Mohammad Tourian

Abstract

In the 30 years of its availability, satellite altimetry has established itself as an important tool for understanding the Earth system. Originally developed for oceanography and geodesy, it has also proven valuable for monitoring water level variation of lakes and rivers. However, when using altimetry for inland waters, there is always a critical issue: retracking i.e. the procedure in which the range from the satellite to the water surface is (re)estimated. The current retracking methods heavily rely on single waveforms, which results in a high sensitivity to every individual peak in the waveform and in a strong dependency on the waveform's shape. Here, we propose the Bin-Space-Time (BiST) retracking method that moves beyond finding a single point in a 1D waveform and instead seeks a retracking line within a 2D radargram, for which the temporal information over different cycles is also considered. The retracking line divides the radargram into two segments: the left (Front) and right-hand side (Back) of the retracking line. Such a segmentation approach can be interpreted as a binary image segmentation problem, for which spatiotemporal information can be incorporated. We follow a Bayesian approach, exploiting a probabilistic graphical model known as a Markov Random Field (MRF). There, the problem is arranged as a Maximum A Posteriori estimation of an MRF (MAP-MRF), which means finding a retracking line that maximizes a posterior probability density or minimizes a posterior energy function. Our posterior energy function is obtained by a prior energy function and a likelihood energy function, both of them depending on signal intensity and bin: 1) The prior: the bin-space energy function defined between first-order neighbouring pixels of a radargram modeling the spatial dependency between their labels for given intensities and bins and 2) The likelihood: the temporal energy function of a pixel for labeling Front or Back given its overall temporal evolution. The realization of the field with the minimum sum of the bin-space and the temporal energy functions is then found through the maxflow algorithm. Consequently, the retracking line, the boundary between the Back and Front region is obtained. We apply our method to both pulse-limited and SAR altimetry data over nine lakes and reservoirs in the USA with different sizes and different altimetry characteristics. The resulting water level time series are validated against in situ data. Across the selected case studies, on average, the BiST retracker improves the RMSE by approximately 0.5m compared to the best existing retracker. The main benefit of the proposed retracker, which operates in bin, space, and time domains, is its robustness against unexpected waveform variations, making it suitable for diverse inland water surfaces.

Abstract

An accurate estimate of river discharge is vital to quantifying the global hydrological cycle and managing water resources. In view of the steadily deteriorating data provision from gauge networks, hydrological monitoring through spaceborne sensors becomes a necessity. The SWOT mission is the first satellite to conduct a global survey of the Earth's surface waters, measuring water surface elevation, river width, and surface slope for estimating discharge. Since SWOT can only sample mid-latitude locations approximately twice per its 21-day cycle, we develop a linear dynamic system for daily discharge estimation over a continuous single-branch river network. The linear dynamic system includes a process model based on a physically-based spatiotemporal correlation and observation equations utilizing SWOT discharge products. We solve this dynamic system through a Kalman filter, which is simultaneously executed in the time and space domain to obtain daily discharge. Since SWOT discharge products are currently inaccessible, we use a perturbed version of synthetic SWOT datasets obtained by Monte Carlo simulation to test the feasibility of our approach. The validation of the estimated discharge against true discharge in the synthetic datasets over all rivers leads to a median correlation as high as 0.95, a median NSE for residuals as high as 0.82, and a median relative bias as high as 5.22%, respectively. Our method delivers promising results and the hope of obtaining daily discharge once the required SWOT data is available.

Abstract

The availability and variations of continental water storage are of great importance for society, as they influence agricultural, industrial, and domestic water use. Among the water storage components, terrestrial surface water specifically lakes and reservoirs are essential for wildlife and human habitats as they store freshwater in the most accessible way, control seasonal floods and generate hydropower. Despite the importance, the estimation of surface water storage variation at a global scale is usually obtained from simplified models due to the absence of necessary gauge and remote sensing measurements. In this study, we produce monthly water volume anomaly time series of 182260 global lakes and reservoirs larger than 1 km² for 1985-2018. To do so, water area time series of lakes and reservoirs are obtained from the Joint Research Center Global Surface Water data set. We gather all publicly available in situ water level time series and generate water level time series using satellite altimetry data from various missions and data sets. For the remaining lakes and reservoirs, water height information is extracted from the TerraSAR-X digital elevation model. After collecting the required data, first, the empirical water area-level model is developed for each object and then the water volume variation time series are estimated. With this data set, we can investigate the temporal and spatial variations of surface water stored in lakes and reservoirs from 1985-2018 on a global scale. This study aims to answer these fundamental questions: 1) What are the temporal behaviors of surface water volume variations in different river basins? 2) Does water volume variation trend agree with other hydrological parameters’ temporal variation, And 3) what are the major natural and anthropogenic 45 factors that explain the long-term water volume variation?

Abstract

he quantification of river discharge is essential for understanding a broad range of scientific questions focused on hydrology, hydraulics and water resource management. However, the 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. For extending the discharge estimates of inactive GRDC stations, we develop the Remote Sensing-based Extension for the GRDC (RSEG) data set that can ingest legacy gauge discharge and remote sensing observations. First, we evaluated the feasibility of extending discharge estimates of gauges in the GRDC dataset benefiting from river water height time series obtained from satellite altimetry missions (2000--2020) and river width estimates obtained from Landsat 4-8 mission images (1984–2020). Then we employ a stochastic nonparametric mapping algorithm to extend the discharge time series for inactive GRDC stations, benefiting from satellite imagery- and altimetry-derived river width and water height observations. Finally, we conduct a rigorous quality assessment on our discharge estimates, involving statistical validation, tests and visual inspection, resulting in the salvation of discharge records for 3377 out of 6015 GRDC stations with an average discharge exceeding 10 m^3/s. 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, providing valuable insight into Earth’s river systems with comprehensive and up-to-date information.

Abstract

The Copernicus Sentinel-3 Surface Topography Mission (S3 STM) provides critical surface elevation information for inland waters, sea ice, and land ice. This valuable data is acquired through its high-resolution radar altimeter and a unique orbit that reaches the polar regions. To ensure reliable measurements and maximize the mission's benefits, adequate validation is essential. This process involves comparing the mission's geophysical retrieval methods, processing algorithms, and corrections against independent observations known as Fiducial Reference Measurements (FRM). The St3TART project addresses the need for an operational framework for the provision of FRM to support validation activities and foster the exploitation of the Sentinel-3 SAR altimeter Hydro-Cryo Thematic data products. The project tackles the specific challenges associated with three types of surfaces: inland waters, sea ice and land ice. To achieve this, St3TART sets forth several objectives, including the identification and operation of super and opportunity sites, the acquisition, processing, and delivery of FRM for Calibration / Validation (Cal/Val) activities, and the characterization of uncertainties 171 associated with each FRM data product and measurand. It also involves the exploitation of FRM for Cal/Val activities, and the preparation of a roadmap for future Altimetry missions beyond S3 STM, focusing on the Cryosphere and Hydrology. These achievements pave the way for the successful implementation of operational FRM provision, ultimately contributing to the Sentinel-3 mission's goal of delivering accurate and reliable Earth observation data. We will present the project's objectives, tools, accomplishments, and future directions.

Abstract

Validation, including the determination of measurement uncertainties, is a key component of a satellite mission. Without adequate validation of the geophysical retrieval methods, of the processing algorithms and the corrections , the computed geophysical parameters derived from satellite measurements cannot be used with confidence. In this context, and in anticipation of the operational delivery of dedicated inland water products based on Copernicus Sentinel-3 measurements, the ESA St3TART project (Sentinel-3 Topography mission Assessment through Reference Techniques), prepared a roadmap and provided a preliminary proof of concept for the operational provision of Fiducial Reference Measurements (FRM) in support of the validation activities of the Copernicus Sentinel-3 (S3) radar altimeter over land surfaces of interest (inland water bodies, sea ice and land ice). In the framework of this project, the activities related to hydrology include a review of existing methodologies and associated ground instrumentation for validating and monitoring the performance and stability of the Sentinel-3 altimeter measurement via FRM. Methodologies and procedures have been defined considering the errors and uncertainties coming from 151 the measurements of in-situ sensors, satellite measurements and the environment of the validation site. Based on these protocols and procedures, a roadmap has been prepared for the operational provision of FRM to support the validation activities and foster exploitation of the Sentinel-3 SAR altimeter Land data products, over inland waters. Then, field campaign implementation and realization have been performed as demonstrators based on the procedures and protocols defined in the roadmap. In this project, a comprehensive review of altimeter uncertainties over inland water bodies was carried out on the basis of a literature review, leading to the identification of the different sources of error with their associated uncertainty level. A full review of all in-situ sensors that have been used for many years for Cal/Val activities for inland waters has also been performed, combined with an analysis of innovative sensors that can fulfil the needs and potentially be used in the framework of the St3TART project. Cal/Val “super-sites” have been carefully selected to implement the roadmap for operational FRM provision. Several campaigns have been performed on the different super sites. In addition of the super sites, opportunity sites from existing national hydrological networks have been studied to significantly increase the number of FRM. Finally, comparison with Sentinel-3 data and FRM have been conducted on all the super sites and the opportunity sites. We propose here to present the final results of these hydrology activities. A Follow-on of this project has been published by ESA. This new project aims to implement the RoadMap defined in the St3TART project. The consortium that worked on St3TART answered to this call. If the project is won, results from the regular production of FRM will be presented.

Abstract

Abstract The Surface Water and Ocean Topography (SWOT) mission will vastly expand measurements of global rivers, providing critical new datasets for both gaged and ungaged basins. SWOT discharge products (available approximately one year after launch) will provide discharge for all river reaches wider than 100 m. In this paper, we describe how SWOT discharge produced and archived by the US and French space agencies will be computed from measurements of river water surface elevation, width, and slope and ancillary data, along with expected discharge accuracy. We present for the first time a complete estimate of the SWOT discharge uncertainty budget, with separate terms for random (standard error) and systematic (bias) uncertainty components in river discharge timeseries. We expect that discharge uncertainty will be less than 30\% for two thirds of global reaches and will be dominated by bias. Separate river discharge estimates will combine both SWOT and in situ data; these “gage constrained” discharge estimates can be expected to have lower systematic uncertainty. Temporal variations in river discharge timeseries will be dominated by random error and are expected to be estimated to within 15\% for nearly all reaches, allowing accurate inference of event flow dynamics globally, including in ungaged basins. We believe this level of accuracy lays the groundwork for SWOT to enable breakthroughs in global hydrologic science.

Abstract

The 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 476þinspacekm3 to 502þinspacekm3, unevenly distributed throughout the region, with 63\% being stored in the southernmost sub-basins, Kasaï (220--228þinspacekm3) and Lualaba (109--169þinspacekm3), while the northern sub-basins contribute only 173þinspace±þinspace8þinspacekm3. We further estimate the hydraulic time constant for draining its entire water storage to be 4.3þinspace±þinspace0.1 months, but, regionally, permanent wetlands and large lakes act as resistors resulting in greater time constants of up to 105þinspace±þinspace3 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.

Abstract

The Arctic is undergoing dramatic climate and environmental changes. The long-term alterations in river discharges from the boreal catchments, which serve as vital links between the ocean and land, are having a profound impact on various environmental factors, particularly ocean circulation and sea-ice content. However, comprehensive and continuous monitoring of Arctic river discharge at seasonal or higher temporal resolutions remains challenging. In this study, we propose a new approach to estimate runoff by generating monthly runoff time series at the basin scale. Our method is based on changes in water storage observed by the gravimetric satellites Gravity Recovery and Climate Experiment (GRACE) and its follow-on mission since 2002. The method utilizes an empirical runoff-storage (R-S) relationship, offering simplicity and low computational burden while maintaining good accuracy in estimating runoff. To validate our method, we utilize in-situ runoff measurements from the seven largest boreal drainage basins spanning the period from 2002 to 2019, encompassing a total of 18 years. We divide these 18 years of observations into two phases: a training phase (8 years) and a testing phase (10 years). The results indicate that the R-S method established during the training phase yields monthly Nash-Sutcliffe efficiency (NSE) values ranging from 0.65 to 0.92 when compared to in-situ runoff measurements. Moreover, the method demonstrates a consistent performance in estimating runoff during the testing phase (monthly NSE: 0.67–0.84). With the exception of the Ob and Mackenzie basins, which exhibit distinct climatic conditions and hydrological networks, the R-S models are interchangeable across basins. This makes it suitable for both temporal and spatial extrapolation to fill data gaps, provided that accurate water and snow storage data are available. All in all, our method enables the reconstruction of monthly surface runoff across the entire boreal basins between 2002 and 2019. The results indicate an average annual runoff of 3200 ± 160 Gt over the study area of 1.58 × 107 km2. To evaluate the accuracy of our estimates, we compare the total runoff estimates obtained using the R-S method with those from 12 model estimates. Our estimates exhibit the highest correlation with available in-situ runoff measurements and yield monthly NSE values >0.57 for five out of the twelve model estimates. This study presents a convenient method to address the urgent need for comprehensive, continuous, and monthly temporal resolution of runoff estimates throughout the entire boreal region.

Abstract

Surface currents in oceanic environment are of vital importance from economical, biological and environmental aspects. Modelling ocean currents has generally been performed using numerical ocean circulation models as a solution to initial-boundary value problems in oceanic domain. Due to lack of knowledge about model parameters as well as initial and boundary values, they need to be externally calibrated for accurate local and regional applications. In this study, an alternative approach is proposed to incorporate spaceborne geodetic observations as well as hydrographic data to estimate the total surface current in the Persian Gulf and the Oman Sea. Being the data-driven approach, the method is comparable to numerical ocean models and regionally it is more accurate and simpler in application. The proposed method focuses on the computation of dynamic topography (DT) by least squares variance component estimation combining two different schemes. They are (1) DT estimation via direct observations of sea surface height from satellite altimetry and (2) steric and non-steric modeling of sea level anomaly using temperature and salinity data for the steric component; and Gravity Recovery and Climate Experiment observations for the non-steric component. Ultimately, the total surface current is obtained by computing the horizontal gradient of DT using geostrophic equation and adding the components of the Ekman current. Moreover, the estimated total surface current is further improved by assimilating with in situ current meter data using 3D-Variational data assimilation method and it is validated against two control stations. This assimilation leads to improvement of about 3 to 15 cm/s in total surface current computed using geostrophic equation and Ekman current. Besides, to illustrate the significance of the proposed approach, the estimated total surface current is externally validated and compared with the output of Copernicus Marine Environment Monitoring Service (CMEMS), as a numerical ocean model developed for oceanographic applications. Our comparison reveals that the proposed method is more accurate and reliable than CMEMS products. As for the circulation and current pattern, the estimated surface velocities reveal the existence of eddies in the region of the Persian Gulf and the Oman Sea, indicating the occurrence of cyclonic and anti-cyclonic circulations. Moreover, they elucidate that the velocities are lower in spring and summer and higher in autumn and winter.

Abstract

The accurate monitoring of the surface water storage as an essential component of the global water cycle requires a realistic representation of river networks and channel characteristics. Since such a representation has not been available for many rivers and is becoming less available even for many gauged rivers, crucial questions about the spatio-temporal dynamics of freshwater in river networks cannot be answered properly. The global coverage and fine temporal resolution of satellite imagery provide the opportunity to obtain time series of surface water extent at the global scale for almost all rivers. However, despite recent advances in satellite imaging sensors, water extraction algorithms, and big data processing capabilities, none of the available global water extent data sets can meet the necessary requirements in terms of accuracy and spatio-temporal resolutions. Due to the inherent complexity of monitoring the river surface extent, efforts have been limited to the development of global river extent data sets with a limited number of temporal layers usually obtained from long-term averaged satellite imagery. In this study, we propose a region-based image restoration algorithm to obtain the river surface extent from a pre-existing global inland water data set by incorporating temporal and spatial constraints between pixel labels. We employ our algorithm on the Monthly Water History maps of the Global Surface Water data set (Pekel et al., 2016). We validate the proposed method on 98 river reaches that their average width ranges from approximately 36m to 3400m with in situ discharge measurements in the Mississippi, Amazon, Niger and Po river basins. The obtained river width time series exhibit a strong monotonic relationship with discharge measurements as the Spearman correlation coefficients are predominantly larger than 0.70 (on average 0.74 for Mississippi, 0.84 for Amazon, 0.86 for Niger and 0.77 for Po rivers). Such a performance confirms that the proposed method can facilitate the acquisition of a global dynamic river extent data set, which plays a key role in better understanding the distribution and availability of freshwater across continents.

Abstract

Like many other Middle East countries, Iran has been suffering from severe water shortages over the last two decades, as evidenced by significant decline in surface water and groundwater levels. The observed changes in water storage can be attributed to the mutually reinforcing effects of human activities, climatic variability, and of course the climate change. The objective of this study is to analyze the dependency of atmospheric CO2 increase on the water shortage of Iran, for which we investigate the spatial relationship between water storage change and CO2 concentration using large scale satellite data. We conduct our analysis using water storage change data from GRACE satellite and atmospheric CO2 concentration from GOSAT and SCIAMACHY satellites during 2002–2015. To analyze the long-term behavior of time series we benefit from Mann-Kendal test and for the investigation of the relationship between atmospheric CO2 concentration and total water storage we use Canonical Correlation Analysis (CCA) and Regression model. Our Results show that the water storage change anomaly and CO2 concentration are negatively correlated especially in northern, western, southwest (Khuzestan province), and also southeast (Kerman, Hormozgan, Sistan, and Baluchestan provinces) of Iran. CCA results reveal that in the most of northern regions, the decrease in water storage is significantly influenced by the increase of CO2 concentration. The results further show that precipitation in the highland and peaks does not seem to be influenced by the long and short-term variation in CO2 concentration. Besides, our results show that the CO2 concentration is slightly correlated with a weak positive trend in evapotranspiration over agricultural areas. Thus, the indirect effect of CO2 on increasing evapotranspiration is observed spatially in the whole of Iran. The results of the regression model between total water storage change and carbon dioxide (R2 = 0.91)/water discharge/water consumption show that carbon dioxide has the highest effect on total water storage change at large scale. The results of this study will contribute to both water resource management and mitigation plans to achieve the goal of CO2 emission reduction.

Abstract

The Copernicus Sentinel-3 Surface Topography Mission (STM) Land Altimetry provides valuable surface elevation information over inland waters, sea ice, and land ice, thanks to its synthetic aperture radar (SAR) altimeter and its orbit that covers high-latitude polar regions. To ensure that these measurements are reliable and to maximise the return on investment, adequate validation of the geophysical retrieval methods, processing algorithms, and corrections must be performed using independent observations. The EU-ESA project St3TART (started July 2021) aims to generalise the concept of Fiducial Reference Measurements (FRMs) for the Copernicus Sentinel-3 STM. This work has gathered existing data, made new observations during field campaigns, and ensured that these observations meet the criteria of FRM standards so that they can be used to validate Sentinel-3 STM Land Altimetry products operationally. A roadmap for the operational provision of the FRM, including the definition, consolidation, and identification of the most relevant and cost-effective methods and protocols to be maintained, supported, or implemented, has been developed. The roadmap includes guidelines for SI traceability, definitions of FRM measurement procedures, processing methods, and uncertainty budget estimations.

Abstract

Abstract European Center for Medium-Range Weather Forecasts Reanalysis (ERA), one of the most widely used precipitation products, has evolved from ERA-40 to ERA-20CM, ERA-20C, ERA-Interim, and ERA5. Studies evaluating the performance of individual ERA products cannot adequately assess the evolution of the products. We compared the performance of all ERA precipitation products at daily, monthly, and annual data (1980–2018) using more than 2100 Iran precipitation gauges. Results indicated that ERA-40 performed worst, followed by ERA-20CM, which showed only minor improvements over ERA-40. ERA-20C considerably outperformed its predecessors, benefiting from the assimilation of observational data. Although several previous studies have reported full superiority of ERA5 over ERA-Interim, our results revealed several shortcomings in ERA5 compared with the ERA-Interim estimates. Both ERA-Interim and ERA5 performed best overall, with ERA-Interim showing better statistical and categorical skill scores, and ERA5 performing better in estimating extreme precipitations. These results suggest that the accuracy of ERA precipitation products has improved from ERA-40 to ERA-Interim, but not consistently from ERA-Interim to ERA5. This study employed a grid-grid comparison approach by first creating a gridded reference data set through the spatial aggregation of point source observations, however, the results from a point-grid approach showed no change in the overall ranking of products (despite the slight changes in the error index values). These findings are useful for model development at a global scale and for hydrological applications in Iran.

Abstract

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.

Abstract

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.

Abstract

Abstract The number of active gauges with open-data policy for discharge monitoring along rivers has decreased over the last decades. Therefore, spaceborne measurements are investigated as alternatives. Among different techniques for estimating river discharge from space, developing a rating curve between the ground-based discharge and spaceborne river water level or width is the most straightforward one. However, this does not always lead to successful results, since the river section morphology often cannot simply be modeled by a limited number of parameters. Moreover, such methods do not deliver a proper estimation of the discharge’s uncertainty as a result of the mismodeling and also the coarse assumptions made for the uncertainty of inputs. Here we propose a nonparametric model for estimating river discharge and its uncertainty from spaceborne river width measurements. The model employs a stochastic quantile mapping scheme by, iteratively: 1) generating realizations of river discharge and width time series using Monte Carlo simulation, 2) obtaining a collection of quantile mapping functions by matching all possible permutations of simulated river discharge and width quantile functions, 3) adjusting the measurement uncertainties according to the point cloud scatter. We validate our method over 14 different river reaches along the Niger, Congo, Po rivers and several river reaches in the Mississippi river basin. Our results show that the proposed algorithm can mitigate the effect of measurement noise and also possible mismodelling. Moreover, the proposed algorithm delivers a meaningful uncertainty for the estimated discharge and allows us to calibrate the error bars of in situ discharge measurements.

Abstract

By combining long-term ground-based data on water withdrawal with climate model projections, this study quantifies the compounding effects of human activities and climate change on surface water availability in Iran over the twenty-first century. Our findings show that increasing water withdrawal in Iran, due to population growth and increased agricultural activities, has been the main source of historical water stress. Increased levels of water stress across Iran are expected to continue or even worsen over the next decades due to projected variability and change in precipitation combined with heightened water withdrawals due to increasing population and socio-economic activities. The greatest rate of decreased water storage is expected in the Urmia Basin, northwest of Iran, (varying from \$\backslash$textasciitilde\ −\$\backslash$thinspace\8.3 mm/year in 2010--2039 to \$\backslash$textasciitilde\ −\$\backslash$thinspace\61.6 mm/year in 2070--2099 compared with an observed rate of 4 mm/year in 1976--2005). Human activities, however, strongly dominate the effects of precipitation variability and change. Major shifts toward sustainable land and water management are needed to reduce the impacts of water scarcity in the future, particularly in Iran's heavily stressed basins like Urmia Basin, which feeds the shrinking Lake Urmia.

Abstract

Although total storage deficit index (TSDI) is a well-known GRACE-based drought index, it is not efficient for the basins with high consumption because water harvesting in these areas affect GRACE signal. To overcome this limitation, the modified total storage deficit index (MTSDI) is introduced in this paper. To develop MTSDI, residuals of the signal were used instead of total signal. The proposed approach was applied to monitor drought in the Markazi Basin, Iran, during 2002–2016. Based on the obtained results, TSDI detected a moderate drought event in 2012 and a long-term severe drought from 2013 to 2016, which is not compatible with the recorded results of standardized precipitation index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI). Meanwhile MTSDI identified two drought events in 2008–09 and 2010–11 that coincided with droughts detected using SPI and SPEI. Results revealed that MTSDI is highly correlated with SPI and SPEI at a 12 month-scale, showing a correlation coefficient of 0.75 and 0.62, respectively. On the other hand, the coefficient of correlation between TSDI and SPI12 was 0.42 and between TSDI and SPEI12 was 0.26. A pixel-by-pixel analysis showed MTSDI and SPI/SPEI had a significant correlation in most areas of the basin. Further, the investigation of droughts in Markazi Basin revealed that dry years coincide with the occurrence of strong La Niña events. Overall, the results indicated a good potential of GRACE observations for developing a drought monitoring system, which its performance can be enhanced with respect to large-scale climate signals.

Abstract

The Bakhtegan catchment, an important agricultural region in south-western Iran, has suffered groundwater depletion in recent years. As groundwater is considered the main source of fresh water in the catchment, especially for agriculture, monitoring groundwater responses to irrigation is important. Gravity Recovery and Climate Experiment (GRACE) satellite data can help determine water mass changes in catchments and assess water volume changes. In this study, we compared GRACE-derived water mass data against groundwater volume variations measured in situ. We also assessed the efficiency of GRACE-derived data in catchments smaller than the 200,000 km2 recommended area when using GRACE. For the study period (January 2002 through December 2011), the GRACE data showed a 7.6 mm annual decline in groundwater level, with a total volume loss of 2.6 km3 during the period. The in situ monthly measurements of groundwater level showed an average depletion of 10 m in catchment aquifers during the study period. This depletion rate was supported by the recorded decrease in precipitation volume, especially in the post-drought period after 2007. These results demonstrate that GRACE can be useful tool for monitoring groundwater depletion in arid catchments.

Abstract

© Springer International Publishing Switzerland 2016. Ocean tides cause notable aliasing errors in the gravity field from single pair space-borne gravimetry missions like GRACE. Several studies into future gravity missions have shown that constellations with two or more GRACE-like tandems lead to a significant reduction of aliasing error from all kinds of high-frequency signal sources. Despite such reduction, tidal aliasing will remain an error source. We here investigate the efficiency of tidal error de-aliasing in the post-processing mode for such future double-pair missions. To that purpose, we analyze how a certain satellite mission samples each tidal constituent. Given the repeat orbit patterns and the observation time span, we examine and model the alias periods and amplitudes constituent by constituent based on data-driven analysis. Results show that a double-pair formation has indeed better de-aliasing properties than a single-pair formation in terms of distribution and amplitude of ocean tide aliasing error. After least-squares (LS) spectral estimation of the tidal aliases at the derived alias periods, the aliasing error is reduced significantly.

Abstract

© 2018 COSPAR. Tropospheric correction is one of the most important corrections in satellite altimetry measurements. Tropospheric wet and dry path delays have strong dependence on temperature, pressure and humidity. Tropospheric layer has particularly high variability over coastal regions due to humidity, wind and temperature gradients. Depending on the extent of water body and wind conditions over an inland water, Wet Tropospheric Correction (WTC) is within the ranges from a few centimeters to tens of centimeters. Therefore, an extra care is needed to estimate tropospheric corrections on the altimetric measurements over inland waters. This study assesses the role of tropospheric correction on the altimetric measurements over the Urmia Lake in Iran. For this purpose, four types of tropospheric corrections have been used: (i) microwave radiometer (MWR) observations, (ii) tropospheric corrections computed from meteorological models, (iii) GPS observations and (iv) synoptic station data. They have been applied to Jason-2 track no. 133 and SARAL/AltiKa track no. 741 and 356 corresponding to 117-153 and the 23-34 cycles, respectively. In addition, the corresponding measurements of PISTACH and PEACHI, include new retracking method and an innovative wet tropospheric correction, have also been used. Our results show that GPS observation leads to the most accurate tropospheric correction. The results obtained from the PISTACH and PEACHI projects confirm those obtained with the standard SGDR, i.e., the role of GPS in improving the tropospheric corrections. It is inferred that the MWR data from Jason-2 mission is appropriate for the tropospheric corrections, however the SARAL/AltiKa one is not proper because Jason-2 possesses an enhanced WTC near the coast. Furthermore, virtual stations are defined for assessment of the results in terms of time series of Water Level Height (WLH). The results show that GPS tropospheric corrections lead to the most accurate WLH estimation for the selected virtual stations, which improves the accuracy of the obtained WLH time series by about 5\%.

Abstract

\textlessp\textgreater\textlessstrong\textgreaterAbstract.\textless/strong\textgreater The Bakhtegan catchment, an important agricultural region in south-western Iran, has suffered groundwater depletion in recent years. As groundwater is considered the main source of fresh water in the catchment, especially for agriculture, monitoring groundwater responses to irrigation is important. Gravity Recovery and Climate Experiment (GRACE) satellite data can help determine water mass changes in catchments and assess water volume changes, but have been under-used to date in water resources management. In this study, we compared GRACE-derived water mass data against groundwater volume variations measured in situ. We also assessed the efficiency of GRACE-derived data in catchments smaller than the 200,000 km\textlesssup\textgreater2\textless/sup\textgreater recommended area when using GRACE. For the study period (January 2002 through December 2011), the GRACE data showed a 7.6 mm annual decline in groundwater level, with a total volume loss of 2.6 km\textlesssup\textgreater3\textless/sup\textgreater during the period. The in situ monthly measurements of groundwater level showed an average depletion of 10 m in catchment aquifers during the study period. This depletion rate was supported by the recorded decrease in precipitation volume, especially in the post-drought period after 2007. These results demonstrate that GRACE can be useful in groundwater resources management of catchments facing groundwater depletion and increasing water demand.\textless/p\textgreater

Abstract

© 2018 Informa UK Limited, trading as Taylor & Francis Group In this study, a waveform retracking algorithm based on finding the inflection-point of the waveform is proposed. After two-steps pre-processing procedure, we employ this method for 145 cycles of Jason-2 data for two tracks 81 and 16 over the Strait of Hormuz. Moreover, we obtain the corrected SSH by the common empirical methods namely Offset Centre of Gravity, Beta and Threshold as well as the ALES. We compare the SSH time series from proposed algorithm with those from common empirical methods. Results are validated against three nearby tide-gauges in the case study. The correlation coefficient and RMSE between the corrected SSH and tide-gages data were computed for three distance classes from the coastline: 0∼5, 5∼10 and 10∼15 kilometer. Our method improves the averaged RMSE of raw SSH up to 41\%, 41\% and 24\%, for these classes over track 81 and 51\%, 38\% and 41\% over track 16, respectively. The averaged correlation values of the proposed method indicate 33\%, 11\% and 2\% improvement over track 81 and are 29\%, 14\% and 3\% over track 16 for three distance groups, respectively. Our method leads to slightly better results than the successful ALES method, especially within the range of 0∼5 km.

Abstract

© 2018. American Geophysical Union. In the Amazon River basin, water stored for months to years in the soils and subsurface provide a persistent resource that regulates local and global climate via teleconnections and provides water to plants in times of little rain. While Gravity Recovery and Climate Experiment (GRACE) satellites have provided the hydrological community with large advances in the understanding of large river basin dynamics and for water resources monitoring applications, the relative storage measurements from GRACE satellites have not yet been utilized for the quantification of total available freshwater on landmasses. In this study, we quantify the total drainable water storage (TDWS) in the Amazon basin by characterizing the relationship between the water storage anomaly of a large catchment and its base flow. Previous studies have shown that these two hydrological parameters behave like a linear time-independent system over a catchment like the Amazon dominated by fully humid tropical climate. This linear relationship allows us to determine a zero water storage level, at which the storage-driven discharge becomes zero. We quantify the TDWS of the Amazon basin as 1,766 km 3 and estimate an average 2 month residence time for stored waters. An independent analysis over nine major subbasins confirms our estimate for the entire basin. We show that our estimated TDWS values for individual subbasins, varying between 15 and 654 km 3 , follow two different linear relationships with their mean annual input. Through these relationships we also quantify the storage-driven portion of the streamflow and the total streamflow for Amazon and its subbasins. Furthermore, we estimate surface water storage variation of the Amazon River system by combining the water level time series from satellite altimetry and surface water extent time series from satellite imagery. Our results show that 61\% of total basin water storage variation occurs in the surface water compartment. In addition, we find that 65\% of the TDWS is available in its surface water system.