scholarly journals Comparison of generalized non-data-driven lake and reservoir routing models for global-scale hydrologic forecasting of reservoir outflow at diurnal time steps

2020 ◽  
Vol 24 (5) ◽  
pp. 2711-2729 ◽  
Author(s):  
Joseph L. Gutenson ◽  
Ahmad A. Tavakoly ◽  
Mark D. Wahl ◽  
Michael L. Follum
Keyword(s):  
Large Scale ◽  
Spatial Scales ◽  
Model Performance ◽  
Global Scale ◽  
Army Corps ◽  
Time Step ◽  
Large Spatial Scale ◽  
Reservoir Routing ◽  
Routing Methods ◽  
Improved Model

Abstract. Large-scale hydrologic forecasts should account for attenuation through lakes and reservoirs when flow regulation is present. Globally generalized methods for approximating outflow are required but must contend with operational complexity and a dearth of information on dam characteristics at global spatial scales. There is currently no consensus on the best approach for approximating reservoir release rates in large spatial scale hydrologic forecasting, particularly at diurnal time steps. This research compares two parsimonious reservoir routing methods at daily steps: Döll et al. (2003) and Hanasaki et al. (2006). These reservoir routing methods have been previously implemented in large-scale hydrologic modeling applications and have been typically evaluated seasonally. These routing methods are compared across 60 reservoirs operated by the U.S. Army Corps of Engineers. The authors vary empirical coefficients for both reservoir routing methods as part of a sensitivity analysis. The method proposed by Döll et al. (2003) outperformed that presented by Hanasaki et al. (2006) at a daily time step and improved model skill over most run-of-the-river conditions. The temporal resolution of the model influences model performances. The optimal model coefficients varied across the reservoirs in this study and model performance fluctuates between wet years and dry years, and for different configurations such as dams in series. Overall, the method proposed by Döll et al. (2003) could enhance large-scale hydrologic forecasting, but can be subject to instability under certain conditions.

scholarly journals Comparison of Generalized Non-Data-Driven Reservoir Routing Models for Global-Scale Hydrologic Modeling

2019 ◽  
Author(s):  
Joseph L. Gutenson ◽  
Ahmad A. Tavakoly ◽  
Mark D. Wahl ◽  
Michael L. Follum
Keyword(s):  
Large Scale ◽  
Hydrologic Modeling ◽  
Model Performance ◽  
Global Scale ◽  
Army Corps ◽  
Time Step ◽  
Large Spatial Scale ◽  
Reservoir Routing ◽  
Routing Methods ◽  
Empirical Coefficients

Abstract. Large-scale hydrologic simulations should account for attenuation through lakes and reservoirs when flow regulation is present. Generalized methods for approximating outflow are required since reservoir operation is complex and specific real-time release information is typically unavailable at global scales. There is currently no consensus on the best approach for approximating reservoir release rates in large spatial scale hydrologic forecasting. This research compares two parsimonious reservoir routing methods previously implemented in large-scale hydrologic modeling applications, requiring minimal data so as not to limit their usage. The methods considered are those proposed by Döll et al. (2003) and Hanasaki et al. (2006). This paper compares the two methodologies across 60 reservoirs operated from 2006–2012 by the U.S. Army Corps of Engineers. The authors vary empirical coefficients for both reservoir routing methods as part of a sensitivity analysis. The Döll method generally outperformed the Hanasaki method at a daily time step, improving model skill in most cases beyond run-of-the-river conditions. The temporal resolution of the model influences performance. The optimal model coefficients varied across the reservoirs in this study and model performance fluctuates between wet years and dry years, and for different configurations such as dams in series. Overall, the Döll and Hanasaki Methods could enhance large scale hydrologic forecasting, but can be subject to instability under certain conditions.

scholarly journals 8-Day and Daily Maximum and Minimum Air Temperature Estimation via Machine Learning Method on a Climate Zone to Global Scale

2021 ◽  
Vol 13 (12) ◽  
pp. 2355
Author(s):  
Linglin Zeng ◽  
Yuchao Hu ◽  
Rui Wang ◽  
Xiang Zhang ◽  
Guozhang Peng ◽  
...  
Keyword(s):  
Spatial Scale ◽  
Air Temperature ◽  
Spatial Scales ◽  
Model Performance ◽  
Global Scale ◽  
Scale Model ◽  
Validation Dataset ◽  
Daily Maximum ◽  
Climate Zone ◽  
Temporal And Spatial

Air temperature (Ta) is a required input in a wide range of applications, e.g., agriculture. Land Surface Temperature (LST) products from Moderate Resolution Imaging Spectroradiometer (MODIS) are widely used to estimate Ta. Previous studies of these products in Ta estimation, however, were generally applied in small areas and with a small number of meteorological stations. This study designed both temporal and spatial experiments to estimate 8-day and daily maximum and minimum Ta (Tmax and Tmin) on three spatial scales: climate zone, continental and global scales from 2009 to 2018, using the Random Forest (RF) method based on MODIS LST products and other auxiliary data. Factors contributing to the relation between LST and Ta were determined based on physical models and equations. Temporal and spatial experiments were defined by the rules of dividing the training and validation datasets for the RF method, in which the stations selected in the training dataset were all included or not in the validation dataset. The RF model was first trained and validated on each spatial scale, respectively. On a global scale, model accuracy with a determination coefficient (R2) > 0.96 and root mean square error (RMSE) < 1.96 °C and R2 > 0.95 and RMSE < 2.55 °C was achieved for 8-day and daily Ta estimations, respectively, in both temporal and spatial experiments. Then the model was trained and cross-validated on each spatial scale. The results showed that the data size and station distribution of the study area were the main factors influencing the model performance at different spatial scales. Finally, the spatial patterns of the model performance and variable importance were analyzed. Both daytime and nighttime LST had a significant contribution in the 8-day Tmax estimation on all the three spatial scales; while their contribution in daily Tmax estimation varied over different continents or climate zones. This study was expected to improve our understanding of Ta estimation in terms of accuracy variations and influencing variables on different spatial and temporal scales. The future work mainly includes identifying underlying mechanisms of estimation errors and the uncertainty sources of Ta estimation from a local to a global scale.

scholarly journals Comparative analysis of abundance–occupancy relationships for species at risk at both broad taxonomic and spatial scales

2015 ◽  
Vol 93 (7) ◽  
pp. 515-519 ◽  
Author(s):  
Nancy E. Roney ◽  
Anna Kuparinen ◽  
Jeffrey A. Hutchings
Keyword(s):  
At Risk ◽  
Comparative Analysis ◽  
Large Scale ◽  
Spatial Scales ◽  
Freshwater Fishes ◽  
Reference Points ◽  
Species At Risk ◽  
Large Spatial Scale ◽  
Terrestrial Mammals ◽  
Taxonomic Groups

The abundance–occupancy relationship is one of the most well-examined relationships in ecology. At the species level, a positive association has been widely documented. However, until recently, research on the nature of this relationship at broad taxonomic and spatial scales has been limited. Here, we perform a comparative analysis of 12 taxonomic groups across a large spatial scale (Canada), using data on Canadian species at risk: amphibians, arthropods, birds, freshwater fishes, lichens, marine fishes, marine mammals, molluscs, mosses, reptiles, terrestrial mammals, and vascular plants. We find a significantly positive relationship in all taxonomic groups with the exception of freshwater fishes (negative association) and lichens (no association). In general, our work underscores the strength and breadth of this apparently fundamental relationship and provides insight into novel applications for large-scale population dynamics. Further development of species-independent abundance–occupancy relationships, or those of a similar nature, might well prove instrumental in serving as starting points for developing species-independent reference points and recovery strategies.

scholarly journals Cloud Inhomogeneity from MODIS

2005 ◽  
Vol 18 (23) ◽  
pp. 5110-5124 ◽  
Author(s):  
Lazaros Oreopoulos ◽  
Robert F. Cahalan
Keyword(s):  
Optical Thickness ◽  
Large Scale ◽  
Spatial Scales ◽  
Temporal Variations ◽  
Global Scale ◽  
Time Of Day ◽  
Wide Range ◽  
Level 3 ◽  
Inhomogeneity Parameter ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract Two full months (July 2003 and January 2004) of Moderate Resolution Imaging Spectroradiometer (MODIS) Atmosphere Level-3 data from the Terra and Aqua satellites are analyzed in order to characterize the horizontal variability of vertically integrated cloud optical thickness (“cloud inhomogeneity”) at global scales. The monthly climatology of cloud inhomogeneity is expressed in terms of standard parameters, initially calculated for each day of the month at spatial scales of 1° × 1° and subsequently averaged at monthly, zonal, and global scales. Geographical, diurnal, and seasonal changes of inhomogeneity parameters are examined separately for liquid and ice phases and separately over land and ocean. It is found that cloud inhomogeneity is overall weaker in summer than in winter. For liquid clouds, it is also consistently weaker for local morning than local afternoon and over land than ocean. Cloud inhomogeneity is comparable for liquid and ice clouds on a global scale, but with stronger spatial and temporal variations for the ice phase, and exhibits an average tendency to be weaker for near-overcast or overcast grid points of both phases. Depending on cloud phase, hemisphere, surface type, season, and time of day, hemispheric means of the inhomogeneity parameter ν (roughly the square of the ratio of optical thickness mean to standard deviation) have a wide range of ∼1.7 to 4, while for the inhomogeneity parameter χ (the ratio of the logarithmic to linear mean) the range is from ∼0.65 to 0.8. The results demonstrate that the MODIS Level-3 dataset is suitable for studying various aspects of cloud inhomogeneity and may prove invaluable for validating future cloud schemes in large-scale models capable of predicting subgrid variability.

scholarly journals Development of a global dataset of Wetland Area and Dynamics for Methane Modeling (WAD2M)

2020 ◽  
Author(s):  
Zhen Zhang ◽  
Etienne Fluet-Chouinard ◽  
Katherine Jensen ◽  
Kyle McDonald ◽  
Gustaf Hugelius ◽  
...  
Keyword(s):  
Land Surface ◽  
Large Scale ◽  
Southern Oscillation ◽  
Microwave Remote Sensing ◽  
Global Scale ◽  
Annual Maximum ◽  
Time Step ◽  
Wetland Area ◽  
Wetland Extent

Abstract. Seasonal and interannual variations in global wetland area is a strong driver of fluctuations in global methane (CH4) emissions. Current maps of global wetland extent vary with wetland definition, causing substantial disagreement and large uncertainty in estimates of wetland methane emissions. To reconcile these differences for large-scale wetland CH4 modeling, we developed a global Wetland Area and Dynamics for Methane Modeling (WAD2M) dataset at ~25 km resolution at equator (0.25 arc-degree) at monthly time-step for 2000–2018. WAD2M combines a time series of surface inundation based on active and passive microwave remote sensing at coarse resolution (~25 km) with six static datasets that discriminate inland waters, agriculture, shoreline, and non-inundated wetlands. We exclude all permanent water bodies (e.g. lakes, ponds, rivers, and reservoirs), coastal wetlands (e.g., mangroves and sea grasses), and rice paddies to only represent spatiotemporal patterns of inundated and non-inundated vegetated wetlands. Globally, WAD2M estimates the long-term maximum wetland area at 13.0 million km2 (Mkm2), which can be separated into three categories: mean annual minimum of inundated and non-inundated wetlands at 3.5 Mkm2, seasonally inundated wetlands at 4.0 Mkm2 (mean annual maximum minus mean annual minimum), and intermittently inundated wetlands at 5.5 Mkm2 (long-term maximum minus mean annual maximum). WAD2M has good spatial agreements with independent wetland inventories for major wetland complexes, i.e., the Amazon Lowland Basin and West Siberian Lowlands, with high Cohen's kappa coefficient of 0.54 and 0.70 respectively among multiple wetlands products. By evaluating the temporal variation of WAD2M against modeled prognostic inundation (i.e., TOPMODEL) and satellite observations of inundation and soil moisture, we show that it adequately represents interannual variation as well as the effect of El Niño-Southern Oscillation on global wetland extent. This wetland extent dataset will improve estimates of wetland CH4 fluxes for global-scale land surface modeling. The dataset can be found at http://doi.org/10.5281/zenodo.3998454 (Zhang et al., 2020).

scholarly journals Analysis and Reduction of Systematic Errors through a Seamless Approach to Modeling Weather and Climate

2010 ◽  
Vol 23 (22) ◽  
pp. 5933-5957 ◽  
Author(s):  
G. M. Martin ◽  
S. F. Milton ◽  
C. A. Senior ◽  
M. E. Brooks ◽  
S. Ineson ◽  
...  
Keyword(s):  
Time Scales ◽  
Land Surface ◽  
Large Scale ◽  
Climate Models ◽  
Spatial Scales ◽  
Full Range ◽  
Model Development ◽  
Model Performance ◽  
Systematic Errors ◽  
Wide Range

Abstract The reduction of systematic errors is a continuing challenge for model development. Feedbacks and compensating errors in climate models often make finding the source of a systematic error difficult. In this paper, it is shown how model development can benefit from the use of the same model across a range of temporal and spatial scales. Two particular systematic errors are examined: tropical circulation and precipitation distribution, and summer land surface temperature and moisture biases over Northern Hemisphere continental regions. Each of these errors affects the model performance on time scales ranging from a few days to several decades. In both cases, the characteristics of the long-time-scale errors are found to develop during the first few days of simulation, before any large-scale feedbacks have taken place. The ability to compare the model diagnostics from the first few days of a forecast, initialized from a realistic atmospheric state, directly with observations has allowed physical deficiencies in the physical parameterizations to be identified that, when corrected, lead to improvements across the full range of time scales. This study highlights the benefits of a seamless prediction system across a wide range of time scales.

Dependence of spatial periods of travelling ionospheric disturbances on their relative amplitudes

2020 ◽  
Vol 26 (6) ◽  
pp. 38-59
Author(s):  
Yu.P. Fedorenko ◽  
Keyword(s):  
Linear Approximation ◽  
Large Scale ◽  
Spatial Scales ◽  
Global Scale ◽  
Relative Amplitude ◽  
Spatial Period ◽  
Ionospheric Disturbances ◽  
Radio Waves ◽  
Traveling Ionospheric Disturbances ◽  
National University

The relationship between the horizontal spatial period L and the relative amplitude Ad of traveling ionospheric disturbances (TID) at various levels of solar (SA) and geomagnetic (GA) activity is experimentally studied. In the vast majority of cases, the TIDs observed during our study were generated by high-latitude sources. It was found that the period L and amplitude Ad of the medium-scale (MS) TIDs (L = 100 – 800 km) are related by a linear dependence, which does not depend upon the SA level. For large-scale (LS) TID with L = 1000 – 4000 km, the linear approximation of the function L(Ad) at low and high SA levels are increasing or decreasing functions, respectively. For global-scale (GM) TIDs with L = 5000 – 35000 km at low SA levels, the linear approximation L(Ad) is an increasing function. The function L(Ad) for TIDs of all spatial scales does not depend upon the GA level. The data were collected at the radio-physical observatory of V. N. Karazin Kharkiv National University (j = 49.63°N, l = 36.32°E) in 1999—2010 with the ionosphere radio sounding by using coherent radio waves at frequencies of about 150 and 400 MHz radiated by low-orbit navigation satellites Parus and Cicada orbiting at circular near-polar orbit with a height of about 1000 km. The experimental dependence of the horizontal period L of TID upon their relative amplitude Ad is explained based on the global prognostic semi-empirical model of the generation and propagation of acoustic-gravitational waves and traveling ionospheric disturbances.

MUSICA - Modeling for Chemistry, Weather and Climate

2020 ◽  
Author(s):  
Gabriele Pfister ◽  
Andrew Conley ◽  
Mary Barth ◽  
Louisa Emmons ◽  
Forrest Lacey ◽  
...  
Keyword(s):  
Air Quality ◽  
Atmospheric Chemistry ◽  
Large Scale ◽  
Spatial Scales ◽  
Spectral Element ◽  
Global Scale ◽  
Earth System ◽  
Early Results ◽  
Consistent Manner ◽  
Model Components

&lt;p&gt;Current chemical transport models inadequately account for the two-way coupling of atmospheric chemistry with other Earth System components over the range of urban/local to regional to global scales and from the surface up to the top of the atmosphere.&amp;#160; To meet future challenges, future modeling systems need to have the ability to (1) change spatial scales in a consistent manner, (2) resolve multiple spatial scales in a single simulation, (3) couple model components which represent different Earth system processes, and (4) easily mix-and-match model components. This is the motivation behind MUSICA - the Multi-Scale Infrastructure for Chemistry and Aerosols, which we develop together with the atmospheric chemistry community. MUSICA will allow simulation of large-scale atmospheric phenomena while still resolving chemistry at scales relevant for representing societal and scientific critical phenomena (e.g. urban air quality, or convection in monsoon regions) and also enable connections to other components of the earth system by fully coupling to land and ocean models. MUSICA objectives will be achieved through development of a global modeling system capable of regional refinement and the new Model Independent Chemistry Module (MICM). We will discuss the infrastructure and show preliminary results of atmospheric chemistry simulations being conducted in a global model with regional refinement: the Community Atmosphere Model with chemistry using spectral element grids that refine from one-degree resolution to ~14 km resolution over the conterminous United States. These early results confirm that model resolution does matter for representing regional air quality and that the two-way feedback between the local and global scale can play an important role.&lt;/p&gt;

scholarly journals Assessing the large-scale impacts of environmental change using a coupled hydrology and soil erosion model

2018 ◽  
Author(s):  
Joris P. C. Eekhout ◽  
Wilco Terink ◽  
Joris de Vente
Keyword(s):  
Soil Erosion ◽  
Environmental Change ◽  
Sediment Yield ◽  
Large Scale ◽  
Spatial Scales ◽  
Catchment Scale ◽  
Time Step ◽  
Vegetation Development ◽  
Erosion Model ◽  
Large Catchment

Abstract. Assessing the impacts of environmental change on soil erosion and sediment yield at the large catchment scale remains one of the main challenges in soil erosion modelling studies. Here, we present a process-based soil erosion model, based on the integration of the Morgan-Morgan-Finney erosion model in a daily-based hydrological model. The model overcomes many of the limitations of previous large-scale soil erosion models, as it includes a more complete representation of crucial processes like surface runoff generation, dynamic vegetation development, and sediment deposition, and runs at the catchment scale with a daily time step. This makes the model especially suited for evaluation of the impacts of environmental change on soil erosion and sediment yield at large spatial scales. The model was successfully applied in a large catchment in southeastern Spain. We demonstrate the models capacity to perform impact assessments of environmental change scenarios, specifically simulating the scenario impacts of intra- and inter-annual variations in climate, land management and vegetation development on soil erosion and sediment yield.

scholarly journals Lifecycle of light-absorbing carbonaceous aerosols in the atmosphere

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Dantong Liu ◽  
Cenlin He ◽  
Joshua P. Schwarz ◽  
Xuan Wang
Keyword(s):  
Optical Properties ◽  
Radiative Forcing ◽  
Water Solubility ◽  
Model Performance ◽  
Global Scale ◽  
Climate Impacts ◽  
Carbonaceous Aerosols ◽  
Earth System ◽  
The Earth ◽  
Improved Model

Abstract Light-absorbing carbonaceous aerosols (LACs), including black carbon and light-absorbing organic carbon (brown carbon, BrC), have an important role in the Earth system via heating the atmosphere, dimming the surface, modifying the dynamics, reducing snow/ice albedo, and exerting positive radiative forcing. The lifecycle of LACs, from emission to atmospheric evolution further to deposition, is key to their overall climate impacts and uncertainties in determining their hygroscopic and optical properties, atmospheric burden, interactions with clouds, and deposition on the snowpack. At present, direct observations constraining some key processes during the lifecycle of LACs (e.g., interactions between LACs and hydrometeors) are rather limited. Large inconsistencies between directly measured LAC properties and those used for model evaluations also exist. Modern models are starting to incorporate detailed aerosol microphysics to evaluate transformation rates of water solubility, chemical composition, optical properties, and phases of LACs, which have shown improved model performance. However, process-level understanding and modeling are still poor particularly for BrC, and yet to be sufficiently assessed due to lack of global-scale direct measurements. Appropriate treatments of size- and composition-resolved processes that influence both LAC microphysics and aerosol–cloud interactions are expected to advance the quantification of aerosol light absorption and climate impacts in the Earth system. This review summarizes recent advances and up-to-date knowledge on key processes during the lifecycle of LACs, highlighting the essential issues where measurements and modeling need improvement.

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