Showing papers in "Journal of Hydrometeorology in 2006"

GLACE: The Global Land-Atmosphere Coupling Experiment. Part I: Overview

Abstract: The Global Land–Atmosphere Coupling Experiment (GLACE) is a model intercomparison study focusing on a typically neglected yet critical element of numerical weather and climate modeling: land–atmosphere coupling strength, or the degree to which anomalies in land surface state (e.g., soil moisture) can affect rainfall generation and other atmospheric processes. The 12 AGCM groups participating in GLACE performed a series of simple numerical experiments that allow the objective quantification of this element for boreal summer. The derived coupling strengths vary widely. Some similarity, however, is found in the spatial patterns generated by the models, with enough similarity to pinpoint multimodel “hot spots” of land–atmosphere coupling. For boreal summer, such hot spots for precipitation and temperature are found over large regions of Africa, central North America, and India; a hot spot for temperature is also found over eastern China. The design of the GLACE simulations are described in full detai...

Modeling the Recent Evolution of Global Drought and Projections for the Twenty-First Century with the Hadley Centre Climate Model

Abstract: Meteorological drought in the Hadley Centre global climate model is assessed using the Palmer Drought Severity Index (PDSI), a commonly used drought index. At interannual time scales, for the majority of the land surface, the model captures the observed relationship between the El Nino–Southern Oscillation and regions of relative wetness and dryness represented by high and low values of the PDSI respectively. At decadal time scales, on a global basis, the model reproduces the observed drying trend (decreasing PDSI) since 1952. An optimal detection analysis shows that there is a significant influence of anthropogenic emissions of greenhouse gasses and sulphate aerosols in the production of this drying trend. On a regional basis, the specific regions of wetting and drying are not always accurately simulated. In this paper, presentday drought events are defined as continuous time periods where the PDSI is less than the 20th percentile of the PDSI distribution between 1952 and 1998 (i.e., on average 20% of the land surface is in drought at any one time). Overall, the model predicts slightly less frequent but longer events than are observed. Future projections of drought in the twenty-first century made using the Special Report on Emissions Scenarios (SRES) A2 emission scenario show regions of strong wetting and drying with a net overall global drying trend. For example, the proportion of the land surface in extreme drought is predicted to increase from 1% for the present day to 30% by the end of the twenty-first century.

Simulation of Global Land Surface Conditions from 1948 to 2004. Part I: Forcing Data and Evaluations

Abstract: Because of a lack of observations, historical simulations of land surface conditions using land surface models are needed for studying variability and changes in the continental water cycle and for providing initial conditions for seasonal climate predictions. Atmospheric forcing datasets are also needed for land surface model development. The quality of atmospheric forcing data greatly affects the ability of land surface models to realistically simulate land surface conditions. Here a carefully constructed global forcing dataset for 1948–2004 with 3-hourly and T62 (∼1.875°) resolution is described, and historical simulations using the latest version of the Community Land Model version 3.0 (CLM3) are evaluated using available observations of streamflow, continental freshwater discharge, surface runoff, and soil moisture. The forcing dataset was derived by combining observation-based analyses of monthly precipitation and surface air temperature with intramonthly variations from the National Center...

A meteorological distribution system for high-resolution terrestrial modeling (MicroMet)

Abstract: An intermediate-complexity, quasi–physically based, meteorological model (MicroMet) has been developed to produce high-resolution (e.g., 30-m to 1-km horizontal grid increment) atmospheric forcings required to run spatially distributed terrestrial models over a wide variety of landscapes. The following eight variables, required to run most terrestrial models, are distributed: air temperature, relative humidity, wind speed, wind direction, incoming solar radiation, incoming longwave radiation, surface pressure, and precipitation. To produce these distributions, MicroMet assumes that at least one value of each of the following meteorological variables are available for each time step, somewhere within, or near, the simulation domain: air temperature, relative humidity, wind speed, wind direction, and precipitation. These variables are collected at most meteorological stations. For the incoming solar and longwave radiation, and surface pressure, either MicroMet can use its submodels to generate these fields, or it can create the distributions from observations as part of a data assimilation procedure. MicroMet includes a preprocessor component that analyzes meteorological data, then identifies and corrects potential deficiencies. Since providing temporally and spatially continuous atmospheric forcing data for terrestrial models is a core objective of MicroMet, the preprocessor also fills in any missing data segments with realistic values. Data filling is achieved by employing a variety of procedures, including an autoregressive integrated moving average calculation for diurnally varying variables (e.g., air temperature). To create the distributed atmospheric fields, spatial interpolations are performed using the Barnes objective analysis scheme, and subsequent corrections are made to the interpolated fields using known temperature–elevation, wind–topography, humidity–cloudiness, and radiation–cloud–topography relationships.

Effects of Frozen Soil on Snowmelt Runoff and Soil Water Storage at a Continental Scale

Abstract: The presence of ice in soil dramatically alters soil hydrologic and thermal properties. Despite this important role, many recent studies show that explicitly including the hydrologic effects of soil ice in land surface models degrades the simulation of runoff in cold regions. This paper addresses this dilemma by employing the Community Land Model version 2.0 (CLM2.0) developed at the National Center for Atmospheric Research (NCAR) and a simple TOPMODEL-based runoff scheme (SIMTOP). CLM2.0/ SIMTOP explicitly computes soil ice content and its modifications to soil hydrologic and thermal properties. However, the frozen soil scheme has a tendency to produce a completely frozen soil (100% ice content) whenever the soil temperature is below 0°C. The frozen ground prevents infiltration of snowmelt or rainfall, thereby resulting in earlier- and higher-than-observed springtime runoff. This paper presents modifications to the above-mentioned frozen soil scheme that produce more accurate magnitude and seasonality of runoff and soil water storage. These modifications include 1) allowing liquid water to coexist with ice in the soil over a wide range of temperatures below 0°C by using the freezing-point depression equation, 2) computing the vertical water fluxes by introducing the concept of a fractional permeable area, which partitions the model grid into an impermeable part (no vertical water flow) and a permeable part, and 3) using the total soil moisture (liquid water and ice) to calculate the soil matric potential and hydraulic conductivity. The performance of CLM2.0/SIMTOP with these changes has been tested using observed data in cold-region river basins of various spatial scales. Compared to the CLM2.0/SIMTOP frozen soil scheme, the modified scheme produces monthly runoff that compares more favorably with that estimated by the University of New Hampshire–Global Runoff Data Center and a terrestrial water storage change that is in closer agreement with that measured by the Gravity Recovery and Climate Experiment (GRACE) satellites.

Impact of Climate Change on River Discharge Projected by Multimodel Ensemble

Abstract: This study investigates the projections of river discharge for 24 major rivers in the world during the twenty-first century simulated by 19 coupled atmosphere–ocean general circulation models based on the Special Report on Emissions Scenarios A1B scenario. To reduce model bias and uncertainty, a weighted ensemble mean (WEM) is used for multimodel projections. Although it is difficult to reproduce the present river discharge in any single model, the WEM results produce more accurate reproduction for most rivers, except those affected by anthropogenic water usage. At the end of the twenty-first century, the annual mean precipitation, evaporation, and runoff increase in high latitudes of the Northern Hemisphere, southern to eastern Asia, and central Africa. In contrast, they decrease in the Mediterranean region, southern Africa, southern North America, and Central America. Although the geographical distribution of the changes in precipitation and runoff tends to coincide with that in the river disch...

A Distributed Snow-Evolution Modeling System (SnowModel)

Abstract: SnowModel is a spatially distributed snow-evolution modeling system designed for application in landscapes, climates, and conditions where snow occurs. It is an aggregation of four submodels: MicroMet defines meteorological forcing conditions, EnBal calculates surface energy exchanges, SnowPack simulates snow depth and water-equivalent evolution, and SnowTran-3D accounts for snow redistribution by wind. Since each of these submodels was originally developed and tested for nonforested conditions, details describing modifications made to the submodels for forested areas are provided. SnowModel was created to run on grid increments of 1 to 200 m and temporal increments of 10 min to 1 day. It can also be applied using much larger grid increments, if the inherent loss in high-resolution (subgrid) information is acceptable. Simulated processes include snow accumulation; blowing-snow redistribution and sublimation; forest canopy interception, unloading, and sublimation; snow-density evolution; and snowp...

Soil Moisture Memory in AGCM Simulations: Analysis of Global Land–Atmosphere Coupling Experiment (GLACE) Data

Abstract: Soil moisture memory is a key aspect of land–atmosphere interaction and has major implications for seasonal forecasting. Because of a severe lack of soil moisture observations on most continents, existing analyses of global-scale soil moisture memory have relied previously on atmospheric general circulation model (AGCM) experiments, with derived conclusions that are probably model dependent. The present study is the first survey examining and contrasting global-scale (near) monthly soil moisture memory characteristics across a broad range of AGCMs. The investigated simulations, performed with eight different AGCMs, were generated as part of the Global Land–Atmosphere Coupling Experiment. Overall, the AGCMs present relatively similar global patterns of soil moisture memory. Outliers are generally characterized by anomalous water-holding capacity or biases in radiation forcing. Water-holding capacity is highly variable among the analyzed AGCMs and is the main factor responsible for intermodel diffe...

GEOtop: A Distributed Hydrological Model with Coupled Water and Energy Budgets

Abstract: This paper describes a new distributed hydrological model, called GEOtop. The model accommodates very complex topography and, besides the water balance, unlike most other hydrological models, integrates all the terms in the surface energy balance equation. GEOtop uses a discretization of the landscape based on digital elevation data. These digital elevation data are preprocessed to allow modeling of the effect of topography on the radiation incident on the surface, both shortwave (including shadowing) and longwave (accounting for the sky view factor). For saturated and unsaturated subsurface flow, GEOtop makes use of a numerical solution of the 3D Richards’ equation in order to properly model, besides the lateral flow, the vertical structure of water content and the suction dynamics. These characteristics are deemed necessary for consistently modeling hillslope processes, initiation of landslides, snowmelt processes, and ecohydrological phenomena as well as discharges during floods and interstorm...

Do Global Models Properly Represent the Feedback between Land and Atmosphere

Abstract: The Global Energy and Water Cycle Experiment/Climate Variability and Predictability (GEWEX/CLIVAR) Global Land–Atmosphere Coupling Experiment (GLACE) has provided an estimate of the global distribution of land–atmosphere coupling strength during boreal summer based on the results from a dozen weather and climate models. However, there is a great deal of variation among models, attributable to a range of sensitivities in the simulation of both the terrestrial and atmospheric branches of the hydrologic cycle. It remains an open question whether any of the models, or the multimodel estimate, reflects the actual pattern and strength of land–atmosphere coupling in the earth’s hydrologic cycle. The authors attempt to diagnose this by examining the local covariability of key atmospheric and land surface variables both in models and in those few locations where comparable, relatively complete, long-term measurements exist. Most models do not encompass well the observed relationships between surface and a...

Snow Data Assimilation via an Ensemble Kalman Filter

Abstract: A snow data assimilation study was undertaken in which real data were used to update a conceptual model, SNOW-17. The aim of this study is to improve the model’s estimate of snow water equivalent (SWE) by merging the uncertainties associated with meteorological forcing data and SWE observations within the model. This is done with a view to aiding the estimation of snowpack initial conditions for the ultimate objective of streamflow forecasting via a distributed hydrologic model. To provide a test of this methodology, the authors performed experiments at 53 stations in Colorado. In each case the situation of an unobserved location is mimicked, using the data at any given station only for validation; essentially, these are withholding experiments. Both ensembles of model forcing data and assimilated data were derived via interpolation and stochastic modeling of data from surrounding sources. Through a process of cross validation the error for the ensemble of model forcing data and assimilated obser...

Fractal Distribution of Snow Depth from Lidar Data

Abstract: Snowpack properties vary dramatically over a wide range of spatial scales, from crystal microstructure to regional snow climates. The driving forces of wind, energy balance, and precipitation interact with topography and vegetation to dominate snow depth variability at horizontal scales from 1 to 1000 m. This study uses land surface elevation, vegetation surface elevation, and snow depth data measured using airborne lidar at three sites in north-central Colorado. Fractal dimensions are estimated from the slope of a log-transformed variogram and demonstrate scale-invariant, fractal behavior in the elevation, vegetation, and snow depth datasets. Snow depth and vegetation topography each show two distinct fractal distributions over different scale ranges (multifractal behavior), with short-range fractal dimensions near 2.5 and long-range fractal dimensions around 2.9 at all locations. These fractal ranges are separated by a scale break at 15–40 m, depending on the site, which indicates a process cha...

Understanding Precipitation Changes in Iberia in Early Spring: Weather Typing and Storm-Tracking Approaches

Abstract: March monthly accumulated precipitation in the central and western regions of the Iberian Peninsula presents a clear continuous decline of 50% during the 1960-97 period. A finer analysis using daily data reveals that this trend is exactly confined to the month of March. However, this is merely the most visible aspect of a larger phenomenon over the North Atlantic/European sector. The European precipitation trends in March for the period 1960-2000 show a clear distribution of increasing precipitation in the northern regions (the British Isles and parts of Scandinavia) together with decreasing trends throughout the western Mediterranean Basin. Relevant circulation changes over the North Atlantic and European sectors explain these precipitation trends. First, a regional Eulerian approach by means of a weather-type (WT) classification shows that the major rainfall contributors in March display significantly decreasing frequencies for the Iberian Peninsula, in contrast to the corresponding "wet" weather types for the U.K./Ireland sector, which display increasing frequencies. Within a larger context, a Lagrangian approach, based oil the analysis of storm tracks over Europe and the North Atlantic region, reveals dramatic changes in the location of cyclones in the last four decades that coincide with the corresponding precipitation trends in Europe. The North Atlantic Oscillation is suggested to be the most important large-scale factor controlling both the circulation changes and the precipitation trends over the Euro-Atlantic area in March. Finally, the potential impact of reduced precipitation for rivers and water resources in the Iberian Peninsula is considered.

Mapping “At Risk” Snow in the Pacific Northwest

Abstract: One of the most visible and widely felt impacts of climate warming is the change (mostly loss) of low-elevation snow cover in the midlatitudes. Snow cover that accumulates at temperatures close to the ice-water phase transition is at greater risk to climate warming than cold climate snowpacks because it affects both precipitation phase and ablation rates. This study maps areas in the Pacific Northwest region of the United States that are potentially at risk of converting from a snow-dominated to a rain-dominated winter precipitation regime, under a climate-warming scenario. A data-driven, climatological approach of snow cover classification is used to reveal these “at risk” snow zones and also to examine the relative frequency of warm winters for the region. For a rain versus snow temperature threshold of 0°C the at-risk snow class covers an area of about 9200 km2 in the Pacific Northwest region and represents approximately 6.5 km3 of water. Many areas of the Pacific Northwest would see an increa...

Data Assimilation for Estimating the Terrestrial Water Budget Using a Constrained Ensemble Kalman Filter

Abstract: A procedure is developed to incorporate equality constraints in Kalman filters, including the ensemble Kalman filter (EnKF), and is referred to as the constrained ensemble Kalman filter (CEnKF). The constraint is carried out as a two-step filtering approach, with the first step being the standard (ensemble) Kalman filter. The second step is the constraint step carried out by another Kalman filter that optimally redistributes any imbalance from the first step. The CEnKF is implemented over a 75 000 km2 domain in the southern Great Plains region of the United States, using the terrestrial water balance as the constraint. The observations, consisting of gridded fields of the upper two soil moisture layers from the Oklahoma Mesonet system, Atmospheric Radiation Measurement Program Cloud and Radiation Testbed (ARM-CART) energy balance Bowen ratio (EBBR) latent heat estimates, and U.S. Geological Survey (USGS) streamflow from unregulated basins, are assimilated into the Variable Infiltration Capacity (...

Probabilistic Quantitative Precipitation Estimation in Complex Terrain

Abstract: This paper describes a flexible method to generate ensemble gridded fields of precipitation in complex terrain. The method is based on locally weighted regression, in which spatial attributes from station locations are used as explanatory variables to predict spatial variability in precipitation. For each time step, regression models are used to estimate the conditional cumulative distribution function (cdf) of precipitation at each grid cell (conditional on daily precipitation totals from a sparse station network), and ensembles are generated by using realizations from correlated random fields to extract values from the gridded precipitation cdfs. Daily high-resolution precipitation ensembles are generated for a 300 km × 300 km section of western Colorado (dx = 2 km) for the period 1980–2003. The ensemble precipitation grids reproduce the climatological precipitation gradients and observed spatial correlation structure. Probabilistic verification shows that the precipitation estimates are reliab...

Multimodel Combination Techniques for Analysis of Hydrological Simulations: Application to Distributed Model Intercomparison Project Results

Abstract: This paper examines several multimodel combination techniques that are used for streamflow forecasting: the simple model average (SMA), the multimodel superensemble (MMSE), modified multimodel superensemble (M3SE), and the weighted average method (WAM). These model combination techniques were evaluated using the results from the Distributed Model Intercomparison Project (DMIP), an international project sponsored by the National Weather Service (NWS) Office of Hydrologic Development (OHD). All of the multimodel combination results were obtained using uncalibrated DMIP model simulations and were compared against the best-uncalibrated as well as the best-calibrated individual model results. The purpose of this study is to understand how different combination techniques affect the accuracy levels of the multimodel simulations. This study revealed that the multimodel simulations obtained from uncalibrated single-model simulations are generally better than any single-member model simulations, even the bestcalibrated single-model simulations. Furthermore, more sophisticated multimodel combination techniques that incorporated bias correction step work better than simple multimodel average simulations or multimodel simulations without bias correction.

Real-Time Data Assimilation for Operational Ensemble Streamflow Forecasting

Abstract: Operational flood forecasting requires that accurate estimates of the uncertainty associated with model-generated streamflow forecasts be provided along with the probable flow levels. This paper demonstrates a stochastic ensemble implementation of the Sacramento model used routinely by the National Weather Service for deterministic streamflow forecasting. The approach, the simultaneous optimization and data assimilation method (SODA), uses an ensemble Kalman filter (EnKF) for recursive state estimation allowing for treatment of streamflow data error, model structural error, and parameter uncertainty, while enabling implementation of the Sacramento model without major modification to its current structural form. Model parameters are estimated in batch using the shuffled complex evolution metropolis stochastic-ensemble optimization approach (SCEM-UA). The SODA approach was implemented using parallel computing to handle the increased computational requirements. Studies using data from the Leaf River...

Using TRMM/TMI to Retrieve Surface Soil Moisture over the Southern United States from 1998 to 2002

Abstract: Passive microwave remote sensing has been recognized as a potential method for measuring soil moisture. Combined with field observations and hydrological modeling brightness temperatures can be used to infer soil moisture states and fluxes in real time at large scales. However, operationally acquiring reliable soil moisture products from satellite observations has been hindered by three limitations: suitable low-frequency passive radiometric sensors that are sensitive to soil moisture and its changes; a retrieval model (parameterization) that provides operational estimates of soil moisture from top-of-atmosphere (TOA) microwave brightness temperature measurements at continental scales; and suitable, large-scale validation datasets. In this paper, soil moisture is retrieved across the southern United States using measurements from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) X-band (10.65 GHz) radiometer with a land surface microwave emission model (LSMEM) developed by the authors. Surface temperatures required for the retrieval algorithm were obtained from the Variable Infiltration Capacity (VIC) hydrological model using North American Land Data Assimilation System (NLDAS) forcing data. Because of the limited information content on soil moisture in the observed brightness temperatures over regions characterized by heavy vegetation, active precipitation, snow, and frozen ground, quality control flags for the retrieved soil moisture are provided. The resulting retrieved soil moisture database will be available through the NASA Goddard Space Flight Center (GSFC) Distributed Active Archive Center (DAAC) at a 1/8° spatial resolution across the southern United States for the 5-yr period of January 1998 through December 2002. Initial comparisons with in situ observations obtained from the Oklahoma Mesonet resulted in seasonal correlation coefficients exceeding 0.7 for half of the time covered by the dataset. The dynamic range of the satellite-derived soil moisture dataset is considerably higher compared to the in situ data. The spatial pattern of the TMI soil moisture product is consistent with the corresponding precipitation fields.

Impact of Incorrect Model Error Assumptions on the Sequential Assimilation of Remotely Sensed Surface Soil Moisture

Abstract: Data assimilation approaches require some type of state forecast error covariance information in order to optimally merge model predictions with observations. The ensemble Kalman filter (EnKF) dynamically derives such information through a Monte Carlo approach and the introduction of random noise in model states, fluxes, and/or forcing data. However, in land data assimilation, relatively little guidance exists concerning strategies for selecting the appropriate magnitude and/or type of introduced model noise. In addition, little is known about the sensitivity of filter prediction accuracy to (potentially) inappropriate assumptions concerning the source and magnitude of modeling error. Using a series of synthetic identical twin experiments, this analysis explores the consequences of making incorrect assumptions concerning the source and magnitude of model error on the efficiency of assimilating surface soil moisture observations to constrain deeper root-zone soil moisture predictions made by a land surface model. Results suggest that inappropriate model error assumptions can lead to circumstances in which the assimilation of surface soil moisture observations actually degrades the performance of a land surface model (relative to open-loop assimilations that lack a data assimilation component). Prospects for diagnosing such circumstances and adaptively correcting the culpable model error assumptions using filter innovations are discussed. The dual assimilation of both runoff (from streamflow) and surface soil moisture observations appears to offer a more robust assimilation framework where incorrect model error assumptions are more readily diagnosed via filter innovations.

Estimating Large-Scale Precipitation Minus Evapotranspiration from GRACE Satellite Gravity Measurements

Abstract: Currently, observations of key components of the earth's large-scale water and energy budgets are sparse or even nonexistent. One key component, precipitation minus evapotranspiration (P − ET), remains largely unmeasured due to the absence of observations of ET. Precipitation minus evapotranspiration describes the flux of water between the atmosphere and the earth's surface, and therefore provides important information regarding the interaction of the atmosphere with the land surface. In this paper, large-scale changes in continental water storage derived from satellite gravity data from the Gravity Recovery and Climate Experiment (GRACE) project are combined with river discharge data to obtain estimates of areally averaged P − ET. After constructing an equation describing the large-scale terrestrial water balance reflecting the temporal sampling of GRACE water storage estimates, GRACE-derived P − ET estimates are compared to those obtained from a reanalysis dataset [NCEP/Department of Energy (DO...

RainFARM: Rainfall Downscaling by a Filtered Autoregressive Model

Abstract: A method is introduced for stochastic rainfall downscaling that can be easily applied to the precipitation forecasts provided by meteorological models. Our approach, called the Rainfall Filtered Autoregressive Model (RainFARM), is based on the nonlinear transformation of a Gaussian random field, and it conserves the information present in the rainfall fields at larger scales. The procedure is tested on two radar-measured intense rainfall events, one at midlatitude and the other in the Tropics, and it is shown that the synthetic fields generated by RainFARM have small-scale statistical properties that are consistent with those of the measured precipitation fields. The application of the disaggregation procedure to an example meteorological forecast illustrates how the method can be implemented in operational practice.

Feasibility Test of Multifrequency Radiometric Data Assimilation to Estimate Snow Water Equivalent

Abstract: A season-long, point-scale radiometric data assimilation experiment is performed in order to test the feasibility of snow water equivalent (SWE) estimation. Synthetic passive microwave observations at Special Sensor Microwave Imager (SSM/I) and Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) frequencies and synthetic broadband albedo observations are assimilated simultaneously in order to update snowpack states in a land surface model using the ensemble Kalman filter (EnKF). The effects of vegetation and atmosphere are included in the radiative transfer model (RTM). The land surface model (LSM) was given biased precipitation to represent typical errors introduced in modeling, yet was still able to recover the true value of SWE with a seasonally integrated rmse of only 2.95 cm, despite a snow depth of around 3 m and the presence of liquid water in the snowpack. This ensemble approach is ideal for investigating the complex theoretical relationships between the snowpack proper...

Seasonal Variations in Terrestrial Water Storage for Major Midlatitude River Basins

Abstract: This paper presents a new diagnostic dataset of monthly variations in terrestrial water storage for 37 midlatitude river basins in Europe, Asia, North America, and Australia. Terrestrial water storage is the sum of all forms of water storage on land surfaces, and its seasonal and interannual variations are in principle determined by soil moisture, groundwater, snow cover, and surface water. The dataset is derived with the combined atmospheric and terrestrial water-balance approach using conventional streamflow measurements and atmospheric moisture convergence data from the ECMWF 40-yr Re-Analysis (ERA-40). A recent study for the Mississippi River basin (Seneviratne et al. 2004) has demonstrated the validity of this diagnostic approach and found that it agreed well with in situ observations in Illinois. The present study extends this previous analysis to other regions of the midlatitudes. A systematic analysis is presented of the slow drift that occurs with the water-balance approach. It is shown ...

Snow Distribution and Melt Modeling for Mittivakkat Glacier, Ammassalik Island, Southeast Greenland

Abstract: A physically based snow-evolution modeling system (SnowModel) that includes four submodels—the Micrometeorological Model (MicroMet), EnBal, SnowPack, and SnowTran-3D—was used to simulate five full-year evolutions of snow accumulation, distribution, sublimation, and surface melt on the Mittivakkat Glacier, in southeast Greenland. Model modifications were implemented and used 1) to adjust underestimated observed meteorological station solid precipitation until the model matched the observed Mittivakkat Glacier winter mass balance, and 2) to simulate glacier-ice melt after the winter snow accumulation had ablated. Meteorological observations from two meteorological stations were used as model inputs, and glaciological mass balance observations were used for model calibration and testing of solid precipitation observations. The modeled end-of-winter snow-water equivalent (w.eq.) accumulation increased with elevation from 200 to 700 m above sea level (ASL) in response to both elevation and topographic...

Improvement of modeled soil wetness conditions and turbulent fluxes through the assimilation of observed discharge

Abstract: The objective of this paper is to improve the performance of a hydrologic model through the assimilation of observed discharge. Since an observation of discharge at a certain time is always influenced by the catchment wetness conditions and meteorology in the past, the assimilation method will have to modify both the past and present soil wetness conditions. For this purpose, a bias-corrected retrospective ensemble Kalman filter has been used as the assimilation algorithm. The assimilation methodology takes into account bias in the forecast state variables for the calculation of the optimal estimates. A set of twin experiments has been developed, in which it is attempted to correct the model results obtained with erroneous initial conditions and strongly over- and underestimated precipitation data. The results suggest that the assimilation of observed discharge can correct for erroneous model initial conditions. When the precipitation used to force the model is underestimated, the assimilation of...

On the uncertainties of flash flood guidance: toward probabilistic forecasting of flash floods

Abstract: Quantifying uncertainty associated with flash flood warning or forecast systems is required to enable informed decision making by those responsible for operation and management of natural hazard protection systems. The current system used by the U.S. National Weather Service (NWS) to issue flash-flood warnings and watches over the Unites States is a purely deterministic system. The authors propose a simple approach to augment the Flash Flood Guidance System (FFGS) with uncertainty propagation components. The authors briefly discuss the main components of the system, propose changes to improve it, and allow accounting for several sources of uncertainty. They illustrate their discussion with examples of uncertainty quantification procedures for several small basins of the Illinois River basin in Oklahoma. As the current FFGS is tightly coupled with two technologies, that is, threshold-runoff mapping and the Sacramento Soil Moisture Accounting Hydrologic Model, the authors discuss both as sources of...

Extending the Predictability of Hydrometeorological Flood Events Using Radar Rainfall Nowcasting

Abstract: The predictability of hydrometeorological flood events is investigated through the combined use of radar nowcasting and distributed hydrologic modeling. Nowcasting of radar-derived rainfall fields can extend the lead time for issuing flood and flash flood forecasts based on a physically based hydrologic model that explicitly accounts for spatial variations in topography, surface characteristics, and meteorological forcing. Through comparisons to discharge observations at multiple gauges (at the basin outlet and interior points), flood predictability is assessed as a function of forecast lead time, catchment scale, and rainfall spatial variability in a simulated real-time operation. The forecast experiments are carried out at temporal and spatial scales relevant for operational hydrologic forecasting. Two modes for temporal coupling of the radar nowcasting and distributed hydrologic models (interpolation and extended-lead forecasting) are proposed and evaluated for flood events within a set of nes...

Impact of Watershed Geomorphic Characteristics on the Energy and Water Budgets

Abstract: The GEOtop model makes it possible to analyze the short- and long-term effects of geomorphic variation on the partitioning of the lateral surface and subsurface water and surface energy fluxes. The topography of the Little Washita basin (Oklahoma) and of the Serraia basin (Trentino, Italy) have been used as base topographies from which virtual topographies with altered slopes and elevations have been created with corresponding modifications of the soil thickness and the extension of the channel network, according to applicable geomorphological theories, in order to quantify the contribution of these topographic features to the spatial and temporal variability of energy and water fluxes. Simulation results show that both a more extended channel network and more accentuated slopes cause an increase in the discharge balanced by a diminution of the evapotranspiration. The diminution of the latent heat flux is balanced by the increase in the sensible heat flux. Net radiation shows a minor sensitivity ...

Impact of an Exponential Profile of Saturated Hydraulic Conductivity within the ISBA LSM: Simulations over the Rhône Basin

Abstract: This study focuses on the influence of an exponential profile of saturated hydraulic conductivity, ksat, with soil depth on the water budget simulated by the Interaction Soil Biosphere Atmosphere (ISBA) land surface model over the French Rhone River basin. With this exponential profile, the saturated hydraulic conductivity at the surface increases by approximately a factor of 10, and its mean value increases in the root zone and decreases in the deeper region of the soil in comparison with the values given by Clapp and Hornberger. This new version of ISBA is compared to the original version in offline simulations using the Rhone-Aggregation high-resolution database. Low-resolution simulations, where all atmospheric data and surface parameters have been aggregated, are also performed to test the impact of the modified ksat profile at the typical scale of a climate model. The simulated discharges are compared to observations from a dense network consisting of 88 gauging stations. Results of the high-resolution experiments show that the exponential profile of ksat globally improves the simulated discharges and that the assumption of an increase in saturated hydraulic conductivity from the soil surface to a depth close to the rooting depth in comparison with values given by Clapp and Hornberger is reasonable. Results of the scaling experiments indicate that this parameterization is also suitable for largescale hydrological applications. Nevertheless, low-resolution simulations with both model versions overestimate evapotranspiration (especially from the plant transpiration and the wet fraction of the canopy) to the detriment of total runoff, which emphasizes the need for implementing subgrid distribution of precipitation and land surface properties in large-scale hydrological applications.