Gravity recovery and climate experiment (GRACE) and GRACE Follow-On (GRACE-FO) are Earth's gravity satellite missions with hydrological monitoring applications. However, caused by measuring instrumental problems, there are several temporal missing values in the dataset of the two missions where a long gap between the mission dataset also exists. Recent studies utilized different gap-filling methodologies to fill those data gaps. In this article, we employ a variety of singular spectrum analysis (SSA) algorithms as well as the least squares-harmonic estimation (LS-HE) approach for the data gap-filling. These methods are implemented on six hydrological basins, where the performance of the algorithms is validated for different artificial gap scenarios. Our results indicate that each hydrological basin has its special behaviour. LS-HE outperforms the other algorithms in half of the basins, whereas in the other half, SSA provides a better performance. This highlights the importance of different factors affecting the deterministic signals and stochastic characteristics of climatological time series. To fill the missing values of such time series, it is therefore required to investigate the time series behaviour on their time-invariant and time-varying characteristics before processing the series.

The Urmia lake in north-west Iran has dried up to perilously low levels in the past two decades. In this study, we investigate the drivers behind the decline in lake water level with the help of in-situ and remote sensing data. We use total water storage (TWS) changes from the gravity recovery and climate experiment (GRACE) satellite mission. TWS from GRACE includes all the water storage compartments in a column and is the only remote sensing product that can help in estimating groundwater storage (GWS) changes. The coarse spatial (approx. 300 km) resolution of GRACE does not allow us to identify local changes that may have led to the Urmia lake disaster. In this study, we tackle the poor resolution of the GRACE data by employing three machine learning (ML) methods including random forest (RF), support vector regression (SVR) and multi-layer perceptron (MLP). The methods predict the groundwater storage anomaly (GWSA), derived from GRACE, as a function of hydro-climatic variables such as precipitation, evapotranspiration, land surface temperature (LST) and normalized difference vegetation index (NDVI) on a finer scale of 0.25° × 0.25°. We found that i) The RF model exhibited highest R (0.98), highest NSE (0.96) and lowest RMSE (18.36 mm) values. ii) The RF downscaled data indicated that the exploitation of groundwater resources in the aquifers is the main driver of groundwater storage and changes in the regional ecosystem, which has been corroborated by few other studies as well. The impact of precipitation and evapotranspiration on the GWSA was found to be rather weak, indicating that the anthropogenic derivers had the most significant impact on the GWSA changes. iii) We generally observed a significant negative trend in GWSA, having also significant positive correlations with the well data. However, over regions with dam construction significant negative correlations were found.

Drought is one of the most complex and threatening disasters, defined as a period of drier-than-normal conditions which results in water-related problems. In addition to climate variation, human activities can cause or intensify drought through groundwater overextraction, unsupervised expansion of agricultural lands, etc. One prime example of such drought has been observed in the Zayandehrud River basin in central Iran. We investigate the long-term (1986 to 2019) drought of the Zayandehrud River basin using meteorological, hydrological, and agricultural drought indices. The main focus of this research is to explore the impact of the irrigated area expansion in the upstream sub-basins on the agricultural drought of the downstream. To this end, different drought indices are employed over the whole basin and its sub-basins. The results indicate that the long-term precipitation index and snow reserves do not show a long-term trend during the study period, while the combined precipitation-evapotranspiration index and groundwater storage decline. The research concludes that the expansion of the agricultural lands in the river upstream, rising air temperature, and increasing population and industrial activities in the basin are most likely the driving forces to intensify the agricultural drought in the midstream and downstream parts of the basin.

Many studies in the past have discussed potential orbit configurations of future satellite gravity missions. Most of those works have targeted orbit optimization of the satellite missions of the next generation in the so-called Bender formation. The studies have investigated the impact of the Keplerian orbital parameters, especially the influence of the repeat orbits and mission altitude of both satellite pairs and the inclination of the second pair in Bender formation on the satellite configurations’ gravity field recovery quality performance. Obviously, the search space for the orbit optimization in the Bender formation is vast and, therefore, different approaches have been suggested for optimal orbit design. Among approaches, however, different assumptions about input geophysical models as well as the error models into the simulation software play a role. Our paper shows how different assumptions for input models change the orbit optimization results. For this purpose, the genetic algorithm has been utilized for orbit optimization of the Bender formation where different input models were considered. Those input models include (1) the updated ESA geophysical models, and (2) error models for the Ocean Tide (OT error) and Atmosphere-Ocean (AO error). Here, we focus on the impact of the models on relative difference of the longitude of ascending nodes between the two pairs in Bender formation. The results of the paper clearly state that our current and future knowledge about signal and error models can significantly affect the orbit optimization problem.