Application Of Regional Models Research Articles

Groundwater Sustainability and Land Subsidence in California’s Central Valley

The Central Valley of California is one of the most prolific agricultural regions in the world. Agriculture is reliant on the conjunctive use of surface-water and groundwater. The lack of available surface-water and land-use changes have led to pumping-induced groundwater-level and storage declines, land subsidence, changes to streamflow and the environment, and the degradation of water quality. As a result, in part, the Sustainable Groundwater Management Act (SGMA) was developed. An examination of the components of SGMA and contextualizing regional model applications within the SGMA framework was undertaken to better understand and quantify many of the components of SGMA. Specifically, the U.S. Geological Survey (USGS) updated the Central Valley Hydrologic Model (CVHM) to assess hydrologic system responses to climatic variation, surface-water availability, land-use changes, and groundwater pumping. MODFLOW-OWHM has been enhanced to simulate the timing of land subsidence and attribute its inelastic and elastic portions. In addition to extending CVHM through 2019, the new version, CVHM2, includes several enhancements as follows: managed aquifer recharge (MAR), pumping with multi-aquifer wells, inflows from ungauged watersheds, and more detailed water-balance subregions, streamflow network, diversions, tile drains, land use, aquifer properties, and groundwater level and land subsidence observations. Combined with historical approximations, CVHM2 estimates approximately 158 km3 of storage loss in the Central Valley from pre-development to 2019. About 15% of the total storage loss is permanent loss of storage from subsidence that has caused damage to infrastructure. Climate extremes will likely complicate the efforts of water managers to store more water in the ground. CVHM2 can provide data in the form of aggregated input datasets, simulate climatic variations and changes, land-use changes or water management scenarios, and resulting changes in groundwater levels, storage, and land subsidence to assist decision-makers in the conjunctive management of water supplies.

High-Resolution Reanalysis of Daily Precipitation using AROME Model Over France

Among the various meteorological variables, precipitation is one of significant interest, especially for hydrological studies. However, obtaining a reliable precipitation data set is a difficult challenge as precipitation can be very discontinuous in space and time. In this study, a method to obtain a high resolution precipitation reanalysis over France is purposed based on a study from 01/01/2016 to 31/12/2018. The French operational regional model Application de la Recherche à l’Opérationnel à Méso-Echelle (AROME) is combined with precipitation observations, which have been quality controlled, using an optimum interpolation data assimilation algorithm. To use this technique, some hypotheses have to be verified, such as the Gaussian distribution of the innovations. Since the precipitation distribution is highly asymmetric, a Box-Cox transformation is applied to both background and observations to work with variables which behave like Gaussian variables. Then, the background and observation standard deviation errors are determined thanks to the semi-variogram technique, which provides daily values. Results show that the Box-Cox transformation provides better scores for light precipitation and has the same quality as the reference – analysis in the physical space – for high precipitation amounts.

Coupling Rivers and Estuaries with an Ocean Model: An Improved Methodology

Freshwater sources are essential inputs for regional ocean models covering coastal areas such as the western Iberian Peninsula. The problem is how to include the mixture between fresh and salt water, typically performed by estuaries and in the adjacent areas of river mouths, without unsustainable increases of computational time and human setup errors. This work provides a proof-of-concept solution to both these problems through the use of an offline two-way methodology, where local schematic rivers and estuaries are responsible for mixing river freshwater with salt water of a regional model application. Two different offline upscaling methodologies—which focus on the implementation of tidal fluxes from local domains to regional domains in the context of operational modelling—are implemented in the Portuguese Coast Operational Modelling System (PCOMS) regional model application as well as in a version without rivers. A comparison between results produced by these methodologies, field data, and satellite imagery was performed, which confirmed that the proposed methodology of using schematic rivers and estuaries, combined with the new offline upscaling methodology proposed herein, represents a good solution for operational modelling of coastal areas subject to a high dominance of freshwater inputs.

Evaluation of Daily Precipitation from the ERA5 Global Reanalysis against GHCN Observations in the Northeastern United States

Precipitation is a primary input for hydrologic, agricultural, and engineering models, so making accurate estimates of it across the landscape is critically important. While the distribution of in-situ measurements of precipitation can lead to challenges in spatial interpolation, gridded precipitation information is designed to produce a full coverage product. In this study, we compare daily precipitation accumulations from the ERA5 Global Reanalysis (hereafter ERA5) and the US Global Historical Climate Network (hereafter GHCN) across the northeastern United States. We find that both the distance from the Atlantic Coast and elevation difference between ERA5 estimates and GHCN observations affect precipitation relationships between the two datasets. ERA5 has less precipitation along the coast than GHCN observations but more precipitation inland. Elevation differences between ERA5 and GHCN observations are positively correlated with precipitation differences. Isolated GHCN stations on mountain peaks, with elevations well above the ERA5 model grid elevation, have much higher precipitation. Summer months (June, July, and August) have slightly less precipitation in ERA5 than GHCN observations, perhaps due to the ERA5 convective parameterization scheme. The heavy precipitation accumulation above the 90th, 95th, and 99th percentile thresholds are very similar for ERA5 and the GHCN. We find that daily precipitation in the ERA5 dataset is comparable to GHCN observations in the northeastern United States and its gridded spatial continuity has advantages over in-situ point precipitation measurements for regional modeling applications.

Revisiting Snow Cover Variability and Canopy Structure Within Forest Stands: Insights From Airborne Lidar Data

AbstractThe retrieval of detailed, co‐located snow depth and canopy cover information from airborne lidar has advanced our understanding of links between forest snow distribution and canopy structure. In this study, we present two recent high‐resolution (1 m) lidar data sets acquired in (i) a 2017 mission in the Eastern Swiss Alps and (ii) NASA's 2017 SnowEx field campaign at Grand Mesa, Colorado. Validation of derived snow depth maps against extensive manual measurements revealed a RMSE of 6 and 3 cm for plot‐level mean and standard deviation of snow depth, respectively, demonstrating that within‐stand snow distribution patterns were captured reliably. Lidar data were further processed to obtain canopy structure metrics. To this end, we developed a novel approach involving a continuous measure of local distance to canopy edge (DCE), which enabled creating spatially aggregated nondirectional and directional descriptors of the canopy structure. DCE‐based canopy metrics were correlated to mean and standard deviation of snow depth over areas representing grid‐cell sizes typical of watershed and regional model applications (20–200 m). Snow depth increased along the DCE gradient from dense canopy to the center of canopy gaps for all sites and acquisition times, while directional effects particularly evolved during the ablation season. These findings highlight the control of canopy gap distribution on snow distribution in discontinuous forests, with higher snow depths where the open fraction is concentrated in few large gaps rather than many fragmented small gaps. In these environments, dedicated canopy structure metrics such as DCE should advance spatially distributed snow modeling.

Biomass and Volume Models Based on Stump Diameter for Assessing Degradation of Miombo Woodlands in Tanzania

Models to estimate forest degradation in terms of removed volume and biomass from the extraction of wood fuel and logging using stump diameter (SD) are lacking. The common method of estimating removals is through estimating diameter at breast height (D) by applying equations relating measured D and SD. The estimated D is then used to estimate biomass and volume by means of allometric equations, which utilize D. Through this sequence of procedures, it is apparent that there is an accumulation of errors. This study developed equations for estimating volume, aboveground biomass (ABG), and belowground biomass (BGB) using SD in miombo woodlands of mainland Tanzania. Volume models were developed from 114 sample trees while AGB and BGB models were developed from 127 and 57 sample trees, respectively. Both site specific and regional models were developed. Over 70% of the variations in BGB, AGB, and volume were explained by SD. It was apparent that SD is inferior compared to measured D in explaining variation in volume and BGB but not AGB. However, the accuracy of BGB and volume estimates emanating directly from SD were far better than those obtained indirectly, i.e., volume or BGB estimates obtained from estimated D from SD, since the latter is affected by accumulation of regression equation errors. For improved accuracy of ABG, BGB, and volume estimates, we recommend the use of site specific models. However, for areas with no site specific models, application of regional models is recommended. The developed models will facilitate the addition of forest degradation as a REDD+ activity into the forthcoming FREL.

The impact of resolution on meteorological, chemical and aerosol properties in regional simulations with WRF-Chem

Abstract. Limited area (regional) models applied at high resolution over specific regions of interest are generally expected to more accurately capture the spatiotemporal variability of key meteorological and climate parameters. However, improved performance is not inevitable, and there remains a need to optimize use of numerical resources and to quantify the impact on simulation fidelity that derives from increased resolution. The application of regional models for climate forcing assessment is currently limited by the lack of studies quantifying the sensitivity to horizontal spatial resolution and the physical–dynamical–chemical schemes driving the simulations. Here we investigate model skill in simulating meteorological, chemical and aerosol properties as a function of spatial resolution, by applying the Weather Research and Forecasting model with coupled Chemistry (WRF-Chem) over eastern North America at different resolutions. Using Brier skill scores and other statistical metrics it is shown that enhanced resolution (from 60 to 12 km) improves model performance for all of the meteorological parameters and gas-phase concentrations considered, in addition to both mean and extreme aerosol optical depth (AOD) in three wavelengths in the visible relative to satellite observations, principally via increase of potential skill. Some of the enhanced model performance for AOD appears to be attributable to improved simulation of meteorological conditions and the concentration of key aerosol precursor gases (e.g., SO2 and NH3). Among other reasons, a dry bias in the specific humidity in the boundary layer and a substantial underestimation of total monthly precipitation in the 60 km simulations are identified as causes for the better performance of WRF-Chem simulations at 12 km.

Evaluation of different approaches to describe the sorption and desorption of phosphorus in soils on experimental data

Phosphorus is an essential element to enhance the needed increase in crop production in the forthcoming century. On the other hand environmental losses of phosphorus cause eutrophication of surface waters. Both problems call for reliable models to predict the behaviour of phosphorus in agricultural soils. In this study the performances of five different sorption approaches were evaluated. The ultimate aim was to identify the most suitable concept for large scale predictions of P dynamics in soils, in terms of a high comparability between observations and predictions with a minimum amount of input data. The model results were compared with unique data from long term (10–15years) experimental field studies of grassland including situations with P mining, equilibrium P fertilization and P surpluses and a pot experiment with P mining. The model performance was evaluated while using site specific constants and generic constants for adsorption and desorption. Three rate limited models (DPPS, INITIATOR and ANIMO) showed good performance when site specific constants were used but the performance of the equilibrium model (NEWS-Dynamic) was reasonably comparable. Model performance was better for experiments with a P surplus than for P mining and was also better for sandy soils as compared to clay and peat soils. However, long term desorption rates had to be calibrated for each application rate. The performance of all models declined when generic data were used. We conclude that none of the included models properly describe what happens when the soil changes its P status, considering that parameterization needs to be treatment-specific to obtain reliable predictions. Considering this flaw, models of intermediate complexity, including both equilibrium and rate limited sorption, and a limited data demand, like DPPS and INITIATOR, seem most suited for regional model application.

A Semi-Structured MODFLOW-USG Model to Evaluate Local Water Sources to Wells for Decision Support.

In order to better represent the configuration of the stream network and simulate local groundwater-surface water interactions, a version of MODFLOW with refined spacing in the topmost layer was applied to a Lake Michigan Basin (LMB) regional groundwater-flow model developed by the U.S. Geological. Regional MODFLOW models commonly use coarse grids over large areas; this coarse spacing precludes model application to local management issues (e.g., surface-water depletion by wells) without recourse to labor-intensive inset models. Implementation of an unstructured formulation within the MODFLOW framework (MODFLOW-USG) allows application of regional models to address local problems. A "semi-structured" approach (uniform lateral spacing within layers, different lateral spacing among layers) was tested using the LMB regional model. The parent 20-layer model with uniform 5000-foot (1524-m) lateral spacing was converted to 4 layers with 500-foot (152-m) spacing in the top glacial (Quaternary) layer, where surface water features are located, overlying coarser resolution layers representing deeper deposits. This semi-structured version of the LMB model reproduces regional flow conditions, whereas the finer resolution in the top layer improves the accuracy of the simulated response of surface water to shallow wells. One application of the semi-structured LMB model is to provide statistical measures of the correlation between modeled inputs and the simulated amount of water that wells derive from local surface water. The relations identified in this paper serve as the basis for metamodels to predict (with uncertainty) surface-water depletion in response to shallow pumping within and potentially beyond the modeled area, see Fienen et al. (2015a).

Partial lateral forcing experiments reveal how multi-scale processes induce devastating rainfall: a new application of regional modeling

The 1998 extremely heavy rainfall events over East Asia are investigated through partial lateral forcing (PLF) experiments with the Weather Research and Forecasting model to determine the impacts of the synoptic (SY), intra-seasonal (IS), and inter-annual (IA) forcing across the lateral boundary on the extreme climate anomalies. The large-scale lateral boundary forcing was derived from an ensemble reanalysis dataset and decomposed into climatological, SY, IS, and IA components. The PLF experiments show that the IS forcing not only triggers the monsoon onset and drives two northward propagation events of the subtropical front but also has dominant contributions to the two heaviest rainfall events over the Yangtze River Basin (YRB) and South China, suggesting the critical role of the intra-seasonal variability in the devastating 1998 floods. Previous studies perceived that the northward propagating IS oscillation from the tropics regulates the extreme heavy rainfall of East Asia summer monsoon in 1998. However, we find that the IS forcing from the mid-latitude plays a more important role than the forcing from the tropics in generating the two extreme rainfall events in 1998. During the first extreme event in June, the IS forcing across the western boundary is the major cause of the northward advance of the subtropical front and the heavy rainfall over the YRB and South China, with the IS forcing across the northern boundary providing the second largest contribution. During the second extreme event (July 15–August 5), the IS forcing from the eastern boundary plays a dominant role in driving the southward retreat and northward advance of the subtropical front, causing another heavy rainfall over the YRB and South China. The western and northern IS forcing also has large contributions to the second extreme event. We have estimated the contributions to the seasonal anomalous rainfall by the three types of forcing. The SY forcing tends to have a moderate effect on the YRB rainfall but significant reduction of the rainfall in South China. The IS forcing has dominant contributions to the seasonal-mean rainfall anomalies over all three sub-regions of China (North China, the YRB, and South China). The IA forcing mainly enhances the rainfall in South China but reduces the precipitation in the YRB slightly. This study portends a promising application of regional climate models to identify key factors causing extreme climate events. The PLF methodology can be used to study a broad range of climate phenomena and to understand the effects of variety of dynamic and physical processes in climate variability and predictability.

Quality Assessment of Meteorological Data for the Beaufort and Chukchi Sea Coastal Region using Automated Routines

Meteorological observations from more than 250 stations in the Beaufort and Chukchi Sea coastal, interior, and offshore regions were gathered and quality-controlled for the period 1979 through 2009. These stations represent many different observing networks that operate in the region for the purposes of aviation, fire weather, coastal weather, climate, surface radiation, and hydrology and report data hourly or sub-hourly. A unified data quality control (QC) has been applied to these multi-resource data, incorporating three main QC procedures: the threshold test (identifying instances of an observation falling outside of a normal range); the step change test (identifying consecutive values that are excessively different); and the persistence test (flagging instances of excessively high or low variability in the observations). Methods previously developed for daily data QC do not work well for hourly data because they flag too many data entries. Improvements were developed to obtain the proper limits for hourly data QC. These QC procedures are able to identify the suspect data while producing far fewer Type I errors (the erroneous flagging of valid data). The fraction of flagged data for the entire database illustrates that the persistence test was failed the most often (1.34%), followed by the threshold (0.99%) and step change tests (0.02%). Comparisons based on neighboring stations were not performed for the database; however, correlations between nearby stations show promise, indicating that this type of check may be a viable option in such cases. This integrated high temporal resolution dataset will be invaluable for weather and climate analysis, as well as regional modeling applications, in an area that is undergoing significant climatic change.

The wildland fire emission inventory: western United States emission estimates and an evaluation of uncertainty

Abstract. Biomass burning emission inventories serve as critical input for atmospheric chemical transport models that are used to understand the role of biomass fires in the chemical composition of the atmosphere, air quality, and the climate system. Significant progress has been achieved in the development of regional and global biomass burning emission inventories over the past decade using satellite remote sensing technology for fire detection and burned area mapping. However, agreement among biomass burning emission inventories is frequently poor. Furthermore, the uncertainties of the emission estimates are typically not well characterized, particularly at the spatio-temporal scales pertinent to regional air quality modeling. We present the Wildland Fire Emission Inventory (WFEI), a high resolution model for non-agricultural open biomass burning (hereafter referred to as wildland fires, WF) in the contiguous United States (CONUS). The model combines observations from the MODerate Resolution Imaging Spectroradiometer (MODIS) sensors on the Terra and Aqua satellites, meteorological analyses, fuel loading maps, an emission factor database, and fuel condition and fuel consumption models to estimate emissions from WF. WFEI was used to estimate emissions of CO (ECO) and PM2.5 (EPM2.5) for the western United States from 2003–2008. The uncertainties in the inventory estimates of ECO and EPM2.5 (uECO and uEPM2.5, respectively) have been explored across spatial and temporal scales relevant to regional and global modeling applications. In order to evaluate the uncertainty in our emission estimates across multiple scales we used a figure of merit, the half mass uncertainty, ũEX (where X = CO or PM2.5), defined such that for a given aggregation level 50% of total emissions occurred from elements with uEX ũEX. The sensitivity of the WFEI estimates of ECO and EPM2.5 to uncertainties in mapped fuel loading, fuel consumption, burned area and emission factors have also been examined. The estimated annual, domain wide ECO ranged from 436 Gg yr−1 in 2004 to 3107 Gg yr−1 in 2007. The extremes in estimated annual, domain wide EPM2.5 were 65 Gg yr−1 in 2004 and 454 Gg yr−1 in 2007. Annual WF emissions were a significant share of total emissions from non-WF sources (agriculture, dust, non-WF fire, fuel combustion, industrial processes, transportation, solvent, and miscellaneous) in the western United States as estimated in a national emission inventory. In the peak fire year of 2007, WF emissions were ~20% of total (WF + non-WF) CO emissions and ~39% of total PM2.5 emissions. During the months with the greatest fire activity, WF accounted for the majority of total CO and PM2.5 emitted across the study region. Uncertainties in annual, domain wide emissions was 28% to 51% for CO and 40% to 65% for PM2.5. Sensitivity of ũECO and ũEPM2.5 to the emission model components depended on scale. At scales relevant to regional modeling applications (Δx = 10 km, Δt = 1 day) WFEI estimates 50% of total ECO with an uncertainty <133% and half of total EPM2.5 with an uncertainty <146%. ũECO and ũEPM2.5 are reduced by more than half at the scale of global modeling applications (Δ x = 100 km, Δ t = 30 day) where 50% of total emissions are estimated with an uncertainty <50% for CO and <64% for PM2.5. Uncertainties in the estimates of burned area drives the emission uncertainties at regional scales. At global scales ũECO is most sensitive to uncertainties in the fuel load consumed while the uncertainty in the emission factor for PM2.5 plays the dominant role in ũEPM2.5. Our analysis indicates that the large scale aggregate uncertainties (e.g. the uncertainty in annual CO emitted for CONUS) typically reported for biomass burning emission inventories may not be appropriate for evaluating and interpreting results of regional scale modeling applications that employ the emission estimates. When feasible, biomass burning emission inventories should be evaluated and reported across the scales for which they are intended to be used.

Modelling vadose zone moisture dynamics in shallow water table settings with the Integrated Hydrologic Model: field-scale testing and application

Reproducing the moisture retention behaviour of the vadose zone in shallow water table settings, especially for the near-surface water table aquifer of the Florida coastal plain, provides unique challenges for integrated hydrological models. A comprehensive distributed parameter model, the Integrated Hydrologic Model (IHM), is developed based on deterministic and physically based soil and vegetative properties. The theory and vertical behaviour of IHM is examined through comparisons with data collected at a study site in west-central Florida. The objectives of this exploration are to test a model of the vertical processes controlling water table behaviour, and to also investigate model parameter effects and offer recommendations for improvements and parameterization for regional model application. Rigorous testing is performed to better understand the robustness and/or the limitations of the methodology of the IHM for vadose zones. Simulation results indicate that IHM is capable of providing reasonable predictions of infiltration, depth to water table (DTWT) elevation response, evapotranspiration (ET) distributions from the upper, lower soil and water table while incorporating field-scale variability of soil and land cover properties.

An iterative solution of flux–profile relationships in the surface layer for regional model applications

Due to the implicit character of flux–profile relationships in the atmospheric surface layer, in mesoscale modeling it is common practice to calculate fluxes of momentum, heat, and moisture with approximate formulas. The study presented here shows that a Newton–Raphson iterative procedure to calculate surface fluxes using broadly accepted formulations for universal stability functions is always convergent when an interval for the solution is properly specified. Independent of surface type ( z 0 , z H / z 0 ) , iterations converge to a solution for Obukhov stability parameter ζ with an accuracy of 1 × 10 - 3 within three iterations for the unstable stratification, and within four iterations for the stable stratification. Thus, this investigation suggests that a proper iterative procedure to obtain surface fluxes is not only always convergent but is also computationally efficient and remains a practical alternative to approximate explicit methods.

A quantitative assessment of the influence of grid resolution on predictions of future-year air quality in North Carolina, USA

Increased focus has been directed at fine-scale modeling for improving the ability of air quality modeling systems to capture local phenomena. While numerous studies have investigated model performance at finer resolution (4–5 km), there is relatively limited information available for choosing the optimum grid resolution for predicting future air quality in attainment demonstration studies. We demonstrate an evaluation of the MM5–SMOKE–MAQSIP modeling system for four 8-h ozone episodes in the summers of 1995, 1996 and 1997 in North Carolina using a one-way nested 36/12/4-km application. After establishing acceptable base-case model performance for ozone predictions during each episode, we developed future-year emissions control scenarios for 2007 and 2012, and finally computed relative reduction factors (RRFs) using model outputs from each of the three grid resolutions. Our analyses, based upon qualitative as well as quantitative approaches like the Student's t-test, indicate that RRFs computed at specific monitoring locations—and hence predicted future-year air quality—are not very different between the 4- and 12-km results, while the differences are slightly larger between the 4- and 36-km results. The results imply that grid resolution contributes to a variability of about 1–3 ppb in the projected future-year design values; this variability needs to be incorporated into policy-relevant decision-making. Since this assessment was performed for four different episodes under diverse meteorological, physical and chemical regimes, one can generalize the results from this study. They are also relevant for regional modeling applications that are currently ongoing for studying PM 2.5 nonattainment issues, where the need for annual base-year and future-year simulations for demonstrating attainment may place a large demand on computing resources. Based upon the results from this study, future studies may consider using results from 12-km modeling to address future-year air quality goals for ozone and PM 2.5 and its components, and then incorporate grid-resolution uncertainties into the computed results.

Plant parameter values for models in temperate climates

Ecological, and especially hydrological models used to assess the effects of land cover changes require various input parameters for plants. Regional model applications rely on detailed information about the properties of the vegetation, especially if process-based approaches are chosen. As raising acceptable data is a time consuming issue, scientists often use globally approximated plant parameter ranges, rather than considering published data sets. The plant parameters summarised in this overview, i.e. albedo, interception capacity, maximum leaf area index, rooting depth, plant height and stomatal conductance, can be used as data for a wide range of published ecological and hydrological models. We concentrate on a presentation of values for temperate regions in order to list a manageable amount of data. Information on plant species is grouped into four main land cover types, crops, pasture (herbs, forbs, grasses), coniferous and deciduous trees. Overall, more than 1300 values for the described parameters have been gathered and present a solid data base for future applications. Further properties of species and sites, such as stand age, basal area, stock density, plant height, mean annual precipitation, mean annual temperature, coordinates and country are given, if available. In many cases of model applications scientists used parameter spans, with no further information or testing of the distribution of data. Twenty-two of the total of 26 data sets subsumed in this data base contained sufficient values to perform a Kolmogorov–Smirnov-test. Twenty of these 22 data sets are normally distributed. In order to investigate spatial differences, the data for stomatal conductance, leaf area index and interception capacity were grouped into North American and European land cover species. Significant differences could only be determined for the leaf area index of deciduous trees and pasture species between the continents.

Possibilities and limitations of regional hydrological models applied within an environmental change study in Benin (West Africa)

The main goal of the hydrological part within the IMPETUS project (‘an integrated approach to the efficient management of scarce water resources in West Africa’) is the prediction of environmental change effects on the water cycle and the water availability of regional scale West African catchments. As a prognosis of possible environmental change effects on hydrology has to be based on scenario analyses, an appropriate regional hydrological model is required. Therefore a preliminary goal is the adaptation and validation of a regional hydrological model in the target region. Due to the fact that a validation of spatially distributed simulation results is not feasible at the regional scale, two different strategies of modelling regional water fluxes are followed. First a multi-scale approach is proposed. Based on highly resolved pedological and hydrological measurements at the local scale (30 km 2) a parameterisation scheme for the spatially distributed TOPLATS approach is developed. After validation at the local scale regional model applications based on parameterisation rules developed at the local scale are performed at the regional scale where only gauge data are available for comparison. Due to data and parameter uncertainty and the fact that the distributed model cannot be calibrated at the regional scale, the model results remain uncertain. For comparison a simplified, lumped model approach is applied directly at the regional scale. The model requires less input data and seems to be more robust with regard to input data and model parameter uncertainties which is mainly due to the fact that uncertainties and errors are smoothed out by the calibration. As it can be calibrated a noticeable better correlation between observed and simulated discharges is obtained because also data uncertainties are calibrated. While a good prediction of the discharge at the regional scale can more easily be obtained by applying the simplified model, environmental change scenario analyses require that land cover change is considered as detailed as possible. Therefore, distributed approaches are still indispensable although model calibration often fails. Model parameter and data uncertainties have to be taken into account and have to be quantified.

Coordinate Transformation on a Sphere Using Conformal Mapping*

When setting up global ocean circulation models one faces the problem of including the Arctic Ocean where the traditional spherical coordinate system has a singularity at the pole. In addition, in regional model applications one has to deal with open boundaries where assumptions are made about the normally poorly known boundary conditions. Here an analytical reversible coordinate transformation on a sphere that preserves the orthogonality and the shape of infinitesimal figures is presented. Starting from a standard spherical coordinate system, the transformation is able to map the North and South Poles to two arbitrary locations of the earth and this is readily done with the aid of a conformal mapping in the extended complex plane. The resulting coordinate system will have enhanced resolution along the geodesic curve between the new poles. Examples are given where the transformation is used to strongly increase the resolution in a particular region of interest in the model domain.

Errors Associated with the Use of Soil Survey Data for Estimating Plant‐Available Water at a Regional Scale

Agricultural models generally provide estimation procedures for soil properties that regularly are missing in data sets. In regional model applications, the inputs to these procedures are often derived from soil survey information. This study was conducted to determine two types of errors associated with the use of soil survey data for estimating plant‐available water (PAW) for the Peace River region of British Columbia: the error associated with the use of an estimation procedure and the error associated with the use of soil survey data rather than measured data as inputs for the procedure. Two PAW estimation procedures (one used in CERES–Maize and in EPIC, and a recent update) were evaluated against laboratory‐measured water‐holding capacity. The original procedure did not perform adequately, with a prediction error of 0.10 compared with 0.04 for the updated procedure. Prediction error for procedure inputs derived from soil survey data were 8 to 18% of the value of the measured mean for particle size and as much as 51% for organic C. The updated procedure was relatively insensitive to input prediction errors. Prediction errors for horizon thickness were 38 mm for the Ap and 95 mm for the main B horizons, the single largest source of error in this study. Prediction errors for total PAW were 25 and 33% of the mean for the Ap and main B horizons, respectively. Tests for unbiasedness for total PAW failed. Field measurements are needed to validate the best of the two estimation procedures and to supplement the present horizon thickness values found in soil survey. These field measurements represent a significant investment of time and money, but are essential to optimize the allocation of resources for a modeling project at the regional scale.

The use of models to integrate information and understanding of soil C at the regional scale

Regional analysis of ecosystem properties, including soil C, is a rapidly developing area of research. Regional analyses are being used to quantify existing soil C stocks, predict changes in soil C as a function of changing landuse patterns, and assess possible responses to climate change. The tools necessary for such analyses are simulation models coupled with spatially-explicit databases of vegetation, soils, topography, landuse and climate. A general framework for regional analyses which integrates models with site-specific and spatially-resolved data is described. Two classes of models are currently being used for analyses at regional scales, ecosystem-level models, which were originally designed for local scale studies, and more aggregated “macro-scale” models developed for continental and global scale applications. A consideration in applying both classes of models is the need to minimize errors associated with aggregating information to apply to coarser spatial and temporal scales. For model input data, aggregation bias is most severe for variables which enter into non-linear model functions, such as soil textural effects on organic matter decomposition and water balance or the temperature response of decomposer organisms. Aggregation of model structure also needs to be considered, particularly for macro-scale models. For example, representations of litter and soil organic matter by only one or two pools may be suitable for representing equilibrium conditions but rates of change will tend to be overestimated for transient-state conditions using highly aggregated models. Geographic soils data, derived from field surveys, are a key component for regional analyses. Issues of data quality and interpretation of soil survey data are discussed in the context of regional analyses of soil C. Areas for further development of data and modeling capabilities, including refining soil C maps, developing spatial databases on landuse and management practices, using remotely sensed data in regional model applications, and linking terrestrial ecosystem models with global climate models, are discussed.