• Assessing XMT‐Measurement Variability of Air‐Water Interfacial Areas in Natural Porous Media

      Araujo, Juliana B.; Brusseau, Mark L.; Univ Arizona, Dept Environm Sci (AMER GEOPHYSICAL UNION, 2020-01-07)
      This study investigates the accuracy and reproducibility of air-water interfacial areas measured with high-resolution synchrotron X-ray microtomography (XMT). Columns packed with one of two relatively coarse-grained monodisperse granular media, glass beads or a well-sorted quartz sand, were imaged over several years, encompassing changes in acquisition equipment, improved image quality, and enhancements to image acquisition and to processing software. For the glass beads, the specific solid surface area (SSSA-XMT) of 31.6 +/- 1 cm(-1) determined from direct analysis of the segmented solid-phase image data is statistically identical to the independently calculated geometric smooth-sphere specific solid surface area (GSSA, 32 +/- 1 cm(-1)) and to the measured SSSA (28 +/- 3 cm(-1)) obtained with the N-2-Brunauer, Emmett, and Teller method. The maximum specific air-water interfacial area (A(max)) is 27.4 (+/- 2) cm(-1), which compares very well to the SSSA-XMT, GSSA, and SSSA-N-2-Brunauer, Emmett, and Teller values. For the sand, the SSSA-XMT (111 +/- 2 cm(-1)) and GSSA (113 +/- 1 cm(-1)) are similar. The mean A(max) is 96 +/- 5 cm(-1), which compares well to both the SSSA and the GSSA values. The XMT-SSSA values deviated from the GSSA values by 7-16% for the first four experiments but were essentially identical for the later experiments. This indicates that enhancements in image acquisition and processing improved data accuracy. The Amax values ranged from 74 cm(-1) to 101 cm(-1), with a coefficient of variation (COV) of 9%. The maximum capillary interfacial area ranged from 12 cm(-1) to 19 cm(-1), for a COV of 10%. The COVs for both decreased to 5-6% for the latter five experiments. These results demonstrate that XMT imaging provides accurate and reproducible measurements of total and capillary interfacial areas.
    • Characterizing the Fluxes and Age Distribution of Soil Water, Plant Water, and Deep Percolation in a Model Tropical Ecosystem

      Evaristo, Jaivime; Kim, Minseok; Haren, Joost; Pangle, Luke A.; Harman, Ciaran J.; Troch, Peter A.; McDonnell, Jeffrey J.; Univ Arizona, Dept Hydrol & Atmospher Sci; Univ Arizona, Biosphere 2 (AMER GEOPHYSICAL UNION, 2019-04-25)
      Recent field observations indicate that in many forest ecosystems, plants use water that may be isotopically distinct from soil water that ultimately contributes to streamflow. Such an assertion has been met with varied reactions. Of the outstanding questions, we examine whether ecohydrological separation of water between trees and streams results from a separation in time, or in space. Here we present results from a 9-month drought and rewetting experiment at the 26,700-m(3) mesocosm, Biosphere 2-Tropical Rainforest biome. We test the null hypothesis that transpiration and groundwater recharge water are sampled from the same soil volume without preference for old nor young water. After a 10-week drought, we added 66 mm of labeled rainfall with 152 parts per thousand delta H-2 distributed over four events, followed by background rainfall (-60 parts per thousand delta H-2) distributed over 13 events. Our results show that mean transit times through groundwater recharge and plant transpiration were markedly different: groundwater recharge was 2-7 times faster (similar to 9 days) than transpired water (range 17-62 days). The "age" of transpired water showed strong dependence on species and was linked to the difference between midday leaf water potential and soil matric potential. Moreover, our results show that trees used soil water (89% +/- 6) and not the "more mobile" (represented by "zero tension" seepage) water (11% +/- 6). The finding, which rejects our null hypothesis, is novel in that this partitioning is established based on soil water residence times. Our study quantifies mean transit times for transpiration and seepage flows under dynamic conditions. Plain Language Summary Recent studies suggest that plants use a type of water that is different to the water that recharges the ground, a phenomenon described as the two water worlds. It is unclear, however, whether these waters are segregated in space or in time. That is, do plants draw water from parts of the soil different to groundwater recharge, or do plant water withdrawals happen at a different time from groundwater recharge? Evidence from well-controlled experiments is badly needed because the two water worlds, if true, means that our understanding of the water cycle is incomplete. Here we perform a 9-month drought and rainfall experiment, taking fingerprints of the water molecule, to follow a raindrop from the moment it enters the ground through to its exit via plants or groundwater recharge. Results point to two main discoveries: (1) the travel time of water via root water uptake is much longer than the travel time of water leading to groundwater recharge and (2) the water taken by tree roots comes from parts of the soil that are different to the water leading to groundwater recharge. These discoveries show the segregation of these two components of the water cycle in space and in time.
    • The Climatic Water Balance and Topography Control Spatial Patterns of Atmospheric Demand, Soil Moisture, and Shallow Subsurface Flow

      Hoylman, Zachary H.; Jencso, Kelsey G.; Hu, Jia; Holden, Zachary A.; Martin, Justin T.; Gardner, W. Payton; Univ Arizona, Sch Nat Resources & Environm (AMER GEOPHYSICAL UNION, 2019-03-25)
      Catchment hydrometeorology and the organization of shallow subsurface flow are key drivers of active contributing areas and streamflow generation. However, understanding how the climatic water balance and complex topography contribute to these processes from hillslope to catchment scales remains difficult. We compared time series of vapor pressure deficits and soil moisture to the climatic water balance and topographic variables across six zero-order catchments in the Lubrecht Experimental Forest (Montana, USA). We then evaluated how local hydrometeorology (volumetric water content and atmospheric vapor pressure deficit) affected the spatial occurrence of shallow subsurface flow. Generalized linear mixed model analysis revealed significant, temporally stable (monthly and seasonal average) patterns of hydrometeorology that can be predicted by the topographic wetness index and the dynamic climatic water deficit (CWD = potential evapotranspiration - actual evapotranspiration). Intracatchment patterns were significantly correlated to the topographic wetness index, while intercatchment patterns were correlated to spatiotemporal variance in the CWD during each time period. Spatial patterns of shallow subsurface flow were related to the hydrometeorological conditions of the site. We observed persistent shallow subsurface flow in convergent hillslope positions, except when a catchment was positioned in locations with high CWDs (low elevations and southerly aspects). Alternatively, we observed persistent subsurface flow across all hillslope positions (even 70-m upslope from the hollow) when catchments were positioned in locations with especially low CWDs (northerly aspects and high elevations). These results highlight the importance of considering the superposition of the catchment-scale climatic water balance and hillslope-scale topography when characterizing hydrometeorology and shallow subsurface flow dynamics.
    • Equivalence of Discrete Fracture Network and Porous Media Models by Hydraulic Tomography

      Dong, Yanhui; Fu, Yunmei; Yeh, Tian‐Chyi Jim; Wang, Yu‐Li; Zha, Yuanyuan; Wang, Liheng; Hao, Yonghong; Univ Arizona, Dept Hydrol & Atmospher Sci (AMER GEOPHYSICAL UNION, 2019-04-23)
      Hydraulic tomography (HT) has emerged as a potentially viable method for mapping fractures in geologic media as demonstrated by recent studies. However, most of the studies adopted equivalent porous media (EPM) models to generate and invert hydraulic interference test data for HT. While these models assign significant different hydraulic properties to fractures and matrix, they may not fully capture the discrete nature of the fractures in the rocks. As a result, HT performance may have been overrated. To explore this issue, this study employed a discrete fracture network (DFN) model to simulate hydraulic interference tests. HT with the EPM model was then applied to estimate the distributions of hydraulic conductivity (K) and specific storage (S-s) of the DFN. Afterward, the estimated fields were used to predict the observed heads from DFN models, not used in the HT analysis (i.e., validation). Additionally, this study defined the spatial representative elementary volume (REV) of the fracture connectivity probability for the entire DFN dominant. The study showed that if this spatial REV exists, the DFN is deemed equivalent to EPM and vice versa. The hydraulic properties estimated by HT with an EPM model can then predict head fields satisfactorily over the entire DFN domain with limited monitoring wells. For a sparse DFN without this spatial REV, a dense observation network is needed. Nevertheless, HT is able to capture the dominant fractures.
    • Exploring Deep Neural Networks to Retrieve Rain and Snow in High Latitudes Using Multisensor and Reanalysis Data

      Tang, Guoqiang; Long, Di; Behrangi, Ali; Wang, Cunguang; Hong, Yang; Univ Arizona, Dept Hydrol & Atmospher Sci (AMER GEOPHYSICAL UNION, 2018-10)
      Satellite remote sensing is able to provide information on global rain and snow, but challenges remain in accurate estimation of precipitation rates, particularly in snow retrieval. In this work, the deep neural network (DNN) is applied to estimate rain and snow rates in high latitudes. The reference data for DNN training are provided by two spaceborne radars onboard the Global Precipitation Measurement (GPM) Core Observatory and CloudSat. Passive microwave data from the GPM Microwave Imager (GMI), infrared data from MODerate resolution Imaging Spectroradiometer and environmental data from European Centre for Medium-Range Weather Forecasts are trained to the spaceborne radar-based reference precipitation. The DNN estimates are compared to data from the Goddard Profiling Algorithm (GPROF), which is used to retrieve passive microwave precipitation for the GPM mission. First, the DNN-based retrieval method performs well in both training and testing periods. Second, the DNN can reveal the advantages and disadvantages of different channels of GMI and MODerate resolution Imaging Spectroradiometer. Additionally, infrared and environmental data can improve precipitation estimation of the DNN, particularly for snowfall. Finally, based on the optimized DNN, rain and snow are estimated in 2017 from orbital GMI brightness temperatures and compared to ERA-Interim and Modern-Era Retrospective analysis for Research and Applications Version 2 reanalysis data. Evaluation results show that (1) the DNN can largely mitigate the underestimation of precipitation rates in high latitudes by GPROF; (2) the DNN-based snowfall estimates largely outperform those of GPROF; and (3) the spatial distributions of DNN-based precipitation are closer to reanalysis data. The method and assessment presented in this study could potentially contribute to the substantial improvement of satellite precipitation products in high latitudes. Plain Language Summary Snow has a significant influence on the hydrological cycle and water resource availability. Compared to ground gauges and radars with limited coverage, satellite remote sensing can provide global rain and snow observations from space. However, traditional satellite precipitation retrieval methods are prone to errors in snow estimation at high latitudes. In this study, we developed a new rain and snow estimation method at high latitudes using deep neural networks. The reference data sets were from two spaceborne radars that provide the most direct precipitation observations from space. Passive microwave, infrared, and environmental data were trained to the reference data sets for rain and snow estimation. Results show that the neural network-based method can largely reduce the underestimation of rain and snow rates in high latitudes in many prior algorithms. Statistical indices for snow estimation are notably improved. Furthermore, combining data from passive microwave, infrared, and environmental data sets contribute to better precipitation estimation than a single source. We suggest that deep neural networks could potentially contribute to the improvement of satellite precipitation at high latitudes, which is valuable for expanding the spatial coverage of current satellite products.
    • Exploring the Influence of Smallholders' Perceptions Regarding Water Availability on Crop Choice and Water Allocation Through Socio-Hydrological Modeling

      Kuil, L.; Evans, T.; McCord, P. F.; Salinas, J.L.; Blöschl, G.; Univ Arizona, Sch Geog & Dev (AMER GEOPHYSICAL UNION, 2018-04)
      While it is known that farmers adopt different decision-making behaviors to cope with stresses, it remains challenging to capture this diversity in formal model frameworks that are used to advance theory and inform policy. Guided by cognitive theory and the theory of bounded rationality, this research develops a novel, socio-hydrological model framework that can explore how a farmer's perception of water availability impacts crop choice and water allocation. The model is informed by a rich empirical data set at the household level collected during 2013 in Kenya's Upper Ewaso Ng'iro basin that shows that the crop type cultivated is correlated with water availability. The model is able to simulate this pattern and shows that near-optimal or satisficing crop patterns can emerge also when farmers were to make use of simple decision rules and have diverse perceptions on water availability. By focusing on farmer decision making it also captures the rebound effect, i.e., as additional water becomes available through the improvement of crop efficiencies it will be reallocated on the farm instead of flowing downstream, as a farmer will adjust his (her) water allocation and crop pattern to the new water conditions. This study is valuable as it is consistent with the theory of bounded rationality, and thus offers an alternative, descriptive model in addition to normative models. The framework can be used to understand the potential impact of climate change on the socio-hydrological system, to simulate and test various assumptions regarding farmer behavior and to evaluate policy interventions.
    • Formulating an Elasticity Approach to Quantify the Effects of Climate Variability and Ecological Restoration on Sediment Discharge Change in the Loess Plateau, China

      Zhang, Jianjun; Gao, Guangyao; Fu, Bojie; Gupta, Hoshin V.; Univ Arizona, Dept Hydrol & Atmospher Sci (AMER GEOPHYSICAL UNION, 2019-11-11)
      Suspended sediment yields (SSY) respond strongly to ecological restoration (ER) efforts, and significant improvements in SSY control have been achieved in the Loess Plateau of China. However, it remains challenging to quantify the net impacts of ER on SSY. Here, we formulate the notion of elasticity of sediment discharge, by associating SSY change to climate variability and ER over the period 1950s to 2014. All ten of the subcatchments studied experienced significant decreases in annual SSY, streamflow, and suspended sediment concentration. Our results strongly support the hypothesis that changes to both streamflow volumes and to the suspended sediment concentration versus water discharge (C-Q) relationships result in reduced SSY, so that streamflow is reduced but runs clearer. We find that two of the ER strategies resulted in weaker relative impacts of climate variability, largely by reducing streamflow (by 55% to 75%). Meanwhile, ER predominantly decreased SSY (by 63% to 81%). Regarding ER practices, (i) the predominant measure acting to reduce SSY changed, over time, from engineering to reforestation; (ii) check dams preferentially act to regulate the C-Q relationships, whereas reforestation preferentially acts to moderate streamflow. Overall, our results suggest that a combination of engineering and vegetation measures is critical to achieving high-efficiency ER. While change to the ER strategy increased the efficiency of streamflow for SSY control, the lost water discharge per unit SSY reduction increased from 5.2 to 6.4 m(3)t(-1). Conflicting demands for water necessitate that further ER should target precision management by revegetation of targeted areas in the Loess Plateau.
    • A High-Resolution Global Map of Soil Hydraulic Properties Produced by a Hierarchical Parameterization of a Physically Based Water Retention Model

      Zhang, Yonggen; Schaap, Marcel G.; Zha, Yuanyuan; Univ Arizona, Dept Soil Water & Environm Sci (AMER GEOPHYSICAL UNION, 2018-12)
      A correct quantification of mass and energy exchange processes among Earth's land surface, groundwater, and atmosphere requires an accurate parameterization of soil hydraulic properties. Pedotransfer functions (PTFs) are useful in this regard because they estimate these otherwise difficult to obtain characteristics using texture and other ubiquitous soil data. Most PTFs estimate parameters of empirical hydraulic functions with modest accuracy. In a continued pursuit of improving global-scale PTF estimates, we evaluated whether improvements can be obtained when estimating parameters of hydraulic functions that make physically based assumptions. To this end, we developed a PTF that estimates the parameters of the Kosugi retention and hydraulic conductivity functions (Kosugi, 1994, , 1996, ), which explicitly assume a lognormal pore size distribution and apply the Young-Laplace equation to derive a corresponding pressure head distribution. Using a previously developed combination of machine learning and bootstrapping, the developed five hierarchical PTFs allow for estimates under practical data-poor to data-rich conditions. Using an independent global data set containing nearly 50,000 samples (118,000 retention points), we demonstrated that the new Kosugi-based PTFs outperformed two van Genuchten-based PTFs calibrated on the same data. The new PTFs were applied to a 1x1km(2) global map of texture and bulk density, thus producing maps of the parameters, field capacity, wilting point, plant available water, and associated uncertainties. Soil hydraulic parameters exhibit a much larger variability in the Northern Hemisphere than in the Southern Hemisphere, which is likely due to the geographical distribution of climate zones that affect weathering and sedimentation processes.
    • Hillslope Hydrology in Global Change Research and Earth System Modeling

      Fan, Y.; Clark, M.; Lawrence, D. M.; Swenson, S.; Band, L. E.; Brantley, S. L.; Brooks, P. D.; Dietrich, W. E.; Flores, A.; Grant, G.; et al. (AMER GEOPHYSICAL UNION, 2019-02)
      Earth System Models (ESMs) are essential tools for understanding and predicting global change, but they cannot explicitly resolve hillslope-scale terrain structures that fundamentally organize water, energy, and biogeochemical stores and fluxes at subgrid scales. Here we bring together hydrologists, Critical Zone scientists, and ESM developers, to explore how hillslope structures may modulate ESM grid-level water, energy, and biogeochemical fluxes. In contrast to the one-dimensional (1-D), 2- to 3-m deep, and free-draining soil hydrology in most ESM land models, we hypothesize that 3-D, lateral ridge-to-valley flow through shallow and deep paths and insolation contrasts between sunny and shady slopes are the top two globally quantifiable organizers of water and energy (and vegetation) within an ESM grid cell. We hypothesize that these two processes are likely to impact ESM predictions where (and when) water and/or energy are limiting. We further hypothesize that, if implemented in ESM land models, these processes will increase simulated continental water storage and residence time, buffering terrestrial ecosystems against seasonal and interannual droughts. We explore efficient ways to capture these mechanisms in ESMs and identify critical knowledge gaps preventing us from scaling up hillslope to global processes. One such gap is our extremely limited knowledge of the subsurface, where water is stored (supporting vegetation) and released to stream baseflow (supporting aquatic ecosystems). We conclude with a set of organizing hypotheses and a call for global syntheses activities and model experiments to assess the impact of hillslope hydrology on global change predictions. Plain Language Summary Hillslopes are key landscape features that organize water availability on land. Valley bottoms are wetter than hilltops, and sun-facing slopes are warmer and drier than shaded ones. This hydrologic organization leads to systematic differences in soil and vegetation between valleys and hilltops, and between sunny and shady slopes. Although these patterns are fundamental to understanding the structures and functions of water and terrestrial ecosystems, they are too fine grained to be represented in global-scale Earth System Models. Here we bring together Critical Zone scientists who study the interplay of vegetation, the porous upper layer of the continental crust from vegetation to bedrock, and moisture dynamics deep into the weathered bedrock underlying hillslopes and Earth System Model scientists who develop global models, to ask: Do hillslope-scale processes matter to predicting global change? The answers will help scientists understand where and why hillslopes matter, and to better predict how terrestrial ecosystems, including societies, may affect and be affected by our rapidly changing planet.
    • Hydromechanical Impacts of Pleistocene Glaciations on Pore Fluid Pressure Evolution, Rock Failure, and Brine Migration Within Sedimentary Basins and the Crystalline Basement

      Zhang, Yipeng; Person, Mark; Voller, Vaughan; Cohen, Denis; McIntosh, Jennifer; Grapenthin, Ronni; Univ Arizona, Dept Hydrol & Atmospher Sci (AMER GEOPHYSICAL UNION, 2018-10)
      The effects of Pleistocene glacial loading on rock failure, permeability increases, pore pressure evolution, and brine migration within two linked sedimentary basins were evaluated using a multiphysics control volume finite element model. We applied this model to an idealized cross section that extends across the continent of North America from the Hudson Bay to the Gulf of Mexico. Our analysis considered lithosphere geomechanical stress changes (sigma(yy) > 35 MPa) in response to 10 cycles of ice sheet loading. Hydrologic boundary conditions, lithosphere rheological properties, and aquifer/confining unit configuration were varied in a sensitivity study. We used a Coulomb Failure Stress change metric (Delta CFSp > 0.1 MPa) to increase permeability by a factor of 100 in some simulations. Results suggest that a buildup of anomalous pore pressures up to about 3 MPa occurred in confining units during periods of glaciations, but this had only a second-order effect on triggering rock failure. In regions prone to failure, permeability increases during glaciations help to explain observations of brine flushing in sedimentary basin aquifers. During the Holocene to present day, deglaciation resulted in underpressure formation in confining units primarily along the northern margin of the northern basin. Holocene-modern geomechanical stress fields were relatively small (<0.6 MPa). However, pore pressure increases associated with postglacial rebound, especially when a basal sedimentary basin aquifer is present, induced rock failure and seismicity up to 150 km beyond the terminus of the ice sheet. Sedimentary basin salinity patterns did not equilibrate after 10 simulated glacial cycles.
    • Hyper‐Resolution Continental‐Scale 3‐D Aquifer Parameterization for Groundwater Modeling

      de Graaf, Inge; Condon, Laura; Maxwell, Reed; Univ Arizona, Dept Hydrol & Atmospher Sci (AMER GEOPHYSICAL UNION, 2020-04-04)
      Groundwater is the world's most important freshwater resource. Despite this importance, groundwater flow and interactions between groundwater and other parts of the hydrological cycle are often neglected or simplified in large-scale hydrological models. One of the challenges in simulating groundwater flow and continental to global scales is the lack of consistent globally available hydrogeological data. These input data are needed for a more realistic physical representation of the groundwater system, enabling the simulation of groundwater head dynamics and lateral flows. A realistic representation of the subsurface is especially important as large-scale hydrological models move to finer resolutions and aim to provide accurate and locally relevant hydrologic information everywhere. In this study, we aim at improving and extending on current available large-scale data sets providing information of the subsurface. We present a detailed aquifer representation for the continental United States and Canada at hyper resolution (250 x 250 m). We integrate local hydrogeological information, including observations of aquifer layer thickness, conductivity, and vertical structure, to obtain representative aquifer parameter values applicable to the continental scale. The methods used are simple and can be expanded to other parts of the world. Hydrological simulations were performed using the integrated hydrological model ParFlow and demonstrated improved model performance when using the new aquifer parameterization. Our results support that more detailed and accurate aquifer parameterization will advance our understanding of the groundwater system at larger scales.
    • Implementing Dynamic Root Optimization in Noah-MP for Simulating Phreatophytic Root Water Uptake

      Wang, Ping; Niu, Guo-Yue; Fang, Yuan-Hao; Wu, Run-Jian Run-Jian; Yu, Jing-Jie; Yuan, Guo-Fu; Pozdniakov, Sergey P.; Scott, Russell L.; Univ Arizona, Dept Hydrol & Atmospher Sci; Univ Arizona, Biosphere 2 (AMER GEOPHYSICAL UNION, 2018-03)
      Widely distributed in arid and semiarid regions, phreatophytic roots extend into the saturated zone and extract water directly from groundwater. In this paper, we implemented a vegetation optimality model of root dynamics (VOM-ROOT) in the Noah land surface model with multiparameterization options (Noah-MP LSM) to model the extraction of groundwater through phreatophytic roots at a riparian site with a hyperarid climate (with precipitation of 35 mm/yr) in northwestern China. VOM-ROOT numerically describes the natural optimization of the root profile in response to changes in subsurface water conditions. The coupled Noah-MP/VOM-ROOT model substantially improves the simulation of surface energy and water fluxes, particularly during the growing season, compared to the prescribed static root profile in the default Noah-MP. In the coupled model, more roots are required to grow into the saturated zone to meet transpiration demand when the groundwater level declines over the growing season. The modeling results indicate that at the study site, the modeled annual transpiration is 472 mm, accounting for 92.3% of the total evapotranspiration. Direct root water uptake from the capillary fringe and groundwater, which is supplied by lateral groundwater flow, accounts for approximately 84% of the total transpiration. This study demonstrates the importance of implementing a dynamic root scheme in a land surface model for adequately simulating phreatophytic root water uptake and the associated latent heat flux.
    • Improved Dynamic System Response Curve Method for Real‐Time Flood Forecast Updating

      Si, Wei; Gupta, Hoshin V.; Bao, Weimin; Jiang, Peng; Wang, Wenzhuo; Univ Arizona, Dept Hydrol & Atmospher Sci (AMER GEOPHYSICAL UNION, 2019-09-02)
      The dynamic system response curve (DSRC) method has been shown to effectively use error feedback correction to obtain updated areal estimates of mean rainfall and thereby improve the accuracy of real‐time flood forecasts. In this study, we address two main shortcomings of the existing method. First, ridge estimation is used to deal with ill‐conditioning of the normal equation coefficient matrix when the method is applied to small basins, or when the length of updating rainfall series is short. Second, the effects of spatial heterogeneity of rainfall on rainfall error estimates are accounted for using a simple index. The improved performance of the method is demonstrated using both synthetic and real data studies. For smaller basins with relatively homogeneous spatial distributions of rainfall, the use of ridge regression provides more accurate and robust results. For larger‐scale basins with significant spatial heterogeneity of rainfall, spatial rainfall error updating provides significant improvements. Overall, combining the two strategies results in the best performance for all cases, with the effects of ridge estimation and spatially distributed updating complementing each other.
    • Improving Information Extraction From Simulated Discharge Using Sensitivity‐Weighted Performance Criteria

      Guse, B.; Pfannerstill, M.; Fohrer, N.; Gupta, H.; Univ Arizona, Dept Hydrol & Atmospher Sci (AMER GEOPHYSICAL UNION, 2020-07-13)
      Due to seasonal or interannual variability, the relevance of hydrological processes and of the associated model parameters can vary significantly throughout the simulation period. To achieve accurately identified model parameters, temporal variations in parameter dominance should be taken into account. This is not achieved if performance criteria are applied to the entire model output time series. Even when using complementary performance criteria, it is often only possible to identify some of the model parameters precisely. We present an innovative approach to improve parameter identifiability that exploits the information available regarding temporal variations in parameter dominance. Using daily parameter sensitivity time series, we construct a set of sensitivity-weighted performance criteria, one for each parameter, whereby periods of higher dominance of a model parameter and its corresponding process are assigned higher weights in the calculation of the associated performance criterion. These criteria are used to impose constraints on parameter values. We demonstrate this approach by constraining 12 model parameters for three catchments and examine ensemble hydrological simulations generated using these constrained parameter sets. The sensitivity-weighted approach improves in particular the identifiability for parameters whose corresponding processes are dominant only for short periods of time or have strong seasonal patterns. This results overall in slight improvement of model performance for a set of 10 contrasting performance criteria. We conclude that the sensitivity-weighted approach improves the extraction of hydrologically relevant information from data, thereby resulting in improved parameter identifiability and better representation of model parameters.
    • Improving Snow Water Equivalent Maps With Machine Learning of Snow Survey and Lidar Measurements

      Broxton, Patrick D.; van Leeuwen, Willem J.D.; Biederman, Joel A.; Univ Arizona, Sch Nat Resources & Environm; Univ Arizona, Sch Geog & Dev (AMER GEOPHYSICAL UNION, 2019-05)
      In the semiarid interior western USA, where a majority of surface water supply comes from mountain forests, high-resolution aerial lidar-based surveys are commonly used to study snow. These surveys provide rich information about snow depth, but they are usually not accompanied with spatially explicit measurements of snow density, which leads to uncertainty in the estimation of snow water equivalent (SWE). In this study, we use a novel approach to distribute similar to 300 field measurements of snow density with artificial neural networks. We combine the resulting density maps with aerial lidar snow depth measurements, bias corrected with a very large and precisely geolocated array of field-measured snow depths (similar to 4,000 observations), to create and validate maps of snow depth, snow density, and SWE over two sites along Arizona's Mogollon Rim in February and March 2017. These maps show differences between midwinter and late-winter snow conditions. In particular, compared to that of snow depth, the spatial variability of snow density is smaller for the later snow survey than the earlier snow survey. These gridded data also show that the representativeness of Snow Telemetry and other point measurements is different for the midwinter and late-winter snow surveys. Overall, the lidar artificial neural network SWE estimates can be as much as 30% different than if Snow Telemetry density were used with lidar snow depths to estimate SWE. Plain Language Summary In the western USA, a majority of surface water originates from mountain snowmelt. Knowing the quantity of water in the snowpack, called snow water equivalent (SWE), is critical for water supply forecasts and management of rivers and streams for water delivery and hydropower. In this study, we develop a new method to estimate SWE by combining aerial remote sensing maps of snow depth with snow density maps generated through machine learning of hundreds of field measurements of snow density. This study finds that on a given date, snow density can vary widely, highlighting the importance of considering its spatial variability when estimating SWE. These gridded data show that the representativeness of Snow Telemetry and other point measurements is different for the midwinter versus late winter snow surveys. In addition, we show that using spatially variable maps of snow density can impact watershed-scale SWE estimates by up to 30% as compared to using snow density measurements from commonly used snow monitoring stations. The method described in this study will be useful for generating SWE estimates for water supply monitoring, evaluating snow models, and understanding how changing mountain forests might impact SWE.
    • Investigating Runoff Efficiency in Upper Colorado River Streamflow Over Past Centuries

      Woodhouse, Connie A.; Pederson, Gregory T.; Univ Arizona, Sch Geog & Dev; Univ Arizona, Tree Ring Res Lab (AMER GEOPHYSICAL UNION, 2018-01-05)
      With increasing concerns about the impact of warming temperatures on water resources, more attention is being paid to the relationship between runoff and precipitation, or runoff efficiency. Temperature is a key influence on Colorado River runoff efficiency, and warming temperatures are projected to reduce runoff efficiency. Here, we investigate the nature of runoff efficiency in the upper Colorado River (UCRB) basin over the past 400 years, with a specific focus on major droughts and pluvials, and to contextualize the instrumental period. We first verify the feasibility of reconstructing runoff efficiency from tree-ring data. The reconstruction is then used to evaluate variability in runoff efficiency over periods of high and low flow, and its correspondence to a reconstruction of late runoff season UCRB temperature variability. Results indicate that runoff efficiency has played a consistent role in modulating the relationship between precipitation and streamflow over past centuries, and that temperature has likely been the key control. While negative runoff efficiency is most common during dry periods, and positive runoff efficiency during wet years, there are some instances of positive runoff efficiency moderating the impact of precipitation deficits on streamflow. Compared to past centuries, the 20th century has experienced twice as many high flow years with negative runoff efficiency, likely due to warm temperatures. These results suggest warming temperatures will continue to reduce runoff efficiency in wet or dry years, and that future flows will be less than anticipated from precipitation due to warming temperatures.
    • Long‐Term Hydroclimatic Patterns in the Truckee‐Carson Basin of the Eastern Sierra Nevada, USA

      Biondi, F.; Meko, D. M.; Univ Arizona, Lab Tree Ring Res (AMER GEOPHYSICAL UNION, 2019-07-08)
      The Truckee/Carson Basin, like other semiarid basins in the western United States, faces challenges to water management and planning under a changing climate. We analyzed tree-ring data, along with instrumental climatic and hydrologic records, to provide a perspective on extreme drought in the 21st century. Drought indices highlighted a recent increase in the average duration of hydroclimatic episodes: in the new millennium average duration was 74% longer for the 24-month Standardized Precipitation Index (SPI-24) and 62% longer for the Palmer Drought Severity Index (PDSI) than in the previous century. Average snow water equivalent (SWE) declined 7% per decade from 1965 to 2018. The 2012-2015 drought, in particular, stood out for its intensity and expression in snowpack, streamflow, and drought indices. Likely because of recent warming, this 4-year drought event had a very low likelihood based on observed Carson River flows from the first half of the 20th century. A 501-year tree-ring reconstruction (1500-2000 CE) of average water-year streamflow for the Carson River indicated that positive (wet) spells had slightly longer duration (mean of 2.7 years and range from 1 to 10 years) than negative (dry) intervals (mean of 2.4 years and range from 1 to 9 years). The early 1900s pluvial, that is, 1905-1911 in this record, was the third strongest episode in the entire reconstruction. The driest years were 1580 and 1934, both well-known widespread and severe droughts in the western United States. Noise-added reconstructions suggest that 2012-2015, while not unique in the 401 years prior to the start of the Carson River gaged flows in 1901, was a less than one-in-a-century event.
    • Measuring Aquifer Specific Yields With Absolute Gravimetry: Result in the Choushui River Alluvial Fan and Mingchu Basin, Central Taiwan

      Chen, Kuan‐Hung; Hwang, Cheinway; Chang, Liang‐Cheng; Tsai, Jui‐Pin; Yeh, Tian‐Chyi Jim; Cheng, Ching‐Chung; Ke, Chien‐Chung; Feng, Wei; Univ Arizona, Dept Hydrol & Atmospher Sci (AMER GEOPHYSICAL UNION, 2020-06-08)
      Due to seasonal or interannual variability, the relevance of hydrological processes and of the associated model parameters can vary significantly throughout the simulation period. To achieve accurately identified model parameters, temporal variations in parameter dominance should be taken into account. This is not achieved if performance criteria are applied to the entire model output time series. Even when using complementary performance criteria, it is often only possible to identify some of the model parameters precisely. We present an innovative approach to improve parameter identifiability that exploits the information available regarding temporal variations in parameter dominance. Using daily parameter sensitivity time series, we construct a set of sensitivity-weighted performance criteria, one for each parameter, whereby periods of higher dominance of a model parameter and its corresponding process are assigned higher weights in the calculation of the associated performance criterion. These criteria are used to impose constraints on parameter values. We demonstrate this approach by constraining 12 model parameters for three catchments and examine ensemble hydrological simulations generated using these constrained parameter sets. The sensitivity-weighted approach improves in particular the identifiability for parameters whose corresponding processes are dominant only for short periods of time or have strong seasonal patterns. This results overall in slight improvement of model performance for a set of 10 contrasting performance criteria. We conclude that the sensitivity-weighted approach improves the extraction of hydrologically relevant information from data, thereby resulting in improved parameter identifiability and better representation of model parameters.
    • Mountain‐Block Recharge: A Review of Current Understanding

      Markovich, Katherine H.; Manning, Andrew H.; Condon, Laura E.; McIntosh, Jennifer C.; Univ Arizona, Dept Hydrol & Atmospher Sci (American Geophysical Union (AGU), 2019-11-11)
      Mountain‐block recharge (MBR) is the subsurface inflow of groundwater to lowland aquifers from adjacent mountains. MBR can be a major component of recharge but remains difficult to characterize and quantify due to limited hydrogeologic, climatic, and other data in the mountain block and at the mountain front. The number of MBR‐related studies has increased dramatically in the 15 years since the last review of the topic was conducted by Wilson and Guan (2004), generating important advancements. We review this recent body of literature, summarize current understanding of factors controlling MBR, and provide recommendations for future research priorities. Prior to 2004, most MBR studies were performed in the southwestern United States. Since then, numerous studies have detected and quantified MBR in basins around the world, typically estimating MBR to be 5–50% of basin‐fill aquifer recharge. Theoretical studies using generic numerical modeling domains have revealed fundamental hydrogeologic and topographic controls on the amount of MBR and where it originates within the mountain block. Several mountain‐focused hydrogeologic studies have confirmed the widespread existence of mountain bedrock aquifers hosting considerable groundwater flow and, in some cases, identified the occurrence of interbasin flow leaving headwater catchments in the subsurface—both of which are required for MBR to occur. Future MBR research should focus on the collection of high‐priority data (e.g., subsurface data near the mountain front and within the mountain block) and the development of sophisticated coupled models calibrated to multiple data types to best constrain MBR and predict how it may change in response to climate warming.
    • On Lack of Robustness in Hydrological Model Development Due to Absence of Guidelines for Selecting Calibration and Evaluation Data: Demonstration for Data-Driven Models

      Zheng, Feifei; Maier, Holger R.; Wu, Wenyan; Dandy, Graeme C.; Gupta, Hoshin V.; Zhang, Tuqiao; Univ Arizona, Dept Hydrol & Atmospher Sci (AMER GEOPHYSICAL UNION, 2018-01-30)
      Hydrological models are used for a wide variety of engineering purposes, including streamflow forecasting and flood-risk estimation. To develop such models, it is common to allocate the available data to calibration and evaluation data subsets. Surprisingly, the issue of how this allocation can affect model evaluation performance has been largely ignored in the research literature. This paper discusses the evaluation performance bias that can arise from how available data are allocated to calibration and evaluation subsets. As a first step to assessing this issue in a statistically rigorous fashion, we present a comprehensive investigation of the influence of data allocation on the development of data-driven artificial neural network (ANN) models of streamflow. Four well-known formal data splitting methods are applied to 754 catchments from Australia and the U.S. to develop 902,483 ANN models. Results clearly show that the choice of the method used for data allocation has a significant impact on model performance, particularly for runoff data that are more highly skewed, highlighting the importance of considering the impact of data splitting when developing hydrological models. The statistical behavior of the data splitting methods investigated is discussed and guidance is offered on the selection of the most appropriate data splitting methods to achieve representative evaluation performance for streamflow data with different statistical properties. Although our results are obtained for data-driven models, they highlight the fact that this issue is likely to have a significant impact on all types of hydrological models, especially conceptual rainfall-runoff models.