Hydrological Modelling Framework Research Articles

A practitioner-oriented regional hydrology data product for use in site-specific hydraulic applications

In the 465,000 km2 Canadian Prairies ecozone, robust hydrological input data for hydraulic model applications are uncommon because of the sparse monitoring network and the intermittently connected stream network. New hydrological datasets can offer a valuable advancement for making water management decisions and designing infrastructure in this water stressed region. The Prairie Hydrology Design and Analysis Product (PHyDAP) was created to address existing limitations, and provides a comprehensive regional dataset for use in hydraulic modelling applications. PHyDAP is a collection of outputs from a physically based hydrological modelling framework, run for periods ranging from 38 to 150 years, according to three climate forcing datasets. The dataset includes vertical and lateral fluxes (rainfall, snowmelt, upland runoff, and open water evaporation) and basin streamflow, at hourly or 3-hourly intervals for the >4000 small basins of approximately 100 km2 that span the region. This contribution describes the motivation for this work and methodology used to derive the data product, summarizes the data and its accessibility, and provides an overview of potential use cases.

Using hydrological modeling and satellite observations to elucidate subsurface and surface hydrological responses to the extreme drought

Climate change and anthropogenic activities have intensified extreme weather events globally. In the summer of 2022, the Yangtze River Basin (YRB) in China experienced an extreme drought, significantly impacting the ecosystems and society. However, the specific effects of this extreme drought on surface and subsurface hydrological dynamics remain unclear. Here we employed satellite-observed terrestrial water storage anomaly (TWSA) and a modified hydrological model with consideration of reservoir operation, human water consumption, and water diversion engineering to quantify how subsurface and surface water in YRB responded to such an extreme drought in 2022. Validation against a series of observations shows that the modified model has good performance in reproducing daily streamflow, reservoir water storage, lake water storage, and snow water equivalent. It achieved more precise GRACE TWSA estimates in the YRB with significant human intervention, and therefore it can fairly accurately quantify both surface and subsurface hydrological responses to the 2022 extreme drought. Compared to the same months (July-December) in 2015–2021, the drought in 2022 resulted in a decrease in precipitation and discharge of 373 km3 (36 %) and 324 km3 (50 %), respectively, while an increase in evapotranspiration of 156 km3 (29 %) in the YRB. In general, the surface water storage (SWS) is relatively low from July 2022, followed by subsurface water storage (SSWS) from August 2022, indicating an approximately one-month lag from the former to the latter. During the latter half year of 2022, the SWS and SSWS reduced by 48 km3 and 83 km3, respectively, suggesting the changes in the latter dominated the TWS variations. This study sheds light on the responses of surface and subsurface hydrology to extreme droughts, and the hydrological modeling framework with consideration of human activities proposed here holds applicability beyond the YRB.

Assessing the suitability of groundwater resources based on farmers’ predicted intentions and human–water feedbacks

In this study, a behavioral −hydrological simulation modeling framework is developed to explore the functioning of human-water systems. The main objective is to identify the psycho-social factors affecting water conservation intentions among farmers, and then realize the interactions and bidirectional feedback between humans and water resources. For this purpose, the agent-based modeling (ABM) and extended theory of planned behavior (TPB) is recruited to reflect on the agents’ behaviors. As the first step, the framework of the extended TPB together with the agent behavior was designed via field researches through the completion of 169 questionnaires upon proportional-to-size (PPS) random sampling. The partial least squares-structural equation modeling (PLS-SEM) is then implemented, at the second step, to analyze the data and obtain the weights of the extended TPB constructs. The results reveal that the constructs supplemented to the TPB, including moral norms (MNs) and perceived risk (PR), could have significant effects on water conservation intentions and behaviors among farmers. At the last step, the farmers and their decision-making as the dynamic elements by obtaining feedbacks of water resources were modeled using the ABM. To evaluate the proposed framework, the recorded data of groundwater fluctuations and cultivated areas in the Lenjanat plain is utilized and compared with the socio-hydrological simulation modeling outcomes using the Nash-Sutcliffe efficiency (NSE) coefficient. Then, five scenarios are defined to shed light on the changes in the agent behavior on the aquifer suitability, including water consumption rate and groundwater levels in the aquifer. The results correspondingly demonstrated that merging the second and third scenarios in an effort to raise the prices of crops with low water need along with the increase in utility costs (here, water and electricity) could lead to a 75% increase of farmers’ income and a 21.5% reduction in the surface and groundwater uses in this region, implying the highest level of water conservation among other scenarios.

Enhancing physically-based hydrological modeling with an ensemble of machine-learning reservoir operation modules under heavy human regulation using easily accessible data

Dams and reservoirs have significantly altered river flow dynamics worldwide. Accurately representing reservoir operations in hydrological models is crucial yet challenging. Detailed reservoir operation data is often inaccessible, leading to relying on simplified reservoir operation modules in most hydrological models. To improve the capability of hydrological models to capture flow variability influenced by reservoirs, this study proposes a hybrid hydrological modeling framework, which combines a process-based hydrological model with a machine-learning-based reservoir operation module designed to simulate runoff under reservoir operations. The reservoir operation module employs an ensemble of three machine learning models: random forest, support vector machine, and AutoGluon. These models predict reservoir outflows using precipitation and temperature data as inputs. The Soil and Water Assessment Tool (SWAT) then integrates these outflow predictions to simulate runoff. To evaluate the performance of this hybrid approach, the Xijiang Basin within the Pearl River Basin, China, is used as a case study. The results highlight the superiority of the SWAT model coupled with machine learning-based reservoir operation models compared to alternative modeling approaches. This hybrid model effectively captures peak flows and dry period runoff. The Nash-Sutcliffe Efficiency (NSE) in daily runoff simulations shows substantial improvement, ranging from 0.141 to 0.780, with corresponding enhancements in the coefficient of determination (R2) by 0.098–0.397 when compared to the original reservoir operation modules in SWAT. In comparison to parameterization techniques lacking a dedicated reservoir module, NSE enhancements range from 0.068 to 0.537, and R2 improvements range from 0.027 to 0.139. The proposed hybrid modeling approach effectively characterizes the impact of reservoir operations on river flow dynamics, leading to enhanced accuracy in runoff simulation. These findings offer valuable insights for hydrological forecasting and water resources management in regions influenced by reservoir operations.

Wflow_sbm v0.7.3, a spatially distributed hydrological model: from global data to local applications

Abstract. The wflow_sbm hydrological model, recently released by Deltares, as part of the Wflow.jl (v0.7.3) modelling framework, is being used to better understand and potentially address multiple operational and water resource planning challenges from a catchment scale to national scale to continental and global scale. Wflow.jl is a free and open-source distributed hydrological modelling framework written in the Julia programming language. The development of wflow_sbm, the model structure, equations and functionalities are described in detail, including example applications of wflow_sbm. The wflow_sbm model aims to strike a balance between low-resolution, low-complexity and high-resolution, high-complexity hydrological models. Most wflow_sbm parameters are based on physical characteristics or processes, and at the same time wflow_sbm has a runtime performance well suited for large-scale high-resolution model applications. Wflow_sbm models can be set a priori for any catchment with the Python tool HydroMT-Wflow based on globally available datasets and through the use of point-scale (pedo)transfer functions and suitable upscaling rules and generally result in a satisfactory (0.4 ≥ Kling–Gupta efficiency (KGE) < 0.7) to good (KGE ≥ 0.7) performance for discharge a priori (without further tuning). Wflow_sbm includes relevant hydrological processes such as glacier and snow processes, evapotranspiration processes, unsaturated zone dynamics, (shallow) groundwater, and surface flow routing including lakes and reservoirs. Further planned developments include improvements on the computational efficiency and flexibility of the routing scheme, implementation of a water demand and allocation module for water resource modelling, the addition of a deep groundwater concept, and computational efficiency improvements through for example distributed computing and graphics processing unit (GPU) acceleration.

Hydrologic Evaluation of the Global Precipitation Measurement Mission over the U.S.: Effect of Spatial and Temporal Scales

This research focuses on the effects of spatio-temporal resolutions of global satellite precipitation on simulated flood events characteristics. The analysis is carried out by spatially, temporally, and spatiotemporally upscaling fine-scale precipitation forcings from the ground-radar-based Multi-Radar Multi-Sensor system (MRMS) to the resolution of the Integrated Multi-satellitE Retrievals for GPM (IMERG) satellite precipitation product. These upscaled products were then run through the Ensemble Framework for Flash Flood Forecasting hydrologic modeling framework over the U.S. to assess how the different spatial and temporal resolutions impact the simulated outputs of flood event magnitude, duration, and timing. It was found that the quantitative uncertainties generated by the IMERG Early (IMERG-E) simulations overpowered those associated with resolution across all flood characteristics. When compared to native MRMS simulations, however, the upscaled precipitation estimates tended to underestimate peak discharges and event durations, associated with distinct negative water balance errors. It was also found that the upscaled precipitation simulations exhibited a scaling effect with regards to error contribution of peak discharge, with higher contributions at smaller basin sizes and decreasing contributions as basin sizes increased. Looking at how upscaling can affect the modeled output desired allows for improved understanding of which resolutions lead to the greatest changes in simulation accuracy, increasing the potential global use and utility of current or future satellite products, especially in regions where high-resolution ground radars are sparse.

Fuzzy C-Means clustering for physical model calibration and 7-day, 10-year low flow estimation in ungaged basins: comparisons to traditional, statistical estimates

In the northeast U.S., resource managers commonly apply 7-day, 10-year (7Q10) low flow estimates for protecting aquatic species in streams. In this paper, the efficacy of process-based hydrologic models is evaluated for estimating 7Q10s compared to the United States Geological Survey's (USGS) widely applied web-application StreamStats, which uses traditional statistical regression equations for estimating extreme flows. To generate the process-based estimates, the USGS's National Hydrologic Modeling (NHM-PRMS) framework (which relies on traditional rainfall-runoff modeling) is applied with 36 years of forcings from the Daymet climate dataset to a representative sample of ninety-four unimpaired gages in the Northeast and Mid-Atlantic U.S. The rainfall-runoff models are calibrated to the measured streamflow at each gage using the recommended NHM-PRMS calibration procedure and evaluated using Kling-Gupta Efficiency (KGE) for daily streamflow estimation. To evaluate the 7Q10 estimates made by the rainfall-runoff models compared to StreamStats, a multitude of error metrics are applied, including median relative bias (cfs/cfs), Root Mean Square Error (RMSE) (cfs), Relative RMSE (RRMSE) (cfs/cfs), and Unit-Area RMSE (UA-RMSE) (cfs/mi2). The calibrated rainfall-runoff models display both improved daily streamflow estimation (median KGE improving from 0.30 to 0.52) and 7Q10 estimation (smaller median relative bias, RMSE, RRMSE, and UA-RMSE, especially for basins larger than 100 mi2). The success of calibration is extended to ungaged locations using the machine learning algorithm Fuzzy C-Means (FCM) clustering, finding that traditional K-Means clustering (FCM clustering with no fuzzification factor) is the preferred method for model regionalization based on (1) Silhouette Analysis, (2) daily streamflow KGE, and (3) 7Q10 error metrics. The optimal rainfall-runoff models created with clustering show improvement for daily streamflow estimation (a median KGE of 0.48, only slightly below that of the calibrated models at 0.52); however, these models display similar error metrics for 7Q10 estimation compared to the uncalibrated models, neither of which provide improved error compared to the statistical estimates. Results suggest that the rainfall-runoff models calibrated to measured streamflow data provide the best 7Q10 estimation in terms of all error metrics except median relative bias, but for all models applicable to ungaged locations, the statistical estimates from StreamStats display the lowest error metrics in every category.

Assimilation of Sentinel-1 Backscatter into a Land Surface Model with River Routing and Its Impact on Streamflow Simulations in Two Belgian Catchments

Abstract Accurate streamflow simulations rely on good estimates of the catchment-scale soil moisture distribution. Here, we evaluated the potential of Sentinel-1 backscatter data assimilation (DA) to improve soil moisture and streamflow estimates. Our DA system consisted of the Noah-MP land surface model coupled to the HyMAP river routing model and the water cloud model as a backscatter observation operator. The DA system was set up at 0.01° resolution for two contrasting catchments in Belgium: (i) the Demer catchment dominated by agriculture and (ii) the Ourthe catchment dominated by mixed forests. We present the results of two experiments with an ensemble Kalman filter updating either soil moisture only or soil moisture and leaf area index (LAI). The DA experiments covered the period from January 2015 through August 2021 and were evaluated with independent rainfall error estimates based on station data, LAI from optical remote sensing, soil moisture retrievals from passive microwave observations, and streamflow measurements. Our results indicate that the assimilation of Sentinel-1 backscatter observations can partly correct errors in surface soil moisture due to rainfall errors and overall improve surface soil moisture estimates. However, updating soil moisture and LAI simultaneously did not bring any benefit over updating soil moisture only. Our results further indicate that streamflow estimates can be improved through Sentinel-1 DA in a catchment with strong soil moisture–runoff coupling, as observed for the Ourthe catchment, suggesting that there is potential for Sentinel-1 DA even for forested catchments. Significance Statement The purpose of this study is to improve streamflow estimation by integrating soil moisture information from satellite observations into a hydrological modeling framework. This is important preparatory work for operational centers that are responsible for producing the most accurate flood forecasts for the society. Our results provide new insights into how and where streamflow forecasting could benefit from high-spatial-resolution Sentinel-1 radar backscatter observations.

A hydrologic and river ice modelling framework for assessing ungauged sub-basin streamflow impact in a large cold-region river basin

ABSTRACT Modelling snow- and ice-affected streamflow in cold regions is challenging. Neglecting the streamflow from ungauged regions/sub-basins of a river basin in the inflow boundaries of a river ice model adds further uncertainties. This study combined a river ice model (River1D) with a hydrological model (Soil and Water Assessment Tool, SWAT) to investigate the impacts of ungauged sub-basin streamflow on peak flow simulation under both open water and river ice breakup conditions in the Peace River Basin (PRB). Results showed that ungauged sub-basins of the PRB can greatly affect peak flow simulation of the River1D for both open water and river ice breakup events, especially for flood events. Although they represent only about 26.54% of the whole modelled area in the PRB, the SWAT model simulated results show that the ungauged sub-basins can contribute nearly 50% of peak flow for the open water and river ice breakup flood events.

Assessing water security and footprint in hypersaline Lake Urmia

The quantitative assessment of water security including its distribution helps plan and manage adequate amounts of quality water to sustain livelihoods. This research introduces a hydrological modeling framework designed to evaluate the spatial and temporal fluctuations of blue water and green water, aiming to quantify the water security status of Lake Urmia in Iran. This lake, recognized as the second largest hypersaline lake globally, has experienced a significant decline in its water storage in recent years. This model, based on water consumption and population size was used to investigate water security and footprint while quantifying the spatial and temporal changes in the availability of blue water and green water. As the results demonstrate, the monthly and annual coefficient of variation for blue water (41.1 % and 27.6%, respectively) was greater than for green water (32.3% and 21.7%, respectively), hence green water flow showed more homogeneous than blue water flow distribution among different sub-basins. At the same time, the coefficient of variation for the spatial distribution differs between various components of the water footprint. Agricultural use requires the most water in this basin, with a blue water scarcity of 0.27. The blue water vulnerability value for total water consumption indicates a lower risk of a water crisis under low-flow conditions. In addition, the freshwater provision indicator in each sub-basin varies between the middle (0.64) and lower (0.80) parts. Thus, in these parts, water can be consumed with less negative impact on the environment. In conclusion, the environmental situation of Lake Urmia and its water security has clearly decreased over the last decade which requires urgent management action (e.g., changing irrigation pattern) to mitigate an environmental crisis. This research offers precise and quantifiable references related to water resources and water security, and can enhance targeted water footprint management within the study area. Our hydrological modeling framework provides the basis to inform water management in various similar watersheds across the globe.

Developing a stochastic hydrological model for informing lake water level drawdown management

Winter drawdown (WD) is a common lake management tool for multiple purposes such as flood control, aquatic vegetation reduction, and lake infrastructure maintenance. To minimize adverse impacts to a lake’s ecosystem, regulatory agencies may provide managers with general guidelines for drawdown and refill timing, drawdown magnitude, and outflow limitations. However, there is significant uncertainty associated with the potential to meet management targets due to variability in lake characteristics and hydrometeorology of each lake’s basin, making the use of modeling tools a necessity. In this context, we developed a hydrological modeling framework for lake water level drawdown management (HMF-Lake) and evaluated it at 15 Massachusetts lakes where WDs have been applied over multiple years for vegetation control. HMF-Lake is based on the daily lake water balance, with inflows simulated by a lumped rainfall-runoff model (Cemaneige-GR4J) and outflow rate calculated by a modified Target Storage and Release Based Method (TSRB). The model showed a satisfactory performance of simulating historical water levels (0.53 ≤ NSE ≤ 0.86), however, uncertainties from meteorological inputs and TSRB determined lake outflow rate affected the result accuracy. To account for these uncertainties, the model was executed stochastically to assess the ability of study lakes to follow the Massachusetts’ general WD guidelines: drawdown by Dec 1 and fully refilled by Apr 1. By using the stochastic HMF-Lake, the probabilities of each lake to reach the drawdown level by Dec 1 were calculated for different drawdown magnitudes (1–6 ft). The probability results suggest it was generally less possible for most of study lakes to achieve a drawdown of 3 ft or more by Dec 1. Moreover, we employed the stochastic model to derive the annual latest refill starting dates that ensure a 95 % probability of reaching the normal water level by Apr 1. We found starting a refill in March for drawdowns up to 6 ft was feasible for most of study lakes. These results provide lake managers with a quantitative understanding of the lake’s ability to follow the state guidelines. The model may be used to systematically evaluate current WD management strategies at state or regional scales and support adaptive WD management under changing climates.

Introducing APEXMOD - A QGIS plugin for developing coupled surface-subsurface hydrologic modeling framework of APEX, MODFLOW, and RT3D-Salt

Introducing APEXMOD - A QGIS plugin for developing coupled surface-subsurface hydrologic modeling framework of APEX, MODFLOW, and RT3D-Salt

The Benefits of Continental-Scale High-Resolution Hydrological Modeling in the Detection of Extreme Hydrological Events in China

High-resolution hydrological modeling is crucial for detecting extreme hydrological events and understanding fundamental terrestrial processes. However, spatial resolutions in current hydrological modeling studies have been mostly constrained to relatively coarse resolution (~10–100 km), and they therefore have a difficult time addressing flooding or drought issues with fine resolutions. In this study, a continental-scale high-resolution hydrological modeling framework (0.0625°, ~6 km) driven by remote sensing products was used to detect extreme hydrological event occurrences in China and evaluated based on the Variable Infiltration Capacity (VIC) model. The results showed that the developed model provided more detailed information than the coarser resolution models (a 0.25° and 1°), thereby capturing the timing, duration, and spatial extent of extreme hydrologic events regarding the 2012 Beijing flood and 2009/10 drought in Hai River Basin. Here, the total water storage changes were calculated based on the VIC model (−0.017 mm/year) and Gravity Recovery and Climate Experiment (GRACE) satellite (−0.203 mm/year) to reflect the water availability caused by climate change and anthropogenic factors. This study found that the 0.0625° dataset could capture detailed changes, thereby providing reliable information during occurrences of extreme hydrological events. The high-resolution model integrated with remote sensing products could be used for accurate evaluations of continental-scale extreme hydrological events and can be valuable in understanding its long-term occurrence and water resource security.

Construction of a daily streamflow dataset for Peru using a similarity-based regionalization approach and a hybrid hydrological modeling framework

Study regionA total of 11,913 sub-catchments in Peru and transboundary catchments with neighboring countries in South America. Study focusThis paper aims to develop a national hydrological model using physiographic and climatic characteristics to identify donor and receptor sub-catchments (sub-zones). Therefore, we use the hydrometeorological PISCO dataset (0.1º x 0.1º) to drive a sub-catchment conceptual rainfall-runoff (ARNO/VIC) model and a river-routing (RAPID) model in thousands of river reaches. We identify 43 hydrological zones (with 122 sub-zones) to run the hybrid hydrological modeling framework (ARNO/VIC+RAPID) with previously calibrated and validated parameters with 43 fluviometric stations for 1981–2020. Simulated flow series show a higher performance at daily scale (KGE ≥ 0.75, NSEsqrt ≥ 0.65, MARE ≤ 1, and −25% ≤ PBIAS ≤ 25%) for catchments located at the Pacific coast and the Andes-Amazon transition, and good representation (R≥0.75) of seasonal and interannual variability. New hydrological insights for the regionIncreasing hydrological hazards such as floods highlight the importance of a systematic hydrological analysis and modeling at national level in gauged and ungauged catchments in Peru. This study introduces a new hydrological dataset of simulated daily flow series. The results are helpful for short-term flood risk scenario simulations and long-term water resource planning as the outcomes can provide valuable information for hydrologists and water resource managers in Peruvian regions with limited or no access to in-situ networks.

A hybrid hydrologic modelling framework with data-driven and conceptual reservoir operation schemes for reservoir impact assessment and predictions

Reservoirs have been built worldwide to address the water-related issues. To fully understand their potential impacts on the hydrologic regime, explicitly parameterizing reservoir operation in hydrologic models is often required. In this study, two data-driven reservoir operation schemes based on extreme gradient boosting (XGBoost) and artificial neural network (ANN) are respectively developed to predict the reservoir release and storage in hydrologic models for reservoirs with historic in-situ inflow, storage, release data. Then, a hybrid modelling framework is proposed by coupling a high-resolution (3 km) hydrologic model with (1) the developed data-driven reservoir operation schemes and (2) a calibration-free conceptual reservoir operation scheme designed for data-scarce reservoirs. This allows quantitative assessment of the cumulative impacts of dam operation on the hydrologic regime under different reservoir data availability. The framework is applied to the Upper Yangtze River Basin (UYRB) in China that is one of the most regulated river basins across the country due to extensive reservoir construction. Results indicate that both data-driven reservoir operation schemes can well reconstruct the reservoir releases and storage in the UYRB (daily NSE of ∼ 0.9), and the XGBoost performs slightly better than ANN. By coupling reservoir operation, the model shows a remarkably improved performance in reconstructing the daily streamflow of the basin. The major reservoirs in the UYRB can redistribute excessive water in the wet season to the dry season and attenuate the high and low flows, leading to enhanced water security along the river. Our approach provides a practical framework for reservoir impact assessment and predictions.

Snowmelt Runoff in the Yarlung Zangbo River Basin and Runoff Change in the Future

Comprehending the impacts of climate change on regional hydrology and future projections of water supplies is of great value to manage the water resources in the Yarlung Zangbo River Basin (YZRB). However, large uncertainties from both input data and the model itself exert obstacles to accurate projections. In this work, a hydrological modeling framework was established over the YZRB linking the Variable Infiltration Capacity (VIC) with an empirical formulation, called the degree-day glacier-melt scheme (VIC–Glacier). The model performance was evaluated through three aspects, including streamflow, snow cover area, and glacier area. Nine GCM models and three emission scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5) in CMIP6 were chosen to drive the calibrated VIC–Glacier model. The results showed that both precipitation and temperature resulted in an increase of around 25% and 13%, respectively, in multi-year average runoff from June to September, under SSP5-8.5 and SSP1-2.6. The precipitation runoff was projected to increase, as compensation for the decrease of glacier runoff and snow runoff by the end of the 21st century. An apparent increasing trend in the runoff was expected over the YZRB before 2050 and after the year 2060 under SSP 5-8.5, with a steeply decreasing trend from 2050 to 2060, and a negligible decreasing trend under SSP1-2.6 from 2020 to 2060, in contrast to an increasing trend from 2060 to 2100.

A computationally efficient hydrologic modeling framework to simulate surface-subsurface hydrological processes at the hillslope scale

Considering surface and subsurface interactions is imperative to predict water movement, water quantity and quality in the environment. However, substantial execution time and over-parameterization presently limit the applicability of integrated hydrologic models at larger scales. Herein, a new efficient coupling routine for one-dimensional (1D) surface–subsurface modeling was developed by externally coupling two widely used open-source codes. KINEROS2 (K2) solves the 1D kinematic wave equation for overland flow, and HYDRUS-1D (H1D) solves the Richards equation for subsurface flow. A novel approach, combined with water balance and boundary condition switching, is used to account for surface ponding and water exchange between the two model domains. A weighting factor related to the surface water depth is used to assign the time series of exchange fluxes in one H1D profile to multiple surface nodes in the K2 calculation. This novel approach enables us to represent the entire subsurface below each overland-flow plane by one vertical soil column with different soil layering. When soil properties vary horizontally, multiple soil columns can represent spatial heterogeneities. The performance of the coupled H1D-K2 model is examined by comparing simulation results with the HYDRUS-2D (H2D) model for six benchmark problems. The solution's robustness, stability, and accuracy are assessed for a wide variety of cases, including multiple rainstorms, different slopes, heterogeneous subsurface, and different bottom boundary conditions resulting in 41 cases. The simulated hydrographs, surface water levels, and water balance components are all in good agreement. Relative mass balance errors are very small, typically not exceeding 4.0% for the coupled H1D-K2 model. Compared with the H2D model, the coupled model achieves a factor of 1.3–10 speedup by applying one H1D soil profile to an entire overland flow plane rather than to individual surface nodes.

RavenR v2.1.4: an open-source R package to support flexible hydrologic modelling

Abstract. In recent decades, advances in the flexibility and complexity of hydrologic models have enhanced their utility in scientific studies and practice alike. However, the increasing complexity of these tools leads to a number of challenges, including steep learning curves for new users and issues regarding the reproducibility of modelling studies. Here, we present the RavenR package, an R package that leverages the power of scripting to both enhance the usability of the Raven hydrologic modelling framework and provide complementary analyses that are useful for modellers. The RavenR package contains functions that may be useful in each step of the model-building process, particularly for preparing input files and analyzing model outputs. The utility of the RavenR package is demonstrated with the presentation of six use cases for a model of the Liard River basin in Canada. These use cases provide examples of visually reviewing the model configuration, preparing input files for observation and forcing data, simplifying the model discretization, performing realism checks on the model output, and evaluating the performance of the model. All of the use cases are fully reproducible, with additional reproducible examples of RavenR functions included with the package distribution itself. It is anticipated that the RavenR package will continue to evolve with the Raven project and will provide a useful tool to new and experienced users of Raven alike.

Evaluating Multiple Canopy‐Snow Unloading Parameterizations in SUMMA With Time‐Lapse Photography Characterized by Citizen Scientists

AbstractCanopy‐snow unloading is the complex physical process of snow unloading from the canopy through meltwater drip, sublimation to the atmosphere, or solid snow unloading to the snowpack below. This process is difficult to parameterize due to limited observations. Time‐lapse photographs of snow in the canopy were characterized by citizen scientists to create a data set of snow interception observations at multiple locations across the western United States. This novel interception data set was used to evaluate three snow unloading parameterizations in the Structure for Unifying Multiple Modeling Alternatives (SUMMA) modular hydrologic modeling framework. SUMMA was modified to include a third snow unloading parameterization, termed Wind‐Temperature (Roesch et al., 2001, https://doi.org/10.1007/s003820100153), which includes wind‐dependent and temperature‐dependent unloading functions. It was compared to a meltwater drip unloading parameterization, termed Melt (Andreadis et al., 2009, https://doi.org/10.1029/2008wr007042), and a time‐dependent unloading parameterization, termed Exponential‐Decay (Hedstrom & Pomeroy, 1998, https://doi.org/10.1002/(SICI)1099-1085(199808/09)12:10/11<1611::AID-HYP684>3.0.CO;2-4). Wind‐Temperature performed well without calibration across sites, specifically in cold climates, where wind dominates unloading and rime accretion is low. At rime prone sites, Wind‐Temperature should be calibrated to account for longer interception events with less sensitivity to wind, otherwise Melt can be used without calibration. The absence of model physics in Exponential‐Decay requires local calibration that can only be transferred to sites with similar unloading patterns. The choice of unloading parameterization can result in 20% difference in SWE on the ground below the canopy and 10% difference in estimated average winter canopy albedo. These novel observations shed light on processes that are often overlooked in hydrology.

An Analysis of the Impact of Groundwater Overdraft on Runoff Generation in the North China Plain with a Hydrological Modeling Framework

The long-term overexploitation of groundwater has caused sharp decreases in groundwater table depth and water storage in the agricultural areas of the North China Plain, which has led to obvious changes in the runoff process of the hydrological cycle, affecting the mechanism of runoff generation. Evaluating the impact of groundwater overdraft on runoff generation using hydrological models is the focus of the current work. Herein, a hydrological modeling framework is proposed based on the Variable Infiltration Capacity (VIC) model. The optimal parameters of the VIC model were determined by the synergetic calibration method, combining runoff, evaporation, and water storage levels. Meanwhile, a sliding calibration scheme was employed to explore the implied relationships among runoff coefficient, groundwater exploitation, and model parameters, particularly for the thickness of the second soil layer (i.e., parameter d2), both for the whole period and the sliding window periods. Overall, the VIC model showed good applicability in the southern Haihe river plain, as demonstrated by the low absolute value of the relative error (RE) between the simulated and observed data for runoff and evaporation, with all REs < 8%, as well as large correlation coefficients (CC, all > 0.8). In addition, the CCs between the simulated and the observed data for water storage were all above 0.7. The calibrated optimal parameter d2 increased as the sliding window period increased, and the average d2 gradually increased from 0.372 m to 0.415 m, for which we also found high correlations with both the groundwater table and water storage levels. Additionally, increases in the parameter d2 led to decreases in the runoff coefficient. From 2003 to 2016, the parameter d2 increased from 0.36 m to 0.42 m, and the runoff coefficient decreased by about 0.02.