Article
Engineering, Civil
Sooraj Krishnan, Ankita Pradhan, J. Indu
Summary: Soil moisture and precipitation are crucial for land surface and atmospheric processes. High-resolution data is essential for hydroclimatology studies. This study uses a bottom-up approach to estimate precipitation from soil moisture, combined with land surface temperature, vegetation index, and soil moisture. The Ganga basin in India is used as a case study, and the results show that high-resolution precipitation can be accurately estimated using satellite-derived soil moisture data.
JOURNAL OF HYDROLOGY
(2022)
Article
Meteorology & Atmospheric Sciences
Shankar N. R. Ram, V. M. Chowdary, Venkateshwar Rao Vala, Chandra Shekhar Jha
Summary: This study comprehensively evaluated different satellite-based gridded rainfall products (SGRPs) and found that different products perform better in different time scales and purposes. The best performing seasonal scale rainfall product was determined through ensemble approach.
THEORETICAL AND APPLIED CLIMATOLOGY
(2022)
Article
Engineering, Electrical & Electronic
Linguang Miao, Zushuai Wei, Fengmin Hu, Zheng Duan
Summary: The SM2RAIN model was evaluated using four widely used satellite microwave soil moisture products for rainfall estimation over the Tibetan Plateau. The results showed that SM2RAIN-SMAP had the highest accuracy, and combining satellite soil moisture products significantly improved the estimation. The SMAP product or combined soil moisture products outperformed the benchmark rainfall products (IMERG and ERA5).
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2023)
Article
Engineering, Civil
Ankita Pradhan, J. Indu
Summary: The study evaluates the impact of different satellite-based precipitation products on LSM simulated soil moisture over India, with results showing biases and RMSE during the monsoon period for each product.
JOURNAL OF HYDROLOGY
(2021)
Article
Environmental Sciences
Ying Zhang, Jinliang Hou, Chunlin Huang
Summary: Precipitation monitoring is crucial for earth system modeling and environmental management. Traditional gauge measurements and satellite-derived rainfall have limitations in spatial representativeness and resolution. This study proposes an integration framework using multiple soil moisture datasets and machine learning methods to improve the accuracy of rainfall estimation, resulting in a successful rainfall product.
Article
Environmental Sciences
Mohammad Saeedi, Sina Nabaei, Hyunglok Kim, Ameneh Tavakol, Venkataraman Lakshmi
Summary: The SM2RAIN algorithm estimates rainfall by inverting the soil-water equation based on soil moisture knowledge. The SM2RAIN-NWF algorithm further improves the estimation by integrating the SM2RAIN algorithm and the net water flux model. The performance of the algorithms is influenced by LSC characteristics and rainfall intensity.
REMOTE SENSING OF ENVIRONMENT
(2023)
Article
Engineering, Civil
Mohammad Saeedi, Ahmad Sharafati, Luca Brocca, Ameneh Tavakol
Summary: The sparse distribution of rain gauge networks poses challenges for estimating rainfall variability. This study developed the SM2RAIN-NWF algorithm by integrating the SM2RAIN algorithm and the NWF model, which improved the accuracy of rainfall estimation. The algorithm showed considerable improvement compared to the SM2RAIN algorithm in terms of correlation coefficient and error indices, and better simulated the variation trend of rainfall.
JOURNAL OF HYDROLOGY
(2022)
Article
Environmental Sciences
Aashutosh Aryal, Thanh-Nhan-Duc Tran, Brijesh Kumar, Venkataraman Lakshmi, Bahman Naser, Hongwei Lu, Lei Wang, Genxu Wang
Summary: This study evaluates the performance of four Satellite-derived Precipitation Products (SPPs) in Nepal's Myagdi Khola watershed and validates their accuracy using a hydrological model. The results show that SM2RAIN-ASCAT and GPM IMERGF perform better than MSWEP and CHIRPS in simulating daily and monthly streamflow.
Article
Environmental Sciences
Ke Zhang, Long Zhao, Kun Yang, Lisheng Song, Xiang Ni, Xujun Han, Mingguo Ma, Lei Fan
Summary: This study quantifies the uncertainties in SM2RAIN-estimated precipitation using SMAP soil moisture data in the Tibetan Plateau. The original SM2RAIN algorithm underestimates precipitation, and the descending SMAP product performs better than the ascending one. The combination of both orbits improves the overall estimation accuracy.
Article
Environmental Sciences
Roghayeh Ghasempour, Mohammad Taghi Aalami, V. S. Ozgur Kirca
Summary: This study investigates the efficiency of the SM2RAIN-ASCAT precipitation product in monitoring drought in the Northwest part of Iran. A new multi-scale method is developed and validated, showing promising results in drought monitoring. The study also explores the influences of different climate indices on the drought index in the selected area.
GEOCARTO INTERNATIONAL
(2022)
Article
Environmental Sciences
Sakine Koohi, Asghar Azizian, Luca Brocca
Summary: Drought, a significant natural phenomenon affecting water resources studies, agriculture, and environmental societies globally, is monitored in diverse climate regions of Iran using a newly developed precipitation dataset. The study shows that the SM2RAIN-ASCAT product performs well in detecting rainy days in extra-arid regions but tends to overestimate precipitation in these areas, while underestimating in humid and per-humid climates. Results also demonstrate good agreement between SM2RAIN-ASCAT and ground-based observations for monitoring drought at different time scales.
SCIENCE OF THE TOTAL ENVIRONMENT
(2021)
Article
Environmental Sciences
Muhammad Asif, Muhammad Umer Nadeem, Muhammad Naveed Anjum, Bashir Ahmad, Gulakhmadov Manuchekhr, Muhammad Umer, Muhammad Hamza, Muhammad Mashood Javaid, Tie Liu
Summary: This study evaluated the performance of four soil moisture-based satellite precipitation products (SPPs) and compared them with weather station data in Pakistan. The results showed that GPM-SM2Rain performed best on a seasonal scale and had the highest probability of detection in summer. Therefore, the daily and monthly use of GPM-SM2Rain and SM2Rain is recommended for hydroclimatic applications in a semi-arid climate zone like Pakistan.
Article
Engineering, Civil
Yuefen Zhang, Chuanhao Wu, Pat J. -F. Yeh, Jianzhu Li, Jiayun Li, Bill X. Hu, Ping Feng
Summary: This study comprehensively evaluates the drought monitoring capabilities of two top-down quantitative precipitation estimation methods and one bottom-up method in mainland China. The results show that the top-down methods perform better in detecting drought in humid regions, while the bottom-up method is more suitable for arid areas.
JOURNAL OF HYDROLOGY
(2023)
Article
Multidisciplinary Sciences
Siswanto Siswanto, Kartika Kusuma Wardani, Babag Purbantoro, Andry Rustanto, Faris Zulkarnain, Evi Anggraheni, Ratih Dewanti, Triarko Nurlambang, Muhammad Dimyati
Summary: Meteorological drought, caused by reduced rainfall, is a major challenge to food security. This study develops a drought indicator using satellite-based precipitation products and applies it to rice-producing districts in Java, Indonesia. The results show the potential of satellite-based precipitation monitoring in predicting and preparing for meteorological drought conditions.
Article
Meteorology & Atmospheric Sciences
Hanqing Chen, Debao Wen, Yanan Du, Luyun Xiong, Leyang Wang
Summary: Revealing the errors and impact of elevation on satellite precipitation products (SPPs) for different rainfall intensity groups is crucial for their applications in hydro-meteorological field, however their spatial distribution and impact are still unclear.
ATMOSPHERIC RESEARCH
(2023)
Article
Geosciences, Multidisciplinary
Anwar Hussain, Khan Zaib Jadoon, Khalil Ur Rahman, Songhao Shang, Muhammad Shahid, Nuaman Ejaz, Himayatullah Khan
Summary: This study evaluates the impact of drought on Pakistan's agriculture sector at national and provincial scales. The results show that Punjab, Balochistan, and Sindh provinces are most vulnerable to drought, and the decrease in drought severity has a positive impact on maize, sugarcane, tobacco, and wheat yields.
Article
Engineering, Civil
Yeqiang Wen, Heyang Wan, Songhao Shang
Summary: Water scarcity and soil salinization are major problems in arid irrigation districts with shallow groundwater table. A water and salt balance model was developed to study these issues, and it was successfully applied to the Hetao Irrigation District in China. The model revealed that evapotranspiration is the dominant water consumption component, and drainage through ditches plays a crucial role in salt balance. The study suggests the need for appropriate measures, such as decreasing irrigation water diversion and improving the drainage system, to address these problems and improve the sustainability of the irrigation district.
JOURNAL OF HYDROLOGY
(2023)
Article
Meteorology & Atmospheric Sciences
Khalil Ur Rahman, Songhao Shang, Khaled Balkhair, Ammara Nusrat
Summary: This study investigates the propagation of meteorological and hydrological droughts in the Indus basin, Pakistan, using principal component analysis (PCA) and wavelet analysis. PCA is used to calculate principal components of precipitation, temperature, and streamflow, enabling the systematic propagation of drought from one catchment to another. Wavelet analysis is used to assess the variability and propagation of drought. The findings enhance our understanding of drought behavior at a catchment scale and can aid in the development of drought mitigation plans in similar regions worldwide.
JOURNAL OF HYDROMETEOROLOGY
(2023)
Article
Geosciences, Multidisciplinary
Khalil Ur Rahman, Anwar Hussain, Nuaman Ejaz, Songhao Shang, Khaled S. Balkhair, Kaleem Ullah Jan Khan, Mahmood Alam Khan, Naeem Ur Rehman
Summary: This study assesses the impact of drought severity on wheat and rice crops in Punjab province, Pakistan, and quantifies crop yield losses under variable drought conditions. Severe drought events were observed during 2000-2020, with the severity increasing from northern to southern Punjab. Wheat losses were highest in Attock, Bahawalpur, Chakwal, Pakpattan, and Sargodha, while the highest loss in rice yield was observed in Mandi Baha Uddin.
INTERNATIONAL JOURNAL OF DISASTER RISK REDUCTION
(2023)
Article
Agronomy
Ximin Qian, Hongwei Qi, Songhao Shang, Heyang Wan, Khalil Ur Rahman, Ruiping Wang
Summary: This study proposed a remote sensing-based LSTM model for near-real-time monitoring of autumn/winter irrigation extent. The model combined MODIS and Sentinel-2 data, solved the mixed pixel issue, and calibrated Sentinel-2 thresholds for AI identification. Validated in the Hetao Irrigation District, the model achieved reasonable performance and revealed changes in irrigation patterns over time, attributed to land policies. The proposed model demonstrates potential for AI scheduling and insight into irrigation patterns and practices.
AGRICULTURAL WATER MANAGEMENT
(2023)
Article
Environmental Sciences
Nuaman Ejaz, Jarbou Bahrawi, Khalid Mohammed Alghamdi, Khalil Ur Rahman, Songhao Shang
Summary: This study investigates the application of remote sensing techniques to monitor drought in the hyper-arid region of Saudi Arabia. The results show that the Vegetation Health Index (VHI) derived from remote sensing has a higher correlation with the Standardized Precipitation Evapotranspiration Index (SPEI), indicating its suitability for drought monitoring in data-scarce hyper-arid regions.
Article
Green & Sustainable Science & Technology
Muhammad Shehzad Ashraf, Muhammad Shahid, Muhammad Waseem, Muhammad Azam, Khalil Ur Rahman
Summary: The use of hydro-climatological time series to identify patterns is crucial for understanding climate change and drought. In this study, hydrological drought variability based on the standard drought index (SDI) was investigated in the Upper Indus River Basin (UIRB) of Pakistan. The findings showed a significant decreasing trend in hydrological drought from October to March and a significant increasing trend from April to September. The IITA method was found to be reliable and effective in analyzing these trends.
Article
Green & Sustainable Science & Technology
Huzaifah Zahran, Muhammad Zeeshan Ali, Khan Zaib Jadoon, Hammad Ullah Khan Yousafzai, Khalil Ur Rahman, Nadeem Ahmed Sheikh
Summary: This study investigates the impact of land cover change on groundwater depletion and explores the spatial distribution of surface temperature due to urbanization. GRACE data and Landsat data are used for groundwater storage and land cover mapping respectively. The results show a rapid increase in groundwater depletion and urbanization rates over the past decade, which is consistent with the spatial pattern of surface temperature. This study highlights the limitation of effective policies for regulating groundwater extraction in urban areas and emphasizes the importance of proper planning for the long-term sustainability of freshwater resources.
Article
Green & Sustainable Science & Technology
Nuaman Ejaz, Mohamed Elhag, Jarbou Bahrawi, Lifu Zhang, Hamza Farooq Gabriel, Khalil Ur Rahman
Summary: This study utilizes the TerrSet model integrated with remote sensing and GIS techniques to examine sediment retention in Wadi Baysh. The study contributes by accurately estimating sediment loads using high resolution datasets, providing valuable information for dam operation and efficiency improvement. The ALPS PALSAR data is used to delineate the basin and as input for the TerrSet model. Rainfall erosivity, soil erodibility, and land use/land cover categorization are calculated using various datasets. The results show significant soil loss in the basin, particularly in the northeast and south, indicating the need for proactive measures to reduce the impact on dam operations.
Article
Agronomy
Heyang Wan, Hongwei Qi, Songhao Shang
Summary: Soil water and salt contents are important soil physical parameters that have significant impacts on hydrological, ecological, environmental, and agricultural processes. Time domain reflectometry (TDR) is commonly used to measure in-situ soil water and salt contents, and provide possible solutions to quickly obtain soil bulk density (BD). This study designed different model input schemes and applied machine learning algorithms to accurately estimate various soil properties based on TDR measurements. The results showed the importance of soil particle-size fractions (psfs) in predicting soil properties, and extreme gradient boosting (XGB) and gradient boosting regression tree (GBRT) algorithms demonstrated good robustness and strong learning capacity.
AGRICULTURAL WATER MANAGEMENT
(2023)