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RAINFALL CHANGE DETECTION IN AFRICA USING REMOTE SENSING AND GIS BETWEEN 1999 – 2018

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2bdwre2020-52-54

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ABSTRACT

RAINFALL CHANGE DETECTION IN AFRICA USING REMOTE SENSING AND GIS BETWEEN 1999 – 2018

Journal: Big Data In Water Resources Engineering (BDWRE)

Author: Abdullahi Muktar, Wali Elekwachi, Nwankwoala Hycienth, Stephen Hemba

This is an open access article distributed under the Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

DOI: 10.26480/bdwre.02.2020.52.54

Many researchers used gauge data from weather stations for rainfall estimate across Africa. Since Africa lies within the tropics, there is possibility for variations in rain received from place to place. Therefore, there is need for excessive density of the gauges for accurate estimate of Africa’s rainfall. Due to numerous challenges, these cannot be achieved. This necessitates the application of remote sensing and GIS to detect changes in rainfall amount in Africa between 1999 and 2018. The data used was obtained from remote sensing satellite (TRMM) and analyzed using GIS application (IDRISI Taiga). The Simple Image Differencing was performed on the two annual mean images covering January to December, 1999 and January to December, 2018. This provides reliable information on rainfall estimate that can complement sparsely and unevenly distributed rain gauge network in Africa. The analysis shows that latitudinal locations, to some extent, determine spatial distribution of rainfall in Africa. It is also observed that significant changes in rainfall rate are mainly found around coastal regions. It was recommended that adequate ground data it needed to confirm these findings. African countries should provide adequate and justly distributed weather stations with on-net database for easy access to the data.
Pages 52-54
Year 2020
Issue 2
Volume 1
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2bdwre2020-49-51

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A REVIEW ON TECHNIQUES FOR WATER QUALITY MONITORING USING IOT DEVICES

Journal: Big Data In Water Resources Engineering (BDWRE)

Author: Ali Javaid, Ahthasham Sajid, Afia Zafar, Zaheer Ahemed

This is an open access article distributed under the Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

DOI: 10.26480/bdwre.02.2020.49.51

In this paper is discussed the different kinds of environment monitoring systems related to water goodness. Different parameters are discussed to elaborate each water monitoring system with different aspects. The technology aspects of different approach techniques related to water quality monitoring with their way of implementation with the IOT aspect has been evaluated of past four years. In this paper, the workflow of different approaches of the technologies exploited will be discussed critically and also that which approaches focused on what parameters.
Pages 49-51
Year 2020
Issue 2
Volume 1
Posted by din

2bdwre2020-43-48

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ESTIMATING SEDIMENT YIELD AT TARBELA DAM AND FLOOD FORECASTING THROUGH CONTINUOUS PRECIPITATION-RUNOFF MODELING OF UPPER INDUS BASIN

Journal: Big Data In Water Resources Engineering (BDWRE)

Author: Rana Muhammad Amir, Sikandar Ali, Muhammad Jehanzeb Masud Cheema, Saddam Hussain, Muhammad Mohsin Waqas, Muhammad Sohail Waqas, Rao Husnain Arshad, Muhammad Salam, Ahsan Raza and Muhammad Aslam

This is an open access article distributed under the Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

DOI: 10.26480/bdwre.02.2020.43.48

The live water storage of the reservoirs is decreasing by the sedimentation, which is affecting the reservoir’s capacity and cause a severe problem for the irrigation system at the downstream side. Floods occur at the downstream by the poor management at upstream due to the heavy rainfall and snow melting. For annual accumulations of sediment load and estimation of the peak flow at Tarbela reservoir near Besham Qila station having area of 170,000 km2 was selected. Estimation of the peak flow and sediment yield at the Tarbela reservoir, SWAT (distributed hydrological model) was used. The expected decrease in reservoir storage capacity was also estimated with the SWAT model. For runoff modelling, calibration was done for three years (2004-2006) and validation was also done for three years (2007-2009). Nash-Sutcliffe Efficiency (NSE) and Standard Error of Estimate existed the statistical indices to evaluate the results. Coefficient of determination (R2) was found as 0.75 for the calibration period and 0.80 for the validation. Whereas, NSE for calibration was observed 0.69 and 0.70 for the validation. Monthly mean sediment yield was about 0.13 BCM estimated at the Tarbela reservoir near Besham Qila.
Pages 43-48
Year 2020
Issue 2
Volume 1
Posted by din

2bdwre2020-36-42

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ASSESSMENT OF SEDIMENT YIELD USING SWAT MODEL: CASE STUDY OF KEBIR WATERSHED, NORTHEAST OF ALGERIA

Journal: Big Data In Water Resources Engineering (BDWRE)

Author: Kamel Khanchoul, Amina Amamra, Bachir Saaidia

This is an open access article distributed under the Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

DOI: 10.26480/bdwre.02.2020.36.42

Erosion is identified as one of the most significant threats to land in increasing rates of soil loss and reservoir sedimentation. An integrated approach therefore requires sediment assessment for identification of its sources for efficient watershed management. The present study is aimed to examine the spatial and temporal sediment yield distribution potential and to identify the critical erosion prone zones within Kebir watershed, Algeria using Soil and Water Assessment Tool interfaced in GIS for the period from 1982 to 2014. The model is calibrated by adjusting sensitive parameters and validation is done using observed data from 1982 to 1998. The model performance checked by the coefficient of determination (0.76), Nash–Sutcliffe coefficient (0.75) and relative error (+8.19%) suggests that the model has performed satisfactorily for sediment yield prediction. The simulated outputs of the model show that the 33-year period of sediment load production is estimated to be 19.24×106 tons and a mean annual sediment yield of 856.14 T/km²/yr. Temporally, sixty-four percent (50%) of sediment yield generated in the watershed occurs in five months of the winter and fall seasons. The most erosion vulnerable sub-basins that could have a significant impact on the sediment yield of the reservoirs are identified. Based on this, sub-basin 16, 14, 13, 11 and 8 are found to be the most erosion sensitive areas that could have a significant contribution, of 50%, to the increment of sediment yield. Best management practices are highly recommended for the land sustainability because of the high sediment supply to the dams.
Pages 36-42
Year 2020
Issue 2
Volume 1
Posted by din

2bdwre2020-32-35

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CROP AREA MAPPING BY INTELLIGENT PIXEL INFORMATION INFERRED USING 250M MODIS VEGETATION TIMESERIES IN TRANSBOUNDARY INDUS BASIN

Journal: Big Data In Water Resources Engineering (BDWRE)

Author: Muhammad Mohsin Khan, Muhammad Jehanzeb Masud Cheema, Talha Mahmood, Saddam Hussain, Muhammad Sohail Waqas, Hafiz Muhammad Nauman, Mohsin Nawaz, Muhammad Saifullah

This is an open access article distributed under the Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

DOI: 10.26480/bdwre.02.2020.32.35

Irrigation water could be managed properly by mapping area of various crops. Remote sensing data can provide useful Land Use Land Cover (LULC) for assessment of different crop area and change detection. The present study was carried out with core objective to map crop area within the Indus Basin’s transboundary. Four major crops (i.e. wheat, rice, cotton and sugarcane) were identified using Normalize Difference Vegetation Index (NDVI) time series that was picked up from MODIS sensors aboard Terra (EOS AM) and Aqua (EOS PM) satellites with 250m pixel resolution. Crop phonological information was used to train each pixel intelligently for interpretation of unanalyzed NDVI data into crops. Eight days of time series data was used for identification and mapping of various crops on the basis of their phenology for the years 2008, 2010 and 2013. Error matrix was prepared to reveal mapping accurateness and ground truthing was also done in particular canal commands within the Indus basin. Furthermore, the temporal variation in cropped area was determined and for accuracy check, secondary data was matched with prepared maps. LULC maps for year 2008, 2010 and 2013 were defined for Rabi and kharif seasons.
Pages 32-35
Year 2020
Issue 2
Volume 1
Posted by din

1bdwre2020-10-15

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ABSTRACT

GROUNDWATER STORAGE CHANGE ESTIMATION USING GRACE SATELLITE DATA IN INDUS BASIN

Journal: Big Data In Water Resources Engineering (BDWRE)

Author: Muhammad Salam, Muhammad Jehanzeb Masud Cheema, Wanchang Zhang, Saddam Hussain, Azeem Khan, Muhammad Bilal, Arfan Arshad, Sikandar Ali, Muhammad Awais Zaman

This is an open access article distributed under the Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

DOI: 10.26480/bdwre.01.2020.10.15

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.
Pages 10-15
Year 2020
Issue 1
Volume 1
Posted by din

1bdwre2020-06-09

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GULLY PLUGGING SPILLWAY IS AN EFFECTIVE GULLY REHABILITATION MEASURE: A CASE STUDY OF DISTRICT GUJRAT-PAKISTAN

Journal: Big Data In Water Resources Engineering (BDWRE)

Author: Aqsa Ayub

This is an open access article distributed under the Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

DOI: 10.26480/bdwre.01.2020.06.09

Soil erosion is the universal land degradation event which invites an enormous challenge for its rehabilitation. Among all forms, gully erosion is the most worst and visible form of water erosion which cannot be controlled without any permanent gully stabilization structure. Therefore, the study was conducted to evaluate the impact of gully plugging spillway to rehabilitate the eroded land. For this purpose, a highly eroded site was selected within the study area, surveyed to estimate the structural design and brick masonry work was executed accordingly. The consequences of study clearly illustrated that straight drop spillway is an effective hydraulic structure which considerably fulfilled its objectives by plugging the gully to stop further erosion as well as stabilizing the eroded land.
Pages 06-09
Year 2020
Issue 1
Volume 1
Posted by din

1bdwre2020-01-05

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EFFECT OF WATER QUALITY AND DIFFERENT MEALS ON GROWTH OF CATLA CATLA AND LABEO ROHITA

Journal: Big Data In Water Resources Engineering (BDWRE)

Author: Saba Malik, Saddam Hussain and Muhammad Sohail Waqas

This is an open access article distributed under the Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

DOI: 10.26480/bdwre.01.2020.01.05

The human body cannot make significant required quantities of vital nutrients but fish is minimum-fat, big-protein nutrition that delivers a variety of health advantages. Several external factors including temperature, oxygen level, alkalinity and photoperiod have impact on growth rate while water is also an important parameter in fish rising. Therefore, there is need to work and evaluate impact of fish food and water quality to improve the fish growth. For this purpose, twelve glass aquaria (six with ground water and six with surface water) were considered to assess growth and food conversion ratio (FCR) of Catla Catla and Labeo Rohita and two feeds (i.e. sunflower and bone meal) were provided. The feed has been given twice a day and changed 4% on the rate of body weight of fingerlings in the ground water pond and surface water pond as well. The fingerlings get the most elevated body weight in ground water and on sunflower meal (1.43 ± 0.01 g) as compared to surface water and bone meal (1.39 ± 0.03 g). The general lengths obtained using the fish feed have gotten to be (5.78 ± 0.03cm) on sunflower meal and (5.47±0.03 cm) on the bone meal. The values of FCR had been lower (better) on sunflower meal (2.13±0.01) as compared to bone feast (2.32±0.03). In conclusion, it was observed from the results that fishes fed on sunflower meals had shown better growth with improved morphometric parameters and lower FCR values.
Pages 01-05
Year 2020
Issue 1
Volume 1
Posted by din

1bdwre2020-01-03

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HEALTH RISK ASSESSMENT OF HEAVY METALS DUE TO UNTREATED WASTEWATER IRRIGATED VEGETABLES

Journal: Big Data In Water Resources Engineering (BDWRE)

Author: Ayesha Nawaz, Saddam Hussain, Muhammad Sohail Waqas, Haroon Rasheed, Sikandar Ali, Muhammad Mazhar Iqbal, Zarina Yasmeen, Muhammad Mohsin Waqas

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

DOI: 10.26480/bdwre.01.2020.01.03

Immense amount of wastewater is used for irrigation of vegetables and crops in order to cope with water scarcity issues in Pakistan. Wastewater is highly contaminated due to the disposal of untreated industrial wastewater into the main drains. Therefore, crops and vegetables have concentrations of heavy metals and other contaminants. The study was conducted at Chokera wastewater treatment plant in district Faisalabad, where wastewater is used for irrigation of vegetables. Samples of wastewater were collected from the drain during different time interval to find out variations in wastewater characteristics. Samples of irrigation wastewater and cultivated vegetables by wastewater were collected and investigated to check the Arsenic concentrations in vegetables. Soil samples and groundwater samples from the nearby vicinity have been taken so as to investigate the impact of wastewater characteristics which is being used for irrigation purposes. Furthermore, health risk assessment due to arsenic was conducted because consumption of wastewater irrigated vegetables. Results of selected vegetables shown that the edible portions of vegetables had average concentrations of Ni, Pb, Cd and Cr as 30.14, 27.49, 27.67 and 7.56 mg kg-1, respectively. All the water samples were alkaline. The mean concentrations of Ni, Pb, Cd and Cr in leaves samples were 30.78, 15.58, 12.37 and 3.74 mg kg-1, respectively. Waste water without treatment is not fit for irrigation to the vegetables.
Pages 01-03
Year 2020
Issue 1
Volume 1
Posted by din