Variable Infiltration Capacity Hydrologic Model Research Articles

Hydrologic Sensitivity of a Critical Turkish Watershed to Inform Water Resource Management in an Altered Climate

This study introduces a novel sensitivity analysis approach to assess the resilience and susceptibility of hydrologic systems to the stresses of climate change, moving away from conventional top-down methodologies. By exploring the hydrological sensitivity of the upper Kızılırmak River basin using the Variable Infiltration Capacity (VIC) hydrologic model, we employed a sensitivity-based approach as an alternative to the traditional Global Climate Model (GCM)-based methods, providing more insightful information for water managers. Considering the consistent projections of increasing temperature over this region in GCMs, the hydrologic system was perturbed to examine gradients of a more challenging climate characterized by warming and drying conditions. The sensitivity of streamflow, snow water equivalent, and evapotranspiration to temperature (T) and precipitation (P) variations under each perturbation or “reference” climate was quantified. Results indicate that streamflow responds to T negatively under all warming scenarios. As the reference climates become drier, streamflow sensitivity to P increases, indicating that meteorological drought impacts on water availability could be exacerbated. These results suggest that there will be heightened difficulty in managing water resources in the region if it undergoes both warming and drying due to the following setbacks: (1) water availability will shift away from the summer season of peak water demand due to the warming effects on the snowpack, (2) annual water availability will likely decrease due to a combination of warming and lower precipitation, and (3) streamflow sensitivity to hydroclimatic variability will increase, meaning that there will be more extreme impacts to water availability. Water managers will need to plan for a larger set of extreme conditions.

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

Reliable drought monitoring is a prerequisite for minimizing potential agricultural losses. Soil moisture is a key variable for monitoring agricultural drought assessment. The 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 from 1998 to 2016. Crop yield (CY) was linked with SMDI and other covariates using random forest (RF) machine learning (ML) 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 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.

A new non-stationary standardised streamflow index using the climate indices and the optimal anthropogenic indices as covariates in the Wei River Basin, China

Study regionCatchment area above the Huaxian station along the Wei River Basin, China. Study focusThis study attempts to construct a new Non-stationary Standardized Streamflow Index (NSSI) applicable to the variable streamflow sequence of the Wei River Basin based on the climate index and the optimal anthropogenic index, and analyse the drought characteristics of the basin. The climate index is used to quantify climate change factors and three anthropogenic indices are used to quantify the factor of human activities, including the reservoir index, the human-induced index calculated based on the Variable Infiltration Capacity (VIC) hydrological model and the Long Short-Term Memory (LSTM) model machine learning approach, respectively. New hydrological insights for the regionThe human-induced index based on the LSTM model is more suitable for quantifying anthropogenic factors in the Wei River Basin. The NSSI performs better than the SSI in drought identification. The NSSI based on the LSTM model can capture more frequent severe drought and extreme drought events. The frequency of severe drought and extreme drought is higher in summer and autumn than in the others. The NSSI can better characterize the hydrological drought processes under a non-stationary condition, thus it can provide a more effective reference for regional drought assessment and related policy-making from the perspective of a changing environment.

Impacts of precipitation uncertainty on hydrological ensemble simulations over the Ganjiang River basin

Study regionThe Ganjiang River basin (113°−117°E, 24°−30°N; 83,374 km2) is a large watershed with complex topography in the Poyang Lake basin in Jiangxi province, China. Study focusThis study evaluates three quantitative precipitation estimates (QPEs) over the Ganjiang River basin, namely the China Gauged-Based Daily Precipitation Analysis (CGDPA) data, the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) data, and the Asian Precipitation - Highly-Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE). The study also investigates the impacts of precipitation uncertainty on hydrological ensemble streamflow simulations using the three QPEs as precipitation inputs of the Variable Infiltration Capacity (VIC) hydrological model. New hydrological insights for the regionAPHRODITE underestimates precipitation compared to CGDPA, while PERSIANN-CDR shows greater spatiotemporal variability. The ensemble mean streamflow demonstrates greater improvement compared to the results obtained from a single parameter set. Among the three QPEs, the simulations forced by CGDPA show the best deterministic and probabilistic verification scores, followed by APHRODITE. PERSIANN-CDR tends to underestimate evaporation and leads to the lowest score of ensemble streamflow simulations, but shows advantages in simulating extremely low streamflow. The study highlights that high-density gauge-based QPEs remain the most accurate source of precipitation inputs for reliable hydrological simulations, while satellite-gauge merged QPEs can provide valuable inputs for hydrological simulations over the basins where meteorological stations are scarce.

Spatio-Temporal Analysis and Contribution of Agricultural Drought in Daling River Basin: A VIC Model-Based Soil Moisture Simulation and SMAPI Evaluation

Soil moisture is a crucial factor that directly influences agricultural drought. As such, investigating drought-monitoring methods utilizing soil moisture data is of significant importance for accurately evaluating and predicting agricultural drought. However, the current soil moisture data for the Daling River Basin is insufficient. Therefore, the variable infiltration capacity (VIC) hydrological model was utilized to simulate soil moisture in the Daling River Basin. The simulated data were then analyzed in conjunction with the standardized moisture anomaly index (SMAPI) to analyze and evaluate the spatio-temporal characteristics of agricultural drought in the Darling River Basin. The results indicate that the frequency of drought occurrence in the basin follows a seasonal pattern of winter > spring > autumn > summer. Between 1981 and 2019, 24 out of 39 years experienced slight or greater drought, 15 years experienced moderate or more severe drought, and 4 years experienced severe drought. Drought conditions have become exceptionally severe in the 21st century. Specifically, the frequency of drought occurrence from 2001 to 2019 was nearly 10 times higher compared to the period from 1981 to 2000. The droughts were most severe in the southeast and southwest of the Daling River Basin, while the northeast and northwest experienced relatively mild drought. Agricultural drought is influenced by numerous complex factors. The contribution of climate change (CC) and other factors (OF) to agricultural drought was quantified by using a partial derivative under six different scenarios. Results showed that SMAPI was positively correlated with precipitation and solar radiation, while negatively correlated with temperature. From 1981 to 2000, SMAPI exhibited an increasing trend that accounted for 61.66% of variability, while a decreasing trend accounted for 38.34%. From 2001 to 2019, SMAPI exhibited a significant decreasing trend that accounted for 93.53% of the variability, while the increasing trend only accounted for 6.47%. CC was the dominant factor in most of the areas with increased SMAPI. OF was the main controlling factor for areas with decreased SMAPI.

An Improved Satellite‐Based Evapotranspiration Procedure for China

AbstractEvapotranspiration (ET) is a critical process that regulates the transfer of heat and water between land and the atmosphere. While satellite‐based ET algorithms can provide area‐wide daily ET estimates, few are independent of local meteorological measurements. The Variant of the Moderate Resolution Imaging Spectroradiometer Standard Evapotranspiration Algorithm (VISEA) can potentially run without ground‐based observations. However, the surface energy budgets used in VISEA generally overpredict daily net radiation and related ET. To improve the accuracy of net radiation and ET, we incorporated Brutsaert's atmosphere emissivity model and corrected the routine to calculate the downward long‐wave radiation on cloudy days (which we called VISEA2023 model). The VISEA2023 model predicted net radiation and ET that agreed well with measurements at seven ChinaFlux sites, with an R2 of 0.8 and an Root Mean Square Error (RMSE) of 30 W m−2 for net radiation, and an R2 of 0.6 and an RMSE of 1 mm day−1 for ET, respectively. Additionally, the VISEA2023 model is more robust in comparison to the Variable Infiltration Capacity hydrologic model output in 10 major river basins and 80 sub‐river basins, with an R2 of 0.91 (0.78) and an RMSE of 85 (127.4) mm year−1, than MOD16, which has an R2 of 0.81 (0.82) and an RMSE of 134.8 (157.7) mm year−1, and Advanced Very High Resolution Radiometer ET, which has an R2 of 0.92 (0.62) and an RMSE of 90.1 (136.1) mm year−1. The improved VISEA2023 model can offer near‐real‐time ET on a larger scale, improving our understanding of water cycles and their relation to climate change.

Vegetation Representation Influences Projected Streamflow Changes in the Colorado River Basin

Abstract Vegetation parameters for the Variable Infiltration Capacity (VIC) hydrologic model were recently updated using observations from the Moderate Resolution Imaging Spectroradiometer (MODIS). Previous work showed that these MODIS-based parameters improved VIC evapotranspiration simulations when compared to eddy covariance observations. Due to the importance of evapotranspiration within the Colorado River basin, this study provided a basin-by-basin calibration of VIC soil parameters with updated MODIS-based vegetation parameters to improve streamflow simulations. Interestingly, while both configurations had similar historic streamflow performance, end-of-century hydrologic projections, driven by 29 downscaled global climate models under the RCP8.5 emissions scenario, differed between the two configurations. The calibrated MODIS-based configuration had an ensemble mean that simulated little change in end-of-century annual streamflow volume (+0.4%) at Lees Ferry, Arizona, relative to the historical period (1960–2005). In contrast, the previous VIC configuration, which is used to inform decisions about future water resources in the Colorado River basin, projected an 11.7% decrease in annual streamflow. Both VIC configurations simulated similar amounts of evapotranspiration in the historical period. However, the MODIS-based VIC configuration did not show as much of an increase in evapotranspiration by the end of the century, primarily within the upper basin’s forested areas. Differences in evapotranspiration projections were the result of the MODIS-based vegetation parameters having lower leaf area index values and less forested area compared to previous vegetation estimates used in recent Colorado River basin hydrologic projections. These results highlight the need to accurately characterize vegetation and better constrain climate sensitivities in hydrologic models. Significance Statement Understanding systemic changes in annual Colorado River basin flows is critical for managing long-term reservoir levels. Single-digit percentage decreases have the potential to degrade the regions’ water supply, hydropower generation, and environmental concerns. Hydrology projections under climate change have largely been based on simulations from the Variable Infiltration Capacity model. Updating the model’s vegetation representation based on updated satellite information highlighted the sensitivity of the hydrologic projections to the models’ vegetation representation primarily within forested areas. This updated model did not increase in evapotranspiration by the end of the century as much as previous simulations. This increased the mean and ensemble spread of the projected streamflow changes, emphasizing the need to properly characterize the hydrologic model’s vegetation parameters and better constrain model climate sensitivity.

Lagged compound dry and wet spells in Northwest North America under 1.5 °C–4 °C global warming levels

The increasing variability and rapid transitions between hydroclimatic extremes and their compounding economic and environmental effects can cause severe consequences. Understanding the changing characteristics of such transitions, including their spatiotemporal frequency and magnitude is critical to developing effective mitigation and adaptation strategies. In this study, lagged compound dry and wet spells are investigated in Northwest North America (NWNA) based on the Standardised Precipitation Index and Standardised Precipitation Evapotranspiration Index for multiple accumulation periods (1, 3, and 6 months). The indices are estimated based on six downscaled Global Climate Models that participated in the 5th phase of the Coupled Model Intercomparison Project under the Representative Concentration Pathways (RCPs) 4.5 and 8.5, in addition to the Variable Infiltration Capacity hydrologic model simulations. We characterize changing behaviour of the lagged compound dry and wet spells under 1.5 °C–4 °C global warming levels. Projections show overall increases in the frequency of wet and dry swings and decreases in the corresponding transition times in NWNA under climate change. In addition, the magnitude, intensity, and duration of wet and dry components of such lagged compound events are projected to increase. With some differences, both indices identify the entire Columbia and southern Fraser basins as hotspots for frequent compound event occurrences. The study identifies increasing hotspots across the domain affected by abrupt transitions under climate change and asserts the necessity of integrating mitigation measures targeting such lagged compound events into disaster risk reduction strategies.

Meteorological Driving Datasets for the U.S. Midwest and Great Lakes Region Incorporating Precipitation Gauge Undercatch Corrections

Abstract Precipitation (P) gauge undercatch (PUC) is an important source of error when using observed meteorological datasets for hydrologic modeling studies in regions with cold and windy winters. Preliminary simulations using the Variable Infiltration Capacity (VIC) hydrological model forced with different meteorological datasets showed significant underprediction of simulated streamflow throughout the domain. A new hybrid gridded meteorological dataset at 1/16° resolution based on observed station data was assembled over the U.S. Midwest and Great Lakes region from 1915 to 2021 at a daily time step. Correction of primary station data using existing techniques is generally difficult or infeasible in the United States due to missing station metadata and lack of local wind speed (WS) measurements. We developed and tested several different postprocessing adjustment techniques using regridded WS obtained from the NCEP–NCAR reanalysis. The most effective approach corrected rain or mixed P using WS alone, and P as snow using a regressed snow-to-P ratio from a group of wind-shielded reference stations (to account for different and generally unknown snow measurement techniques). The PUC-corrected gridded products were validated against high-quality shielded stations and corrected Global Historical Climatology Network stations with in situ WS, showing good overall agreement. Observed monthly streamflow at 40 river basins was also compared to hydrologic model simulations forced by datasets with and without PUC corrections. The best PUC-corrected dataset produced improvements in streamflow simulations in at least 80% of the streamflow locations for three validation metrics (r2, Nash–Sutcliff efficiency, bias in the mean), demonstrating its value for hydrometeorological studies in the greater Midwest region. Significance Statement Many applications in hydrology require in situ precipitation (P) measurements, which are known to have a systematic low bias due to the effects of wind, also known as precipitation undercatch (PUC). Addressing PUC is problematic in the United States due to limited access to detailed station metadata (SMD) and local wind speed (WS) measurements. In this paper we develop a set of procedures to create gridded precipitation datasets for the U.S. Midwest region that incorporate corrections for PUC without needing either (i) detailed SMD or (ii) local WS measurements. Among other tests, results in 40 test basins throughout the Midwest show substantial improvements in simulated streamflow in 32 out of 40 basins when PUC corrections are included in meteorological driving datasets.

Bark beetle impacts on forest evapotranspiration and its partitioning

Insect outbreaks affect forest structure and function and represent a major category of forest disturbance globally. However, the resulting impacts on evapotranspiration (ET), and especially hydrological partitioning between the abiotic (evaporation) and biotic (transpiration) components of total ET, are not well constrained. As a result, we combined remote sensing, eddy covariance, and hydrological modeling approaches to determine the effects of bark beetle outbreak on ET and its partitioning at multiple scales throughout the Southern Rocky Mountain Ecoregion (SRME), USA. At the eddy covariance measurement scale, 85 % of the forest was affected by beetles, and water year ET as a fraction of precipitation (P) decreased by 30 % relative to a control site, with 31 % greater reductions in growing season transpiration relative to total ET. At the ecoregion scale, satellite remote sensing masked to areas of >80 % tree mortality showed corresponding ET/P reductions of 9–15 % that occurred 6–8 years post-disturbance, and indicated that the majority of the total reduction occurred during the growing season; the Variable Infiltration Capacity hydrological model showed an associated 9–18 % increase in the ecoregion runoff ratio. Long-term (16–18 year) ET and vegetation mortality datasets extend the length of previously published analyses and allowed for clear characterization of the forest recovery period. During that time, transpiration recovery outpaced total ET recovery, which was lagged in part due to persistently reduced winter sublimation, and there was associated evidence of increasing late summer vegetation moisture stress. Overall, comparison of three independent methods and two partitioning approaches demonstrated a net negative impact of bark beetles on ET, and a relatively greater negative impact on transpiration, following bark beetle outbreak in the SRME.

Identifying stream temperature variation by coupling meteorological, hydrological, and water temperature models

AbstractIn this study, we demonstrate a physically based semi‐Lagrangian water temperature model known as the River Basin Model (RBM) coupled with the Variable Infiltration Capacity (VIC) hydrological model and Weather Research & Forecasting Model in the Mississippi River Basin (MRB). The results of this coupling compare favorably with observed water temperature data available from six river gages located in the MRB. Further sensitivity analysis indicates that the mean water temperatures may increase by 1.3, 1.5, and 1.8°C in northern, central, and southern MRB zones under a hypothetical uniform air temperature increase of 3.0°C. If air temperatures increase uniformly by 6.0°C in this scenario, then water temperatures are projected to increase by 3.3, 3.5, and 4.0°C. Lastly, downscaled air temperatures from a global climate model are used to drive the coupled VIC and RBM model from 2020 to 2099. Average stream temperatures from 2020 to 2099 increase by 1.0 to 8.0°C above 1950 to 2010 average water temperatures, with non‐uniform increases along the river. In some portions of the MRB, stream temperatures could increase above survival thresholds for several native fish species, which are critical components of the stream ecosystem. In addition, increased water temperatures interact with nutrient loadings from sources throughout the MRB, which is expected to exacerbate harmful algal blooms and dead zones in the Gulf of Mexico.

Quantifying the Role of Internal Climate Variability and Its Translation from Climate Variables to Hydropower Production at Basin Scale in India

Abstract Quantifying uncertainties in estimating future hydropower production directly or indirectly affects India’s energy security, planning, and management. The chaotic and nonlinear nature of atmospheric processes results in considerable internal climate variability (ICV) for future projections of climate variables. Multiple initial condition ensembles (MICE) and multimodel ensembles (MME) are often used to analyze the role of ICV and model uncertainty in precipitation and temperature. However, there are limited studies focusing on quantifying the role of internal variability on impact variables, including hydropower production. In this study, we analyze the role of ICV and model uncertainty on three prominent hydropower plants in India using MICE of EC-Earth3 and MME from CMIP6. We estimate the streamflow projections for all ensemble members using the Variable Infiltration Capacity hydrological model. We estimate maximum hydropower production generated using monthly release and hydraulic head available at the reservoir. We also analyzed the role of bias correction in hydropower production. The results show that ICV plays a significant role in estimating streamflow and hydropower potential for monsoon and throughout the year, respectively. Model uncertainty contributes more to total uncertainty than ICV in estimating the streamflow and potential hydropower. However, ICV is increasing toward the far term (2075–2100). We also show that bias correction does not preserve ICV in estimating the streamflow. Although there is an increase in uncertainty for estimated streamflow, mean hydropower shows a decrease toward the far term for February–May, more prominent for MICE than MME. The results suggest a need to incorporate uncertainty due to internal variability for addressing power security in changing climate scenarios.

Streamflow Composition and the Contradicting Impacts of Anthropogenic Activities and Climatic Change on Streamflow in the Amu Darya Basin, Central Asia

Abstract Transboundary rivers are often the cause of water-related international disputes. One example is the Amu Darya River, with a catchment area of 470 000 km2, which passes through five countries and provides water resources for 89 million people. Intensified human activities and climate change in this region have altered hydrological processes and led to water-related conflicts and ecosystem degradation. Understanding streamflow composition and quantifying the change impacts on streamflow in the Amu Darya basin (ADB) are imperative to water resources management. Here, a degree-day glacier-melt scheme coupled offline with the Variable Infiltration Capacity hydrological model (VIC-glacier), forced by daily precipitation, maximum and minimum air temperature, and wind speed, is used to examine streamflow composition and changes during 1953–2019. Results show large differences in streamflow composition among the tributaries. There is a decrease in the snowmelt component (−260.8 m3 s−1) and rainfall component (−30.1 m3 s−1) at Kerki but an increase in the glacier melt component (160.0 m3 s−1) during drought years. In contrast, there is an increase in the snowmelt component (378.6 m3 s−1) and rainfall component (12.0 m3 s−1) but a decrease in the glacier melt component (−201.8 m3 s−1) during wet years. Using the VIC-glacier and climate elasticity approach, impacts of human activities and climate change on streamflow at Kerki and Kiziljar during 1956–2015 are quantified. Both methods agree and show a dominant role played by human activities in streamflow reduction, with contributions ranging 103.2%–122.1%; however, the contribution of climate change ranges from −22.1% to −3.2%.

Skill and sources of skill in seasonal streamflow hindcasts for South America made with ECMWF’s SEAS5 and VIC

The first aim of the present paper is the determination of the magnitude, annual variation and spatial distribution of skill in seasonal hindcasts of runoff and discharge in the entire continent of South America. We evaluated 35 years of hindcasts generated with the Variable Infiltration Capacity (VIC) hydrological model forced with SEAS5 hindcasts. Initial conditions of terrestrial water and so-called pseudo-observations were computed with a reference (i.e. historic) simulation. Skill was determined with monthly temporal resolution for the entire annual cycle and mostly using the pseudo-observations for verification. The second aim of the paper is the explanation of skill in terms of its sources, namely meteorological forcing and the initial conditions. Therefore, two sets of restricted hindcasts, which isolate the sources of skill, were analysed. The SEAS5 precipitation hindcasts exhibit significant skill even at the longest lead times (7 months). Beyond the first lead month we found significant skill in 13–43 % of the grid cells, depending on target and lead month. Levels of skill are higher in the full hindcasts (significant skill in 31–89 % of the grid cells). At the continental scale more of the skill is caused by the forcing than by the initial conditions. The runoff hindcasts are skilful in large parts of the continent. In a 1000 km wide band along the north coast of the continent and in southeast South America, most of the skill is due to the forcing. A typical feature of these regions is an increase of skill with lead time during specific parts of the year, which is against the common tendency. In Argentina and north Chile most of the skill in the runoff hindcasts can be attributed to the initial conditions of soil moisture. Verification with real observations of discharge broadly confirmed the skill pattern obtained with pseudo-observations.

Corrected ERA5 Precipitation by Machine Learning Significantly Improved Flow Simulations for the Third Pole Basins

Abstract Precipitation is one of the most important atmospheric inputs to hydrological models. However, existing precipitation datasets for the Third Pole (TP) basins show large discrepancies in precipitation magnitudes and spatiotemporal patterns, which poses a great challenge to hydrological simulations in the TP basins. In this study, a gridded (10 km × 10 km) daily precipitation dataset is constructed through a random-forest-based machine learning algorithm (RF algorithm) correction of the ERA5 precipitation estimates based on 940 gauges in 11 upper basins of TP for 1951–2020. The dataset is evaluated by gauge observations at point scale and is inversely evaluated by the Variable Infiltration Capacity (VIC) hydrological model linked with a glacier melt algorithm (VIC-Glacier). The corrected ERA5 (ERA5_cor) agrees well with gauge observations after eliminating the severe overestimation in the original ERA5 precipitation. The corrections greatly reduce the original ERA5 precipitation estimates by 10%–50% in 11 basins of the TP and present more details on precipitation spatial variability. The inverse hydrological model evaluation demonstrates the accuracy and rationality, and we provide an updated estimate of runoff components contribution to total runoff in seven upper basins in the TP based on the VIC-Glacier model simulations with the ERA5_cor precipitation. This study provides good precipitation estimates with high spatiotemporal resolution for 11 upper basins in the TP, which are expected to facilitate the hydrological modeling and prediction studies in this high mountainous region. Significance Statement The Third Pole (TP) is the source of water to the people living in the areas downstream. Precipitation is the key driver of the terrestrial hydrological cycle and the most important atmospheric input to land surface hydrological models. However, none of the current precipitation data are equally good for all the TP basins because of high variabilities in their magnitudes and spatiotemporal patterns, posing a great challenge to the hydrological simulation. Therefore, in this study, a gridded daily precipitation dataset (10 km × 10 km) is reconstructed through a random-forest-based machine learning algorithm correction of ERA5 precipitation estimates based on 940 gauges in 11 TP basins for 1951–2020. The data eliminate the severe overestimation of original ERA5 precipitation estimates and present more reasonable spatial variability, and also exhibit a high potential for hydrological application in the TP basins. This study provides long-term precipitation data for climate and hydrological studies and a reference for deriving precipitation in high mountainous regions with complex terrain and limited observations.

Reliability of satellite-derived precipitation data in driving hydrological simulations: A case study of the upper Huaihe River basin, China

• SPD are better at capturing temporal rather than spatial variations in precipitation. • SPD-driven models are typically better at characterizing temporal variations in HFS. • SPD-driven models exhibited large uncertainty in reproducing daily HFS. • Effective reproduction of HFS has more stringent requirements on the quality of SPD. • Model calibration based on streamflow may hinder the best reproduction of HFS. The vigorous development of satellite precipitation observations has gained widespread attention in the field of hydrology. Many studies have explored the reliability of hydrological models driven by satellite precipitation data (SPD) to reproduce streamflow. However, the reliability of SPD in the simulation of other hydrological processes remains unclear, even though a full understanding of the dynamic variations in hydrological processes is essential for the scientific management of water resources. In this study, we investigated the effect of SPD uncertainty on the distributed hydrological model in reproducing hydrological simulations. The investigation was conducted in the upper Huaihe River basin, which has a high-density ground precipitation observation network. The uncertainty inherent in 8 types of SPD in characterizing the temporal and spatial variations in precipitation was comprehensively examined. The Variable Infiltration Capacity hydrological model (VIC) was then applied to further explore the reliability of the simulated hydrological fluxes and states (HFS) driven by the selected SPD over multiple temporal-spatial scales. The results showed that: (1) the selected SPD tend to have greater uncertainty in recognizing spatial variations in precipitation than in identifying temporal variations; the VIC models driven by the selected SPD generally performed better in characterizing temporal variations in HFS than in reproducing their spatial variations. In addition, the corrected SPD (CSPD) are more suitable for HFS simulations than the uncorrected SPD (USPD); (2) the effective reproduction of the spatio-temporal variations in HFS has more stringent requirements on the quality of SPD than the satisfactory reproduction of streamflow. For the gridded scale simulations, the selected SPD-driven models exhibited large uncertainties in reproducing spatio-temporal variations in daily HFS. In contrast, although these models significantly improved the simulation performance of monthly HFS, they were still relatively deficient in reproducing their spatial variations. In terms of the basin-scale simulations, the CSPD-driven models tend to reliably reproduce monthly HFS; and (3) independent calibration can allow the selected SPD-driven models to reproduce streamflow more effectively, but it may not result in the best reproduction of HFS. This study provides guidance for strengthening the application of SPD in hydrological modeling and provides feedback for further improving the quality of SPD.

Snowpack driven streamflow predictability under future climate: contrasting changes across two western Canadian river basins

Abstract Anthropogenic climate change induced snowpack loss is affecting streamflow predictability, as it becomes less dependent on the initial snowpack conditions and more dependent on meteorological forecasts. We assess future changes to seasonal streamflow predictability over two large river basins, Liard and Athabasca in western Canada, by approximating streamflow response from the variable infiltration capacity (VIC) hydrologic model with the Bayesian regularized neutral network (BRNN) machine learning emulator. We employ the BRNN emulator in a test-bed ensemble streamflow prediction system by treating VIC simulated snow water equivalent (SWE) as a known predictor, and precipitation and temperature from GCMs as ensemble forecasts, thereby isolating the effect of SWE on streamflow predictability. We assess warm-season mean and maximum flow predictability over 2041-2070 and 2071-2100 future periods against 1981-2010 historical period. The results indicate contrasting patterns of change, with the predictive skills for mean flow generally declining for the two basins, and marginally increasing or decreasing for the headwater subbasins. The predictive skill for maximum flow declines for the relatively warmer Athabasca basin, and improves for the colder Liard basin and headwater subbasins. While the decreasing skill for the Athabasca is attributable to substantial loss in SWE, the improvement for the Liard and headwaters can be attributed to an earlier maximum flow timing that reduces the forecast horizon and offsets the effect of SWE loss. Overall, while the future change in SWE does affect the streamflow prediction skill, the loss of SWE alone is not a sufficient condition for the reduction in streamflow predictability.

Characterizing Drought Behavior in the Colorado River Basin Using Unsupervised Machine Learning

AbstractDrought is a pressing issue for the Colorado River Basin (CRB) due to the social and economic value of water resources in the region and the significant uncertainty of future drought under climate change. Here, we use climate simulations from various Earth System Models (ESMs) to force the Variable Infiltration Capacity hydrologic model and project multiple drought indicators for the sub‐watersheds within the CRB. We apply an unsupervised machine learning (ML) based on Non‐Negative Matrix Factorization using K‐means clustering (NMFk) to synthesize the simulated historical, future, and change in drought indicators. The unsupervised ML approach can identify sub‐watersheds where key changes to drought indicator behavior occur, including shifts in snowpack, snowmelt timing, precipitation, and evapotranspiration. While changes in future precipitation vary across ESMs, the results indicate that the Upper CRB will experience increasing evaporative demand and surface‐water scarcity, with some locations experiencing a shift from a radiation‐limited to a water‐limited evaporation regime in the summer. Large shifts in peak runoff are observed in snowmelt‐dominant sub‐watersheds, with complete disappearance of the snowmelt signal for some sub‐watersheds. The work demonstrates the utility of the NMFk algorithm to efficiently identify behavioral changes of drought indicators across space and time and to quickly analyze and interpret hydro climate model results.

La Plata Basin Hydroclimate Response to Solar Radiation Modification With Stratospheric Aerosol Injection

This study analyses the La Plata Basin (LPB) hydroclimate response to Solar Radiation Modification (SRM) with Stratospheric Aerosol Injection (SAI) in terms of changes in mean and extreme temperature, rainfall, and river flows simulations from the Geoengineering Large Ensemble (GLENS). The GLENS data used consists of three members simulations of a single model under the high anthropogenic emission scenario RCP8.5 with and without sulfur injection in the tropical stratosphere. The response of such an intervention is compared to a historical period (1980–2010) and to projections under the RCP8.5 scenario for two time slices: 2021–2050 and 2051–2080. The analysis of changes in extreme temperature and precipitation focuses on a subset of climate indices from the Expert Team of Climate Change Detection and Indices (ETCCDI). To determine the possible effects of SRM in the LPB water cycle, we use the distributed Variable Infiltration Capacity (VIC) hydrologic model in combination with daily extreme temperature and precipitation outputs from GLENS. Systematic errors in both fields were corrected by the application of a statistical bias correction method. Results show robust impacts on the water cycle of the LPB due to SRM implementation, particularly associated with increased precipitation over the northern part of the region and a general reduced warming compared to the RCP8.5 scenario. These variations would lead to regional-dependent responses in the river flows, mostly related to reductions in the severity of the extremely low flow conditions. Additionally, under the SRM scenario a reduction in extreme precipitation and a cooling effect on extreme temperatures could be expected over the LPB.

Quantifying the potential impacts of meltwater on cotton yields in the Tarim River Basin, Central Asia

Snow and glaciers provide water to the densely populated downstream area of the Tarim River Basin, which is an important irrigated agricultural area in China. Cotton is an important cash crop, and meltwater is an important irrigation water source for cotton in this region. In this study, the spatiotemporal dependence of cotton yield on mountain meltwater resources in the subbasins of the Tarim River basin was quantified by the variable infiltration capacity (VIC) hydrologic model with the degree-day and CROPR models during 1960–2017. The results showed that the changes in meltwater in all subbasins had a significantly increasing trend. Meltwater contributions to cotton irrigation and yield varied spatiotemporally. Along the area south of the Tian Shan Mountains, the meltwater contribution to irrigation showed a decreasing trend from west to east, and the highest contribution of meltwater to cotton yield occurred in the Weigan River basin, followed by the Aksu River basin and Kaidu River basin. Along the northern Karakoram Mountains, the meltwater contributions to cotton irrigation and yield first decreased and then increased from west to east. In the whole basin, 48.6% of total irrigation withdrawals originated from mountain snow and glacial meltwater and contributed an additional 55.9% to total cotton production during the study period. The results provide important agricultural information for locations where shifts in water availability and demand are projected as a result of socioeconomic growth.