Enhancing spatial resolution of GRACE-derived groundwater storage anomalies in Urmia catchment using machine learning downscaling methods

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.

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.

Sea Level Budgets Should Account for Ocean Bottom Deformation

The conventional sea level budget (SLB) equates changes in sea surface height with the sum of ocean mass and steric change, where solid-Earth movements are included as corrections but limited to the impact of glacial isostatic adjustment. However, changes in ocean mass load also deform the ocean bottom elastically. Until the early 2000s, ocean mass change was relatively small, translating into negligible elastic ocean bottom deformation (OBD), hence neglected in the SLB equation. However, recently ocean mass has increased rapidly; hence, OBD is no longer negligible and likely of similar magnitude to the deep steric sea level contribution. Here, we use a mass-volume framework, which allows the ocean bottom to respond to mass load, to derive a SLB equation that includes OBD. We discuss the theoretical appearance of OBD in the SLB equation and its implications for the global SLB.