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Mu Xiao, Bart Nijssen, and Dennis P. Lettenmaier

Abstract

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.

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Bart Nijssen, Reiner Schnur, and Dennis P. Lettenmaier

Abstract

A daily set of surface meteorological forcings, model-derived surface moisture fluxes, and state variables for global land areas for the period of 1979–93 is described. The forcing dataset facilitates global simulations and evaluation of land surface parameterizations without relying heavily on GCM output. Daily precipitation and temperature are based on station observations, daily wind speeds are based on National Centers for Environmental Prediction–National Center for Atmospheric Research reanalysis data, and the remaining meteorological forcing variables (shortwave radiation, longwave radiation, and vapor pressure) are derived from the precipitation and temperature series. The Variable Infiltration Capacity (VIC) land surface model is used to produce a set of derived fluxes and state variables, including snow water equivalent, evapotranspiration, runoff, and soil moisture storage. The main differences between the new dataset and other, similar datasets are the daily time step, the use of a specified simulation period as opposed to climatological averages, the length of the simulation period, the use of observed meteorological data, and the use of a more realistic hydrological model. Comparison with observations and existing climatologies shows that 1) the interannual variation in simulated snow cover extent is similar to observations in Eurasia but is somewhat underpredicted in North America; 2) the components of the global and continental water balance are similar to those in previously produced climatologies, although runoff is somewhat lower; 3) patterns of simulated soil moisture storage are similar to the climatology of Mintz and Serafini, but the more sophisticated VIC hydrological model produces a larger range in soil moisture; and 4) the annual cycle and spatial patterns in soil moisture compare well with soil moisture observations in Illinois and in central Eurasia, but mean modeled soil moisture is somewhat lower than observed, and observed soil moisture shows a greater interannual persistence than do the simulations.

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Yixin Mao, Wade T. Crow, and Bart Nijssen

Abstract

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.

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Tian Zhou, Bart Nijssen, Huilin Gao, and Dennis P. Lettenmaier

Abstract

Man-made reservoirs play a key role in the terrestrial water system. They alter water fluxes at the land surface and impact surface water storage through water management regulations for diverse purposes such as irrigation, municipal water supply, hydropower generation, and flood control. Although most developed countries have established sophisticated observing systems for many variables in the land surface water cycle, long-term and consistent records of reservoir storage are much more limited and not always shared. Furthermore, most land surface hydrological models do not represent the effects of water management activities. Here, the contribution of reservoirs to seasonal water storage variations is investigated using a large-scale water management model to simulate the effects of reservoir management at basin and continental scales. The model was run from 1948 to 2010 at a spatial resolution of 0.25° latitude–longitude. A total of 166 of the largest reservoirs in the world with a total capacity of about 3900 km3 (nearly 60% of the globally integrated reservoir capacity) were simulated. The global reservoir storage time series reflects the massive expansion of global reservoir capacity; over 30 000 reservoirs have been constructed during the past half century, with a mean absolute interannual storage variation of 89 km3. The results indicate that the average reservoir-induced seasonal storage variation is nearly 700 km3 or about 10% of the global reservoir storage. For some river basins, such as the Yellow River, seasonal reservoir storage variations can be as large as 72% of combined snow water equivalent and soil moisture storage.

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Tian Zhou, Bart Nijssen, George J. Huffman, and Dennis P. Lettenmaier

Abstract

The Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) near-real-time (RT) data are considered less accurate than the TMPA research quality (RP) data because of the simplified data processing algorithm and the lack of gauge adjustments. However, for near-real-time hydrological applications, such as drought nowcasting, the RT data must play a key role given latency considerations and consistency is essential with products like RP, which have a long-term climatology. The authors used a bivariate test to examine the consistency between the monthly RT and RP precipitation estimates for 12 yr (2000–12) and found that, for over 75% of land cells globally, RT and RP were statistically consistent at 0.05 significance level. The inconsistent grid cells are spatially clustered in western North America, northern South America, central Africa, and most of Australia. The authors also show that RT generally increases with time relative to RP in northern South America and western Australia, while in western North America and eastern Australia, RT decreases relative to RP. In other areas such as the eastern part of North America, Eurasia, and southern part of the South America, the RT data are statistically consistent with the RP data and are appropriate for global- or macroscale hydrological applications.

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Grey S. Nearing, Benjamin L. Ruddell, Martyn P. Clark, Bart Nijssen, and Christa Peters-Lidard

Abstract

We propose a conceptual and theoretical foundation for information-based model benchmarking and process diagnostics that provides diagnostic insight into model performance and model realism. We benchmark against a bounded estimate of the information contained in model inputs to obtain a bounded estimate of information lost due to model error, and we perform process-level diagnostics by taking differences between modeled versus observed transfer entropy networks. We use this methodology to reanalyze the recent Protocol for the Analysis of Land Surface Models (PALS) Land Surface Model Benchmarking Evaluation Project (PLUMBER) land model intercomparison project that includes the following models: CABLE, CH-TESSEL, COLA-SSiB, ISBA-SURFEX, JULES, Mosaic, Noah, and ORCHIDEE. We report that these models (i) use only roughly half of the information available from meteorological inputs about observed surface energy fluxes, (ii) do not use all information from meteorological inputs about long-term Budyko-type water balances, (iii) do not capture spatial heterogeneities in surface processes, and (iv) all suffer from similar patterns of process-level structural error. Because the PLUMBER intercomparison project did not report model parameter values, it is impossible to know whether process-level error patterns are due to model structural error or parameter error, although our proposed information-theoretic methodology could distinguish between these two issues if parameter values were reported. We conclude that there is room for significant improvement to the current generation of land models and their parameters. We also suggest two simple guidelines to make future community-wide model evaluation and intercomparison experiments more informative.

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Maik Renner, Axel Kleidon, Martyn Clark, Bart Nijssen, Marvin Heidkamp, Martin Best, and Gab Abramowitz

Abstract

The diurnal cycle of solar radiation represents the strongest energetic forcing and dominates the exchange of heat and mass of the land surface with the atmosphere. This diurnal heat redistribution represents a core of land–atmosphere coupling that should be accurately represented in land surface models (LSMs), which are critical parts of weather and climate models. We employ a diagnostic model evaluation approach using a signature-based metric that describes the diurnal variation of heat fluxes. The metric is obtained by decomposing the diurnal variation of surface heat fluxes into their direct response and the phase lag to incoming solar radiation. We employ the output of 13 different LSMs driven with meteorological forcing of 20 FLUXNET sites (PLUMBER dataset). All LSMs show a poor representation of the evaporative fraction and thus the diurnal magnitude of the sensible and latent heat flux under cloud-free conditions. In addition, we find that the diurnal phase of both heat fluxes is poorly represented. The best performing model only reproduces 33% of the evaluated evaporative conditions across the sites. The poor performance of the diurnal cycle of turbulent heat exchange appears to be linked to how models solve for the surface energy balance and redistribute heat into the subsurface. We conclude that a systematic evaluation of diurnal signatures is likely to help to improve the simulated diurnal cycle, better represent land–atmosphere interactions, and therefore improve simulations of the near-surface climate.

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Alan D. Ziegler, Justin Sheffield, Edwin P. Maurer, Bart Nijssen, Eric F. Wood, and Dennis P. Lettenmaier

Abstract

Diagnostic studies of offline, global-scale Variable Infiltration Capacity (VIC) model simulations of terrestrial water budgets and simulations of the climate of the twenty-first century using the parallel climate model (PCM) are used to estimate the time required to detect plausible changes in precipitation (P), evaporation (E), and discharge (Q) if the global water cycle intensifies in response to global warming. Given the annual variability in these continental hydrological cycle components, several decades to perhaps more than a century of observations are needed to detect water cycle changes on the order of magnitude predicted by many global climate model studies simulating global warming scenarios. Global increases in precipitation, evaporation, and runoff of 0.6, 0.4, and 0.2 mm yr−1 require approximately 30–45, 25–35, and 50–60 yr, respectively, to detect with high confidence. These conservative detection time estimates are based on statistical error criteria (α = 0.05, β = 0.10) that are associated with high statistical confidence, 1 − α (accept hypothesis of intensification when true, i.e., intensification is occurring), and high statistical power, 1 − β (reject hypothesis of intensification when false, i.e., intensification is not occurring). If one is willing to accept a higher degree of risk in making a statistical error, the detection time estimates can be reduced substantially. Owing in part to greater variability, detection time of changes in continental P, E, and Q are longer than those for the globe. Similar calculations performed for three Global Energy and Water Experiment (GEWEX) basins reveal that minimum detection time for some of these basins may be longer than that for the corresponding continent as a whole, thereby calling into question the appropriateness of using continental-scale basins alone for rapid detection of changes in continental water cycles. A case is made for implementing networks of small-scale indicator basins, which collectively mimic the variability in continental P, E, and Q, to detect acceleration in the global water cycle.

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Andrew J. Newman, Naoki Mizukami, Martyn P. Clark, Andrew W. Wood, Bart Nijssen, and Grey Nearing

Abstract

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.

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Elizabeth A. Clark, Justin Sheffield, Michelle T. H. van Vliet, Bart Nijssen, and Dennis P. Lettenmaier

Abstract

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.

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