06 Fakultät Luft- und Raumfahrttechnik und Geodäsie
Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/7
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Item Open Access Crop water productivity mapping and benchmarking using remote sensing and Google Earth Engine cloud computing(2022) Ghorbanpour, Ali Karbalaye; Kisekka, Isaya; Afshar, Abbas; Hessels, Tim; Taraghi, Mahdi; Hessari, Behzad; Tourian, Mohammad J.; Duan, ZhengScarce water resources present a major hindrance to ensuring food security. Crop water productivity (WP), embraced as one of the Sustainable Development Goals (SDGs), is playing an integral role in the performance-based evaluation of agricultural systems and securing sustainable food production. This study aims at developing a cloud-based model within the Google Earth Engine (GEE) based on Landsat -7 and -8 satellite imagery to facilitate WP mapping at regional scales (30-m resolution) and analyzing the state of the water use efficiency and productivity of the agricultural sector as a means of benchmarking its WP and defining local gaps and targets at spatiotemporal scales. The model was tested in three major agricultural districts in the Lake Urmia Basin (LUB) with respect to five crop types, including irrigated wheat, rainfed wheat, apples, grapes, alfalfa, and sugar beets as the major grown crops. The actual evapotranspiration (ET) was estimated using geeSEBAL based on the Surface Energy Balance Algorithm for Land (SEBAL) methodology, while for crop yield estimations Monteith’s Light Use Efficiency model (LUE) was employed. The results indicate that the WP in the LUB is below its optimum targets, revealing that there is a significant degree of work necessary to ameliorate the WP in the LUB. The WP varies between 0.49-0.55 (kg/m3) for irrigated wheat, 0.27-0.34 for rainfed wheat, 1.7-2.2 for apples, 1.2-1.7 for grapes, 5.5-6.2 for sugar beets, and 0.67-1.08 for alfalfa, which could be potentially increased up to 80%, 150%, 76%, 83%, 55%, and 48%, respectively. The spatial variation of the WP and crop yield makes it feasible to detect the areas with the best and poorest on-farm practices, thereby facilitating the better targeting of resources to bridge the WP gap through water management practices. This study provides important insights into the status and potential of WP with possible worldwide applications at both farm and government levels for policymakers, practitioners, and growers to adopt effective policy guidelines and improve on-farm practices.Item Open Access Evaluating impacts of irrigation and drought on river, groundwater and a terminal wetland in the Zayanderud Basin, Iran(2020) Abou Zaki, Nizar; Torabi Haghighi, Ali; Rossi, Pekka M.; Tourian, Mohammad J.; Bakhshaee, Alireza; Kløve, BjørnThe Zayanderud Basin is an important agricultural area in central Iran. In the Basin, irrigation consumes more than 90 percent of the water used, which threatens both the downstream historical city of Isfahan and the Gavkhuni Wetland reserve-the final recipient of the river water. To analyze impacts of land use changes and the occurrence of metrological and hydrological drought, we used groundwater data from 30 wells, the standardized precipitation index (SPI) and the streamflow drought index (SDI). Changes in the wetland were analyzed using normalized difference water index (NDWI) values and water mass depletion in the Basin was also assessed with gravity recovery and climate experiment (GRACE)-derived data. The results show that in 45 out of studied 50 years, the climate can be considered as normal in respect to mean precipitation amount, but hydrological droughts exist in more than half of the recorded years. The hydrological drought occurrence increased after the 1970s when large irrigation schemes were introduced. In recent decades, the flow rate reached zero in the downstream part of the Zayanderud River. NDWI values confirmed the severe drying of the Gavkhuni Wetland on several occasions, when compared to in situ data. The water mass depletion rate in the Basin is estimated to be 30 (±5) mm annually; groundwater exploitation has reached an average of 365 Mm3 annually, with a constant annual drop of 1 to 2.5 meters in the groundwater level annually. The results demonstrate the connection between groundwater and surface water resources management and highlight that groundwater depletion and the repeated occurrence of the Zayanderud River hydrological drought are directly related to human activities. The results can be used to assess sustainability of water management in the Basin.Item Open Access A probabilistic approach to characterizing drought using satellite gravimetry(2024) Saemian, Peyman; Tourian, Mohammad J.; Elmi, Omid; Sneeuw, Nico; AghaKouchak, AmirIn the recent past, the Gravity Recovery and Climate Experiment (GRACE) satellite mission and its successor GRACE Follow‐On (GRACE‐FO), have become invaluable tools for characterizing drought through measurements of Total Water Storage Anomaly (TWSA). However, the existing approaches have often overlooked the uncertainties in TWSA that stem from GRACE orbit configuration, background models, and intrinsic data errors. Here we introduce a fresh view on this problem which incorporates the uncertainties in the data: the Probabilistic Storage‐based Drought Index (PSDI). Our method leverages Monte Carlo simulations to yield realistic realizations for the stochastic process of the TWSA time series. These realizations depict a range of plausible drought scenarios that later on are used to characterize drought. This approach provides probability for each drought category instead of selecting a single final category at each epoch. We have compared PSDI with the deterministic approach (Storage‐based Drought Index, SDI) over major global basins. Our results show that the deterministic approach often leans toward an overestimation of storage‐based drought severity. Furthermore, we scrutinize the performance of PSDI across diverse hydrologic events, spanning continents from the United States to Europe, the Middle East, Southern Africa, South America, and Australia. In each case, PSDI emerges as a reliable indicator for characterizing drought conditions, providing a more comprehensive perspective than conventional deterministic indices. In contrast to the common deterministic view, our probabilistic approach provides a more realistic characterization of the TWS drought, making it more suited for adaptive strategies and realistic risk management.Item Open Access Improving the modeling of sea surface currents in the Persian Gulf and the Oman Sea using data assimilation of satellite altimetry and hydrographic observations(2022) Pirooznia, Mahmoud; Raoofian Naeeni, Mehdi; Atabati, Alireza; Tourian, Mohammad J.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.Item Open Access Current availability and distribution of Congo Basin’s freshwater resources(2023) Tourian, Mohammad J.; Papa, Fabrice; Elmi, Omid; Sneeuw, Nico; Kitambo, Benjamin; Tshimanga, Raphael M.; Paris, Adrien; Calmant, StéphaneThe Congo Basin is of global significance for biodiversity and the water and carbon cycles. However, its freshwater availability and distribution remain relatively unknown. Using satellite data, here we show that currently the Congo Basin’s Total Drainable Water Storage lies within a range of 476 km 3 to 502 km 3 , unevenly distributed throughout the region, with 63% being stored in the southernmost sub-basins, Kasaï (220-228 km 3 ) and Lualaba (109-169 km 3 ), while the northern sub-basins contribute only 173 ± 8 km 3 . We further estimate the hydraulic time constant for draining its entire water storage to be 4.3 ± 0.1 months, but, regionally, permanent wetlands and large lakes act as resistors resulting in greater time constants of up to 105 ± 3 months. Our estimate provides a robust basis to address the challenges of water demand for 120 million inhabitants, a population expected to double in a few decades.Item Open Access Interrelations of vegetation growth and water scarcity in Iran revealed by satellite time series(2022) Behling, Robert; Roessner, Sigrid; Foerster, Saskia; Saemian, Peyman; Tourian, Mohammad J.; Portele, Tanja C.; Lorenz, ChristofIran 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.Item Open Access Remote sensing-based extension of GRDC discharge time series : a monthly product with uncertainty estimates(2024) Elmi, Omid; Tourian, Mohammad J.; Saemian, Peyman; Sneeuw, NicoThe Global Runoff Data Center (GRDC) data set has faced a decline in the number of active gauges since the 1980s, leaving only 14% of gauges active as of 2020. We develop the Remote Sensing-based Extension for the GRDC (RSEG) data set that can ingest legacy gauge discharge and remote sensing observations. We employ a stochastic nonparametric mapping algorithm to extend the monthly discharge time series for inactive GRDC stations, benefiting from satellite imagery- and altimetry-derived river width and water height observations. After a rigorous quality assessment of our estimated discharge, involving statistical validation, tests and visual inspection, results in the extension of discharge records for 3377 out of 6015 GRDC stations. The quality of discharge estimates for the rivers with a large or medium mean discharge is quite satisfactory (average KGE value > 0.5) however for river reaches with a low mean discharge the average KGE value drops to 0.33.The RSEG data set regains monitoring capability for 83% of total river discharge measured by GRDC stations, equivalent to 7895 km 3 /month.Item Open Access Application of spaceborne geodetic sensors for hydrology(2013) Tourian, Mohammad J.; Sneeuw, Nico (Prof. Dr.-Ing.)How much freshwater do we have on land? How is the freshwater cycle changing with time? Actually, we can not properly answer these questions as our knowledge of the spatial and temporal dynamics of the hydrological cycle is limited. The lack of knowledge is mainly induced by shortage of observational evidence, which motivates the objective of this study: the monitoring of the hydrological cycle using spaceborne geodetic sensors. Among the current space geodetic sensors, GRACE and satellite altimetry are the two active mission concepts, that can capture part of the hydrological cycle. However, monitoring the hydrological cycle using these two sensors is challenging. Satellite altimetry is investigated as an independent spaceborne sensor that provides the water level and discharge time series. An algorithm is developed to improve the quality of water level time series over inland water surfaces. This algorithm particularly deals with the challenges of resolution and uncertainty of altimetry. The obtained altimetric water level time series is validated against in situ measurements showing about 10% improvement in accuracy of the time series. Moreover, this study proposes an algorithm to reduce the random noise from pre-retracked data. The algorithm combines the results of different retrackers and provides water level time series with reduced noise level. The validation shows a significant reduction of noise level and a clear improvement in correlation with in situ measurements. Moreover, this study proposes a statistical approach based on quantile functions to infer a functional relationship between altimetric water level and in situ river discharge without the need for synchronous data sets. This method is based on a scatter diagram of quantile functions, in which the probability-coordinate is eliminated. In contrast, the conventional methods for simultaneous measurements operate directly on time series and eliminate the time-coordinate. The results show that the proposed methodology provides the same range of error as the common conventional empirical method. The good performance of the statistical approach supports the usage of altimetry to salvage pre-satellite altimetry discharge data and turn them into active use for the satellite altimetry time frame. In addition, a stochastic process model is implemented to (i) deal with the data outages in altimetric discharge, (ii) provide a scheme for data assimilation and (iii) smooth the discharge estimation. The model benefits from the cyclostationary behaviour of the discharge and is combined with the estimated discharge from altimetry and available in situ measurements to form a linear dynamic system. The dynamic system is solved using the Kalman filter, that provides an unbiased discharge with minimum variance. The error level of the results is comparable to the empirical approach. In this study, the utility of GRACE data as sensor of hydrological water storage changes is shown to be limited by the following challenges: consistency, resolution, separability and uncertainty. The challenge of inconsistency is addressed by developing two filters for hydrological and hydro-meteorological water storage changes, which lead to a better correlation with GRACE mass storage changes. The challenges of separability and resolution are not specifically investigated in this study, yet their consequences, which appear in different forms of uncertainties is investigated. To deal with the GRACE uncertainties, an algorithm is developed to detect outliers in monthly solutions. The outliers have been corrected by replacing them by an inter-annual monthly mean of the respective month. The results conclude that outlier identification and correction must be performed before further assimilation of GRACE products into hydrological or hydro-meteorological analysis. Further, a longrange correlation has been identified as another source of uncertainty in GRACE monthly solutions. EOF analysis is employed to identify the zonal behaviour of the GRACE C20 errors as the responsible source for the long-range correlation. It is considered as an error source because its residual contains tidal aliasing errors instead of white noise. Therefore, to reduce the uncertainties in GRACE monthly solutions, tidal aliasing errors are also investigated. Primary and secondary tidal aliasing errors of main tidal constituents, S1, S2, P1, K1, K2, M2, O2, O1 and Q1 are identified in GRACE monthly solutions. The effect of tidal aliasing error is estimated using a least squares Fourier analysis indicating errors up to 22mm over the globe. In general, after dealing with GRACE’s challenges and achieving a data set without outliers, long-range correlation and tidal aliasing errors, the noise level of GRACE is quantified. The quantification shows a variation between 2–20mm/month over different parts of the globe, with higher values over tropical and boreal regions. The results specifically confirm that small catchments in the tropics contain more noise contamination. It is also shown that a lower noise level of a catchment does not necessarily lead to a better correlation of GRACE with hydro-meteorological signal. Finally, the joint performance of spaceborne geodetic sensors for estimating the actual evapotranspiration ETa is assessed. There, two approaches are introduced to estimate ETa using the results of GRACE and satellite altimetry. The results of both approaches are compared with different models and their ensemble mean. All in all, given the obtained relative discrepancy, the methods seem to be a viable way for determining ETa for most non-desert catchments containing hot and warm summers.Item Open Access Controls on satellite altimetry over inland water surfaces for hydrological purposes(2012) Tourian, Mohammad J.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.Item Open Access Satellite Altimetry-based Extension of global-scale in situ river discharge Measurements (SAEM)(2025) Saemian, Peyman; Elmi, Omid; Stroud, Molly; Riggs, Ryan; Kitambo, Benjamin M.; Papa, Fabrice; Allen, George H.; Tourian, Mohammad J.River discharge is a crucial measurement, indicating the volume of water flowing through a river cross-section at any given time. However, the existing network of river discharge gauges faces significant issues, largely due to the declining number of active gauges and temporal gaps. Remote sensing, especially radar-based techniques, offers an effective means to this issue. This study introduces the Satellite Altimetry-based Extension of the global-scale in situ river discharge Measurements (SAEM) data set, which utilizes multiple satellite altimetry missions and estimates discharge using the existing worldwide networks of national and international gauges. In SAEM, we have explored 47 000 gauges and estimated height-based discharge for 8730 of them, which is approximately 3 times the number of gauges of the largest existing remote-sensing-based data set. These gauges cover approximately 88 % of the total gauged discharge volume. The height-based discharge estimates in SAEM demonstrate a median Kling–Gupta efficiency (KGE) of 0.48, outperforming current global data sets. In addition to the river discharge time series, the SAEM data set comprises three more products, each contributing a unique facet to better usage of our data. (1) A catalog of virtual stations (VSs) is defined by certain predefined criteria. In addition to each station's coordinates, this catalog provides information on satellite altimetry missions, distance to the discharge gauge, and relevant quality flags. (2) The altimetric water level time series of those VSs are included, for which we ultimately obtained good-quality discharge data. These water level time series are sourced from both existing Level-3 water level time series and newly generated ones within this study. The Level-3 data are gathered from pre-existing data sets, including Hydroweb.Next (formerly Hydroweb), the Database of Hydrological Time Series of Inland Waters (DAHITI), the Global River Radar Altimetry Time Series (GRRATS), and HydroSat. (3) SAEM's third product is rating curves for the defined VSs, which map water level values into discharge values, derived using a nonparametric stochastic quantile mapping function approach. The SAEM data set can be used to improve hydrological models, inform water resource management, and address nonlinear water-related challenges under climate change. The SAEM data set is available from https://doi.org/10.18419/darus-4475 .