Water Resources Research

Zhang, Chen; Chen, Lingwei; Brett, Michael T.

doi: 10.1029/2023wr035914pmid: N/A

Wind is a critical driving force in hydrodynamic and water quality modeling of large shallow lakes, and is characterized by the wind drag coefficient Cd, representing the momentum transfer at the air‐water interface. Contemporary empirical formulae for Cd estimation were derived over oceans and some of which are solely wind velocity U10 dependent. These formulae were previously found to be inadequate in inland lake models often resulting in the water velocity underestimation. To address this problem, a physical scale experiment was designed, in which Cd was measured using a wind profile and eddy covariance methodology. A new wind‐induced wave‐dependent Cd parameterization was also established and validated in two lake studies. The driving force was modified by the wave‐dependent Cd formula in a hydrodynamic model of the shallow Upper Klamath Lake (UKL), OR, USA. The experimental Cd was negatively correlated to the wind velocity up until the critical U10 = 1.6 m s−1 which was 1.0~3.1 times previous empirical extrapolations at light winds. The variation partitioning results showed that wave parameters contributed to more than 30% of Cd variation combined with wind parameters. The modified wind stress field was spatially heterogeneous and the modeled water velocity was closer to the observations at two sites. Significant main circulation and outer bank circulation were modeled accompanied by higher surface vorticity, compared to the original UKL model. Overall, the wave‐dependent Cd formula provided an improvement of the surface flow field in the UKL model and will improve the management of the lake ecosystems.

Hu, Xiaobo; Jiang, Yuanjun; Ning, Po; Liang, Heng; Fan, Xiaoyi; Liu, Wei; Xia, Xin; Zhu, Yuanjia

doi: 10.1029/2023wr036289pmid: N/A

Accurate prediction of shallow landslide occurrence and the subsequent motion range after transformation into debris flows is crucial for reducing disaster‐induced losses. The use of Cellular Automata‐based (CA) hydrodynamic models has seen increasing application in predicting landslides, debris flows, and other related hazards. However, previous CA‐based models have primarily focused on the motion and evolution process of debris flows, lacking detailed description about the dynamic instability associated with shallow landslides. In this study, we propose a comprehensive CA‐based model that is based on existing theories and improved models for simulating hydrological processes, sliding surface identification algorithms, threshold‐based mechanical interactions, material entrainment, and deposition. The model was applied to the Yindongzi (YDZ) landslides in Sichuan, China. The accuracy of the model was validated through comparisons with field investigation results and calculations based on shallow water equations. This model enables efficient and rapid prediction of shallow landslide occurrence time, volume, spatial distribution, and runout distance. Evaluation of the model’s predictive performance reveals an error range of −23.12% to +44.26% for YDZ landslides. Moreover, the influence of different shallow landslide failure patterns on the deposition and erosion of debris flows was analyzed. The results indicate that the failure patterns of shallow landslides significantly affect the deposition and entrainment capacity of debris flows. This study provides a novel approach for predicting the occurrence of shallow landslides and the subsequent motion, entrainment, and deposition after transformation into debris flows.

Brusseau, Mark L.; Araujo, Juliana B.; Narter, Matt; Marble, Justin C.; Bigler, Matt

doi: 10.1029/2023wr036039pmid: N/A

Synchrotron X‐ray microtomography (XMT) was used to measure total air‐water interfacial areas (Aaw) as a function of water saturation (Sw) for several soils that comprise a range of physical and geochemical properties. Measurements were also conducted for glass beads and quartz sands for comparison. Apparent near‐linear Aaw‐Sw relationships are observed for the three sands and the three sandy soils. In contrast, the measured interfacial areas for two soils that contain greater proportions of silt and clay are strongly nonlinear functions of water saturation. The greater degree of nonlinearity observed for these two soils is due to their much greater particle‐size distributions (i.e., uniformity coefficients) and their concomitant greater range in pore sizes. Interfacial areas determined with the thermodynamic method were used to benchmark the XMT measurements. XMT‐measured interfacial areas compare well to the thermodynamic‐determined values for the sands and sandy soils. In contrast, the XMT‐measured interfacial areas for the two soils with larger particle‐size distributions are not fully congruent with the thermodynamic‐determined values. Both of these soils have large fractions of pore space comprising nominal pore diameters smaller than the resolution of the XMT imaging. These results suggest that air‐water interfacial area may not always be fully characterized by standard XMT for soils with large particle‐size distributions.

Scaff, Lucia; Krogh, Sebastian A.; Musselman, Keith; Harpold, Adrian; Li, Yanping; Lillo‐Saavedra, Mario; Oyarzún, Ricardo; Rasmussen, Roy

doi: 10.1029/2023wr034492pmid: N/A

An increase in winter air temperature can amplify snowmelt and sublimation in mountain regions with implications to water resources and ecological systems. Winter Warm Spells (WWS) are defined as a winter period (December to February, DJF) of at least 3 consecutive days with daily maximum temperature anomaly above the 90th percentile (using a moving‐average of 15 days between 2001 and 2013). We calculate WWS for every 4‐km grid cell within an atmospheric model over western North America to characterize WWS and analyze snow ablation and their changes in a warmer climate. We find that days with ablation during WWS represent a small fraction of winter days (0.6 days), however, 49% of total winter ablation (33.4 mm/DJF) occurs during WWS. Greater extreme ablation rates (99th percentile) occur 18% more frequently during WWS than during non‐WWS days. Ablation rates during WWS in humid regions are larger (9 mm d−1) than in dry regions (7 mm d−1) in a warmer climate, which can be explained by differences in the energy balance and the snowpack's cold content. We find that warmer (0.8°C), longer (1.8 days) and more frequent (3.7 more events) WWS increase total winter ablation (on average 109% or 18 mm/DJF) in a warmer climate. Winter melt during WWS in warm and humid places is expected to increase about 3 times more than in the cold and dry region. This study provides a comprehensive description of WWS and their impact on snowpack dynamics, which is relevant to reservoir operations and water security.

Zhang, Yonggang; Cheng, Jinhua; Hassan, Marwan A.; Wang, Ping; Wu, Zi

doi: 10.1029/2023wr036535pmid: N/A

Widely distributed in natural rivers and coasts, vegetation interacts with fluid flows and sediments in a variable and complicated manner. Such interactions make it difficult to predict associated drag forces during sediment transport. This paper investigates the drag coefficient for an emergent vegetated patch area under nonuniform flow and mobile bed conditions, based on an analytical model solving the momentum equation following our previous work (Zhang et al., 2020, https://doi.org/10.1029/2020WR027613). Emergent vegetation was modeled with rigid cylinders arranged in staggered arrays of different vegetation coverage ∅. Laboratory flume tests were conducted to measure variations in both the water and bed surfaces along a vegetated patch on a sand bed. Based on the experimental and theoretical analyses, a dimensionless drag model integrating both terms of flow properties and bed effects is proposed to predict the drag coefficient Cd over a mobile bed. The calculated values of Cd exhibit two different trends, that is, nonmonotonically or monotonically increasing along the streamwise direction, due to the combined effect of water surface gradient and bed slope. The morphodynamic response of the mobile bed to nonuniform flow manifests as an evolution in the bed slope within the vegetated patch. Ongoing scouring directs the flow's energy toward overcoming the rising Cd and bed slope, leading to a relatively stable stage with a low sediment transport rate. This study advances the existing understanding of the drag coefficient's role over a mobile bed within nonuniform flows. It also enhances the applicability of vegetation drag models in riverine restoration.

Guo, Yuxue; Xu, Yue‐Ping; Yu, Xinting; Liu, Li; Gu, Haiting

doi: 10.1029/2023wr035693pmid: N/A

Informing reservoirs with forecasts is highly important for real‐time flood control. This study proposed a forecast‐informed methodology framework for reservoir flood control operation under uncertainty. A new combination of two post‐processing methods, that is, the Cloud model and error‐based copula functions, were developed to merge individual AI‐based forecasts to ensemble flood forecasts, so called stochastic errors‐based Cloud (SE‐Cloud). A multi‐objective robust optimization model (MRO) integrating the risk, resilience, and vulnerability was then proposed to tackle flood control problems under ensemble forecasts; for comparison, a two‐objective stochastic optimization model (TSO) was developed to minimize the expected highest reservoir level and peak release. The proposed methodology was applied to the Lishimen reservoir in the Shifeng River subbasin, China, aiming to comprehensively verify the relationships among deterministic forecasts, ensemble forecasts, and flood control performance. Results showed that the Cloud model could effectively integrate different models and improve forecast accuracy. But a higher deterministic forecast quality did not consistently result in improved flood control performance. SE‐Cloud could capture the peak flow and effectively characterize forecast uncertainties and increased hypervolume values by 13.14%–39.65% compared to the Cloud model, indicating the superiority of ensemble forecasts in generating robust solutions over individual deterministic forecasts. MRO released more inflow than TSO, decreasing the expected highest water level by 0.05 m and incrementing the expected peak release by 4.29%. However, with downstream resilience value remaining at zero, it is demonstrated that MRO improving upstream vulnerability did not necessarily diminish resilience. The enhanced robustness highlights the potential of AI‐based ensemble forecasts in flood control.

Destouni, Georgia; Bierkens, Marc; Castelletti, Andrea; Fatichi, Simone; Islam, Shafiqul; Jha, Madan Kumar; Kollet, Stefan; Miao, Chiyuan; Moradkhani, Hamid; Sanchez‐Vila, Xavier; Sawyer, Audrey; Singha, Kamini; Stahl, Kerstin; Troch, Peter; Wohl, Ellen

doi: 10.1029/2024wr037727pmid: N/A

Gharari, Shervan; Vanderkelen, Inne; Tefs, Andrew; Mizukami, Naoki; Kluzek, Erik; Stadnyk, Tricia; Lawrence, David; Clark, Martyn P.

doi: 10.1029/2022wr032400pmid: N/A

Lakes and reservoirs are an integral part of the terrestrial water cycle. In this work, we present the implementation of different water balance models of lakes and reservoirs into mizuRoute, a vector‐based routing model, termed mizuRoute‐Lake. As the main advantage of mizuRoute‐Lake, users can choose between various parametric models implemented in mizuRoute‐Lake. So far, three parametric models of lake and reservoir water balance, namely Hanasaki, HYPE, and Döll are implemented in mizuRoute‐Lake. In general, the parametric models relate the outflow from lakes or reservoirs to the storage and various parameters including inflow, demand, volume of storage, etc. Additionally, this flexibility allows users to easily evaluate and compare the effect of various water balance models for a lake without needing to reconfigure the routing model or change the parameters of other lakes or reservoirs in the modeling domain. Users can also use existing data such as historical observations or water management models to specify the behavior of a selected number of lakes and reservoirs within the modeling domain using the data‐driven capability of mizuRoute‐Lake. We demonstrate the flexibility of mizuRoute‐Lake by presenting global, regional, and local scale applications. The development of mizuRoute‐Lake paves the way for better integration of water management models, locally measured, and remotely sensed data sets in the context of Earth system modeling.

Hsueh, Han‐Fang; Guthke, Anneli; Wöhling, Thomas; Nowak, Wolfgang

doi: 10.1029/2022wr033280pmid: N/A

Hydrogeological models require reliable uncertainty intervals that honestly reflect the total uncertainties of model predictions. The operation of a conventional Bayesian framework only produces realistic (interpretable in the context of the natural system) inference results if the model structure matches the data‐generating process, that is, applying Bayes' theorem implicitly assumes the underlying model to be true. With an imperfect model, we may obtain a too‐narrow‐for‐its‐bias uncertainty interval when conditioning on a long time‐series of calibration data, because the assumption of a quasi‐true model becomes too strict. To overcome the problem of overconfident posteriors, we propose a non‐parametric Bayesian method, called Tau‐averaging method: it applies Bayesian analysis on sliding time windows along the data time series for calibration. Thus, it obtains so‐called transitional posteriors per time window. Then, we average these into a wider predictive posterior. With the proposed routine, we explicitly capture the time‐varying impact of model error on prediction uncertainty. The length of the calibration window is optimized to maximize goal‐oriented statistical skill scores for predictive coverage. Our method loosens the perfect‐model‐assumption by conditioning only on small windows of the data set at a time, that is, it assumes that “the model is sufficient to follow the system dynamics for a smaller duration.” We test our method on two cases of soil moisture modeling and show how it improves predictive coverage as compared to the conventional Bayesian approach. Our findings demonstrate that the proposed method convincingly overcomes the overconfidence drawback of Bayesian inference under model misspecification and long calibration time‐series.

Karan, Sachin; Jensen, Karsten H.; Sonnenborg, Torben O.

doi: 10.1029/2023wr036557pmid: N/A

With modern agricultural practices, it is essential to quantify flow and solute transport fluxes by numerical models and associated predictions. A major challenge in modeling preferential flow settings is the ability to constrain the often numerous parameters needed to physically represent these systems. Following this, there is a lack of understanding of what parameters and observations carry the most worth for a model to reduce its prediction uncertainty. Here, first‐order second moment (FOSM) analyses were used for a heavily monitored clay till field with preferential flow to investigate which parameters and observation types contribute the most to reducing the uncertainty of bromide predictions at various depths. Using a multi‐objective regularized optimization approach, a 1‐D preferential flow model was calibrated and subjected to FOSM analyses. Key parameters contributing to the prediction uncertainty of bromide concentrations in 0.25–3 m were limited to the lower boundary condition, the mass transfer coefficient, the hydraulic conductivity of the micro‐ and macropore, the macropore porosity, and the water content at wilting point. The data with the largest worth and ability to reduce the pre‐calibration prediction uncertainty were concentration observations closest to the sought prediction depth, drain concentrations, and averaged water table measurements from the entire field. The post‐calibration prediction uncertainty was increased primarily by removing concentration observations closest to the prediction depths. While this study provided new insights into parameter importance and data worth further research is required to understand if these findings apply broadly to clay till settings (or any soil setting) with preferential flow.