Your search found 5 records
1 Flerchinger, G. N.; Shang, S.; Finnie, J. I. 1996. Simulating three-dimensional ground water response in a small mountainous watershed. Water Resources Bulletin, 32(5):1081-1088.
(Location: IWMI-HQ Call no: PER Record No: H019545)
2 Shang, S.; Li, X.; Mao, X.; Lei, Z. 2004. Simulation of water dynamics and irrigation scheduling for winter wheat and maize in seasonal frost areas. Agricultural Water Management, 68(2):117-133.
Evapotranspiration ; Soil water ; Simulation models ; Irrigation scheduling ; Wheat ; Maize / China / Beijing
(Location: IWMI-HQ Call no: PER Record No: H035391)
3 Yu, B.; Shang, S.. 2020. Estimating growing season evapotranspiration and transpiration of major crops over a large irrigation district from HJ-1A/1B data using a remote sensing-based dual source evapotranspiration model. Remote Sensing, 12(5):865. (Special issue: Remote Sensing in Agricultural Hydrology and Water Resources Modeling) [doi: https://doi.org/10.3390/rs12050865]
Crops ; Evapotranspiration ; Plant growth ; Irrigation water ; Remote sensing ; Satellite imagery ; Water balance ; Maize ; Sunflowers ; Models ; Normalized difference vegetation index / China / Inner Mongolia Autonomous Region / Hetao Irrigation District / Dengkou / Hangjinhouqi / Linhe / Wuyuan
(Location: IWMI HQ Call no: e-copy only Record No: H049725)
(6.10 MB) (6.10 MB)
Crop evapotranspiration (ET) is the largest water consumer of agriculture water in an irrigation district. Remote sensing (RS) technique has provided an effective way to map regional ET using various RS-based ET models over the past several decades. To map growing season ET of different crops and partition ET into evaporation (E) and transpiration (T) at regional scale, appropriate ET models should be further integrated with crop distribution maps in different years and crop growing seasons determined for each crop pixel. In this study, a hybrid dual-source scheme and trapezoid framework-based ET Model (HTEM) fed with HJ-1A/1B data was applied in Hetao Irrigation District (HID) of China from 2009 to 2015 to map crop growing season ET and T at 30 m resolution. The HTEM model with HJ-1A/1B data performed well in estimating ET in HID, and the finer spatial resolution of model input data can improve the estimation accuracy of ET. Combined with the annual crop planting map identified in previous study, and crop growing seasons determined from fitted Normalized Difference Vegetation Index (NDVI) curves for crop pixels, the spatial and temporal variations of growing season ET and T of major crops (maize and sunflower) were examined. The results indicate that ET and T of maize and sunflower reach their minimum values in the southwest HID with smaller crop planting density, and reach their maximum values in northwest HID with higher crop planting density. Over the study period with a decreasing trend of available irrigation water, ET and T in maize and sunflower growing seasons show decreasing trends, while ratios of T/ET show increasing trends, which implies that the adverse effect of decreased irrigation water diversion on crop growth is diminished due to the favorable portioning of E and T in cropland of HID. In addition, the calculation results of crop coefficients show that there is water stress to crop growth in the study area. The present results are helpful to better understand the spatial pattern of crop water consumption and water stress of different crops during crop growing season, and provide the basis for optimizing the spatial distribution of crop planting with less water consumption and more crop yield.
4 Wen, Y.; Wan, H.; Shang, S.; Rahman, K. U. 2022. A monthly distributed agro-hydrological model for irrigation district in arid region with shallow groundwater table. Journal of Hydrology, 609:127746. [doi: https://doi.org/10.1016/j.jhydrol.2022.127746]
Irrigation water ; Groundwater table ; Hydrological modelling ; Arid zones ; Evapotranspiration ; Drainage systems ; Irrigation canals ; Water balance ; Precipitation ; Soil water ; Groundwater flow ; Irrigated land ; Salinity ; Farmland ; Soil texture ; Land use mapping ; Remote sensing / China / Inner Mongolia / Hetao Irrigation District / Yellow River
(Location: IWMI HQ Call no: e-copy only Record No: H051126)
(14.10 MB)
Agro-hydrological processes in arid irrigation districts mainly include precipitation, water diversion, irrigation, drainage, evapotranspiration (ET), and soil water and groundwater flow, which interact with each other and are controlled by complex natural and anthropogenic drivers. To better understand the agro-hydrological processes in arid irrigation districts with shallow groundwater table, we developed a novel monthly distributed agro-hydrological model for irrigation districts (DAHMID) based on the concepts of canal command area (CCA) and sub-drainage command area (SDCA). The DAHMID model is driven by meteorology, irrigation, and evapotranspiration (ET) estimated by remote sensing-based ET model, and considers soil water and groundwater balances in both irrigated and non-irrigated lands and interior drainage between them. The model was applied to Hetao Irrigation District (HID), the largest irrigation district in arid region of China with a total irrigated area of 0.68 million ha. The DAHMID model was calibrated with groundwater table depth measurements in 13 CCAs of HID from 2008 to 2010, and validated from 2012 to 2013. Results depicted that the root mean square errors (RMSEs), normalized RMSEs (NRMSEs), Nash-Sutcliffe efficiency coefficients (NSEs), and coefficients of determination (r2) of groundwater table depth in both irrigated and non-irrigated lands for all CCAs were in the ranges of 0.19–0.34 m, 0.10–0.25, 0.30–0.82, and 0.68–0.91, respectively. The simulation results from 2008 to 2014 indicated that interior drainage from irrigated land to non-irrigated land is an important approach of drainage in HID, which is about 14.3% of total irrigation water diversion and 34.9% more than the drainage through ditches. The interior drainage process is basically similar to irrigation and ditch drainage processes, all reaching their peaks in May and October. ET is the major water consumption in HID, which is about 95% of total irrigation water diversion and precipitation in average. The net capillary rise of irrigated land is significantly less than that of non-irrigated land due to the impact of irrigation infiltration. The DAHMID model has less parameters and requires less inputs, and can be better applied to continuous simulation of agro-hydrological processes in irrigation districts in medium and long periods with satisfactory simulation accuracy.
5 Rahman, K. Ur.; Ejaz, N.; Shang, S.; Balkhair, K. S.; Alghamdi, K. M.; Zaman, K.; Khan, M. A.; Hussain, A. 2024. A robust integrated agricultural drought index under climate and land use variations at the local scale in Pakistan. Agricultural Water Management, 295:108748. [doi: https://doi.org/10.1016/j.agwat.2024.108748]
Integrated management ; Agriculture ; Drought ; Climate change ; Land use ; Agroecological zones ; Soil moisture ; Remote sensing ; Vegetation ; Precipitation ; Evapotranspiration ; Crop yield / Pakistan / Punjab / Rajanpur
(Location: IWMI HQ Call no: e-copy only Record No: H052746)
https://www.sciencedirect.com/science/article/pii/S0378377424000830/pdfft?md5=dc36213e823d7f8cd6e83555fbc73245&pid=1-s2.0-S0378377424000830-main.pdf
https://vlibrary.iwmi.org/pdf/H052746.pdf
https://vlibrary.iwmi.org/pdf/H052746.pdf
(18.50 MB) (18.5 MB)
This study aims to develop an integrated agricultural drought index (IADI) that incorporates multiple remote sensing-based agricultural drought indices, considering variations in climate and land use at a local scale in ten different agro-ecological zones (AEZs) of Pakistan from 2000 to 2020. The IADI focuses on soil moisture stress to vegetation, utilizing the soil moisture condition index (SMCI) derived from various soil moisture datasets. The correlation between SMCI and the Standardized Precipitation Evapotranspiration Index (SPEI) is assessed at different time scales (1-, 3-, 6-, and 12-month) using Pearson correlation coefficient (r). Results indicate that SMCIERA5-Land is superior to others and is selected to develop the IADI. To account for local variations in climate and land use, agricultural drought indices (ADIs) are calculated on a 0.25°×0.25° grids. These ADIs are then averaged over a 3×3 window and analyzed using structural equation modeling (SEM) to understand the causal relationship between SMCIERA5-Land and ADIs. Temperature condition index (TCI) and vegetation health index (VHI) demonstrate a significant causal relationship with SMCIERA5-Land in most AEZs, while the relationship between temperature-vegetation dryness index (TVDI) and vegetation condition index (VCI) with SMCIERA5-Land is insignificant across the AEZs except in northern Pakistan. The dominance of land surface temperature (LST)-derived indices in most AEZs highlights the role of temperature in drought onset and propagation. To develop the IADI, Bayesian principal component analysis (BPCA) is employed to calculate dynamic weights between the selected ADIs and SMCIERA5-Land. The distribution of BPCA weights exhibits extreme variability across different AEZs, with some windows being sensitive to LST-derived ADIs and others to NDVI-derived ADIs. The average BPCA weights for VCI, TCI, VHI, and TVDI range from 9.42%–23.05%, 23.20–49.83%, 13.00–31.08%, and 16.38–25.50%, respectively. The IADI demonstrates the best fit with TCI and VHI, while showing good agreement with SPEI, VCI, and TVDI. The correlation between IADI and TCI/VHI ranges from 0.62/0.72–0.94/0.97. Furthermore, ordinary least square regression (OLSR) is used to assess the accuracy of IADI, ADIs, and SPEI in analyzing the impact of drought on wheat/maize yield at the district level in Punjab province, Pakistan. OLSR analysis reveals the dominance of IADI, followed by VHI, in analyzing the role of drought on crop yield. For example, in Rajanpur district, one unit increase in drought severity results in an 11.94 t/ha decrease in maize yield, with IADI explaining 59% of the variations in maize yield. Similarly, one unit increase in drought severity leads to a 7.22 t/ha decrease in wheat yield, with IADI explaining 49% of the variations in wheat yield. Overall, results indicate that the developed IADI effectively captures agricultural drought with high spatial and temporal resolutions across different agro-ecological zones, providing valuable insights for monitoring agricultural drought in data-scarce regions.
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