Determining water storage depletion within Iran by assimilating GRACE data into the W3RA hydrological model

Abstract

Link to publication record in Explore Bristol Research PDF-document This is the author accepted manuscript (AAM). The final published version (version of record) is available online via Elsevier at https://www.sciencedirect.com/science/article/pii/S0309170817306322?via%3Dihub#! . Please refer to any applicable terms of use of the publisher. University of Bristol -Explore Bristol Research General rightsThis document is made available in accordance with publisher policies. Please cite only the published version using the reference above. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.ACCEPTED MANUSCRIPT A C C E P T E D M A N U S C R I P T Highlights• We assimilate GRACE data to improve a hydrological model estimations over Iran• Ensemble Square-Root Filter is used for data assimilation• We estimate sub-surface water storage changes within the country• Climate and anthropogenic impacts on the water storages are investigated• Independent in-situ measurements are used to evaluate the results 1 ACCEPTED MANUSCRIPT A C C E P T E D M A N U S C R I P T AbstractGroundwater depletion, due to both unsustainable water use and a decrease in precipitation, has

Assimilating GRACE Into a Land Surface Model in the Presence of an Irrigation‐Induced Groundwater Trend

“…In recent years, a number of studies have reported on efforts to optimize GRACE‐DA performance by investigating (1) the spatial scales of assimilation (Eicker et al, ; Schumacher et al, ), (2) the representation of horizontal error covariance of models and GRACE observations (Forman & Reichle, ; Khaki et al, ; Khaki, Schumacher, et al, ), (3) the choice of GRACE products and scaling factors (Kumar et al, ; Schumacher et al, ), and (4) the choice of data assimilation techniques (Girotto et al, ; Khaki et al, ; Khaki, Hoteit, et al, ; Schumacher et al, ). Despite these efforts, challenges persist, particularly for regions that have substantial TWS depletion.…”

Nonparametric Data Assimilation Scheme for Land Hydrological Applications

“…Here we choose to test both AUKF and Kalman‐Takens filters to improve upon model simulations between 2003 and 2013 over Iran (32.4279°N, 53.6880°E). The rationale behind choosing Iran for this synthetic analysis, and not Australia, is that a remarkable water storage decline is reported over this region, mainly due to anthropogenic impacts, which cannot be detected by W3RA (see Khaki, Forootan, Kuhn, Awange, van Dijk, et al, ). A major part of the negative water storage trend is due to human impacts (see details in ; Forootan et al, ; Khaki, Forootan, Kuhn, Awange, van Dijk, et al, ).…”

“…The rationale behind choosing Iran for this synthetic analysis, and not Australia, is that a remarkable water storage decline is reported over this region, mainly due to anthropogenic impacts, which cannot be detected by W3RA (see Khaki, Forootan, Kuhn, Awange, van Dijk, et al, ). A major part of the negative water storage trend is due to human impacts (see details in ; Forootan et al, ; Khaki, Forootan, Kuhn, Awange, van Dijk, et al, ). Synthetic observations are produced using the WaterGAP Global Hydrology Model (WGHM; Döll et al, ; Müller Schmied et al, ) monthly TWS outputs, which contain the anthropogenic impacts (Khaki, Forootan, Kuhn, Awange, van Dijk, et al, ), at two different spatial resolution of 1° × 1° and 3° × 3°.…”

“…A major part of the negative water storage trend is due to human impacts (see details in ; Forootan et al, ; Khaki, Forootan, Kuhn, Awange, van Dijk, et al, ). Synthetic observations are produced using the WaterGAP Global Hydrology Model (WGHM; Döll et al, ; Müller Schmied et al, ) monthly TWS outputs, which contain the anthropogenic impacts (Khaki, Forootan, Kuhn, Awange, van Dijk, et al, ), at two different spatial resolution of 1° × 1° and 3° × 3°. This can help to test whether data assimilation can account for human impacts on water storage and also to investigate the effect of spatial resolution on the final results.…”

“…Three degree 1 coefficients (C10, C11, and S11) and degree 2 and order 0 (C20) coefficient are replaced by those of Swenson et al (2008) and that of Cheng and Tapley (2004), respectively. Further, we apply the DDK2 smoothing filter (Kusche et al, 2009) to mitigate a colored/correlated noise in the coefficients (see also Khaki, Forootan, Kuhn, Awange, Longuevergne, & Wada, 2018), and thereafter convert them into 1 ∘ × 1 ∘ TWS fields following Wahr et al (1998). The mean TWS for the study period is taken from the W3RA model and is aggregated to the GRACE TWS change time series to reach absolute values related to W3RA (Zaitchik et al, 2008).…”