Your search found 5 records
1 Bricquet, Jean P.; Migraine, J. B.; Boonsaner, A.; Janeau, Jean; Valentin, Christian; Maglinao, Amado R. 2003. Development and validation of the PLER (Predict and Localize Erosion and Runoff) Model. In Maglinao, Amado R.; Valentin, Christian; Penning de Vries, Frits (Eds.), From soil research to land and water management: Harmonizing people and nature – Proceedings of the IWMI-ADB Project Annual Meeting and 7th MSEC Assembly. Bangkok, Thailand: IWMI. pp.217-226.
(Location: IWMI-HQ Call no: IWMI 631.45 G570 MAG Record No: H036276)
(0.55 MB)
2 Phai, D. D.; Orange, Didier; Migraine, J. B.; Toan, Tran Duc; Vinh, N. C. 2007. Applying GIS-assisted modelling to predict soil erosion for a small agricultural watershed within sloping lands in Northern Vietnam. Paper presented at the 2nd International Conference on “Sustainable Sloping Lands and Watershed Management”, LuangPhrabang, Laos. 12-15 December 2006. pp. 212-228.
(Location: IWMI HQ Call no: IWMI 631.45 G784 PHA Record No: H040813)
GIS-assisted distributed modelling is particularly useful for supplying information to decision-makers regarding land-use, water management and environmental protection. This study deals with the prediction of soil losses by a simple distributed and GIS-assisted model within a small experimental agricultural watershed on sloping lands in northern Vietnam (<1 km2). The Predict and Localise Erosion and Runoff (PLER) model predicts the spatial and temporal distribution of soil erosion rates; thus it can be used to identify erosion hot spots in a watershed. The model has been built specifically to take into account steep slopes. It is a conceptual erosion model on a physical base. Indeed, the model imitates soil erosion as a dynamic process which includes three phases: i) detachment, ii) transport and iii) deposition. In this study the PLER model was used for two complete years, 2003 and 2004. The disparity for the soil erosion quantity between the experiment and the run model was 5.1% in 2003 and 4.9% in 2004, even though these two years had a very different annual amount of rain. Indeed, 40% of the rainfall events were of a strong intensity (>75 mm hr-1) in 2003 as apposed to only 4% in 2004. The amount of rainfall in 2003 and 2004 was 1,583 mm and 1,353 mm, respectively. The PLER model took into account this discrepancy in the rainfall characteristics between the two years. Between April to September, the disparity fluctuates between just 4.7%-5.3%. The maps drawn by the PLER model underline that the erosion process occurs mainly at the top of the landscape and highlights a different behaviour for detachability and soil erosion between the western and the eastern parts of the studied watershed.
3 Phai, D. D.; Orange, Didier; Migraine, J. B.; Toan, T. D.; Vinh, N. C. 2007. Applying GIS-assisted modelling to predict soil erosion for a small agricultural watershed within sloping lands in Northern Vietnam. In Gebbie, L.; Glendinning, A.; Lefroy-Braun, R.; Victor, M. (Eds.). Proceedings of the International Conference on Sustainable Sloping Lands and Watershed Management: Linking Research to Strengthen upland Policies and Practices, National Agriculture and Forestry Research Institute of Lao PDR (NAFRI), Vientiane, Lao PDR, 2007. Vientiane, LAO PDR: National Agriculture and Forestry Research Institute of Lao PDR (NAFRI) pp.212-228.
(Location: IWMI HQ Call no: IWMI 333.91 G784 PHA Record No: H041518)
4 Cullmann, J.; Dilley, M. (Ed.); Egerton, P.; Grasso, V. F. (Ed.); Honore, C.; Lucio, F.; Luterbacher, J.; Nullis, C.; Power, M.; Rea, A.; Repnik, M.; Stander, J.; Idle, T. (Ed.); Msemo, N. (Ed.); Baubion, N.; Roudier, P.; Woillez, M.- N.; Gomes, A. M.; Dobardzic, S.; Pina, C. L.; Naran, B.; Richmond, M.; Harding, J.; Macasil, M. L. K.; Chaponniere, E.; Hoyer, B.; Losenno, C.; Vaananen, E.; Baugh, C.; Prudhomme, C.; Brovko, E.; Giusti, S.; Hoogeveen, J.; Maher, S.; Neretin, L.; Pek, E.; Gutierrez, A.; Ramage, S.; Venturini, S.; Intsiful, J.; Barnwal, A.; Iqbal, F.; Aich, V.; Debevec, L.; Grey, S.; Sumner, T.; Marsden, K.; Katsanakis, R.; Sengupta, R.; Bensada, A.; Olhoff, A.; Ivanova, O.; Kappelle, M.; Nield, M.; Wang, Y.; Bertule, M.; Glennie, P.; Lloyd, G. J.; Benchwick, G.; Creitaru, L.; Larroquette, B.; Stephens, E.; Properzi, F.; Schade, M.; Bogdanova, A.- M.; Kull, D.; de France, J.; Aich, V.; Alexieva, A.; Bastani, H.; Berit, A.; Berod, D.; Bode, G.; Boscolo, R.; Chernov, I.; de Coning, E.; Eggleston, S.; Ehlert, K.; Delju, A.; Douris, J.; Gallo, I.; Kim, H.; Migraine, J.- B.; Msemo, N.; Polcher, J.; Sparrow, M.; Stefanski, R.; Tripathi, R.; Vara, R. L. S.; Woolnough, S.; Zuniga, J. A.; Christiana, P.; Luo, T.; Saccoccia, L. 2021. 2021 state of climate services: water. Geneva, Switzerland: WMO. 46p. (WMO No.1278)
Water resources ; Climate change ; Information services ; Early warning systems ; Socioeconomic aspects ; Communities ; Flooding ; Water stress ; Drought ; Forecasting ; Governance ; Water supply ; Gender ; Decision making ; Disasters ; Economic losses ; Hurricanes ; Resilience ; Policies ; Hydroelectric power generation ; Meteorological stations ; Disaster risk management ; Disaster risk reduction ; Natural disasters ; Case studies / Asia / Thailand / Africa / Gambia / Europe / Slovakia / North America / Central America / Hondura / Caribbean / South America
(Location: IWMI HQ Call no: e-copy only Record No: H050659)
(4.62 MB) (4.62 MB)
5 Alvar-Beltran, J.; Saturnin, C.; Gregoire, B.; Camacho, J. L.; Dao, A.; Migraine, J. B.; Marta, A, D. 2023. Using AquaCrop as a decision-support tool for improved irrigation management in the Sahel Region. Agricultural Water Management, 287:108430. (Online first) [doi: https://doi.org/10.1016/j.agwat.2023.108430]
Decision support systems ; Irrigation management ; Tomatoes ; Maize ; Quinoa ; Food security ; Agricultural extension ; Water productivity ; Models ; Water resources ; Precipitation ; Evapotranspiration ; Drought stress ; Canopy ; Irrigation schemes ; Yields ; Crop water use ; Water requirements ; Early warning systems / Sahel / West Africa / Burkina Faso
(Location: IWMI HQ Call no: e-copy only Record No: H052111)
https://www.sciencedirect.com/science/article/pii/S0378377423002950/pdfft?md5=071f68c31d21c94884a3be995aaa27d5&pid=1-s2.0-S0378377423002950-main.pdf
https://vlibrary.iwmi.org/pdf/H052111.pdf
https://vlibrary.iwmi.org/pdf/H052111.pdf
(3.41 MB) (3.41 MB)
Operational systems providing irrigation advisories to agricultural extension workers are paramount, particularly in West Africa where the yield gap represents the greatest agriculture growth-led opportunity. The proposed framework for Burkina Faso, an irrigation decision support system (DSS), is based on in-situ weather and field observations necessary for feeding the atmosphere, soil, and crop modules of crop-water productivity models (e.g., AquaCrop). To optimize water resources, incoming irrigation and precipitation, and outgoing evapotranspiration are constantly monitored and adjusted. The findings of the proposed semi-automatic irrigation DSS indicate that water stresses affecting the canopy cover and stomatal closure are minimized if the proposed irrigation schemes are generated and improved with five-day weather observations. The source of uncertainty in crop models’ evapotranspiration estimations is reduced by systematically comparing the observed crop evapotranspiration (ETc) with historical ETc records. An increase in yields is observed in all studied crops, from 1960 to 2018 kg/ha (tomato dry yields), from 2571 to 2799 kg/ha (maize), and from 1279 to 1385 kg/ha (quinoa) when comparing the 2020–21 and 2021–22 experiments. Results show an optimization of water resources, with a higher evapotranspired water productivity (WPET, expressed as dry weight) when comparing the two experiments, from 0.86 to 0.97 kg/m3 for tomato, from 0.85 to 0.86 kg/m3 for maize, and from 0.67 to 0.73 kg/m3 for quinoa, respectively in 2020–21 and 2021–22. The proposed irrigation DSS can be used to inform extension workers and technical agronomic experts about real-time crop water requirements and, thus, assist the Climate Risk and Early Warning Systems (CREWS) initiative that aims to improve access to weather information for decision-support in agriculture. Afterwards, extension agents can catalyze irrigation advisories and support farmers improve irrigation management at the field level to, ultimately, obtain higher yields.
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