Your search found 7 records
1 Marsalek, J.; Stancalie, G.; Balint, G. (Eds.) 2006. Transboundary floods: reducing risks through flood management. Dordrecht, Netherlands: Springer. 336p. (NATO Science Series IV - Earth and Environmental Sciences, vol. 72)
Flood control ; Forecasting ; Disasters ; Risks ; International waters ; River basins ; Remote sensing ; GIS ; Discharges ; Hydrometeorology ; Hydrology ; Telemetry ; Sensors ; Land use mapping ; Models ; Weather forecasting ; Rainfall-runoff relationships ; Urban areas ; History ; Decision making ; Dykes ; Disaster preparedness ; Reservoirs ; International cooperation / Czech Republic / Azerbaijan / Romania / Hungary / Koros River Basin / Upper Tisza Region / Crisul Alb River Basin / Crisul Negru River Basin / Hron River Basin / Crisul Repede River Basin
(Location: IWMI HQ Call no: 551.489 G000 MAR Record No: H043960)
(0.13 MB)
2 Tiwari, K.; Goyal, R.; Sarkar, A. 2018. GIS-based methodology for identification of suitable locations for rainwater harvesting structures. Water Resources Management, 32(5):1811-1825. [doi: https://doi.org/10.1007/s11269-018-1905-9]
Rainwater ; Water harvesting ; GIS ; Remote sensing ; Surface runoff ; Drainage systems ; Estimation ; Land use mapping ; Land cover mapping ; Soil types ; Slopes ; Models / India / Rajasthan / Alwar
(Location: IWMI HQ Call no: e-copy only Record No: H048510)
(4.10 MB)
Presently, the water resources across the world are being continuously depleted. It is essential to find sustainable solutions for this shortage of water. Rainwater harvesting is one such promising solution to this problem. This paper presents a new GIS-based methodology to identify suitable locations for rainwater harvesting structures using only freely available imageries/remote sensing data and data from other sources. The methodology has been developed for the semi-arid environment of Khushkhera-Bhiwadi-Neemrana Investment Region (KBNIR) in Alwar district of Rajasthan. For identifying locations suitable for rainwater harvesting structures, the layers of surface elevation (ASTER-DEM), landuse/landcover, soil map, drainage map and depression map are used and further analyzed for their depression volume, and availability of surface runoff using Soil Conservation Service - Curve Number (SCS-CN) method. Based on the proposed criteria total seven locations were identified, out of which two locations are excellent; three locations are good, (if provisions of overflow structure are made for them) and two locations are not suitable for rain water harvesting. The total rainwater harvesting potential of the study area is 54.49 million cubic meters which is sufficient to meet the water requirements if harvested and conserved properly. This methodology is time-saving and cost-effective. It can minimize cost of earthwork and can be utilized for the planning of cost effective water resource management.
3 Nhamo, Luxon; van Dijk, R.; Magidi, J.; Wiberg, David; Tshikolomo, K. 2018. Improving the accuracy of remotely sensed irrigated areas using post-classification enhancement through UAV [Unmanned Aerial Vehicle] capability. Remote Sensing, 10(5):1-12. (Special issue: Remote Sensing for Crop Water Management). [doi: https://doi.org/10.3390/rs10050712]
Irrigated sites ; Remote sensing ; Unmanned aerial vehicles ; Land use mapping ; Land cover mapping ; Satellite imagery ; Landsat ; Farmland ; Vegetation index ; Crops / South Africa / Limpopo Province / Venda / Gazankulu
(Location: IWMI HQ Call no: e-copy only Record No: H048752)
(2.23 MB) (2.23 MB)
Although advances in remote sensing have enhanced mapping and monitoring of irrigated areas, producing accurate cropping information through satellite image classification remains elusive due to the complexity of landscapes, changes in reflectance of different land-covers, the remote sensing data selected, and image processing methods used, among others. This study extracted agricultural fields in the former homelands of Venda and Gazankulu in Limpopo Province, South Africa. Landsat 8 imageries for 2015 were used, applying the maximum likelihood supervised classifier to delineate the agricultural fields. The normalized difference vegetation index (NDVI) applied on Landsat imageries on the mapped fields during the dry season (July to August) was used to identify irrigated areas, because years of satellite data analysis suggest that healthy crop conditions during dry seasons are only possible with irrigation. Ground truth points totaling 137 were collected during fieldwork for pre-processing and accuracy assessment. An accuracy of 96% was achieved on the mapped agricultural fields, yet the irrigated area map produced an initial accuracy of only 71%. This study explains and improves the 29% error margin from the irrigated areas. Accuracy was enhanced through post-classification correction (PCC) using 74 post-classification points randomly selected from the 2015 irrigated area map. High resolution aerial photographs of the 74 sample fields were acquired by an unmanned aerial vehicle (UAV) to give a clearer picture of the irrigated fields. The analysis shows that mapped irrigated fields that presented anomalies included abandoned croplands that had green invasive alien species or abandoned fruit plantations that had high NDVI values. The PCC analysis improved irrigated area mapping accuracy from 71% to 95%.
4 Fayas, C. M.; Abeysingha, N. S.; Nirmanee, K. G. S.; Samaratunga, D.; Mallawatantri, A. 2019. Soil loss estimation using RUSLE model to prioritize erosion control in Kelani River Basin in Sri Lanka. International Soil and Water Conservation Research, 7(2):130-137. [doi: https://doi.org/10.1016/j.iswcr.2019.01.003]
Revised Universal Soil Loss Equation ; Estimation ; Soil erosion models ; Erosion control ; Land degradation ; Land use mapping ; Land cover mapping ; River basins ; Slope ; Rain ; Runoff / Sri Lanka / Kelani River Basin
(Location: IWMI HQ Call no: e-copy only Record No: H049211)
https://www.sciencedirect.com/science/article/pii/S2095633918301734/pdfft?md5=a3753a3c707e963d96f83f94ed76ed9d&pid=1-s2.0-S2095633918301734-main.pdf
https://vlibrary.iwmi.org/pdf/H049211.pdf
https://vlibrary.iwmi.org/pdf/H049211.pdf
(3.17 MB) (3.17 MB)
Soil erosion contributes negatively to agricultural production, quality of source water for drinking, ecosystem health in land and aquatic environments, and aesthetic value of landscapes. Approaches to understand the spatial variability of erosion severity are important for improving landuse management. This study uses the Kelani river basin in Sri Lanka as the study area to assess erosion severity using the Revised Universal Soil Loss Equation (RUSLE) model supported by a GIS system. Erosion severity across the river basin was estimated using RUSLE, a Digital Elevation Model (15 15 m), twenty years rainfall data at 14 rain gauge stations across the basin, landuse and land cover, and soil maps and cropping factors. The estimated average annual soil loss in Kelani river basin varied from zero to 103.7 t ha-1 yr1 , with a mean annual soil loss estimated at 10.9 t ha1 yr1 . About 70% of the river basin area was identified with low to moderate erosion severity (o12 t ha1 yr1 ) indicating that erosion control measures are urgently needed to ensure a sustainable ecosystem in the Kelani river basin, which in turn, is connected with the quality of life of over 5 million people. Use of this severity information developed with RUSLE along with its individual parameters can help to design landuse management practices. This effort can be further refined by analyzing RUSLE results along with Kelani river sub-basins level real time erosion estimations as a monitoring measure for conservation practices.
5 Rana, V. K.; Suryanarayana, T. M. V. 2020. Performance evaluation of MLE [Maximum Likelihood Estimation], RF [Random Forest Tree] and SVM [Support Vector Machine] classification algorithms for watershed scale land use/land cover mapping using sentinel 2 bands. Remote Sensing Applications: Society and Environment, 19:100351. [doi: https://doi.org/10.1016/j.rsase.2020.100351]
Watersheds ; Land use mapping ; Land cover mapping ; Hydrology ; Models ; Performance evaluation ; Remote sensing ; Satellites ; Vegetation ; Cultivated land ; Rain ; Machine learning ; Principal component analysis ; Multivariate analysis / India / Gujarat / Vishwamitri Watershed
(Location: IWMI HQ Call no: e-copy only Record No: H049839)
(14.80 MB)
The land use and land cover map plays a significant role in agricultural, water resources planning, management, and monitoring programs at regional and national levels and is an input to various hydrological models. Land use and land cover maps prepared using satellite remote sensing techniques in conjunction with landform-soil-vegetation relationships and ground truth are popular for locating suitable sites for the construction of water harvesting structures, soil and water conservation measures, runoff computations, irrigation planning and agricultural management, analyzing socio-ecological concerns, flood controlling, and overall watershed management. Here we use a novel approach to analyze Sentinel–2 multispectral satellite data using traditional and principal component analysis based approaches to evaluate the effectiveness of maximum likelihood estimation, random forest tree, and support vector machine classifiers to improve land use and land cover categorization for Soil Conservation Service Curve Number model. Additionally, we use stratified random sampling to evaluate the accuracies of resulted land use and land cover maps in terms of kappa coefficient, overall accuracy, producer's accuracy, and user's accuracy. The classifiers were used for classifying the data into seven major land use and land cover classes namely water, built-up, mixed forest, cultivated land, barren land, fallow land with vertisols dominance, and fallow land with inceptisols dominance for the Vishwamitri watershed. We find that principal component analysis with support vector machine is able to produce highly accurate land use and land cover classified maps. Principal component analysis extracts the useful spectral information by compressing redundant data embedded in each spectral channel. The study highlights the use of principal component analysis with support vector machine classifier to improve land use and land cover classification from which policymakers can make better decisions and extract basic information for policy amendments.
6 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.
7 Akpoti, Komlavi; Dembele, Moctar; Forkuor, G.; Obuobie, E.; Mabhaudhi, Tafadzwanashe; Cofie, Olufunke. 2023. Integrating GIS and remote sensing for land use/land cover mapping and groundwater potential assessment for climate-smart cocoa irrigation in Ghana. Scientific Reports, 13:16025. [doi: https://doi.org/10.1038/s41598-023-43286-5]
Climate-smart agriculture ; Cocoa ; Groundwater irrigation ; Land-use mapping ; Land cover mapping ; Groundwater potential ; Groundwater assessment ; Geographical information systems ; Remote sensing ; Surface water ; Water availability ; Climate change / Ghana
(Location: IWMI HQ Call no: e-copy only Record No: H052236)
(12.00 MB) (12.0 MB)
Although Ghana is a leading global cocoa producer, its production and yield have experienced declines in recent years due to various factors, including long-term climate change such as increasing temperatures and changing rainfall patterns, as well as drought events. With the increasing exposure of cocoa-producing regions to extreme weather events, the vulnerability of cocoa production is also expected to rise. Supplemental irrigation for cocoa farmers has emerged as a viable adaptation strategy to ensure a consistent water supply and enhance yield. However, understanding the potential for surface and groundwater irrigation in the cocoa-growing belt remains limited. Consequently, this study aims to provide decision-support maps for surface and groundwater irrigation potential to aid planning and investment in climate-smart cocoa irrigation. Utilizing state-of-the-art geospatial and remote sensing tools, data, and methods, alongside in-situ groundwater data, we assess the irrigation potential within Ghana's cocoa-growing areas. Our analysis identified a total area of 22,126 km2 for cocoa plantations and 125.2 km2 for surface water bodies within the cocoa-growing regions. The multi-criteria analysis (MCA) revealed that approximately 80% of the study area exhibits moderate to very high groundwater availability potential. Comparing the MCA output with existing borehole locations demonstrated a reasonable correlation, with about 80% of existing boreholes located in areas with moderate to very high potential. Boreholes in very high potential areas had the highest mean yield of 90.7 l/min, while those in low groundwater availability potential areas registered the lowest mean yield of 58.2 l/min. Our study offers a comprehensive evaluation of water storage components and their implications for cocoa irrigation in Ghana. While groundwater availability shows a generally positive trend, soil moisture and surface water have been declining, particularly in the last decade. These findings underline the need for climate-smart cocoa irrigation strategies that make use of abundant groundwater resources during deficit periods. A balanced conjunctive use of surface and groundwater resources could thus serve as a sustainable solution for maintaining cocoa production in the face of climate change.
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