Macroscale Hydrologic Model Research Articles

Assessing the hydrological impacts of climate change in the headwater catchment of the Tarim River basin, China

Two statistical downscaling models were used to downscale regional climate change scenarios, on the basis of the outputs of three general circulation models (GCMs) and three emission scenarios. Driven by these climate change scenarios, a distributed macro-scale hydrological model (the Variable Infiltration Capacity (VIC) model) was applied to assess the impact of climate change on hydrological processes in the headwater catchment (HC) of the Tarim River basin, China. The results showed that the HC tends to experience warmer and drier conditions under the combined climate change scenarios. The predictions show a decreasing trend of the runoff in the HC, driven by the combined climate change scenarios. The results predicted an increasing trend for winter runoff however, which was consistent with the forecasts from most previous studies on other locations such as the region of St Lawrence tributaries (Quebec, Canada) and the Willamette River Basin (Oregon, USA). There was an inconsistent intra-annual distribution of the changes in precipitation and runoff in the HC; these inconsistencies may be explained by increasing snowmelt runoff resulting from higher air temperature. It was concluded that uncertainties within different GCM outputs are more significant than emission scenarios in the assessment of the potential impact of climate change.

A new global river network database for macroscale hydrologic modeling

Coarse‐resolution (upscaled) river networks are critical inputs for runoff routing in macroscale hydrologic models. Recently, Wu et al. (2011) developed a hierarchical dominant river tracing (DRT) algorithm for automated extraction and spatial upscaling of river networks using fine‐scale hydrography inputs. We applied the DRT algorithms using combined HydroSHEDS and HYDRO1k global fine‐scale hydrography inputs and produced a new series of upscaled global river network data at multiple (1/16° to 2°) spatial resolutions. The new upscaled results are internally consistent and congruent with the baseline fine‐scale inputs and should facilitate improved regional to global scale hydrologic simulations.

Bayesian parameter uncertainty modeling in a macroscale hydrologic model and its impact on Indian river basin hydrology under climate change

Macroscale hydrologic models (MHMs) were developed to study changes in land surface hydrology due to changing climate over large domains, such as continents or large river basins. However, there are many sources of uncertainty introduced in MHM hydrological simulation, such as model structure error, ineffective model parameters, and low‐accuracy model input or validation data. It is hence important to model the uncertainty arising in projection results from an MHM. The objective of this study is to present a Bayesian statistical inference framework for parameter uncertainty modeling of a macroscale hydrologic model. The Bayesian approach implemented using Markov Chain Monte Carlo (MCMC) methods is used in this study to model uncertainty arising from calibration parameters of the Variable Infiltration Capacity (VIC) MHM. The study examines large‐scale hydrologic impacts for Indian river basins and changes in discharges for three major river basins with distinct climatic and geographic characteristics, under climate change. Observed/reanalysis meteorological variables such as precipitation, temperature and wind speed are used to drive the VIC macroscale hydrologic model. An objective function describing the fit between observed and simulated discharges at four stations is used to compute the likelihood of the parameters. An MCMC approach using the Metropolis‐Hastings algorithm is used to update probability distributions of the parameters. For future hydrologic simulations, bias‐corrected GCM projections of climatic variables are used. The posterior distributions of VIC parameters are used for projection of 5th and 95th percentile discharge statistics at four stations, namely, Farakka, Jamtara, Garudeshwar, and Vijayawada for an ensemble of three GCMs and three scenarios, for two time slices. Spatial differences in uncertainty projections of runoff and evapotranspiration for years 2056–2065 for the a1b scenario at the 5th and 95th percentile levels are also projected. Results from the study show increased mean monthly discharges for Farakka and Vijayawada stations, and increased low, mid and high duration flows at Farakka, Jamtara and Vijayawada for the future. However, it is seen that uncertainty introduced due to choice of GCM, is larger than that due to parameter uncertainty for the VIC MHM. The largest effects of runoff predictive uncertainty due to uncertainty in VIC parameters are seen in the Himalayan foothills belt, and the high‐precipitation Northeast region of the country. It is demonstrated through the study that it is relevant and feasible to provide Bayesian uncertainty estimates for macroscale models in projection of large‐scale and regional hydrologic impacts.

Application of a Macroscale Hydrologic Model to Estimate Streamflow across Southeast Australia

Abstract Reliable predictions of water availability and streamflow characteristics, and the impact of climate and land use change on water availability, are central to water resources planning and management. This paper assesses the application of the widely used macroscale hydrologic model, the three-layer Variable Infiltration Capacity model (VIC-3L), to estimate daily streamflow in 191 unregulated catchments across southeast Australia and evaluates the regionalization of model parameters to predict streamflow in ungauged catchments. The parameter values in the VIC-3L model are estimated using three methods: default values, optimized values based on model calibration, and regionalized values based on spatial proximity method. The modeled streamflows from VIC-3L are assessed against the observed streamflows from the catchments. The authors discuss the model performance based on different parameter estimation methods and the effects of rainfall regimes on streamflow prediction. Also the implication of using a priori estimates of parameter values versus optimizing parameter values against observed streamflow to predict the impact of climate and land use change on streamflow is discussed. The VIC-3L model can simulate the streamflow in the catchments across southeast Australia reasonably well, with comparable results to those reported for the same region using conceptual rainfall-runoff models. The model performed better in summer-dominant rainfall catchments and wet catchments than in other catchments. The regionalization based on spatial proximity method performed reasonably well, which demonstrated the potential of VIC-3L model to predict streamflow in ungauged catchments in Australia.

CLIMATE AND LULC CHANGE SCENARIOS TO STUDY ITS IMPACT ON HYDROLOGICAL REGIME

Abstract. Climate change, whether as a natural cycle variability and/or due to anthropogenic reasons, is affecting and likely to further affect the water resources, which is a vital necessity for existence of life form. The predicted intensification of hydrological cycle would change all of its constituents both in time and space domain. This is a long term phenomenon and the necessity is to understand the intensity of the effects on various aspects of water resources by way of scientific studies backed by the available field data. Therefore, in the present study, the impact of climate and land use land cover change on entire India under different assumed plausible hypothetical scenarios has been studied. These scenarios were developed by increasing; temperature by 1, 2 and 30C; rainfall by 5, 10 and 15%; and then the combination of both. To carry out this analysis, variable infiltration capacity (VIC) semi-distributed macroscale hydrological model has been investigated. It was found that slight change in climate may pose huge difference on hydrological cycle and its component.

Coupling a glacier melt model to the Variable Infiltration Capacity (VIC) model for hydrological modeling in north-western China

For the sustainable utilization of rivers in the mid and downstream regions, it is essential that land surface hydrological processes are quantified in high cold mountains regions, as it is in these regions where most of the larger rivers in China acquire their headstreams. Glaciers are one of the most important water resources of north-west China. However, they are seldom explicitly considered within hydrological models, and climate-change effects on glaciers, permafrost and snow cover will have increasingly important consequences for runoff. In this study, an energy-balance ice-melt model was integrated within the Variable Infiltration Capacity (VIC) macroscale hydrological model. The extended VIC model was applied to simulate the hydrological processes in the Aksu River basin, a large mountainous and glaciered catchment in north-west China. The runoff components and their response to climate change were analyzed based on the simulated and observed data. The model showed an acceptable performance, and achieved an efficiency coefficient R2 ≈ 0.8 for the complete simulation period. The results indicate that a large proportion of the catchment runoff is derived from ice meltwater and snowmelt water. In addition, over the last 38 years, rising temperature caused an extension in the snow/ice melting period and a reduction in the seasonality signal of runoff. Due to teh increased precipitation runoff, the Aksu catchment annual runoff had a positive trend, increasing by about 40.00 × 106 m3 per year, or 25.7 %.

A grid‐based approach for simulating stream temperature

Applications of grid‐based systems are widespread in many areas of environmental analysis. In this study, the concept is adapted to the modeling of water temperature by integrating a macroscale hydrologic model, variable infiltration capacity (VIC), with a computationally efficient and accurate water temperature model. The hydrologic model has been applied to many river basins at scales from 0.0625° to 1.0°. The water temperature model, which uses a semi‐Lagrangian numerical scheme to solve the one‐dimensional, time‐dependent equations for thermal energy balance in advective river systems, has been applied and tested on segmented river systems in the Pacific Northwest. The state‐space structure of the water temperature model described in previous work is extended to include propagation of uncertainty. Model results focus on proof of concept by comparing statistics from a study of a test basin with results from other studies that have used either process models or statistical models to estimate water temperature. The results from this study compared favorably with those of selected case studies using data‐driven statistical models. The results for deterministic process models of water temperature were generally better than the grid‐based method, particularly for those models developed from site‐specific, data‐intensive studies. Biases in the results from the grid‐based system are attributed to heterogeneity in hydraulic characteristics and the method of estimating headwater temperatures.

An Operation-Based Scheme for a Multiyear and Multipurpose Reservoir to Enhance Macroscale Hydrologic Models

Abstract This paper develops an operation-based numerical scheme for simulating storage in and outflow from a multiyear and multipurpose reservoir at a daily time step in order to enhance the simulation capacity of macroscale land surface hydrologic models. In the new scheme, besides the purpose of flood control, three other operational purposes—hydropower generation, downstream water supply, and water impoundment—are considered, and accordingly three related decision-based parameters are introduced. The new scheme is then integrated into the Soil and Water Assessment Tool (SWAT), which is a macroscale hydrologic model. The observed water storage and outflow from a multiyear and multipurpose reservoir, the Xinfengjiang Reservoir in southern China, are used to examine the new scheme. Compared with two other reservoir operation schemes—namely, a modified existing reservoir operation scheme in SWAT (i.e., the target release scheme) and a multilinear regression scheme—the new scheme can give a consistently better simulation of the reservoir storage and outflow. Furthermore, through a sensitivity analysis, this study shows that the three decision-based parameters can represent the significance of each operational purpose in different periods and the new scheme can advance the flexibility and capability of the simulation of the reservoir storage and outflow.

Evaluation of streamflow estimates for the Rovuma River

Reliable estimates of historic streamflow are important when estimating future flows and water resources availability based on factors such as climate change, population growth, and changes in land use or land cover. Many regions across the globe have limited streamflow observations. Additional information about streamflow in these basins is critical to water resources planning and economic development strategies. In southeastern Africa, the remote Rovuma River lies on the border between Mozambique and Tanzania. There are limited historic measurements in the main tributary, the Lugenda River, and no publicly available observations from recent years. Improved knowledge of the water resources availability and seasonal and annual variability of this river will enhance transboundary river basin management discussions. A combination of methods, including index-gauge methods and a macro-scale hydrological model are used to estimate historic streamflow conditions in the Rovuma River. These methods incorporate data from remote sensing, gridded global soil data, a composite runoff dataset, and in situ observations. The hydrological model was tested in a nearby gauged basin yielding a Nash–Sutcliffe efficiency ratio of 0.8, an efficiency ratio based on mean historical streamflow by month of 0.6, an efficiency ratio based on inverse flows (sensitive to low flows) of 0.9, and a coefficient of determination equal to 0.99. In the Rovuma River, the mean and standard deviation of the index gauge-estimated mean monthly flows agree with streamflow estimates using the Variable Infiltration Capacity (VIC) hydrologic model with a 0.25 decimal degree spatial resolution. A closer look at precipitation records suggests that the model results provide a more accurate historic flow record than the index gauge methods due to small-scale precipitation events. Model inputs and results are evaluated by leveraging available regional in situ data in comparison to remote sensing data input data. Uncertainties in the streamflow estimates are high, however, additional in situ measurements can reduce these uncertainties. This combination of methods could prove useful for estimating flows in other rivers in southern Africa and other regions with intermittent or sparse streamflow observations.

How Much Can A Priori Hydrologic Model Predictability Help in Optimal Merging of Satellite Precipitation Products?

AbstractIn this study, the authors ask the question: Can a more superior precipitation product be developed by merging individual products according to their a priori hydrologic predictability? The performance of three widely used high-resolution satellite precipitation products [Tropical Rainfall Measuring Mission (TRMM) real-time precipitation product 3B42 (3B42-RT), the NOAA/Climate Prediction Center morphing technique (CMORPH), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS)] was evaluated in terms streamflow predictability for the entire Mississippi River basin using the Variable Infiltration Capacity (VIC) macroscale hydrologic model. A merging concept that was not based on a single universal merging formula for the whole basin but rather used a “localized” (grid box by grid box) approach for merging precipitation products was then explored. In this merging technique, the a priori (historical) hydrologic predictive skill of each product for each grid box was first identified. Prior to streamflow routing, the corresponding accuracy of the spatially distributed simulations of soil moisture and runoff were used as proxy for weights in merging the precipitation products. It was found that the merged product derived on the basis of runoff predictability outperformed its counterpart merged product derived on the basis of soil moisture simulation. Results indicate that such a grid box by grid box merging concept that leverages a priori information on predictability of individual products has the potential to yield a more superior product for streamflow prediction than what the individual products can deliver for hydrologic prediction.

Reconstructing and analyzing China's fifty-nine year (1951–2009) drought history using hydrological model simulation

Abstract. The 1951–2009 drought history of China is reconstructed using daily soil moisture values generated by the Variable Infiltration Capacity (VIC) land surface macroscale hydrology model. VIC is applied over a grid of 10 458 points with a spatial resolution of 30 km × 30 km, and is driven by observed daily maximum and minimum air temperature and precipitation from 624 long-term meteorological stations. The VIC soil moisture is used to calculate the Soil Moisture Anomaly Percentage Index (SMAPI), which can be used as a measure of the severity of agricultural drought on a global basis. We have developed a SMAPI-based drought identification procedure for practical uses in the identification of both grid point and regional drought events. As a result, a total of 325 regional drought events varying in time and strength are identified from China's nine drought study regions. These drought events can thus be assessed quantitatively at different spatial and temporal scales. The result shows that the severe drought events of 1978, 2000 and 2006 are well reconstructed, which indicates that the SMAPI is capable of identifying the onset of a drought event, its progression, as well as its termination. Spatial and temporal variations of droughts in China's nine drought study regions are studied. Our result shows that on average, up to 30% of the total area of China is prone to drought. Regionally, an upward trend in drought-affected areas has been detected in three regions (Inner Mongolia, Northeast and North) from 1951–2009. However, the decadal variability of droughts has been weak in the rest of the five regions (South, Southwest, East, Northwest, and Tibet). Xinjiang has even been showing steadily wetter since the 1950s. Two regional dry centres are discovered in China as the result of a combined analysis on the occurrence of drought events from both grid points and drought study regions. The first centre is located in the area partially covered by the North and the Northwest, which extends to the southeastern portion of Inner Mongolia and the southwest part of Northeast. The second one is found on the central to southern portion of the South. Our study demonstrates the applicability and the value of using modeled soil moisture for reconstructing drought histories, and the SMAPI is useful for analyzing drought at different spatial and temporal scales.

Climate change impacts on snow water availability in the Euphrates-Tigris basin

Abstract. This study investigates the effects of projected climate change on snow water availability in the Euphrates-Tigris basin using the Variable Infiltration Capacity (VIC) macro scale hydrologic model and a set of regional climate-change outputs from 13 global circulation models (GCMs) forced with two greenhouse gas emission scenarios for two time periods in the 21st century (2050 and 2090). The hydrologic model produces a reasonable simulation of seasonal and spatial variation in snow cover and associated snow water equivalent (SWE) in the mountainous areas of the basin, although its performance is poorer at marginal snow cover sites. While there is great variation across GCM outputs influencing snow water availability, the majority of models and scenarios suggest a significant decline (between 10 and 60 percent) in available snow water, particularly under the high-impact A2 climate change scenario and later in the 21st century. The changes in SWE are more stable when multi-model ensemble GCM outputs are used to minimize inter-model variability, suggesting a consistent and significant decrease in snow-covered areas and associated water availability in the headwaters of the Euphrates-Tigris basin. Detailed analysis of future climatic conditions point to the combined effects of reduced precipitation and increased temperatures as primary drivers of reduced snowpack. Results also indicate a more rapid decline in snow cover in the lower elevation zones than the higher areas in a changing climate but these findings also contain a larger uncertainty. The simulated changes in snow water availability have important implications for the future of water resources and associated hydropower generation and land-use management and planning in a region already ripe for interstate water conflict. While the changes in the frequency and intensity of snow-bearing circulation systems or the interannual variability related to climate were not considered, the simulated changes in snow water availability presented here are likely to be indicative of climate change impacts on the water resources of the Euphrates-Tigris basin.

Evaluation of mesoscale convective systems in South America using multiple satellite products and an object-based approach

[1] In this study, an object-based verification method was used to reveal the existence of systematic errors in three satellite precipitation products: Tropical Rainfall Measurement Mission (TRMM), Climate Prediction Center Morphing Technique (CMORPH), and Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN). Mesoscale convective systems (MCSs) for the austral summer 2002–2003 in the La Plata river basin, southeastern South America, were analyzed with the Contiguous Rain Area (CRA) method. Errors in storms intensity, volume, and spatial location were evaluated. A macroscale hydrological model was used to assess the impact of spatially shifted precipitation on streamflows simulations. PERSIANN underestimated the observed average rainfall rate and maximum rainfall consistent with the detection of storm areas systematically larger than observed. CMORPH overestimated the average rainfall rate while the maximum rainfall was slightly underestimated. TRMM average rainfall rate and rainfall volume correlated extremely well with ground observations whereas the maximum rainfall was systematically overestimated suggesting deficiencies in the bias correction procedure to filter noisy measurements. The preferential direction of error displacement in satellite-estimated MCSs was in the east-west direction for CMORPH and TRMM. Discrepancies in the fine structure of the storms dominated the error decomposition of all satellite products. Errors in the spatial location of the systems influenced the magnitude of simulated peaks but did not have a significant impact on the timing indicating that the system's response to precipitation was mitigating the effect of the errors.

Automated upscaling of river networks for macroscale hydrological modeling

We developed a hierarchical dominant river tracing (DRT) algorithm for automated extraction and spatial upscaling of basin flow directions and river networks using fine‐scale hydrography inputs (e.g., flow direction, river networks, and flow accumulation). In contrast with previous upscaling methods, the DRT algorithm utilizes information on global and local drainage patterns from baseline fine‐scale hydrography to determine upscaled flow directions and other critical variables including upscaled basin area, basin shape, and river lengths. The DRT algorithm preserves the original baseline hierarchical drainage structure by tracing each entire flow path from headwater to river mouth at fine scale while prioritizing successively higher order basins and rivers for tracing. We applied the algorithm to produce a series of global hydrography data sets from 1/16° to 2° spatial scales in two geographic projections (WGS84 and Lambert azimuthal equal area). The DRT results were evaluated against other alternative upscaling methods and hydrography data sets for continental U.S. and global domains. These results show favorable DRT upscaling performance in preserving baseline fine‐scale river network information including: (1) improved, automated extraction of flow directions and river networks at any spatial scale without the need for manual correction; (2) consistency of river network, basin shape, basin area, river length, and basin internal drainage structure between upscaled and baseline fine‐scale hydrography; and (3) performance largely independent of spatial scale, geographic region, and projection. The results of this study include an initial set of DRT upscaled global hydrography maps derived from HYDRO1K baseline fine‐scale hydrography inputs; these digital data are available online for public access at ftp://ftp.ntsg.umt.edu/pub/data/DRT/.

Assessing the impacts of vegetation heterogeneity on energy fluxes and snowmelt in boreal forests

Aims In the mid- and high-latitude regions, three quarters of the land surface is covered by boreal conifer forests, and snow lasts for 6–8 months of the year. Correctly modeling surface energy balance and snowmelt at mid- and high-latitudes has a significant influence on climate and hydrological processes. However, the heterogeneous and clumped forest structure exerts important control over the radiative energy at the forest floor, which results in large variations of underneath snow cover and snowmelt rate. The goal of this study is to investigate the impact of hierarchically clumped vegetation structure in boreal forest on snowmelt and exchanges of energy and water. Methods We used a simple Clumped Canopy Scheme (CCS) for canopy radiation transfer to characterize the impact of the clumped forest structure on net radiation at the snow surface underneath forests. The CCS was integrated with the Variable Infiltration Capacity macroscale hydrological model (herein referred to as VIC-CCS) to characterize the impact of clumped vegetation structure on surface energy balance and snowmelt during the snow season. A twin simulation, VICCCS and the standard VIC model, was performed to isolate the impact of CCS on the energy and water fluxes and snowmelt rates. The simulation results were compared to in situ measurements at four different forest stands: old aspen forest in the Southern Study Area (SOA), black spruce forests in the Southern and Northern Study Areas (SOBS and NOBS) and fen wetland in the Northern Study Area (NFEN) within the Boreal Ecosystem–Atmosphere Study (BOREAS) region in central Canada during 1994 to1996. Important Findings Simulations showed that the implementation of CCS has reduced incoming long-wave radiation at the underlying snow surface and, thereby, lowered the snowmelt rate. Comparison against ground observations of net radiation and surface flux rates showed a reasonable agreement while demonstrating implementation of CCS can markedly improve model surface energy budget and energy inputs computation for snowmelt. The modeled snowmelt matches reasonably well with observations with root mean square error (RMSE) ranging from 16.51 to 19.81 mm using VIC-CCS versus 29.86 to 32.61 mm for VIC only in the four forest sites. The improvement is the most significant for the deciduous forest (old aspen) site, reducing RMSE by16 mm. This study demonstrates that taking into account the effect of the clumped forest structure in land surface parameterization schemes is critical for snowmelt prediction in the boreal regions.

Reconstructing sixty year (1950-2009) daily soil moisture over the Canadian Prairies using the Variable Infiltration Capacity model

The Variable Infiltration Capacity (VIC) land surface macroscale hydrology model was used to reconstruct 60 years (1950-2009) of daily soil moisture values for three soil layers (0–20 cm, 20–100 cm, and 0–100 cm) over the three Canadian Prairie Provinces with a total area of 1,964,000 km2. VIC was applied over a grid of 4,393 points with a resolution of 0.25° × 0.25°, and was driven by observed daily maximum and minimum air temperature and precipitation from 1,167 meteorological stations. The model was first calibrated using observed hydrographs from seven catchments with drainage areas varying from 3,750 to 7,870 km2. Special attention was given to modelling of rainfall-runoff processes over the non-contributing drainage area of the Prairies. VIC was then validated over these seven catchments at different periods and over an additional five catchments with drainage areas ranging from 36,500 to 131,000 km2. An estimation procedure to determine model parameters was developed and applied to catchments where hydrographs are not available for the standard calibration process. In situ soil moisture measurements from six Alberta sites were also used for model validation. VIC performed well over both calibration and validation catchments. The results clearly demonstrate that incorporating non-contributing drainage areas into runoff calculations could substantially improve the ability of VIC to simulate surface and sub-surface runoff in regions where poor drainage network development is a dominant feature of drainage basins. The VIC reconstructed 60–year average of the soil moisture in the top 1 m shows some expected climatological features of the Prairies. For example, the reconstructed soil moisture climatology portrays the dry Palliser Triangle region and the Prairie Dry Belt in the southern Prairies. The VIC simulated soil moisture was used to calculate the daily Soil Moisture Anomaly Percentage Index (SMAPI) for the three soil layers; SMAPI can be used as an index of agricultural drought severity taking into account climatology. The value of the calculated SMAPI in quantifying and documenting prairie drought events is demonstrated through an intensive examination of the April 2002 drought case.

Macroscale hydrologic modeling of ecologically relevant flow metrics

Stream hydrology strongly affects the structure of aquatic communities. Changes to air temperature and precipitation driven by increased greenhouse gas concentrations are shifting timing and volume of streamflows potentially affecting these communities. The variable infiltration capacity (VIC) macroscale hydrologic model has been employed at regional scales to describe and forecast hydrologic changes but has been calibrated and applied mainly to large rivers. An important question is how well VIC runoff simulations serve to answer questions about hydrologic changes in smaller streams, which are important habitat for many fish species. To answer this question, we aggregated gridded VIC outputs within the drainage basins of 55 streamflow gages in the Pacific Northwest United States and compared modeled hydrographs and summary metrics to observations. For most streams, several ecologically relevant aspects of the hydrologic regime were accurately modeled, including center of flow timing, mean annual and summer flows and frequency of winter floods. Frequencies of high and low flows in the summer were not well predicted, however. Predictions were worse for sites with strong groundwater influence, and some sites showed errors that may result from limitations in the forcing climate data. Higher resolution (1/16th degree) modeling provided small improvements over lower resolution (1/8th degree). Despite some limitations, the VIC model appears capable of representing several ecologically relevant hydrologic characteristics in streams, making it a useful tool for understanding the effects of hydrology in delimiting species distributions and predicting the potential effects of climate shifts on aquatic organisms.

Estimating the water budget of major US river basins via remote sensing

Nine satellite-based products, each of which provides information about land surface water budget terms, are used to estimate seasonal and annual variations in the water budget of the major river basins of the conterminous USA from 2003 to 2006. The remotely sensed terms are compared with gridded gauge precipitation, and estimates of evapotranspiration (E) and total water storage (TWS) derived from the Variable Infiltration Capacity (VIC) macroscale hydrology model. Among the remote sensing estimates, precipitation has the largest uncertainties. In general, apparent errors for E and TWS show substantial spatial variations, but the consistencies among these remote sensing products are greater than among precipitation products, possibly due in part to similarities in methodology, especially for TWS. Inferred run-off (as a residual of remote sensing estimates of precipitation, E, and TWS) is generally overestimated, due both to excessive precipitation and underestimation of combined E and terrestrial water storage change (TWSC) from remote sensing.

Terrestrial hydrological features of the Pearl River basin in South China

This paper presents the terrestrial hydrological features of the Pearl River basin in South China by using a macro-scale hydrological model, the Variable Infiltration Capacity (VIC) model, and a routing scheme. Without calibration, the VIC model is used to simulate streamflow, evapotranspiration and soil moisture change at a daily time step for the period 1951–2000. After aggregation of daily output, it is observed that the VIC streamflow simulation is comparable to the observation at a month step. Moreover, from the model simulation, the study reveals that the monthly soil moisture change varies dynamically for maintaining the basin water balance, and both of the streamflow and evapotranspiration are dominant hydrological processes over the basin. With the routing scheme, the hydrological simulation from the VIC model is investigated at a daily step. It is observed that the scheme can improve the simulation of the timings and magnitudes of the daily streamflow peaks significantly, and the temporal scale of the influence of the routing on the streamflow simulation is less than 2–3 weeks in the Pearl River basin.

Modeling the Effects of Lakes and Wetlands on the Water Balance of Arctic Environments

AbstractLakes, ponds, and wetlands are common features in many low-gradient arctic watersheds. Storage of snowmelt runoff in lakes and wetlands exerts a strong influence on both the interannual and interseasonal variability of northern rivers. This influence is often not well represented in hydrology models and the land surface schemes used in climate models. In this paper, an algorithm to represent the evaporation and storage effects of lakes and wetlands within the Variable Infiltration Capacity (VIC) macroscale hydrology model is described. The model is evaluated with respect to its ability to represent water temperatures, net radiation, ice freeze–thaw, and runoff production for a variety of high-latitude locations. It is then used to investigate the influence of surface storage on the spatial and temporal distribution of water and energy fluxes for the Kuparuk and Putuligayuk Rivers, on the Alaskan arctic coastal plain. Inclusion of the lake and wetland algorithm results in a substantial improvement of the simulated streamflow hydrographs, as measured using the monthly Nash–Sutcliffe efficiency. Simulations of runoff from the Putuligayuk watershed indicate that up to 80% of snow meltwater goes into storage each year and does not contribute to streamflow. Approximately 46% of the variance in the volume of snowmelt entering storage can be explained by the year-to-year variation in maximum snow water equivalent and the lake storage deficit from the previous summer. The simulated summer lake storage deficit is much lower than the cumulative precipitation minus lake evaporation (−47 mm, on average) as a result of simulated recharge from the surrounding uplands.