Controls on satellite altimetry over inland water surfaces for hydrological purposes

The global available and freely accessible in situ measurements of hydrological cycles is unsatisfactory, limited and has been on the decline, lately. This together with large modeling error for hydrological cycles, support the efforts to seek for alternative measuring techniques. In the recent past, satellite altimetry has been used to measure non-ocean water level variations for hydrological purposes. Due to the effect of topography and heterogeneity of reflecting surface and atmospheric propagation, the expected echo shape for altimeter returns over land differs from that over ocean surfaces. As a result, altimetry measurements over inland waters are erroneous and include missing data. In the present study, we have developed an algorithm to improve the quality of water level time series over non-ocean surfaces. This algorithm contains an outlier identification and elimination process, an algorithm for excluding the noisy waveforms, an unsupervised classification of the satellite waveforms and finally a retracking procedure. The two preliminary steps of outlier identification and noisy waveforms exclusion allow to achieve better results for further classification and retracking steps. We have employed data snooping algorithm to identify and eliminate outliers in the water level time series. Further, an algorithm based on comparing each waveform with fitted waveform from 5β algorithm is developed to identify the noisy waveforms. An unsupervised classification algorithm is implemented to classify the waveforms into consistent groups, for which the appropriate retracking algorithms are performed. The classification algorithm is based on computing the heterogeneity of data sets, which is computed through the difference between median and modal waveforms. We have employed the algorithm to improve the water level time series in Balaton (Hungary) and Urmia (Iran) lakes. After then, we validated the results of proposed algorithm against the available in situ measurements.In letzter Zeit ist die global verfügbare und frei zugängliche in situ-Messungen von hydrologischen Zyklen unbefriedigend, beschränkt und rückgängig geworden. Dies zusammen mit großen Modellierungsfehler der hydrologischen Zyklen unterstützen die Suche nach alternativen Messverfahren. In der jüngsten Vergangenheit hat die Satellitenaltimetrie verwendet worden, um die Variationen des kontinentalen Wasserstands für hydrologische Zwecke zu messen. Aufgrund der Wirkung der Topographie und der Heterogenität der reflektierenden Oberfläche und atmosphärische Ausbreitung unterscheidet sich die erwartete Echoform des Höhenmessers über das Land vom Echoform über die Ozeanoberflächen. In der vorliegenden Arbeit haben wir einen Algorithmus entwickelt, um die Qualität der Wasserstandszeitreihen über die kontinentale Oberflächen zu verbessern. Dieser Algorithmus enthält: • eine Ausreißer-Identifikation und einen Beseitigungsprozess • einen Algorithmus zum Ausschluss der gestörten Echoform • eine unüberwachten Klassifizierung der Echoform • ein „retracking“ Verfahren Die vorbereitende Schritte zur Ermittlung von Ausreißern und verrauschten Echoformen ermöglichen bessere Ergebnisse zur weiteren Klassifizierung und retracking Schritte. Wir haben Daten-Snooping-Algorithmus zur Identifizierung und Beseitigung von Ausreißern in der Wasserstand Zeitreihen verwendet. Um die verrauschten Echoformen zu identifizieren ist ein Algorithmus entwickelt, der sich auf den Vergleich jeder Echoform mit gepasster Echoform aus 5β-Algorithmus basiert. Ein überwachten Klassifizierungsalgorithmus ist implementiert, um die Echoformen in kohärente Gruppen zu klassifizieren. Für jede kohärente Gruppe ist ein entsprechender retracking-Algorithmus durchgeführt worden. Die Klassifizierung Algorithmus basiert sich auf der Berechnung der Heterogenität der Datensätze, die durch die Differenz zwischen Median und Modal-Echoformen berechnet wird. Wir haben diese Algorithmen verwendet, um die Wasserstandszeitreihen im Balaton (Ungarn) und Urmia (Iran) Seen zu verbessern. Danach haben wir die Ergebnisse der vorgeschlagenen Algorithmen gegen lokalen Daten geprüft

Improving the Modeling of Sea Surface Currents in the Persian Gulf and the Oman Sea Using Data Assimilation of Satellite Altimetry and Hydrographic Observations

Sea surface currents are often modeled using numerical models without adequately addressing the issue of model calibration at the regional scale. The aim of this study is to calibrate the MIKE 21 numerical ocean model for the Persian Gulf and the Oman Sea to improve the sea surface currents obtained from the model. The calibration was performed through data assimilation of the model with altimetry and hydrographic observations using variational data assimilation, where the weights of the objective functions were defined based on the type of observations and optimized using metaheuristic optimization methods. According to the results, the calibration of the model generally led the model results closer to the observations. This was reflected in an improvement of about 0.09 m/s in the obtained sea surface currents. It also allowed for more accurate evaluations of model parameters, such as Smagorinsky and Manning coefficients. Moreover, the root mean square error values between the satellite altimetry observations at control stations and the assimilated model varied between 0.058 and 0.085 m. We further showed that the kinetic energy produced by sea surface currents could be used for generating electricity in the Oman Sea and near Jask harbor

Analyzing the Lake Urmia restoration progress using ground-based and spaceborne observations

Lake Urmia, located in the North West of Iran, was once the most extensive permanent hypersaline lake in the world. Unsustainable water management in response to increasing demand together with climatic extremes have given rise to the lake's depletion during the last two decades. The Urmia Lake Restoration Program (ULRP) was established in 2013 and aims to restore the lake within a 10-year program. This study aims to monitor these restoration endeavours using spaceborne and ground-based observations. We analyzed the in-situ water level, the surface water extent, and the water volume of the lake. The water storage change of the Urmia Lake catchment is quantified using the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On satellite observations, which gives us a holistic view of hydrological components. Our analysis shows a positive trend of 14.5 cm/yr, 204 km2/yr, and 0.42 km3/yr in the time series of lake water level, lake water area, and water volume from 2015 to 2019 which indicates a short-lived stabilization of Lake Urmia. This has been achieved mainly due to an increase of 0.35 km3/yr in inflow from rivers to the lake, predominantly driven by anomalous precipitation events in 2016 and early 2019. However, the long-term trend from 2003 to 2019 still shows negative values of −22 cm/yr, −200 km2/yr, and −0.72 km3/yr for the water level, the surface area, and the water volume of the lake, respectively. The stabilization seems to be fragile however, since most of the increase in the water volume of the lake has spread over the large shallow southern region with high evaporation potential during hot seasons. Furthermore, due to the high correlation between the lake water level and precipitation, the recovery observed in 2016 and the first half of 2019 might not continue in case of a long drought period

Basin-scale runoff prediction: An Ensemble Kalman Filter framework based on global hydrometeorological data sets

In order to cope with the steady decline of the number of in situ gauges worldwide, there is a growing need for alternative methods to estimate runoff. We present an Ensemble Kalman Filter based approach that allows us to conclude on runoff for poorly or irregularly gauged basins. The approach focuses on the application of publicly available global hydrometeorological data sets for precipitation (GPCC, GPCP, CRU, UDEL), evapotranspiration (MODIS, FLUXNET, GLEAM, ERA interim, GLDAS), and water storage changes (GRACE, WGHM, GLDAS, MERRA LAND). Furthermore, runoff data from the GRDC and satellite altimetry derived estimates are used. We follow a least squares prediction that exploits the joint temporal and spatial auto- and cross-covariance structures of precipitation, evapotranspiration, water storage changes and runoff. We further consider time-dependent uncertainty estimates derived from all data sets. Our in-depth analysis comprises of 29 large river basins of different climate regions, with which runoff is predicted for a subset of 16 basins. Six configurations are analyzed: the Ensemble Kalman Filter (Smoother) and the hard (soft) Constrained Ensemble Kalman Filter (Smoother). Comparing the predictions to observed monthly runoff shows correlations larger than 0.5, percentage biases lower than ± 20%, and NSE-values larger than 0.5. A modified NSE-metric, stressing the difference to the mean annual cycle, shows an improvement of runoff predictions for 14 of the 16 basins. The proposed method is able to provide runoff estimates for nearly 100 poorly gauged basins covering an area of more than 11,500,000 km2 with a freshwater discharge, in volume, of more than 125,000 m3/s

Interrelations of vegetation growth and water scarcity in Iran revealed by satellite time series

Iran has experienced a drastic increase in water scarcity in the last decades. The main driver has been the substantial unsustainable water consumption of the agricultural sector. This study quantifies the spatiotemporal dynamics of Iran’s hydrometeorological water availability, land cover, and vegetation growth and evaluates their interrelations with a special focus on agricultural vegetation developments. It analyzes globally available reanalysis climate data and satellite time series data and products, allowing a country-wide investigation of recent 20+ years at detailed spatial and temporal scales. The results reveal a wide-spread agricultural expansion (27,000 km$^2$) and a significant cultivation intensification (48,000 km$^2$). At the same time, we observe a substantial decline in total water storage that is not represented by a decrease of meteorological water input, confirming an unsustainable use of groundwater mainly for agricultural irrigation. As consequence of water scarcity, we identify agricultural areas with a loss or reduction of vegetation growth (10,000 km$^2$), especially in irrigated agricultural areas under (hyper-)arid conditions. In Iran’s natural biomes, the results show declining trends in vegetation growth and land cover degradation from sparse vegetation to barren land in 40,000 km$^2$, mainly along the western plains and foothills of the Zagros Mountains, and at the same time wide-spread greening trends, particularly in regions of higher altitudes. Overall, the findings provide detailed insights in vegetation-related causes and consequences of Iran’s anthropogenic drought and can support sustainable management plans for Iran or other semi-arid regions worldwide, often facing similar conditions