Hydrology in Water Resources Management

This book is a collection of 12 papers describing the role of hydrology in water resources management. The papers can be divided s according to their area of focus as 1) modeling of hydrological processes, 2) use of modern techniques in hydrological analysis, 3) impact of human pressure and climate change on water resources, and 4) hydrometeorological extremes. Belonging to the first area is the presentation of a new Muskingum flood routing model, a new tool to perform frequency analysis of maximum precipitation of a specified duration via the so-named PMAXΤP model (Precipitation MAXimum Time (duration) Probability), modeling of interception processes, and using a rainfall-runoff GR2M model to calculate monthly runoff. For the second area, the groundwater potential was evaluated using a model of multi-influencing factors in which the parameters were optimized by using geoprocessing tools in geographical information system (GIS) in combination with satellite altimeter data and the reanalysis of hydrological data to simulate overflow transport using the Nordic Sea as an example. Presented for the third area are a water balance model for the comparison of water resources with the needs of water users, the idea of adaptive water management, impacts of climate change, and anthropogenic activities on the runoff in catchment located in the western Himalayas of Pakistan. The last area includes spatiotemporal analysis of rainfall variability with regard to drought hazard and use of the copula function to meteorologically analyze drought

Vertical mixing in atmospheric tracer transport models: error characterization and propagation

International audienceImperfect representation of vertical mixing near the surface in atmospheric transport models leads to uncertainties in modelled tracer mixing ratios. When using the atmosphere as an integrator to derive surface-atmosphere exchange from mixing ratio observations made in the atmospheric boundary layer, this uncertainty has to be quantified and taken into account. A comparison between radiosonde-derived mixed layer heights and mixed layer heights derived from ECMWF meteorological data during May?June 2005 in Europe revealed random discrepancies of about 40% for the daytime with insignificant bias errors, and much larger values approaching 100% for nocturnal mixed layers with bias errors also exceeding 50%. The Stochastic Time Inverted Lagrangian Transport (STILT) model was used to propagate this uncertainty into CO2 mixing ratio uncertainties, accounting for spatial and temporal error covariance. Average values of 3 ppm were found for the 2 month period, indicating that this represents a large fraction of the overall uncertainty. A pseudo data experiment shows that the error propagation with STILT avoids biases in flux retrievals when applied in inversions. The results indicate that transport models driven by current generation data assimilation for meteorological fields is by far not sufficient for inversions of continental mixing ratio data. As a solution we suggest the use of better, higher resolution atmospheric models, and a modification of the overall sampling strategy

A method for evaluating bias in global measurements of CO_2 total columns from space

We describe a method of evaluating systematic errors in measurements of total column dry-air mole fractions of CO_2 (X_(CO_2)) from space, and we illustrate the method by applying it to the v2.8 Atmospheric CO_2 Observations from Space retrievals of the Greenhouse Gases Observing Satellite (ACOS-GOSAT) measurements over land. The approach exploits the lack of large gradients in X_(CO_2) south of 25° S to identify large-scale offsets and other biases in the ACOS-GOSAT data with several retrieval parameters and errors in instrument calibration. We demonstrate the effectiveness of the method by comparing the ACOS-GOSAT data in the Northern Hemisphere with ground truth provided by the Total Carbon Column Observing Network (TCCON). We use the observed correlation between free-tropospheric potential temperature and X_(CO_2) in the Northern Hemisphere to define a dynamically informed coincidence criterion between the ground-based TCCON measurements and the ACOS-GOSAT measurements. We illustrate that this approach provides larger sample sizes, hence giving a more robust comparison than one that simply uses time, latitude and longitude criteria. Our results show that the agreement with the TCCON data improves after accounting for the systematic errors, but that extrapolation to conditions found outside the region south of 25° S may be problematic (e.g., high airmasses, large surface pressure biases, M-gain, measurements made over ocean). A preliminary evaluation of the improved v2.9 ACOS-GOSAT data is also discussed

Spatiotemporal complexity patterns of resting‐state bioelectrical activity explain fluid intelligence : sex matters

Neural complexity is thought to be associated with efficient information processing but the exact nature of this relation remains unclear. Here, the relationship of fluid intelligence (gf) with the resting‐state EEG (rsEEG) complexity over different timescales and different electrodes was investigated. A 6‐min rsEEG blocks of eyes open were analyzed. The results of 119 subjects (57 men, mean age = 22.85 ± 2.84 years) were examined using multivariate multiscale sample entropy (mMSE) that quantifies changes in information richness of rsEEG in multiple data channels at fine and coarse timescales. gf factor was extracted from six intelligence tests. Partial least square regression analysis revealed that mainly predictors of the rsEEG complexity at coarse timescales in the frontoparietal network (FPN) and the temporo‐parietal complexities at fine timescales were relevant to higher gf. Sex differently affected the relationship between fluid intelligence and EEG complexity at rest. In men, gf was mainly positively related to the complexity at coarse timescales in the FPN. Furthermore, at fine and coarse timescales positive relations in the parietal region were revealed. In women, positive relations with gf were mostly observed for the overall and the coarse complexity in the FPN, whereas negative associations with gf were found for the complexity at fine timescales in the parietal and centro‐temporal region. These outcomes indicate that two separate time pathways (corresponding to fine and coarse timescales) used to characterize rsEEG complexity (expressed by mMSE features) are beneficial for effective information processing

Training the Emotional Brain: Improving Affective Control through Emotional Working Memory Training

Affective cognitive control capacity (e.g., the ability to regulate emotions or manipulate emotional material in the service of task goals) is associated with professional and interpersonal success. Impoverished affective control, by contrast, characterizes many neuropsychiatric disorders. Insights from neuroscience indicate that affective cognitive control relies on the same frontoparietal neural circuitry as working memory (WM) tasks, which suggests that systematic WM training, performed in an emotional context, has the potential to augment affective control. Here we show, using behavioral and fMRI measures, that 20 d of training on a novel emotional WM protocol successfully enhanced the efficiency of this frontoparietal demand network. Critically, compared with placebo training, emotional WM training also accrued transfer benefits to a “gold standard” measure of affective cognitive control–emotion regulation. These emotion regulation gains were associated with greater activity in the targeted frontoparietal demand network along with other brain regions implicated in affective control, notably the subgenual anterior cingulate cortex. The results have important implications for the utility of WM training in clinical, prevention, and occupational settings

EQUADIFF 15

Equadiff 15 – Conference on Differential Equations and Their Applications – is an international conference in the world famous series Equadiff running since 70 years ago. This booklet contains conference materials related with the 15th Equadiff conference in the Czech and Slovak series, which was held in Brno in July 2022. It includes also a brief history of the East and West branches of Equadiff, abstracts of the plenary and invited talks, a detailed program of the conference, the list of participants, and portraits of four Czech and Slovak outstanding mathematicians

All-Sky Search for Gravitational-Wave Bursts in the First Joint LIGO-GEO-Virgo Run

We present results from an aU-sky search for unmodeled gravitational-wave bursts in the data collected by the LIGO, GEO 600 and Virgo detectors between November 2006 and October 2007. The search is performed. by three different analysis algorithms over the frequency band 50 - 6000 Hz. Data are analyzed for times with at least two of the four LIGO-Virgo detectors in coincident operation, with a total live time of 266 days, No events produced by the search algorithms survive the selection cuts. We set a frequentist upper limit on the rate of gravitational-wave bursts impinging on our network of detectors. When combined with the previous LIGO search of the data collected between November 2005 and November 2006, the upper limit on the rate of detectable gra.vitational. wave bursts in the 64-2048 Hz band is 2,0 events per year at 90% confidence. We also present event rate versus strength exclusion plots for several types of plausible burst waveforms. The sensitivity of the combined search is expressed in terms of the root-sum-squared strain amplitude for a variety of simulated waveforms and lies in the range 6 X 10(exp -22) Hz(exp - 1/2) to 2 X 10(exp -20) Hz(exp -l/2). This is the first untriggered burst search to use data from the LIGO and Virgo detectors together, and the most sensitive untriggered burst search performed so far

Trace gas concentration retrieval from short-wave infrared nadir sounding spaceborne spectrometers

The remote sensing of short wave infrared (SWIR) radiation reflected from the Earth allows to infer atmospheric trace gas concentrations by solving the inverse problem. The retrieval algorithm BIRRA (Beer InfraRed Retrieval Algorithm) has been developed at the DLR (Deutsches Zentrum f¨ur Luft- und Raumfahrt) Remote Sensing Technology Institute (IMF) since around 2005 and is one of multiple algorithms to infer molecular concentrations from calibrated radiance spectra. BIRRA’s forward model is based on the Generic Atmospheric Radiation Line-by-line Infrared Code (GARLIC) which has also been developed at the DLR-IMF. First, the BIRRA retrieved carbon monoxide (CO) columns from SCIAMACHY (SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY) 2.3 μm observations from 2003–2011 were validated against eighteen stations from the ground-based networks TCCON (Total Carbon Column Observing Network) and NDACC (Network for the Detection of Atmospheric Composition Change). The BIRRA inferred CO concentrations were found to be ≈10% low biased which is in large agreement with other similar studies. Next, the latest updates from the radiative transfer code GARLIC were incorporated in BIRRA’s forward model and the physical results of both, the old (but validated) and the latest (updated) BIRRA algorithms were verified and found to be numerically consistent for SCIAMACHY input data. Subsequently, the forward model was extended by upgrading its capabilities with respect to spectroscopy, i.e., enhanced line models were incorporated in order to utilize latest spectroscopic information from line lists such as the SEOM–IAS (Scientific Exploitation of Operational Missions – Improved Atmospheric Spectroscopy). More specifically, ‘beyond Voigt’ line profiles were implemented and the impact of the SEOM–IAS spectroscopy was studied with respect to latest compilations of HITRAN (HIgh-resolution TRANsmission molecular absorption database) and GEISA (Gestion et Etude des Informations Spectroscopiques Atmosphériques) for a large set of SCIAMACHY measurements. It was found that the SEOM–IAS line data and corresponding line models have significant impact on the spectral fitting: the residuals become smaller and the retrieved CO concentrations are also slightly different. The same methodology was then applied to study the spectroscopic impact on CO from S5P/TROPOMI measurements. The impact of the SEOM–IAS spectroscopy revealed to be even more pronounced, in particular with respect to the fitting residuals and smaller retrieval errors (higher precision) of the CO and co-retrieved parameters. Overall, the TROPOMI results are in agreement with that found for SCIAMACHY. A subsequent part of the thesis examines instrument spectral response functions (ISRF), in particular appropriate parameterizations for the TROPOMI’s SWIR band responses. A first assessment with tabulated instrument profiles indicates that the parameterized variants can mimic the tabulated responses within ≈3–6 %, depending on the instrument model and spectral position. The positive impact of the SEOM–IAS spectroscopy on the spectral fitting residuals could also be identified with the parameterized response functions. Moreover, the presented instrument profiles are considered promising candidates for the description of responses from upcoming sensors due to their flexibility. Finally, the co-retrieval of aerosol parameters in the CO fit is presented. Based on a simple model for the aerosol optical thickness the feasibility to co-retrieve aerosol extinction was investigated. In this context two different inverse solvers, namely the ’classical’ nonlinear least squares and separable least squares, were examined with respect to convergence. First results show a stable CO retrieval for the separable least squares solver, however, the co-retrieved aerosol and reflectivity parameters indicate issues due to degeneracies. This thesis improved the retrieval of CO from SCIAMACHY observations. Moreover, the upgraded BIRRA algorithm successfully retrieved CO concentrations from cloud-free TROPOMI measurements. Many aspects investigated in this study are also relevant for the retrieval of other atmospheric constituents, such such CO2 or CH4. The study does hence provide a proven basis for further developments.Aus der Beobachtung reflektierter Sonnenstrahlung im kurzwelligen Infrarot (SWIR) können Spurengaskonzentrationen in der Erdatmosphäre abgeleitet werden, wobei die Lösung des inversen Problems eine Schätzung des wahren Atmosphärenzustands liefert. Die Inversionsmethode BIRRA (Beer InfraRed Retrieval Algorithm) ist einer von mehreren am DLR (Deutsches Zentrum für Luft- und Raumfahrt) am Institut für Methodik der Fernerkundung (IMF) entwickelten Algorithmen zur Bestimmung von Molekülkonzentrationen aus spektroskopischen Messungen. Das Vorwärtsmodell von BIRRA basiert auf dem ebenfalls am DLR-IMF entwickelten Generic Atmospheric Radiation Line-by-line Infrared Code (GARLIC). Am Anfang stand die Validierung der mit BIRRA abgeleiteten Kohlenmonoxid (CO) Gesamtsäulen aus SCIAMACHY (SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY) Messungen im 2.3 μm Bereich. Dazu wurden die BIRRA Gesamtsäulen mit jenen der bodengebundenen Beobachtungsstationen der Netzwerke TCCON (Total Carbon Column Observing Network) and NDACC (Network for the Detection of Atmospheric Composition Change) im Zeitraum von 2003–2011 verglichen. Die mit BIRRA ermittelten CO Konzentrationen zeigen eine ≈10% negative Abweichung und stimmen mit den Ergebnissen ähnlicher Studien anderer Autoren weitgehend überein. Nach erfolgter Validierung wurden Neuerungen des Strahlungstransportmodells GARLIC in das BIRRA Vorwärtsmodell eingebaut und die Ergebnisse des aktualisierten Inversionsalgorithmus mit jenen des Vorgängers verglichen. Auf Basis von SCIAMACHY Daten wurde numerische Übereinstimmung der Ergebnisse festgestellt. Anschließend wurde das Vorwärtsmodell mit Blick auf die Verwendung neuester spektroskopischer Liniendaten, wie SEOM–IAS (Scientific Exploitation of Operational Missions – Improved Atmospheric Spectroscopy), erweitert. Um genauere Molekülabsorptionsquerschnitte berechnen zu können, musste das (klassische) Voigt-Absorptionslinienprofil erweitert werden. Der Einfluss der neuen Spektroskopie wurde zuerst auf Basis von SCIAMACHY Messungen untersucht und anhand von Vergleichsrechnungen auf Basis aktueller HITRAN (HIgh-resolution TRANsmission molecular absorption database) und GEISA (Gestion et Etude des Informations Spectroscopiques Atmosphériques) Daten bewertet. Es stellte sich heraus, dass die SEOM–IAS Liniendaten einen signifikanten Einfluss auf die Inversion haben: die Residuen werden kleiner und auch die abgeleiteten CO Konzentrationen unterscheiden sich leicht. Die gleiche Methodik wurde anschließend dazu verwendet, den Einfluss der Spektroskopie für das CO Retrieval aus TROPOMI Messungen zu bestimmen. Dabei zeigten sich die Auswirkungen noch deutlicher – signifikant kleinere Residuen und eine damit einhergehend höhere Genauigkeit (kleinere Fehler) der CO Säulen sowie der (mit-)abgeleiteten Parameter. Desweiteren besteht weitgehende Übereinstimmung mit den Resultaten der SCIAMACHY Studie. Ein weiterer Teil der Arbeit beschäftigt sich mit Instrumentenfunktionen (auch bekannt als Instrumentenprofile), speziell mit der Untersuchung einer passenden Parameterisierung der TROPOMI-Funktion im SWIR Band. Die tabellierten TROPOMI Instrumentenprofile können mit geeigneten Parameterisierungen gut modelliert werden. Darüber hinaus konnte der positive Einfluss der SEOM–IAS Spektroskopie auf die spektralen Residuen auch mit einem parameterisierten Instrumentenprofil nachgewiesen werden. Aufgrund der Flexibilität der vorgestellten Parameterisierungen könnten diese auch für zukünftige Sensoren zum Einsatz kommen. Abschließend wird der Einfluss von Aerosolen im CO Retrieval analysiert. Auf Basis einer einfachen Parameterisierung wurde versucht, die Extinktion bzw. die optische Tiefe (mit) zu bestimmen. In diesem Zusammenhang wurden auch der (klassische) nichtlineare Least Squares und der separierbare Least Squares hinsichtlich des Konvergenzverhaltens beim Lösen des inversen Problems untersucht. Erste Ergebnisse zeigen ein stabiles CO Retrieval unter Verwendung der separierbaren Least Squares Methode, wobei die (mit-)abgeleiteten Aerosol- und Reflektivitätsparameter auf Probleme durch Entartung hinweisen. Die vorliegende Arbeit hat gezeigt, wie das CO Retrieval aus SCIAMACHY Messungen verbessert werden kann. Mit dem weiterentwickelten BIRRA Code wurden darüberhinaus erfolgreich CO Konzentrationen aus wolkenfreien TROPOMI Messungen bestimmt. Viele Aspekte der Arbeit sind auch für die präzise Konzentrationsbestimmung anderer Moleküle wie CO2 oder CH4 von Bedeutung. Damit bietet die vorliegende Arbeit eine valide Grundlage für die Weiterentwicklung