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Dag Lohmann

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Papers by Dag Lohmann

The Rhone-Aggregation land surface scheme intercomparison project: An overview

The Rhône-Aggregation (Rhône-AGG) Land Surface Scheme (LSS) intercomparison project is an initiat... more The Rhône-Aggregation (Rhône-AGG) Land Surface Scheme (LSS) intercomparison project is an initiative within the Global Energy and Water Cycle Experiment (GEWEX)/Global Land-Atmosphere System Study (GLASS) panel of the World Climate Research Programme (WCRP). It is a intermediate step leading up to the next phase of the Global Soil Wetness Project (GSWP) (Phase 2), for which there will be a broader investigation of the aggregation between global scales (GSWP-1) and the river scale. This project makes use of the Rhône modeling system, which was developed in recent years by the French research community in order to study the continental water cycle on a regional scale. The main goals of this study are to investigate how 15 LSSs simulate the water balance for several annual cycles compared to data from a dense observation network consisting of daily discharge from over 145 gauges and daily snow depth from 24 sites, and to examine the impact of changing the spatial scale on the simulations. The overall evapotranspiration, runoff, and monthly change in water storage are similarly simulated by the LSSs, however, the differing partitioning among the fluxes results in very different river discharges and soil moisture equilibrium states. Subgrid runoff is especially important for discharge at the daily timescale and for smallerscale basins. Also, models using an explicit treatment of the snowpack compared better with the observations than simpler composite schemes. Results from a series of scaling experiments are examined for which the spatial resolution of the computational grid is decreased to be consistent with large-scale atmospheric models. The impact of upscaling on the domainaveraged hydrological components is similar among most LSSs, with increased evaporation of water intercepted by the canopy and a decrease in surface runoff representing the most consistent inter-LSS responses. A significant finding is that the snow water equivalent is greatly reduced by upscaling in all LSSs but one that explicitly accounts for subgrid-scale orography effects on the atmospheric forcing.

Correlation, simulation and uncertainty in catastrophe modeling

The science of catastrophic risk modeling helps people to understand the physical and financial i... more The science of catastrophic risk modeling helps people to understand the physical and financial implications of natural catastrophes (hurricanes, flood, earthquakes, etc.), terrorism, and the risks associated with changes in life expectancy. As such it depends on simulation techniques that integrate multiple disciplines such as meteorology, hydrology, structural engineering, statistics, computer science, financial engineering, actuarial science, and more in virtually every field of technology. In this paper we will explain the techniques and underlying assumptions of building catastrophe models end to end. We especially will pay attention to correlation (spatial and temporal), simulation and uncertainty in each of the various components in the development process. Our Next Generation Simulation Platform will enable clients (insurance and re-insurance companies) to look at their catastrophe risks objectively while at the same time being able to make use of an open model architecture ...

Quantifying and Monetizing Renewable Energy Resiliency

Sustainability

Energy resiliency has been thrust to the forefront by recent severe weather events and natural di... more Energy resiliency has been thrust to the forefront by recent severe weather events and natural disasters. Billions of dollars are lost each year due to power outages. This article highlights the unique value renewable energy hybrid systems (REHS), comprised of solar, energy storage, and generators, provide in increasing resiliency. We present a methodology to quantify the amount and value of resiliency provided by REHS, and ways to monetize this resiliency value through insurance premium discounts. A case study of buildings in New York City demonstrates how implementing REHS in place of traditional backup diesel generators can double the amount of outage survivability, with an added value of $781,200. For a Superstorm Sandy type event, results indicate that insurance premium reductions could support up to 4% of the capital cost of REHS, and the potential exists to prevent up to $2.5 billion in business interruption losses with increased REHS deployment.

Validation of North American LDAS retrospective forcing with station observations and model experiments

A 5-year LDAS retrospective forcing data set (September 1996-September 2001) was produced by the ... more A 5-year LDAS retrospective forcing data set (September 1996-September 2001) was produced by the NASA/GSFC group for the North American Land Data Assimilation System (N-LDAS) project, and is being used to test the N-LDAS land surface models. The forcing data set covers the LDAS domain with a 1/8 resolution. It was produced based on near-surface analysis/assimilation fields from the NCEP Eta Data Assimilation System (EDAS), actual rain gauge and radar observations of precipitation, and satellite retrievals of ...

The Baltic Sea Experiment BALTEX: A brief overview and some selected results of the authors

Surv Geophys, 1998

The mechanisms responsible for the transfer of energy and water within the climate system are und... more The mechanisms responsible for the transfer of energy and water within the climate system are under worldwide investigation within the framework of the Global Energy and Water Cycle Experiment (GEWEX) to improve the predictability of natural and man-made climate changes at short and long ranges and their impact on water resources. Five continental-scale experiments have been established within GEWEX to enable a more complete coupling between atmospheric and hydrological models. One of them is the Baltic Sea Experiment (BALTEX).

Global flood risk under a changing climate

NWS-CPC's Monitoring and Prediction of US Soil Moisture & Associated Land Surface Variables: Land Data Reanalysis

Development and Application of a Large-Scale Flood Risk and Financial Impact

ABSTRACT Flood risk models as developed for the insurance sector are typically comprised of three... more ABSTRACT Flood risk models as developed for the insurance sector are typically comprised of three major components: i) a hazard module describing where flooding may occur and what the inundation will be, ii) an exposure module describing the value of properties at a particular location, and iii) a vulnerability module which, based on a particular property type and property use (e.g., residential, industrial) describes the relationship between flood depth and the fraction of the building value that is affected or lost. Return period of losses can be determined from combining results from the three components. For the insurance sector it is of great relevance to be able to estimate the type of losses that happen frequently, but also those that happen infrequently: the former is determinant for insurance pricing, and the latter is more important for reinsurance purchasing and solvency issues. To estimate the infrequent (i.e., severe) events, long simulation times are required. Moreover, the sector puts a specific requirement on the scale on which flood modelling should occur: insurers typically have insured properties across a larger area and are interested in losses locally, but also in spatial and temporal correlation of losses. Both these requirements (i.e., long simulation times and assessment of spatio-temporal correlation of losses) make that factors such as model runtime often put constraints on the model choice and modelling resolution. Furthermore, it also limits the calibration and validation data that can be used, given that they have to be consistently available over a large area and preferably over a long time period. In this work we will present the different model components for the flood model that was developed for the UK by Risk Management Solutions (RMS). We will mainly focus on the hazard module components that were used and developed (i.e., the hydrological, hydraulic and river defence models), and we will discuss input data used in this type of large-scale flood modelling (e.g., the stochastic representation of precipitation data and other meteorological forcings), and the calibration data used (e.g., river flow data and existing flood maps). Finally, we will show some of the model results obtained from the RMS flood model in the UK.

Eegional scale hydrology: I. Formulation of the VIC2L model coupled to a routing model

A grid network version of the two-layer Variable Infiltration Capacity (VIC-2L) macroscale hydrol... more A grid network version of the two-layer Variable Infiltration Capacity (VIC-2L) macroscale hydrological model is described. The VIC-2L model is a hydrologically-based SVAT (Soil Vegetation Atmospheric Transfer) scheme designed to represent the land surface in numerical weather prediction and climate models. It is coupled to a linear routing scheme which is optimized with measured precipitation and streamflow data and is

A large-scale horizontal routing model to be coupled to land surface parametrization schemes

Tellus A, 1996

Anthropogenic greenhouse gas contribution to flood risk in England and Wales in autumn 2000

Nature, 2011

Interest in attributing the risk of damaging weather-related events to anthropogenic climate chan... more Interest in attributing the risk of damaging weather-related events to anthropogenic climate change is increasing 1 . Yet climate models used to study the attribution problem typically do not resolve the weather systems associated with damaging events 2 such as the UK floods of October and November 2000. Occurring during the wettest autumn in England and Wales since records began in 1766 3,4 , these floods damaged nearly 10,000 properties across that region, disrupted services severely, and caused insured losses estimated at £1.3 billion (refs 5, 6). Although the flooding was deemed a 'wakeup call' to the impacts of climate change at the time 7 , such claims are typically supported only by general thermodynamic arguments that suggest increased extreme precipitation under global warming, but fail 8,9 to account fully for the complex hydrometeorology 4,10 associated with flooding. Here we present a multi-step, physically based 'probabilistic event attribution' framework showing that it is very likely that global anthropogenic greenhouse gas emissions substantially increased the risk of flood occurrence in England and Wales in autumn 2000. Using publicly volunteered distributed computing 11,12 , we generate several thousand seasonal-forecast-resolution climate model simulations of autumn 2000 weather, both under realistic conditions, and under conditions as they might have been had these greenhouse gas emissions and the resulting large-scale warming never occurred. Results are fed into a precipitation-runoff model that is used to simulate severe daily river runoff events in England and Wales (proxy indicators of flood events). The precise magnitude of the anthropogenic contribution remains uncertain, but in nine out of ten cases our model results indicate that twentiethcentury anthropogenic greenhouse gas emissions increased the risk of floods occurring in England and Wales in autumn 2000 by more than 20%, and in two out of three cases by more than 90%.

Evaluation of Soil Moisture in the NCEP–NCAR and NCEP–DOE Global Reanalyses

Journal of Hydrometeorology, 2005

This study compares soil moisture analyses from the National Centers for Environmental Prediction... more This study compares soil moisture analyses from the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) global reanalysis (R-1) and the later NCEP– Department of Energy (DOE) Atmospheric Model Intercomparison Project (AMIP) global reanalysis (R-2). The R-1 soil moisture is strongly controlled by nudging it to a prescribed climatology, whereas the R-2 soil moisture is adjusted according to differences between model-generated and observed precipitation. While mean soil moisture fields from R-1 and R-2 show many geographic similarities, there are some major differences. This study uses in situ observations from the Global Soil Moisture Data Bank to evaluate the two global reanalysis products. In general, R-2 does a better job of simulating interannual variations, the mean seasonal cycle, and the persistence of soil moisture, when compared to observations. However, the R-2 reanalysis does not necessarily represent observed soil moisture cha...

The Effect of Large-Scale Atmospheric Uncertainty on Streamflow Predictability

Journal of Hydrometeorology, 2009

Hydrological processes are strongly coupled with atmospheric processes related, for example, to p... more Hydrological processes are strongly coupled with atmospheric processes related, for example, to precipitation and temperature, and a coupled atmosphere–land surface system is required for a meaningful hydrological forecast. Since the atmosphere is a chaotic system with limited predictability, ensemble forecasts offer a practical tool to predict the future state of the coupled system in a probabilistic fashion, potentially leading to a more complete and informative hydrologic prediction. As ensemble forecasts with coupled meteorological–hydrological models are operationally running at major numerical weather prediction centers, it is currently possible to produce a gridded streamflow prognosis in the form of a probabilistic forecast based on ensembles. Evaluation and improvement of such products require a comprehensive assessment of both components of the coupled system. In this article, the atmospheric component of a coupled ensemble forecasting system is evaluated in terms of its a...

Continental-scale water and energy flux analysis and validation for the North American Land Data Assimilation System project phase 2 (NLDAS-2): 1. Intercomparison and application of model products

Journal of Geophysical Research, 2012

1] This is the second part of a study on continental-scale water and energy flux analysis and val... more 1] This is the second part of a study on continental-scale water and energy flux analysis and validation conducted in phase 2 of the North American Land Data Assimilation System project (NLDAS-2). The first part concentrates on a model-by-model comparison of mean annual and monthly water fluxes, energy fluxes and state variables. In this second part, the focus is on the validation of simulated streamflow from four land surface models (Noah, Mosaic, Sacramento Soil Moisture Accounting (SAC-SMA), and Variable Infiltration Capacity (VIC) models) and their ensemble mean. Comparisons are made against 28-years (1 October 1979-30 September 2007) of United States Geological Survey observed streamflow for 961 small basins and 8 major basins over the conterminous United States (CONUS). Relative bias, anomaly correlation and Nash-Sutcliffe Efficiency (NSE) statistics at daily to annual time scales are used to assess model-simulated streamflow. The Noah (the Mosaic) model overestimates (underestimates) mean annual runoff and underestimates (overestimates) mean annual evapotranspiration. The SAC-SMA and VIC models simulate the mean annual runoff and evapotranspiration well when compared with the observations. The ensemble mean is closer to the mean annual observed streamflow for both the 961 small basins and the 8 major basins than is the mean from any individual model. All of the models, as well as the ensemble mean, have large daily, weekly, monthly, and annual streamflow anomaly correlations for most basins over the CONUS, implying strong simulation skill. However, the daily, weekly, and monthly NSE analysis results are not necessarily encouraging, in particular for daily streamflow. The Noah and Mosaic models are useful (NSE > 0.4) only for about 10% of the 961 small basins, the SAC-SMA and VIC models are useful for about 30% of the 961 small basins, and the ensemble mean is useful for about 42% of the 961 small basins. As the time scale increases, the NSE increases as expected. However, even for monthly streamflow, the ensemble mean is useful for only 75% of the 961 small basins. Citation: Xia, Y., et al. (2012), Continental-scale water and energy flux analysis and validation for North American Land Data Assimilation System project phase 2 (NLDAS-2): 2. Validation of model-simulated streamflow,

Comment on “Modeling ground heat flux in land surface parameterization schemes” by Xu Liang, Eric F. Wood, and Dennis P. Lettenmaier

Journal of Geophysical Research, 2001

Streamflow and water balance intercomparisons of four land surface models in the North American Land Data Assimilation System project

Journal of Geophysical Research, 2004

This paper compares and evaluates streamflow and water balance results from four different Land S... more This paper compares and evaluates streamflow and water balance results from four different Land Surface Models (LSMs) which participated in the multi-institutional North American Land Data Assimilation System (NLDAS). These LSMs have been run for the retrospective period 10/01/1996 to 09/30/1999 forced by atmospheric observations from the Eta Data Assimilation System (EDAS) analysis and ETA model output, measured precipitation and downward solar radiation. We have evaluated these simulations using measured daily streamflow data within 9 large major basins within the US and with 1145 smaller basins from 23 km 2 to 10,000 km 2 distributed over the NLDAS domain from the U.S. Geological Survey (USGS). Model runoff was routed with a distributed and a lumped optimized linear routing model. The diagnosis of the model results shows that the LSMs have a wide spread in their partitioning of precipitation into evapotranspiration and runoff. The modeled mean annual runoff shows large regional differences by a factor of up to four between models. The corresponding difference in mean annual evapotranspiration is about a factor of two. Runoff timing for the LSMs is influenced by snow melt timing with differences in the streamflow peaks of up to four months. While the modeled mean annual runoff shows large regional differences among the models and between the models and observations, it is under-estimated in areas with significant snowfall by all models. The monthly water budget shows that in the summer the model differences in runoff are as large as are soil water storage changes.

Surface radiation budgets in support of the GEWEX Continental-Scale International Project (GCIP) and the GEWEX Americas Prediction Project (GAPP), including the North American Land Data Assimilation System (NLDAS) project

Journal of Geophysical Research, 2003

In support of the World Climate Research Program (WCRP) GEWEX Continental-scale International Pro... more In support of the World Climate Research Program (WCRP) GEWEX Continental-scale International Project (GCIP) and the GEWEX Americas Prediction Project (GAPP), real time estimates of shortwave radiative fluxes, both at the surface and at the top of the atmosphere, are being produced operationally by the National Oceanic and Atmospheric Administration (NOAA)/National Environmental Satellite Data and Information Service (NESDIS), using observations from the GOES-8 Imager. The inference scheme has been developed at the Department of Meteorology, University of Maryland (UMD), and the atmospheric and surface model input parameters are produced and provided by the NOAA/National Centers for Environmental Prediction (NCEP). The radiative fluxes are being evaluated on hourly, daily, and monthly time scales, using observations at about fifty stations. The satellite estimates have been found to be within acceptable limits during snow-free periods, but the difficulty to detect clouds over snow affects the accuracy during the winter season. In what follows, this activity is discussed, and evaluation results of the derived fluxes against ground observations for time periods of one to two years are presented.

Snow process modeling in the North American Land Data Assimilation System (NLDAS): 1. Evaluation of model-simulated snow cover extent

Journal of Geophysical Research, 2003

This is the second part of a study on the cold season process modeling in the North American Land... more This is the second part of a study on the cold season process modeling in the North American Land Data Assimilation System (NLDAS). The first part concentrates on the assessment of model simulated snow cover extent. In this second part, the focus is on the evaluation of simulated snow water equivalent (SWE) from the four land surface models (Noah, MOSAIC, SAC and VIC) in the NLDAS. Comparisons are made with observational data from the Natural Resources Conservation Service's SNOTEL network for a 3-year retrospective period at selected sites in the mountainous regions of the western United States. All models show systematic negative bias in the maximum annual simulated SWE that is most notable in the Cascade and Sierra Nevada regions where differences can approach 1000mm. Comparison of NLDAS precipitation forcing with SNOTEL measurements revealed a large bias in the NLDAS annual precipitation which may be lower than the SNOTEL record by up to 2000mm at certain stations.

Land surface model spin-up behavior in the North American Land Data Assimilation System (NLDAS)

Journal of Geophysical Research, 2003

1] The process of a model adjusting to its forcing (model spin-up) can severely bias land surface... more 1] The process of a model adjusting to its forcing (model spin-up) can severely bias land surface simulations, and result in questionable land surface model (LSM) output during the spin-up process. To gain a better understanding of how spin-up processes affect complex spatial and temporal land surface modeling situations in general, and the Retrospective North American Land Data Assimilation System (NLDAS) simulations in particular, a two-phase study was conducted. The first phase examined results from Control, Wet, and Dry 11 year-long Mosaic simulations, while the second phase attempted to explain spin-up behavior in NLDAS Retrospective simulations from the Mosaic, Noah, VIC and Sacramento LSMs based in part on the results from phase 1. Total column and root zone soil moisture spin up slowly, while evaporation and deep soil temperature spin up more quickly. Mosaic soil moisture initialization with NCEP/DOE Global Reanalysis 2 (NCEP/DOE R-2) data (Control run) leads to a faster spin-up time than saturated (Wet run) or dry (Dry run) initialization, with the Control run reaching equilibrium 1 to 2 years sooner than the Wet run and 3 to 4 years more quickly than the Dry run. Overall, practical drift of land surface stores and output ceased in the Control run within approximately 1 year, and fine-scale equilibrium was reached within 5.5 years. Spin-up times exhibited large spatial variability, and although no single causal factor could be determined, they were correlated most strongly with precipitation and temperature forcing. In general, NLDAS models reach a state of rough equilibrium within the first 1 to 2 years of the 3-year Retrospective simulation. The Sacramento LSM has the shortest spin-up phase, followed by the Mosaic, VIC, and Noah LSMs. Initial NCEP/DOE R-2 conditions were too dry in general for the VIC and Noah LSMs, and too moist for the Mosaic and Sacramento LSMs. These results indicate that in most cases, the 1-year spinup time used in the Retrospective NLDAS simulations eliminated spin-up problems from the subsequent period that was used for analysis.

Analysis of process controls in land surface hydrological cycle over the continental United States

Journal of Geophysical Research, 2004