Impact of Land Model Calibration on Coupled Land-Atmosphere Prediction

Land-atmosphere (L-A) interactions play a critical role in determining the diurnal evolution of both planetary boundary layer (PBL) and land surface heat and moisture budgets, as well as controlling feedbacks with clouds and precipitation that lead to the persistence of dry and wet regimes. Recent efforts to quantify the strength of L-A coupling in prediction models have produced diagnostics that integrate across both the land and PBL components of the system. In this study, we examine the impact of improved specification of land surface states, anomalies, and fluxes on coupled WRF forecasts during the summers of extreme dry and wet land surface conditions in the U.S. Southern Great Plains. The improved land initialization and surface flux parameterizations are obtained through calibration of the Noah land surface model using the new optimization and uncertainty estimation subsystem in NASA's Land Information System (LIS-OPT/UE). The impact of the calibration on the a) spinup of the land surface used as initial conditions, and b) the simulated heat and moisture states and fluxes of the coupled WRF simulations is then assessed. Changes in ambient weather and land-atmosphere coupling are evaluated along with measures of uncertainty propagation into the forecasts. In addition, the sensitivity of this approach to the period of calibration (dry, wet, average) is investigated. Results indicate that the offline calibration leads to systematic improvements in land-PBL fluxes and near-surface temperature and humidity, and in the process provide guidance on the questions of what, how, and when to calibrate land surface models for coupled model prediction

Regional climate projections for the South West of Western Australia to simulate changes in mean and extreme rainfall and temperature

The southwest of Western Australia (SWWA) is an area of significant agricultural production and an internationally recognised biodiversity hotspot. The region has experienced marked rainfall reductions over the last four decades and there is uncertainty as to the extent of future changes to the hydrological regime. Hence, there is a need for regional climate information in SWWA to better inform climate adaptation strategies for several key sectors, including agriculture and forestry. The overarching aim of this project is to provide such information, with a focus on changes in rainfall and temperature extremes. The Weather Research and Forecasting (WRF) model was used as a regional climate model for SWWA. Given the known sensitivity of WRF to physics options and driving data, the most appropriate physical parameterisations were tested on a yearly time-scale. Based on these findings, a 30-year climatology was produced for SWWA (1981-2010) at a 5 km resolution by downscaling ERA-Interim reanalysis. Comparisons against observations showed that the model was able to simulate the daily, seasonal and annual variation of temperature and precipitation well, including extreme events. The model was then used to downscale an ensemble of 4 general circulation models (GCMs) for the historical period (1970- 1999) and compared against both observations and the GCMs. WRF was shown to add value to the GCM data for 3 out of the 4 GCMs evaluated, particularly in the spatio-temporal distribution of winter rainfall. Finally, the ensemble was run from 2030-2059 to examine projected climate change in SWWA. Results project that maximum temperature extremes will increase, consistent with mean changes however the variance of maximum temperatures is not projected to change significantly. While mean minimum temperatures are not projected to increase as much as maximum temperatures, there is strong evidence that the variability of minimum temperatures will increase. This has the potential to raise the likelihood of night time temperature extremes. Simulations project a reduction in rainfall, particularly during winter. This decline is related to fewer frontal systems traversing the SWWA and hence fewer rain days. The study found no evidence to suggest that the intensity of rain bearing winter storms is likely to change

On the multi-scale analysis of land-surface mass and energy exchanges for the tropical Andean páramo of Southern Ecuador

Studies on the atmosphere-surface exchange processes over montane regions represent a growing field. This is particular on the scope of ecohydrological investigations, which assess the estimation and prediction of water, carbon and energy fluxes, among other functional key-indicators of the biomes. Therefore, the understanding of the complexity of energy-driven processes such as actual evapotranspiration (ETa) and carbon dioxide (CO2) exchange is fundamental, particularly for the biodiverse tropical highlands, which are exposed to human-induced treats (e.g., global warming and land cover change). Herein, the Andean páramo region of Ecuador (3200-5000 m a.s.l.) supports vital ecosystem services, such as water supply, carbon storage and biodiversity. This biome is crucially important for the sustainability of the inter-Andean valleys’ inhabitants, mainly due to its role as a massive water reservoir. Atmosphere-surface exchange processes in the páramo are largely unknown. The aim of this thesis is to analyze the water, energy and carbon flux transfer mechanisms in páramo catchments of Southern Ecuador, by a multi-scale approach, which considers point-scale (ecosystem) and spatial-scale (catchment), through ground-level (instrumental-based) and remote sensing level (model-based). This methodology allows to quantify the accuracy of each technique, and to identify the most appropriate method according to site characteristics. This work has contemplated: (i) The analysis of the spatial dynamics of ETa derived from satellite products (Landsat and MODIS) with a calibrated energy balance-based model (METRIC), and the evaluation with a global ETa product (MOD16) and with ETa obtained as water balance residual (WB); (ii) the detection and analysis of ground-level and ecosystem-scale ETa, energy and CO2 fluxes (through eddy covariance (EC) and micrometeorological methods), their interaction with environmental controls, and the comparison of EC ETa with widely applied reference ET models (ETr); and finally, (iii) the evaluation of simulated ETa and energy fluxes obtained from a state-of-art land-surface model (CLM) parameterized to the biophysical and climate conditions of two páramo catchments. The CLM outcomes evaluation was performed with METRIC ETa, energy fluxes observations (EC) and with WB-derived ETa. The findings of the first analysis reveal that spatial ETa can be successfully estimated when a proper calibration of the model parameters and a high resolution satellite product is used simultaneously. The ETa temporal dynamics from this approach showed consistent results with WB ETa. The results of the second part, demonstrate the plausibility of EC for gas and energy flux detection on this mountain ecosystem. The CO2 budget (at different time scales) reveals the ‒carbon source‒ behavior of the páramo, which constitutes an outstanding discovery in the knowledge about this region. Mathematical functions between carbon fluxes and biophysical controls (available light and soil temperature) are also reported. The quantification of water loss in the form of ETa, and its comparison with modeled ETr, allowed for the first time, to report truthful crop coefficients for the páramo grasslands. Finally, the third analysis revealed the plausibility of CLM for ETa prediction, in spite of a poor performance of the model for the simulation of specific energy fluxes (net radiation, sensible and soil heat). The evaluation between methods, also demonstrated that METRIC ETa values are closer to the EC ETa observations, and revealed that WB ETa rendered poorly. These analyses provide insights on the methods’ selection for future studies in similar locations. The current investigation provides solid answers to unsolved questions about the dynamics of the ETa, CO2 and energy fluxes of the páramo, and the multiscale approach adopted enhance our understanding of the ecohydrological processes of this unique Andean ecosystem

Fire

Vegetation plays a crucial role in regulating environmental conditions, including weather and climate. The amount of water and carbon dioxide in the air and the albedo of our planet are all influenced by vegetation, which in turn influences all life on Earth. Soil properties are also strongly influenced by vegetation, through biogeochemical cycles and feedback loops (see Volume 1A—Section 4). Vegetated landscapes on Earth provide habitat and energy for a rich diversity of animal species, including humans. Vegetation is also a major component of the world economy, through the global production of food, fibre, fuel, medicine, and other plantbased resources for human consumptio

Remote Sensing of Natural Hazards

Each year, natural hazards such as earthquakes, cyclones, flooding, landslides, wildfires, avalanches, volcanic eruption, extreme temperatures, storm surges, drought, etc., result in widespread loss of life, livelihood, and critical infrastructure globally. With the unprecedented growth of the human population, largescale development activities, and changes to the natural environment, the frequency and intensity of extreme natural events and consequent impacts are expected to increase in the future.Technological interventions provide essential provisions for the prevention and mitigation of natural hazards. The data obtained through remote sensing systems with varied spatial, spectral, and temporal resolutions particularly provide prospects for furthering knowledge on spatiotemporal patterns and forecasting of natural hazards. The collection of data using earth observation systems has been valuable for alleviating the adverse effects of natural hazards, especially with their near real-time capabilities for tracking extreme natural events. Remote sensing systems from different platforms also serve as an important decision-support tool for devising response strategies, coordinating rescue operations, and making damage and loss estimations.With these in mind, this book seeks original contributions to the advanced applications of remote sensing and geographic information systems (GIS) techniques in understanding various dimensions of natural hazards through new theory, data products, and robust approaches

Validation and evaluation of the DNDC model to simulate soil water content, mineral N and N2O emission in the North China Plain

Using measured datasets (various soil properties, the soil water content, daily N2O emissions, and different crop parameters) from a multi-factorial field experiment (N fertilisation, irrigation, and straw removal) in the years 1999-2002 on the experimental site Dong Bei Wang (DBW) in the North China Plain (NCP), the ability of the process-oriented model DNDC (DeNitrification-DeComposition) was tested to simulate soil processes, and especially N2O trace gas emissions. The soil is classified as ?calcaric cambisol? (16 % clay content), while the site itself is further characterised by the regime of a continental monsoon climate. The central hypothesis in this work was that a thorough testing of the model (using a considerable range of different datasets) will allow the identification of shortcomings or discrepancies in the model, and that, given the linear succession of model calculation steps, the model calculation can be improved step by step, starting with improvements of initial calculation steps before continuing the improvement of following calculation steps. Due to increases in the N2O atmospheric concentration, and a lifetime of 100 to 150 years for one molecule (as well as a global warming potential 32 times that of a CO2 molecule), N2O is estimated to account for 7.9 % of the global warming potential. 70 % ? 90 % of the anthropogenic N2O emissions are thought to origin from agriculture. The formation of nitrous oxide is dependent on the availability of reactive nitrogen, and, therefore, mainly influenced by the N fertilisation rate, fertiliser type, application timing and method. China, and the main cropping area NCP, are expected to contribute considerably to the anthropogenic N2O emissions. The DNDC model consists of two compartments, which first calculate soil temperature, moisture, pH, redox potential and substrate concentration profiles from climate, soil, vegetation and anthropogenic activity datasets, and in a second step NO, N2O, CH4 and NH3 fluxes. In accordance with the data availability, the simulation of the soil water content, the mineral nitrogen concentration, and the N2O fluxes were investigated. An automated parameter optimisation (using the software UCODE_2005) and programmed changes in the source code were conducted to improve the model simulations. In result, neither the automated parameter optimisations, nor the programmed changes, were able to improve the unsatisfying default simulations of the DNDC model. The results of the cascade model, employed by the DNDC model to simulate soil water dynamics, suggest that conceptual errors exist in the model calculation. Also the results of the mineral nitrogen and N2O emissions simulations suggest shortcomings in the model calculation. The best agreement between measured and simulated total cumulative N2O fluxes was achieved using an adapted (90 cm soil depth, adjusted SOC fractioning, and added atmospheric N deposition) default model version, despite unsatisfactory simulations of soil water content, mineral nitrogen, and daily N2O fluxes. Thus, in conclusion, the investigated DNDC model version appears to be able to give an approximation of seasonal N2O fluxes, without being able to simulate the underlying processes accurately in detail. Therefore, caution is suggested when modelling sites on the process level.Die Messergebnisse (generelle Bodenparameter, Bodenwassergehalt, tägliche N2O Emissionen, sowie verschiedene Pflanzenparameter) eines multifaktoriellen Feldversuchs (Stickstoffdüngung, Bewässerung und die Entfernung von Getreidestroh nach der Ernte) in den Jahren 1999-2002, erstellt auf der Versuchsfläche Dong Bei Wang in der Nordchinesischen Tiefebene, wurden verwendet um die Genauigkeit des Prozess-orientierten Simulationsmodells DNDC (DeNitrification-DeComposition) zu untersuchen. In diesem Sinne standen die Simulation von Bodenprozessen, und insbesondere die Simulation von N2O Treibhausgas-Emissionen, im Mittelpunkt der Arbeit. Der Boden der Versuchsfläche ist klassifiziert als ?kalkiger Cambisol? (16% Tongehalt), eine weitere charakteristische Eigenschaft des untersuchten Bodens ist der Einfluss des kontinentalen Monsun-Klimas. Zentrale Hypothese der Arbeit war, dass die schrittweise Verbesserung einzelner (möglicherweise) fehlerhafter Kalkulationsschritte es erlauben würde, am Ende eine Übereinstimmung zwischen simulierten und gemessenen Bodenprozess-Datensätzen zu erzielen. Der Anstieg der atmosphärischen N2O Konzentration, die geschätzte Lebensdauer von 100 bis 150 Jahren eines N2O Moleküls (und einem Treibhauspotential, welches das 32-fache des Treibhauspotentials eines CO2 Moleküls beträgt), führen zu der Schätzung dass N2O Emissionen für ca. 7.9 % des gesamten Treibhauspotentials verantwortlich sind. Es wird erwartet das 70 % ? 90 % dieser N2O Emissionen aus der Landwirtschaft stammen. Die Menge des emittierten N2Os wird bestimmt durch die Verfügbarkeit von reaktiven Stickstoffverbindungen, und ist damit abhängig von Stickstoff-Düngemengen, Düngertyp, Ausbringungstermin und ?methode. China gilt, und hier insbesondere das Hauptanbaugebiet Nordchinesische Tiefebene, als eine der Hauptquellen menschlich verursachter N2O Emissionen. Das DNDC model besteht aus zwei Teilen, in denen zuerst (aus Eingabewerten von Wetter, Boden, Vegetation und menschlichen Aktivitäten) Bodentemperatur, Bodenfeuchtigkeit, den pH Wert, das Boden Redox Potential, sowie Substratkonzentrationen im Bodenprofil, und in einem zweiten Schritt NO, N2O, CH4 und NH3 Flüsse berechnet werden. In Übereinstimmung mit der Datenverfügbarkeit wurden die Simulation des Bodenwassergehalts, des Stickstoffhaushalts und der N2O Flüsse überprüft. Eine automatisierte Parameter Optimierung (mit Hilfe der Software UCODE_2005) und programmierte Änderungen im DNDC Quellcode wurden genutzt um die Modellsimulationen zu verbessern. Im Ergebnis führten aber weder die automatisierte Parameter Optimierung, noch die programmierten Änderung zu einer Verbesserung der unzulänglichen Simulationsergebnisse des DNDC Modells. Die Resultate des Kaskaden-Modell, welches im DNDC Modell für die Simulation des Bodenwasserhaushalts zuständig ist, legen die Existenz grundlegender Fehler in der Berechnung nahe. Die Resultate der Simulation des Stickstoffhaushalts und der N2O Emissionen deuten ebenfalls auf Unzulänglichkeiten in der Modellberechnung. Die beste Übereinstimmung zwischen gemessenen und simulierten saisonalen N2O Emissionsraten wurde mit einer adaptierten DNDC Version erreicht (90 cm Bodentiefe, angepasste Fraktionierung des organischen Kohlenstoffgehalts und hinzugefügter atmosphärischer Stickstoffablagerung), allerdings basierend auf einer äußerst ungenauen Simulation des Bodenwassergehalts, des Stickstoffhaushalts und der täglichen N2O Emissionen. Deswegen muss geschlussfolgert werden, dass das Modell nicht in der Lage ist die Bodenprozesse auf dem Untersuchungsstandort detailgetreu nachzustellen, und dass Vorsicht geboten ist wenn das Modell zur Simulation der Bodenprozesse anderer Standorte eingesetzt wird. Es bleibt allerdings die Möglichkeit, das DNDC Modell zur Simulation von saisonalen N2O Emissionsraten in hypothetischen Situationen und zur Berechnung von regionalen N2O Emissionsraten zu verwenden

Integrated Water Resources Research

Anthropogenic and natural disturbances to freshwater quantity and quality are a greater issue for society than ever before. To successfully restore water resources requires understanding the interactions between hydrology, climate, land use, water quality, ecology, and social and economic pressures. This Special Issue of Water includes cutting edge research broadly addressing investigative areas related to experimental study designs and modeling, freshwater pollutants of concern, and human dimensions of water use and management. Results demonstrate the immense, globally transferable value of the experimental watershed approach, the relevance and critical importance of current integrated studies of pollutants of concern, and the imperative to include human sociological and economic processes in water resources investigations. In spite of the latest progress, as demonstrated in this Special Issue, managers remain insufficiently informed to make the best water resource decisions amidst combined influences of land use change, rapid ongoing human population growth, and changing environmental conditions. There is, thus, a persistent need for further advancements in integrated and interdisciplinary research to improve the scientific understanding, management, and future sustainability of water resources