A document by Fabian Quichimbo. Click on the document to view its contents.

Forecasting hydroclimatic extremes holds significant importance considering the increasing trends in natural cascading climate-induced hazards such as wildfires, floods, and droughts. This study evaluates the performance of five Copernicus Climate Change Service (C3S) seasonal forecast models (i.e., CMCC, DWD, ECCC, UK-Met, and Météo-France) in predicting extreme precipitation events from 1993 to 2016 using 28 extreme precipitation indices reflecting timing and intensity of precipitation in a seasonal timescale. We design indices using various precipitation thresholds to reflect model skill in capturing the distribution and intensity of precipitation over a season. We use percentage bias, the Kendall Tau rank correlation, and ROC scores for skill evaluation. We introduce an impact-based framework to evaluate model skill in capturing extreme events over regions prone to natural disasters such as floods and wildfires. The performance of models varies across regions and seasons. The model skill is highlighted primarily in the tropical and inter-tropical regions, while skill in extra-tropical regions is markedly lower. Elevated precipitation thresholds correlate with heightened model bias, revealing deficiencies in modelling severe precipitation events. The impact-based framework analysis highlights the superior predictive capabilities of the UK-Met and Météo-France models for extreme event forecasting across many regions and seasons. In contrast, other models exhibit strong performance in specific regions and seasons. These results advance our understanding of an impact-based framework in capturing a broad spectrum of extreme climatic events through the strategic amalgamation of diverse models across different regions and seasons, offering valuable insights for disaster management and risk analysis.

Lakes are the most prevalent and predominant water repositories on land surface. A primary objective of the Surface Water and Ocean Topography (SWOT) satellite mission is to monitor the surface water elevation, area, and storage change in Earth’s lakes. To meet this objective, prior information of global lakes, such as locations and benchmark extents, is required to organize SWOT’s KaRIn observations over time for computing lake storage variation. Here, we present the SWOT mission Prior Lake Database (PLD) to fulfill this requirement. This paper emphasizes the development of the “operational PLD”, which consists of (1) a high-resolution mask of ~6 million lakes and reservoirs with a minimum area of 1 ha, and (2) multiple operational auxiliaries to assist the lake mask in generating SWOT’s standard vector lake products. We built the prior lake mask by harmonizing the UCLA Circa-2015 Global Lake Dataset and several state-of-the-art reservoir databases. Operational auxiliaries were produced from multi-theme geospatial data to provide information necessary to embody the PLD function, including lake catchments and influence areas, ice phenology, relationship with SWOT-visible rivers, and spatiotemporal coverage by SWOT overpasses. Globally, over three quarters of the prior lakes are smaller than 10 ha. Nearly 96% of the lakes, constituting over half of the global lake area, are fully observed at least once per orbit cycle. The PLD will be recursively improved during the mission period and serves as a critical framework for organizing, processing, and interpreting SWOT observations over lacustrine environments with fundamental significance to lake system science.

A document by Sebastian Uhlemann. Click on the document to view its contents.

A document by Filipi Vianna. Click on the document to view its contents.

Despite the growing use of Aquatic Ecosystem Models (AEMs) for lake modelling, there is currently no widely applicable framework for their configuration, calibration, and evaluation. To date, calibration is generally based on direct data comparison of observed vs. modelled state variables using standard statistical techniques, however, this approach may not give a complete picture of the model’s ability to capture system-scale behaviour that is not prevalent in the state observations, but which may be important for resource management. The aim of this study is to compare the performance of ‘naïve’ calibration and a ‘system-inspired’ calibration, a new approach that augments the standard state-based calibration with a range of system-inspired metrics (e.g. thermocline depth, metalimnetic oxygen minima), in an effort to increase the coherence between the simulated and natural ecosystems. This was achieved by applying a coupled physical-biogeochemical model to a focal site to simulate temperature and dissolved oxygen. The model was calibrated according to the new system-inspired modelling convention, using formal calibration techniques. There was a clear improvement in the simulation using parameters optimised on the additional metrics, which helped to focus calibration on aspects of the system relevant to reservoir management, such as the metalimnetic oxygen minima. Extending the use of system-inspired metrics for the calibration of models of nutrient cycling, algal blooms, and greenhouse gas emissions has the potential to greatly improve the prediction of complex ecosystem dynamics.

Nicolás Velásquez1, Witold F. Krajewski11IIHR-Hydrosciences & Engineering, Department of Civil and Environmental Engineering, The University of iowa, Iowa City, IA, USA.Corresponding author: Nicolás Velásquez ([email protected])This is a non-peer review pre-print Key Points:List up to three key points (at least one is required)Key Points summarize the main points and conclusions of the articleEach must be 140 characters or less with no special characters or acronyms.AbstractHydrological models and quantitative precipitation estimation (QPE) are critical elements of flood forecasting systems. Both are subject to considerable uncertainties. Quantifying their relative contribution to the forecasted streamflow and flood uncertainty has remained challenging. Past work documented in the literature focused on one of these elements separately from the other. With this challenge in mind, we present a systematic approach to assess the impact of QPE uncertainty in streamflow forecasting. Our approach explores the operational Iowa Flood Center (IFC) hydrological model performance after altering three radar-based QPE products. We ran the Hillslope Link Model (HLM) for Iowa between 2015 and 2020, altering the Multi-Radar/Multi-Sensor System (MRMS) and two IFC radar-derived products, i.e., reflectivity-based IFCZ and specific attenuation-based IFCA with a multiplicative error term. We assessed the forecasting system performance at 122 USGS stage gauges using the mentioned altered QPE products. Our results suggest that addressing rainfall uncertainty is crucial in flood forecasting uncertainty spatially and seasonally. We identified spatial patterns linking the QPE product improvement to the radar’s location, the rainfall magnitude, and the watershed size. Also, we observed seasonal trends suggesting underestimations during the cold season (October to April). The patterns for different radar products are generally similar but also show some differences, implying that the radars and the QPE algorithm play a significant role. This study’s results are a step towards separating modeling and QPE uncertainties. Future work involving larger areas and different hydrological and error models is essential to improve our understanding of the QPE uncertainty impact.

Existing stochastic rainfall generators (SRGs) are typically limited to relatively small domains due to spatial stationarity assumptions, hindering their usefulness for flood studies in large basins. This study proposes StormLab, an SRG that simulates precipitation events at 6-hour and 0.03° resolution in the Mississippi River Basin (MRB). The model focuses on winter and spring storms caused by strong water vapor transport from the Gulf of Mexico—the key flood-generating storm type in the basin. The model generates anisotropic spatiotemporal noise fields that replicate local precipitation structures from observed data. The noise is transformed into precipitation through parametric distributions conditioned on large-scale atmospheric fields from a climate model, reflecting both spatial and temporal nonstationarity. StormLab can produce multiple realizations that reflect the uncertainty in fine-scale precipitation arising from a specific large-scale atmospheric environment. Model parameters were fitted for each month from December-May, based on storms identified from 1979-2021 ERA5 reanalysis data and AORC precipitation. Validation showed good consistency in key storm characteristics between StormLab simulations and AORC data. StormLab then generated 1,000 synthetic years of precipitation events based on 10 CESM2 ensemble simulations. Empirical return levels of simulated annual maxima agreed well with AORC data and displayed bounded tail behavior. To our knowledge, this is the first SRG simulating nonstationary, anisotropic high-resolution precipitation over continental-scale river basins, demonstrating the value of conditioning such stochastic models on large-scale atmospheric variables. The simulated events provide a wide range of extreme precipitation scenarios that can be further used for design floods in the MRB.

A document by Raj Pandya. Click on the document to view its contents.

Wildfires can induce an abundance of vegetation and soil changes that may trigger higher surface runoff and soil erosion, affecting the water cycling within these ecosystems. In this study, we employed the Advanced Terrestrial Simulator (ATS), an integrated and fully distributed hydrologic model at watershed scale to investigate post-fire hydrologic responses in a few selected watersheds with varying burn severity in the Pacific Northwest region of the United States. The model couples surface overland flow, subsurface flow, and canopy biophysical processes. We developed a new fire module in ATS to account for the fire-caused hydrophobicity in the topsoil. Modeling results show that the watershed-averaged evapotranspiration is reduced after high burn severity wildfires. Post-fire peak flows are increased by 21-34% in the three study watersheds burned with medium to high severity due to the fire-caused soil water repellency (SWR). However, the watershed impacted by a low severity fire only witnessed a 2% surge in post-fire peak flow. Furthermore, the high severity fire resulted in a mean reduction of 38% in the infiltration rate within fire-impacted watershed during the first post-fire wet season. Hypothetical numerical experiments with a range of precipitation regimes after a high severity fire reveal the post-fire peak flows can be escalated by 1-34% due to the SWR effect triggered by the fire. This study implies the importance of applying fully distributed hydrologic models in quantifying the disturbance-feedback loop to account for the complexity brought by spatial heterogeneity.

Global and coastal ocean surface water elevation prediction skill has advanced considerably with improved algorithms, more refined discretizations and high-performance parallel computing. Model skill is tied to mesh resolution, the accuracy of specified bathymetry/topography, dissipation parameterizations, air-sea drag formulations, and the fidelity of forcing functions. Wind forcing skill can be particularly prone to errors, especially at the land-ocean interface. The resulting biases and errors can be addressed holistically with a machine-learning (ML) approach. Herein, we weakly couple the Temporal Fusion Transformer to the National Oceanic and Atmospheric Administration’s (NOAA) Storm and Tide Operational Forecast System (STOFS 2D Global) to improve its forecasting skill throughout a 7-day horizon. We demonstrate the transformer’s ability to enrich the hydrodynamic model’s output at 228 observed water level stations operated by NOAA’s National Ocean Service. We conclude that the transformer is a rapid way to correct STOFS 2D Global forecasted water levels provided that sufficient covariates are supplied. For stations in wind-dominant areas, we demonstrate that including past and future wind-speed covariates make for a more skillful forecast. In general, while the transformer renders consistent corrections at both tidally and wind-dominant stations, it does so most aggressively at tidally-dominant stations. We show notable improvements in Alaska and the Atlantic and Pacific seaboards of the United States. We evaluate several transformers instantiated with different hyperparameters, covariates, and training data to provide guidance on how to enhance performance.

A document by Charlie Mydlarz. Click on the document to view its contents.

Extreme weather events are reshaping hydrological cycles across the globe, yet our understanding of the groundwater response to these extremes remains limited. Here we analyze groundwater levels across the South Coast of British Columbia (BC) in the Pacific Northwest with the objective of determining groundwater responses to atmospheric rivers (ARs) and drought. An AR catalogue was derived and associated to local rainfall defining extreme precipitation. Droughts were quantified using dry day metrics, in conjunction with the standardized precipitation index (SPI). From September to January, approximately 40% of total precipitation is contributed by ARs. From April to September, more than 50% of days receive no precipitation, with typically 26 consecutive dry days. We used the autocorrelation structure of groundwater levels to quantify aquifer memory characteristics and identified two distinct clusters. Cluster 1 wells respond to recharge from local precipitation, primarily rainfall, and respond rapidly to both ARs during winter recharge and significant rainfall deficits during summer. Cluster 2 wells are also driven by local precipitation, and are additionally influenced by the Fraser River’s large summer freshet, briefly providing a secondary recharge mechanism to South Coast aquifers. Accordingly, groundwater recessions are offset to later in the summer, contingent on the Fraser River, mediating drought. The results suggest that groundwater memory encapsulates multiple hydrogeological factors, including boundary conditions, influencing the response outcome to extreme events.

The rate of channel incision in bedrock rivers is often described using a power law relationship that scales erosion with drainage area. However, erosion in landscapes that experience strong rainfall gradients may be better described by discharge instead of drainage area. In this study we test if these two end member scenarios result in identifiable topographic signatures in both idealized numerical simulations and in natural landscapes. We find that in simulations using homogeneous lithology, we can differentiate a posteriori between drainage area and discharge-driven incision scenarios by quantifying the relative disorder of channel profiles, as measured by how well tributary profiles mimic both the main stem channel and each other. The more heterogeneous the landscape becomes, the harder it proves to identify the disorder signatures of the end member incision rules. We then apply these indicators to natural landscapes, and find, among 8 test areas, no clear topographic signal that allows us to conclude a discharge or area-driven incision rule is more appropriate. We then quantify the distortion in the channel steepness index induced by changing the incision rule. Distortion in the channel steepness index can also be driven by changes to the assumed reference concavity index, and we find that distortions in the normalized channel steepness index, frequently used as a proxy for erosion rates, is more sensitive to changes in the concavity index than to changes in the assumed incision rule. This makes it a priority to optimize the concavity index even under an unknown incision mechanism.

We use Wasserstein Generative Adversarial Networks to learn and integrate multi-scale features in segmented three-dimensional images of porous materials, enabling the dependable generation of large-scale representations of complex porous media. A Laplacian pyramid generator is introduced which creates pore-space features with a hierarchy of spatial scales. Feature statistics mixing regularization enhances the ability of the model generation to reliably maintain multi-scale pore-space features of images by increasing diversity. The method is tested on a variety of X-ray images of porous rocks. The generated images can be of any size -- cm-scale ten-billion-cell images are generated to demonstrate the power of the approach -- which have two-point correlation functions, porosity, permeability, Euler characteristic, curvature, and specific surface area close to the training datasets. The images demonstrate a considerable improvement over previously- published studies using generative adversarial networks.

A document by Sushel Unninayar. Click on the document to view its contents.

Satellite-based precipitation observations provide near-global coverage with high spatiotemporal resolution in near-realtime. Their utility, however, is hindered by oftentimes large errors that vary substantially in space and time. Since precipitation uncertainty is, by definition, a random process, probabilistic expression of satellite-based precipitation product uncertainty is needed to advance their operational applications. Ensemble methods, in which uncertainty is depicted via multiple realizations of precipitation fields, have been widely used in other contexts such as numerical weather prediction, but rarely in satellite contexts. Creating such an ensemble dataset is challenging due to the complexity of errors and the scarcity of “ground truth” to characterize it. This challenge is particularly pronounced in ungauged regions, where the benefits of satellite-based precipitation data could otherwise provide substantial benefits. In this study, we propose the first quasi-global (covering all continental land masses within 50°N-50°S) satellite-only ensemble precipitation dataset, derived entirely from NASA’s Integrated Multi-SatellitE Retrievals for Global Precipitation Measurement (IMERG) and GPM’s radar-radiometer combined precipitation product (2B-CMB). No ground-based measurements are used in this generation and it is suitable for near-realtime use, limited only by the latency of IMERG. We compare the results against several precipitation datasets of distinct classes, including global satellite-based, rain gauge-based, atmospheric reanalysis, and merged products. While our proposed approach faces some limitations and is not universally superior to the datasets it is compared to in all respects, it does hold relative advantages due to its combination of accuracy, resolution, latency, and utility in hydrologic and hazard applications.

A document by Hao Zhou. Click on the document to view its contents.

An attempt has been made to quantitatively analyze different degrees of environmental drought events, given the limited scientific understanding of environmental droughts, which hinders practical assessment efforts. This study thus aims to rigorously develop and assess the applicability of a novel heuristic method in conjunction with creating an Environmental Drought Index \cite{Srivastava_2023}. The heuristic method evaluates the combined influences of drought duration and water shortage levels, providing crucial insights into the environmental flow requirements amidst climate change. The Minimum in-stream Flow Requirements (MFR) is first defined as the threshold value essential for sustaining the river basin's ecological functions, aligning with Tennant’s environmental flow concept. Establishing MFR enables a balance between water resource utilization and ecological preservation, fostering sustainable water management. To comprehensively assess the eco-status, the study defined the High Flow Season (HFS) and the Low Flow Season (LFS). Drought status is then determined by comparing MFR with observed streamflow rate, quantifying negative differences as environmental droughts. Drought Duration Length (DDL) and Water Shortage Level (WSL) are introduced as functions of environmental drought. DDL categorizes consecutive months into four classes: DDL 1 (1-3 months), DDL 2 (4-6 months), DDL 3 (7-12 months), and DDL 4 (>12 months). WSL is determined by the most significant water deficit observed during DDL, classified into four categories: WSL 1 (<40%), WSL 2 (40-60%), WSL 3 (60-80%), and WSL 4 (>80%). Integrating DDL and WSL yields an index classifying environmental drought events into slight, moderate, severe, and extreme levels. The index value is obtained by comparing DDL and WSL values and selecting the maximum. The study enhances the scientific rigor of environmental drought identification and analysis, contributing to understanding drought impacts and effective mitigation strategies.  

Pockmarks are morphological depressions commonly observed in ocean and lake floors. Pockmarks form by fluid (typically gas) seepage thorough a sealing sedimentary layer, deforming and breaching the layer. The seepage-induced sediment deformation mechanisms, and their links to the resulting pockmarks morphology, are not well understood. To bridge this gap, we conduct laboratory experiments in which gas seeps through a granular (sand) reservoir, overlaid by a (clay) seal, both submerged under water. We find that gas rises through the reservoir and accumulates at the seal base. Once sufficient gas over-pressure is achieved, gas deforms the seal, and finally escapes via either: (i) doming of the seal followed by dome breaching via fracturing; (ii) brittle faulting, delineating a plug. The gas lifts the plug and seeps through the bounding faults; or (iii) plastic deformation by bubbles ascending through the seal. The preferred mechanism is found to depend on the seal thickness and stiffness: in stiff seals, a transition from doming and fracturing to brittle faulting occurs as the thickness increases, whereas bubbles rise is preferred in the most compliant, thickest seals. Seepage can also occur by mixed modes, such as bubbles rising in faults. Repeated seepage events suspend the sediment at the surface and create pockmarks. We present a quantitative analysis that explains the tendency for the various modes of deformation observed experimentally. Finally, we connect simple theoretical arguments with field observations, highlighting similarities and differences that bound the applicability of laboratory experiments to natural pockmarks.

The geostationary Himawari-8 satellite offers a unique opportunity to monitor sub-daily thermal dynamics over Asia and Oceania, and several operational land surface temperature (LST) retrieval algorithms have been developed for this purpose. However, studies have reported inconsistency between LST data obtained from geostationary and polar-orbiting platforms, particularly for daytime LST, which usually shows directional artefacts and can be strongly impacted by viewing and illumination geometries and shadowing effects. To overcome this challenge, Solar Zenith Angle (SZA) serves as an ideal physical variable to quantify systematic differences between platforms. Here we presented an SZA-based Calibration (SZAC) method to operationally calibrate the daytime component of a split-window retrieved Himawari-8 LST (referred to here as the baseline). SZAC describes the spatial heterogeneity and magnitude of diurnal LST discrepancies from different products. The SZAC coefficient was spatiotemporally optimised against highest-quality assured (error < 1 K) pixels from the MODerate-resolution Imaging Spectroradiometer (MODIS) daytime LST between 01/Jan/2016 and 31/Dec/2020. We evaluated the calibrated LST data, referred to as the Australian National University LST with SZAC (ANUSZAC), against MODIS LST and the Visible Infrared Imaging Radiometer Suite (VIIRS) LST, as well as in-situ LST from the OzFlux network. Two peer Himawari-8 LST products from Chiba University and the Copernicus Global Land Service were also collected for comparisons. The median daytime bias of ANUSZAC LST against Terra-MODIS LST, Aqua-MODIS LST and VIIRS LST was 1.52 K, 0.98 K and -0.63 K, respectively, which demonstrated improved performance compared to baseline (5.37 K, 4.85 K and 3.02 K, respectively) and Chiba LST (3.71 K, 2.90 K and 1.07 K, respectively). All four Himawari-8 LST products showed comparable performance of unbiased root mean squared error (ubRMSE), ranging from 2.47 to 3.07 K, compared to LST from polar-orbiting platforms. In the evaluation against in-situ LST, the overall mean values of bias (ubRMSE) of baseline, Chiba, Copernicus and ANUSZAC LST during daytime were 4.23 K (3.74 K), 2.16 K (3.62 K), 1.73 K (3.31 K) and 1.41 K (3.24 K), respectively, based on 171,289 hourly samples from 20 OzFlux sites across Australia between 01/Jan/2016 and 31/Dec/2020. In summary, the SZAC method offers a promising approach to enhance the reliability of geostationary LST retrievals by incorporating the spatiotemporal characteristics observed by accurate polar-orbiting LST data. Furthermore, it is possible to extend SZAC for LST estimation by using data acquired by geostationary satellites in other regions, e.g., Europe, Africa and Americas, as this could improve our understanding of the error characteristics of overlapped geostationary imageries, allowing for targeted refinements and calibrations to further enhance applicability.KeywordsLand surface temperature; Geostationary; Himawari-8; Diurnal temperature cycle; Calibration; Solar zenith angle; MODIS; VIIRS

Optimising hydropower operations to balance economic profitability and support functioning ecosystem services is integral to river management policy. In this article, we propose a multi-objective optimization framework for small hydropower plants (SHPs) to evaluate trade-offs among environmental flow scenarios. Specifically, we examine the balance between short-term losses in hydropower generation and the potential for compensatory benefits in the form of revenue from recreational ecosystem services, irrespective of the direct beneficiary. Our framework integrates a fish habitat model, a hydropower optimization model, and a recreational ecosystem service model to evaluate each environmental flow scenario. The optimisation process gives three outflow release scenarios, informed by previous streamflow realisations (dam inflow), and designed environmental flow constraints. The framework is applied and tested for the river Kuusinkijoki in North-eastern Finland, which is a habitat for migratory brown trout and grayling populations. We show that the revenue loss due to the environmental flow constraints arises through a reduction in revenue per generated energy unit and through a reduction in turbine efficiency. Additionally, the simulation results reveal that all the designed environmental flow constraints cannot be met simultaneously. Under the environmental flow scenario with both minimum flow and flow ramping rate constraints, the annual hydropower revenue decreases by 16.5%. An annual increase of 8% in recreational fishing visits offsets the revenue loss. The developed framework provides knowledge of the costs and benefits of hydropower environmental flow constraints and guides the prioritizing process of environmental measures.

Across diverse biomes and climate types, plants use water stored in bedrock to sustain transpiration. Bedrock water storage ($S_{bedrock}$, mm), in addition to soil moisture, thus plays an important role in water cycling and should be accounted for in the context of surface energy balances and streamflow generation. Yet, the extent to which bedrock water storage impacts hydrologic partitioning and influences latent heat fluxes has yet to be quantified at large scales. This is particularly important in Mediterranean climates, where the majority of precipitation is offset from energy delivery and plants must rely on water retained from the wet season to support summer growth. Here we present a simple water balance approach and random forest model to quantify the role of $S_{bedrock}$ on controlling hydrologic partitioning and land surface energy budgets. Specifically, we track evapotranspiration in excess of precipitation and mapped soil water storage capacity ($S_{soil}$, mm) across the western US in the context of Budyko’s water partitioning framework. Our findings indicate that $S_{bedrock}$ is necessary to sustain plant growth in forests in the Sierra Nevada — some of the most productive forests on Earth — as early as April every year, which is counter to the current conventional thought that bedrock is exclusively used late in the dry season under extremely dry conditions. We show that the average latent heat flux used in evapotranspiration of $S_{bedrock}$ can exceed 100 $W/m^{2}$ during the dry season and the proportion of water that returns to the atmosphere would decrease dramatically without access to $S_{bedrock}$.

Estimates of flood inundation from tropical cyclones (TCs) are needed to better understand how exposure varies inland and at the coast. While reduced-complexity flood inundation models have been previously shown to efficiently simulate the drivers of TC flooding across large regions, a lack of detailed validation studies of these models, which are being applied globally, has led to uncertainty about the quality of the predictions of inundation depth and extent and how this translates to exposure. In this study, we complete a comprehensive validation of a reduced-complexity hydrodynamic model (SFINCS) for simulating pluvial, fluvial, and coastal flooding. We hindcast Hurricane Florence (2018) flooding in North and South Carolina, USA using high-resolution meteorologic data and coastal water level output from an ocean recirculation model (ADCIRC). We compare modeled water levels to traditional validation datasets (e.g., water level gages, high-water marks) as well as property-level records of insured damage to draw conclusions about the model’s performance. We demonstrate that SFINCS can accurately simulate coastal and runoff drivers of TC flooding at large scales with minimal computational requirements and limited calibration. We use the validated model to attribute flood extent and building exposure to the individual and compound flood drivers during Hurricane Florence. The results highlight the critical role runoff processes have in TC flood exposure and support the need for broader implementation of models that are capable of realistically representing the compound effects resulting from coastal and runoff processes.