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D.P. Lettenmaier

Publications and source records attributed to D.P. Lettenmaier.

9 recordsLinked to original sources
USGS2016

Simulated high-latitude soil thermal dynamics during the past four decades

Soil temperature (Ts ) change is a key indicator of the dynamics of permafrost. On seasonal and inter-annual time scales, the variability of Ts determines the active layer depth, which regulates hydrological soil properties and biogeochemical processes. On the multi-decadal scale, increasing T 5 s not only drives permafrost thaw/retreat, but can also trigger and accelerate the decomposition of soil organic carbon. The magnitude of permafrost carbon feedbacks is thus closely linked to the rate of change of soil thermal regimes. In this study, we used nine process-based ecosystem models with permafrost processes, all forced by different observation-based climate forcing during the period 1960–2000, to characterize the warming rate of Ts 10 in permafrost regions. There is a large spread of Ts trends at 20 cm depth across the models, with trend values ranging from 0.010 ± 0.003 to 0.031 ± 0.005 ◦C yr−1 . Most models show smaller increase in Ts with increasing depth. Air temperature (Ta ) and longwave downward radiation (LWDR) are the main drivers of Ts trends, but their relative contributions differ 15 amongst the models. Different trends of LWDR used in the forcing of models can explain 61 % of their differences in Ts trends, while trends of Ta only explain 5 % of the differences in Ts trends. Uncertain climate forcing contributes a larger uncertainty in Ts trends (0.021 ± 0.008 ◦C yr−1 , mean ± SD) than the uncertainty of model structure (0.012 ± 0.001 ◦C yr−1 ), diagnosed from the range of response between different mod- 20 els, normalized to the same forcing. In addition, the loss rate of near-surface permafrost area, defined as total area where the maximum seasonal active layer thickness (ALT) is less than 3 m loss rate is found to be significantly correlated with the magnitude of the trends of Ts at 1 m depth across the models (R = −0.85, P = 0.003), but not with the initial total near-surface permafrost area (R = −0.30, P = 0.438). The sensitivity of the total boreal near-surface permafrost area to T 25 s at 1 m, is estimated to be of −2.80 ± 0.67 million km2 ◦C −1 . Finally, by using two long-term LWDR datasets and relationships between trends of LWDR and Ts across models, we infer an observationconstrained total boreal near-surface permafrost area decrease comprised between 39 ± 14 × 103 and 75 ± 14 × 103 km2 yr−1 from 1960 to 2000. This corresponds to 9– 18 % degradation of the current permafrost area.

Cryosphere Discussions↗
USGS2015

Assessment of model estimates of land-atmosphere CO 2 exchange across northern Eurasia

A warming climate is altering land-atmosphere exchanges of carbon, with a potential for increased vegetation productivity as well as the mobilization of permafrost soil carbon stores. Here we investigate land-atmosphere carbon dioxide (CO 2 ) cycling through analysis of net ecosystem productivity (NEP) and its component fluxes of gross primary productivity (GPP) and ecosystem respiration (ER) and soil carbon residence time, simulated by a set of land surface models (LSMs) over a region spanning the drainage basin of Northern Eurasia. The retrospective simulations cover the period 1960–2009 at 0.5° resolution, which is a scale common among many global carbon and climate model simulations. Model performance benchmarks were drawn from comparisons against both observed CO 2 fluxes derived from site-based eddy covariance measurements as well as regional-scale GPP estimates based on satellite remote-sensing data. The site-based comparisons depict a tendency for overestimates in GPP and ER for several of the models, particularly at the two sites to the south. For several models the spatial pattern in GPP explains less than half the variance in the MODIS MOD17 GPP product. Across the models NEP increases by as little as 0.01 to as much as 0.79 g C m −2 yr −2 , equivalent to 3 to 340 % of the respective model means, over the analysis period. For the multimodel average the increase is 135 % of the mean from the first to last 10 years of record (1960–1969 vs. 2000–2009), with a weakening CO 2 sink over the latter decades. Vegetation net primary productivity increased by 8 to 30 % from the first to last 10 years, contributing to soil carbon storage gains. The range in regional mean NEP among the group is twice the multimodel mean, indicative of the uncertainty in CO 2 sink strength. The models simulate that inputs to the soil carbon pool exceeded losses, resulting in a net soil carbon gain amid a decrease in residence time. Our analysis points to improvements in model elements controlling vegetation productivity and soil respiration as being needed for reducing uncertainty in land-atmosphere CO 2 exchange. These advances will require collection of new field data on vegetation and soil dynamics, the development of benchmarking data sets from measurements and remote-sensing observations, and investments in future model development and intercomparison studies.

Biogeosciences↗
USGS2011

The importance of warm season warming to western U.S. streamflow changes

Warm season climate warming will be a key driver of annual streamflow changes in four major river basins of the western U.S., as shown by hydrological model simulations using fixed precipitation and idealized seasonal temperature changes based on climate projections with SRES A2 forcing. Warm season (April-September) warming reduces streamflow throughout the year; streamflow declines both immediately and in the subsequent cool season. Cool season (October-March) warming, by contrast, increases streamflow immediately, partially compensating for streamflow reductions during the subsequent warm season. A uniform warm season warming of 3C drives a wide range of annual flow declines across the basins: 13.3%, 7.2%, 1.8%, and 3.6% in the Colorado, Columbia, Northern and Southern Sierra basins, respectively. The same warming applied during the cool season gives annual declines of only 3.5%, 1.7%, 2.1%, and 3.1%, respectively. Copyright 2011 by the American Geophysical Union.

Geophysical Research Letters↗
USGS2009

Assessing the impact of land use change on hydrology by ensemble modeling (LUCHEM). I: Model intercomparison with current land use

This paper introduces the project on 'Assessing the impact of land use change on hydrology by ensemble modeling (LUCHEM)' that aims at investigating the envelope of predictions on changes in hydrological fluxes due to land use change. As part of a series of four papers, this paper outlines the motivation and setup of LUCHEM, and presents a model intercomparison for the present-day simulation results. Such an intercomparison provides a valuable basis to investigate the effects of different model structures on model predictions and paves the ground for the analysis of the performance of multi-model ensembles and the reliability of the scenario predictions in companion papers. In this study, we applied a set of 10 lumped, semi-lumped and fully distributed hydrological models that have been previously used in land use change studies to the low mountainous Dill catchment, Germany. Substantial differences in model performance were observed with Nash-Sutcliffe efficiencies ranging from 0.53 to 0.92. Differences in model performance were attributed to (1) model input data, (2) model calibration and (3) the physical basis of the models. The models were applied with two sets of input data: an original and a homogenized data set. This homogenization of precipitation, temperature and leaf area index was performed to reduce the variation between the models. Homogenization improved the comparability of model simulations and resulted in a reduced average bias, although some variation in model data input remained. The effect of the physical differences between models on the long-term water balance was mainly attributed to differences in how models represent evapotranspiration. Semi-lumped and lumped conceptual models slightly outperformed the fully distributed and physically based models. This was attributed to the automatic model calibration typically used for this type of models. Overall, however, we conclude that there was no superior model if several measures of model performance are considered and that all models are suitable to participate in further multi-model ensemble set-ups and land use change scenario investigations. ?? 2008 Elsevier Ltd. All rights reserved.

Advances in Water Resources↗
USGS2008

Characterization of errors in a coupled snow hydrology-microwave emission model

Traditional approaches to the direct estimation of snow properties from passive microwave remote sensing have been plagued by limitations such as the tendency of estimates to saturate for moderately deep snowpacks and the effects of mixed land cover within remotely sensed pixels. An alternative approach is to assimilate satellite microwave emission observations directly, which requires embedding an accurate microwave emissions model into a hydrologic prediction scheme, as well as quantitative information of model and observation errors. In this study a coupled snow hydrology [Variable Infiltration Capacity (VIC)] and microwave emission [Dense Media Radiative Transfer (DMRT)] model are evaluated using multiscale brightness temperature (TB) measurements from the Cold Land Processes Experiment (CLPX). The ability of VIC to reproduce snowpack properties is shown with the use of snow pit measurements, while TB model predictions are evaluated through comparison with Ground-Based Microwave Radiometer (GBMR), air-craft [Polarimetric Scanning Radiometer (PSR)], and satellite [Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E)] TB measurements. Limitations of the model at the point scale were not as evident when comparing areal estimates. The coupled model was able to reproduce the TB spatial patterns observed by PSR in two of three sites. However, this was mostly due to the presence of relatively dense forest cover. An interesting result occurs when examining the spatial scaling behavior of the higher-resolution errors; the satellite-scale error is well approximated by the mode of the (spatial) histogram of errors at the smaller scale. In addition, TB prediction errors were almost invariant when aggregated to the satellite scale, while forest-cover fractions greater than 30% had a significant effect on TB predictions. ?? 2008 American Meteorological Society.

Journal of Hydrometeorology↗
USGS2003

Mapping the spatial distribution and time evolution of snow water equivalent with passive microwave measurements

This paper presents an algorithm that estimates the spatial distribution and temporal evolution of snow water equivalent and snow depth based on passive remote sensing measurements. It combines the inversion of passive microwave remote sensing measurements via dense media radiative transfer modeling results with snow accumulation and melt model predictions to yield improved estimates of snow depth and snow water equivalent, at a pixel resolution of 5 arc-min. In the inversion, snow grain size evolution is constrained based on pattern matching by using the local snow temperature history. This algorithm is applied to produce spatial snow maps of Upper Rio Grande River basin in Colorado. The simulation results are compared with that of the snow accumulation and melt model and a linear regression method. The quantitative comparison with the ground truth measurements from four Snowpack Telemetry (SNOTEL) sites in the basin shows that this algorithm is able to improve the estimation of snow parameters.

IEEE Transactions on Geoscience and Remote Sensing↗