Variable Infiltration Capacity Research Articles

Improving soil moisture assimilation efficiency via model calibration using SMAP surface soil moisture climatology information

To minimize systematic differences between soil moisture (SM) time series derived from remote sensing (RS) and land surface model (LSM), RS-based SM climatology information is typically discarded during land data assimilation (DA). However, recent studies have demonstrated that SM climatology estimates provided by L-band microwave RS retrievals can significantly outperform comparable estimates derived from LSMs. Consequently, neglecting a RS-based SM climatology may lead to degraded SM spatial patterns generated by land DA. Here, we propose a climatology-optimized SM DA framework, which first calibrates LSM parameters to leverage the RS SM climatology information. Next, multi-sensor RS SM retrievals are assimilated into the calibrated LSM using Ensemble Kalman Filter (EnKF). This framework is demonstrated using the Variable Infiltration Capacity (VIC) model and RS SM retrievals derived from the L-band Soil Moisture Active Passive (SMAP) and C-band Advanced SCATterometer (ASCAT) sensors. Here, both SMAP and ASCAT SM retrievals are assimilated into the VIC model after calibrating VIC to match spatial variations captured in the SMAP SM climatology. The DA SM results are validated using in-situ SM observations derived from 820 stations. Results show that SMAP-based SM climatology calibration directly improves the quality of SM spatial patterns estimated by VIC. In addition, the SMAP-based calibration also benefits our model-error representation and thereby yields better Kalman gains that improve temporal SM DA accuracy. Relative to typical DA approaches, this newly proposed DA framework improves both spatial (0.26 versus 0.51 (−)) and temporal correlations (0.48 versus 0.52 (−)) versus in-situ SM observations. In addition, SM improvements are effectively propagated into improved streamflow estimates – leading to an average increase of Nash and Sutcliffe coefficient from 0.74 to 0.76 (−). Overall, we demonstrate that RS SM climatology information is valuable for land DA and our climatology-optimized framework successfully retains such information to the benefit of land DA performance.

Spatiotemporal variations in evapotranspiration and transpiration fraction following changes in climate and vegetation in a karst basin of southwest China

Evapotranspiration (ET) is a vital factor in terrestrial water and energy cycles. Exploring the spatiotemporal variations in ET and transpiration (T) can improve our understanding of the role of vegetation restoration in the context of climate change. Karst ecosystems are particularly sensitive and vulnerable to environmental change. In this study, we investigated the spatiotemporal changes of ET, T, and the transpiration fraction (T/ET) in the Longjiang River Basin (LRB) of southwest China from 1987 to 2018 using the variable infiltration capacity (VIC) model. In karst areas, the original third soil layer in the VIC model was modified to an epikarst zone and a reservoir module was integrated with the model. Model validation and evaluation showed that the modified VIC model performed well in fitting the observations of runoff and ET. During the 32-year study period, ET, T, and T/ET increased by 2.30 mm·yr−1, 2.21 mm·yr−1, and 0.0006, respectively, with the significant increase concentrated in areas with increased forestland. High positive correlations between NDVI and ET, T, and T/ET were observed in areas where forestland and grassland increased. Temperature and wind speed were positively correlated with ET, T, and T/ET in areas with low altitude and abundant precipitation, where large areas of cropland are present. There was a positive correlation between precipitation and ET in karst areas, whereas the negative correlation between precipitation and T was mainly occurred in non-karst areas. The scenario analysis based on different land use data revealed that ET and T/ET exhibited significant differences in karst areas, but not in non-karst areas, indicating that karst areas were more affected by ecological restoration. Moreover, T did not show a significant increase after vegetation restoration, suggesting that increased ET was mainly caused by evaporation. This study provides insights into the effects of vegetation restoration and climate change on ET, T, and T/ET under different geological conditions and can help implement appropriate afforestation strategies in different geological settings.

Revisiting parameter sensitivities in the variable infiltration capacity model across a hydroclimatic gradient

Abstract. Despite the Variable Infiltration Capacity (VIC) model being used for decades in the hydrology community, there are still model parameters whose sensitivities remain unknown. Additionally, understanding the factors that control spatial variations in parameter sensitivities is crucial given the increasing interest in obtaining spatially coherent parameter fields over large domains. In this study, we investigate the sensitivities of 43 soil, vegetation and snow parameters in the VIC model for 101 catchments spanning the diverse hydroclimates of continental Chile. We implement a hybrid local–global sensitivity analysis approach, using eight model evaluation metrics to quantify sensitivities, with four of them formulated from runoff time series, two characterizing snow processes, and the remaining two based on evaporation processes. Our results confirm an overparameterization for the processes analyzed here, with only 12 (i.e., 28 %) parameters found to be sensitive, distributed among soil (7), vegetation (2) and snow (3) model components. Correlation analyses show that climate variables – in particular, mean annual precipitation and the aridity index – are the main controls on parameter sensitivities. Additionally, our results highlight the influence of the leaf area index on simulated hydrologic processes – regardless of the dominant climate types – and the relevance of hard-coded snow parameters. Based on correlation results and the interpretation of spatial sensitivity patterns, we provide guidance on the most relevant parameters for model calibration according to the target processes and the prevailing climate type. Overall, the results presented here contribute to an improved understanding of model behavior across watersheds with diverse physical characteristics that encompass a wide hydroclimatic gradient from hyperarid to humid systems.

Uncertainty quantification of multi-source hydrological data products for the improvement of water budget estimations in small-scale Sakarya basin, Turkey

ABSTRACT The present study aims to improve the efficacy of water budget (WB) estimations from various hydrological data products, by (1) evaluating the uncertainties of hydrological data products, (2) merging four precipitation and six evapotranspiration products using their error variances, and (3) employing the constrained Kalman filter (CKF) method to distribute residual errors among water budget components based on their relative uncertainties. The results show that applying bias correction before the merging process improved estimations of precipitation products with decreasing root mean square error (RMSE), except Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN). Variable Infiltration Capacity (VIC) and bias-corrected Climate Prediction Center Morphing Technique (CMORPH) products outperformed other evapotranspiration and bias-corrected precipitation products, respectively, in terms of mean merging weights. The terrestrial water storage change is the primary reason for non-closure errors, mainly caused by the coarse resolution of Gravity Recovery and Climate Experiment (GRACE). The CKF results were insensitive to variations in uncertainties of runoff. Precipitation derived from the CKF was the best precipitation output, with the highest correlation coefficient (CC) and smallest root mean square deviation (RMSD).

Variability and changes in hydrological drought in the Volta Basin, West Africa

Study regionVolta River Basin, West Africa Study focusHydrological droughts have a wide range of impacts on societies and economic sectors such as agriculture and energy production. Analysis of hydrological droughts helps better plan and manage water resources under global change. This study analyses the spatial and temporal variability of hydrological drought occurrence (duration and severity) in the Volta River basin during 1979–2013. We used the Variable Infiltration Capacity and vector-based routing (RAPID) models and high-resolution forcing data to simulate streamflow for 10300 river reaches. New hydrological insights for the regionDrought duration and severity show high spatial variability and large differences between the three decades (1980 s, 1990 s and 2000 s). Droughts are more severe in larger catchments as the flows are generally higher. The trend analysis shows a general decreasing trend (up to 5% per event) in drought duration in the north-eastern and an increasing trend in southern parts of the basin. Drought severity shows an increasing and decreasing trend (up to ± 20%) in south and north part of the basin, respectively. Daily streamflow has a maximum correlation (up to 0.78) with upstream precipitation for the previous 30-days with a clear signal of propagation from meteorological to hydrological drought with an average lag-time of two weeks. The results indicate the need to consider site-specific and adaptive drought management measures to minimize the impacts.

A Bayesian Hierarchical Framework for Postprocessing Daily Streamflow Simulations across a River Network

Abstract Despite the potential and increasing interest in physically based hydrological models for streamflow forecasting applications, they are constrained in terms of agility to generate ensembles. Hence, we develop and test a Bayesian hierarchical model (BHM) to postprocess physically based hydrologic model simulations at multiple sites on a river network, with the aim to generate probabilistic information (i.e., ensembles) and improve raw model skill. We apply our BHM framework to daily summer (July–August) streamflow simulations at five stations located in the Narmada River basin in central India, forcing the Variable Infiltration Capacity (VIC) model with observed rainfall. In this approach, daily observed streamflow at each station is modeled with a conditionally independent probability density function with time varying distribution parameters, which are modeled as a linear function of potential covariates that include VIC outputs and meteorological variables. Using suitable priors on the parameters, posterior parameters and predictive posterior distributions—and thus ensembles—of daily streamflow are obtained. The best BHM model considers a gamma distribution and uses VIC streamflow and a nonlinear covariate formulated as the product of VIC streamflow and 2-day precipitation spatially averaged across the area between the current and upstream station. The second covariate enables correcting the time delay in flow peaks and nonsystematic biases in VIC streamflow. The results show that the BHM postprocessor increases probabilistic skill in 60% compared to raw VIC simulations, providing reliable ensembles for most sites. This modeling approach can be extended to combine forecasts from multiple sources and provide skillful multimodel ensemble forecasts.

The Role of Groundwater Withdrawals on River Regulation: Example From the Columbia River Basin

AbstractThe Columbia River Basin (CRB) is heavily regulated by more than 250 dams on its river system while depending significantly on groundwater withdrawals in certain sub‐basins. Neglecting groundwater withdrawals in hydrologic models of the basin could result in inaccurate predictions of its water budget and thus mislead water management decisions in the basin. This study aims to understand the impacts of groundwater pumping on the spatiotemporal patterns of modeling regulated streamflow in the CRB using a modified version of the Variable Infiltration Capacity (VIC) model integrated with a water management component that accounts for groundwater withdrawals, irrigation demands, and reservoir operation (VIC‐GIRR). The VIC‐GIRR simulations showed that considering additional groundwater withdrawals would alleviate the stress of irrigation water deficit in the Snake River Basin with an average reduction of 10 km3/year. Such a reduction in water deficit resulted in slight streamflow increase over the CRB with maximum increase up to 40% during dry period in certain locations. We also note that the implementation of groundwater withdrawal does not, however, improve the overall model performance in long‐term averaged streamflow and storage predictions. Our results highlight the efforts needed to examine additional important processes in representing the interactions between water withdrawals and reservoir operations. Such efforts will aid in better simulation of multi‐reservoir system and improve effectiveness for agricultural productivity, power generation, flood control, and navigation purposes.

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.

Climatic Control on Spatial Distribution of Water Storage at the Catchment Scale: A Framework for Unifying Saturation Excess Runoff Models

AbstractThis paper aims to investigate the connection between TOPography‐based hydrological model (TOPMODEL) and Variable Infiltration Capacity (VIC) model through virtual experiments from the perspective of water table and storage at the catchment scale. A simple finite‐difference groundwater flow model was built for a hypothetical catchment forced by a sequence of recharges. A steady‐state water table under a low recharge rate is used as the climatic lower limit, above which the pore space is considered as the maximum storage capacity. When the water table is shallow, the land surface is a good proxy of the water table as assumed in the original TOPMODEL, and the underlying water storage distribution curve is similar as the maximum storage capacity distribution curve. When the water table is deep, the climatic lower limit is a good proxy of water table, and the storage is approximately spatially uniform over the unsaturated area as assumed in the VIC model. The systematic variation of water table and storage distribution potentially provides a framework for unifying the TOPMODEL and VIC model.

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.

Climate change impacts on conventional and flash droughts in the Mekong River Basin

Recent drought events in the Mekong River Basin (MRB) have resulted in devastating environmental and economic losses, and climate change and human-induced alterations have exacerbated drought conditions. Using hydrologic models and multiple climate change scenarios, this study quantified the future climate change impacts on conventional and flash drought conditions in the MRB. The Soil and Water Assessment Tool (SWAT) and Variable Infiltration Capacity (VIC) models were applied to estimate long-term drought indices for conventional and flash drought conditions over historical and future periods (1966–2099), using two emission scenarios (RCP 4.5 and RCP8.5), and four climate models from the Coupled Model Intercomparison Project Phase 5 (CMIP5). For the conventional drought assessment, monthly scale drought indices were estimated, and pentad-scale (5 days) drought indices were computed for the flash drought evaluations. There were overall increases in droughts from the SWAT model for the conventional drought conditions and overall decreases from the VIC model. For the flash drought conditions, the SWAT-driven drought indices showed overall increases in drought occurrences (up to 165%). On the contrary, the VIC-driven drought indices presented decreases in drought occurrences (up to −44%). The conventional and flash drought evaluations differ between these models as they partition the water budget, specifically soil moisture differently. We conclude that the proposed framework, which includes hydrologic models, various emission scenarios, and projections, allows us to assess the various perspectives on drought conditions. Basin countries have differential impacts, so targeted future adaptation strategy is required.

Evaluation of two new-generation global soil databases for macro-scale hydrological modelling in Norway

Lack of national soil property maps limits the studies of soil moisture (SM) dynamics in Norway. One alternative is to apply the global soil data as input for macro-scale hydrological modelling, but the quality of these data is still unknown. The objectives of this study are 1) to evaluate two recent global soil databases (Wise30sec and SoilGrids) in comparison with data from local soil profiles; 2) to evaluate which database supports better model performance in terms of river discharge and SM for three macro-scale catchments in Norway and 3) to suggest criteria for the selection of soil data for models with different complexity. The global soil databases were evaluated in three steps: 1) the global soil data are compared directly with the Norwegian forest soil profiles; 2) the simulated discharge based on the two global soil databases is compared with observations and 3) the simulated SM is compared with three global SM products. Two hydrological models were applied to simulate discharge and SM: the Soil and Water Integrated Model (SWIM) and the Variable Infiltration Capacity (VIC) model. The comparison with data from local soil profiles shows that SoilGrids has smaller mean errors than Wise30sec, especially for upper soil layers, but both soil databases have large root mean squared errors and poor correlations. SWIM generally performs better in terms of discharge using SoilGrids than using Wise30sec and the simulated SM has higher correlations with the SM products. In contrast, the VIC model is less sensitive to soil input data and the simulated SM using Wise30sec is higher correlated with the SM products than using SoilGrids. Based on the results, we conclude that the global soil databases can provide reasonable soil property information at coarse resolutions and large areas. The selection of soil input data should depend on the characteristics of both models and study areas.

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.

Climate change impacts on concurrences of hydrological droughts and high temperature extremes in a semi-arid river basin of China

Hydrological droughts are natural hazards with devastating impacts on different sectors, which may cause even more adverse impacts if concurring with high temperature extremes. Though many studies have evaluated changes in individual hydrological droughts and high temperature extremes, the impact of climate change on their concurrences (or compound extremes) is still lacking. In this study, we investigated changes in compound hydrological droughts and high temperature extremes (CHDHEs) for the period 1961–2016 in the Luanhe River Basin, a semi-arid basin of China. We employed the Variable Infiltration Capacity (VIC) model to simulate runoff, based on which the temporal and spatial variation of CHDHEs was investigated. Results showed that the frequency of CHDHEs increased by 160% from 1961 to 1988 to the recent period 1989–2016. At the spatial scale, a significant increase in the frequency of CHDHEs was shown over 67.37% of the study area. Temperature was shown to play a significant role in the variation of CHDHEs with an average contribution of 57.57%. Results in this study can provide valuable insights for understanding the risk of compound droughts and high temperature extremes in the Luanhe River Basin under global warming.

Impact on discharge modelling using different spatial and temporal resolution scenarios in South of Chile

Despite ungauged basins exist across countries of a wide range of incomes, assessing water resource availability is an essential issue in low-income zones with less dense hydrologic measurement networks. In a megadrought context, hydrological resources availability is critical in the central south of Chile, leading to water scarcity problems for the population. We develop a one-way coupled discharge model using the Weather and Research Forecast (WRF) and Variable Infiltration Capacity (VIC)/routing model to estimate surface water availability along the basin and subbasins. We studied the Biobío basin in Chile (∼37° S) because of its low spatial density discharge measurements as a case of study. We compare discharge variations simulated between two datasets and in-situ high temporal resolution measurements between the years 2005–2006. Our results show that discharge is unsuitable for a mid-scale basin using atmospheric products over 12 km of spatial resolution. However, our simulations showed high efficiency (Nash Sutcliffe over 0.75) and low RMSE values (less than 40 m3s-1 in most cases) in daily and tri-hourly simulations at 4-km spatial resolution. The percentual root mean square error (pRMSE) improved from the worst scenario (110% in SC1) up to 37% in the best scenario (SC3), showing that the coupled approach is suitable for basins in the south of Chile. These results provide a temporal-spatial basis for improving water resources management in poorly gauged basins in the south of Chile, as is common in South America. Our analysis will contribute to a real-time flow modeling system development for the Biobio basin. Future developments will contribute to surface water availability in a water scarcity context.

Sub‐Seasonal Prediction of Drought and Streamflow Anomalies for Water Management in India

AbstractMeteorological and hydrologic prediction at short to sub‐seasonal scales is essential for reservoir operations to mitigate droughts. We examine the skills in the meteorological forecast from the Subseasonal Experiment (SubX) and Extended Range Forecast System (ERFS) for precipitation, maximum and minimum temperatures at 1, 7, 15, and 30 days lead. We bias‐corrected meteorological forecasts using the multivariate bias correction method for hydrologic prediction. The Variable Infiltration Capacity model was used to simulate total runoff and root‐zone soil moisture for India. We also developed a streamflow forecast for the five major river basins that have large reservoirs. Bias correction of meteorological forecast (precipitation, maximum and minimum temperatures) resulted in a considerable improvement in hydrologic and meteorological forecast skills. The Environmental Modeling Center (EMC) model from the SubX provides either better or equal forecast skills for the raw meteorological forecast compared to ERFS, which is an operational product in India. We examined the forecast skills of the meteorological and hydrological products for the two major droughts that occurred recently. We find that most forecast models effectively captured the onset, peak, and termination of the North Indian drought in 2015–2016 and the South Indian drought in 2016–2017 at a 30‐day lead. Bias correction of the meteorological forecast improved the streamflow forecast for the selected drought event upstream of the major reservoirs. The EMC model showed better forecast skills for the two major droughts than other forecast products. Overall, the SubX products show potential for short‐to‐sub‐seasonal scale hydrologic prediction that can assist water management in India.

Explaining water security indicators using hydrologic and agricultural systems models

Water security plays an important role in socio-economic development, ecosystem management, and environmental sustainability. Over the last four decades, water security assessment has attracted much political and economic attention. An improved understanding of the relationships between water demand and supply is needed to mitigate the impacts of diminishing water resources. This study provides an overview of water security assessment by focusing on the various water security indicators and the concept of water footprint (blue, green, and grey water). The water security indicators based on the water footprint concept is currently receiving more attention because it accounts for the return flow from the total water withdrawn from a watershed. We also investigate the application of different physically-based hydrological models, such as Soil and Water Assessment Tool (SWAT) and Variable Infiltration Capacity (VIC), on water security assessment at a regional to continental scale. However, hydrological/agricultural system models cannot quantify evapotranspiration from irrigation and rainwater separately. Therefore, independent quantification of blue and green water footprint from an irrigated field is challenging. For illustration purposes, we apply the fully distributed Agricultural Ecosystems Services (AgES) model in the Big Dry Creek Watershed (BDCW), an intensively managed and irrigated watershed located in semiarid Colorado. The results indicate that the blue water footprint is higher than the green water footprint in the watershed. In addition, the spatial distribution of grey water footprint is highly correlated with the amount of fertilizer application. The variation of grey water footprint among the irrigated fields is higher than blue and green water footprints. We conclude that applying a physically distributed model can provide useful insight into the impact of climate and anthropogenic activities on water security at different scales.

Climate change impact assessment in a tropical headwater basin

Changes in precipitation and air temperature may produce different impacts on the hydrological regime, compromising water supply. This study focuses on climate change impacts in the Verde River Basin (VRB), a tropical headwater basin in southeast Brazil, located in the state of Minas Gerais. The Variable Infiltration Capacity model (VIC) was calibrated and validated in the Verde River Basin. The downscaling (Eta Regional Climate Model, at 20-km resolution) of three Global Circulation Models (CanESM2, HadGEM2-ES and MIROC5) were used to drive the VIC for a historical baseline (1961-2005) and three time-slices (2011-2040, 2041-2070 and 2071-2099), under RCPs 4.5 and 8.5 scenarios. The scenarios were used as input in the hydrological model after bias correction. The hydrological model (VIC) showed satisfactory statistical performance in calibration and validation, with CNS varying from 0.77 to 0.85 for daily and monthly discharges; however, it overestimated some peak flows and underestimated the recession flows. Multi-model ensemble means predict increases of the minimum and maximum monthly average temperature for the investigated area at the end of the century. The Eta-CanESM2 indicated greater warming, mainly for RCP8.5 at the end the century, whereas Eta-HadGEM2-ES showed higher reduction in the precipitation for RCP4.5 at the beginning of the century and for RCP8.5 at the end the century, negatively impacting the evapotranspiration and discharge. Among the Regional Climate Models (RCMs), the Eta-MIROC5 showed minor changes in the components of the hydrological cycle. This study suggests that Global Circulation Models represent an additional uncertainty, which should be accounted for in the climate change impact assessment.
 Keywords: climate changes, RCP4.5, RCP8.5, VIC model.