Your search found 16 records
1 Thenkabail, P. S.; Lyon, J. G.; Huete, A. (Eds.) 2012. Hyperspectral remote sensing of vegetation. Boca Raton, FL, USA: CRC Press. 705p.
Remote sensing ; Vegetation ; Indicators ; Multispectral imagery ; Satellite observation ; Satellite imagery ; Image analysis ; Data processing ; Data analysis ; Algorithms ; Models ; Sensors ; Water use ; Agriculture ; Crop management ; Cereal crops ; Cotton ; Botany ; Tissue analysis ; Nitrogen content ; Moisture content ; Plant diseases ; Pastures ; Indicator plants ; Species ; Canopy ; Forest management ; Tropical forests ; Wetlands ; Ecosystems ; Soil properties ; Land cover ; Reflectance
(Location: IWMI HQ Call no: 621.3678 G000 THE Record No: H044548)
(0.54 MB)
2 Tang, B.-H.; Shrestha, B.; Li, Z.-L.; Liu, G.; Ouyang, H.; Gurung, D. R.; Amarnath, Giriraj; Aung, K. S. 2013. Determination of snow cover from MODIS data for the Tibetan Plateau Region. International Journal of Applied Earth Observation and Geoinformation, 21:356-365. [doi: https://doi.org/10.1016/j.jag.2012.07.014]
Snow cover ; Cloud cover ; Satellite surveys ; Data ; Mapping ; Indicators ; Algorithms / Central Asia / China / Tibet / Tibetan Plateau
(Location: IWMI HQ Call no: e-copy only Record No: H045039)
(1.65 MB)
This paper addresses a snow-mapping algorithm for the Tibetan Plateau region using Moderate Resolution Imaging Spectroradiometer (MODIS) data. Accounting for the effects of the atmosphere and terrain on the satellite observations at the top of the atmosphere (TOA), particularly in the rugged Tibetan Plateau region, the surface reflectance is retrieved from the TOA reflectance after atmospheric and topographic corrections. To reduce the effect of the misclassification of snow and cloud cover, a normalized difference cloud index (NDCI) model is proposed to discriminate snow/cloud pixels, separate from the MODIS cloud mask product MOD35. The MODIS land surface temperature (LST) product MOD11 L2 is also used to ensure better accuracy of the snow cover classification. Comparisons of the resulting snow cover with those estimated from high spatial-resolution Landsat ETM+ data and obtained from MODIS snow cover product MOD10 L2 for the Mount Everest region for different seasons in 2002, show that the MODIS snow cover product MOD10 L2 overestimates the snow cover with relative error ranging from 20.1% to 55.7%, whereas the proposed algorithm estimates the snow cover more accurately with relative error varying from 0.3% to 9.8%. Comparisons of the snow cover estimated with the proposed algorithm and those obtained from MOD10 L2 product with in situ measurements over the Hindu Kush-Himalayan (HKH) region for December 2003 and January 2004 (the snowy seasons) indicate that the proposed algorithm can map the snow cover more accurately with greater than 90% agreement.
3 Anwar, Arif; Haq, Z. U. 2013. Genetic algorithms for the sequential irrigation scheduling problem. Irrigation Science, 31(4):815-829. [doi: https://doi.org/10.1007/s00271-012-0364-y]
(Location: IWMI HQ Call no: PER Record No: H045325)
(0.73 MB)
A sequential irrigation scheduling problem is the problem of preparing a schedule to sequentially service a set of water users. This problem has an analogy with the classical single machine earliness/tardiness scheduling problem in operations research. In previously published work, integer program and heuristics were used to solve sequential irrigation scheduling problems; however, such scheduling problems belong to a class of combinatorial optimization problems known to be computationally demanding (NP-hard). This is widely reported in operations research. Hence, integer program can only be used to solve relatively small problems usually in a research environment where considerable computational resources and time can be allocated to solve a single schedule. For practical applications, metaheuristics such as genetic algorithms (GA), simulated annealing, or tabu search methods need to be used. These need to be formulated carefully and tested thoroughly. The current research is to explore the potential of GA to solve the sequential irrigation scheduling problems. Four GA models are presented that model four different sequential irrigation scenarios. The GA models are tested extensively for a range of problem sizes, and the solution quality is compared against solutions from integer programs and heuristics. The GA is applied to the practical engineering problem of scheduling water scheduling to 94 water users.
4 Amarnath, Giriraj; Ameer, Mohamed; Aggarwal, Pramod; Smakhtin, Vladimir. 2012. An algorithm for rapid flood inundation mapping from optical data using reflectance differencing technique [Abstract only]. In de Silva, R. P.; Kumar, N.; Mehmood, H. (Eds.). GIT4NDM - reduce exposure to reduce risk: proceedings of the 4th International Conference on Geo-information Technology for Natural Disaster Management (GIT4NDM), Colombo, Sri Lanka, 7-8 November 2012. Pathumthani, Thailand: Geoinformatics Intenational. pp.19.
(Location: IWMI HQ Call no: e-copy only Record No: H045697)
(0.13 MB)
5 Amarnath, Giriraj. 2014. An algorithm for rapid flood inundation mapping from optical data using a reflectance differencing technique. Journal of Flood Risk Management, 7(3):239-250. [doi: https://doi.org/10.1111/jfr3.12045]
Remote sensing ; Algorithms ; Flooding ; Mapping ; Satellite imagery ; Satellite surveys ; Surface water ; Indicators ; Vegetation
(Location: IWMI HQ Call no: e-copy only Record No: H045768)
(1.46 MB)
This paper presents an algorithm for flood inundation mapping in the context of emergency response. Rapid satellite-based flood inundation mapping and delivery of flood inundation maps during a flood event can provide crucial information for decision-makers to put relief measures in place.With the development of remote sensing techniques, flood mapping for large areas can be done easily. The algorithm discussed here involves the use of shortwave infrared, near-infrared and green spectral bands to develop a suitable band rationing technique for detecting surface water changes. This technique is referred to as Normalized Difference Surface Water Index (NDSWI). The NDSWI-based approach produces the best results for mapping of flood-inundated areas when verified with actual satellite data. Analysis of results reveals that NDSWI has the potential to detect floodwater turbidity, which was verified using principal component analysis. The application of the technique is informative about flood damages, which are illustrated using the floods in Pakistan in 2010 as an example.
6 Kiptala, J. K.; Mohamed, Y.; Mul, Marloes L.; Van der Zaag, P. 2013. Mapping evapotranspiration trends using MODIS and SEBAL model in a data scarce and heterogeneous landscape in eastern Africa. Water Resources Research, 49(12):8495-8510. [doi: https://doi.org/10.1002/2013WR014240, 2013]
Mapping ; Evapotranspiration ; Evaporation ; Models ; Algorithms ; Data ; Semiarid climate ; Landscape ; Water use ; Water balance ; Water accounting ; River basins ; Land use ; Land cover ; Reservoirs ; Precipitation / Eastern Africa / Upper Pangani River Basin / Nyumba ya Mungu reservoir
(Location: IWMI HQ Call no: e-copy only Record No: H046302)
[1] Evapotranspiration (ET) accounts for a substantial amount of the water use in river basins particular in the tropics and arid regions. However, accurate estimation still remains a challenge especially in large spatially heterogeneous and data scarce areas including the Upper Pangani River Basin in Eastern Africa. Using multitemporal Moderate-resolution Imaging Spectroradiometer (MODIS) and Surface Energy Balance Algorithm of Land (SEBAL) model, 138 images were analyzed at 250 m, 8 day scales to estimate actual ET for 16 land use types for the period 2008–2010. A good agreement was attained for the SEBAL results from various validations. For open water evaporation, the estimated ET for Nyumba ya Mungu (NyM) reservoir showed a good correlations (R = 0.95; R2 = 0.91; Mean Absolute Error (MAE) and Root Means Square Error (RMSE) of less than 5%) to pan evaporation using an optimized pan coefficient of 0.81. An absolute relative error of 2% was also achieved from the mean annual water balance estimates of the reservoir. The estimated ET for various agricultural land uses indicated a consistent pattern with the seasonal variability of the crop coefficient (Kc) based on Penman-Monteith equation. In addition, ET estimates for the mountainous areas has been significantly suppressed at the higher elevations (above 2300 m a.s.l.), which is consistent with the decrease in potential evaporation. The calculated surface outflow (Qs) through a water balance analysis resulted in a bias of 12% to the observed discharge at the outlet of the river basin. The bias was within 13% uncertainty range at 95% confidence interval for Qs. SEBAL ET estimates were also compared with global ET from MODIS 16 algorithm (R = 0.74; R2 = 0.32; RMSE of 34% and MAE of 28%) and comparatively significant in variance at 95% confidence level. The interseasonal and intraseasonal ET fluxes derived have shown the level of water use for various land use types under different climate conditions. The evaporative water use in the river basin accounted for 94% to the annual precipitation for the period of study. The results have a potential for use in hydrological analysis and water accounting.
7 Awan, U. K.; Ismaeel, Ali. 2014. A new technique to map groundwater recharge in irrigated areas using a SWAT model under changing climate. Journal of Hydrology, 519:1368-1382. [doi: https://doi.org/10.1016/j.jhydrol.2014.08.049]
Groundwater recharge ; Irrigated sites ; Irrigation schemes ; Climate change ; Models ; Soils ; Assessment ; Evapotranspiration ; Energy balance ; Algorithms ; Calibration ; Canals ; Land cover ; Cropping patterns ; Remote sensing / Pakistan / Lower Chenab Canal Irrigation Scheme / Indus Basin
(Location: IWMI HQ Call no: e-copy only Record No: H046715)
(4.92 MB)
The Lower Chenab canal irrigation scheme, the largest irrigation scheme of the Indus Basin irrigation system was selected for an estimate of groundwater recharge using the soil and water assessment tool (SWAT) at high spatial and temporal resolution under changing climate. Groundwater recharge was simulated using the SWAT model for representative concentration pathways (RCP) 4.5 and 8.5 climate change scenarios for the period 2012–2020. Actual evapotranspiration (ETa) was estimated using the SWAT model for the period 2010–2011. This was compared with the ETa determined using the surface energy balance algorithm (SEBAL) calibrated using data for the period 2005–2009. We concluded that the SWAT ETa estimates showed good agreement with those of SEBAL (coefficient of determination = 0.85 ± 0.05, Nash–Sutcliffe efficiency = 0.83 ± 0.07). The total average annual groundwater recharge to the aquifer was 537 mm (±55 mm) with the maximum occurring during July (151 mm). The results showed that groundwater recharge would increase by 40%, as compared to the reference period, by the end of 2020 under RCP 4.5 and by 37% under RCP 8.5. The SWAT can thus be a handy tool for not only estimating the recharge at high spatial and temporal resolution, but also under changing climate.
8 Chemin, Yann H. 2014. Remote sensing raster programming. 3rd ed. Raleigh, NC, USA: Lulu Press Inc. 111p.
Remote sensing ; Computer programming ; Algorithms ; Satellite observation ; Image processing ; GIS ; Computer software
(Location: IWMI HQ Call no: IWMI Record No: H046885)
(0.87 MB)
9 Ebrahim, Girma Yimer; Jonoski, A.; Al-Maktoumi, A.; Ahmed, M.; Mynett, A. 2016. Simulation-optimization approach for evaluating the feasibility of managed aquifer recharge in the Samail Lower Catchment, Oman. Journal of Water Resources Planning and Management, 142(2):1-16. [doi: https://doi.org/10.1061/(ASCE)WR.1943-5452.0000588]
Aquifers ; Groundwater recharge ; Groundwater management ; Water levels ; Water budget ; Water supply ; Catchment areas ; Dams ; Mathematical models ; Simulation models ; Algorithms ; Sensitivity analysis ; Hydraulic conductivity ; Calibration ; Salt water intrusion / Oman / Samail Lower Catchment
(Location: IWMI HQ Call no: e-copy only Record No: H047227)
(16.33 MB)
This article presents a simulation-optimization approach for evaluating the feasibility of managed aquifer recharge (MAR) in the Samail Lower Catchment, Oman. The objective is to provide a maximum recharge and extraction rate through MAR in an annual cycle of two successive injection and recovery periods, while meeting operational and system constraints such as water level, gradient, and travel time. Three groundwater management problems were solved by coupling a simulation model with successive linear programming (SLP) and the nondominated sorting genetic algorithm (NSGA-II) multiobjective genetic algorithm. Sensitivity analysis was also completed to examine the overall response of the simulation-optimization results to changes in hydraulic conductivities and maximum injection rates. Results using the SLP algorithm showed that the total volume of injected water for 4 months of injection without recovery is as high as 8 × 106 m3, and the total recovered volume of water for 4months injection and 8 months recovery is approximately 5.3 × 106 m3, giving a total recovery efficiency of approximately 66%. For the same setup the NSGA-II algorithm derived the entire nondominated front of solutions for two conflicting objectives: maximizing recovery rate and maximizing minimum groundwater head close to the sea (for preventing seawater intrusion). This algorithm includes travel time constraints directly in the optimization process. In conclusion, the proposed approach provides a cost-effective means to evaluate MAR in a coastal aquifer.
10 Anwar, Arif A.; Haq, Z. U. 2016. Arranged-demand irrigation scheduling with nonidentical discharges. Journal of Irrigation and Drainage Engineering, 142(9):1-10. [doi: https://doi.org/10.1061/(ASCE)IR.1943-4774.0001029]
Irrigation water ; Irrigation systems ; Irrigation canals ; Water supply ; Water demand ; Farmers ; Flow discharge ; Mathematical models ; Algorithms / Pakistan / Khyber Pukhtunkhwa Province / Maira Branch Canal
(Location: IWMI HQ Call no: e-copy only Record No: H047678)
Several irrigation water delivery methods are in practice in irrigated agriculture throughout the world, and a variety of classifications have been suggested by different researchers. Demand, arranged-demand, and rotation are the three main types of irrigation schedules/delivery methods. Irrigation systems may also be classified as either sequential or simultaneous. Supplying water sequentially to farmers according to their requested times constitutes an irrigation scheduling problem analogous to the classical earliness/tardiness single machine scheduling problems in Operational Research (OR). In this paper, the authors describe an irrigation scheduling problem analogous to the complex multimachine scheduling problem. The authors develop a genetic algorithm (GA) and test this algorithm against solutions obtained from an integer program to draw conclusions about the solution quality of the GA. The researchers demonstrate the potential of this GA through an engineering application of the Maira Branch Canal. The authors show that if this canal is operated at a constant discharge, the arranged-demand schedule requires the canal to be operated at 75% of the discharge required if this canal were operated on an on-demand schedule.
11 Ul Haq, Z.; Anwar, Arif A. 2017. Simultaneous-irrigation scheduling GA [genetic algorithm] model with identical discharges and travel time. Journal of Irrigation and Drainage Engineering, 143(2):1-6. [doi: https://doi.org/10.1061/(ASCE)IR.1943-4774.0001125]
Irrigation scheduling ; Irrigation systems ; Discharges ; Operations research ; Farmers ; Irrigation canals ; Mathematical models ; Algorithms ; Constraints
(Location: IWMI HQ Call no: e-copy only Record No: H047891)
Multimachine scheduling problems with earliness/tardiness costs and sequence-dependent setup times are analogous to the simultaneous irrigation scheduling problem with water travel times between outlets in a canal irrigation system where all the farmers are supplied with identical discharges at their requested time, i.e., arranged demand irrigation scheduling. The multimachine scheduling problem with earliness/tardiness costs even without setup consideration is computationally very demanding and optimum solutions are not possible in practical time limits. The addition of the sequence-dependent setup time and the dual goal of minimizing earliness/tardiness and the number of machines makes it even more difficult, complicated, and novel. For practical applications, meta-heuristics such as genetic algorithms, simulated annealing, or tabu search methods need to be used. This study employs the genetic algorithm (GA) model. The model presented here is an improvement over earlier work as it considers travel time in a multimachine or simultaneous irrigation system and resolves the issue of computational time by using an approximate algorithm instead of an exact algorithm. However, no quantitative comparison can be donewith earlier models as the current model accommodates travel time; hence, its objectivefunction is numerically different than earlier models. The problem is successfully modeled using GA and its implementation is demonstrated. No comprehensive data set is available that completes the requirements of rigorous testing of the GA model. Therefore, to evaluate the performance of the GA model with travel time, instances were randomly generated from a uniform distribution, for three different values of travel times. The GA model was able to obtain feasible schedules for all the instances tested.
12 Yi, W. 2021. Forecast of agricultural water resources demand based on particle swarm algorithm. Acta Agriculturae Scandinavica, Section B - Soil and Plant Science, 14p. (Online first) [doi: https://doi.org/10.1080/09064710.2021.1990386]
Agriculture ; Water resources ; Water demand ; Forecasting ; Machine learning ; Algorithms ; Models ; Water footprint
(Location: IWMI HQ Call no: e-copy only Record No: H050818)
https://www.tandfonline.com/doi/pdf/10.1080/09064710.2021.1990386
https://vlibrary.iwmi.org/pdf/H050818.pdf
https://vlibrary.iwmi.org/pdf/H050818.pdf
(2.57 MB) (2.57 MB)
The planning and management of water resources are becoming more and more important, and the forecast of water demand as the prerequisite and foundation of the entire planning has become a very important task in agricultural development. This paper combines the particle swarm algorithm to construct the agricultural water resource demand forecasting model, analyzes the shortcomings of the traditional particle swarm algorithm, and makes appropriate improvements to the quantum particle swarm algorithm. Moreover, this paper constructs the functional structure of the agricultural water resource demand forecast model based on the forecast demand of water resources, and analyzes the application process of the particle swarm algorithm in the system of this paper. After the model is constructed, the performance of the model is verified, and the simulation test is designed to evaluate the effect of system forecast with actual data. At the same time, this paper uses the model constructed in this paper to analyze the factors affecting water resources forecast demand. From the results of the experimental analysis, it can be seen that the model constructed in this paper is more effective in the forecast of water resources demand.
13 Leggesse, E. S.; Zimale, F. A.; Sultan, D.; Enku, T.; Srinivasan, R.; Tilahun, Seifu A. 2023. Predicting optical water quality indicators from remote sensing using machine learning algorithms in tropical highlands of Ethiopia. Hydrology, 10(5):110. [doi: https://doi.org/10.3390/hydrology10050110]
Water quality ; Indicators ; Prediction ; Remote sensing ; Machine learning ; Algorithms ; Neural networks ; Modelling ; Total dissolved solids ; Turbidity ; Chlorophyll A ; Landsat ; Satellite imagery ; Monitoring ; Highlands ; Lakes / Ethiopia / Lake Tana
(Location: IWMI HQ Call no: e-copy only Record No: H051963)
https://www.mdpi.com/2306-5338/10/5/110/pdf?version=1684396571
https://vlibrary.iwmi.org/pdf/H051963.pdf
https://vlibrary.iwmi.org/pdf/H051963.pdf
(3.60 MB) (3.60 MB)
Water quality degradation of freshwater bodies is a concern worldwide, particularly in Africa, where data are scarce and standard water quality monitoring is expensive. This study explored the use of remote sensing imagery and machine learning (ML) algorithms as an alternative to standard field measuring for monitoring water quality in large and remote areas constrained by logistics and finance. Six machine learning (ML) algorithms integrated with Landsat 8 imagery were evaluated for their accuracy in predicting three optically active water quality indicators observed monthly in the period from August 2016 to April 2022: turbidity (TUR), total dissolved solids (TDS) and Chlorophyll a (Chl-a). The six ML algorithms studied were the artificial neural network (ANN), support vector machine regression (SVM), random forest regression (RF), XGBoost regression (XGB), AdaBoost regression (AB), and gradient boosting regression (GB) algorithms. XGB performed best at predicting Chl-a, with an R2 of 0.78, Nash–Sutcliffe efficiency (NSE) of 0.78, mean absolute relative error (MARE) of 0.082 and root mean squared error (RMSE) of 9.79 µg/L. RF performed best at predicting TDS (with an R2 of 0.79, NSE of 0.80, MARE of 0.082, and RMSE of 12.30 mg/L) and TUR (with an R2 of 0.80, NSE of 0.81, and MARE of 0.072 and RMSE of 7.82 NTU). The main challenges were data size, sampling frequency, and sampling resolution. To overcome the data limitation, we used a K-fold cross validation technique that could obtain the most out of the limited data to build a robust model. Furthermore, we also employed stratified sampling techniques to improve the ML modeling for turbidity. Thus, this study shows the possibility of monitoring water quality in large freshwater bodies with limited observed data using remote sensing integrated with ML algorithms, potentially enhancing decision making.
14 Chandrasekharan, Kiran M.; Villholth, Karen G.; Kashaigili, J. J.; Gebregziabher, Gebrehaweria; Mandela, P. J. 2023. Land cover changes in the Upper Great Ruaha (Tanzania) and the Upper Awash (Ethiopia) river basins and their potential implications for groundwater resources. Colombo, Sri Lanka: International Water Management Institute (IWMI). 49p. (IWMI Research Report 184) [doi: https://doi.org/10.5337/2023.212]
Land cover change ; River basins ; Groundwater ; Water resources ; Land cover mapping ; Land use change ; Human settlements ; Rainfed farming ; Irrigated farming ; Irrigated areas variety ; Grasslands ; Woodlands ; Forest plantations ; Wetlands ; Catchment areas ; Vegetation index ; Moisture index ; Remote sensing ; Landsat ; Satellite imagery ; Datasets ; Algorithms ; Trends ; Climate change ; Urbanization / Africa South of Sahara / United Republic of Tanzania / Ethiopia / Upper Great Ruaha River Basin / Upper Awash River Basin
(Location: IWMI HQ Call no: IWMI Record No: H052252)
(3.58 MB)
Over the past century, the world has experienced an unprecedented surge in population growth, accompanied by a significant increase in economic activity and fuelled by an intensive utilization of natural resources, including water. This phenomenon has brought about profound alterations in land cover and land use patterns across various regions. Knowledge of land use changes is key to unlocking an understanding of water use changes and associated impacts on water resources, and potential threats to sustainability. However, the pace and nature of land use transitions vary widely across the globe, shaped by a complex interplay of local, regional and global factors, making systematic assessments important. This report presents the results of a land cover change analysis conducted in two river basins in sub-Saharan Africa: the Upper Great Ruaha River Basin (UGRRB) in Tanzania and the Upper Awash River Basin (UARB) in Ethiopia. The spatio-temporal analysis spans a recent 15-20-year period up until 2015/16 and utilizes remote sensing imagery, secondary maps and ground truth information for the two end point times (resolution: 30 m). The basins are significantly different in terms of agricultural development and water resource use. UARB represents an area with emerging commercial farms, urban expansion and diminishing natural vegetation, whereas UGRRB still retains significant natural vegetation but is experiencing an increase in smallholder agriculture as well as intensive commercial irrigation potentially affecting fragile wetland systems. In UGRRB, surface water is the main source of irrigation water, while in UARB, groundwater resources are increasingly used for irrigation by smallholder farmers. The findings reveal a common overall trend in both basins that is similar to many low-income countries, illustrating an expansion of agricultural and irrigated areas and human settlements at the expense of natural land cover. The report presents a detailed systematic remote sensing-based methodology to quantify and compare land cover transitions in time and space with high resolution, within and between agricultural landscapes of larger basins. The study highlights that land cover changes in the basins follow diverse and unique trajectories, providing critical insights into evolving land use patterns. In its conclusion, the study underscores the profound implications of recent land use changes for groundwater resources within these agro-pastoral systems. Overall, the report highlights the importance of sustainable land management and integrated water resources management, and provides valuable insights into the complexities of land use change in these regions.
15 Pattinson, N. B.; Russell, C.; Taylor, J.; Dickens, Chris W. S.; Koen, R. C. J.; Koen, F. J.; Graham, P. M. 2023. Digital innovation with miniSASS, a citizen science biomonitoring tool. Colombo, Sri Lanka: International Water Management Institute (IWMI). CGIAR Initiative on Digital Innovation. 11p.
Digital technology ; Citizen science ; Biomonitoring ; Rivers ; Water quality ; Macroinvertebrates ; Mobile applications ; Machine learning ; Algorithms ; Databases ; Training ; Sustainable Development Goals / South Africa / Mooi River / uMgeni River / Karkloof River
(Location: IWMI HQ Call no: e-copy only Record No: H052345)
(1.32 MB)
The mini stream assessment scoring system (miniSASS) was developed as a citizen science biomonitoring tool for assessing the water quality and health of stream and river systems. A miniSASS survey involves sampling the aquatic macroinvertebrate community in a stream or river reach and using the known sensitivities and tolerances of the taxa present to infer information about the water quality and health of the stream or river. The quality of the outcomes of a miniSASS survey is dependent on good sampling technique and accurate identification of aquatic macroinvertebrates by low-skilled citizen scientists. As such, there is potential for errors in sampling and identification which may impact the accuracy of results. In response, we aimed to 1) develop a smartphone application (miniSASS mobile app with built-in machine learning (ML) algorithm for the automatic, real-time identification of aquatic macroinvertebrates) to assist in miniSASS surveys, 2) modernise and upgrade the miniSASS website to handle new data submissions (including photographs) and improve the user interface (UI), and 3) develop an online miniSASS training course. This report presents the methodology and preliminary results pertaining to these objectives.
16 Mabhaudhi, Tafadzwanashe; Dirwai, Tinashe Lindel; Taguta, C.; Sikka, Alok; Lautze, Jonathan. 2023. Mapping Decision Support Tools (DSTs) on agricultural water productivity: a global systematic scoping review. Agricultural Water Management, 290:108590. [doi: https://doi.org/10.1016/j.agwat.2023.108590]
Decision support systems ; Mapping ; Agricultural water management ; Water productivity ; Integrated development ; Models ; Algorithms ; Spatial analysis ; Systematic reviews ; Geographical information systems ; Remote sensing ; Irrigation scheduling ; Energy balance ; Food security ; Nexus approaches
(Location: IWMI HQ Call no: e-copy only Record No: H052407)
https://www.sciencedirect.com/science/article/pii/S0378377423004559/pdfft?md5=0ab53b59f378e1d440519afef292c719&pid=1-s2.0-S0378377423004559-main.pdf
https://vlibrary.iwmi.org/pdf/H052407.pdf
https://vlibrary.iwmi.org/pdf/H052407.pdf
(5.01 MB) (5.01 MB)
While there is a proliferation of Decision Support Tools (DSTs) to enhance agricultural water productivity (AWP) and related objectives such as food security, an assessment of their adoption and performance is not known to be undertaken. To develop new or improved DSTs for bespoke applications in optimizing AWP, there needs to be a stock-take of the existing tools, their functionality, user-friendliness and uptake. We compiled and assessed existing DSTs for AWP as a starting point for present and future developers who intend to improve existing or develop new DSTs for optimizing AWP. Secondarily, this review identifies DSTs’ key characteristics, availability, and applicability for different typologies and spatio-temporal scales. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach was applied to search for literature from Scopus and WoS databases. The study revealed the existence of 81 documented AWP DSTs whose development started from around the 1970 s, peaked in the 1990 s, and declined after that although the improvement and upgrading of existing DSTs continued. Over half (51%) of the DSTs are not readily available in the public domain. The prevalent spatial and temporal application scales are field and day, respectively. There is limited reporting on the application at scale, partly due to the wide unavailability of DSTs. A gap exists in AWP DSTs with geospatial capabilities (one in 10 or 10% had geographic information systems (GIS) integration capabilities). Most DSTs focus on water and food (yield) components but omit energy and other dimensions of AWP. Regarding format, most tools were available as desktop (35%) and web-based (48%) applications, and codes (27%). Developers should strive to deliver AWP tools in convenient, compatible, and user-friendly for a wide range of users, from novices to experts.
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