Variable Infiltration Capacity Model Research Articles

Water budget-based evapotranspiration product captures natural and human-caused variability

Abstract Evapotranspiration (ET) is one of the most important yet highly uncertain components of the water cycle. Available modelled ET products do not necessarily agree with each other at various spatiotemporal scales, either due to limitations on input data and/or due to model assumptions and simplifications. Therefore, using water budget equation to estimate ET has gained attention. However, a large number of water-budget combinations with large uncertainties are available that increases ambiguity in choosing the best ET estimate. Here, the Kalman filter is employed for ingesting 96 water-budget based ET estimates and produce a global ET product with uncertainty < 2 mm, and it captures the general spatiotemporal pattern of ET and the inter-annual variability over all continents. Since the water budget includes storage change due to human interventions, our ET estimates are superior over regions with strong irrigation signal, such as the Ganges basin. We verify our claim by using a modified variable infiltration capacity model that simulates irrigation activities as well. Our ET estimates have a global mean positive trend of 0.18 ± 0.02 mm/year with larger regional variations that are discussed.

Improving runoff simulation in the Western United States with Noah-MP and variable infiltration capacity

Abstract. Streamflow predictions are critical for managing water resources and for environmental conservation, especially in the water-short Western United States. Land surface models (LSMs), such as the variable infiltration capacity (VIC) model and the Noah LSM with multiparameterization options (Noah-MP), play an essential role in providing comprehensive runoff predictions across the region. Virtually all LSMs require parameter estimation (calibration) to optimize their predictive capabilities. Here, we focus on the calibration of VIC and Noah-MP models at a 1/16° latitude–longitude resolution across the Western United States. We first performed global optimal calibration of parameters for both models for 263 river basins in the region. We find that the calibration significantly improves the models' performance, with the median daily streamflow Kling–Gupta efficiency (KGE) increasing from 0.37 to 0.70 for VIC, and from 0.22 to 0.54 for Noah-MP. In general, post-calibration model performance is higher for watersheds with relatively high precipitation and runoff ratios, and at lower elevations. At a second stage, we regionalize the river basin calibrations using the donor-basin method, which establishes transfer relationships for hydrologically similar basins, via which we extend our calibration parameters to 4816 hydrologic unit code (HUC)-10 basins across the region. Using the regionalized parameters, we show that the models' capabilities to simulate high and low flow conditions are substantially improved following calibration and regionalization. The refined parameter sets we developed are intended to support regional hydrological studies and hydrological assessments of climate change impacts.

Enhancing streamflow simulation accuracy in ungauged catchments via parameter calibration with triple collocation-based merged evapotranspiration and streamflow features

Estimating model parameters for accurate hydrological simulations poses a challenge in ungauged catchments. Evapotranspiration (ET) data derived from remote sensing (RS) holds promise for parameter calibration in ungauged catchments. Nonetheless, uncertainties inherent in raw RS-based ET data can pose challenges in the calibration process. This study evaluates two calibration methods mitigating the impact of this uncertainty. The first, a single-variable calibration, utilizes merged ET data based on the triple collocation technique, while the second incorporates streamflow features alongside ET data for multi-variable calibration. Applying the variable infiltration capacity model to 49 Chinese catchments revealed the superiority of the merged ET-based calibration scheme over the three raw RS ET-based calibrations, with improvements noted in the Nash-Sutcliffe efficiency (NSE) and Kling-Gupta efficiency (KGE) coefficients for simulated streamflow. On average, the NSE and KGE for the simulated streamflow demonstrate improvements ranging from 0.023 to 0.080 and 0.026 to 0.061, respectively. The multi-variable calibration schemes exhibit a significant enhancement in the reliability of streamflow simulations compared to the ET-based single-variable calibration schemes. On average, the NSE and KGE for the simulated streamflow exhibit improvements of 0.054 to 0.133 and 0.062 to 0.122, respectively. The performance of these calibrations was generally higher in humid catchments but showed more pronounced improvements in arid catchments when compared to raw RS ET-based calibrations. In conclusion, the proposed calibrations using streamflow features and/or merged ET data offer viable solutions for parameter calibration in ungauged catchments.

Learning Distributed Parameters of Land Surface Hydrologic Models Using a Generative Adversarial Network

AbstractLand surface hydrologic models adeptly capture crucial terrestrial processes with a high level of spatial detail. Typically, these models incorporate numerous uncertain, spatially varying parameters, the specification of which can profoundly impact the simulation capabilities. There is a longstanding tradition wherein parameter calibration has served as the conventional procedure to enhance model performance. However, calibrating distributed land surface hydrologic models presents a great challenge, often resulting in uneven spatial performance due to the compression of information inherent in model outputs and observations into a single‐value objective function. To address this problem, we propose a novel Generative Adversarial Network‐based Parameter Optimization (GAN‐PO) method. By leveraging a deep neural network to discern model spatial biases, we train a generative network to produce spatially coherent parameter fields, minimizing distinctions between simulations and observations. By leveraging neural network‐based surrogate models to make the physical model differentiable, we employ GAN‐PO to calibrate the Variable Infiltration Capacity (VIC) model against evapotranspiration (ET) over China's Huaihe basin. The results show that GAN‐PO can diminish errors in simulated ET derived from default parameters across nearly all grid cells within the study region, surpassing the conventional calibration approach based on the parameter regionalization technique. Ablation analysis indicates that relying solely on the traditional loss could lead to deteriorated model performance, underscoring the crucial role of the discriminator. Notably, due to the discriminator's explicit identification of model spatial biases, GAN‐PO excels in maintaining spatial consistency, outperforming the state‐of‐the‐art differentiable parameter learning (dPL) method in terms of model spatial performance.

Investigating the utility of satellite-based precipitation products for simulating extreme discharge events: an exhaustive model-driven approach for a tropical river basin in India.

Satellite-based precipitation estimates are a critical source of information for understanding and predicting hydrological processes at regional or global scales. Given the potential variability in the accuracy and reliability of these estimates, comprehensive performance assessments are essential before their application in specific hydrological contexts. In this study, six satellite-based precipitation products (SPPs), namely, CHIRPS, CMORPH, GSMaP, IMERG, MSWEP, and PERSIANN, were evaluated for their utility in hydrological modeling, specifically in simulating streamflow using the Variable Infiltration Capacity (VIC) model. The performance of the VIC model under varying flow conditions and timescales was assessed using statistical indicators, viz., R2, KGE, PBias, RMSE, and RSR. The findings of the study demonstrate the effectiveness of VIC model in simulating hydrological components and its applicability in evaluating the accuracy and reliability of SPPs. The SPPs were shown to be valuable for streamflow simulation at monthly and daily timescales, as confirmed by various performance measures. Moreover, the performance of SPPs for simulating extreme flow events (streamflow above 75%, 90%, and 95%) using the VIC model was assessed and a significant decrease in the performance was observed for high-flow events. Comparative analysis revealed the superiority of IMERG and CMORPH for streamflow simulation at daily timescale and high-flow conditions. In contrast, the performances of CHIRPS and PERSIANN were found to be poor. This study highlights the importance of thoroughly assessing the SPPs in modeling diverse flow conditions.

Drought and its ecological risk bundle from the perspective of watershed hydrological cycle

The mechanisms underlying the impacts of climate change and vegetation dynamics on hydrological drought in humid regions are still lacking. In this study, we connected the four components of meteorology-soil-vegetation-runoff to investigate the spatio-temporal response relationship between vegetation growth and different drought types. Based on the Variable Infiltration Capacity model and the Self-organizing Map Algorithm, we proposed ecological risk bundles at the grid scale to characterize the potential impacts of different types and levels of drought on vegetation. Furthermore, we quantified the driving impact of temporal and spatial changes in vegetation coverage on the propagation of meteorological-hydrological drought. The study found that the centers of gravity for the occurrence frequencies of extreme and mild drought shifted towards regions where vegetation growth was influenced by climate change. In certain regions of the watershed, vegetation exhibits significant spatial and temporal heterogeneity in its response to stress caused by different drought types. From 2004 to 2014, the stress on vegetation caused by moderate and mild meteorological droughts weakened, while soil moisture stress intensified after 2014. Simultaneously, the impacts of climate change and vegetation growth on runoff reached 48.25 % and 35.13 % respectively, and their synergistic effects triggered changes in the risk of co-concurrent return periods for hydrological drought events. Under the 100-year design return period, the co-occurrence return period of runoff shifted from its natural state of 162.9 years to 52.8 years, and the joint return period reversed its scenario, becoming shorter than the co-occurrence return period.

Quantifying the runoff reduction benefits of the ‘Grain for Green’ Programme using the VIC model

ABSTRACT Variable infiltration capacity model is used to quantitatively analyse the runoff reduction benefits of three basins on the Loess Plateau since the implementation of the ‘Grain for Green’ programme. The programme played an important role, contributing to >60% of the runoff variation in the different basins. Furthermore, for every 1000 km2 increase in cumulative forest area in basins, the rate of runoff coefficient reduction changed by −30%, −37.3% and 169% in the late stage of the programme compared to the early stage, respectively (‘-’ represents a decrease in reduction rate). This was closely related to the restoration of vegetation.

On the Sensitivity of Future Hydrology in the Colorado River to the Selection of the Precipitation Partitioning Method

AbstractThe accurate partitioning of precipitation is important for snowpack and streamflow simulations in mountainous regions. However, most hydrologic models employ a partitioning method based on near‐surface air temperature (Ta) and the sensitivity of hydrologic simulations to the selection of the precipitation partitioning method (PPM) under climate change has been underexplored. To address this, we compared simulations using two PPMs, a conventional Ta‐based scheme and a wet‐bulb temperature (Tw)‐based approach (TA and TW) with the Variable Infiltration Capacity model. Our study focused on the Colorado River Basin (CRB) which is vulnerable to future snowfall and streamflow reductions due to climate warming. Historical simulations demonstrated the improved performance of the TW scheme in simulating snowfall fraction (SF) and snow water equivalent (SWE) in relation to in situ observations and a gridded SWE product. Subsequently, we evaluated the influence of the PPM selection on snowpack and streamflow projections from eight climate models under two emissions scenarios. The ensemble median results revealed more substantial decline in annual SF with the TW scheme as compared to the TA method (−12% vs. −9%) from the historical (1976–2005) to the far future (2066–2095) period, especially at lower elevations. Hydrologic simulations showed that the TA scheme underestimated reductions in SWE and streamflow (Q) under warming as compared to the TW scheme. This finding has important implications on future projections for the CRB and in other mountainous basins where climate warming can shift conditions from snowfall to rainfall.

Application of VIC-WUR model for assessing the spatiotemporal distribution of water availability in anthropogenically-impacted basins

Climate change, rapid increase in population, and urbanization have given rise to over-exploitation and shortage of freshwater resources. Acquiring accurate representations of fresh water availability is therefore crucial to sustainable development. Based on the underlying hydrological processes and storage types, fresh water can be divided into blue and green water components. Hence, quantification of the spatio-temporal distribution of blue and green water requires estimating the components of terrestrial water cycle. In this study, a framework is proposed to evaluate the water scarcity using the blue and green water concept. In this framework, the Wageningen Institute and Research (WUR) version of the Variable Infiltration Capacity Model (VIC) is employed to simulate terrestrial water cycle. Compared to VIC model, VIC-WUR model employs additional modules to account for sectoral water demands, return flows, and groundwater withdrawals, however, very limited number of its applications have been reported so far. The Divandareh-Bijar (DBJ) basin in west of Iran is selected because it has experienced severe anthropogenic alterations in the past three decades. To address precipitation and parameter uncertainty, the VIC-WUR model is separately calibrated for three precipitation datasets. As a part of this framework, the calibrated model is used to simulate blue and green water components and their corresponding scarcity metrics for the first time. According to the results, blue and green water resources in the DBJ are relatively scarce. The results also highlight the importance of considering anthropogenic impacts on the spatiotemporal dynamics of green and blue water resources. Additionally, quantification of the uncertainty in blue and green water components can help produce more reliable estimations for intelligent management of water resources. The ANalysis Of VAriance (ANOVA) method is therefore used to quantify the contribution of three major factors, including precipitation inputs, model parameters, and the interaction of these two, on the uncertainty of calculated water scarcity metrics. Structural uncertainty is implicitly addressed through the use of the modified version of the VIC model. The results indicate seasonality of these contributions for the two metrics under investigation and the differences in relative contribution of different sources to the overall uncertainty. The proposed framework could be employed to identify water stress in various spatiotemporal scales across the globe.

Hydrological modelling for post-monsoon agricultural drought assessment and implications for the agro-economy

ABSTRACT Reliable drought monitoring is a prerequisite for minimizing potential agricultural losses. Soil moisture is a key variable for monitoring agricultural drought assessment. This study conducted in the Bundelkhand region of Uttar Pradesh uses a macroscale variable infiltration capacity (VIC) hydrological model to simulate soil moisture and calculate soil moisture deficit index (SMDI) for agricultural drought assessment for the Rabi crop growing season, 1998–2016. Crop yield was linked with SMDI and other covariates using a random forest machine learning-based regression technique. The results show that the VIC model effectively simulated root zone soil moisture when compared with the reference data. Major droughts were identified in the years 2000–01, 2007–08, and 2015–16 in the study region. The RF-based crop yield prediction accuracy improved when irrigational factors were added. The study provides a noteworthy reference for drought assessment and prevention, water resource management, and ensuring food security.

Compound successive dry-hot and wet extremes in China with global warming and urbanization

Global warming has accelerated the interregional hydrological cycle, resulting in a significant increase in the frequency and intensity of extreme events worldwide. These events often involve a combination of spatial and temporal factors, giving rise to compound events. Among them are rapid transitions from dry and hot conditions to wet (DHW) events, which can have more severe impacts on human societies and ecosystems than individual extreme events. Urbanization not only heightens the likelihood of disasters but also exacerbates the exposure of affected populations. However, there has been insufficient attention given to understanding the connections between these successive compound events and the heightened risks posed by the increasing urbanization. In this study, we used bias-corrected daily precipitation and maximum temperature data simulated by the Coupled Model Intercomparison Project Phase 6 (CMIP6) model, along with historical and future daily runoff data simulated by the Variable Infiltration Capacity (VIC) model. We systematically investigated the spatiotemporal changes (i.e., frequency, duration, intensity, and compound probability) in DHW events in China during the upper historical period (1979–2014) and under two medium and high emission scenarios (2015–2100) − Shared Socioeconomic Pathways (SSP245 and SSP585). Furthermore, we examined how extreme runoff responds to variations in maximum temperature and daily precipitation during DHW events. We also evaluated the potential population and urban exposure to DHW events using dynamic future population and urban expansion data to assess the potential risks. Our results indicate that the multi-model ensemble predicts varying spatial patterns of future DHW events under SSP245 and SSP585 scenarios. The frequency and duration of these events are expected to decrease by approximately 20–25% in the Northwest region under both scenarios. At the same time, the middle and lower plains of the Yangtze and Yellow River Basins experience increased occurrence, broader geographical impact, and higher DHW event intensity alongside urban expansion and population growth in these regions. Specifically, event intensity is anticipated to increase by a factor of approximately 7–11. Temporally, we expect short-duration, high-intensity DHW events to occur in the Yangtze and Yellow River basins around 2023, 2038, and 2058, respectively. The primary driver for the future rise in population exposure to DHW events is the expected increase in event frequency in the middle and lower plains of the Yangtze and Yellow River basins, concurrent with population growth in these regions. Under the medium emission scenario, the total exposed population to DHW events in China is predicted to be 1.61 times higher than the historical baseline. In contrast, the high-emission scenario estimates a total population of 2.41 times higher than the historical baseline period. Most of the DHW events occurred in regions that exhibit a positive dependence between high temperature and high runoff events, serving as the primary driver of DHW events. Urbanization has a positive impact on DHW events, with the effect under high emissions being approximately 30% higher than during the historical base period. Additionally, it is indicated that future DHW events will exhibit higher sensitivity to global warming, with intensity projected to increase approximately fourfold and the exposed population to rise by about 1.5 times for every 1 °C of global warming. This research enhances our understanding of forecasting future compound hydrometeorological extreme events under different scenarios and provides insight into the role of climate change and urbanization in shaping these events in China.

Multi-century flow reconstruction of the Lhasa River, China

Study regionLhasa River Basin Study focusMulti-century records are needed to characterize flow variability including the magnitude and frequency of occurrence of extreme events. Such long records are not available without integrating well-calibrated hydrological models and climate proxies. To test whether 50–70 years of observations represent the long-term flow variability for water resources management, daily flows (1472–2000) were reconstructed by integrating the Variable Infiltration Capacity (VIC) model with proxy-based precipitation and temperature data for Lhasa River. New hydrological insights for the regionVIC performed well during calibration and validation for 3 gauging stations with the Nash-Sutcliffe coefficient of efficiency (NSE) exceeding 0.52. Proxy-based precipitation, temperature, and reconstructed flow are broadly stationary over the past 529 years, while recent decades show detectable local trends. The inter-annual/decadal variability is aligned with long-term variations in sea surface temperatures and the intensity of south Asian summer monsoon. Moreover, higher long-term average, greater variabilities, and persistent periods of above/below-average flows are detected. The maximum daily flows with small annual recurrence intervals (ARIs) less than 60 years would be overestimated based on observations, and underestimated for ARIs > 60 years. It is the first time to have reconstructed long-term daily flows using hydrological models coupled with proxy-based climate data, and this approach offers invaluable insights into the pre-instrumental flow responses to changing climate conditions in Lhasa River basin.

Evaluation of Climatological Precipitation Datasets and Their Hydrological Application in the Hablehroud Watershed, Iran

Hydrological modeling is essential for runoff simulations in line with climate studies, especially in remote areas with data scarcity. Advancements in climatic precipitation datasets have improved the accuracy of hydrological modeling. This research aims to evaluate the APHRODITE, PERSIANN-CDR, and ERA5-Land climatic precipitation datasets for the Hablehroud watershed in Iran. The datasets were compared with interpolated ground station precipitation data using the inverse distance weighted (IDW) method. The variable infiltration capacity (VIC) model was utilized to simulate runoff from 1992 to 1996. The results revealed that the APHRODITE and PERSIANN-CDR datasets demonstrated the highest and lowest accuracy, respectively. The sensitivity of the model was analyzed using each precipitation dataset, and model calibration was performed using the Kling–Gupta efficiency (KGE). The evaluation of daily runoff simulation based on observed precipitation indicated a KGE value of 0.78 and 0.76 during the calibration and validation periods, respectively. The KGE values at the daily time scale were 0.64 and 0.77 for PERSIANN-CDR data, 0.62 and 0.75 for APHRODITE precipitation data, 0.50 and 0.66 for ERA5-Land precipitation data during the calibration and validation periods, respectively. These results indicate that despite varying sensitivity, climatic precipitation datasets present satisfactory performance, particularly in poorly gauged basins with infrequent historical datasets.

Superior performance of hybrid model in ungauged basins for real-time hourly water level forecasting – A case study on the Lancang-Mekong mainstream

In the context of climate change and human influence, timely and reliable information about water level variations is crucial for downstream flood control, navigation, and water resource management, particularly for the Lancang-Mekong River, the largest transboundary river in Southeast Asia. However, accurate real-time water level forecasts remain challenging, especially in data-limited regions. This study focused on the Chiang Saen Station, the uppermost Mekong River hydrological station, comparing three methods to predict 48-hour water levels: the Variable Infiltration Capacity (VIC) and Hydrology-Hydraulic (HH) physics-based models, the Gated Recurrent Unit (GRU) model, and hybrid models (VIC-GRU and HH-GRU). Evaluations were conducted at the 1-h, 3-h, 6-h, 12-h, 24-h, and 48-h forecast lead times. Assessments at various lead times showed that hybrid models incorporating physics-based mechanisms significantly enhanced predictions. VIC-GRU demonstrated superior performance, with KGE values surpassing 0.94, NSE values exceeding 0.95, MAE below 0.17 m, and PBIAS within ± 1 % across all lead times. Compared to GRU, PBIAS decreased by 84 %, while MAE dropped by 76 % to 81 % relative to VIC and HH, respectively. Notably, seasonal variations affected hybrid model performance, especially in the dry season. Optimal parameter selections enhanced model accuracy by 48 % to 49 %. These results underscore the potential of combining physics-based and deep learning models for more accurate, high-temporal-resolution real-time water level forecasts in data-scarce regions. A comprehensive study of streamflow characteristics enhances the advantages of this integration. This research contributes valuable insights to water level prediction in data-scarce regions and informs flood risk reduction and management in the Lancang-Mekong mainstream.

A holistic approach to projecting streamflow and analyzing changes in ecologically relevant hydrological indicators under climate and land use/cover change

Understanding streamflow change and its ecological impacts under climate change and land use/cover change (LUCC) makes possible the improvement of water and ecological governance decision-making. Gaining a comprehensive understanding of the drivers of historical streamflow changes can provide valuable insights for effective projections of future streamflow. Meanwhile, future changes in ecologically relevant hydrological indicators need more attention to maintain the ecological stability of rivers. This study has developed a holistic framework based on a physically based model (the Variable Infiltration Capacity model, or VIC) and the indicators of hydrological alteration (IHA) method to decouple the drivers of historical streamflow change and to project changes in future streamflow and ecologically relevant hydrologic indicators. The proposed framework was applied in the upper Han River basin (UHRB), which is the water source for the water diversion project in China. The results showed that climate change contributed 99.67 % of the streamflow decrease in the 1980–1990s and 108.21 % and 102.09 % of the streamflow increase in the 1990–2000s and 2000–2010s respectively. LUCC had a weak impact on streamflow in these three periods. Projected climate change will further exacerbate streamflow change. The projected monthly streamflow is expected to increase from October to December. The counts of streamflow pulses are expected to decrease, but the duration of each high pulse will increase. These changes will cause stress in floodplain vegetation, alterations to stream chemistry, reductions in fish diversity (decreased streamflow pulses), and alterations to river landscape and animal habitat (increased flood duration). The developed framework is expected to provide significant technical guidance for water resource management and river conservation.

Mitigating El Niño impacts on hydro-energy vulnerability through identifying resilient run-of-river small hydropower sites

Study regionThe Godavari River basin (GRB), situated between the geographical coordinates of 73°21′ E to 83°09′ E and 16°07′ N to 22°50′ N, India Study focusWe developed an integrated framework combining the variable infiltration capacity (VIC) model and geospatial tools to identify potential sites for run-of-river small hydropower plants (RoR-SHP) capable of meeting the GRB's energy demands during El Niño events. This study utilized long-term (1951–2020) daily streamflow data, simulated with the VIC model for design discharge computation at 30%, 75%, and 90% flow dependability and provided a thorough assessment of the potential of RoR-SHP in the GRB. New hydrological insights for the regionThe analysis revealed the GRB's potential for RoR-SHP development, identifying 226 potential sites based on the head along the river, with a combined power and annual energy generation estimate of 92 MW and 0.4 TWh/yr, respectively, at 90% flow dependability. After meticulous screening, 11 potential sites based on the head and the power potential demonstrated a decline of 46.03%, 37.97%, and 17.77% in annual energy at 30%, 75%, and 90% flow dependability, respectively, leaving nine sites maintaining the firm power (90% flow dependability) even during El Niño years. Our findings underscore the increased risk of power shortages during El Niño years and emphasize the need to develop appropriate strategies to cope with the risks associated with El Niño events.

Assessing the Applicability of Variable Infiltration Capacity (VIC) Model using Remote Sensing Products for the Analysis of Water Balance: Case Study of the Tons River Basin, India

Assessing the Applicability of Variable Infiltration Capacity (VIC) Model using Remote Sensing Products for the Analysis of Water Balance: Case Study of the Tons River Basin, India

Responses of runoff processes to vegetation dynamics during 1981–2010 in the Yarlung Zangbo River basin

Study regionThe Yarlung Zangbo River basin (YRB), southwest China. Study focusThe vegetation conditions play a significant role in regulating river runoff under climate change. However, the effect of vegetation growth induced by climate change to runoff variation remains controversial as relevant studies are mainly focused on the influences of climate change to runoff variation and vegetation growth, respectively. In this study, the response of runoff generation to change of Leaf Area Index (LAI) inputs during 1981–2010 in YRB were explored by using the Variable Infiltration Capacity (VIC) model. New hydrological insightsThe VIC model with dynamic LAI inputs performs better in the YRB, especially in the sub-basins with apparent peak clipping effect by LAI overestimation. The impact mechanism of how runoff variation is driven by LAI change in the meadow, tundra and cultivated vegetation cover is evapotranspiration-controlled by energy redistribution. However, the LAI changes in coniferous and broad-leaved forest cover affect the runoff generation mainly by redistributing energy-water to alter both evapotranspiration and surface soil moisture, so do the shrub and grassland during the growing season (Apr.∼Sep.). The results highlight implanting grassland would be efficient to reduce the flash flood runoff in a short term and balance groundwater runoff in a long run. The broad-leaved forest and coniferous forest with a longer growth cycle also play a main role in adjusting soil moisture.

Calibrating macroscale hydrological models in poorly gauged and heavily regulated basins

Abstract. The calibration of macroscale hydrological models is often challenged by the lack of adequate observations of river discharge and infrastructure operations. This modeling backdrop creates a number of potential pitfalls for model calibration, potentially affecting the reliability of hydrological models. Here, we introduce a novel numerical framework conceived to explore and overcome these pitfalls. Our framework consists of VIC-Res (a macroscale model setup for the Upper Mekong Basin), which is a novel variant of the Variable Infiltration Capacity (VIC) model that includes a module for representing reservoir operations, and a hydraulic model used to infer discharge time series from satellite data. Using these two models and global sensitivity analysis, we show the existence of a strong relationship between the parameterization of the hydraulic model and the performance of VIC-Res – a codependence that emerges for a variety of performance metrics that we considered. Using the results provided by the sensitivity analysis, we propose an approach for breaking this codependence and informing the hydrological model calibration, which we finally carry out with the aid of a multi-objective optimization algorithm. The approach used in this study could integrate multiple remotely sensed observations and is transferable to other poorly gauged and heavily regulated river basins.

To what extent does river routing matter in hydrological modeling?

Abstract. Spatially distributed hydrology and land surface models are typically applied in combination with river routing schemes that convert instantaneous runoff into streamflow. Nevertheless, the development of such schemes has been somehow disconnected from hydrologic model calibration research, although both seek to achieve more realistic streamflow simulations. In this paper, we seek to bridge this gap to understand the extent to which the configuration of routing schemes affects hydrologic model parameter searches in water resources applications. To this end, we configure the Variable Infiltration Capacity (VIC) model coupled with the mizuRoute routing model in the Cautín River basin (2770 km2), Chile. We use the Latin hypercube sampling (LHS) method to generate 3500 different model parameters sets, for which basin-averaged runoff estimates are obtained directly (no routing or instantaneous runoff case) and are subsequently compared against outputs from four routing schemes (unit hydrograph, Lagrangian kinematic wave, Muskingum–Cunge, and diffusive wave) applied with five different routing time steps (1, 2, 3, 4, and 6 h). The results show that incorporating routing schemes may alter streamflow simulations at sub-daily, daily, and even monthly timescales. The maximum Kling–Gupta efficiency (KGE) obtained for daily streamflow increases from 0.64 (instantaneous runoff) to 0.81 (for the best routing scheme), and such improvements do not depend on the routing time step. Moreover, the optimal parameter sets may differ depending on the routing scheme configuration, affecting the baseflow contribution to total runoff. Including routing models decreases streamflow values in flood frequency curves and may alter the probabilistic distribution of the medium- and low-flow segments of the flow duration curve considerably (compared to the case without routing). More generally, the results presented here highlight the potential impacts of river routing implementations on water resources applications that involve hydrologic models and, in particular, parameter calibration.