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 \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.

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.

The fertile Anatolian lands in Turkey, supporting about 80 million people, rely on abundant water resources. The Kızılırmak River basin in Anatolia is vulnerable to global warming, mainly due to snowmelt in its headwaters. Quantifying the upper watershed’s climate sensitivity is crucial for assessing water availability. Instead of using Global Climate Model (GCM)-driven projections, a sensitivity-based approach was employed with the Variable Infiltration Capacity (VIC) hydrologic model to assess the region’s hydrological vulnerability to potential future climatic changes. Considering the consistent projections of increasing temperature (T) over this region in GCMs, the system was perturbed to examine gradients of a more challenging climate, characterized by warming and drying conditions. The sensitivity of streamflow, snowpack water equivalence, and evapotranspiration to T and Precipitation (P) variations under each perturbation or “reference” climates was quantified. Results indicate that streamflow responds to T negatively under all warming scenarios. Streamflow responding to P increases nonlinearly as P decreases in the reference climates. These results suggest that there will be heightened difficulty in managing water resources in the region if it undergoes both warming and drying due to the following setbacks: 1) water availability will shift away from the summer season of peak water demand due to the warming effects on the snowpack, 2) annual water availability will likely decrease due to a combination of warming and lower precipitation, and 3) streamflow sensitivity to hydroclimatic variability will increase, meaning that water managers will likely need to plan for a system that is more sensitive to weather variations.

Scenarios have emerged as valuable tools in managing complex human-natural systems, but the traditional approach of limiting focus on a small number of predetermined scenarios can inadvertently miss consequential dynamics, extremes, and diverse stakeholder impacts. Exploratory modeling approaches have been developed to address these issues by exploring a wide range of possible futures and identifying those that yield consequential vulnerabilities. However, vulnerabilities are typically identified based on aggregate robustness measures that do not take full advantage of the richness of the underlying dynamics in the large ensembles of model simulations and can make it hard to identify key dynamics and/or narrative storylines that can guide planning or further analyses. This study introduces the FRamework for Narrative Scenarios and Impact Classification (FRNSIC; pronounced “forensic’): a scenario discovery framework that addresses these challenges by organizing and investigating consequential scenarios using hierarchical classification of diverse outcomes across actors, sectors, and scales, while also aiding in the selection of narrative storylines, based on system dynamics that drive consequential outcomes. We present an application of this framework to the Upper Colorado River Basin, focusing on decadal droughts and their water scarcity implications for the basin’s diverse users and its obligations to downstream states through Lake Powell. We show how FRNSIC can explore alternative sets of impact metrics and drought dynamics and use them to identify narrative drought storylines, that can be used to inform future adaptation planning.

Storage and release of surface water by reservoirs can alter the natural streamflow pattern of rivers with negative impacts on the environment. Such reservoir-driven river regulation is poorly understood at a global scale due to a lack of publicly available in-situ data on reservoir operations. However, with rapid advancements in satellite remote sensing-based tracking of reservoir state, this gap in data availability can be bridged. In this study, we modeled regulated flow of rivers using only satellite-observed reservoir state and hydrological modeling forced also with satellite precipitation data. We propose a globally scalable algorithm, ResORR (Reservoir Operations driven River Regulation), to predict regulated river flow and tested it over the heavily regulated basin of the Cumberland River in the US. ResORR was found able to model regulated river flow due to upstream reservoir operations of the Cumberland River. Over a mountainous basin dominated by high rainfall, ResORR was effective in capturing extreme flooding modified by upstream hydropower dam operations. ResORR successfully captured the peak of the regulated river flow altered by hydropower dam and flood control operations during the devastating floods of 2018 in the South Indian state of Kerala. On average, ResORR improved regulation river flow simulation by more than 50% across all performance metrics when compared to a hydrologic model without a regulation module. ResORR is a timely algorithm for understanding human regulation of surface water as satellite-estimated reservoir state is expected to improve globally with the recently launched Surface Water and Ocean Topography (SWOT) mission.

Sap flow observations provide a basis for estimating transpiration and understanding forest water use dynamics and plant-climate interactions. This study developed a continental modeling approach using Long Short-Term Memory networks (LSTMs) to predict hourly tree-level sap flow across Europe based on the SAPFLUXNET database. We developed models with varying levels of training sets to evaluate performance in unseen conditions. The average Kling-Gupta Efficiency was 0.77 for gauged models trained on 50 % of time series across all forest stands and was 0.52 for ungauged models trained on 50 % of the forest stands. Continental models matched or exceeded performance of specialized and baseline models for all genera and forest stands, demonstrating the potential of LSTMs to generalize hourly sap flow across tree, climate, and forest types. This work highlights hence the potential of deep learning models to generalize sap flow for enhancing tree to continental ecohydrological investigations.

Runoff in natural rivers, commonly termed as streamflow, is a major process in the water cycle and a crucial variable in water resources engineering. While the increase in extreme rainfall events over the Conterminous United States (CONUS) has been well-documented, understanding the variability of streamflow remains challenging due to the nonlinear relationship between rainfall and runoff. In this study, daily streamflow data from 18 USGS gauge stations with the largest drainage area in its respective Hydrologic Unit Code 2-digit (HUC-2) region throughout the CONUS with contiguous records spanning from 2003 to 2022 water years is used to gain insights into streamflow variability over the past two decades. The original Mann-Kendall (MK) Test is employed to assess the potential temporal trends in the basic statistics (maximum, mean, minimum, and standard deviation) of annual streamflow data over the past 20 water years. Additionally, the seasonal MK Test is performed to explore the trends in the same basic statistics of the daily streamflow on a monthly basis. Furthermore, the statistical distributions of the normalized daily streamflow within each decade (2003-2012 and 2013-2022) are compared for each HUC-2 region. The results of the original MK Test indicate that no discernible trend in the annual streamflow and its standard deviation for most of the HUC-2 regions. However, the results of the seasonal MK Test suggest either an increasing or decreasing trend in around 30% of the HUC-2 regions. Moreover, low flows demonstrate a more significant change in frequency compared to high flows between the past two decades. Overall, this study highlights the complexity of the streamflow variability and the potential implications for changes in flood or drought risk under a changing climate.  

Characterizing climate change impacts on water resources typically relies on Global Climate Model (GCM) outputs that are bias-corrected using observational datasets. In this process, two pivotal decisions are (i) the Bias Correction Method (BCM) and (ii) how to handle the historically observed time series, which can be used as a continuous whole (i.e., without dividing it into sub-periods), or partitioned into monthly, seasonal (e.g., three months), or any other temporal stratification (TS). Here, we examine how the interplay between the choice of BCM, TS, and the raw GCM seasonality may affect historical portrayals and projected changes. To this end, we use outputs from 29 GCMs belonging to the CMIP6 under the Shared Socioeconomic Pathway 5–8.5 scenario, using seven BCMs and three TSs (entire period, seasonal, and monthly). The results show that the effectiveness of BCMs in removing biases can vary depending on the TS and climate indices analyzed. Further, the choice of BCM and TS may yield different projected change signals and seasonality (especially for precipitation), even for climate models with low bias and a reasonable representation of precipitation seasonality during a reference period. Because some BCMs may be computationally expensive, we recommend using the linear scaling method as a diagnostics tool to assess how the choice of TS may affect the projected precipitation seasonality of a specific GCM. More generally, the results presented here unveil trade-offs in the way BCMs are applied, regardless of the climate regime, urging the hydroclimate community for a careful implementation of these techniques.

Regional flood mapping poses computational and spatial heterogeneity challenges, exacerbated by climate change-induced uncertainties. This study focuses on creating a state-wide flood mapping solution with enhanced accuracy and computational speed to support regional flooding hazard analysis and the assessment of climate change, using New Jersey as a case study. The Height Above Nearest Drainage (HAND) framework was employed for large-scale flood mapping. The model was validated against high water marks (HWMs) collected after Hurricane Irene. Based on the National Water Model (NWM), synthetic rating curves in HAND were calibrated by tuning Manning’s roughness, aligning the predicted and observed flood depths. The roughness values were generalized across the state from the validated water basins to the ungauged ones, using a multivariate regression with the hydrologic and geographic information. To map the future climate-change-induced flooding, a correlation between NOAA historical precipitation totals and NWM flow data from 2010-2020 was established to link precipitation and runoff. This study also invented a novel method for correcting catchment discontinuities, inherent in the HAND model, based on a computer vision scheme, the Sobel filter. The modeling results show that average and worst-case storm events have the potential to increase 10-50% in the state, where mountain areas and major river banks would be exposed to this impact more significantly.

In this study, we identify the key length and time scales associated with CO2 mineralization in basalt reservoirs. This is achieved through the development and application of a simple yet complete model of the fate and transport of a supersaturated CO2-charged fluid moving unidirectionally through an initially uniform basalt rock. The model consists of three coupled equations describing, (i) the spatiotemporal evolution of porosity with the mineralization reaction, (ii) the resulting temporal and spatially varying fluid discharge, and (iii) the fate and transport of the mineralization reactant(s) in the aqueous phase. A dimensional analysis provides length and time scales that characterize the extent and duration of field-scale carbon mineralization. These scales are applied to a field site to estimate poorly constrained mineralization parameters, notably, the effective first-order reaction rate constant.

The U.S. National Water Model (NWM) is a hydrologic modeling framework that uses the Weather Research and Forecasting Hydrological modeling system (WRF-Hydro) to simulate land surface hydrology and energy fluxes at 1-km spatial resolution. Understanding the performance of the operational NWM in simulating evapotranspiration (ET) is necessary to identify problems and biases in streamflow forecasts that may result from poor partitioning of runoff and ET. In this study, we compared NWM ET fluxes against OpenET, a satellite-driven dataset that provides interpretive or diagnostic information on actual ET at 30-m spatial resolution. Monthly ET simulations from the NWM version 2.1 (NWM V2.1) retrospective analysis over the Bear River Basin (BRB), U.S. were compared against OpenET products from 2017 to 2020 for different months and seasons. Comparisons showed that there was general agreement between the ET assessments at the 1-km scale, but with notable discrepancies for some landcover types, such as irrigated agriculture and riparian areas. The NWM showed less spatial variability and tended to predict lower ET fluxes compared to OpenET, particularly from June to August. In comparison with water balance estimates of ET derived from precipitation and USGS streamflow observations in four sub-watersheds within the BRB, OpenET modeled ET was biased high in two watersheds dominated by evergreen forest. The results from this study provide useful information for both NWM and OpenET developers, demonstrating the power of comparing predictive and interpretive modeling systems. This study serves as a prototype for broader assessment of both NWM and OpenET via intercomparison. Plain Language Summary This study compared the retrospective U.S. National Water Model (NWM) version 2.1 evapotranspiration (ET) fluxes with OpenET, a satellite-driven data product offering actual ET information at 30-m resolution from 2017 to 2020, aggregated to match the 1 km NWM grid. Results indicated that the NWM tends to underpredict ET fluxes when compared against the different OpenET component models used in this study. OpenET showed a high bias in comparison with water balance assessments of ET in two natural sub-watersheds characterized by evergreen forest. Significant spatial discrepancies were observed in NWM results for certain landcover types, including irrigated agricultural lands, riparian areas, and in one watershed that appears to be mis-calibrated. Key Points: • Compared with OpenET, the U.S. National Water Model (NWM) tends to underpredict evapotranspiration (ET) fluxes in all seasons. • OpenET overpredicts ET in comparison to water balance estimates from observed streamflow and precipitation in two forested sub-watersheds. • Spatial discrepancies between NWM ET and OpenET were observed in irrigated lands, riparian areas, and one mis-calibrated watershed.

In various research fields such as hydrogeology, environmental science and energy engineering, geological formations with fractures are frequently encountered. Accurately characterizing these fractured media is of paramount importance when it comes to tasks that demand precise predictions of liquid flow and the transport of solute and energy within them. Since directly measuring fractured media poses inherent challenges, data assimilation (DA) techniques are typically employed to derive inverse estimates of media properties using observed state variables like hydraulic head, concentration, and temperature. Nonetheless, the considerable difficulties arising from the strong heterogeneity and non-Gaussian nature of fractured media have diminished the effectiveness of existing DA methods. In this study, we formulate a novel DA approach known as PEDL (parameter estimator with deep learning) that harnesses the capabilities of DL to capture nonlinear relationships and extract non-Gaussian features. To evaluate PEDL’s performance, we conduct two numerical case studies with increasing complexity. Our results unequivocally demonstrate that PEDL outperforms three popular DA methods: ensemble smoother with multiple DA (ESMDA), iterative local updating ES (ILUES), and ES with DL-based update (ESDL). Sensitivity analyses confirm PEDL’s validity and adaptability across various ensemble sizes and DL model architectures. Moreover, even in scenarios where structural difference exists between the accurate reference model and the simplified forecast model, PEDL adeptly identifies the primary characteristics of fracture networks.

A document by Salam A. Abbas. Click on the document to view its contents.

Distributed hydrological water quality models are increasingly being used to manage natural resources at the catchment scale but there are no calibration guidelines for selecting the most useful gauging stations. In this study, we investigated the influence of calibration schemes on the spatiotemporal performance of a fully distributed process-based hydrological water quality model (mHM-Nitrate) for discharge and nitrate simulations at Bode catchment in central Germany. We used a single- and two multi-site calibration schemes where the two multi-site schemes varied in number of gauging stations but each subcatchment represented different dominant land uses of the catchment. To extract a set of behavioral parameters for each calibration scheme, we chose a sequential multi-criteria method with 300.000 iterations. For discharge (Q), model performance was similar among the three schemes (NSE varied from 0.88 to 0.92). However, for nitrate concentration, the multi-site schemes performed better than the single site scheme. This improvement may be attributed to that multi-site schemes incorporated a broader range of data, including low Q and NO3- values, thus provided a better representation of within-catchment diversity. Conversely, adding more gauging stations in the multi-site approaches did not lead to further improvements in catchment representation but showed wider 95% uncertainty boundaries. Thus, adding observations that contained similar information on catchment characteristics did not seem to improve model performance and increased uncertainty. These results highlight the importance of strategically selecting gauging stations that reflect the full range of catchment heterogeneity rather than seeking to maximize station number, to optimize parameter calibration.

Increasing climatic variability has resulted in an unprecedented surge in extreme events, pressing global ecosystems towards systematic breakdown. Yet, the resilience of the soil-vegetation-atmosphere (SVA) system to revert to its natural state indicates the existence of energetic barriers forbidding systems from tipping. Observational and theoretical constraints limit our understanding of these energetic barriers which are crucial for assessing ecosystem sensitivity to atmospheric perturbations. We provide a novel coherent theory on the dissipative energy barriers (𝛥e) which decides the resilience potential of an ecosystem. These barriers are manifestation of lower bounds of entropy produced ( Σ *) for unit anomaly transference from soil moisture (SM) to evapotranspiration (ET). Using remote sensing data, we compute these global entropy bounds by introducing a new metric (Wasserstein distance, dw) for SM-ET coupling. Quantifying these lower bounds from SM-ET coupling, places terrestrial ecosystems in the hierarchy of dissipative energy states spanning from forested regions to barren lands. Furthermore, we show that the optimization of SM-ET coupling translates to entanglement of water potential gradient (∆ω) between land surface and atmospheric boundary layer, and the resulting memory timescale or residence time (τ). This (τ.∆ω) entanglement propels moisture-rich and moisture-deficit systems in complementary evolutionary pathways in responding to imposed anomalies. As a result, we witness an emergence of coupling-aridity tradeoff with temperate climates operating as least efficient systems for unit SM to ET anomaly transfer. Physical basis, and transferability across space and scale makes this theory a potential benchmark for process improvement in the climate and earth system models.  

Climate elasticity of streamflow represents a nondimensional measure of the sensitivity of streamflow to climatic factors. Estimation of such elasticities from observational records has become an important alternative to scenario-based methods of evaluating streamflow sensitivity to climate. Nearly all previous elasticity studies have used a definition of elasticity known as arc elasticity, which measures changes in streamflow about mean values of streamflow and climate. Using observational records in western U.S., our findings reveal that elasticity definitions based on power law models lead to both regional and basin specific estimates of elasticity which are physically more realistic than estimates based on arc elasticity. Evaluating the ability of arc and power law elasticity estimators in reproducing Dooge’s complementary relationship (DCR) between potential evapotranspiration and precipitation elasticities reveal that power law elasticities estimated from at-site, panel and hierarchical statistical models reproduce DCR, whereas corresponding estimators based on arc elasticity cannot reproduce DCR. Importantly, our regional elasticity formulations using either panel and/or hierarchical formulations led to estimates of both regional and basin specific estimates of elasticities, enabling and contrasting streamflow sensitivity to climate across both basins and regions.

Despite advances in hydrological Deep Learning (DL) models using Single Task Learning (STL), the intricate relationships among multiple hydrological components and model inputs might not be comprehensively encapsulated. This study employed a Long Short-Term Memory (LSTM) neural network and the CAMELS dataset to develop a Multi-Task Learning (MTL) model, predicting streamflow and evapotranspiration across multiple basins. An optimal multi-task loss weight ratio was determined manually during the validation phase for all 591 selected basins with streamflow data-gaps under 5%. During test period, MTL showed median Nash-Sutcliffe Efficiency predictions for streamflow and evapotranspiration at 0.69 and 0.92, consistent with two STL models. The MTL’s strength appeared when predicting the non-target variable, surface soil moisture, using probes derived from LSTM cell states—representative of the internal DL model workings. This prediction showed a median correlation coefficient of 0.90, surpassing the 0.88 and 0.89 achieved by the streamflow and evapotranspiration STL models, respectively. This outcome suggests that MTL models could reveal additional rules aligned with hydrological processes through the inherent correlations among multiple hydrological variables, thereby enhancing their reliability. We termed this as “variable synergy,” where MTL can simultaneously predict varied targets with comparable STL performance, augmented by its robust internal representation. Harnessing this, MTL promises enhanced predictions for high-cost observational variables and a comprehensive hydrological model.

In mountain rivers, sediment from landslides or debris flows can alluviate portions or even full reaches of bedrock channel beds, influencing bedrock river incision rates. Various landscape evolution models have been developed to account for the coevolution of alluvial cover and sediment-flux-dependent bedrock incision. Despite the commonality of their aims, one major difference between these models is the way they account for and conserve sediment. We combine two of the most widely used sediment conservation schemes, an Exner-type scheme and an erosion-deposition scheme, with the saltation-abrasion model for bedrock incision to simulate the coevolution of sediment transport and bedrock incision in a mixed bedrock-alluvial river. We compare models incorporating each of these schemes and perform numerical simulations to explore the transient evolution of bedrock incision rates in response to changes in sediment input. Our results show that the time required for bedrock incision rates to reach a time-invariant value in response to changes in sediment supply is over an order of magnitude faster using the Exner-type scheme than the erosion-deposition scheme. These different response times lead to significantly different time-averaged bedrock incision rates, particularly when the sediment supply is periodic. We explore the implications of different model predictions for modeling mixed bedrock-alluvial rivers where sediment is inevitably delivered to rivers episodically during specific tectonic and climatic events.

Climate projections suggest an increase in drought frequency and intensity in various places over the globe, one of them being Southern Europe, expected to become a hotspot. However, 2018 presented an anomaly with the emergence of a rare “water seesaw” phenomenon, leading to severe drought in Central and Northern Europe while Southern Europe experienced high humidity. This unexpected event resulted in significant agricultural disparities, emphasizing the influence of interannual variability. The commentary underscores the danger of overlooking short-term climate variability, vital for accurate adaptation planning, especially for vulnerable regions, when focusing solely on long-term trends. This case serves as a motivation for exploration of global atmospheric circulation changes, emphasizing the need for nuanced modeling approaches to grasp subtle complexities in climate predictions and considering short-term climate variability alongside long-term trends.

Tectonic and/or climatic perturbations can drive drainage adjustment. The capture events, significantly changing the river network topology, are the major events in river network evolution. While they could be identified through field observations and provenance analysis, reconstructing this evolution process and pinpointing the capture time remain challenging. Following a capture event, the steady-state elevation of the captor river will be much lower than that of the beheaded river. Then, the newly-formed drainage divide will migrate towards the beheaded river, a process also known as river-channel reversal. The migration of the newly-formed drainage divide provides a new perspective for identifying the reorganization of the river network. Here, we employ numerical modeling to reproduce the characteristic phenomena of drainage-divide migration following capture events and analyze the effects of different parameters on the migration rate. We find that (1) the migration of newly-formed drainage divides can last for tens of millions of years, with the migration rate decreasing exponentially over time; (2) larger captured area, higher uplift rate, and lower erosional coefficient, all of which cause a higher cross-divide difference in steady-state elevation, will cause higher migration rate of the newly-formed drainage divide. This insight was further applied to the Dadu-Anning and Yarlung-Yigong capture events. We predict the present Dadu-Anning drainage divide would further migrate ~65–92 km southward to reach a steady state in tens of millions of years. The Yarlung-Yigong capture event occurred in the early-middle Cenozoic, which implies that the late-Cenozoic increased exhumation rate is not related to the capture event.

A document by Yi Yang. Click on the document to view its contents.