A Scale‐Aware Parameterization for Estimating Subgrid Variability of Downward Solar Radiation Using High‐Resolution Digital Elevation Model Data

AbstractSubgrid variability of solar downward radiation at the surface can be important in estimating subgrid variability of other radiation‐driven variables, such as snowmelt and soil temperature. However, this information is ignored in current hydrological and weather prediction models as only the mean downward solar radiation of model grid is used. In this study, a parameterization for estimating subgrid variability of downward solar radiation from the model grid mean using high‐resolution digital elevation model (DEM) data is proposed. This scheme considers aspect and slope effects on the subgrid variability. The advantage of this scheme is that computations are performed at the same resolution as the considered hydrological or weather prediction model, and subgrid topographic properties derived from high‐resolution DEM data are used as static inputs. This proposed scheme has been verified in mountainous and flat areas, respectively. It is found that the scheme can well estimate the subgrid variability of downward solar radiation. Also, effects of the DEM resolution on the calculated subgrid variability and the spatial correlation of downward solar radiation are studied. Results show that modeled subgrid variability highly depends on the resolution of the DEM, while the spatial correlation is negligibly time dependent. The proposed scheme can be used in any hydrological and weather prediction model to estimate subgrid variability of downward solar radiation. For example, it is planned to be tested in future NOAA regional and global weather models to account for the effects of the subgrid variability of downward solar radiation on the snow model of the land‐surface component

Downscaling of global solar irradiation in R

A methodology for downscaling solar irradiation from satellite-derived databases is described using R software. Different packages such as raster, parallel, solaR, gstat, sp and rasterVis are considered in this study for improving solar resource estimation in areas with complex topography, in which downscaling is a very useful tool for reducing inherent deviations in satellite-derived irradiation databases, which lack of high global spatial resolution. A topographical analysis of horizon blocking and sky-view is developed with a digital elevation model to determine what fraction of hourly solar irradiation reaches the Earth's surface. Eventually, kriging with external drift is applied for a better estimation of solar irradiation throughout the region analyzed. This methodology has been implemented as an example within the region of La Rioja in northern Spain, and the mean absolute error found is a striking 25.5% lower than with the original database

Comparison of different procedures to map reference evapotranspiration using geographical information systems and regression-based techniques

16 páginas, 6 figuras, 8 tablas.This paper compares different procedures for mapping reference evapotranspiration (ETo) by means of regression-based techniques and geographical information systems (GIS). ETo is calculated following the method of Hargreaves (HG) from a dense database of meteorological stations in the northernmost semi-arid region of Europe, the Ebro valley. The HG method requires the calculation of estimates of extraterrestrial radiation (Ra). We calculated this parameter using two approaches: (1) the common approach that assumes a planar surface and determines the parameter as a function of latitude and (2) using a digital terrain model (DTM) and GIS modelling. The maps were made on a monthly basis using both approaches. We also compared possible propagations of errors in the map calculations for maps derived from modelled layers of maximum and minimum temperatures with those modelled using previously determined local ETo calculations. We demonstrate that calculations of Ra from a DTM and GIS modelling provide a more realistic spatial distribution of ETo than those derived by only considering latitude. It is also preferable to model in advance the variables involved in the calculation of ETo (temperature and Ra) and to subsequently calculate ETo by means of layer algebra in the GIS rather than directly model the local ETo calculations. The obtained maps are useful for the purposes of agriculture and ecological and water resources management in the study area.This work has been supported by the project CGL2005- 04508/BOS financed by the Spanish Comission of Science and Technology (CICYT) and FEDER, PIP176/2005 financed by the Aragón Government, and ‘Programa de grupos de investigación consolidados’ (BOA 48 of 20-04-2005), also financed by the Aragón Government. Research of the third author was supported by postdoctoral fellowship by the Ministerio de Educación, Cultura y Deporte (Spain).Peer reviewe

Space-Time High-Resolution Data of The Potential Insolation and Solar Duration for Montenegro

The assessment of the potential use of renewable energy resources requires reliable and precise data inputs for sustainable energy planning on a regional, national and local scale. In this study, we examine high spatial resolution grids of potential insolation and solar duration in order to determine the location of potential solar power plants in Montenegro. Grids with a 25-m spatial resolution of potential solar radiation and duration were produced based on observational records and publicly available high-resolution digital elevation model provided by the European Environment Agency. These results could be further used for the estimation and selection of a specific location for solar panels. With an average annual potential insolation of 1800 kWh/m² and solar duration of over 2000 h per year for most of its territory, Montenegro is one of the European countries with the highest potential for the development, production, and consumption of solar energy

Predicting glacier accumulation area distributions

A mass balance model based on energy balance at the terrain surface was developed and used to predict glacier accumulation areas in the Jotunheimen, Norway. Spatially distributed melt modelling used local climate and energy balance surfaces to drive predictions, derived from regional climate and topographic data. Predictions had a temporal resolution of 1 month and a spatial resolution of 100 m, which were able to simulate observed glacier accumulation area distributions. Data were stored and manipulated within a GIS and spatial trends and patterns within the data were explored. These trends guided the design of a suite of geomorphologically and climatologically significant variables which were used to simulate the observed spatial organisation of climatic variables, specifically temperature, precipitation and wind speed and direction. DEM quality was found as a critical factor in minimising error propagation. A new method of removing spatially and spectrally organised DEM error is presented using a fast Fourier transformation. This was successfully employed to remove error within the DEM minimising error propagation into model predictions. With no parameter fitting the modeled spatial distribution of snowcover showed good agreement with observed distributions. Topographic maps and a Landsat ETM+ image are used to validate the predictions and identify areas of over or under prediction. Topographically constrained glaciers are most effectively simulated, where aspect, gradient and altitude impose dominant controls on accumulation. Reflections on the causes of over or under prediction are presented and future research directions to address these are outlined. Sensitivity of snow accumulation to climatic and radiative variables was assessed. Results showed the mass balance of accumulation areas is most sensitive to air temperature and cloud cover parameterisations. The model was applied to reconstruct snow accumulation at the last glacial maximum and under IPCC warming scenarios to assess the sensitivity of melt to changing environmental conditions, which showed pronounced sensitivity to summer temperatures Low data requirements: regional climate and elevation data identify the model as a powerful tool for predicting the onset, duration and rate of melt for any geographical area

Geostatistical methods for estimating snowmelt contribution to the seasonal water balance in an alpine watershed

2006 Fall.Includes bibliographical references.The performance of nine spatial interpolation models was evaluated to estimate snowmelt contributions to streamflow in the West Glacier Lake watershed (0.61 km2), in the Snowy Range Mountains of Wyoming. Streamflow from the West Glacier Lake watershed has been previously estimated at 40% to 130% greater than measured precipitation inputs. Additional input into the watershed had been attributed to a permanent snowfield in the upper portion of the watershed covering approximately 2.4% of the watershed area. However, the excess output may be a result of inaccurate estimation of water quantities using current precipitation and stream gauging methods. In April 2005, near peak accumulation snow depth measurements and snow density measurements were collected within West Glacier Lake watershed. The distribution of snow water equivalent (SWE) was calculated as the product of snow depth, snow density, and snow-covered-area (SCA). Snow depths were spatially distributed throughout the watershed through nine spatial interpolation models. Snow densities were spatially distributed through a multiple linear regression. The nine spatial snow depth models explained 18% to 94% of the observed variance in the measured snow depths. Co-kriging with solar radiation produced the best results explaining 94% of the observed variance in snow depth measurements. The annual water balance, expressed as equivalent water depths for water year 2005, was total precipitation (1,481 mm), snowpack sublimation (251 mm), and streamflow (1,000 mm), resulting in an evapotranspiration estimate of 230 mm. Estimated SWE from the field survey data was 67% greater than precipitation gauge estimates and accounted for 85% of the annual streamflow. Summer precipitation was not a significant contributor to the annual hydrograph and was also less than snowpack sublimation. Precipitation gauge values were unrepresentative of actual precipitation depths, and several spatially distributed snow depth models provided better estimates of precipitation inputs

Theoretical Evaluation of Anisotropic Reflectance Correction Approaches for Addressing Multi-Scale Topographic Effects on the Radiation-Transfer Cascade in Mountain Environments

Research involving anisotropic-reflectance correction (ARC) of multispectral imagery to account for topographic effects has been ongoing for approximately 40 years. A large body of research has focused on evaluating empirical ARC methods, resulting in inconsistent results. Consequently, our research objective was to evaluate commonly used ARC methods using first-order radiation-transfer modeling to simulate ASTER multispectral imagery over Nanga Parbat, Himalaya. Specifically, we accounted for orbital dynamics, atmospheric absorption and scattering, direct- and diffuse-skylight irradiance, land cover structure, and surface biophysical variations to evaluate their effectiveness in reducing multi-scale topographic effects. Our results clearly reveal that the empirical methods we evaluated could not reasonably account for multi-scale topographic effects at Nanga Parbat. The magnitude of reflectance and the correlation structure of biophysical properties were not preserved in the topographically-corrected multispectral imagery. The CCOR and SCS+C methods were able to remove topographic effects, given the Lambertian assumption, although atmospheric correction was required, and we did not account for other primary and secondary topographic effects that are thought to significantly influence spectral variation in imagery acquired over mountains. Evaluation of structural-similarity index images revealed spatially variable results that are wavelength dependent. Collectively, our simulation and evaluation procedures strongly suggest that empirical ARC methods have significant limitations for addressing anisotropic reflectance caused by multi-scale topographic effects. Results indicate that atmospheric correction is essential, and most methods failed to adequately produce the appropriate magnitude and spatial variation of surface reflectance in corrected imagery. Results were also wavelength dependent, as topographic effects influence radiation-transfer components differently in different regions of the electromagnetic spectrum. Our results explain inconsistencies described in the literature, and indicate that numerical modeling efforts are required to better account for multi-scale topographic effects in various radiation-transfer components.Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Uncertainty in hydrological estimation

2021 Spring.Includes bibliographical references.Detailed hydrometeorologic analyses and uncertainty assessments are needed to aid water resources decision-making, to account for upstream-downstream linkages and dominant process scale for integrated land and water resources management and planning. The water balance is a fundamental concept in hydrology that inspires many tools for predicting the specific components including precipitation, streamflow, soil moisture, and groundwater storage. A water balance is typically expressed as an equation that relates water inputs, outputs, and storage of a system. The water balance model is applied to analyze the allocation of water among components of the hydrologic system. Knowledge on the components composing inputs and outputs in a water balance are essential to understanding watershed processes. While methods to measure and model water balance components continue to improve, all components of the balance have substantial uncertainty. Methods to analyze a water balance should acknowledge these uncertainties and consider how they propagate through water balance calculations in order to better assist water resources decisions. This research investigated four water balance components: (1) snowpack sublimation, (2) precipitation as snow, (3) precipitation as rain, and (4) stream discharge in mountainous watersheds in order to examine and build our knowledge of uncertainty in the water balance for mountainous environments. The research presented in this dissertation supports a theme that hydrology is a highly uncertain science, where uncertainty is a result of the hydrologic community's knowledge gap to accurately model physics of atmospheric and hydrologic processes. A finding of this work is that no component of the water balance can be quantified at watershed scale without estimating he associated uncertainty. Results highlight that mean cumulative snowpack sublimation uncertainty is 41% with individual input variable uncertainties in the range of 1 to 29%; simulated to observed basin mean snow depth was estimated within 15% for 10-years while extreme dry and wet years were within 5%; and forcing precipitation datasets used in hydrologic models to estimate streamflow have cumulative uncertainties in the range of 30 to 60%. Results of this dissertation identify the importance to account for uncertainty in water resources, i.e., Monte Carlo methods, to properly account for and quantify associated risks in water management and design infrastructure decisions

Multi-Scale Modelling of Cold Regions Hydrology

Numerical computer simulations are increasingly important tools required to address both research and operational water resource issues related to the hydrological cycle. Cold region hydrological models have requirements to calculate phase change in water via consideration of the energy balance which has high spatial variability. This motivates the inclusion of explicit spatial heterogeneity and field-testable process representations in such models. However, standard techniques for spatial representation such as raster discretization can lead to prohibitively large computational costs and increased uncertainty due to increased degrees of freedom. As well, semi-distributed approaches may not sufficiently represent all the spatial variability. Further, there is uncertainty regarding which process conceptualizations are used and the degree of required complexity, motivating modelling approaches that allow testing multiple working hypotheses. This thesis considers two themes. In the first, the development of improved modelling techniques to efficiently include spatial heterogeneity, investigate warranted model complexity, and appropriate process representation in cold region models is addressed. In the second, the issues of non-linear process cascades, emergence, and compensatory behaviours in cold regions hydrological process representations is addressed. To address these themes, a new modelling framework, the Canadian Hydrological Model (CHM), is presented. Key design goals for CHM include the ability to: capture spatial heterogeneity in an efficient manner, include multiple process representations, be able to change, remove, and decouple hydrological process algorithms, work both at point and spatially distributed scales, reduce computational overhead to facilitate uncertainty analysis, scale over multiple spatial extents, and utilize a variety of boundary and initial conditions. To enable multi-scale modelling in CHM, a novel multi-objective unstructured mesh generation software *mesher* is presented. Mesher represents the landscape using a multi-scale, variable resolution surface mesh. It was found that this explicitly captured the spatial heterogeneity important for emergent behaviours and cold regions processes, and reduced the total number of computational elements by 50\% to 90\% from that of a uniform mesh. Four energy balance snowpack models of varying complexity and degree of coupling of the energy and mass budget were used to simulate SWE in a forest clearing in the Canadian Rocky Mountains. It was found that 1) a compensatory response was present in the fully coupled models’ energy and mass balance that reduced their sensitivity to errors in meteorology and albedo and 2) the weakly coupled models produced less accurate simulations and were more sensitive to errors in forcing meteorology and albedo. The results suggest that the inclusion of a fully coupled mass and energy budget improves prediction of snow accumulation and ablation, but there was little advantage by introducing a multi-layered snowpack scheme. This helps define warranted complexity model decisions for this region. Lastly, a 3-D advection-diffusion blowing snow transport and sublimation model using a finite volume method discretization via a variable resolution unstructured mesh was developed. This found that the blowing snow calculation was able to represent the spatial redistribution of SWE over a sub-arctic mountain basin when compared to detailed snow surveys and the use of the unstructured mesh provided a 62\% reduction in computational elements. Without the inclusion of blowing snow, unrealistic homogeneous snow covers were simulated which would lead to incorrect melt rates and runoff contributions. This thesis shows that there is a need to: use fully coupled energy and mass balance models in mountains terrain, capture snow-drift resolving scales in next-generation hydrological models, employ variable resolution unstructured meshes as a way to reduce computational time, and consider cascading process interactions