Assimilation of Satellite-Based Data for Hydrological Mapping of Precipitation and Direct Runoff Coefficient for the Lake Urmia Basin in Iran

Akbari, Mahdi; Haghighi, Ali Torabi; Aghayi, Mohammad Mahdi; Javadian, Mostafa; Tajrishy, Masoud; Kløve, Bjørn

doi:10.3390/w11081624

Cited by 26 publications

(5 citation statements)

References 32 publications

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“…Moreover, gauge observations suffer from sampling errors and undercatch issues (Ehsani & Behrangi, 2021; Kidd et al., 2017; Panahi & Behrangi, 2019; Saemian et al., 2022; Song et al., 2021). Precipitation estimation through gauge data interpolation for sparsely gauged regions is also challenging (Akbari et al., 2019; Yong et al., 2016). Ground‐based precipitation radars provide near real‐time coverage with a high spatiotemporal resolution (Sokol et al., 2021).…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Evolution of ECMWF Precipitation Products Using Observational Data for Iran: From ERA40 to ERA5

Ghajarnia

Akbari

Saemian

et al. 2022

Earth and Space Science

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Precipitation is one of the most important components of the hydrological cycle (Eltahir & Bras, 1996;Oki & Kanae, 2006) and great efforts have been invested in monitoring its spatiotemporal variability (Arabzadeh et al., 2020;Koohi et al., 2021;Teague & Gallicchio, 2017). Multiple methods have been developed to measure and monitor precipitation in the field, but accurate estimation of precipitation is still challenging (Adhikari et al., 2020;Foufoula-Georgiou et al., 2020). For example, this difficulty is due to, high spatiotemporal variability in complex topographical regions (Beck et al., 2019) and different sources of uncertainty associated with different measurement methods and scales (Behrangi & Wen, 2017). These uncertainties impede the study of climate and interconnections of hydroclimatic variables, water resources management, and hydrological forecasts (Foufoula-Georgiou et al., 2020;Sun et al., 2018).There are three main sources of precipitation data: (a) ground-based data (mainly from precipitation gauge networks), (b) satellite remote sensing (RS) data, and (c) reanalysis data sets (Hosseini-Moghari et al., 2018;Shayeghi et al., 2020;Sun et al., 2018). Each of these has its strengths and weaknesses. Gauges provide the most accurate measurements, but they are geographically sparse, especially in remote areas, inaccessible topographies, and harsh climates (

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Section: Introductionmentioning

confidence: 99%

Evaluating the Evolution of ECMWF Precipitation Products Using Observational Data for Iran: From ERA40 to ERA5

Ghajarnia

Akbari

Saemian

et al. 2022

Earth and Space Science

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“…The dry-up of water bodies, i.e. so-called Aral Sea desiccation syndrome (AghaKouchak et al, 2015), has been observed in Central Asia (Micklin, 1988) and northwestern Iran (Akbari et al, 2019;Alborzi et al, 2018;Torabi Haghighi et al, 2018) Hamun Lakes are located in the Iran-Afghanistan border zone in the Sistan region (Figure 1-a) within the transboundary Helmand Basin. The lake system is a Ramsar site (The Convention on Wetlands, 1975) that is crucial for the economy and environment (Rashki et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Desiccation of the Transboundary Hamun Lakes between Iran and Afghanistan in Response to Hyro-climatic Droughts and Anthropogenic Activities

Akbari¹,

Mirchi²,

Roozbahani³

et al. 2022

Preprint

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This paper investigates the hydrologic and water management reasons behind the desiccation of the Hamun Lakes in the Iran-Afghanistan border region. We analysed changes in Hirmand (or Helmand) River flow, the main tributary providing 70% of the lakes’ total inflow, and precipitation during 1960-2016 by calculating standardized indices for precipitation (SPI) and discharge (SDI). Also, we applied Normalized Difference Spectral Indices (NDSIs) using satellite images from 1987 to present to observe monthly areal change of the lakes. The transboundary water body is responding to changes in regional water management, which has severely reduced the lakes’ inflow. Upstream water regulation in Afghanistan coupled with reservoir construction on the Iranian side has caused nearly full desiccation of major parts of the lake system. There is a discernible shift in the relation between the Hirmand River flow at the international border and upstream precipitation over the lakes’ basin before and after 2004. From 1960 to 2003, high river flows were expected to feed the lakes due to high precipitation over the basin. However, the Hirmand River flow at the border declined after 2004 despite large amounts of upstream precipitation, including the largest recorded amounts, especially in the Hindu Kush mountains. Further, environmental water stress caused by anthropocentric water management in Iran by reservoir construction has impacted the area of the lakes. Although a long period of drought from 1998-2004, i.e. climatic driver, decreased the lakes’ area, the lake system is primarily falling victim to anthropogenic flow alterations in the transboundary river basin. The lakes’ shrinkage places socio-economic stress on an already-vulnerable region with important public health implications as the exposed lake beds turn into major sources of sand and dust storms.

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“…Moreover, gauge observations suffer from sampling errors and undercatch issues Kidd et al, 2017;Panahi and Behrangi, 2019;Song et al, 2021). Precipitation estimation through gauge data interpolation for sparsely gauged regions is also challenging (Akbari et al, 2019;Yong et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Evolution of ECMWF Precipitation Products Using Observational Data for Iran: From ERA40 to ERA5

Ghajarnia¹,

Akbari²,

Saemian³

et al. 2022

Preprint

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ECMWF Reanalysis (ERA), one of the most widely used precipitation products evolved over time 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 in the products. We compared the performance of all ERA precipitation products using daily, monthly, and annual data (1980-2018) from 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 assimilation of observational data. Although several previous studies have reported full superiority of ERA5 over ERA-Interim, our results revealed several shortcomings compared with ERA-Interim in ERA5 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 improved from ERA-40 to ERA-Interim, but not consistently from ERA-Interim to ERA5. This study employed a grid-grid comparison approach by firstly creating a gridded reference dataset through spatial aggregation of point source observations, however, the results from a point-grid approach show no change in the overall ranking of products (despite the existence of slight changes in the error index values). These findings are useful for model development at global scale and for hydrological applications in Iran.

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Assimilation of Satellite-Based Data for Hydrological Mapping of Precipitation and Direct Runoff Coefficient for the Lake Urmia Basin in Iran

Cited by 26 publications

References 32 publications

Evaluating the Evolution of ECMWF Precipitation Products Using Observational Data for Iran: From ERA40 to ERA5

Evaluating the Evolution of ECMWF Precipitation Products Using Observational Data for Iran: From ERA40 to ERA5

Desiccation of the Transboundary Hamun Lakes between Iran and Afghanistan in Response to Hyro-climatic Droughts and Anthropogenic Activities

Evaluating the Evolution of ECMWF Precipitation Products Using Observational Data for Iran: From ERA40 to ERA5

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