surface and subsurface water systems due to very sparse observations. Isotope mass models combining tritium and 222Rn measurements are developed here to quantify the contributions of groundwater discharge and thawing permafrost to rivers and lakes in the Source Area of the Yellow River. Results demonstrate the spatial variability of permafrost thaw impacts and confirm significant influence on surface and subsurface runoff processes including effects on water yield capacity and water drainage connectivity. While results to date are conclusive, further work to refine the method and extend observations is warranted. [ABSTRACT FROM AUTHOR]

Abstract Understanding hydrological processes in the Source Area of the Yellow River (SAYR), Qinghai-Tibet Plateau, is vital for protection and management of groundwater and surface water resources in the region. In situ water measurements of exchange rates between surface water and groundwater are, however, hard to conduct because of the harsh natural conditions of the SAYR. We here present an indirect method using in situ 222Rn measurements to estimate groundwater discharge into rivers and lakes in the SAYR. 222Rn was measured in rivers, lakes, groundwater and springs during three sampling periods (2014–2016), and the results indicate large variability in the concentration of the isotope. The data also indicate decreasing 222Rn trends in groundwater in the cold season (the Feb-2015 sampling period) which may be linked to frequency of capturing 222Rn in the frozen ground caused by geocryogenic processes. In addition, permafrost spatial extent and freeze-thaw processes have strongly affected the hydrological conditions in the region. Highlights • 222Rn distribution in natural water of the Source Area of the Yellow River (SAYR). • 222Rn-based estimates of groundwater inflow rates to rivers and lakes in the SAYR. • Effects of frozen ground on 222Rn concentration in water. [ABSTRACT FROM AUTHOR]

Better quantification of the spatiotemporal distribution of soil moisture across different spatial scales contributes significantly to the understanding of land surface processes on the Earth as an integrated system. While observational data for root-zone soil moisture (RZSM) often have sparse spatial coverage, model-simulated soil moisture may provide a useful alternative. TOPMODEL-Based Land Surface–Atmosphere Transfer Scheme (TOPLATS) has been widely studied and actively modified in recent years, while a detailed regional application with evaluation currently is still lacking. Thus, TOPLATS was used to generate high-resolution (30 arc s) RZSM based on coarse-scale (0.125°) forcing data over part of the Arkansas–Red River basin. First, the simulated RZSM was resampled to coarse scale to compare with the results of Mosaic, Noah, and VIC from NLDAS. Second, TOPLATS performance was assessed based on the spatial absolute difference among the models. The comparison shows that TOPLATS performance is similar to VIC, but different from Mosaic and Noah. Last, the simulated RZSM was compared with in situ observations of 16 stations in the study area. The results suggest that the simulated spatial distribution of RZSM is largely consistent with the distribution of topographic index (TI) in most instances, as topography was traditionally considered a major, but not the only, factor in horizontal redistribution of soil moisture. In addition, the finer-resolution RZSM can reflect the in situ soil moisture change at most local sites to a certain degree. The evaluation confirms that TOPLATS is a useful tool to estimate high-resolution soil moisture and has great potential to provide regional soil moisture estimates. [ABSTRACT FROM AUTHOR]

The differences between δ18O and δ2H in throughfall and open rainfall were studied for a selected typhoon event in a watershed within the Taihu Lake drainage basin, eastern China. In this event, the isotopic composition of precipitation exhibited a strong temporal variation. Comparison results show that an isotopic composition difference existed not only between gross rainfall and average incremental rainfall, but also between different calculation methods used to derive average. The differences between incremental precipitation and throughfall isotopic composition were observed in this study. Considering the temporal variation in rainfall and throughfall during this typhoon event, the incremental value can have an effect on hydrograph separation more accurately in evaluating the importance of ‘new’ water. In addition, isotopic fluctuations of surface water and groundwater differed from those of rainfall and throughfall throughout the event. [ABSTRACT FROM PUBLISHER]

There can be marked variations in soil hydraulic properties in a soil vertical profile from the soil surface to the base of the root zone. Many existing two-layer soil moisture ( TLSM) models cannot well describe typical stratified soil profiles. A modified TLSM model is presented in this study. The modified model results and those from two existing models are compared with field observations. The modified TLSM model had the best agreement with the field observations. In both the surface layer and the root zone layer, the root mean square errors of soil moisture estimated by the modified model were smaller than those for the other models. The parameters in the modified TLSM model are relatively easy to determine. The modified TLSM model offers clear advantages over current TLSM models. [ABSTRACT FROM AUTHOR]

In the original publication of the article, the eighth author's name and Figure 1 were published incorrectly. [ABSTRACT FROM AUTHOR]