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Access to mountainous regions for monitoring streamflow, snow and glaciers is often difficult, and many rivers are thus not gauged and hydrological measurements are limited. Consequently, cold-region watersheds, particularly heavily glacierized ones, are poorly represented in large-sample hydrology (LSH) datasets. We present a new LSH dataset for Iceland, termed LamaH-Ice (LArge-SaMple DAta for Hydrology and Environmental Sciences for Iceland). Glaciers and ice caps cover about 10 % of Iceland and, while streamflow has been measured for several decades, these measurements have not previously been published in a consistent manner. The dataset provides daily and hourly hydrometeorological time series and catchment characteristics for 107 river basins in Iceland, covering an area of almost 46 000 km2 (45 % of Iceland's area), with catchment sizes ranging from 4 to 7500 km2. LamaH-Ice conforms to the structure of existing LSH datasets and includes most variables contained in these datasets as well as additional information relevant to cold-region hydrology, e.g., time series of snow cover, glacier mass balance and albedo. LamaH-Ice also includes dynamic catchment characteristics to account for changes in land cover, vegetation and glacier extent. A large majority of the watersheds in LamaH-Ice are not subject to human activities, such as diversions and flow regulations. Streamflow measurements under natural flow conditions are highly valuable to hydrologists seeking to model and comprehend the natural hydrological cycle or estimate climate change trends. The LamaH-Ice dataset (Helgason and Nijssen, 2024) is intended for the research community to improve the understanding of hydrology in cold-region environments. LamaH-Ice is publicly available on HydroShare at https://doi.org/10.4211/hs.86117a5f36cc4b7c90a5d54e18161c91 (Helgason and Nijssen, 2024). [ABSTRACT FROM AUTHOR]

Access to mountainous regions for monitoring streamflow, snow and glaciers is often difficult, and many rivers are thus not gauged and hydrological measurements are limited. Consequently, cold-region watersheds, particularly heavily glacierized ones, are poorly represented in large-sample hydrology (LSH) datasets. We present a new LSH dataset for Iceland, termed LamaH-Ice (LArge-SaMple DAta for Hydrology and Environmental Sciences for Iceland). Glaciers and ice caps cover about 10% of Iceland and while streamflow has been measured for several decades, these measurements have not been published in a consistent manner before. The dataset provides daily and hourly hydrometeorological timeseries and catchment characteristics for 107 river basins in Iceland, covering an area of almost 46,000 km2 (45% of Iceland's area), with catchment sizes ranging from 4 to about 7,500 km2. LamaH-Ice conforms to the structure of existing LSH datasets and includes most variables offered in these datasets, as well as additional information relevant to cold-region hydrology, e.g., timeseries of snow cover, glacier mass balance and albedo. LamaH-Ice also includes dynamic catchment characteristics to account for changes in land cover, vegetation, and glacier extent. A large majority of the watersheds in LamaH-Ice are not subject to human activities, such as diversions and flow regulations. Streamflow measurements under natural flow conditions are highly valuable to hydrologists seeking to model and comprehend the natural hydrological cycle or estimate climate change trends. The LamaH Ice dataset (Helgason and Nijssen, 2023) is intended for the research community to improve the understanding of hydrology in cold-region environments. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The Columbia River basin is a large transboundary basin located in the Pacific Northwest. The basin spans seven US states and one Canadian province, encompassing a diverse range of hydroclimates. Strong seasonality and complex topography are projected to give rise to spatially heterogeneous climate effects on unregulated streamflow. The basin's water resources are economically critical, and regulation across the domain is extensive. Many sensitivity studies have investigated climate impacts on the basin's naturalized hydrology; however, few have considered the large role of regulation. This study investigates where and when regulation affects projected changes in streamflow by comparing climate outcomes across 80‐member ensembles of unregulated and regulated streamflow projections at 75 sites across the basin. Unregulated streamflow projections are taken from an existing data set of climate projections derived from Coupled Model Intercomparison Project version 5 Global Climate Models. Regulated streamflow projections were modeled by the US Army Corps of Engineers and the US Bureau of Reclamation by using these unregulated flows as input to hydro‐regulation models that simulate operations based on current and historical water demands. Regulation dampens shifts in winter and summer streamflow volumes. Regulation generally attenuates changes in cool‐season high flow extremes but amplifies shifts in warm‐season and annual high flow extremes at historically snow‐dominant headwater reservoirs. Regulation reduces dry‐season low flow changes in headwater tributaries where regulation is large but elsewhere has little effect on changes in low flows. Results highlight the importance of accounting for water management in climate sensitivity analysis particularly in snow‐dominant basins. Key Points: Regulation dampens future winter and summer volume changes where the degree of upstream regulation is largeRegulation dampens cool‐season high flow extreme increases and amplifies warm‐season increases at snow‐dominant headwater basinsRegulation dampens low flow changes in tributaries with a large degree of upstream regulation but has little to no effect elsewhere [ABSTRACT FROM AUTHOR]

Water resources planning often uses streamflow predictions made by hydrologic models. These simulated predictions have systematic errors that limit their usefulness as input to water management models. To account for these errors, streamflow predictions are bias corrected through statistical methods that adjust model predictions based on comparisons to reference datasets (such as observed streamflow). Existing bias correction methods have several shortcomings when used to correct spatially distributed streamflow predictions. First, existing bias correction methods destroy the spatiotemporal consistency of the streamflow predictions when these methods are applied independently at multiple sites across a river network. Second, bias correction techniques are usually built on time-invariant mappings between reference and simulated streamflow without accounting for the processes that underpin the systematic errors. We describe improved bias correction techniques that account for the river network topology and allow for corrections that account for other processes. Further, we present a workflow that allows the user to select whether to apply these techniques separately or in conjunction. We evaluate four different bias correction methods implemented with our workflow in the Yakima River basin in the northwestern United States. We find that all four methods reduce systematic bias in the simulated streamflow. The spatially consistent bias correction methods produce spatially distributed streamflow as well as bias-corrected incremental streamflow, which is suitable for input to water management models. We demonstrate how the spatially consistent method avoids creating flows that are inconsistent between upstream and downstream locations, while performing similar to existing methods. We also find that conditioning on daily minimum temperature, which we use as a proxy for snowmelt processes, improves the timing of the corrected streamflow. Significance Statement: To make streamflow predictions from hydrologic models more informative and useful for water resources management they are often postprocessed by a statistical procedure known as bias correction. In this work we develop and demonstrate bias correction techniques that are specifically tailored to streamflow prediction. These new techniques will make modeled streamflow predictions more useful in complex river systems undergoing climate change. [ABSTRACT FROM AUTHOR]

Surface meteorological analyses are an essential input (termed "forcing") for hydrologic modeling. This study investigated the sensitivity of different hydrologic model configurations to temporal variations of seven forcing variables (precipitation rate, air temperature, longwave radiation, specific humidity, shortwave radiation, wind speed, and air pressure). Specifically, the effects of temporally aggregating hourly forcings to hourly daily average forcings were examined. The analysis was based on 14 hydrological outputs from the Structure for Unifying Multiple Modeling Alternatives (SUMMA) model for the 671 Catchment Attributes and Meteorology for Large-Sample Studies (CAMELS) basins across the contiguous United States (CONUS). Results demonstrated that the hydrologic model sensitivity to temporally aggregating the forcing inputs varies across model output variables and model locations. We used Latin hypercube sampling to sample model parameters from eight combinations of three influential model physics choices (three model decisions with two options for each decision, i.e., eight model configurations). Results showed that the choice of model physics can change the relative influence of forcing on model outputs and the forcing importance may not be dependent on the parameter space. This allows for model output sensitivity to forcing aggregation to be tested prior to parameter calibration. More generally, this work provides a comprehensive analysis of the dependence of modeled outcomes on input forcing behavior, providing insight into the regional variability of forcing variable dominance on modeled outputs across CONUS. [ABSTRACT FROM AUTHOR]

Seasonal thermal stratification in reservoirs changes the thermal regime of regulated river systems as well as stream temperature responses to climate change. Cold releases from the reservoir hypolimnion can depress downstream river temperature during warm seasons. Recent large‐scale climate change studies on stream temperature have largely ignored reservoir thermal stratification. In this study, we used established models to develop a framework which considers water demand and reservoir regulation with thermal stratification and applied this model framework to the southeastern United States. About half of all 271 reservoirs in our study area retain strong thermal stratification by the 2080s (2070–2099) under RCP8.5 even as median residence times decrease to 60 days from 69 days in the historic period (1979–2010). Reservoir impacts on downstream temperatures become slightly weaker in the future because of higher air temperature and stronger solar radiation. We defined a "cooling potential" to quantify the thermal energy that a water body can absorb before exceeding a water temperature threshold. In the future, higher river temperatures will reduce the cooling potential for all river segments, but more so for river segments minimally impacted by thermal stratification. Reservoir impacts on cooling potential remain strong for river segments downstream of reservoirs with strong thermal stratification. We conducted a sensitivity analysis to evaluate the robustness of our findings to errors in the hydrological simulations. Although river segments subject to reservoir regulation are more sensitive to errors in hydrology than those without regulation impacts, our overall findings do not materially change due to these errors. Plain Language Summary: River temperature is influenced by both climate change and human activities, especially dam regulation. Large dams impound deep reservoirs where only the top layer is warmed while the bottom stays cold. This phenomenon, with colder, heavier water at the bottom and warmer, lighter water on top, is known as thermal stratification. Large reservoirs usually release water from the bottom and thus cool downstream river temperature in the summer. In this study, we use computer models to simulate river flow and temperature while considering the influence of reservoir regulation and thermal stratification. Thermal stratification will strengthen for about half of the 271 reservoirs in the southeastern United States, but the cold outflow warms up faster due to global warming. Environmental agencies regulate maximum allowable river temperature, constraining a river's ability to provide cooling water for power plants. The cool release from the bottom layer provides more downstream cooling potential, that is, the energy that a river can absorb before exceeding a water temperature threshold, than would be possible in an unregulated river. In the future, the cooling potential will decrease as the climate warms, especially in unregulated rivers. Although our models are subject to errors, these errors do not change our overall findings. Key Points: Summer stream temperatures in regulated rivers are lower than those in unregulated rivers, but they are more sensitive to climate changeReservoirs with long residence times will be more stratified in the future, but the effects on tailwater temperature will dissipate fasterSimulated summer stream temperatures in regulated rivers are more sensitive to errors in hydrology than those in unregulated rivers [ABSTRACT FROM AUTHOR]

Methodological choices can have strong effects on projections of climate change impacts on hydrology. In this study, we investigate the ways in which four different steps in the modeling chain influence the spread in projected changes of different aspects of hydrology. To form the basis of these analyses, we constructed an ensemble of 160 simulations from permutations of two Representative Concentration Pathways, 10 global climate models, two downscaling methods, and four hydrologic model implementations. The study is situated in the Pacific Northwest of North America, which has relevance to a diverse, multinational cast of stakeholders. We analyze the effects of each modeling decision on changes in gridded hydrologic variables of snow water equivalent and runoff, as well as streamflow at point locations. Results show that the choice of representative concentration pathway or global climate model is the driving contributor to the spread in annual streamflow volume and timing. On the other hand, hydrologic model implementation explains most of the spread in changes in low flows. Finally, by grouping the results by climate region the results have the potential to be generalized beyond the Pacific Northwest. Future hydrologic impact assessments can use these results to better tailor their modeling efforts. Plain Language Summary: Future climate change will affect water resources throughout the Pacific Northwest of North America. Simulation experiments and recent observations agree that there will be less snow and it will melt earlier, which will impact the timing and amount of streamflow. However, the magnitudes of these changes are uncertain. In this study, we analyzed the spread among 160 different simulated scenarios of the hydrologic future. We show that the ways we represent the future atmosphere and land surface can have strong effects on our final predictions. Specifically, the way that we model the land surface has a large impact on predictions in arid zones or during dry periods. However, the way we model the atmosphere affects our predictions of changes in snow, snowmelt, and streamflow timing. Our findings are helpful for understanding future hydrologic change more thoroughly, which is of particular importance given international agreements in the Columbia River Basin. Key Points: Methodological choices can have large impacts on projected changes in hydrology, at times affecting the sign of change in streamflow volumeChanges in flow timing are mostly explained by modeled changes in climate while changes in low flows by hydrologic model choiceThe spread among our projected changes remains large, but it is possible to focus modeling efforts to better understand the spread [ABSTRACT FROM AUTHOR]

Atmospheric rivers (ARs) are narrow, elongated corridors of high water vapor content transported from tropical and/or extratropical cyclones. We characterize precipitation and snow water equivalent associated with ARs intersecting the western U.S. coast during the cold season (November– March) of water years 1949–2015. For each AR landfalling date, we retrieved the precipitation and relevant hydrometeorological variables from dynamically downscaled atmospheric reanalyses (NCEP–NCAR) using the WRF mesoscale numerical weather prediction model. Landfalling ARs resulted in higher precipitation amounts throughout the domain than did non-AR storms. ARs contributed the most extreme precipitation events during January and February. Daily snow water equivalent (SWE) changes during ARs indicate that high positive net snow accumulation conditions accompany ARs in December, January, and February. We also assess the historical impact of AR storm duration and precipitation frequency by considering the precipitation depth estimated during a 72-h window bounding the AR landfall date. More extreme precipitation amounts are produced by ARs in the South Cascades and Sierra Nevada ranges compared with ARs with landfall farther north. Most AR extreme precipitation events (and lower SWE accumulations) are produced during warm AR dates, especially toward the northern end of our domain. Analysis of ARs during dry and wet years reveals that ARs during wet years are more frequent and produce heavier precipitation and snow accumulation as compared with dry years. Such conditions are evident in drought events that are associated with a reduced frequency of ARs. [ABSTRACT FROM AUTHOR]

Data assimilation (DA) techniques have been widely applied to assimilate satellite-based soil moisture (SM) measurements into hydrologic models to improve streamflow simulations. However, past studies have reached mixed conclusions regarding the degree of runoff improvement achieved via SM state updating. In this study, a synthetic diagnostic framework is designed to 1) decompose the random error components in a hydrologic simulation, 2) quantify the error terms that originate from SM states, and 3) assess the effectiveness of SM DA to correct these random errors. The general framework is illustrated through a case study in which surface Soil Moisture Active Passive (SMAP) data are assimilated into a large-scale land surface model in the Arkansas–Red River basin. The case study includes systematic error in the simulated streamflow that imposes a first-order limit on DA performance. In addition, about 60% of the random runoff error originates directly from rainfall and cannot be corrected by SM DA. In particular, fast-response runoff dominates in much of the basin but is relatively unresponsive to state updating. Slow-response runoff is strongly controlled by the bottom-layer SM and therefore only modestly improved via the assimilation of surface measurements. Combined, the total runoff improvement in the synthetic analysis is small (<10% over the basin). Improvements in the real SMAP-assimilated case are further limited due to systematic error and other factors such as inaccurate error assumptions and SMAP rescaling. Findings from the diagnostic framework suggest that SM DA alone is insufficient to substantially improve streamflow estimates in large basins. [ABSTRACT FROM AUTHOR]

The concepts of model benchmarking, model agility, and large-sample hydrology are becoming more prevalent in hydrologic and land surface modeling. As modeling systems become more sophisticated, these concepts have the ability to help improve modeling capabilities and understanding. In this paper, their utility is demonstrated with an application of the physically based Variable Infiltration Capacity model (VIC). The authors implement VIC for a sample of 531 basins across the contiguous United States, incrementally increase model agility, and perform comparisons to a benchmark. The use of a large-sample set allows for statistically robust comparisons and subcategorization across hydroclimate conditions. Our benchmark is a calibrated, time-stepping, conceptual hydrologic model. This model is constrained by physical relationships such as the water balance, and it complements purely statistical benchmarks due to the increased physical realism and permits physically motivated benchmarking using metrics that relate one variable to another (e.g., runoff ratio). The authors find that increasing model agility along the parameter dimension, as measured by the number of model parameters available for calibration, does increase model performance for calibration and validation periods relative to less agile implementations. However, as agility increases, transferability decreases, even for a complex model such as VIC. The benchmark outperforms VIC in even the most agile case when evaluated across the entire basin set. However, VIC meets or exceeds benchmark performance in basins with high runoff ratios (greater than ~0.8), highlighting the ability of large-sample comparative hydrology to identify hydroclimatic performance variations. [ABSTRACT FROM AUTHOR]

The Regional Arctic System Model (RASM) is a fully coupled, regional Earth system model applied over the pan-Arctic domain. This paper discusses the implementation of the Variable Infiltration Capacity land surface model (VIC) in RASM and evaluates the ability of RASM, version 1.0, to capture key features of the land surface climate and hydrologic cycle for the period 1979-2014 in comparison with uncoupled VIC simulations, reanalysis datasets, satellite measurements, and in situ observations. RASM reproduces the dominant features of the land surface climatology in the Arctic, such as the amount and regional distribution of precipitation, the partitioning of precipitation between runoff and evapotranspiration, the effects of snow on the water and energy balance, and the differences in turbulent fluxes between the tundra and taiga biomes. Surface air temperature biases in RASM, compared to reanalysis datasets ERA-Interim and MERRA, are generally less than 2°C; however, in the cold seasons there are local biases that exceed 6°C. Compared to satellite observations, RASM captures the annual cycle of snow-covered area well, although melt progresses about two weeks faster than observations in the late spring at high latitudes. With respect to derived fluxes, such as latent heat or runoff, RASM is shown to have similar performance statistics as ERA-Interim while differing substantially from MERRA, which consistently overestimates the evaporative flux across the Arctic region. [ABSTRACT FROM AUTHOR]

The severity-area-duration (SAD) method is used in conjunction with the Variable Infiltration Capacity model (VIC) to identify the major historical total moisture (TM; soil moisture plus snow water equivalent) droughts over the Pacific Northwest region, defined as the Columbia River basin and the region's coastal drainages, for the period 1920-2013. The motivation is to understand how droughts identified using TM (a measure similar to that used in the U.S. Drought Monitor) relate to sector-specific drought measures that are more relevant to users. It is found that most of the SAD space is dominated by an extended drought period during the 1930s, although the most severe shorter droughts are in the 1970s (1976-78) and early 2000s (2000-04). The impact of the three severe TM droughts that dominate most of the SAD space are explored in terms of sector-specific measures relevant to dryland and irrigated agriculture, hydropower generation, municipal water supply, and recreation. It is found that in many cases the most severe droughts identified using the SAD method also appear among the most severe sector-specific droughts; however, there are important exceptions. Two types of inconsistencies are examined and the nature of the conditions that give rise to them are explored. [ABSTRACT FROM AUTHOR]

Gridded precipitation and temperature products are inherently uncertain because of myriad factors, including interpolation from a sparse observation network, measurement representativeness, and measurement errors. Generally uncertainty is not explicitly accounted for in gridded products of precipitation or temperature; if it is represented, it is often included in an ad hoc manner. A lack of quantitative uncertainty estimates for hydrometeorological forcing fields limits the application of advanced data assimilation systems and other tools in land surface and hydrologic modeling. This study develops a gridded, observation-based ensemble of precipitation and temperature at a daily increment for the period 1980-2012 for the conterminous United States, northern Mexico, and southern Canada. This allows for the estimation of precipitation and temperature uncertainty in hydrologic modeling and data assimilation through the use of the ensemble variance. Statistical verification of the ensemble indicates that it has generally good reliability and discrimination of events of various magnitudes but has a slight wet bias for high threshold events (>50 mm). The ensemble mean is similar to other widely used hydrometeorological datasets but with some important differences. The ensemble product produces a more realistic occurrence of precipitation statistics (wet day fraction), which impacts the empirical derivation of other fields used in land surface and hydrologic modeling. In terms of applications, skill in simulations of streamflow in 671 headwater basins is similar to other coarse-resolution datasets. This is the first version, and future work will address temporal correlation of precipitation anomalies, inclusion of other data streams, and examination of topographic lapse rate choices. [ABSTRACT FROM AUTHOR]

A common term in the continental and oceanic components of the global water cycle is freshwater discharge to the oceans. Many estimates of the annual average global discharge have been made over the past 100 yr with a surprisingly wide range. As more observations have become available and continental-scale land surface model simulations of runoff have improved, these past estimates are cast in a somewhat different light. In this paper, a combination of observations from 839 river gauging stations near the outlets of large river basins is used in combination with simulated runoff fields from two implementations of the Variable Infiltration Capacity land surface model to estimate continental runoff into the world's oceans from 1950 to 2008. The gauges used account for ~58% of continental areas draining to the ocean worldwide, excluding Greenland and Antarctica. This study estimates that flows to the world's oceans globally are 44 200 (±2660) km3 yr−1 (9% from Africa, 37% from Eurasia, 30% from South America, 16% from North America, and 8% from Australia-Oceania). These estimates are generally higher than previous estimates, with the largest differences in South America and Australia-Oceania. Given that roughly 42% of ocean-draining continental areas are ungauged, it is not surprising that estimates are sensitive to the land surface and hydrologic model (LSM) used, even with a correction applied to adjust for model bias. The results show that more and better in situ streamflow measurements would be most useful in reducing uncertainties, in particular in the southern tip of South America, the islands of Oceania, and central Africa. [ABSTRACT FROM AUTHOR]

Increased temperatures and changes in precipitation will result in fundamental changes in the seasonal distribution of streamflow in the Pacific Northwest and will have serious implications for water resources management. To better understand local impacts of regional climate change, we conducted model experiments to determine hydrologic sensitivities of annual, seasonal, and monthly runoff to imposed annual and seasonal changes in precipitation and temperature. We used the Variable Infiltration Capacity (VIC) land-surface hydrology model applied at 1/16° latitude-longitude spatial resolution over the Pacific Northwest (PNW), a scale sufficient to support analyses at the hydrologic unit code eight (HUC-8) basin level. These experiments resolve the spatial character of the sensitivity of future water supply to precipitation and temperature changes by identifying the seasons and locations where climate change will have the biggest impact on runoff. The PNW exhibited a diversity of responses, where transitional (intermediate elevation) watersheds experience the greatest seasonal shifts in runoff in response to cool season warming. We also developed a methodology that uses these hydrologic sensitivities as basin-specific transfer functions to estimate future changes in long-term mean monthly hydrographs directly from climate model output of precipitation and temperature. When principles of linearity and superposition apply, these transfer functions can provide feasible first-order estimates of the likely nature of future seasonal streamflow change without performing downscaling and detailed model simulations. [ABSTRACT FROM AUTHOR]

This work advances a unified approach to process-based hydrologic modeling, which we term the 'Structure for Unifying Multiple Modeling Alternatives (SUMMA).' The modeling framework, introduced in the companion paper, uses a general set of conservation equations with flexibility in the choice of process parameterizations (closure relationships) and spatial architecture. This second paper specifies the model equations and their spatial approximations, describes the hydrologic and biophysical process parameterizations currently supported within the framework, and illustrates how the framework can be used in conjunction with multivariate observations to identify model improvements and future research and data needs. The case studies illustrate the use of SUMMA to select among competing modeling approaches based on both observed data and theoretical considerations. Specific examples of preferable modeling approaches include the use of physiological methods to estimate stomatal resistance, careful specification of the shape of the within-canopy and below-canopy wind profile, explicitly accounting for dust concentrations within the snowpack, and explicitly representing distributed lateral flow processes. Results also demonstrate that changes in parameter values can make as much or more difference to the model predictions than changes in the process representation. This emphasizes that improvements in model fidelity require a sagacious choice of both process parameterizations and model parameters. In conclusion, we envisage that SUMMA can facilitate ongoing model development efforts, the diagnosis and correction of model structural errors, and improved characterization of model uncertainty. [ABSTRACT FROM AUTHOR]

This work advances a unified approach to process-based hydrologic modeling to enable controlled and systematic evaluation of multiple model representations (hypotheses) of hydrologic processes and scaling behavior. Our approach, which we term the Structure for Unifying Multiple Modeling Alternatives (SUMMA), formulates a general set of conservation equations, providing the flexibility to experiment with different spatial representations, different flux parameterizations, different model parameter values, and different time stepping schemes. In this paper, we introduce the general approach used in SUMMA, detailing the spatial organization and model simplifications, and how different representations of multiple physical processes can be combined within a single modeling framework. We discuss how SUMMA can be used to systematically pursue the method of multiple working hypotheses in hydrology. In particular, we discuss how SUMMA can help tackle major hydrologic modeling challenges, including defining the appropriate complexity of a model, selecting among competing flux parameterizations, representing spatial variability across a hierarchy of scales, identifying potential improvements in computational efficiency and numerical accuracy as part of the numerical solver, and improving understanding of the various sources of model uncertainty. [ABSTRACT FROM AUTHOR]

Satellite precipitation estimates are increasingly available at temporal and spatial scales of interest to hydrological applications and with the potential for improving flood forecasts in data-sparse regions. This study evaluates the effect of sampling error on simulated large flood events. Synthetic precipitation fields were generated in Monte Carlo fashion by perturbing observed precipitation fields with sampling errors based on 1, 2 and 6 h intervals. The variable infiltration capacity hydrological model was used to assess the impact of these errors on simulated high flow events in the Iguazu basin, a rain-dominated, subtropical basin in southeastern South America. Results showed that unbiased errors in daily error-corrupted precipitation fields introduced bias in the simulated hydrologic fluxes and states. The overall bias for error-corrupted daily streamflows was positive and its magnitude increased with larger sampling intervals. However, for high flow events, the bias was negative as a result of an increase in simulated infiltration and changes in precipitation variability. Errors in precipitation also affected the magnitude and volume of the peak events but did not change the first two statistical moments of the peaks indicating that non-linearities in the hydrological system preserve the statistical properties of high flows in the basin. Caution is needed when using satellite products for hydrological applications that require the estimation of large peaks and volumes. [ABSTRACT FROM PUBLISHER]

Model results from 21 land-surface schemes (LSSs) designed for use in numerical weather prediction and climate models are compared with each other and with observations in the context of the Project for Intercomparison of Land-surface Parameterization Schemes (PILPS) Phase 2(e) model intercomparison experiment. This experiment focuses on simulations of land-surface water and energy fluxes in the 58,000-km2 Torne and Kalix river systems in northern Scandinavia, during the period 1989–1998. All models participating in PILPS Phase 2(e) capture the broad dynamics of snowmelt and runoff, but large differences in snow accumulation and ablation, turbulent heat fluxes, and streamflow exist. The greatest among-model differences in energy and moisture fluxes in these high-latitude environments occur during the spring snowmelt period, reflecting different model parameterizations of snow processes. Differences in net radiation are governed by differences in the simulated radiative surface temperature during the winter months and by differences in surface albedo during the spring/early summer. Differences in net radiation are smallest during the late summer when snow is absent. Although simulated snow sublimation is small for most models, a few models show annual snow sublimation of about 100 mm. These differences in snow sublimation appear to be largely dependent on differences in snow surface roughness parameterizations. The models with high sublimation generally lose their snowpacks too early compared to observations and underpredict the annual runoff. Differences in runoff parameterizations are reflected in differences in daily runoff statistics. Although most models show a greater variability in daily streamflow than the observations, the models with the greatest variability (as much as double the observed variability), produce most of their runoff through fast response, surface runoff mechanisms. As a group, those models that took advantage of an opportunity to calibrate to selected small catchments and to transfer calibration results to the basin at large had a smaller bias and root mean squared error (RMSE) in daily streamflow simulations compared with the models that did not calibrate. [Copyright &y& Elsevier]

Abstract: This study analyzed the linkages between large-scale climate patterns and regional precipitation variability, in particular the interannual variation of seasonal precipitation in the Colorado River Basin. Two climate indices, the Southern Oscillation Index (SOI) and the Pacific Decadal Oscillation (PDO), were selected to represent climate patterns. Conceptual influence indices, which quantify the strength of linkages between climate patterns and precipitation variability, were developed based on the Standardized Precipitation Index (SPI). In turn, the spatial variability of the influence indices within the Colorado River Basin was examined for different combinations of SOI and PDO phases and lead times from zero to three seasons. Precipitation in two seasons (winter and summer) significantly responded to the El Niño-Southern Oscillation (ENSO) phases by decreasing winter precipitation in the lower basin (to high ENSO phase) and by increasing summer precipitation in the upper basin (to low ENSO phase). The PDO phase enhanced those relationships both in intensity and frequency. The overall results of this study revealed that the precipitation of the Colorado River Basin was significantly influenced by climate fluctuation phases. [Copyright &y& Elsevier]

The Project for Intercomparison of Land Surface Parameterization Schemes (PILPS) Phase 2(e) showed that in cold regions the annual runoff production in Land Surface Schemes (LSSs) is closely related to the maximum snow accumulation, which in turn is controlled in large part by winter sublimation. To help further explain the relationship between snow cover, turbulent exchanges and runoff production, a simple equivalent model (SEM) was devised to reproduce the seasonal and annual fluxes simulated by 13 LSSs that participated in PILPS Phase 2(e). The design of the SEM relates the annual partitioning of precipitation and energy in the LSSs to three primary parameters: snow albedo, effective aerodynamic resistance and evaporation efficiency. Isolation of each of the parameters showed that the annual runoff production was most sensitive to the aerodynamic resistance. The SEM was somewhat successful in reproducing the observed LSS response to a decrease in shortwave radiation and changes in wind speed forcings. SEM parameters derived from the reduced shortwave forcings suggested that increased winter stability suppressed turbulent heat fluxes over snow. Because winter sensible heat fluxes were largely negative, reductions in winter shortwave radiation resulted in an increase in annual average sensible heat. [Copyright &y& Elsevier]

Twenty-one land-surface schemes (LSSs) participated in the Project for Intercomparison of Land-surface Parameterizations (PILPS) Phase 2(e) experiment, which used data from the Torne–Kalix Rivers in northern Scandinavia. Atmospheric forcing data (precipitation, air temperature, specific humidity, wind speed, downward shortwave and longwave radiation) for a 20-year period (1979–1998) were provided to the 21 participating modeling groups for 218 1/4° grid cells that represented the study domain. The first decade (1979–1988) of the period was used for model spin-up. The quality of meteorologic forcing variables is of particular concern in high-latitude experiments and the quality of the gridded dataset was assessed to the extent possible. The lack of sub-daily precipitation, underestimation of true precipitation and the necessity to estimate incoming solar radiation were the primary data concerns for this study. The results from two of the three types of runs are analyzed in this, the first of a three-part paper: (1) calibration–validation runs—calibration of model parameters using observed streamflow was allowed for two small catchments (570 and 1300 km2), and parameters were then transferred to two other catchments of roughly similar size (2600 and 1500 km2) to assess the ability of models to represent ungauged areas elsewhere; and 2) reruns—using revised forcing data (to resolve problems with apparent underestimation of solar radiation of approximately 36%, and certain other problems with surface wind in the original forcing data). Model results for the period 1989–1998 are used to evaluate the performance of the participating land-surface schemes in a context that allows exploration of their ability to capture key processes spatially. In general, the experiment demonstrated that many of the LSSs are able to capture the limitations imposed on annual latent heat by the small net radiation available in this high-latitude environment. Simulated annual average net radiation varied between 16 and 40 W/m2 for the 21 models, and latent heat varied between 18 and 36 W/m2. Among-model differences in winter latent heat due to the treatment of aerodynamic resistance appear to be at least as important as those attributable to the treatment of canopy interception. In many models, the small annual net radiation forced negative sensible heat on average, which varied among the models between −11 and 9 W/m2. Even though the largest evaporation rates occur in the summer (June, July and August), model-predicted snow sublimation in winter has proportionately more influence on differences in annual runoff volume among the models. A calibration experiment for four small sub-catchments of the Torne–Kalix basin showed that model parameters that are typically adjusted during calibration, those that control storage of moisture in the soil column or on the land surface via ponding, influence the seasonal distribution of runoff, but have relatively little impact on annual runoff ratios. Similarly, there was no relationship between annual runoff ratios and the proportion of surface and subsurface discharge for the basin as a whole. [Copyright &y& Elsevier]

Highlights: [•] Algorithms for daily shortwave, longwave, and humidity performed well, globally. [•] Shortwave biases at coasts for low daily temperature range. [•] Humidity and longwave biases concentrated in world's deserts. [•] Good overall performance of air temperature diurnal cycle scheme. [•] Morning/afternoon biases in air temperature caused biases in humidity and longwave. [Copyright &y& Elsevier]