Catchment Model Research Articles

Lithosphere infiltrometer: A low cost, automated infiltrometer (permeameter) for measurement of soil health, infiltration, and hydraulic conductivity

Accurate measurement of infiltration rate and hydraulic conductivity is important for assessing soil physical health, irrigation management and catchment modelling. Commercially available infiltrometers are often expensive, fragile, and require specialist knowledge to operate and perform complex post-measurement calculations. We developed the Lithosphere Infiltrometer (permeameter) to be usable for both experienced soil researchers, and users without prior technical or scientific expertise in soil science. The Lithosphere Infiltrometer is able measure both surface and subsoil infiltration rate and hydraulic conductivity. It is manufactured using 3D-printed parts and off-the-shelf electronic components. It has a robust, quad-leg design that allows for rapid leveling of the device and accurate setting of water depth via a novel 3D-printed bell chamber. The water level is measured using a U85 JRT laser with ±1 mm accuracy connected to an Adafruit Huzzah 32 microcontroller. Infiltration rate and hydraulic conductivity are calculated at 1 to 10 min time steps, and data is presented in real-time on an OLED display. Data is stored on the device and available for download to a mobile device by Wi-Fi.

Characterizing nonlinear, nonstationary, and heterogeneous hydrologic behavior using ensemble rainfall–runoff analysis (ERRA): proof of concept

Abstract. A classical approach to understanding hydrological behavior is the unit hydrograph and its many variants, but these often assume linearity (runoff response is proportional to effective precipitation), stationarity (runoff response to a given unit of rainfall is identical, regardless of when it falls), and spatial homogeneity (runoff response depends only on spatially averaged precipitation). In the real world, by contrast, runoff response is typically nonlinear, nonstationary, and spatially heterogeneous. Quantifying this nonlinearity, nonstationarity, and spatial heterogeneity is essential to unraveling the mechanisms and subsurface properties controlling hydrological behavior. Here, I present proof-of-concept demonstrations illustrating how nonlinear, nonstationary, and spatially heterogeneous rainfall–runoff behavior can be quantified, directly from data, using ensemble rainfall–runoff analysis (ERRA), a data-driven, model-independent method for quantifying rainfall–runoff relationships across a spectrum of time lags. I show how ERRA uses nonlinear deconvolution to quantify how catchments' runoff responses vary with precipitation intensity and to estimate their precipitation-weighted runoff response distributions. I further illustrate how ERRA combines nonlinear deconvolution with de-mixing techniques to reveal how runoff response depends jointly on precipitation intensity and nonstationary ambient conditions, including antecedent wetness and vapor pressure deficit. I demonstrate how ERRA's de-mixing techniques can be used to quantify spatially heterogeneous runoff responses in different parts of a catchment, even if those subcatchments are not separately gauged. I also illustrate how ERRA's broken-stick deconvolution capabilities can be used to quantify multiscale runoff responses that combine hydrograph peaks lasting for hours and recessions lasting for weeks, well beyond the average spacing between storms. ERRA can unscramble these multiple effects on runoff response even if they are overprinted on each other through time and even if they are corrupted by autoregressive moving average (ARMA) noise. Results from this approach may be informative for catchment characterization, process understanding, and model–data comparisons; they may also lead to a better understanding of storage dynamics and landscape-scale connectivity. An R script is provided to perform the necessary calculations, including uncertainty analysis.

A Dimensionless Framework for the Partitioning of Fluvial Inorganic Carbon

AbstractRivers are pivotal in the global carbon cycle, transporting terrestrial carbon to the ocean while emitting significant amount of to the atmosphere. However, the partitioning of fluvial inorganic carbon (IC) between downstream transport and atmospheric evasion remains uncertain due to intricate hydrodynamic and biogeochemical processes. Inspired by Budyko's hydrological work, this study introduces a dimensionless framework to identify critical factors in fluvial IC partitioning: the IC fraction in equilibrium with the atmosphere and the ratio of advection to evasion timescales. River catchment analyses and modeling reveal that the equilibrium ratio determines the fraction of IC stably transported downstream. The hydrodynamic‐driven timescale ratio determines the fate of out‐of‐equilibrium IC, with low‐order streams favoring atmospheric evasion and higher‐order streams promoting downstream transport. This framework provides a simple yet robust approach to predicting river carbon dynamics, with implications for land‐to‐ocean transport, fluvial emissions, and climate mitigation strategies such as enhanced weathering.

Comparative analysis of rainfall-runoff simulation using a long short-term memory (LSTM) deep learning model and a conceptual hydrological model, HEC-HMS: a case study of the mountainous river basin of Nepal

Accurate simulation and stream flow prediction are essential for effective water resources planning and management. Hydrological models have long been used for rainfall-runoff modeling; in recent years, with the availability of large datasets, machine learning algorithms, and deep learning techniques have become powerful tools to explore. This study compares rainfall-runoff simulation between the Long and Short-Term Memory (LSTM) (a deep learning model) and the Hydrologic Engineering Centre Hydrologic Modeling System (HEC-HMS) (a conceptual model) for a steep mountainous catchment in Nepal. During calibration (1989–2006) and validation (2007–2016), the HEC-HMS model demonstrated a root mean square error (RMSE) and Nash Sutcliffe Efficiency (NSE) of 18.3 and 0.73 and 22.93 and 0.66, respectively, for the calibration and validation periods. During the same periods, the LSTM model showed values of 7.25 and 0.87 and 19.59 and 0.64, respectively, for the calibration and validation periods. The performance indices show that the LSTM model has performed better than the HEC-HMS model. The outcomes of this study suggest that the LSTM deep learning model can simulate the discharge successfully across steep mountainous catchments characterized by monsoon and snowmelt runoff, even with a wide range of precipitation variation, making it an asset for hydrological modeling and water resources management in the data-scarce region.

Simulating streamflow in a transboundary river catchment: The implications of hybrid rainfall data

The lack of observed data for the hydrological modelling of catchments across borders has hindered the management of transboundary water resources. This study investigated the implications of different degrees of hybridization of observed rainfall data using ERA5-Land reanalysis precipitation data in simulating streamflow in the Ruvubu River catchment across the Burundi-Tanzania border. The hydrological model of the Ruvubu River catchment was set up and iteratively updated using 0%–100% hybrid rainfall data, parameterized, simulated, and then evaluated against observed streamflow data at the catchment outlet. The findings show that the performance of the hydrological model decreased as the degree of rainfall data hybridization increased. However, model parameters satisfactorily compensated for input uncertainty when the model had hybrid rainfall data not exceeding 15% hybridization. Subsequently, errors in the simulated flow were also minimal. This implies that the simulated flow from the model when it has hybrid rainfall data not exceeding 15% hybridization can represent the simulated flow when it has 0% hybrid rainfall data. These findings show the need to develop thresholds of rainfall data hybridization for different global precipitation datasets in data-scarce transboundary river catchments.

Urban flooding simulation and flood risk assessment based on the InfoWorks ICM model: A case study of the urban inland rivers in Zhengzhou, China.

Urban flooding intensifies with escalating urbanization. This study focuses on Xiong'er river as the study area and couples a 1D/2D urban flooding model using InfoWorks ICM (Integrated Catchment Modeling). Ten scenarios are set respectively with a rainfall return period of 5a 10a, 20a, 50a, and 100a, alongside rainfall durations of 1 and 24 h. Subsequently, the H-V (hazard-vulnerability) method was applied to evaluate urban flooding risk. Three indicators were selected for each of hazard factors and vulnerability factors. The relative weight values of each indicator factor were calculated using the AHP method. The result shows that (1) flood depth, rate, and duration escalate with longer rainfall return periods, yet decrease as the duration of rainfall increases; (2) as the rainfall return period lengthens, the proportion of node overflow rises, whereas it diminishes with longer rainfall durations, leading to an overall overloaded state in the pipeline network; and (3) the distribution in the research area is mainly low-risk areas, with very few extremely high-risk. Medium to high-risk areas are mainly distributed on both sides of the river, in densely built and low-lying urban areas. This study demonstrates that the model can accurately simulate urban flooding and provide insights for flood analyses in comparable regions.

Hydrodynamic modeling of dam breach floods for predicting downstream inundation scenarios using integrated approach of satellite data, unmanned aerial vehicles (UAVs), and Google Earth Engine (GEE)

Dam breach floods pose significant threats to downstream areas, necessitating accurate prediction of inundation scenarios to mitigate potential damage. This paper presents a novel methodology for hydrodynamic modeling of dam breach floods, leveraging a comprehensive approach that integrates satellite imagery, unmanned aerial vehicles (UAVs), and Google Earth Engine (GEE) to forecast downstream inundation scenarios. Specifically, UAVs were utilized to generate high-resolution Digital Elevation Models (DEMs) of the flood-affected areas, ensuring precise representation of topography in the model. The approach incorporates Cartosat DEM data for catchment modeling, while NASA's Global Precipitation Measurement mission data, integrated with GEE, facilitated accurate estimation of rainfall in ungagged catchment areas. Furthermore, the Hydrological Engineering Center-Hydrological Modeling System was employed for rainfall-runoff simulation and flood hydrograph derivation, followed by application of the HEC River Analysis System (RAS) for hydrodynamic modeling under dam breach conditions. This integrated modeling approach was applied as a case study of Banaskantha district, Gujarat, India. The outcome was the generation of scenario maps based on HEC-RAS results, which include flood extent, water depth, and flow velocity, highlighting downstream areas affected by flooding. Validation of the hydrodynamic dam breach model performance was conducted using actual field measurements and simulated results, employing statistical analysis methods including Support Vector Regression (SVR) and linear regression to determine coefficient of determination (R2), Root-Mean-Square Error, and Mean Absolute Error of observed and simulated data. The coefficient of determination (R2) values for measured and simulated flow (0.91) and water level (0.86) calculated using SVR demonstrate strong correlation between observed and simulated values. This integrated study of hydrodynamic modeling in data-scarce areas aids in accurate estimation of future probable flooding in downstream areas in the event of a dam break, underscoring the potential of advanced surveying and modeling techniques in flood assessment and management. Ultimately, this integration of technologies aims to enhance community resilience and mitigate socioeconomic costs associated with dam breach floods.

Transferability of a Bayesian Belief Network across diverse agricultural catchments using high-frequency hydrochemistry and land management data

Biogeochemical catchment models are often developed for a single catchment and, as a result, often generalize poorly beyond this. Evaluating their transferability is an important step in improving their predictive power and application range. We assess the transferability of a recently developed Bayesian Belief Network (BBN) that simulated monthly stream phosphorus (P) concentrations in a poorly-drained grassland catchment through application to three further catchments with different hydrological regimes and agricultural land uses. In all catchments, flow and turbidity were measured sub-hourly from 2009 to 2016 and supplemented with 400–500 soil P test measurements. In addition to a previously parameterized BBN, five further model structures were implemented to incorporate in a stepwise way: in-stream P removal using expert elicitation, additional groundwater P stores and delivery, and the presence or absence of septic tank treatment, and, in one case, Sewage Treatment Works. Model performance was tested through comparison of predicted and observed total reactive P (TRP) concentrations and percentage bias (PBIAS). The original BBN accurately simulated the absolute values of observed flow and TRP concentrations in the poorly and moderately drained catchments (albeit with poor apparent percentage bias scores; 76 % ≤ PBIAS≤94 %) irrespective of the dominant land use, but performed less well in the groundwater-dominated catchments. However, including groundwater total dissolved P (TDP) and Sewage Treatment Works (STWs) inputs, and in-stream P uptake improved model performance (−5 % ≤ PBIAS≤18 %). A sensitivity analysis identified redundant variables further helping to streamline the model applications. An enhanced BBN model capable for wider application and generalisation resulted.

Effects of a landslide on the geochemistry of dissolved major and trace elements in a granite-gneiss forest catchment of Southeast Brazil

This study investigated the hydrogeochemical variations in a scenario following a landslide event in the Soberbo pristine rainforest small catchment (13 km2). The selected catchment is underlain by granite gneiss and located at the Serra dos Órgãos a mountainous region in the Rio de Janeiro, Brazil, that faces frequent high-intensity rainfall events. Stream water sampling, water discharge, and pH and electrical conductivity measurements were conducted monthly from May 2018 to October 2019. In January 2019, a landslide occurred upstream. Concentrations of dissolved organic carbon (DOC), HCO3−, major elements (Ca, K, Mg, Na, and Si), and trace elements (Al, Ba, Ce, Fe, Mn, Nd, Sr, Rb, La, Pr and Sm) were determined. The Spearman correlation and the multivariate analysis (PCA) indicated that during baseflow, rock weathering hydrolysis reactions are the main source of Si, Na, Mg, Ba and Sr. On the other hand, the forest plays a significant role during the rainy months by remobilizing colloidal litter material (represented by the DOC concentration) closely associated with colloidal Al. The landslide promoted a change in local redox conditions, resulting in the early release of Mn-scavenged elements, and about a month later, Fe and LREE, following a deepening in reduced conditions. The Ce/La molar ratio appeared to be a reliable hydrogeochemical redox proxy. The landslide effects persisted for at least nine months. In this case, the traceability of natural geochemical impacts of landslides was achieved through the river hydrogeochemistry in a mountainous small catchment model area.

Groundwater modelling in SWAT+: Considerations for a realistic baseflow simulation

Hydrological models are valuable tools that support the evaluation and management of water resources. Among catchment scale models, SWAT has been widely used, and a revision of this model, SWAT+, was recently released. Groundwater simulation has been pointed out as one of the main weaknesses of these models, and despite the introduction of some improvements in the new version, an accurate simulation of groundwater flow is still a pending task. One of the most common issues is the inability of the model to maintain baseflow during long dry periods. Baseflow is common in aquifer areas and has great relevance for water quality and ecosystems. Although some authors proposed different solutions to its inaccurate simulation (e.g., editing aquifers configuration, coupling with other models, etc.) this work aimed to determine if a realistic groundwater simulation is possible using SWAT+ and determine the reasons why this is not being achieved. For this purpose, a groundwater dominated catchment was modelled, and different calibration approaches were carried out and compared, including strategies such as hard calibration, soft calibration, or sensitivity analysis. A comprehensive analysis of model outputs was crucial to achieve a model with a realistic simulation of groundwater contribution to the streamflow, both in amount and timing. The properly parameterization of some parameters (perco, latq_co, lat_ttime, and especially alpha) was key. Limitations of hard calibration were evidenced, such as the necessity of complementing this strategy with an evaluation of the simulation of hydrological processes. For those parameters governing streamflow components simulation, a detailed description of its functioning is included in this manuscript, together with some guidelines to achieve a statistically accurate model with a realistic groundwater flow simulation. Follow these recommendations will be useful for SWAT users, and will serve to create more reliable models.

Dimensionality and scales of preferential flow in soils of Shale Hills hillslope simulated using HYDRUS

AbstractPreferential flow (PF) processes are governed by subsurface soil structures at various scales. Still, model validation and mechanistic understanding of PF are very lacking. We hypothesize that PF at hillslope and larger scales cannot be described and quantified when neglecting small‐scaled spatially variable processes and simplifying the model dimensionality. The objective was to learn from comparing simulation results of multidimensional (1D, 2D, and 3D) and multiscale (pedon, catena, and catchment) modeling approaches with comprehensive datasets, and so as to evaluate PF simulations based on the Richards’ equation (solved by the HYDRUS software). Results showed limited alignment between 1D simulations and soil moisture data, mainly affected by vertical changes in porosity, permeability, and precipitation features. 2D and 3D simulations outperformed 1D models. 3D simulations provided satisfactory description of PF dynamics at the pedon scale, considering accurate representations of soil and bedrock structures for three dimensions (vertical, horizontal, and surrounding area). In 2D simulations at the pedon scale, models incorporating dual‐porosity and anisotropy of soils yielded more accurate predictions of water dynamics than single‐porosity and isotropic models. Furthermore, the application of 2D simulation at the catena scale identify PF pathways owing to the enhanced representation of the hydraulic connectivity between different locations along the slope. The results confirmed the significance of multidimensional and multiscale modeling approaches for PF simulations in hillslope hydrology. Considering the complexity and parameterization of 2D and 3D “bottom‐up” physically based models in representing spatial variability within and between soil profiles and/or underlying bedrock geology, the results contribute to creating a modeling framework applicable to identify the PF processes and thus their implications in managing water resources.

On the Challenges of Simulating Streamflow in Glacierized Catchments of the Himalayas Using Satellite and Reanalysis Forcing Data

Abstract Hydrologic assessment of climate change impacts on complex terrains and data-sparse regions like High Mountain Asia is a major challenge. Combining hydrological models with satellite and reanalysis data for evaluating changes in hydrological variables is often the only available approach. However, uncertainties associated with the forcing dataset, coupled with model parameter uncertainties, can have significant impacts on hydrologic simulations. This work aims to understand and quantify how the uncertainty in precipitation and its interaction with the model uncertainty affect streamflow estimation in glacierized catchments. Simulations for four precipitation datasets [Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG), Climate Hazards Group Infrared Precipitation with Station (CHIRPS), ERA5-Land, and Asian Precipitation–Highly Resolved Observational Data Integration Toward Evaluation (APHRODITE)] and two glacio-hydrological models [Glacio-Hydrological Degree-Day Model (GDM) and Hydrological Model for Distributed Systems (HYMOD_DS)] are evaluated for the Marsyangdi and Budhigandaki River basins in Nepal. Temperature sensitivity of streamflow simulations is also investigated. Relative to APHRODITE, which compared well with ground stations, ERA5-Land overestimates the catchment average precipitation for both basins by more than 70%; IMERG and CHIRPS overestimate by ∼20%. Precipitation uncertainty propagation to streamflow exhibits strong dependencies to model structure and streamflow components (snowmelt, ice melt, and rainfall-runoff), but overall uncertainty dampens through precipitation-to-streamflow transformation. Temperature exerts a significant additional source of uncertainty in hydrologic simulations of such environments. GDM was found to be more sensitive to temperature variations, with >50% increase in total flow for 20% increase in actual temperature, emphasizing that models that rely on lapse rates for the spatial distribution of temperature have much higher sensitivity. Results from this study provide critical insight into the challenges of utilizing satellite and reanalysis products for simulating streamflow in glacierized catchments. Significance Statement This work investigates the uncertainty of streamflow simulations due to climate forcing and model parameter/structure uncertainty and quantifies the relative importance of each source of uncertainty and its impact on simulating different streamflow components in glacierized catchments of High Mountain Asia. Results highlight that in high mountain regions, temperature uncertainty exerts a major control on hydrologic simulations and models that do not adequately represent the spatial variability of temperature are more sensitive to bias in the forcing data. These findings provide guidance on important aspects to be considered when modeling glacio-hydrological response of catchments in such areas and are thus expected to impact both research and operation practice related to hydrologic modeling of glacierized catchments.

Understanding Washington State’s Low Uptake of Lung Cancer Screening in Two Steps: A Geospatial Analysis of Patient Travel Time and Health Care Availability of Imaging Sites

BackgroundEarly detection of lung cancer reduces cancer mortality; yet uptake for lung cancer screening (LCS) has been limited in Washington State. Geographic disparities contribute to low uptake, but do not wholly explain gaps in access for underserved populations. Other factors, such as an adequate workforce to meet population demand and the capacity of accredited screening facility sites must also be considered. Research QuestionWhat proportion of the eligible population for LCS have access to LCS facilities in Washington State? Study Design and MethodsWe used the enhanced two-step floating catchment (E2SFCA) model to evaluate how geographic accessibility in addition to availability of LCS imaging centers contribute to disparities. We used available data on radiologic technologist volume at each American College of Radiology (ACR) accredited screening facility site to estimate each site’s capacity to meet potential population demand. Spearman rank correlation coefficients of the spatial access ratios (SPARs) were compared with the 2010 Rural-Urban Commuting Area (RUCA) codes and Area Deprivation Index (ADI) quintiles to identify characteristics of populations at risk for lung cancer with greater and lesser levels of access. ResultsA total of 549 radiologic technologists were identified across the 95 ACR accredited screening facilities. We observed that 95% of the eligible population had proximate geographic access to any ACR facility. However, when we incorporated the E2SFCA method, we found significant variation of access for eligible populations. The inclusion of the availability measure attenuated access for most of the eligible population. Furthermore, we observed that rural areas were substantially correlated and areas with greater socioeconomic disadvantage were modestly correlated with lower access. InterpretationRural and socioeconomically disadvantaged areas face significant disparities. The E2SFCA models demonstrated that capacity is an important component and how geographic access and availability jointly contribute to disparities in access to LCS.

Evaluation of Near Real-Time Global Precipitation Measurement (GPM) Precipitation Products for Hydrological Modelling and Flood Inundation Mapping of Sparsely Gauged Large Transboundary Basins—A Case Study of the Brahmaputra Basin

Limited availability of hydrometeorological data and lack of data sharing practices have added to the challenge of hydrological modelling of large and transboundary catchments. This research evaluates the suitability of latest near real-time global precipitation measurement (GPM)-era satellite precipitation products (SPPs), IMERG-Early, IMERG-Late and GSMaP-NRT, for hydrological and hydrodynamic modelling of the Brahmaputra Basin. The HEC-HMS modelling system was used for the hydrological modelling of the Brahmaputra Basin, using IMERG-Early, IMERG-Late, and GSMaP-NRT. The findings showed good results using GPM SPPs for hydrological modelling of large basins like Brahmaputra, with Nash–Sutcliffe efficiency (NSE) and R2 values in the range of 0.75–0.85, and root mean square error (RMSE) between 7000 and 9000 m3 s−1, and the average discharge was 20611 m3 s−1. Output of the GPM-based hydrological models was then used as input to a 1D hydrodynamic model to assess suitability for flood inundation mapping of the Brahmaputra River. Simulated flood extents were compared with Landsat satellite-captured images of flood extents. In critical areas along the river, the probability of detection (POD) and critical success index (CSI) values were above 0.70 with all the SPPs used in this study. The accuracy of the models was found to increase when simulated using SPPs corrected with ground-based precipitation datasets. It was also found that IMERG-Late performed better than the other two precipitation products as far as hydrological modelling was concerned. However, for flood inundation mapping, all of the three selected products showed equally good results. The conclusion is reached that for sparsely gauged large basins, particularly for trans-boundary ones, GPM-era SPPs can be used for discharge simulation and flood inundation mapping.

Spatial and Temporal Changes in Nutrient Source Contribution in a Lowland Catchment Within the Baltic Sea Region Under Climate Change Scenarios

AbstractCurrently, climate change is considered as an important factor affecting nutrient loads introduced through riverine systems into the Baltic Sea. Although the prospect of a large increase in pollution has long seemed very real, it still does not translate into planning of effective remedial actions. One of the factors limiting the development of such activities is the scale of simulations, focusing generally on catchment outlet profiles. To fill this gap and enable a step forward in understanding responses toward future predictions in a higher resolution scale (subcatchment), we assessed nutrient load contribution using calculation profiles localized along a main watercourse and its tributaries. To track spatial and seasonal changes of total nitrogen and phosphorus for the Wełna River (central Poland), we used climate change data and the SWAT model. Having at our disposal a catchment model with a good performance we could follow not only total load changes in particular subcatchments, but also track localization of the pollution sources and their direct impact on load estimations. Our results showed an increase of the loads, especially from the agricultural land use type, up to 34% for TN and 85% for TP, in the most extreme scenario. Moreover, forest areas have been noted as highly reactive to climate change, and through their localisation are able to distinctly alter nutrient outflow. Finally, the contribution of urban areas should be further investigated since the dynamics of nitrogen and phosphorus release from impervious surfaces is noticeably different here than from the other diffuse sources.

Hybrid Analytical-Numerical Modeling of Surface Geometry Evolution and Deposition Integrity in a Multi-Track Laser-Directed Energy Deposition Process

Abstract Modeling multitrack laser-directed energy deposition (LDED) is different from single-track deposition. There is a temporal variation in the deposition geometry and integrity in a multitrack deposition, which is not well understood. This article employs an analytical model for power attenuation and powder catchment in the melt pool in conjunction with a robust fully coupled metallurgical-thermomechanical finite element (FE) model iteratively to simulate the multitrack deposition. The novel hybrid analytical–numerical approach incorporates the effect of preexisting tracks on melt pool formation, powder catchment, geometry evolution, dilution, residual stress, and defect generation. CPM 9V steel powder was deposited on the H13 tool steel substrate for validating the model. The deposition height is found to be a function of the track sequence but reaches a steady-state height after a finite number of tracks. The height variation determines the waviness of the deposited surface and, therefore, the effective layer height. The inter-track spacing (I) plays a vital role in steady-state height evolution. A larger value of I facilitates faster convergence to the steady-state height but increases the surface waviness. The FE model incorporates the effects of differential thermal contraction, volume dilation, and transformation-induced plasticity. It predicts the deposition geometry and integrity as a function of inter-track spacing and powder feed rate. The insufficient remelting of the substrate or the preceding track can induce defects. A method to predict and mitigate these defects has also been presented in this article.

Land surface modeling informed by earth observation data: toward understanding blue–green–white water fluxes in High Mountain Asia

ABSTRACT Mountains are important suppliers of freshwater to downstream areas, affecting large populations in particular in High Mountain Asia (HMA). Yet, the propagation of water from HMA headwaters to downstream areas is not fully understood, as interactions in the mountain water cycle between the cryo-, hydro- and biosphere remain elusive. We review the definition of blue and green water fluxes as liquid water that contributes to runoff at the outlet of the selected domain (blue) and water lost to the atmosphere through vapor fluxes, that is evaporation from water, ground, and interception plus transpiration (green) and propose to add the term white water to account for the (often neglected) evaporation and sublimation from snow and ice. We provide an assessment of models that can simulate the cryo-hydro-biosphere continuum and the interactions between spheres in high mountain catchments, going beyond disciplinary separations. Land surface models are uniquely able to account for such complexity, since they solve the coupled fluxes of water, energy, and carbon between the land surface and atmosphere. Due to the mechanistic nature of such models, specific variables can be compared systematically to independent remote sensing observations – providing vital insights into model accuracy and enabling the understanding of the complex watersheds of HMA. We discuss recent developments in spaceborne earth observation products that have the potential to support catchment modeling in high mountain regions. We then present a pilot study application of the mechanistic land surface model Tethys & Chloris to a glacierized watershed in the Nepalese Himalayas and discuss the use of high-resolution earth observation data to constrain the meteorological forcing uncertainty and validate model results. We use these insights to highlight the remaining challenges and future opportunities that remote sensing data presents for land surface modeling in HMA.

Influence of Subsurface Critical Zone Structure on Hydrological Partitioning in Mountainous Headwater Catchments

AbstractHeadwater catchments play a vital role in regional water supply and ecohydrology, and a quantitative understanding of the hydrological partitioning in these catchments is critically needed, particularly under a changing climate. Recent studies have highlighted the importance of subsurface critical zone (CZ) structure in modulating the partitioning of precipitation in mountainous catchments; however, few existing studies have explicitly taken into account the 3D subsurface CZ structure. In this study, we designed realistic synthetic catchment models based on seismic velocity‐estimated 3D subsurface CZ structures. Integrated hydrologic modeling is then used to study the effects of the shape of the weathered bedrock and the associated storage capacity on various hydrologic fluxes and storages in mountainous headwater catchments. Numerical results show that the weathered bedrock affects not only the magnitude but also the peak time of both streamflow and subsurface dynamic storage.

SWAT + input data preparation in a scripted workflow: SWATprepR

Input data collection, quality assurance and preparation are central but time_consuming steps in environmental modeling. Errors due to manual processing of model input data can result in an incorrect representation of an environmental system and may consequently lead to implausible model simulations. Correct input data preparation and thorough quality check at an early stage of the model setup procedure are essential to build confidence in model simulation results. Typically, in environmental model applications, many steps in the input data preparation phase have to be repeated with the inflow of new, additional or corrected data. In this study, we selected the widely used SWAT + ecohydrological model as an illustrative example to investigate challenges related to input data preparation. To assist in these tasks, we developed an R package named SWATprepR, which provides functions for typical and repeating SWAT + model input data preparation tasks. The package supports the preparation of weather input files, atmospheric deposition, soil parameters, crop rotations, and observed (control or calibration) data, to name a few, presently with focus on European applications. The SWATprepR functions are integrated in R script workflows and can help SWAT + modelers to avoid repetitive tasks, secure reproducibility and transparently document the data processing steps. Application of the package is illustrated with a test case of a SWAT + model for a small catchment in central Poland.

On the development and analysis of coupled surface–subsurface models of catchments. Part 3. Analytical solutions and scaling laws

The objective of this three-part work is to formulate and rigorously analyse a number of reduced mathematical models that are nevertheless capable of describing the hydrology at the scale of a river basin (i.e. catchment). Coupled surface and subsurface flows are considered. In this third part, we focus on the development of analytical solutions and scaling laws for a benchmark catchment model that models the river flow (runoff) generated during a single rainfall. We demonstrate that for catchments characterised by a shallow impenetrable bedrock, the shallow-water approximation allows a reduction of the governing formulation to a coupled system of one-dimensional time-dependent equations for the surface and subsurface flows. Asymptotic analysis is used to derive semi-analytical solutions for the model. We provide simple asymptotic scaling laws describing the peak flow formation, and demonstrate its accuracy through a comparison with the two-dimensional model developed in Part 2. These scaling laws can be used as an analytical benchmark for assessing the validity of other physical, conceptual or statistical models of catchments.