Search Results (6)

In water-scarce regions such as Jordan, accurate tracking of water flows is critical for informed water management. This study applied the Water Accounting Plus (WA+) framework using open-source remote sensing data from the FAO WaPOR portal to develop agricultural water accounting (AWA) for the Amman–Zarqa Basin (AZB) during 2014–2022. Inflows, outflows, and water consumption were quantified using WaPOR and other open datasets. The results showed a strong correlation between WaPOR precipitation (P) and rainfall station data, while comparisons with other remote sensing sources were weaker. WaPOR evapotranspiration (ET) values were generally lower than those from alternative datasets. To improve classification accuracy, a correction of the WaPOR-derived land cover map was performed. The revised map achieved a producer’s accuracy of 15.9% and a user’s accuracy of 86.6% for irrigated areas. Additionally, ET values over irrigated zones were adjusted, resulting in a fivefold improvement in estimates. These corrections significantly enhanced the reliability of key AWA indicators such as basin closure, ET fraction, and managed fraction. The findings demonstrate that the accuracy of P and ET data strongly affects AWA outputs, particularly the estimation of percolation and beneficial water use. Therefore, calibrating remote sensing data is essential to ensure reliable water accounting, especially in agricultural settings where data uncertainty can lead to misleading conclusions. This study recommends the use of open-source datasets such as WaPOR—combined with field validation and calibration—to improve agricultural water resource assessments and support decision making at basin and national levels. Full article

Drought is a complex threat where its propagation is not yet controllable, causing more environmental, social, and economic damage. This research assesses the effects of incessant warming and decrescent precipitation by calculating SPI and SPEI from 1985 to 2021 in the Amman –Zarqa Basin based on five grid points on time and space scales. The study applied the Pearson Correlation Coefficient (PCC) between each one-to-one index at different time scales and the Mann–Kendall test (MKT) to determine trends with different data sources to measure the inferential capturing of historical drought features. Machine learning algorithms are used to predict near-future droughts from 2022 to 2025. TBATS and ARIMA models run diverse input datasets, including observations, CSIC, and CMIP6-ssp126 datasets. The longest drought duration was 14 months. Drought severity and average intensity were found to be −24.64 and −1.76, −23.80 and −1.83, −23.57 and −1.96, and −23.44 and −2.0 where the corresponding drought categories were SPI-12 Sweileh, SPI-9 Sweileh, SPI-12 Wadi Dhullal, SPI-12 Amman Airport, respectively. The dominant drought event occurred between Oct 2020 and Dec 2021. CMIP6-ssp126 can capture the drought occurrence and severity by measuring SPI but did not capture the severity magnitude as the observations (SPI was −2.87 by observation and −1.77 by CMIP6). There are significant differences in drought dimensions between SPI and SPEI, where SPI was more sensitive to drought assessment than SPEI. Using CMIP6-ssp126, ARIMA was more accurate than TBATS, as well as using the observed historical SPEI and CSIC across all stations. The performance metrics ME, RMSE, MAE, and MASE implied significantly promising forecasting models with values of −0.0046, 0.278, 0.179, and 0.193, respectively, for ARIMA and −0.0181, 0.538, 0.416, and 0.466, respectively, for TBATS. The outcomes suggest an increased risk of drought incidents and, consequently, water deficits in the future. Hybrid modelling is suggested for more consistency and robustness of forecasting approaches. Full article

Land resources are under relentless pressure from metropolitan regions, pollution, and climate shifts. The urge to monitor Land Use/Land Cover (LULC) and climate changes based on technology and sustainable management are addressed. This study analyzes the historical land cover maps to calculate growth patterns for the years 1985–2022 and uses Logistic Regression (LR) and Artificial Neural Networks (ANN) to project future dynamics forecasts for the years 2030–2040 in the Amman-Zarqa Basin (AZB). The state of the climate and the extreme indices projections of CMIP5 under RCP8.5 are linked to the corrected historical LULC maps and assessed. Given greater dry covering of large surface runoff, little rainfall, and high evapotranspiration rates, the state of the climate across the AZB notably showed instability in key climatic indices and a major exacerbation of warmth and drier soil in the basin. Both climate change and land use are contributing dynamics, but land-use alterations are much more dramatic changes than climate changes. Since the effects of climate alterations are mostly identifiable through land cover forms, land use practices put the phase that may be influenced by climate change. The results revealed that the daily extremes in 1992 are aligned with the corresponding increase of barren lands and diminished the half area of forest, cultivated, rainfed, and pasture lands in 1995. Rainfed regions were converted to agriculture or shrubland with an accuracy of 0.87, and urban encroachment caused the acreage of woodland, cultivated, rainfed, and grazing fields to decrease by almost half. Predicted land cover maps were created using LR in 2030 (Kappa = 0.99) and 2040 (Kappa = 0.90), in contrast to the ANN approach (Kappa = 0.99 for 2030 and 0.90 for 2040). By combining ANN and LR, decreasing bare soil was anticipated between 325 km² and 344 km². As a result, 20% of the total area of the major AZB cities’ urban areas will be doubled. More subjective analysis is required to study and predict drought in the future to improve the resilience of various LULC types. Full article

Treated wastewater is an important component of the water resource in Jordan. As Samra wastewater treatment plant—the largest treatment plant in Jordan—discharges ~110 MCM per year of secondary treated municipal wastewater to Zarqa River, and eventually to Jordan Valley. This research aims at assessing the impact of treated wastewater reuse on the hydrology and environment in the most vulnerable areas within Amman-Zarqa Basin, specifically from As Samra treatment plant to Jerash Bridge. Historical data is collected, field survey is performed, and chemical and biological analyses are performed at eleven selected locations along the study area. Afterwards, all collected data is managed using suitable tools to address the impact. The findings of this research demonstrate high improvement in biological and microbial parameters along the flow path, yet the salinity is increased downstream. It is found that this increase is due to brackish water intrusion, apparently from sandstone aquifer. Analysis of BOD and COD carried out as part of this research showed effective system recovery with COD reduction from 130 mg/L at the effluent to less than 50 mg/l in the downstream. Moreover, microbial activities are reduced, mainly due to self-purification in the river. Full article

This research shows a case from Jordan where geospatial techniques were utilized for irrigation water auditing. The work was based on assessing records of groundwater abstraction in relation to irrigated areas and estimated crop water consumption in three water basins: Yarmouk, Amman-Zarqa and Azraq. Mapping of irrigated areas and crop water requirements was carried out using remote sensing data of Landsat 8 and daily weather records. The methodology was based on visual interpretation and the unsupervised classification for remote sensing data, supported by ground surveys. Net (NCWR) and gross (GCWR) crop water requirements were calculated by merging crop evapotranspiration (ETc), calculated from daily weather records, with maps of irrigated crops. Gross water requirements were compared with groundwater abstractions recorded at a farm level to assess the levels of abstraction in relation to groundwater safe yield. Results showed that irrigated area and GCWR were higher than officially recorded cropped area and abstracted groundwater. The over abstraction of groundwater was estimated to range from 144% to 360% of the safe yield in the three basins. Overlaying the maps of irrigation and groundwater wells enabled the Ministry of Water and Irrigation (MWI) to detect and uncover violations and illegal practices of irrigation, in the form of unlicensed wells, incorrect metering of pumped water and water conveyance for long distances. Results from the work were utilized at s high level of decision-making and changes to the water law were made, with remote sensing data being accredited for monitoring water resources in Jordan. Full article

Jordan faces a sincere water crisis. Groundwater is the major water resource in Jordan and most of the ground water systems are already exploited beyond their estimated safe yield. The Amman Zarqa Basin is one of the most important groundwater systems in Jordan, which supplies the three largest cities in Jordan with drinking and irrigation water. Based on new data the groundwater drawdown in the Amman Zarqa Basin is studied. This basin is the most used drainage area in Jordan. Groundwater drawdown in eight central representative monitoring wells is outlined. Based on almost continuous data for the last 15 years (2000–2015) an average drawdown for the whole basin in the order of 1.1 m·a⁻¹ is calculated. This result is in accordance with results of previous studies in other areas in Jordan and shows that, until now, no sustainable water management is applied. Groundwater management in such a basin presents a challenge for water managers and experts. The applicability of satellite data for estimating large-scale groundwater over exploitation, such as gravity products of the Gravity Recovery and Climate Experiment (GRACE) mission, along with supplementary data, is discussed. Although the size of the basin is below the minimum resolution of GRACE, the data generally support the measured drawdown. Full article