Application of new science tools in integrated watershed management for enhancing impacts.

Not AvailableApplication of new science tools in rainfed agriculture opens up new vistas for development through IWMPs. These tools can help in improving the rural livelihoods and contributing substantially to meet the millennium development goals of halving the number of hungry people by 2015 and achieving food security through enhanced use efficiency of scarce natural resources such as land and water in the tropical countries. Till now rainfed areas of the SAT did not get much benefit of new science tools but the recent research using these tools such as simulation modeling, remote sensing, GIS as well as satellite-based monitoring of the natural resources in the SAT has shown that not only the effectiveness of the research is enhanced substantially but also the cost efficiency and impact are enhanced. The remarkable developments in space technology currently offer satellites which provide better spatial and spectral resolutions, more frequent revisits, stereo viewing, and on-board recording capabilities. Thus, the high spatial and temporal resolution satellite data could be effectively used for watershed management and monitoring activities at land ownership level. By using crop simulation modeling approach, yield gap analyses for the major crops in Asia, Africa, and WANA regions revealed that the yields could be doubled with the existing technologies if the improved crop land, nutrient, and water management options are scaled-out. Similarly, technology application domains could be easily identified for better success and greater adoption of the particular technologies considering the biophysical as well as socioeconomic situations. GIS helped in speedy analysis of voluminous data and more rationale decision in less time to target the investments as well as to monitor the large number of interventions in the SAT. The satellite-based techniques along with GIS helped in identifying the vast fallow areas (2 million ha) in Madhya Pradesh during the rainy season. Similarly, 14 million ha rice-fallows in the Indo-Gangetic Plain offer excellent potential to grow second crop on residual soil moisture by using shortduration chickpea cultivars and simple seed priming technology. These techniques are also successfully used for preparing detailed thematic maps, watershed development plans, and continuous monitoring of the natural resources in the country in rainfed areas. Further, such data could be of immense help in tracking the implementation, applying midcourse corrections, and for assessing long-term effectiveness of the program implemented. The synergy of GIS and Web Technology allows access to dynamic geospatial watershed information without burdening the users with complicated and expensive software. Further, these web-based technologies help the field data collection and analysis in a collaborative way. However the availability of suitable software for watershed studies and their management in open GIS platform is very limited. Hence, there is a requirement to strengthen this area through collaborative efforts between various line organizations. Use of ICT in IWMP can bridge the existing gap to reach millions of small farm holders who have no access to new technologies for enhancing agricultural productivity on their farms. Use of smart sensor network along with GIS, remote sensing, Wani Ch006.tex 8/7/2011 19: 41 Page 201 Application of new science tools in integrated watershed management 201 simulation modeling and ICT opens up new opportunities for developing intelligent watershed management information systems. However, it calls for a new partnership involving corporates, development agencies, researchers from various disciplines and most importantly to reach millions of small farm holders in rainfed areas of the world. Application of new science tools in IWMP have helped to substantially enhance productivity as well as income from rainfed agriculture and improved livelihoods of the rural people.Not Availabl

Temperature-Vegetation-soil Moisture-Precipitation Drought Index (TVMPDI); 21-year drought monitoring in Iran using satellite imagery within Google Earth Engine

Remote Sensing (RS) offers efficient tools for drought monitoring, especially in countries with a lack of reliable and consistent in-situ multi-temporal datasets. In this study, a novel RS- based Drought Index (RSDI) named Temperature-Vegetation-soil Moisture-Precipitation Drought Index (TVMPDI) was proposed. To the best of our knowledge, TVMPDI is the first RSDI using four different drought indicators in its formulation. TVMPDI was then validated and compared with six conventional RSDIs including VCI, TCI, VHI, TVDI, MPDI and TVMDI. To this end, precipitation and soil temperature in-situ data have been used. Different time scales of meteorological Standardized Precipitation Index (SPI) index have also been used for the validation 2 of the RSDIs. TVMPDI was highly correlated with the monthly precipitation and soil temperature in-situ data at 0.76 and 0.81 values respectively. The correlation coefficients between the RSDIs and 3-month SPI ranged from 0.07 to 0.28, identifying the TVMPDI as the most suitable index for subsequent analyses. Since the proposed TVMPDI could considerably outperform the other selected RSDIs, all spatiotemporal drought monitoring analyses in Iran were conducted by TVMPDI over the past 21 years. In this study, different products of the Moderate Resolution Imaging Spectrometer (MODIS), Tropical Rainfall Measuring Mission (TRMM), and Global Precipitation Measurement (GPM) datasets containing 15206 images were used on the Google Earth Engine (GEE) cloud computing platform. According to the results, Iran experienced the most severe drought in 2000 with a 0.715 TVMPDI value lasting for almost two years. Conversely, the TVMPDI showed a minimum value equal to 0.6781 in 2019 as the lowest annual drought level. The drought severity and trend in the 31 provinces of Iran have also been mapped. Consequently, various levels of decrease over the 21 years were found for different provinces, while Isfahan and Gilan were the only provinces showing an ascending drought trend (with a 0.004% and 0.002% trendline slope respectively). Khuzestan also faced a worrying drought prevalence that occurred in several years. In summary, this study provides updated information about drought trends in Iran using an advanced and efficient RSDI implemented in the cloud computing GEE platform. These results are beneficial for decision-makers and officials responsible for environmental sustainability, agriculture and the effects of climate change.Peer ReviewedPostprint (author's final draft

Doctor of Philosophy

dissertationSin Nombre virus (SNV), a strain of hantavirus, causes hantavirus pulmonary syndrome (HPS) in humans, a deadly disease with high mortality rate (>50%). The primary virus host is deer mice, and greater deer mice abundance has been shown to increase the human risk of HPS. There is a great need in understanding the nature of the virus host, its temporal and spatial dynamics, and its relation to the human population with the purpose of predicting human risk of the disease. This research studies SNV dynamics in deer mice in the Great Basin Desert of central Utah, USA using multiyear field data and integrated geospatial approaches including remote sensing, Geographic Information System (GIS), and a spatially explicit agent-based model. The goal is to advance our understanding of the important ecological and demographic factors that affect the dynamics of deer mouse population and SNV prevalence. The primary research question is how climate, habitat disturbance, and deer mouse demographics affect deer mouse population density, its movement, and SNV prevalence in the sagebrush habitat. The results show that the normalized difference vegetation index (NDVI) and the enhanced vegetation index (EVI) can be good predictors of deer mouse density and the number of infected deer mice with a time lag of 1.0 to 1.3 years. This information can be very useful in predicting mouse abundance and SNV risk

Mapping and Assessing Impacts of Land Use and Land Cover Change by Means of Advanced Remote Sensing Approach:: Mapping and Assessing Impacts of Land Use and Land Cover Change by Means of Advanced Remote Sensing Approach:: A case Study of Gash Agricultural Scheme, Eastern Sudan

Risks and uncertainties are unavoidable in agriculture in Sudan, due to its dependence on climatic factors and to the imperfect nature of the agricultural decisions and policies attributed to land cover and land use changes that occur. The current study was conducted in the Gash Agricultural Scheme (GAS) - Kassala State, as a semi-arid land in eastern Sudan. The scheme has been established to contribute to the rural development, to help stability of the nomadic population in eastern Sudan, particularly the local population around the Gash river areas, and to facilitate utilizing the river flood in growing cotton and other cash crops. In the last decade, the scheme production has declined, because of drought periods, which hit the region, sand invasion and the spread of invasive mesquite trees, in addition to administrative negligence. These have resulted also in poor agricultural productivity and the displacement of farmers away from the scheme area. Recently, the scheme is heavily disturbed by human intervention in many aspects. Consequently, resources of cultivated land have shrunk and declined during the period of the study, which in turn have led to dissatisfaction and increasing failure of satisfying increasing farmer’s income and demand for local consumption. Remote sensing applications and geospatial techniques have played a key role in studying different types of hazards whether they are natural or manmade. Multi-temporal satellite data combined with ancillary data were used to monitor, analyze and to assess land use and land cover (LULC) changes and the impact of land degradation on the scheme production, which provides the managers and decision makers with current and improved data for the purposes of proper administration of natural resources in the GAS. Information about patterns of LULC changes through time in the GAS is not only important for the management and planning, but also for a better understanding of human dimensions of environmental changes at regional scale. This study attempts to map and assess the impacts of LULC change and land degradation in GAS during a period of 38 years from 1972-2010. Dry season multi-temporal satellite imagery collected by different sensor systems was selected such as three cloud-free Landsat (MSS 1972, TM 1987 and ETM+ 1999) and ASTER (2010) satellite imagery. This imagery was geo-referenced and radiometrically and atmospherically calibrated using dark object subtraction (DOS). Two approaches of classification (object-oriented and pixel-based) were applied for classification and comparison of LULC. In addition, the study compares between the two approaches to determine which one is more compatible for classification of LULC of the GAS. The pixel-based approach performed slightly better than the object-oriented approach in the classification of LULC in the study area. Application of multi-temporal remote sensing data proved to be successful for the identification and mapping of LULC into five main classes as follows: woodland dominated by dense mesquite trees, grass and shrubs dominated by less dense mesquite trees, bare and cultivated land, stabilized fine sand and mobile sand. After image enhancement successful classification of imagery was achieved using pixel and object based approaches as well as subsequent change detection (image differencing and change matrix), supported by classification accuracy assessments and post-classification. Comparison of LULC changes shows that the land cover of GAS has changed dramatically during the investigated period. It has been discovered that more significant of LULC change processes occurred during the second studied period (1987 to 1999) than during the first period (1972-1987). In the second period nearly half of bare and cultivated lands was changed from 41372.74 ha (20.22 %) in 1987 to 28020.80 ha (13.60 %) in 1999, which was mainly due to the drought that hit the region during the mentioned period. However, the results revealed a drastic loss of bare and cultivated land, equivalent to more than 40% during the entire period (1972-2010). Throughout the whole period of study, drought and invasion of both mesquite trees and sand were responsible for the loss of more than 40% of the total productive lands. Change vector analysis (CVA) as a useful approach was applied for estimating change detection in both magnitude and direction of change. The promising approach of multivariate alteration detection (MAD) and subsequent maximum autocorrelation factor (MAD/MAF) transformation was used to support change detection via assessment of maximum correlation between the transformed variates and the specific original image bands related to specific land cover classes. However, both CVA and MAD/MAD strongly prove the fact that bare and cultivated land have dramatically changed and decreased continuously during the studied period. Both CVA and MAD/MAD demonstrate adequate potentials for monitoring, detecting, identifying and mapping the changes. Moreover, this research demonstrated that CVA and MAD/MAF are superior in providing qualitative details about the nature of all kinds of change. Vegetation indices (VI) such as normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), modified adjusted vegetation index (MSAVI) and grain soil index (GSI) were applied to measure the quantitative characterization of temporal and spatial vegetation cover patterns and change. All indices remain very sensitive to structure variation of LULC. The results reveal that the NDVI is more effective for detecting the amount and status of the vegetation cover in the study area than SAVI, MSAVI and GSI. Therefore, it can be stated that NDVI can be used as a response variable to identify drought disturbance and land degradation in semi-arid land such as the GAS area. Results of detecting vegetation cover observed by using SAVI were found to be more reasonable than using MSAVI, although MSAVI reduces the background of bare soil better than SAVI. GSI proves high efficiency in determining the different types of surface soils, and producing a change map of top soil grain size, which is useful in assessment of land degradation in the study area. The linkage between socio-economic data and remotely sensed data was applied to determine the relationships between the different factors derived and to analyze the reasons for change in LULC and land degradation and its effects in the study area. The results indicate a strong relationship between LULC derived from remotely sensed data and the influencing socioeconomic variables. The results obtained from analyzing socioeconomic data confirm the findings of remote sensing data analysis, which assure that the decline and degradation of agricultural land is a result of further spread of mesquite trees and of increased invasion of sand during the study period. High livestock density and overgrazing, drought, invasion of sand, spread of invasive mesquite trees, overexploitation of land, improper management, and population growth were considered as the main direct factors responsible for degradation in the study area

Exploring A Stable Aspen Niche Within Aspen-Conifer Forests of Utah

Quaking aspen (Populus tremuloides Michx.) is the most widespread broadleaf tree species of North America. Increasing evidence shows that aspen has diverging ecological roles across its range as both “seral” and “stable” aspen community types. This leads us to believe that the successional pathway of aspen may not always lead to a climax conifer sere, but may in some cases consist of persisting stands of pure aspen. This study is an attempt to understand the relationship of aspen community types to climatic, physical, and biophysical variables by modeling patterns of aspen and conifer distribution using remote sensing and GIS technology. Study methodologies and results were specifically designed to aid land managers in identifying extent and status of aspen populations as well as prioritizing aspen restoration projects. Four study sites were chosen in order to capture the geographic and climatic range of aspen. Photointerpretation of NAIP color infrared imagery and linear unmixing of Landsat Thematic Mapper imagery were used to classify dominant forest cover. A Kappa analysis indicates photointerpretation methods to be more accurate (Khat=92.07%, N=85) than linear unmixing (Khat=51.05%, N=85). At each plot, variables were calculated and derived from DAYMET data, digital elevation models, and soil surveys, then assessed for precision and ability to model aspen and conifer distributions. A generalized linear model and discriminant analysis were used to assess habitat overlap between aspen and conifer and to predict areas where “stable” aspen communities are likely to occur. Results do not provide definitive evidence for a “stable” aspen niche. However, the model indicates 60 to 90 cm of total annual precipitation and topographic positions receiving greater than 4,500 Wh m‐2 d‐1 of solar radiation have a higher potential for “stable” aspen communities. Model predictions were depicted spatially within GIS as probability of conifer encroachment. In addition, prediction‐conditioned fallout rates and receiver operating characteristic curves were used to partition the continuous model output. Categorical maps were then produced for each study site delineating potential “stable” and “seral” aspen community types using an overlay analysis with landcover maps of aspen‐conifer forests

An Integrated Approach to Assessing Spread of Commercial Horticulture and Related Environmental Impacts on Watersheds : Cases in Central Highlands of Kenya

Intensive horticulture production has broad environmental implications due to the high dependency on natural resources. Numerous reports indicate positive socio-economic gains associated with the Kenyan horticulture sub-sector. Even so, few highlight the extent of the negative environmental impacts. We adopt a holistic approach that integrates deskwork, Geographical Information Systems (GIS), field study and remote sensing tools to evaluate the spread and growth of commercial horticulture, and the effects on: i) surface water quality, and ii) vegetation condition, in watersheds experiencing increased production within the central highlands. The desk research utilized Google Earth archives and GIS data, to map greenhouse distribution, determining area under production and factors predicting choice of location. This was followed by a field study to sample and characterize surface water quality in select sub-watersheds with intensive horticulture, thereby highlighting potential pollutant source-processes. Twenty five years of remote sensing data were also analyzed to establish vegetation condition and responses to increased farming and human disturbances. This was followed by a detailed study to quantify land use and land cover changes, and finally a chapter illustrating trends in horticulture exports volumes. Results from the desk research showed heterogeneous spread of farming, where area under production increased rapidly between 2000 and 2011. Population density, average slope, average rainfall and dams were significant predictors to farming location. Results from the field study show predominance of anthropogenic trace elements of cadmium, phosphate, and zinc in waters draining from regions with intensive large scale horticulture. The long-term vegetation study indicates spatially varying inter-annual NDVI, which continuously declined post 1990s in sub watersheds with increased farming. The study to quantify land transformation dynamics, indicate varying magnitudes of change with rates of change differing between land-uses, and between case studies, attributable to socio-economic drivers. We also find that horticultural exports had positive trends until 2008/2009, and 2010, where the effects of post-election violence and volcanic eruption are evident. Overall, the research has demonstrated the efficacy of integrated approaches in understanding implications intensified production on watershed resources. This knowledge is important in developing policies and regulatory frameworks that supports sustainable resource utilization and best management practices

Nature-based solutions efficiency evaluation against natural hazards: Modelling methods, advantages and limitations

Nature-based solutions (NBS) for hydro-meteorological risks (HMRs) reduction and management are becoming increasingly popular, but challenges such as the lack of well-recognised standard methodologies to evaluate their performance and upscale their implementation remain. We systematically evaluate the current state-of-the art on the models and tools that are utilised for the optimum allocation, design and efficiency evaluation of NBS for five HMRs (flooding, droughts, heatwaves, landslides, and storm surges and coastal erosion). We found that methods to assess the complex issue of NBS efficiency and cost-benefits analysis are still in the development stage and they have only been implemented through the methodologies developed for other purposes such as fluid dynamics models in micro and catchment scale contexts. Of the reviewed numerical models and tools MIKE-SHE, SWMM (for floods), ParFlow-TREES, ACRU, SIMGRO (for droughts), WRF, ENVI-met (for heatwaves), FUNWAVE-TVD, BROOK90 (for landslides), TELEMAC and ADCIRC (for storm surges) are more flexible to evaluate the performance and effectiveness of specific NBS such as wetlands, ponds, trees, parks, grass, green roof/walls, tree roots, vegetations, coral reefs, mangroves, sea grasses, oyster reefs, sea salt marshes, sandy beaches and dunes. We conclude that the models and tools that are capable of assessing the multiple benefits, particularly the performance and cost-effectiveness of NBS for HMR reduction and management are not readily available. Thus, our synthesis of modelling methods can facilitate their selection that can maximise opportunities and refute the current political hesitation of NBS deployment compared with grey solutions for HMR management but also for the provision of a wide range of social and economic co-benefits. However, there is still a need for bespoke modelling tools that can holistically assess the various components of NBS from an HMR reduction and management perspective. Such tools can facilitate impact assessment modelling under different NBS scenarios to build a solid evidence base for upscaling and replicating the implementation of NBS

Nature-based solutions efficiency evaluation against natural hazards: modelling methods, advantages and limitations

Nature-based solutions (NBS) for hydro-meteorological risks (HMRs) reduction and management are becoming increasingly popular, but challenges such as the lack of well-recognised standard methodologies to evaluate their performance and upscale their implementation remain. We systematically evaluate the current state-of-the art on the models and tools that are utilised for the optimum allocation, design and efficiency evaluation of NBS for five HMRs (flooding, droughts, heatwaves, landslides, and storm surges and coastal erosion). We found that methods to assess the complex issue of NBS efficiency and cost-benefits analysis are still in the development stage and they have only been implemented through the methodologies developed for other purposes such as fluid dynamics models in micro and catchment scale contexts. Of the reviewed numerical models and tools MIKE-SHE, SWMM (for floods), ParFlow-TREES, ACRU, SIMGRO (for droughts), WRF, ENVI-met (for heatwaves), FUNWAVE-TVD, BROOK90 (for landslides), TELEMAC and ADCIRC (for storm surges) are more flexible to evaluate the performance and effectiveness of specific NBS such as wetlands, ponds, trees, parks, grass, green roof/walls, tree roots, vegetations, coral reefs, mangroves, sea grasses, oyster reefs, sea salt marshes, sandy beaches and dunes. We conclude that the models and tools that are capable of assessing the multiple benefits, particularly the performance and cost-effectiveness of NBS for HMR reduction and management are not readily available. Thus, our synthesis of modelling methods can facilitate their selection that can maximise opportunities and refute the current political hesitation of NBS deployment compared with grey solutions for HMR management but also for the provision of a wide range of social and economic co-benefits. However, there is still a need for bespoke modelling tools that can holistically assess the various components of NBS from an HMR reduction and management perspective. Such tools can facilitate impact assessment modelling under different NBS scenarios to build a solid evidence base for upscaling and replicating the implementation of NBS

Characterizing geomorphological change to support sustainable river restoration and management

The hydrology and geomorphology of most rivers has been fundamentally altered through a long history of human interventions including modification of river channels, floodplains, and wider changes in the landscape that affect water and sediment delivery to the river. Resultant alterations in fluvial forms and processes have negatively impacted river ecology via the loss of physical habitat, disruption to the longitudinal continuity of the river, and lateral disconnection between aquatic, wetland, and terrestrial ecosystems. Through a characterization of geomorphological change, it is possible to peel back the layers of time to investigate how and why a river has changed. Process rates can be assessed, the historical condition of rivers can be determined, the trajectories of past changes can be reconstructed, and the role of specific human interventions in these geomorphological changes can be assessed. To achieve this, hydrological, geomorphological, and riparian vegetation characteristics are investigated within a hierarchy of spatial scales using a range of data sources. A temporal analysis of fluvial geomorphology supports process-based management that targets underlying problems. In this way, effective, sustainable management and restoration solutions can be developed that recognize the underlying drivers of geomorphological change, the constraints imposed on current fluvial processes, and the possible evolutionary trajectories and timelines of change under different future management scenarios. Catchment/river basin planning, natural flood risk management, the identification and appraisal of pressures, and the assessment of restoration needs and objectives would all benefit from a thorough temporal analysis of fluvial geomorphology