Your search found 19 records
1 Amjath-Babu, T. S.; Krupnik, T. J.; Kaechele, H.; Aravindakshan, S.; Sietz, D. 2016. Transitioning to groundwater irrigated intensified agriculture in Sub-Saharan Africa: an indicator based assessment. Agricultural Water Management, 168:125-135. [doi: https://doi.org/10.1016/j.agwat.2016.01.016]
Groundwater irrigation ; Farming systems ; Transitional farming ; Intensive farming ; Indicators ; Principal component analysis ; Agricultural development ; Sustainable agriculture ; Population growth ; Market access ; Economic impact ; Social aspects ; Food security / Africa South of Sahara / Burkina Faso / Ghana / Malawi / Ethiopia / Nigeria / Zambia / Namibia / Cameroon / Zimbabwe / Kenya
(Location: IWMI HQ Call no: e-copy only Record No: H047631)
(0.89 MB)
Growing populations, changing market conditions, and the food security risks posed by rainfed cropping and climate change collectively indicate that Sub-Saharan African nations could benefit from transforming agricultural production to more intensive yet resilient and sustainable systems. Although highly underutilized, emerging evidence indicates that groundwater may be more widely available than previously thought, highlighting its potential role in facilitating such a transformation. Nevertheless, the possibility for such a transition is conditioned by number of complex factors. We therefore construct a transition index that integrates data considering groundwater and energy availability and cost, market access, infrastructural needs, farm conditions and natural resource stocks, labor availability, climate, population density, as well as economic and political framework variables, using a principal component analysis based methodology. Using the consequent multi-dimensional transition index and constituent intermediate indices, we provide an assessment of groundwater irrigation potential discussed in consideration of Burkina Faso, Ghana, Malawi, Ethiopia, Nigeria, Zambia, Namibia, Cameroon, and Zimbabwe. Our results, though preliminary, provide a methodology for conducting such an integrated assessment, while deriving a holistic set of policy options considering the transition towards appropriate use of groundwater for agricultural development.
2 Gunawardhana, W. D. T. M.; Jayawardhana, J. M. C. K.; Udayakumara, E. P. N. 2016. Impacts of agricultural practices on water quality in Uma Oya catchment area in Sri Lanka. Procedia Food Science, 6:339-343. [doi: https://doi.org/10.1016/j.profoo.2016.02.068]
Water quality ; Agricultural practices ; Catchment areas ; Habitats ; Invertebrates ; Species ; Chemical compounds ; Pollutant load ; Ecological factors ; Farmland ; Land use ; Principal component analysis / Sri Lanka / Uma Oya
(Location: IWMI HQ Call no: e-copy only Record No: H047770)
http://www.sciencedirect.com/science/article/pii/S2211601X16000699/pdf?md5=4576562be7b258c1f31fbba8a85dc634&pid=1-s2.0-S2211601X16000699-main.pdf
https://vlibrary.iwmi.org/pdf/H047770.pdf
https://vlibrary.iwmi.org/pdf/H047770.pdf
(0.24 MB) (240 KB)
Sustainability of global food production is highly depending on the quality of the environment. In many parts of the world increase of agricultural production heavily depend on intensive agricultural practices which are having negative impact on the environment. The impacts of agricultural practices on surface water quality is given special attention currently since the safe and ample supply of freshwater is fundamental to humans and for the sustainability of ecosystem function. Intensive agricultural practices in river catchments often pose threat to the ecological integrity of river ecosystems. Uma Oya watershed in the upper Mahaweli watershed in Sri Lanka is an intensively cultivated landscape. In most parts of the catchment previously forested lands have been cleared and converted to agricultural lands. However, the empirical evidence on quantitative assessment of such land use conversion impacts on stream ecological health is lacking in the context of river catchments in Sri Lanka. Therefore the present study was aimed at evaluating the agricultural land use impacts on stream physical habitat quality, water quality and macroinvertebrate indices in the Uma Oya catchment at different spatial scales. The relationship between catchment and site scale % agricultural lands, water quality and macroinvertebrate indices were evaluated using univariate and multivariate approaches. The results indicated that stream physical habitat quality, water quality parameters and macroinvertebrate indices are significantly (p<0.05) affected by catchment scale % agricultural land cover. Among the water quality variables that were tested NO2-N, NH3-N, PO4-P and BOD5 level in sites with higher percentage of agricultural land cover exceeded the drinking water quality standards during dry season. PO4-P and BOD5 level in those sites exceeded the proposed ambient water quality standards for inland waters in Sri Lanka for aquatic life and for irrigation purposes. Findings of the present study suggest that catchment scale interventions are crucial for the management of Uma Oya watershed and for the improvement of water quality and sustainable agricultural production.
3 Kim, J.-H.; Kim, K.-H; Thao, N. T.; Batsaikhan, B.; Yun, S.-T. 2017. Hydrochemical assessment of freshening saline groundwater using multiple end-members mixing modeling: a study of Red River delta aquifer, Vietnam. Journal of Hydrology, 549:703-714. [doi: https://doi.org/10.1016/j.jhydrol.2017.04.040]
Groundwater ; Salinity ; Aquifers ; Hydrology ; Chemical composition ; Geochemistry ; Cation exchange capacity ; Sulphates ; Models ; Principal component analysis ; Rivers ; Sea water ; Deltas / Vietnam / Red River Delta
(Location: IWMI HQ Call no: e-copy only Record No: H048159)
(2.80 MB)
In this study, we evaluated the water quality status (especially, salinity problems) and hydrogeochemical processes of an alluvial aquifer in a floodplain of the Red River delta, Vietnam, based on the hydrochemical and isotopic data of groundwater samples (n = 23) from the Kien Xuong district of the Thai Binh province. Following the historical inundation by paleo-seawater during coastal progradation, the aquifer has been undergone progressive freshening and land reclamation to enable settlements and farming. The hydrochemical data of water samples showed a broad hydrochemical change, from Na-Cl through NaHCO3 to Ca-HCO3 types, suggesting that groundwater was overall evolved through the freshening process accompanying cation exchange. The principal component analysis (PCA) of the hydrochemical data indicates the occurrence of three major hydrogeochemical processes occurring in an aquifer, namely: 1) progressive freshening of remaining paleo-seawater, 2) water-rock interaction (i.e., dissolution of silicates), and 3) redox process including sulfate reduction, as indicated by heavy sulfur and oxygen isotope compositions of sulfate. To quantitatively assess the hydrogeochemical processes, the end-member mixing analysis (EMMA) and the forward mixing modeling using PHREEQC code were conducted. The EMMA results show that the hydrochemical model with the two-dimensional mixing space composed of PC 1 and PC 2 best explains the mixing in the study area; therefore, we consider that the groundwater chemistry mainly evolved by mixing among three end-members (i.e., paleo-seawater, infiltrating rain, and the K-rich groundwater). The distinct depletion of sulfate in groundwater, likely due to bacterial sulfate reduction, can also be explained by EMMA. The evaluation of mass balances using geochemical modeling supports the explanation that the freshening process accompanying direct cation exchange occurs through mixing among three end-members involving the K-rich groundwater. This study shows that the multiple end-members mixing model is useful to more successfully assess complex hydrogeochemical processes occurring in a salinized aquifer under freshening, as compared to the conventional interpretation using the theoretical mixing line based on only two end-members (i.e., seawater and rainwater).
4 Alemu, T.; Bahrndorff, S.; Hundera, K.; Alemayehu, E.; Ambelu, A. 2017. Effect of riparian land use on environmental conditions and riparian vegetation in the East African highland streams. Limnologica, 66:1-11. [doi: https://doi.org/10.1016/j.limno.2017.07.001]
Riparian zones ; Land use ; Environmental effects ; Water quality ; Farmland ; Riparian vegetation ; Highlands ; Rivers ; Plantations ; Ecosystems ; Chemicophysical properties ; Principal component analysis / East Africa / Ethiopia
(Location: IWMI HQ Call no: e-copy only Record No: H048292)
(0.43 MB)
Agricultural land use is expanding and at an accelerated rate. In Ethiopia, most of this expansion has occurred in highland areas and involve deforestation of natural riparian vegetation. However, the impacts on the water quality of streams are poorly understood, especially with regard to the influence of land use patterns on highland streams. In this study, we investigated the effects of land use modifications on the water quality and riparian condition of highland streams and examined whether the preservation of riparian vegetation would help mitigate the negative impacts of intensive agriculture practices. Our results show significant differences in the water quality of streams with different land use. Several parameters commonly used to indicate water quality, such as the concentrations of orthophosphate, turbidity, and suspended solids were significantly higher in the agricultural streams than in the forest stream. The preservation of riparian vegetation in the surrounding highland streams was associated with overall better riparian condition, floristic quality, and water quality such as lower turbidity, total suspended solids, orthophosphate, and higher dissolved oxygen. We conclude, that increases in vegetation cover improved riparian condition and water quality relative to other non-vegetated areas. Therefore, we strongly recommend the preservation of riparian vegetation in tropical highland streams surrounded by intensive agriculture. More studies on the effects of best management practices in areas dominated by agriculture can greatly improve our capacity to prevent the degradation of water quality in tropical highland streams of Africa.
5 Bhardwaj, R.; Gupta, A.; Garg, J. K. 2017. Evaluation of heavy metal contamination using environmetrics and indexing approach for River Yamuna, Delhi stretch, India. Water Science, 31(1):52-66. [doi: https://doi.org/10.1016/j.wsj.2017.02.002]
Water pollution ; Heavy metals ; Chemical contamination ; Water quality ; Industrial wastes ; Environmental effects ; Evaluation techniques ; Principal component analysis ; Correlation analysis ; Monsoon climate / India / Delhi / River Yamuna
(Location: IWMI HQ Call no: e-copy only Record No: H048762)
https://www.sciencedirect.com/science/article/pii/S1110492916300923/pdfft?md5=648ea7a4051748131a23781653bfee96&pid=1-s2.0-S1110492916300923-main.pdf
https://vlibrary.iwmi.org/pdf/H048762.pdf
https://vlibrary.iwmi.org/pdf/H048762.pdf
(1.23 MB) (1.23 MB)
The objective of the present study is to investigate the current status of heavy metal pollution in River Yamuna, Delhi stretch. The concentrations of Nickel, Cadmium, Chromium, Copper, Iron, Lead, and Zinc in water samples have been studied during December 2013–August 2015. The overall mean concentration of heavy metals was observed in the following order Fe >Cu > Zn > Ni >Cr > Pb >Cd. Correlation analysis formed two distinct groups of heavy metals highlighting similar sources. This was further corroborated by results from principal components analysis that showed similar grouping of heavy metals (Ni, Zn, Fe, Pb, Cd) into PC1 having one common source for these heavy metals and PC2 (Cu, Cr) having another common source. Further, our study pointed out two sites i.e. Najafgarh drain and Shahdara drain outlet in river Yamuna as the two potential sources responsible for the heavy metal contamination. Based on heavy metal pollution index value (1491.15), we concluded that our study area as a whole is critically polluted with heavy metals under study due to pollutant load from various anthropogenic activities.
6 Rao, M. P.; Cook, E. R.; Cook, B. I.; Palmer, J. G.; Uriarte, M.; Devineni, N.; Lall, U.; D’Arrigo, R. D.; Woodhouse, C. A.; Ahmed, M.; Zafar, M. U.; Khan, N.; Khan, A.; Wahab, M. 2018. Six centuries of Upper Indus Basin streamflow variability and its climatic drivers. Water Resources Research, 54(8):5687-5701. [doi: https://doi.org/10.1029/2018WR023080]
River basins ; Stream flow ; Climatic factors ; Temperature ; Precipitation ; Discharges ; Forecasting ; Models ; Regression analysis ; Principal component analysis / Pakistan / Upper Indus Basin / Partab Bridge / Doyian / Gilgit / Kachora
(Location: IWMI HQ Call no: e-copy only Record No: H048920)
(3.32 MB)
Our understanding of the full range of natural variability in streamflow, including how modern flow compares to the past, is poorly understood for the Upper Indus Basin because of short instrumental gauge records. To help address this challenge, we use Hierarchical Bayesian Regression with partial pooling to develop six centuries long (1394–2008 CE) streamflow reconstructions at three Upper Indus Basin gauges (Doyian, Gilgit, and Kachora), concurrently demonstrating that Hierarchical Bayesian Regression can be used to reconstruct short records with interspersed missing data. At one gauge (Partab Bridge), with a longer instrumental record (47 years), we develop reconstructions using both Bayesian regression and the more conventionally used principal components regression. The reconstructions produced by principal components regression and Bayesian regression at Partab Bridge are nearly identical and yield comparable reconstruction skill statistics, highlighting that the resulting tree ring reconstruction of streamflow is not dependent on the choice of statistical method. Reconstructions at all four reconstructions indicate that flow levels in the 1990s were higher than mean flow for the past six centuries. While streamflow appears most sensitive to accumulated winter (January–March) precipitation and summer (May–September) temperature, with warm summers contributing to high flow through increased melt of snow and glaciers, shifts in winter precipitation and summer temperatures cannot explain the anomalously high flow during the 1990s. Regardless, the sensitivity of streamflow to summer temperatures suggests that projected warming may increase streamflow in coming decades, though long-term water risk will additionally depend on changes in snowfall and glacial mass balance.
7 Misi, A.; Gumindoga, W.; Hoko, Z. 2018. An assessment of groundwater potential and vulnerability in the upper manyame sub-catchment of Zimbabwe. Physics and Chemistry of the Earth, 105:72-83. [doi: https://doi.org/10.1016/j.pce.2018.03.003]
Groundwater assessment ; Groundwater pollution ; Water quality ; Drinking water ; Groundwater recharge ; Aquifers ; Mapping ; Geographical information systems ; Rain ; Catchment areas ; Principal component analysis ; Models / Zimbabwe / Upper Manyame Sub-Catchment
(Location: IWMI HQ Call no: e-copy only Record No: H049298)
(2.75 MB)
Severe depletion and pollution of groundwater resources are of rising concern in the Upper Manyame Sub-Catchment (UMSC); Zimbabwe's most urbanised sub-catchment. Despite groundwater playing a pivotal role in the provision of potable water in the sub-catchment, it is under serious threat from anthropogenic stressors which include sewage effluents and leachates from landfills, among others. Inadequate scientific knowledge pertaining to the spatio-temporal variability of groundwater storage and vulnerability in the UMSC is further compromising its sustainability. Therefore, comprehensive assessments of UMSC's Groundwater Potential (GP) and vulnerability are crucial for its effective management. This study assessed GP and vulnerability in the UMSC using Geographic Information Systems and Remote Sensing techniques. Groundwater conditioning factors: geology, slope, land-use, drainage density, topographic index, altitude, recharge and rainfall were used to develop GP zones. Validation of the GP map was done by correlating estimated GP with historical borehole yields. An assessment of groundwater vulnerability was done at micro-catchment level (Marimba) using the GOD model; a three parameter Index Overlay Model. Marimba is the most urbanised and has the second highest borehole density. It also exhibits similar landuse characteristics as the UMSC. Furthermore, groundwater quality in Marimba was assessed from 15 sampling sites. Fifteen drinking water parameters were analysed based on the standard methods for Water and Wastewater Examination. The potability of groundwater was then assessed by comparing the measured water quality parameters with the Standards Association of Zimbabwe (SAZ) drinking water standards and/or WHO guidelines for drinking water. Repeated Measures ANOVA and Principal Component Analysis (PCA) were used to assess the spatio-temporal variations in groundwater quality and to identify key parameters, respectively. About 72% (2725.9 km2) of the UMSC was found to be of moderate GP, while 19% and 9% accounted for high and low GP, respectively. Marimba vulnerability status was dominantly moderate (77.3%). Parameters: EC, pH, coliforms, TDS, total hardness, Fe, NH4+ and turbidity exceeded SAZ and/or WHO drinking water limits on most sampling sites with DO, total and faecal coliforms showing significant variations (p < 0.05). Four Principal Components representing 84% of the cumulative variance were extracted; with PC1, PC2, PC3 and PC4 contributing 38%, 19.1%, 14.3% and 12.85%, respectively. PC1 was characterized by pH, TDS, EC and total hardness. PC2's variance was associated with elevated levels of Cl-, Zn and Cu. PC3 had high loadings of total and faecal coliforms, Fl- and turbidity while PC4 was characterized by high loadings of Pb, Fe, ammonia and turbidity. The variation in the nature of the parameters across PCs explains the complexity of pollutants within the micro-catchment. PC2 and PC4 were largely characterized by metallic compounds, suggesting pollution from mineral dissolution into the aquifers e.g. from industrial areas and dumpsites. PC3 indicate the contribution of domestic waste e.g. faecal waste from waste pipe leakages and poorly constructed pit latrines. The findings of this study are useful decision-making tools on groundwater utilisation and groundwater protection.
8 Rajora, Chesta. 2019. Climate change vulnerability assessment with a focus on agriculture sector - a district level study of Assam and Odisha. Project Dissertation submitted to the Department of Energy and Environment, TERI School of Advanced Studies, New Delhi, India, in partial fulfillment of the requirement for the Master of Science in Environmental Studies and Resource Management. 51p.
Climate change adaptation ; Agricultural sector ; Smallholders ; Farmers ; Living standards ; Indicators ; Assessment ; Monsoon climate ; Rain ; Socioeconomic environment ; Population density ; Rural areas ; Principal component analysis / India / Assam / Odisha
(Location: IWMI HQ Call no: e-copy only Record No: H049473)
(1.49 MB)
Climate change is posing a serious challenge for developing countries like India. The agriculture sector is one of the most vulnerable sectors to climate change. In turn, it is making food security and livelihoods of smallholders, more vulnerable to climate change. This study adopted the IPCC’s integrated indicator approach for assessing the vulnerability of the agriculture sector to climate change in Assam and Odisha by means of creating a vulnerability index and by comparing the spatial profile of vulnerability across the districts of the two states. Several socio-economic and biophysical indicators were identified and categorized into 3 components of vulnerability: sensitivity, exposure and, adaptive capacity. Running PCA on these indicators generated weights. Since, Principal Component 1 explains the maximum variance in the dataset, the correlation of indicators with Principal Component 1 has been used for computing the composite climate vulnerability indices. The districts are ranked on the basis of their performance on indices based on 3 components of vulnerability and composite vulnerability. District-wise spatial vulnerability profile has been created to identify and prioritize the most vulnerable districts. The results of the study indicate that the most vulnerable districts of Assam are – Tinsukia, Karbi Anglong, and Dima Hasao; and that of Odisha are - Nabarangpur, Kandhamal, Mayurbhanj, Sundargarh, Malkangiri, Nuapada, Kalahandi, and Koraput. The predominant indicators contributing to vulnerability have been identified which suggest that vulnerability in Assam is more due to high exposure while in Odisha, it is largely attributed to low adaptive capacity and high sensitivity. There exists a large difference in the extent of vulnerability among the districts and there is a need to develop specific policy interventions to address climate change at the district level in order to reduce the vulnerability of smallholders and to increase the resilience of the agriculture sector to climate change.
9 Chen, M.; Luo, Y.; Shen, Y.; Han, Z.; Cui, Y. 2020. Driving force analysis of irrigation water consumption using principal component regression analysis. Agricultural Water Management, 234:106089 (Online first) [doi: https://doi.org/10.1016/j.agwat.2020.106089]
Irrigation water ; Water use ; Water resources ; Climatic factors ; Economic development ; Planting methods ; Models ; Techniques ; Principal component analysis ; Regression analysis ; Cluster analysis / China
(Location: IWMI HQ Call no: e-copy only Record No: H049567)
(4.91 MB)
The effective management of irrigation water consumption is one of the main countermeasures to combat water shortages. This paper introduced an integrated approach to determine the major factors influencing irrigation water consumption in China. It combined multiple linear regression and principal component analysis to analyze the relationship between irrigation water consumption and influencing factors and then applied analytic hierarchy process and cluster analysis to analyze the spatial variation in driving factors of irrigation water consumption. Based on statistical data from the 31 provinces of China from 2000 to 2015, the results showed that irrigation water consumption was positively affected by the planting size, the ratio of surface water in water consumption (RSW), the planting structure, the annual ET0 (AE) and the annual average temperature (AAT); in contrast, consumption was generally negatively affected by irrigation technique, economic development, and annual rainfall (AR). The water consumption structure, irrigation technique and planting structure were major influential factors in most provinces of China, and there were significant differences in different regions; thus, regions should be restructured to be studied as subregions. For the total consumption of irrigation water, Central China was mainly affected by the water consumption structure, irrigation technique and climatic conditions, and North and Northwest China were hardly influenced by planting structure. Northeast, Southwest and southeastern coastal China were slightly affected by climatic conditions. For the per unit area irrigation water consumption, Central China was mainly affected by the water consumption structure, irrigation technique, planting size and climatic conditions, Southwest, South, East and Northeast China were mainly affected by the planting structure and planting size, and Northwest and North China were mainly influenced by the irrigation technique, water consumption structure and planting size.
10 Akhtar, N.; Syakir, M. I.; Rai, S. P.; Saini, R.; Pant, N.; Anees, M. T.; Qadir, A.; Khan, U. 2020. Multivariate investigation of heavy metals in the groundwater for irrigation and drinking in Garautha Tehsil, Jhansi District, India. Analytical Letters, 53(5):774-794. [doi: https://doi.org/10.1080/00032719.2019.1676766]
Groundwater pollution ; Groundwater assessment ; Groundwater irrigation ; Drinking water ; Heavy metals ; Water quality ; Anthropogenic factors ; Hydrogeology ; Principal component analysis ; Multivariate analysis / India / Jhansi / Garautha
(Location: IWMI HQ Call no: e-copy only Record No: H049649)
(2.42 MB)
Groundwater is an important source for drinking and irrigation purposes. Due to anthropogenic activities, heavy metals have been leaching due to industrial waste and agricultural activities to the groundwater causing pollution. The assessment of groundwater quality is necessary to reduce the pollution to acceptable levels. Therefore, the aim of this study is to investigate heavy metal concentrations in the groundwater of the villages of Garautha Tehsil, Jhansi where the anthropogenic activities are active. The groundwater samples were analyzed by inductively coupled plasma – mass spectrometry (ICP-MS) and the results were compared to the 2012 Bureau of Indian Standard limits. Three multivariate statistical methods were used to analyze the groundwater quality for irrigation and drinking purposes and to investigate the geological and hydrogeological processes. The results of principal component analysis (PCA) identified four factors responsible for the data structure by illuminating the total variance of 77.83% of the dataset. The majority of groundwater samples contained Al, Co, Cu, Mn, Ni, Cr, Pb, and Fe within the acceptable limits except at few locations. However, the Al, Fe, and Mn concentration were high at a few sites due to rock–water interactions, whereas the concentration of As, Cd, and Zn were lower than their respective permissible limits in all groundwater samples. Furthermore, the groundwater quality for the use of irrigation is found to be acceptable at 19 locations, with only one high result.
11 Gao, F.; Wang, Y.; Zhang, Y. 2020. Evaluation of the crosta method for the retrieval of water quality parameters from remote sensing data in the Pearl River Estuary. Water Quality Research Journal, 55(2):209-220. [doi: https://doi.org/10.2166/wqrj.2020.024]
Rivers ; Estuaries ; Water quality ; Parameters ; Remote sensing ; Satellite imagery ; Landsat ; Thematic mapper ; Sediment ; Coastal waters ; Principal component analysis ; Models / China / Pearl River Estuary
(Location: IWMI HQ Call no: e-copy only Record No: H049885)
https://iwaponline.com/wqrj/article-pdf/55/2/209/709563/wqrjc0550209.pdf
https://vlibrary.iwmi.org/pdf/H049885.pdf
https://vlibrary.iwmi.org/pdf/H049885.pdf
(0.65 MB) (668 KB)
In recent decades, many algorithms have been developed for the retrieval of water quality parameters using remotely sensed data. However, these algorithms are specific to a certain geographical area and cannot be applied to other areas. In this study, feature-orientated principal component (PC) selection, based on the Crosta method and using Landsat Thematic Mapper (TM) for the retrieval of water quality parameters (i.e., total suspended sediment concentration (TSM) and chlorophyll a (Chla)), was carried out. The results show that feature-orientated PC TSM, based on the Crosta method, obtained a good agreement with the MERIS-based TSM product for eight Landsat TM images. However, the Chla information, selected using the feature-orientated PC, has a poor agreement with the MERIS-based Chla product. The accuracy of the atmospheric correction method and MERIS product may be the main factors influencing the accuracy of the TSM and Chla information identified by the Landsat TM images using the Crosta method. The findings of this study would be helpful in the retrieval of spatial distribution information on TSM from the long-term historical Landsat image archive, without using coincident ground measurements.
12 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.
13 Jin, Y.; Li, A.; Bian, J.; Nan, X.; Lei, G.; Muhammad, K. 2020. Spatiotemporal analysis of ecological vulnerability along Bangladesh-China-India-Myanmar economic corridor through a grid level prototype model. Ecological Indicators, 120:106933. (Online first) [doi: https://doi.org/10.1016/j.ecolind.2020.106933]
Ecological factors ; Vulnerability ; Indicators ; Sustainable development ; Human activity ; Remote sensing ; Biodiversity ; Models ; Principal component analysis ; Normalized difference vegetation index / Bangladesh / China / India / Myanmar
(Location: IWMI HQ Call no: e-copy only Record No: H050077)
(9.78 MB)
Bangladesh-China-India-Myanmar economic corridor, a critical part of the Belt and Road Initiative program, is subject to the impact of various natural disasters and intense human activities, which have led to serious ecological vulnerability. This study proposed a prototype model using geographically weighted principal component analysis to quantify ecological vulnerability at the grid level, and an analysis was conducted on the dynamic changes of ecological vulnerability along Bangladesh-China-India-Myanmar economic corridor during 2005–2015. An indicator system for 23 spatial variables was established based on Driver-Pressure-State-Impact-Response framework to calculate the ecological vulnerability index. The cluster principle was adopted to split the ecological vulnerability into five vulnerability levels, namely, potential, light, medium, heavy, and very heavy. Given the spatial recognition of ecologically vulnerable areas along Bangladesh-China-India-Myanmar economic corridor, several suggestions on ecological management were offered. As revealed from the results, the ecological vulnerability has been rising progressively, particularly in the mountainous areas, and most of the protected areas are at medial to very heavy vulnerability level. The ecological vulnerability was tightly correlated with vulnerability events and impacts. As suggested from the results, ecological restoration and protection measures should be strictly implemented to minimize the adverse impact on the protected areas under the construction of economic corridor. Our results indicated that the geographically weighted principal component analysis can effectively quantify environmental vulnerability, and these space management policies has implications for ecological protection, resource utilization, and sustainable development in other similar regions.
14 Smichowski, B. C.; Durand, C.; Knauss, S. 2021. Participation in global value chains and varieties of development patterns. Cambridge Journal of Economics, 45(2):271-294. [doi: https://doi.org/10.1093/cje/beaa046]
Value chains ; Participation ; Socioeconomic development ; Indicators ; Investment ; Income ; Labour ; Employment ; Economic aspects ; Principal component analysis
(Location: IWMI HQ Call no: e-copy only Record No: H050273)
(0.45 MB)
This paper relates participation in global value chains (GVCs) to development patterns at the country level. It accounts for the diversity and interdependence of development through a cross-country analysis for 51 countries between 1995 and 2008. We identify three patterns of socio-economic development related to various degrees and modes of GVC participation: a social upgrading mirage, the reproduction of the core and unequal growth. This result is achieved thanks to the introduction of two new elements to the literature: first, the introduction of new macroeconomic indicators of GVC participation and economic gains that are explicitly based in a theoretically consistent definition of GVCs; second, the identification of a variety of interdependent development patterns related to GVC participation through the use of principal component analysis and cluster analysis.
15 Phali, L.; Mudhara, M.; Ferrer, S.; Makombe, G. 2021. Household-level perceptions of governance in smallholder irrigation schemes in KwaZulu-Natal Province. Irrigation and Drainage, 12p. (Online first) [doi: https://doi.org/10.1002/ird.2659]
Water governance ; Irrigation schemes ; Households ; Smallholders ; Farmers ; Small scale systems ; Stakeholders ; Institutions ; Land tenure ; Irrigation water ; Principal component analysis / South Africa / KwaZulu-Natal
(Location: IWMI HQ Call no: e-copy only Record No: H050765)
(0.78 MB)
Good governance is a prerequisite for better management of common-use resources. Awareness of institutions, inclusion of members in decision-making processes, stakeholder engagement and transparency are needed for good governance, which enhances the sustainable use of communal water resources. This paper therefore considers perceptions of farmers on irrigation scheme governance in its various dimensions. The study uses household data of 341 farmers drawn from four irrigation schemes in KwaZulu-Natal. The results show that farmers who are satisfied with the informal institutions, being the rules and norms set locally to govern the scheme farmers, value the involvement of the tribal authorities in scheme management, including their contribution to rule enforcement. Age, agricultural training, water adequacy, participation in scheme activities, psychological capital and land tenure have a positive effect on perceptions of governance constructs. Farmers are satisfied with the informal institutions governing the schemes and therefore the study recommends the inclusion of informal institutions in policy formulation. Farmers should be empowered through training and be made aware of formal institutions applicable to their irrigation scheme, and stakeholder engagement in the schemes should be strengthened.
16 Qaisrani, Z. N.; Nuthammachot, N.; Techato, K.; Asadullah; Jatoi, G. H.; Mahmood, B.; Ahmed, R. 2022. Drought variability assessment using standardized precipitation index, reconnaissance drought index and precipitation deciles across Balochistan, Pakistan. Brazilian Journal of Biology, 84:e261001. (Online first) [doi: https://doi.org/10.1590/1519-6984.261001]
Drought ; Assessment ; Precipitation ; Water resources ; Climate change ; Arid zones ; Temperature data ; Evapotranspiration ; Principal component analysis / Pakistan / Balochistan
(Location: IWMI HQ Call no: e-copy only Record No: H051324)
https://www.scielo.br/j/bjb/a/jRMpsrhmZnQDQrmVPvrdFyq/?lang=en
https://vlibrary.iwmi.org/pdf/H051324.pdf
https://vlibrary.iwmi.org/pdf/H051324.pdf
(2.30 MB) (2.30 MB)
Drought variability analysis is of utmost concern for planning and efficiently managing water resources and food security in any specific area. In the current study, drought spell occurrence has been investigated in the Balochistan province of Pakistan during the past four decades (1981-2020) using standardized precipitation index (SPI), reconnaissance drought index (RDI), and precipitation deciles (PD) at an annual timescale. Precipitation and temperature data collected from 13 synoptic meteorological stations located in Balochistan were used to calculate the SPI, the RDI, and the PD for calculation of drought severity and duration. Based on these indices, temporal analysis shows adverse impacts of drought spells in Nokkundi during 1991-1993, in Barkhan, Dalbandin, Quetta stations during 1999-2000, whereas Barkhan, Dalbandin, Lasbella, Sibi during 2002-2003, Zhob during 2010-2011, Kalat and Khuzdar during 2014-2015, and Panjgur during 2017-2018. Also, the aridity index for each station was calculated based on the UNEP method shows that major part of Balochistan lies in the arid zone, followed by the hyper-arid in the southwestern part and the semi-arid zones in the northeastern part of the province. SPI and RDI results were found more localized than PD, as PD shows extensive events. Furthermore, principal component analysis shows a significant contribution from all the indices. For SPI, RDI, and PD, the first three principal components have more than 70% share, contributing 73.63%, 74.15%, and 72.30% respectively. By integrating drought patterns, long-term planning, and preparedness to mitigate drought impacts are only possible. The RDI was found more suitable and recommended in case of temperature data availability.
17 Parween, S.; Siddique, N. A.; Diganta, M. T. M.; Olbert, A. I.; Uddin, Md G. 2022. Assessment of urban river water quality using modified NSF [National Sanitation Foundation] water quality index model at Siliguri City, West Bengal, India. Environmental and Sustainability Indicators, 16:100202. (Online first) [doi: https://doi.org/10.1016/j.indic.2022.100202]
Water quality ; River water ; Assessment ; Urban areas ; Surface water ; Water pollution ; Monitoring ; Faecal coliforms ; Anthropogenic factors ; Sewage ; Principal component analysis ; Models ; Indicators / India / West Bengal / Siliguri / Mahananda River
(Location: IWMI HQ Call no: e-copy only Record No: H051352)
https://www.sciencedirect.com/science/article/pii/S2665972722000344/pdfft?md5=3c51e5b803523233c330f8b2d9a35318&pid=1-s2.0-S2665972722000344-main.pdf
https://vlibrary.iwmi.org/pdf/H051352.pdf
https://vlibrary.iwmi.org/pdf/H051352.pdf
(6.97 MB) (6.97 MB)
Rivers are the source of freshwater for any urban community and hence, monitoring of river water is an obligatory yet challenging task. This study was conducted in a subtropical urban river in India with the view of developing a quantitative approach to assess its water quality (WQ) status. For the purposes of this study, water samples were collected from five locations across the Mahananda River main streams encompassing both urbanised and non-urbanised parts of the Siliguri city during April to June of 2021 and collected samples were analysed for fourteen common WQ indicators: pH, Temperature, Conductivity, TDS, Turbidity, Total Hardness (TH), DO, BOD, COD, NO3-, PO43-, Cl-, Fecal Coliform (FC) and E. coli for assessing water quality. In order to obtain WQ status, the present study utilised the modified national sanitation foundation (NSF) water quality index (WQI) model, whereas the crucial WQ indicators were identified using the principal components analysis (PCA) technique. All WQ indicators were considered to compute the NSF-WQI except water pH and TH. Most WQ indicators were breached the guideline values of the Bureau of Indian Standards (BIS) and Indian Standards (IS) for surface water. The modified NSF-WQI results revealed that the Mahananda River water quality was “good” to “medium” quality and the water is only suitable for limited purposes under certain conditions. The findings of this study provided evidence that the river WQ is heavily influenced by urban pressures because relatively “good” WQ was found at the sampling location of the outer part of the urban area. The results of this research could be effective in improving the Mahananda River's water quality and maintaining its complex ecosystem in order to ensure sustainable urban growth.
18 Tedla, H. Z.; Haile, Alemseged Tamiru; Walker, D. W.; Melesse, A. M. 2022. Evaluation of factors affecting the quality of citizen science rainfall data in Akaki Catchment, Addis Ababa, Ethiopia. Journal of Hydrology, 612(Part C):128284. [doi: https://doi.org/10.1016/j.jhydrol.2022.128284]
Citizen science ; Rain ; Weather data ; Data quality ; Catchment areas ; Monitoring ; Principal component analysis / Ethiopia / Addis Ababa / Akaki Catchment
(Location: IWMI HQ Call no: e-copy only Record No: H051572)
(4.66 MB)
Citizen Science can fulfill the quest for high-quality and sufficient environmental data, such as rainfall. However, the factors affecting the quality of rainfall data collected by the citizen scientists are not well understood. In this study, we examined the effect of citizen scientists’ attributes on the quality of rainfall data. For this purpose, Principal Component Analysis (PCA), stepwise regression and Multiple Linear Regressions (MLR) were used. A quality control procedure was developed and applied for daily observed rainfall data collected in the summer rainy season of 2020. Attributes of the citizen scientists’ were gathered for those who collected rainfall data in the urban and peri-urban Akaki catchment which is located in the Upper Awash sub-basin, Ethiopia. We found that easy-to-detect errors, which were identified during the initial stage of quality control, formed most of the errors in the rainfall data. The PCA and the stepwise regression results revealed that four dominant attributes (education level, gauge relative location, use of smartphone app, and supervisor’s travel distance) highly affected the rainfall data quality. The MLR model using these four prominent dominant variables performed very well with R2 value of 0.98. The k-fold cross validation result showed that the developed model can be used to predict the relationships between data quality and attributes of citizen scientists with high accuracy. Hence, the PCA technique, stepwise regression and MLR model can provide useful information regarding the influence of citizen scientists’ attributes on rainfall data quality. Therefore, future studies should carefully consider citizen scientists’ attributes when engaging and supervising citizen scientists, with a comprehensive data quality control while monitoring rainfall.
19 Raahalya, S.; Balasubramaniam, P.; Devi, M. N.; Maragatham, N.; Selvi, R. G. 2024. Farmers' resilience index: a tool to metricize the resilience of the farmers towards natural disasters affecting agriculture in India. Water Policy, 26(1):79-93. [doi: https://doi.org/10.2166/wp.2023.152]
Natural disasters ; Agriculture ; Farmers ; Resilience ; Factor analysis ; Principal component analysis ; Models ; Cyclones ; Livelihoods ; Indicators ; Human capital ; Social capital ; Natural capital / India / Andhra Pradesh / Krishna Godavari Basin / East Godavari District / West Godavari District / Krishna District / Guntur District
(Location: IWMI HQ Call no: e-copy only Record No: H052601)
https://iwaponline.com/wp/article-pdf/26/1/79/1358363/026010079.pdf
https://vlibrary.iwmi.org/pdf/H052601.pdf
https://vlibrary.iwmi.org/pdf/H052601.pdf
(0.72 MB) (740 KB)
In the present paper farmers' resilience index (FRI) was constructed considering the natural disaster using five dimensions including physical, social, economic, human and natural. The scale is administered to the 240 paddy farmers in two coastal districts of Andhra Pradesh. Principal component analysis was performed in order to fix the weightage for each variable. About (39.58%) of farmers are resilient to natural disasters with the highest resilience score for financial capital (0.641) and natural capital with less resilience score (0.401). Confirmatory factor analysis (CFA) was performed to determine how well the generated model of the scale fits the data. The structural equation modelling (SEM) path diagram was developed based on the conceptual model, which uses resilience as a latent variable. The SEM analysis revealed that four dimensions of capital positively affect farmers' resilience except for the human capital which negatively affects resilience. To reduce the effects of natural catastrophes in the upcoming years, the adaptation strategies from the highly resilient places can be examined and put into practice in the less resilient areas. It is imperative that development programmes at all levels incorporate climate awareness and stakeholder capacity building.
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