Bayesian multimodel estimation of global terrestrial latent heat flux from eddy covariance, meteorological, and satellite observations

Accurate estimation of the satellite-based global terrestrial latent heat flux (LE) at high spatial and temporal scales remains a major challenge. In this study, we introduce a Bayesian model averaging (BMA) method to improve satellite-based global terrestrial LE estimation by merging five process-based algorithms. These are the Moderate Resolution Imaging Spectroradiometer (MODIS) LE product algorithm, the revised remote-sensing-based Penman-Monteith LE algorithm, the Priestley-Taylor-based LE algorithm, the modified satellite-based Priestley-Taylor LE algorithm, and the semi-empirical Penman LE algorithm. We validated the BMA method using data for 2000–2009 and by comparison with a simple model averaging (SA) method and five process-based algorithms. Validation data were collected for 240 globally distributed eddy covariance tower sites provided by FLUXNET projects. The validation results demonstrate that the five process-based algorithms used have variable uncertainty and the BMA method enhances the daily LE estimates, with smaller root mean square errors (RMSEs) than the SA method and the individual algorithms driven by tower-specific meteorology and Modern Era Retrospective Analysis for Research and Applications (MERRA) meteorological data provided by the NASA Global Modeling and Assimilation Office (GMAO), respectively. The average RMSE for the BMA method driven by daily tower-specific meteorology decreased by more than 5 W/m2 for crop and grass sites, and by more than 6 W/m2 for forest, shrub, and savanna sites. The average coefficients of determination (R2) increased by approximately 0.05 for most sites. To test the BMA method for regional mapping, we applied it for MODIS data and GMAO-MERRA meteorology to map annual global terrestrial LE averaged over 2001–2004 for spatial resolution of 0.05°. The BMA method provides a basis for generating a long-term global terrestrial LE product for characterizing global energy, hydrological, and carbon cycles

Soil Trace Metals Concentrations in A Mining Impacted Agricultural Watershed: Comparison of Analytical Methods, Geospatial Distribution, and Evaluation of Risk

This study investigated four aspects surrounding lead, zinc, and cadmium soil trace metals concentrations within a mining impacted watershed: (1) a comparison of three soil trace metal quantification methods relating measurements from field portable X-ray fluorescence spectroscopy (XRFS) in in situ and laboratory environments, and inductively coupled plasma-optical emission spectrometry (ICP-OES), (2) distribution of soil trace metals in riparian terraces of a creek, (3) distribution of soil trace metals in an upland environment, (4) analysis of trace metals uptake into white-tailed deer (Odocoileus virginianus) and the human health risk associated with consuming said deer. This study was conducted within the Elm Creek watershed, located in Ottawa County in northeastern Oklahoma, and situated to the west and south of the Tar Creek Superfund Site, part of the historic Tri-State Lead-Zinc Mining District (TSMD). Trace metals contamination has been documented in Elm Creek, however, questions remain about broader impacts in the Elm Creek watershed. Elm Creek watershed properties purchased by the Grand River Dam Authority (GRDA), a public power provider, are designated to be used as offsite mitigation for fish and wildlife impacts under the Pensacola Dam Hydropower License under the Federal Energy Regulatory Commission. This study found: (1) In situ XRFS analysis on soils with less than 10% moisture content yielded statistical similarities to laboratory XRFS concentrations for lead and zinc when the samples were homogenized, dried and sieved, while samples with moisture continents exceeding 20% showed no similarities. Organic contents greater than 10% caused underreporting of lead XRFS values when compared to ICP concentrations and ICP and laboratory XRFS concentrations were not statistically different for lead but were for zinc (p < 0.05). The XRFS overreported zinc concentrations when compared to ICP values. (2) The creek branch with headwaters originating within the Tar Creek Superfund Site had the most influence on downstream soils concentrations and concentrations of trace metals within creek terraces decreased with increasing distances from the headwaters. (3) Areas with elevated trace metals concentrations within upland environments were located closest to the stream at lower elevations suggesting that the creek is depositing contaminated material during flood events. Creek terraces and upland soils within 100 m of the creek reflected background soil concentrations 11.5 km downstream from the headwaters of the branch originating within the Tar Creek Superfund Site. (4) Uptake of trace metals into white-tailed deer tissues were accurate for lead and cadmium, and conservative estimates of risk to humans from consumption of white-tailed deer found no associated human health risk (HI < 1). This study highlights the differences in trace metals detection methods and impacts of trace metals within a mining impacted agricultural watershed. The results of this study will influence long-term land use in the watershed

Transferability of hydrological models and ensemble averaging methods between contrasting climatic periods

Understanding hydrological model predictive capabilities under contrasting climate conditions enables more robust decision making. Using Differential Split Sample Testing (DSST), we analyze the performance of six hydrological models for 37 Irish catchments under climate conditions unlike those used for model training. Additionally, we consider four ensemble averaging techniques when examining interperiod transferability. DSST is conducted using 2/3 year noncontinuous blocks of (i) the wettest/driest years on record based on precipitation totals and (ii) years with a more/less pronounced seasonal precipitation regime. Model transferability between contrasting regimes was found to vary depending on the testing scenario, catchment, and evaluation criteria considered. As expected, the ensemble average outperformed most individual ensemble members. However, averaging techniques differed considerably in the number of times they surpassed the best individual model member. Bayesian Model Averaging (BMA) and the Granger-Ramanathan Averaging (GRA) method were found to outperform the simple arithmetic mean (SAM) and Akaike Information Criteria Averaging (AICA). Here GRA performed better than the best individual model in 51%–86% of cases (according to the Nash-Sutcliffe criterion). When assessing model predictive skill under climate change conditions we recommend (i) setting up DSST to select the best available analogues of expected annual mean and seasonal climate conditions; (ii) applying multiple performance criteria; (iii) testing transferability using a diverse set of catchments; and (iv) using a multimodel ensemble in conjunction with an appropriate averaging technique. Given the computational efficiency and performance of GRA relative to BMA, the former is recommended as the preferred ensemble averaging technique for climate assessment

Confronting input, parameter, structural, and measurement uncertainty in multi-site multiple-response watershed modeling using Bayesian inferences

2012 Fall.Includes bibliographical references.Simulation modeling is arguably one of the most powerful scientific tools available to address questions, assess alternatives, and support decision making for environmental management. Watershed models are used to describe and understand hydrologic and water quality responses of land and water systems under prevailing and projected conditions. Since the promulgation of the Clean Water Act of 1972 in the United States, models are increasingly used to evaluate potential impacts of mitigation strategies and support policy instruments for pollution control such as the Total Maximum Daily Load (TMDL) program. Generation, fate, and transport of water and contaminants within watershed systems comprise a highly complex network of interactions. It is difficult, if not impossible, to capture all important processes within a modeling framework. Although critical natural processes and management actions can be resolved at varying spatial and temporal scales, simulation models will always remain an approximation of the real system. As a result, the use of models with limited knowledge of the system and model structure is fraught with uncertainty. Wresting environmental decisions from model applications must consider factors that could conspire against credible model outcomes. The main goal of this study is to develop a novel Bayesian-based computational framework for characterization and incorporation of uncertainties from forcing inputs, model parameters, model structures, and measured responses in the parameter estimation process for multisite multiple-response watershed modeling. Specifically, the following objectives are defined: (i) to evaluate the effectiveness and efficiency of different computational strategies in sampling the model parameter space; (ii) to examine the role of measured responses at various locations in the stream network as well as intra-watershed processes in enhancing the model performance credibility; (iii) to facilitate combining predictions from competing model structures; and (iv) to develop a statistically rigorous procedure for incorporation of errors from input, parameter, structural and measurement sources in the parameter estimation process. The proposed framework was applied for simulating streamflow and total nitrogen at multiple locations within a 248 square kilometer watershed in the Midwestern United States using the Soil and Water Assessment Tool (SWAT). Results underlined the importance of simultaneous treatment of all sources of uncertainty for parameter estimation. In particular, it became evident that incorporation of input uncertainties was critical for determination of model structure for runoff generation and also representation of intra-watershed processes such as denitrification rate and dominant pathways for transport of nitrate within the system. The computational framework developed in this study can be implemented to establish credibility for modeling watershed processes. More importantly, the framework can reveal how collection of data from different responses at different locations within a watershed system of interest would enhance the predictive capability of watershed models by reducing input, parametric, structural, and measurement uncertainties

Making the best use of GRACE, GRACE‐FO and SMAP data through a constrained Bayesian data‐model integration

The Gravity Recovery and Climate Experiment (GRACE, 2003–2017) and its Follow-On mission GRACE-FO (2018-now) provide global estimates of the vertically integrated Terrestrial Water Storage Changes (TWSC). Since 2015, the Soil Moisture Active Passive (SMAP) radiometer observes global L-band brightness temperatures, which are sensitive to near-surface soil moisture. In this study, we introduce our newly developed Constrained Bayesian (ConBay) optimization approach to merge the TWSC of GRACE/GRACE-FO along with SMAP soil moisture data into the ∼10 km resolution W3RA water balance model. ConBay is formulated based on two hierarchical multivariate state-space models to (I) separate land hydrology compartments from GRACE/GRACE-FO TWSC, and (II) constrain the estimation of surface soil water storage based on the SMAP data. The numerical implementation is demonstrated over the High Plain (HP) aquifer in the United States between 2015 and 2021. The implementation of ConBay is compared with an unconstrained Bayesian formulation, and our validations are performed against in-situ USGS groundwater level observations and the European Space Agency (ESA)'s Climate Change Initiative (CCI) soil moisture data. Our results indicate that the single GRACE/GRACE-FO assimilation improves particularly the groundwater compartment. Adding SMAP data to the ConBay approach controls the updates assigned to the surface storage compartments. For example, correlation coefficients between the ESA CCI and the ConBay-derived surface soil water storage (0.8) that are considerably higher than those derived from the unconstrained experiment (−0.3) in the North HP. The percentage of updates introduced to the W3RA groundwater storage is also decreased from 64% to 57%

Assessing uncertainties in flood forecasts for decision making: prototype of an operational flood management system integrating ensemble predictions

Ensemble forecasts aim at framing the uncertainties of the potential future development of the hydro-meteorological situation. A probabilistic evaluation can be used to communicate forecast uncertainty to decision makers. Here an operational system for ensemble based flood forecasting is presented, which combines forecasts from the European COSMO-LEPS, SRNWP-PEPS and COSMO-DE prediction systems. A multi-model lagged average super-ensemble is generated by recombining members from different runs of these meteorological forecast systems. A subset of the super-ensemble is selected based on a priori model weights, which are obtained from ensemble calibration. Flood forecasts are simulated by the conceptual rainfall-runoff-model ArcEGMO. Parameter uncertainty of the model is represented by a parameter ensemble, which is a priori generated from a comprehensive uncertainty analysis during model calibration. The use of a computationally efficient hydrological model within a flood management system allows us to compute the hydro-meteorological model chain for all members of the sub-ensemble. The model chain is not re-computed before new ensemble forecasts are available, but the probabilistic assessment of the output is updated when new information from deterministic short range forecasts or from assimilation of measured data becomes available. For hydraulic modelling, with the desired result of a probabilistic inundation map with high spatial resolution, a replacement model can help to overcome computational limitations. A prototype of the developed framework has been applied for a case study in the Mulde river basin. However these techniques, in particular the probabilistic assessment and the derivation of decision rules are still in their infancy. Further research is necessary and promising

Potential aboveground biomass in drought-prone forest used for rangeland pastoralism

The restoration of cleared dry forest represents an important opportunity to sequester atmospheric carbon. In order to account for this potential, the influences of climate, soils, and disturbance need to be deciphered. A data set spanning a region defined the aboveground biomass of mulga (Acacia aneura) dry forest and was analyzed in relation to climate and soil variables using a Bayesian model averaging procedure. Mean annual rainfall had an overwhelmingly strong positive effect, with mean maximum temperature (negative) and soil depth (positive) also important. The data were collected after a recent drought, and the amount of recent tree mortality was weakly positively related to a measure of three-year rainfall deficit, and maximum temperature (positive), soil depth (negative), and coarse sand (negative). A grazing index represented by the distance of sites to watering points was not incorporated by the models. Stark management contrasts, including grazing exclosures, can represent a substantial part of the variance in the model predicting biomass, but the impact of management was unpredictable and was insignificant in the regional data set. There was no evidence of density-dependent effects on tree mortality. Climate change scenarios represented by the coincidence of historical extreme rainfall deficit with extreme temperature suggest mortality of 30.1% of aboveground biomass, compared to 21.6% after the recent (2003-2007) drought. Projections for recovery of forest using a mapping base of cleared areas revealed that the greatest opportunities for restoration of aboveground biomass are in the higher-rainfall areas, where biomass accumulation will be greatest and droughts are less intense. These areas are probably the most productive for rangeland pastoralism, and the trade-off between pastoral production and carbon sequestration will be determined by market forces and carbon-trading rules

Integrated watershed modeling in Central Brazil: Toward robust process-based predictions

Over the last decades, fast growing population along with urban and agricultural sprawl has drastically increased the pressure on water resources of the Federal District (DF), Brazil. Various socio-environmental problems, such as soil erosion, non-point source pollution, reservoir silting, and conflicts among water users evoked the need for more efficient and sustainable ways to use land and water. Due to the complexity of processes relevant at the scale of river basins, a prior analysis of impacts of certain land use and/or land management changes is only feasible by means of modeling. The Soil and Water Assessment Tool (SWAT) has been proven to be useful in this context, across the globe and for different environmental conditions. In this thesis, the SWAT model is utilized to evaluate the impact of Best Management Practices (BMPs) on catchment hydrology and sediment transport. However, model applications in tropical regions, such as the DF, are hampered by severe challenges, (i) the lack of input and control data in an adequate temporal and spatial resolution and (ii) model structural failures in representing processes under tropical conditions. The present (cumulative) thesis addresses these challenges in model simulations for two contrasting watersheds, which both are important sources of the DF’s drinking water supply, i.e. (i) the agriculture-dominated Pipiripau river basin where conflicting demands put immense pressure on the available water resources and (ii) the Santa Maria / Torto river basin, which is to large parts protected as national park and, thus, covered by native vegetation of the Cerrado biome. Perhaps one of the most challenging issues facing watershed modelers in tropical regions is the fact that rain gauge networks can usually not reflect the high spatio-temporal variability of mostly convective precipitation patterns. Therefore, an ensemble of different reasonable input precipitation data-sets was used to examine the uncertainty in parameterization and model output. Acceptable streamflow and sediment load predictions could be achieved for each input data-set. However, the best-fit parameter values varied widely across the ensemble. Due to its enhanced consideration of parameter uncertainty, this ensemble approach provides more robust predictions and hence is reasonable to be used also for scenario simulations. BMP scenarios for the Pipiripau River Basin revealed that erosion control constructions, such as terraces and small retention basins along roads (Barraginhas) are promising measures to reduce sediment loads (up to 40%) while maintaining streamflow. Tests for a multi-diverse crop rotation system, in contrast, showed a high vulnerability of the hydrologic system against any increase in irrigation. Considering the BMP implementation costs, it was possible to estimate cost-abatement curves, which can provide useful information for watershed managers, especially when BMPs are supported by Payments for Environmental Services as it is the case in the study area due to the program Produtor de Água. While for agricultural areas the model has proven to generate plausible results, the plant growth module of SWAT was found to be not suitable for simulating perennial tropical vegetation, such as Cerrado (savanna) or forest, which can also play a crucial role in river basin management. For temperate regions SWAT uses dormancy to terminate growing seasons of trees and perennials. However, there is no mechanism considered to reflect seasonality in the tropics, i.e. the phenological change between wet and dry season. Therefore, a soil moisture based approach was implemented into the plant growth module to trigger new growing cycles in the transition period from dry to wet season. The adapted model was successfully tested against LAI and ET time series derived from remote sensing products (MODIS). Since the proposed changes are process-based but also allow flexible model settings, the modified plant growth module can be seen as a fundamental improvement useful for future model application in the tropics. The present thesis shows insights into the workflow of a watershed model application in the semi-humid tropics – from input data processing and model setup over source code adaptation, model calibration and uncertainty analysis to its use for running scenarios. It depicts region-specific challenges but also provides practical solutions. Hence, this work might be seen as one further step toward robust and process-based model predictions to assist land and water resources management.Starkes Bevölkerungswachstum, ungeplante Suburbanisierung und Landnutzungsänderungen (z.B. Intensivierung in der Landwirtschaft) verstärkten innerhalb der letzten Jahrzehnte zunehmend den Druck auf die Wasserressourcen des Bundesdistrikts Brasilien (zentralbrasilianisches Hochland), in dessen Mitte die junge Hauptstadt Brasília liegt. Damit verbundene negative Umweltauswirkungen, wie Bodenerosion, Stoff- und Sedimenteinträge in Fließgewässer und Talsperren sowie Konflikte zwischen den Wassernutzern erfordern daher dringend effektive und nachhaltige Lösungen im Land- und Wasserressourcen-management. Der Einfluss von möglichen zukünftigen Landnutzungs- und Bewirtschaftungsänderungen auf Wasserverfügbarkeit und -qualität hängt vom jeweiligen, oftmals sehr komplexen, landschaftsökologischen Prozessgefüge ab und kann nur mithilfe von prozessbasierten Simulationsmodellen quantitativ auf der Ebene von Einzugsgebieten abgeschätzt werden. Das “Soil and Water Assessment Tool” (SWAT) ist ein solches Modell. Es findet weltweite Anwendung für verschiedene Umweltbedingungen in Einzugsgebieten der Meso- bis Makroskala, um Landnutzungseffekte auf den Wasserhaushalt und den Transport von Nährstoffen, Pestiziden und Sedimenten zu prognostizieren. Seine Anwendung in tropischen Regionen, wie etwa in Zentralbrasilien, ist jedoch mit erheblichen Herausforderungen verbunden. Das betrifft sowohl die Verfügbarkeit von Eingangs- und Referenzdaten in ausreichender raum-zeitlicher Auflösung, als auch modellstrukturelle Unzulänglichkeiten bei der Prozessabbildung. Die vorliegende kumulative Dissertation zeigt dies anhand von Modellanwendungen für zwei unterschiedliche wasserwirtschaftlich relevante Einzugsgebiete (EZG): Das landwirtschaftlich intensiv genutzte EZG des Rio Pipiripau mit aktuell besonders konfliktträchtiger Wassernutzung, und das Santa Maria/Torto-EZG, welches - geschützt als Nationalpark - durch größtenteils natürliche Vegetationsformationen der brasilianischen Savanne (Cerrado) gekennzeichnet ist. Eine der größten Herausforderungen für die Einzugsgebietsmodellierung in tropischen Regionen liegt in der Abschätzung des Gebietsniederschlages, da vorhandene Messstationsdichten oft nicht ausreichen, um die hohe räumliche und zeitliche Variabilität der meist konvektiven Niederschläge zu erfassen. Mithilfe eines Ensembles verschiedener, plausibel generierter Niederschlagsreihen ist der Einfluss von Niederschlagsdaten-Unsicherheit auf die Modellparametrisierung und -vorhersage explizit berücksichtigt und untersucht worden. Zufriedenstellende Abfluss- und Sedimentfrachtsimulationen waren mit jeder der als Modelinput verwendeten Niederschlagsreihen möglich, jedoch nur bei entsprechender, z.T. stark voneinander abweichender Einstellung der Kalibrierungsparameter. Da diese umfassendere Betrachtung von Parameterunsicherheit zu robusteren Modellvorhersagen führt, wurde der Ensemble-Ansatz auch in der Simulation von Bewirtschaftungsszenarien, dem eigentlichen Modellzweck, verwendet. Die Szenariosimulationen zeigten, dass Maßnahmen zur Erosionsvermeidung (Terrassierung) und zum Sedimentrückhalt (kleine Sedimentrückhaltebecken entlang von Straßen - Barraginhas) die Sedimentfracht des Rio Pipiripau durchschnittlich um bis zu 40% reduzieren können, ohne dabei die Wasserverfügbarkeit zu beeinträchtigen. Modellszenarien mit einer vielgliedrigen Fruchtfolge auf großer Fläche verdeutlichten dagegen die hohe Vulnerabilität des Niedrigwasserabflusses in der Trockenzeit gegenüber jedweder Erhöhung der Bewässerungsmenge. Auf Grundlage von Kostenschätzungen für einzelne Maßnahmen konnten Kostenkurven zur Verringerung der Sedimentfracht und damit nützliche Informationen für das Wasserressourcen-Management abgeleitet werden, insbesondere weil eine Auswahl solcher Agrar-Umweltmaßnahmen im Pipiripau-EZG durch das Programm Produtor de Água finanziell gefördert werden sollen. Während das Modell in landwirtschaftlich genutzten Gebieten plausible Ergebnisse produzierte, wurden erhebliche Schwachstellen in der Simulation ausdauernder Vegetation (z.B. Cerrado) identifiziert. Zur Unterbrechung jährlicher Vegetationszyklen verwendet SWAT eine tageslängenabhängige Dormanzperiode. Diese ist zwar zweckmäßig zur Abbildung der Vegetationsdynamik in den gemäßigten Breiten, steuert aber nicht tropische Vegetationszyklen. Um den Wechsel zwischen Trocken- und Regenzeit in der pflanzenphänologischen Simulation in SWAT abzubilden, wurde daher im Rahmen dieser Arbeit das Pflanzenwachstumsmodul modifiziert, und zwar unter anderem durch Einbeziehung der simulierten Bodenfeuchte zur Unterbrechung der Wachstumszyklen. Das angepasste Modul wurde erfolgreich anhand von Fernerkundungsdaten (MODIS) zum zeitlichen Verlauf von Blattflächenindex und Evapotranspiration getestet. Es ist prozessbasiert und erlaubt flexible Einstellungen, so dass es als grundlegende Modellverbesserung auch für andere SWAT-Anwender von großem Nutzen sein kann. Die vorliegende Dissertation bringt neue Einsichten in verschiedene wichtige Aspekte der integrierten Modellierung tropischer Einzugsgebiete, von der Eingangsdatenaufbereitung über Quellcode-Anpassung, Modellkalibrierung und Unsicherheitsanalyse bis hin zu Szenariosimulationen. Sie veranschaulicht regionsspezifische Herausforderungen, liefert gleichzeitig aber auch praktikable Lösungen und damit einen wichtigen Beitrag für robustere prozessbasierte Modellanwendungen als Entscheidungsunterstützung im Bereich Land- und Wasserressourcenmanagement

NCR-days 2008 : 10 years NCR: November 20-21

De verschillende subthema’s van de NCR-dagen 2008, (i) Stroomgebied en Overstromingsrisico management (ii) Hydrologie en (iii) Geomorfodynamica en Morfologie, dekken een groot gedeelte van het hedendaagse onderzoek dat in Nederland op rivierkundig gebied wordt uitgevoerd