Inverse problems are ubiquitous in hydrological modelling for parameter estimation, system understanding, sustainable water resources management, and the operation of digital twins. While statistical inversion is especially popular, its sampling-based nature often inhibits the inversion of computationally costly models, which has compromised the use of the Generalized Likelihood Uncertainty Estimation (GLUE) methodology, e.g., for spatially distributed (partial) differential equation based models. In this study we introduce multilevel GLUE (MLGLUE), which alleviates the computational burden of statistical inversion by utilizing a hierarchy of model resolutions. Inspired by multilevel Monte Carlo, most parameter samples are evaluated on lower levels with computationally cheap low-resolution models and only samples associated with a likelihood above a certain threshold are subsequently passed to higher levels with costly high-resolution models for evaluation. Inferences are made at the level of the highest-resolution model but substantial computational savings are achieved by discarding samples with low likelihood already on levels with low resolution and low computational cost. Two test problems demonstrate the similarity of inferred parameter posteriors and uncertainty estimates of MLGLUE and GLUE as well as increased computational efficiency. Findings are furthermore compared to inversion results from Markov-chain Monte Carlo (MCMC) and from multilevel delayed acceptance MCMC. The computation time of inversion of a groundwater flow model was decreases by ≈45% and ≈57% when using MLGLUE instead of conventional formulations of GLUE and MCMC, respectively.

The Horn of Africa region has frequently been affected by severe droughts and food crises over the last several decades, and this will increase under projected global-warming and socio-economic pathways. Therefore, exploring novel methods of increasing early warning capabilities is of vital importance to reducing food-insecurity risk. In this study, we present the XGBoost machine-learning model to predict food-security crises up to 12 months in advance. We used >20 datasets and the FEWS IPC current-situation estimates to train the machine-learning model. Food-security dynamics were captured effectively by the model up to three months in advance (R2 > 0.6). Specifically, we predicted 20% of crisis onsets in pastoral regions (n = 84) and 40% of crisis onsets in agro-pastoral regions (n = 23) with a 3-month lead time. We also compared our 8-month model predictions to the 8-month food-security outlooks produced by FEWS NET. Over a relatively short test period (2020–2022), results suggest the performance of our predictions is similar to FEWS NET for agro-pastoral and pastoral regions. However, our model is clearly less skilled in predicting food security for crop-farming regions than FEWS NET. With the well-established FEWS NET outlooks as a basis, this study highlights the potential for integrating machine-learning methods into operational systems like FEWS NET.

Causal loop diagrams (CLDs) based on expert and/or stakeholder inputs inform the quantitative structure of socio-hydrological models (SHMs). However, a systematic exploration of the sensitivity of CLDs and SHMs to different levels of stakeholder inputs is lacking. For a large multi-purpose reservoir in southern India, we explore this sensitivity by developing three CLDs that integrate reservoir water balance, groundwater pumping, and consumer water use patterns. CLD1 is a conventional water balance-based reservoir model, while CLD2 additionally incorporates the reservoir operatorâ\euro™s judgment and groundwater pumping. CLD3 further incorporates the adaptive behavior of water users by adjusting demands in response to long-term (5-year) droughts. The correlation between observed and simulated monthly reservoir storage (2000-2013) for SHM1, SHM2, and SHM3 is 0.57, 0.85, and 0.87, respectively. SHM3 also outperforms SHM1 and SHM2 in simulating the relative use of surface and groundwater for irrigation purposes in the command area of the reservoir. Simulated demand deficits, command area groundwater levels, and minimum environmental flow satisfaction downstream of the reservoir for 1968-2013 using the three models exhibit substantial differences. SHM1 and SHM2 simulate deteriorating groundwater levels under the multi-year drought of 2001-2003 while SHM3 does not due to the consideration of adaptive farmer behavior. Thus, our understanding of water and food security during a multi-year drought can be significantly affected by the level of stakeholder inputs incorporated in the models. We highlight the importance of testing different SHMs structures to better understand human-water interactions under extreme conditions.

A document by G. Aaron Alexander. Click on the document to view its contents.

Channel networks across fluvial landscapes are believed to have evolved to minimize energy expenditure[1–3], as evidenced by the similarities between computer-generated optimal channel networks (OCNs) and real networks[4,5]. However, the specific mechanisms driving energy minimization in fluvial landscapes remain largely elusive[6]. Here we propose that randomness has a profound role in landscape evolution[7] and that efficient channel networks emerge when the probability of a channel pixel changing its flow direction decreases with drainage area. The proposed probabilistic growth model then employs a power function to simulate channel-network evolution, with positive exponent (𝜂) values leading to asymptotic decrease of energy expenditure. An interpretation of this result is energy minimization tendency of river networks is a result of landscape evolution following specific adaptive rules rather than being the cause of landscape evolution itself. A greater 𝜂 ensures a greater restriction on the role of randomness and thus results in a more stable channel network configuration, and vice versa. Interestingly, the most efficient networks are observed to emerge always at 𝜂 =0.5, suggesting that randomness plays an important but limited role in the emergence of efficient channel networks. The proposed framework holds promise for explaining the evolution of other tree-like networks in nature and for developing more efficient optimization methods for practical applications.

A reliable understanding of linkages between meteorological, hydrological and agricultural droughts (MD, HD and AD respectively) is crucial to building resilience and planning for future climate changes. Despite Australia being prone to severe droughts, lagtimes of propagation (and recovery) from meteorological to hydrological and agricultural droughts across its large hydroclimatic regions are not well understood. Therefore, we investigate the characteristics of drought propagation and recovery time lags for droughts of four timescales and a combination of drought onset and cessation criteria in 407 unregulated catchments within six major precipitation zones across Australia. We find that the propagation and recovery lags are dependent on climatic conditions, drought criteria and timescales. The average propagation times from MD to HD across Australia varied from 0.8 to 1.7 months for monthly timescales, increasing to 2 to 4.5 months for 12-monthly timescales. The corresponding recovery lagtimes were 1.3 to 3.7 and 1.7 to 7.5 months respectively. Similarly, the average propagation times from MD to AD ranged from 0.9 to 1.9 months for monthly timescales, increasing to 0.8 to 5 months for 12-monthly timescales. The corresponding recovery lagtimes were 0.7 to 2.8 and 0.3 to 9.4 months respectively. For droughts of smaller timescales, propagation and recovery lags are linearly correlated with recovery lagtimes consistently greater than the propagation times. As the timescale increases, these relationships weaken suggesting effects of other catchment attributes (e.g. groundwater contributions) on lag relationships. Notably, recovery lagtimes are generally longer for the high-yielding catchments in eastern Australia compared to the other regions

This study presents a novel approach to improving the accuracy of flood forecast models with limited training data. Flood forecast information is crucial for early evacuation planning. However, the probability of flooding caused by continuous heavy rainfall is increasing, even in areas where we have not installed flood forecasts. New methods exist to provide flood forecasts, but they require long-term observations and regular updating of extensive data on the basin. Existing methods of providing new flood forecast information require long-term observations and regular updates of extensive data on the watershed. These requirements are related to the construction time and cost of providing flood forecasts. To address this issue, we propose Informed Neural Networks (INN) that integrate existing domain knowledge of river engineering to enhance the performance of flood forecasts with a limited amount of training data. We evaluated the performance of our proposed method with Japanese real-world river water levels and compared it to conventional methods such as artificial neural networks (ANN). Our results demonstrate that the INN can significantly improve forecasting accuracy with only a small amount of training data, comparable to conventional methods trained with eight times the amount of flood data. This study highlights the potential of INN as a novel approach for accurate and efficient flood forecasting with limited training data.

This work is published in Water Resources Research.     https://doi.org/10.1029/2023WR035013

Streambed grain sizes and hydro-biogeochemistry (HBGC) control river functions. However, measuring their quantities, distributions, and uncertainties is challenging due to the diversity and heterogeneity of natural streams. This work presents a photo-driven, artificial intelligence (AI)-enabled, and theory-based workflow for extracting the quantities, distributions, and uncertainties of streambed grain sizes and HBGC parameters from photos. Specifically, we first trained You Only Look Once (YOLO), an object detection AI, using 11,977 grain labels from 36 photos collected from 9 different stream environments. We demonstrated its accuracy with a coefficient of determination of 0.98, a Nash–Sutcliffe efficiency of 0.98, and a mean absolute relative error of 6.65% in predicting the median grain size of 20 testing photos. The AI is then used to extract the grain size distributions and determine their characteristic grain sizes, including the 5th, 50th, and 84th percentiles, for 1,999 photos taken at 66 sites. With these percentiles, the quantities, distributions, and uncertainties of HBGC parameters are further derived using existing empirical formulas and our new uncertainty equations. From the data, the median grain size and HBGC parameters, including Manning’s coefficient, Darcy-Weisbach friction factor, interstitial velocity magnitude, and nitrate uptake velocity, are found to follow log-normal, normal, positively skewed, near log-normal, and negatively skewed distributions, respectively. Their most likely values are 6.63 cm, 0.0339 s·m-1/3, 0.18, 0.07 m/day, and 1.2 m/day, respectively. While their average uncertainty is 7.33%, 1.85%, 15.65%, 24.06%, and 13.88%, respectively. Major uncertainty sources in grain sizes and their subsequent impact on HBGC are further studied.

A document by Ghazal Shabestanipour. Click on the document to view its contents.

The land-lake interface is a unique zone where terrestrial and aquatic ecosystems meet, forming part of the Earth’s most geochemically and biologically active zones. The unique characteristics of this interface are yet to be properly understood due to the inherently high spatiotemporal variability of subsurface properties, which are difficult to capture with the traditional soil sampling methods. Geophysical methods offer non-invasive techniques to capture variabilities in soil properties at a high resolution across various spatiotemporal scales. We combined electromagnetic induction (EMI), electrical resistivity tomography (ERT), and ground penetrating radar (GPR) with data from soil cores and in-situ sensors to investigate hydrostratigraphic heterogeneities across land-lake interfaces along the western basin of Lake Erie. Our Apparent electrical conductivity (ECa) maps matched soil maps from a public database with the hydric soil units delineated as high conductivity zones (ECa > 40 mS/m) and also detected additional soil units that were missed in the traditional soil maps. This implies that electromagnetic induction (EMI) could be relied upon for non-invasive characterization of soils in sampling-restricted sites where only non-invasive measurements are feasible. Results from ERT and GPR are consistent with the surficial geology of the study area and revealed variation in the vertical silty-clay and till sequence down to 3.5 m depth. These results indicate that multiple geophysical methods can be used to extrapolate soil properties and map stratigraphic structures at land-lake interfaces, thereby providing the missing information required to improve the earth system model (ESM) of coastal interfaces.

Functional relationships capture how variables co-vary across specific spatial or temporal domains. However, these relationships often take complex forms beyond linear, and they may only hold for sub-sets of the domain. More problematically, it is often a priori unknown how such sub-domains are defined. Here we present a new method called SONAR (diScovery Of fuNctionaAl Relationships) that enables the automated discovery of functional relationships in large datasets. SONAR operates on existing unstructured data and is designed to be an explorative tool for large datasets where manual search for functional relationships would be impossible. We test the method on groundwater recharge outputs of several global hydrological models to explore its usefulness and limitations. Further, we compare SONAR to the established CART (Classification and Regression Trees) and CIT (Conditional Inference Trees) methods. SONAR results in smaller trees with functional relationships in the leaf nodes instead of specific classes or numbers. SONAR provides a robust and automated method for the exploration of functional relationships.

The Gotvand dam was built on the most important Iranian river to support a number of populated cities with freshwater, provide irrigation water for million hectares of fertile farmlands, and meet water demand for the country’s hub industrial zones. This dam is known as one of the worst engineering failures in Iran’s history because its impoundment submerged the enormous salty unit of Gachsaran evaporite formation (GEF) outcropped in the reservoir, leading to reservoir water salinization in deep layers up to several times greater than that of in the high-seas. Given the failed practical application of direct intervention strategies to control the salinity crisis, we suggested a low-cost salinity management strategy based on the reservoir operation to mitigate the dam outlet salinity and preserve the downstream environment from the salinity hazards. The three-dimensional MIKE3 model, was run to calculate the GEF dissolution rate, accumulated salt in the reservoir, and the dam outlet salinity. Then, we ran the model considering different outlet salinity levels to explore the best reservoir operation strategy to prohibit the accumulated salt in the reservoir and keep the safe salinity for downstream irrigation-use. Simulation results suggested that the GEF dissolution rate varied from 0.5 to 7 cm/hr, mainly due to incremental submergence of the GEF during multi-stage impoundment of the reservoir. Considering the final dissolution rate of 0.5 cm/hr and inlet salinity from the upstreams, salt accumulation inside the reservoir can be gradually prevented by setting the outlet salinity to its maximum historical downstream level, i.e., 1400 µmhos/cm.

Global flood mapping has developed rapidly over the past decade, but previous approaches have limited scope, function, and accuracy. These limitations restrict the applicability and fundamental science questions that can be answered with existing model frameworks. Harnessing recently available data and modelling methods, this paper presents a new global ~30 m resolution Global Flood Map (GFM) with complete coverage of fluvial, pluvial, and coastal perils, for any return period or climate scenario, including accounting for uncertainty. With an extensive compilation of global benchmark case studies – ranging from locally collected event water levels, to national inventories of engineering flood maps – we execute a comprehensive validation of the new GFM. For flood extent comparisons, we demonstrate that the GFM achieves a critical success index of ~0.75. In the more discriminatory tests of flood water levels, the GFM deviates from observations by ~0.6 m on average. Results indicating this level of global model fidelity are unprecedented in the literature. With an optimistic scenario of future warming (SSP1-2.6), we show end-of-century global flood hazard increases are limited to 9% (likely range -6–29%); this is within the likely climatological uncertainty of -8–12% in the current hazard estimate. In contrast, pessimistic scenario (SSP5-8.5) hazard changes emerge from the background noise in the 2040s, rising to a 49% (likely range of 7–109%) increase by 2100. This work verifies the fitness-for-purpose of this new-generation GFM for impact analyses with a variety of beneficial applications across policymaking, planning, and commercial risk assessment.

In this study, we explore the relationship between anvil cloud fraction and horizontal model resolution in small domain radiative-convective equilibrium (RCE) simulations, building on the findings of \citeA{jeevanjee22}. Using the System of Atmosphere Modeling (SAM) model, we find that finer resolutions yield higher anvil cloud fractions due to larger convective updrafts mass flux and increased mass detrainment at anvil levels. Employing two different microphysics schemes, we illustrate that finer resolution can enhance mass flux through either stronger cloud evaporation or weaker upper-troposphere stability, as the consequence of enhanced horizontal mixing. Moreover, we refine an analytical zero-buoyancy plume model to investigate the effects of adjusting entrainment rate and evaporation rate on vertical atmosphere profiles in a simple theoretical framework. Our solutions of the zero-buoyancy plume model suggest that stronger horizontal mixing can lead to larger convective updraft mass flux, consistent with the analysis in numerical simulations. We also observe the likelihood of atmospheric profiles converging at a grid size of approximately 100m, potentially as a result of converging entrainment rate and mixing strength. These insights have implications for global storm-resolving simulations, implying a possible convergence of high cloud and deep convection properties as the horizontal resolution approaches around 100m.

Porous materials, such as carbonate rocks, frequently have pore sizes which span many orders of magnitude. This is a challenge for models that rely on an image of the pore space, since much of the pore space may be unresolved. There is a trade off between image size and resolution. For most carbonates, to have an image sufficiently large to be representative of the pore structure, many fine details cannot be captured. In this work, sub-resolution porosity in X-ray images is characterized using differential imaging which quantifies the difference between a dry scan and 30 wt\% KI brine saturated rock images. Once characterized, we develop a robust workflow to incorporate the sub-resolution pore space into network model using Darcy-type elements called micro-links. Each grain voxel with sub-resolution porosity is assigned to the two nearest resolved pores using an automatic dilation algorithm. By including these micro-links with empirical models in flow modeling, we simulate single-phase and multiphase flow. By fine-tuning the micro-link empirical models, we achieve effective permeability, formation factor, and drainage capillary pressure predictions that align with experimental results. We then show that our model can successfully predict steady-state relative permeability measurements on a water-wet Estaillades carbonate sample within the uncertainty of the experiments and modeling. Our approach of incorporating sub-resolution porosity in two-phase flow modeling using image-based multiscale pore network techniques can capture complex pore structures and accurately predict flow behavior in porous materials with a wide range of pore size.

Accurate global river discharge estimation is crucial for advancing our scientific understanding of the global water cycle and supporting various downstream applications. In recent years, data-driven machine learning models, particularly the Long Short-Term Memory (LSTM) model, have shown significant promise in estimating discharge. Despite this, the applicability of LSTM models for global river discharge estimation remains largely unexplored. In this study, we diverge from the conventional basin-lumped LSTM modeling in limited basins. For the first time, we apply an LSTM on a global 0.25° grid, coupling it with a river routing model to estimate river discharge for every river reach worldwide. We rigorously evaluate the performance over 5332 evaluation gauges globally for the period 2000-2020, separate from the training basins and period. The grid-scale LSTM model effectively captures the rainfall-runoff behavior, reproducing global river discharge with high accuracy and achieving a median Kling-Gupta Efficiency (KGE) of 0.563. It outperforms an extensively bias-corrected and calibrated benchmark simulation based on the Variable Infiltration Capacity (VIC) model, which achieved a median KGE of 0.466. Using the global grid-scale LSTM model, we develop an improved global reach-level daily discharge dataset spanning 1980 to 2020, named GRADES-hydroDL. This dataset is anticipated to be useful for a myriad of applications, including providing prior information for the Surface Water and Ocean Topography (SWOT) satellite mission. The dataset is openly available via Globus.

It is increasingly acknowledged that the acceleration of the global water cycle, largely driven by anthropogenic climate change, has a disproportionate impact on sub-daily and small-scale hydrological extreme events such as flash floods. These events occur thereby at local scales within minutes to hours, typically in response to high-intensity rainfall events associated with convective storms. Despite their local scale and rapid onset, the effects of flash floods can be devastating, making their prediction and mitigation of critical importance. However, the modeling and analysis of such events in data-scarce regions present a unique set of challenges. In the present work, we show that by employing physically based representative hillslope models that resolve the main gradients controlling overland flow hydrology and hydraulics, we can get reliable simulations of flash flood response in small data-scarce catchments. To this end, we use climate reanalysis products and transfer soil parameters previously obtained for hydrological predictions in an experimental catchment in the same landscape. The inverted mass balance of flood reservoirs downstream is employed to derive a target data set for model evaluation in these nearly ungauged basins. We show that our approach using representative hillslopes and climate datasets can provide reasonable uncalibrated estimates of the overland runoff response in three of the four catchments considered. Given that flash floods typically occur at scales of a few km2 and in ungauged places, our results have implications for operational flash flood forecasting and the design of small and medium flood retention basins around the world.

Himalayan rivers are prone for drying subject to continuous drop in the glacial meltwater contribution and groundwater level. The present study is conducted in the region of West Kameng District of Arunachal Pradesh, India, covering catchment of 2 tributaries of Kameng river, viz., Tenga & Dirang-Bichom. We used stable isotopic method to trace the origin of water feeding the river as it flows from the headwater to the region of flood plain. Our observation allowed defining the Local Water line (LWL) in this region for the dry period, with the relation δD = (8.1 ± 0.3)×δ 18O + (11.6 ± 2.5‰). This equation is derived from analysis of river water sample collected during March 2021. This LWL is identical to that reconstructed using the monthly (from India, April – October 2007 ) precipitation isotope data from a station at Mawlong, Meghalaya, [δD = (8.1 ± 0.1) ×δ 18O + (11.8 ± 0.9) ‰]. The d-excess values from the two set of data are similar at 11.2± 1.8 ‰ and 11.3± 2.7‰, respectively, implying that river water is mainly derived from rainwater. Such coincidence of observation is interpreted as a common source of water for river and groundwater. Further, we compared present observation with other studies on the surface water composition in other Himalayan River systems and documented a consistent elevation pattern for stable isotopes. Our observation on spatial variability showed maximum altitude effect in the North-Western Himalaya and drop in isotope ratios with height with pronounced participation of recycled moisture in the Eastern Himalaya with presence of terrestrial biosphere.

Spontaneous imbibition flows within confined geometries are commonly encountered in both natural phenomena and industrial applications. A profound knowledge of the underlying flow dynamics benefits a broad spectrum of engineering practices. Nonetheless, within this area, especially concerning complex geometries, there exists a substantial research gap. This work centers on the cylinder-plane geometry, employing a combined theoretical and numerical approach to investigate the process of a wetting film wrapping a cylinder corner. It is found that the advance of the liquid front generally follows the Lucas-Washburn kinetics, i.e., $\thalf$ scaling, but it also depends on the dynamics of the liquid source. Furthermore, we provide a theoretical estimation of the timescale associated with the imbibition process. Notably, this timescale is highly dependent on the wettability condition and the properties of the involved liquid. Importantly, the practicability of our theoretical framework is well confirmed by the numerical experiments.

A document by Shelley Stall. Click on the document to view its contents.

The Lattice-Boltzmann (LB) method is applied here for the first time to simulate bedform-induced hyporheic exchange flow in a reduced complexity model. The flexibility of the LB allows surface and hyporheic flows to be resolved together, in contrast to other approaches for similar model domains, in which surface flow is usually solved independently, and then the solution of the surface flow provides the boundary conditions to model the hyporheic exchange flow. At the same time, the superior computational efficiency of LB allows the use of Large Eddy Simulations within transient simulations. Numerical results show a faithful reproduction of pressure along the bedform surface—especially, the pressure drop leeward to the dune. Results also show short-time-dependent phenomena which were previously described only in the context of DNS studies over reduced-size computational domains. Short-time-dependent phenomena include pressure oscillations and time-dependence of hyporheic zone morphology, with the latter eventually extending beyond the limits of a single bedform element.

Heavy-tailed flood distributions depict the higher occurrence probability of extreme floods. Understanding the spatial distribution of heavy tail floods is essential for effective risk assessment. Conventional methods often encounter data limitations, leading to uncertainty across regions. To address this challenge, we utilize hydrograph recession exponents derived from common streamflow dynamics, which have proven to be a robust indicator of flood tail propensity across analyses with varying data lengths. Analyzing extensive datasets from Germany, the United Kingdom (UK), Norway, and the United States (US), we uncover distinct patterns: prevalent heavy tails in Germany and the UK, diverse behavior in the US, and predominantly nonheavy tails in Norway. The regional tail behavior has been observed in relation to the interplay between terrain and meteorological characteristics, and we further conducted quantitative analyses to assess the influence of hydroclimatic conditions using Köppen classifications. Notably, temporal variations in catchment storage are a crucial mechanism driving highly nonlinear catchment responses that favor heavy-tailed floods, often intensified by concurrent dry periods and high temperatures. Furthermore, this mechanism is influenced by various flood generation processes, which can be shaped by both hydroclimatic seasonality and catchment scale. These insights deepen our understanding of the interplay between climate, physiographical settings, and flood behavior, while highlighting the utility of hydrograph recession exponents in flood hazard assessment.

Global warming has led to significant glacier retreat around the Tarim River Basin. This has resulted in a rise in water resources in southern Xinjiang. Meanwhile, the development of human society has driven a substantial increase in water consumption. This has disrupted the regional water supply-demand balance, making the risk of water resource stress more prominent. Given the characteristics of water resources utilization in arid areas and taking into account the changing trends in precipitation, glacial meltwater, and runoff, along with population and economic development, we employed the water stress index method to assess the current situation and potential future changes in water stress in the three regions of southern Xinjiang. The results indicated the following: The synergistic effects of precipitation and glacial meltwater have significantly increased river runoff, resulting in increased available water. The total water demand in the Aksu and Kashgar regions has shown a substantial increase, while the Hotan region has experienced a decrease. The Aksu and Kashgar regions have exhibited an upword trend in water stress, while the Hotan region has seen some relief. Nevertheless, all the three regions still face high water stress levels. In comparison to the historical period (2000-2020), the available water and total water demand are projected to increase during the next four periods (2030s, 2050s, 2070s and 2090s) under the SSP2-4.5 and SSP5-8.5 scenarios of the CMIP6 model. Notably, the Aksu region is expected to face increasing water stress, indicating a significant risk of water scarcity and insecurity in the future.