Estimation of spatio-temporal groundwater storage variations in the Lower Transboundary Indus Basin using GRACE satellite

2021 ◽  
pp. 127315
Author(s):  
Shoaib Ali ◽  
Qiumei Wang ◽  
Dong Liu ◽  
Qiang Fu ◽  
Md. Mafuzur Rahaman ◽  
...  
Keyword(s):  
Indus Basin ◽  
Groundwater Storage ◽  
Grace Satellite ◽  
Spatio Temporal

scholarly journals GROUNDWATER STORAGE CHANGE ESTIMATION USING GRACE SATELLITE DATA IN INDUS BASIN

2020 ◽  
Vol 1 (1) ◽  
pp. 10-15
Author(s):  
Muhammad Salam ◽  
Muhammad Jehanzeb Masud Cheema ◽  
Wanchang Zhang ◽  
Saddam Hussain ◽  
Azeem Khan ◽  
...  
Keyword(s):  
Water Table ◽  
Land Surface ◽  
Large Scale ◽  
Water Storage ◽  
Indus Basin ◽  
Groundwater Storage ◽  
Grace Satellite ◽  
Over Exploitation ◽  
Flooding Events ◽  
Storage Change

Over exploitation of Ground Water (GW) has resulted in lowering of water table in the Indus Basin. While waterlogging, salinity and seawater intrusion has resulted in rising of water table in Indus Basin. The sparse piezometer network cannot provide sufficient data to map groundwater changes spatially. To estimate groundwater change in this region, data from Gravity Recovery and Climate Experiment (GRACE) satellite was used. GRACE measures (Total Water Storage) TWS and used to estimate groundwater storage change. Net change in storage of groundwater was estimated from the change in TWS by including the additional components such as Soil Moisture (SM), Surface water storage (Qs) and snowpack equivalent water (SWE). For the estimation of these components Global Land Data Assimilation system (GLDAS) Land Surface Models (LSMs) was used. Both GRACE and GLDAS produce results for the Indus Basin for the period of April 2010 to January 2017. The monitoring well water-level records from the Scarp Monitoring Organization (SMO) and the Punjab Irrigation and Drainage Authority (PIDA) from April 2009 to December 2016 were used. The groundwater results from different combinations of GRACE products GFZ (GeoforschungsZentrum Potsdam) CSR (Center for Space Research at University of Texas, Austin) JPL (Jet Propulsion Laboratory) and GLDAS LSMs (CLM, NOAH and VIC) are calibrated (April 2009-2014) and validated (April 2015-April 2016) with in-situ measurements. For yearly scale, their correlation coefficient reaches 0.71 with Nash-Sutcliffe Efficiency (NSE) 0.82. It was estimated that net loss in groundwater storage is at mean rate of 85.01 mm per year and 118,668.16 Km3 in the 7 year of study period (April 2010-Jan 2017). GRACE TWS data were also able to pick up the signals from the large-scale flooding events observed in 2010 and 2014. These flooding events played a significant role in the replenishment of the groundwater system in the Indus Basin. Our study indicates that the GRACE based estimation of groundwater storage changes is skillful enough to provide monthly updates on the trend of the groundwater storage changes for resource managers and policy makers of Indus Basin.

scholarly journals GROUNDWATER STORAGE CHANGE ESTIMATION USING GRACE SATELLITE DATA IN INDUS BASIN

2020 ◽  
Vol 1 (1) ◽  
pp. 13-18 ◽  
Author(s):  
Muhammad Salam ◽  
Muhammad Jehanzeb Masud Cheema ◽  
Wanchang Zhang ◽  
Saddam Hussain ◽  
Azeem Khan ◽  
...  
Keyword(s):  
Satellite Data ◽  
Indus Basin ◽  
Groundwater Storage ◽  
Grace Satellite ◽  
Storage Change

scholarly journals Appraisal of Remote Sensing Technology for Groundwater Resource Management Perspective in Indus Basin

2021 ◽  
Vol 13 (17) ◽  
pp. 9686
Author(s):  
Gulraiz Akhter ◽  
Yonggang Ge ◽  
Naveed Iqbal ◽  
Yanjun Shang ◽  
Muhammad Hasan
Keyword(s):  
Remote Sensing ◽  
Groundwater Resources ◽  
Management Strategies ◽  
Indus Basin ◽  
Space Technology ◽  
Water Balance Components ◽  
Remote Sensing Technology ◽  
Sensing Technology ◽  
Grace Satellite ◽  
Spatio Temporal

The dynamic nature and unsustainable exploitation of groundwater aquifers pose a range of management challenges. The accurate basin-wide hydrological assessment is very critical for the quantification of abstraction rates, spatial patterns of groundwater usage, recharge and discharge processes, and identification of critical areas having groundwater mining. This study provides the appraisal of remote sensing technology in comparison with traditionally prevailing tools and methodologies and introduces the practical use of remote sensing technology to bridge the data gaps. It demonstrates the example of Gravity Recovery and Climate Experiment (GRACE) satellite inferred Total Water Storage (TWS) information to quantify the behavior of the Upper Indus Plain Aquifer. The spatio-temporal changes in aquifer usage are investigated particularly for irrigation and anthropogenic purposes in general. The GRACE satellite is effective in capturing the water balance components. The basin-wide monthly scale groundwater storage monitoring is a big opportunity for groundwater managers and policymakers. The remote sensing integrated algorithms are useful tools to provide timely and valuable information on aquifer behavior. Such tools are potentially helpful to support the implementation of groundwater management strategies, especially in the developing world where data scarcity is a major challenge. Groundwater resources have not grown to meet the growing demands of the population, consequently, overexploitation of groundwater resources has occurred in these decades, leading to groundwater decline. However, future developments in the field of space technology are envisioned to overcome the currently faced spatio-temporal challenges.

scholarly journals Spatio-Temporal Patterns of Mass Changes in Himalayan Glaciated Region from EOF Analyses of GRACE Data

2021 ◽  
Vol 13 (2) ◽  
pp. 265
Author(s):  
Harika Munagapati ◽  
Virendra M. Tiwari
Keyword(s):  
Temperature Anomaly ◽  
Mass Gain ◽  
Snow Accumulation ◽  
Empirical Orthogonal Functions ◽  
Total Water ◽  
Orthogonal Functions ◽  
Grace Data ◽  
Grace Satellite ◽  
Spatio Temporal ◽  
Gravity Recovery

The nature of hydrological seasonality over the Himalayan Glaciated Region (HGR) is complex due to varied precipitation patterns. The present study attempts to exemplify the spatio-temporal variation of hydrological mass over the HGR using time-variable gravity from the Gravity Recovery and Climate Experiment (GRACE) satellite for the period of 2002–2016 on seasonal and interannual timescales. The mass signal derived from GRACE data is decomposed using empirical orthogonal functions (EOFs), allowing us to identify the three broad divisions of HGR, i.e., western, central, and eastern, based on the seasonal mass gain or loss that corresponds to prevailing climatic changes. Further, causative relationships between climatic variables and the EOF decomposed signals are explored using the Granger causality algorithm. It appears that a causal relationship exists between total precipitation and total water storage from GRACE. EOF modes also indicate certain regional anomalies such as the Karakoram mass gain, which represents ongoing snow accumulation. Our causality result suggests that the excessive snowfall in 2005–2008 has initiated this mass gain. However, as our results indicate, despite the dampening of snowfall rates after 2008, mass has been steadily increasing in the Karakorum, which is attributed to the flattening of the temperature anomaly curve and subsequent lower melting after 2008.

scholarly journals Spatio‐temporal evaluation of gridded precipitation products for the high‐altitude Indus basin

2021 ◽  
Author(s):  
Zakir Hussain Dahri ◽  
Fulco Ludwig ◽  
Eddy Moors ◽  
Shakil Ahmad ◽  
Bashir Ahmad ◽  
...  
Keyword(s):  
High Altitude ◽  
Indus Basin ◽  
Spatio Temporal

Evaluation of GRACE derived groundwater storage changes in different agro-ecological zones of the Indus Basin

2021 ◽  
pp. 127369
Author(s):  
Fazlullah Akhtar ◽  
Rana Ali Nawaz ◽  
Mohsin Hafeez ◽  
Usman Khalid Awan ◽  
Christian Borgemeister ◽  
...  
Keyword(s):  
Indus Basin ◽  
Groundwater Storage ◽  
Ecological Zones ◽  
Agro Ecological Zones

scholarly journals Spatio-Temporal Groundwater Drought Monitoring Using Multi-Satellite Data Based on an Artificial Neural Network

Water ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 1953 ◽  
Author(s):  
Seo ◽  
Lee
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Satellite Data ◽  
Severity Index ◽  
Drought Severity ◽  
Human Society ◽  
Groundwater Storage ◽  
Groundwater Drought ◽  
Artificial Neural ◽  
Spatio Temporal

Drought is a complex phenomenon caused by lack of precipitation that affects water resources and human society. Groundwater drought is difficult to assess due to its complexity and the lack of spatio-temporal groundwater observations. In this study, we present an approach to evaluate groundwater drought based on relatively high spatial resolution groundwater storage change data. We developed an artificial neural network (ANN) that employed satellite data (Gravity Recovery and Climate Experiment (GRACE) and Tropical Rainfall Measuring Mission (TRMM)) as well as Global Land Data Assimilation System (GLDAS) models. The Standardized Groundwater Level Index (SGI) was calculated by normalizing ANN-predicted groundwater storage changes from 2003 to 2015 across South Korea. The ANN-predicted 25 km groundwater storage changes correlated well with both the in situ and the water balance equation (WBE)-estimated groundwater storage changes, with mean correlation coefficients of 0.87 and 0.64, respectively. The Standardized Precipitation–Evapotranspiration Index (SPEI), having an accumulation time of 1–6 months, and the Palmer Drought Severity Index (PDSI) were used to validate the SGI. The results showed that the SGI had a pattern similar to that of SPEI-1 and SPEI-2 (1- and 2-month accumulation periods, respectively), and PDSI. However, the SGI performance fluctuated slightly due to its relatively short study period (13 years) as compared to SPEI and PDSI (more than 30 years). The SGI, which was developed using a new approach in this study, captured the characteristics of groundwater drought, thus presenting a framework for the assessment of these characteristics.

Trends of the Degree-Day Factors in the mountainous regions

2020 ◽  
Author(s):  
Muhammad Fraz Ismail
Keyword(s):  
River Basin Management ◽  
Winter Precipitation ◽  
Indus Basin ◽  
Mountainous Regions ◽  
Degree Day ◽  
Air Temperatures ◽  
University Of Munich ◽  
University Of Applied Sciences ◽  
Spatio Temporal ◽  
Energy Balances

<p><strong>Trends of the Degree-Day Factors in the mountainous regions</strong></p><p>Muhammad Fraz Ismail<sup>1, 2</sup>, Prof. Dr. –Ing. Markus Disse<sup>1</sup>, Prof. Dr. –Ing. Wolfgang Bogacki<sup>2</sup>,</p><p>Alexander Brandt<sup>3</sup>, M. Larry Lopez C.<sup>3</sup></p><p><sup> </sup></p><p><sup>1 </sup>Chair of Hydrology and River Basin Management, Department of Civil, Geo and Environmental Engineering, Technical University of Munich.</p><p><sup>2</sup> Department of Civil Engineering, Koblenz University of Applied Sciences.</p><p><sup>3 </sup>Faculty of Agriculture, Yamagata University, Tsuruoka, Japan.</p><p> </p><p>Melt generated through snow and glaciers are considered to be a vital fresh water resource because they store the solid winter precipitation as then act as a reservoir to provide water when it is mostly needed i.e. during the summer season. Recently, a lot of studies based on hydrological modelling showed that the changing climate will adversely affect the snow and glacial melt patterns around the globe. Considering this situation it is quite critical to know more about these melting processes and the factors driving them.</p><p>Degree-day approach for simulating the flows generated through the snow and glacial melt has proved to be a handsome one because it uses the temperatures as an index variable to address the complex energy balances as well as its only dependency over the air temperatures to generate the melt make it feasible especially for the high mountainous data scare regions (e.g. Upper Indus Basin). Degree-day models use the Degree-Day Factor (DDF) as a ‘key’ parameter which transforms one degree-day [°C.day<sup>-1</sup>] into daily melt depth [mm.day<sup>-1</sup>]. Literature enlightens that the DDF is not a constant parameter but it changes with the ripening of the snowpack.</p><p>In the present research, snow measurement datasets from three different locations e.g. Japan (Enshurin 173m a.s.l.), Germany (Brunnenkopfhütte 1602m a.s.l.), and Pakistan (Deosai 4149m a.s.l.) have been collected and evaluated for the estimation of the DDFs. Initial findings show that there exists a considerable spatio-temporal variation of the DDFs. Which ranges from 0.3 – 6.8 [mm°C<sup>-1</sup> day<sup>-1</sup>] in the German Alps, 0.2 – 7.9 [mm°C<sup>-1</sup> day<sup>-1</sup>] in Yamagta Forest Japan and reaches ≥10 [mm°C<sup>-1</sup> day<sup>-1</sup>] in the Himalayan ranges during the snowmelt season.</p><p>In general, the DDFs show an increasing trend during the snowmelt season at different elevations, which not only demonstrates the altitude influence on the variability of the DDFs but also links to changing snow densities. Latter suggests that the DDFs should not be taken as constant because it changes with the location and needs to be estimated for different regions.</p><p><strong> </strong></p><p><strong>KEYWORDS</strong>: Degree-Day Factor, Snow and glacial melt, Measurements</p>

scholarly journals Evaluating the Spatio-Temporal Distribution of Irrigation Water Components for Water Resources Management using Geo-Informatics Approach

2021 ◽  
Author(s):  
Muhammad Mohsin Waqas ◽  
Muhammad Waseem ◽  
Sikandar Ali ◽  
Megersa Kebede Leta ◽  
Adnan Noor Shah ◽  
...  
Keyword(s):  
Irrigation Water ◽  
Temporal Distribution ◽  
Indus Basin ◽  
Irrigated Agriculture ◽  
Command Area ◽  
Groundwater Extraction ◽  
Canal Water ◽  
Average Value ◽  
Groundwater Irrigation ◽  
Spatio Temporal

Irrigation water management components evaluation is mandatory for sustainable irrigated agriculture production in the era of water scarcity. In this research spatio-temporal distribution of irrigation water components were evaluated at canal command area in Indus Basin Irrigation System (IBIS) using remote sensing based geo-informatics approach. Satellite derived MODIS product-based Surface Energy Balance Algorithm for Land (SEBAL) was used for the estimation of the Actual Evapotranspiration (ETa). Satellite derived SEBAL based ETa was calibrated and validated using the ground data-based advection aridity method (AA). Statistical analysis of the SEBAL based ETa and AA shows the mean 87.1 mm and 47.9 mm and, 100 mm and 77 mm, Standard deviation of 27.7 mm and 15.9 mm and, 34.9 mm and 16.1 mm, R of 0.93 and 0.94, NSE of 0.72 and 0.85, PBIASE -12.9 and -4.4, RMSE 34.9 and 5.76 for the Kharif and Rabi season, respectively. Rainfall data was acquired from the Tropical Rainfall Measuring Mission (TRMM). TRMM based rainfall was calibrated with the point observatory data of the Pakistan Metrological Department Stations. Canal water data was collected from the Punjab Irrigation department for the assessment of canal water availability. Water The water balance approach was applied in the unsaturated zone for the quantification of the gross and net Groundwater irrigation. Mmonthly variation of ETa with the minimum average value of 63.3 mm in January and the maximum average value of 110.6 mm in August was found. While, the average annual of four cropping years (2011-12 to 2014-15) ETa was found 899 mm. Average of the sum of Net Canal Water Use (NCWU) and Rainfall during the study period of four years was only 548 mm (36% of ETa) and this resulted the 739.6 mm of groundwater extraction. While the annual based variation in groundwater extraction of 632 mm and 780 mm was found. Seasonal analysis revealed 39% and 61% of groundwater extraction proportion during Rabi and Kharif season, respectively. The variation in four cropping year’s monthly groundwater extraction was found 28.7 mm to 120.3 mm. This variation was high in the 2011-12 to 2012-13 cropping year (0 mm to 148.7 mm), dependent upon the occurrence of rainfall and crop phenology. Net groundwater irrigation, estimated after incorporating the efficiencies was 503 mm year-1 on average for the four cropping years.

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