Remote Sensing in Hydrology
... Ground surveys are commonly used to prepare groundwater potential maps (Ganapuram et al. 2009). Being a powerful tool in the analysis and management of groundwater resource, remote sensing (RS) provides a better understanding and systematic analysis of various hydro-geomorphic units/landforms/ lineaments features (Engman & Gurney 1991; Sharma & Jugran 1992; Meijerink 2007; Jha et al. 2010). On the other hand, geographic information system (GIS) has also proved to be a useful tool for groundwater studies (Krishnamurthy et al. 1996; Meijerink 1996) due to its capability of handling large and complex spatial data for resource management and decision-making (Stafford 1991). ...
... Being a vital component of various hydrological and hydro-geological factors, the 'groundwater potential' signifies the amount of groundwater availability of an area (Jha et al. 2010). The proficiency of remote sensing satellite sensors is limited regarding direct detection of groundwater but provides rapid and valuable ground-level information about the various factors controlling directly or indirectly the existence and movement of groundwater (Engman & Gurney 1991; Meijerink 1996;). Certain baseline information, for instance, soil, geomorphology, fractures/lineaments, topographic slope, land use/land cover and surface water bodies serving as indicators of groundwater existence can be derived from satellite imagery (Todd 1980; Jha & Peiffer 2006; Meijerink 2007). ...
The study aims at delineating groundwater potential zones using geospatial technology and analytical hierarchy process (AHP) techniques in mining impacted hard rock terrain of Ramgarh and part of Hazaribagh districts, Jharkhand, India. Relevant thematic layers were prepared and assigned weight based on Saaty’s 9 point scale and normalized by eigenvector technique of AHP to identify groundwater prospect in the study area. The weighted linear combination (WLC) method was applied to prepare the groundwater potential index in GIS. Final groundwater prospects were classified as excellent, very good, good, moderate, poor and very poor groundwater potential zones. Study thus revealed that the excellent, very good and good groundwater potential zones respectively cover 148.3 km², 373.66 km² and 438.86 km² of the study area, whereas the poor groundwater potential zone covers 180.05 km². Validation was done through a receiver operating characteristic (ROC) curve, which indicated that AHP had good prediction accuracy (AUC=75.45%).
... For example, assessments of the location of highly productive groundwater are often problematic due to the lack of a systematic approach and the requirement of expensive laboratory analyses [23]. However, this type of research can be improved by using GIS data to measure the hydraulic characteristics of the source and then using GIS spatial analysis methods to obtain hydrogeological data regarding groundwater [24,25]. GIS and remote sensing technologies have great potential for use in analyses of groundwater potential; many studies have applied these techniques together with those concerned with geomorphology, drainage, lithology, and soils [26][27][28][29]. ...
In the spacious inundation area on the left bank of the lower course of the Sava River in Serbia, there is an abandoned meander Special Nature Reserve, “Obedska bara”, which represents a very important floodplain in this part of Europe. This area is characterized by an exceptional wealth of biodiversity with a significant presence of rare and endangered species of national and international importance. Hydrological conditions in the mentioned area were analyzed from the aspect of surface water movement in nature and conditions altered by human factors (after the construction of the road network, canals, etc.). The movement of surface water, i.e., the filling and emptying of the investigated area, parallel to the water level of the Sava River, is shown using a digital terrain model. Our simulation of the change in surface water level within the studied area included the display of underwater areas, both with the formation of a flood wave (i.e., increasing water level of the Sava) and with the outflow of water from the pond when the water level in the Sava was reduced in both scenarios (natural and conditions altered by human factors). GIS and terrain digitalization were used for geospatial and hydrological analyses and, based on this, maps that display endangered areas could be made. The obtained results show that the largest human impact was recorded at the water level of the Sava River 74 m above sea level. The aforementioned water regime changes were shown to negatively affect dominant vegetation, such as pedunculate oak and ash.
... There is generally little or no snowmelt during the accumulation period. Precipitation falls as snow is temporarily stored in the snowpack until the beginning of the melt season (Engman and Gurney 1991). ...
Spiti Valley, a cold desert mountain is located in the Trans Himalaya region where snow cover is a dominant type
of land cover. Characterized, as a rain shadow area where precipitation occurs in the form of snowfall and
almost negligible rainfall. Snow/glacier melt water is a single source of freshwater which is used in agricultural
and household activities by mountain dwellers. The objective of the study is to determine the effects of Land
Surface Temperature (LST) on snow cover and snow line position in Spiti Valley. The study is based on geospatial
techniques in which Landsat imagery is the main source of data for snow cover, snow line position, and Land
Surface Temperature (LST) analysis. It includes Operational Land Imager/Thermal Infrared Sensor (OLI / TIRS)
and Thematic Mapper (TM) scenes of all the twelve months (January to December) of 1990 and 2015. Snow
cover was extracted from January to December; using Normalised Difference Snow Index (NDSI). As per the
result, the average snow cover was 4, 68,998.50 ha (61.68%) in 1990 and 3, 69,676.4 ha (48.71%) in 2015.
Approximately 98,422.08 ha (0.51%) of snow cover was converted into a non-snow cover area at an average
rate of 3,937 ha/year. Analysis of the altitudinal position of the snow line indicates that it moved 445.11 m
upwards in Spiti Valley during the study period. In 1990, the snow line was visible at the height of 5,159.37 m
at the end of the summer, but reached at 5,604.48 m height in 2015, marking the loss of snow accumulation in
the lower heights. In addition, Land Surface Temperature (LST) was calculated to examine the impact on snow
cover where the mean LST of snow cover was -3°C in 1990 and -2°C in 2015, indicating that the temperature
increased by 1°C in the last 25 years. The Karl Pearson correlation method was used to establish the relationship
between LST and NDSI. It shows a negative relationship with strong correlation coefficients of R2 0.9,416 and
0.9,684 in 1990 and 2015. It indicates that LST and snow cover have a negative relationship with respect to the
physical changes in the soil, emissivity of the land surface and the albedo of the region. The impact of declining
snow cover can be easily understood by a simple cause-and-effect relationship where most of the people are
engaged in the primary sector such as farming, growing orchards, and animal husbandry. As per the primary
survey, most of the people felt that the availability of water in the study area decreases in the late summer season
which leads to water scarcity.
... Further reviews of the remote sensing to determine hydrological processes have been prepared by Engman and Gurney (1991), Schultz (1988), Schmugge et al. (2002), Pietroniro and Prowse (2002), Neale and Cosh (2012) and Frappart and Bourrel (2018). ...
Strategic planning of water management at the river-basin scale requires (1) measurement and accounting of individual hydrological processes, (2) quantification of water resources, and (3) their optimal allocation. Scalable Water Balances from Earth Observations (SWEO) is an open-access parameterization enabling automated reporting of water footprints and Sustainable Development Goal (SDG) indicators. We present its systematic arrangement and input datasets, and demonstrate its accuracy by independent riverflow measurements. We also review some achievements in remote sensing for hydrology during the last 50 years in quantifying hydrological and water management processes, flows, fluxes and changes in storage from various independent sources; and append mathematical formulations.
... Comprehensive comparisons between different retrieval techniques can be found in Bryant et al. (2003) and Moran et al. (2004). Engman and Gurney (1991) present an overview of soil moisture retrieval techniques and analyse their capabilities, advantages and disadvantages. The following methods mentioned have been successfully used to model soil moisture and are briefly described: ...
Soil moisture is an important element in hydrological land-surface processes as well as land-atmosphere interactions and has proven useful in numerous agronomical, climatological and meteorological studies. Since hydrological soil moisture estimates are usually point-based measurements at a specific site and time, spatial and temporal dynamics of soil moisture are difficult to capture. Soil moisture retrieval techniques in remote sensing present possibilities to overcome the abovementioned limitations by continuously providing distributed soil moisture data at different scales and varying temporal resolutions. The main purpose of this study is to derive soil moisture estimates for the Stockholm region by means of two different approaches from a hydrological and a remote sensing point of view and the comparison of both methods. Soil moisture is both modelled with the Topographic Wetness Index (TWI) based on digital elevation data and with the Temperature‐Vegetation Dryness Index (TVDI) as a representation of land surface temperature and Normalized Difference Vegetation Index (NDVI) ratio. Correlations of both index distributions are investigated. Possible index dependencies on
vegetation cover and underlying soil types are explored. Field measurements of soil moisture are related to the derived indices. The results indicate that according to a very low Pearson correlation coefficient of 0.023, no linear dependency between the two indices existed. Index classification in low, medium and high value categories did not result in higher correlations. Neither index distribution is found to be related to soil types and only the TVDI correlates alongside changes in vegetation cover distribution. In situ measured values correlate better with TVDIs, although neither index is considered to give superior results in the area due to low correlation coefficients. The decision which index to apply is dependent on available data, intent of usage and scale. The TWI surface is considered to be a more suitable soil moisture representation for analyses on smaller scales whereas the TVDI should prove more valuable on a larger, regional scale. The lack of correlation between the indices is attributed to the fact that they differ greatly in their underlying theories. However, the synthesis of hydrologic modelling and remote sensing is a promising field of research. The establishment of combined effective models for soil moisture determination over large areas requires more extensive in situ measurements and methods to fully assess the models’ capabilities, limitations and value for hydrological predictions.
... Furthermore, for an area which is spatially heterogeneous, measurements made in a single location may not be representative of the full area [9]. For a given area in a humid climate, annual ET can be as large as half of the precipitation, whereas for arid and semi-arid areas ET is almost equivalent to the total annual rainfall [10]. ...
Almost fifty years have passed since the idea to retrieve a value for Evapotranspiration (ET) using remote sensing techniques was first considered. Numerous ET models have been proposed, validated and improved along these five decades, as the satellites and sensors onboard were enhanced. This study reviews most of the efforts in the progress towards providing a trustworthy value of ET by means of thermal remote sensing data. It starts with an in-depth reflection of the surface energy balance concept and of each of its terms, followed by the description of the approaches taken by remote sensing models to estimate ET from it in the last thirty years. This work also includes a chronological review of the modifications suggested by several researchers, as well as representative validations studies of such ET models. Present limitations of ET estimated with remote sensors onboard orbiting satellites, as well as at surface level, are raised. Current trends to face such limitations and a future perspective of the discipline are also exposed, for the reader’s inspiration.
... Such methods are usually combined with information provided by perspective-view tools wherein the mapping process is integrated within GIS. These tools, including aerial photographs and near-infrared satellite images, have had limited success due to the absence of spectral resolution (Engman & Gurney, 1991). Still, remote sensing (RS) can be a quick and powerful tool for obtaining spatiotemporal information over a large area, including factors influencing catchment hydrology like geology, geomorphology, land use/cover and drainage patterns (Jha et al., 2007Yeh et al., 2014). ...
Proper knowledge of potential groundwater recharge (PGR) and its spatiotemporal distribution are essential for sustainable groundwater management, especially within the context of climate change. Here, a robust GIS-based water budget framework was developed to estimate PGR at a regional scale and map its spatial distribution. This framework is demonstrated over the Saguenay-Lac-Saint-Jean region (13,200 km2) of Quebec (Canada). The PGR mapping process was based on a model incorporating water budget components. The vertical inflows (VI) include water amounts from rainfall and snowmelt, whereby the latter was assessed using HYDROTEL model. VI were combined with the maximum and minimum temperatures to estimate actual evapotranspiration (AET), while the surface runoff (RuS) was assessed using the curve number method. Field observations of annual variation in temperatures and the water budget components, over a period of 100 years (1910–2009), were used to provide a comprehensive overview of the effects of climate change on PGR. The last 10 years of the observation period (i.e., 2000-2009) indicate that 6% of the study area have PGR rates of 35–50%. PGR rates of 20–35% occur in 58% of the study area, while 36% have PGR of 5–20%. The trend analysis of temperature time series reveals an average of 1.1±0.6 °C increase over 100 years. Also, an increase in the water budget components is observed. Despite the increasing trends of RuS and AET, PGR still showed an increasing trend with an average increase of 0.7±0.4 mm/yr over the past 100 years. This observation indicates that the increase in VI was enough to compensate for the increases in AET and RuS. This finding of an increasing PGR in the study area provides useful information for future studies focusing on predicting long-term PGR evolution and for the development of efficient long-term groundwater management strategies.
... The extraction methods for various parameters including soil moisture vary from empirical to physical methods. An extensive review of soil moisture retrieval methods, merits and demerits can be found in (Engman and Gurney, 1991) and the references therein. Information derived from 'scatter-plot' between satellitederived NDVI and LST is adopted for soil moisture assessment (Petropoulos et al., 2002). ...
Abstract
Agricultural waste management, as practiced by the vast majority of farmers and farm owners in agricultural areas in the district of Kuala Terengganu, involves four process scenarios, namely S1: On farm burial, S2: On-farm burning, S3: Landfill and S4: Recycled. However, the disposal of agricultural waste has produced an environmental impact on the ecosystem in different ways. The aim of this study is to compare the potential environmental damage incurred from the disposal of agricultural waste in the four scenarios above. Data collection was conducted in ten farms located in Kuala Terengganu. The research includes the analysis of agricultural waste, the analysis of crop residue characteristics, and the analysis of potential contamination using the Life Cycle Assessment (LCA) approach that utilizes the Simapro software. The results showed that the scenarios S3 and S1 have resulted in a value credit for the destruction of the environment, compared to the debit value of both S2 and S4. In this study, Material Flow Analysis- Life Cycle Assessment model of integration, with Integration of Material Flow Analysis-Life Cycle Assessment, has been developed and tested based on real data to confirm their validity as a valuable tool for assessing the environmental conditions for agricultural waste management system in the study region.
... In the past three decades, breakthroughs in satellites and remote sensing have highly demonstrated their potential to characterize and model the various components of the hydrological cycle (Engman & Gurney, 1991;McCabe et al., 2017;Tang, Gao, Lu, & Lettenmaier, 2009). This has become a fast-growing field in hydrology. ...
In the past three decades, breakthroughs in satellites and remote sensing have highly demonstrated their potential to characterize and model the various components of the hydrological cycle. A wealth of satellite missions are launched and some of the missions are specifically designed for hydrological research. Given the massive big data for hydrology, it is time for hydrology to embrace the fourth paradigm, data intensive science. This paper aims to highlight available and emergent technologies and missions in the field of Earth observation that have contributed greatly to hydrological science, the current status of those technologies and their improvements in our understanding of hydrological components, and to identify the important and emerging issues in Earth observation data applications in hydrology. This review will provide the readers with detail of Earth observation progress applications in hydrology.
... Land cover refers to the area ratio of a region covered by forests, agricultural land, water bodies, or other types of landscapes, whereas land use refers to how an area is used by people (e.g., building, conservation, park). Land-cover changes are usually determined by interpreting multi-period satellite images or remote-sensed data assisted by spatial analysis tools such as GIS (Geographical Information System) for map overlaying [5,6]; this is cost-effective and thus suitable for periodically updating hydrological parameters on a large scale [7][8][9]. Land-use changes cannot be determined from image processing but are determined from a filed survey or the investigation of environmental, economic, and social activities [10][11][12]. Thus, land-use data are often localized and related to policies regulating human activities on a small scale [13,14]. ...
Evaluating land and runoff variations caused by urbanization is crucial to ensure the safety of people living in highly developed areas. Based on spatial scales, runoff analysis involves different methods associated with the interpretation of land cover and land use, the application of hydrological models, and the consideration of flood mitigation measures. Most studies have focused on analyzing the phenomenon on a certain scale by using a single data source and a specific model without discussing mutual influences. In this study, the runoff changes caused by urbanization are assessed and cross-analyzed on three sizes of study areas in the Zhuoshui River Basin in Taiwan, including basin (large), watershed (medium), and city (small) scales. The results demonstrate that, on the basin scale, land-cover changes interpreted from satellite images are very helpful for identifying the watersheds with urbanization hotspots that might have larger runoff outputs. However, on the watershed scale, the resolution of the land-cover data is too low, and land-cover data should be replaced by investigated land-use data for sophisticated hydrological modeling. The mixed usage of land-cover and land-use data is not recommended because large discrepancies occur when determining hydrological parameters for runoff simulation. According to present and future land-use scenarios, the influence of urbanization on runoff is simulated by HEC-1 and SWMM on watershed and city scales, respectively. The results of both models are in agreement and show that runoff peaks will obviously increase as a result of urbanization from 2008 to 2030. For low return periods, the increase in runoff as a result of urbanization is more significant and the city’s contribution to runoff is much larger than its area. Through statistical regression, the watershed runoff simulated by HEC-1 can be perfectly predicted by the city runoff simulated by SWMM in combination with other land/rainfall parameters. On the city scale, the installation of LID satisfactorily reduces the runoff peaks to pre-urbanization levels for low return periods, but the effects of LID are not as positive and are debatable for higher return periods. These findings can be used to realize the applicability and limitations of different approaches for analyzing and mitigating urbanization-induced runoff in the process of constructing a sponge city.
... During the water stress, water resources considered as renewable can be drawn upon beyond their renewable threshold, rendering the resource unsustainable. In case of north-west India where the depletion of water level and deterioration of water quality is quite faster than any other parts of India, it leads to the scarcity of safe drinking water supply ( Waters et al., 1990;Engman and Gurney, 1991;Meijerink, 1996;Sander et al., 1996;Kumar et al., 2008;Siebert et al., 2010;Gontia and Patil, 2012). Remote Sensing (RS) techniques have gained increasing applications in monitoring regional hydrological processes in the recent decades. ...
This study represents the first attempt to examine spatial and seasonal variations of the surface water budget by using the Gravity Recovery and Climate Experiment (GRACE) by measuring gravity anomalies on earth to estimate changes in Total Water Storage (TWS) content over the north-western region of the India including New Delhi and states of Rajasthan, Uttar Pradesh and Haryana, covering an area of 676,917 km². The TWS (surface plus ground) and its changes were estimated from 2003 to 2012. Additionally, Global Land Data Assimilation System (GLDAS) variables were used to infer as to how TWS was partitioned into canopy water and soil moisture components. To evaluate monthly accumulated rainfall, Tropical Rainfall Measuring Mission (TRMM) data, processed by the Global Precipitation Climatology Center (GPCC) were used. By computing storage changes in GRACE, TWS, GLDAS land surface state variables and terrestrial-based water balance approach, we calculated groundwater storage changes. The time-series comparisons show good agreement between the GRACE satellite data, GLDAS model data and computed groundwater data. The change in soil moisture storage is less than that in saturated storage. Both the GRACE and calculated groundwater storage changes indicate storage loss in the range of 86.43 km³/y ± average of 10 years data (in terms of equivalent water thickness). The average groundwater loss for was calculated as 9.7 ± km³/y, states of Haryana as 9.7 ± km³/y, Rajasthan as 33.199 ± km³/y and Uttar Pradesh as 44.4827 ± km³/y. Our results are convincing of a credible GRACE hydrology data which can be handy in monitoring storage dynamics and water availability at regional scale. As GRACE data are available for virtually every region of the world, their application in conjunction with hydrological models will improve applications of hydrological studies which may lead not only to water balance closures, but also to sustainable water resource management at regional scale.
... This procedure is particularly essential during unusual hydrological events and after a long period of dryness [3][4][5][6][7][8][9][10]. However conventional in situ measurements are currently limited to discrete measurements at particular locations. Furthermore they are too sparse to represent the spatial soil moisture distribution and are therefore not suitable for basin level studies [7,[11][12][13][14]. Alternatively, satellite remote sensing techniques are a major tool in observing soil moisture information at a large scale [15], which provide near real-time global coverage. ...
Accurate soil moisture information is very important for real-time flood forecasting. Although satellite soil moisture observations are useful information, their validations are generally hindered by the large spatial difference with the point-based measurements, and hence they cannot be directly applied in hydrological modelling. This study adopts a widely applied operational hydrological model Xinanjiang (XAJ) as a hydrological validation tool. Two widely used microwave sensors (SMOS and AMSR-E) are evaluated, over two basins (French Broad and Pontiac) with different climate types and vegetation covers. The results demonstrate SMOS outperforms AMSR-E in the Pontiac basin (cropland), while both products perform poorly in the French Broad basin (forest). The MODIS NDVI thresholds of 0.81 and 0.64 (for cropland and forest basins, resp.) are very effective in dividing soil moisture datasets into " denser " and " thinner " vegetation periods. As a result, in the cropland, the statistical performance is further improved for both satellites (i.e., improved to NSE = 0.74, RMSE = 0.0059 m and NSE = 0.58, RMSE = 0.0066 m for SMOS and AMER-E, resp.). The overall assessment suggests that SMOS is of reasonable quality in estimating basin-scale soil moisture at moderate-vegetated areas, and NDVI is a useful indicator for further improving the performance.
... Due to their unique capability, such as, synoptic coverage, temporal data acquisition and spatial characteristics, one can map the entire watershed by using remotely sensed data and GIS environment [33]. By using the GIS technique, remotely sensed data can be processed and converted to necessary information about LU/LC (land use/land cover) mapping, change detection, ground water potential zone, soil erosion analysis, runoff estimation, site suitability for rain water harvesting, etc. [44][45][46][47][48][49][50][51]. ...
Geo-informatics technology is basically comprises of 3S component, Remote sensing (RS), Geographic Information System (GIS) and Global Positioning System (GPS). Nowadays Geographic Information System and Remote Sensing are playing a crucial role in our environmental development, raw materials assessment, urbanization, study of watershed, survey and management of cultivable land, study of forestry, geological structure, disaster management and supervision, etc. GIS and RS have emerged as key instruments for retrieving data and information on the earth during the last 30 years. These days, spatial, temporal and spectral resolve satellite data are accessible and using GIS environment their applications have multiplied for the purpose of research work. The objective of the present paper is to present an overview of the state-of-the-art technology behind GIS and RS. This study also highlights the importance of GIS and RS in managing, monitoring and analysing of contemporary issues, such as, urbanization and watershed management, etc.
... In fact, microwave emissivity is sensitive to water conductivity variations, and thus to water composition. Teledetection in the thermal IR domains and microwave radiation can be used to evaluate surface water temperature (examples in Engman and Gurney, 1991). Microwave radiation is less sensitive to atmospheric conditions and thus it will be more often used, but its resolution is rough compared with that of the IR (Shutko, 1985Shutko, , 1986). ...
... However, a critical limitation in passive microwave observations is the coarse spatial resolution. It is important to develop simple and robust procedures to downscale a passive microwave-based soil moisture from its nominal scale to that needed for hydrologic application and watershed management [2]. In this study, we compared two different downscaling methods using AMSR-2 soil moisture by merging information derived from MODIS. ...
... Remote sensing (RS) and geographical information system (GIS) provide cost-effective and time-effective means of assessing and managing groundwater resources (Jha et al., 2007;Meijerink, 2007;Avtar et al., 2010;Singh et al., 2011) and particularly are of great significance for remote as well as data-scarce regions (Machiwal et al., 2011). Satellite data provide quick and useful baseline information about various factors that directly or indirectly control the occurrence and movement of groundwater such as geomorphology, soil types, land slope, land use/land cover (LULC), drainage patterns and lineaments (Waters et al., 1990;Engman and Gurney 1991;Meijerink, 1996;Jha and Peiffer 2006;Jha et al., 2007). In the past, several researchers have used RS and GIS for the delineation of groundwater potential zones (GWPZs) (Shahid et al., 2000;Jaiswal et al., 2003;Rao and Jugran 2003;Sikdar et al., 2004;Sener et al., 2005;Solomon and Quiel, 2006;Madrucci et al., 2008;Chowdhury et al., 2009;Jha et al., 2010;Singh et al., 2010aSingh et al., ,2010bMachiwal et al., 2011;Adiat et al., 2012;Mukherjee et al., 2012) with good and effective results as the outcomes are in good correlation with field measurements. ...
Geospatial techniques to delineate groundwater potential zones in Delhi
Chander Kumar Singh, Anand Kumar, Sonal Bindal, Rashmi Jha, Vikram Singh Kahlon
Dept. of Natural Resources
TERI University
New Delhi-70
Corresponding Author: chander.singh@teriuniversity.ac.in
Abstract
From the ancient time human depends on the water resources for their survival, cultural and socio-economic development. Groundwater is the one of the most reliable sources of freshwater supply and much more significant than surface water in terms of quality and quantity. After industrial revolution rapid and unplanned growth of population and developmental activities has created extra pressure on groundwater resources. As in climate change scenario hydro-meteorological parameters are also expected to change, effective and sustainable management of groundwater extraction and aquifer recharge became an important aspect for water resource management.
Geospatial technology is proficient and cost effective tool which provides information’s about the various factors which controls the occurrence and movement of groundwater. In present study thematic layers of geomorphology, geology, soil, digital elevation model, land use/land cover, drainage density, proximity of surface water bodies, surface temperature, post monsoon depth of groundwater and lineament density are considered by using remote sensing and geographical information system to delineate groundwater potential zones in study area. These thematic layers were assigned suitable weights on the basis of saaty’s scale according to their relative significance in occurrence of groundwater. The assigned weight and their feature were normalized by analytic hierarchical process. The groundwater potential zone map was validated by groundwater discharge data of 28 pumping wells which showed good correlation The final results shows that about 51% (approximately 700 km2) of area is covered by very good and good GWPZs while 19% of area (268 km2) is under poor and very poor GWPZs rest 30% area is having moderate GWPZ.
In Northern Ethiopia, land degradation, together with population pressure foster soil erosion. Soil erosion in turn escalates surface runoff which is a serious challenge to agricultural production and economic growth in the region. Understanding the characteristics and dynamics of hydrometeorological variables are important elements in water resources development projects. Present study was carried out to evaluate hydrometeorological characteristics in the Northern Ethiopia, Gerado Catchment. Long term meteorological data such as precipitation, temperature and other climatic factors were collected from existing meteorological stations. Thornthwaite empirical equation and thornthwaite soil water balance models were employed in estimating potential and actual evapotranspiration. The first approach used air temperature as an index of energy available for evapotranspiration. Similarly, groundwater recharge of the catchment was computed as a difference of between outflow and change in a water storage. The runoff of the area was calculated based on the rainfall coefficient, annual precipitation and aerial coverage. On the other hand, groundwater potential (GWP) of the area was mapped based on important selected controlling factors. The result indicated that the annual potential and actual evapotranspiration of the catchment was found to be 755 mm/year and 723 mm/year respectively. The actual evapotranspiration was evaluated and weighted based on the dominant soil textures, depth root soil, and the respective land uses. As result, high evapotranspiration was observed in moderate deep rooted cereal crops and sandy loam soil texture which accounted 48.5% influence. But, cereal crops with moderate deep rooted and clay loam type have low AET (42.2%). Because of absence of gauging stations in the catchment, the volume runoff was computed using the runoff coefficient method. Accordingly, surface runoff from the catchment was calculated to be 120,581,841 cubic meter (m3) or 326 mm. Whereas, the groundwater recharge of the area was also found to be 52,208,159.5 cubic meter (141.5mm). Thus, out of the given mean annual precipitation, 27.6% and 12% of the mean annual rainfall lost because of runoff and recharge and the rest (60.4%) due to evapotranspiration. Regarding GWP suitability mapping, lithology, liniment density and geomorphology were found the most essential factors affecting groundwater mapping. The suitable GWP areas were lied within lithology and geomorphology class. Moreover, areas with flat slope and low lineament density are also located in most rich groundwater areas. Furthermore, installation of rain gauges at appropriate areas are essential for all inclusive and consistent data availability. Keywords: Hydro-meteorological, Gerdo Catchment, groundwater recharge, runoff, evapotranspiration, GWP
Water is an important natural resource for human survival, and it is the base for all vital activities. Water needs rise along with population growth, but water supplies largely remain stable. There is not a shortage of water to fulfill our needs; rather, there is a problem with water management. Water supplies are being depleted for a variety of reasons, including population expansion, industrialization, deforestation, a lack of rainfall, and changes in land use and land cover (LULC). The four elements of hydrological processes that are most likely to be impacted by changes in LULC in terms of their quantity and pattern are surface runoff, base flow, interflow, and evapotranspiration. Information on current patterns of land use and temporal land use changes is a fundamental necessity for the effective use of land. Accurate, useful, and current data on land usage are crucial in this dynamic context. Due to changes in the features of the land surface, the LULC alterations could have an impact on infiltration or percolation. To allocate resources for planning and management, it is required to identify the land use change in the past and current accessible land use. Remote sensing and a Geographic Information System (GIS) are good options for properly monitoring LULC and its effects on water quality and water pollution. Therefore, the purpose of this research is to investigate how LULC changes affect water resources and how they are managed, as well as how well remote sensing and GIS technologies function as monitoring tools. Therefore, the purpose of this research is to investigate the impact of LULC on water resources and their management as well as how well remote sensing and GIS technologies function as monitoring tools. This paper concludes that water resource monitoring and management is important for the human being. Through satellite imageries, we can timely diagnose and predict various attributes of water that will be helpful in the management of surface and groundwater resources.KeywordsLULCWater resourcesRemote sensingGeographical information system
Surface runoff estimation of ungauged watershed is one of the major problem, so estimation of surface runoff using Natural Resource Conservation Service is one of the most widely used method. The hydrologic soil groups, land use land cover and slope maps were generated with GIS tools. The curve number values from NRCS-SCS CN standard tables were assigned to the intersected hydrologic soil groups and land use maps to generate CN values map. The curve number method was followed to estimate runoff depth for selected storm events in the watershed. Weighted CN value and the storage retention (S) for the entire basin of Tavarja lake catchment are estimated as 86 to 94 respectively. So it indicates that the study area is characterized by high runoff potential. Runoff values are estimated on different rainfall sections of the study area. The study ultimately leads to the development of rainfall runoff method based on NRCS-SCS-CN method. The runoff for any rainfall event occurred can be estimated by feeding the inputs into the corresponding method which would in turn be helpful on significant conservation of water in the study area.
Natural resources are gradually becoming insufficient, and the results of human actions are omnipresent. In such a case, the focus should be on how to reduce impacts and improve sustainability using the best available tools for environmental characterization, impact assessment, and plan development. A clear understanding of the process, how water is collected and stored, and an understanding of how runoff changes in mountainous areas is useful for developing water resource use and planning. Groundwater is mainly used for drinking and irrigation purpose. Pollution in groundwater can lead to unsafe drinking water, water supply loss, excessive cleaning cost, and increase in cost of other water supply sources and/or potential health risks. With the Geographic Information Systems (GIS) application, better and innovative methods can be developed to process the huge amount of data and information related to groundwater. This chapter provides a comprehensive review of GIS applications for groundwater resource assessment, exploration, groundwater contamination risk assessment, and protection planning. In this chapter, the relevant literature in different locations and in various ways has been collected to provide a comprehensive review. Conclusions are drawn based on identified gaps and on research prospectus in groundwater assessment of groundwater resources and pollution risk using GIS.
Water salinity is one of the most critical water properties which considerably affects the lives of marine flora and fauna. In this study, the water salinity of Lake Urmia was mapped using sentinel-2 Multispectral Images (MSI). A Support Vector Regression (SVR) was developed to predict the water salinity using sentinel-2 spectral bands and indices. Three main scenarios were considered when input features were used in the SVR model. In scenario 1, the SVR was fed by all the features generated from Sentinel-2 data, and in the other two scenarios, a Genetic Algorithm (GA) and a Sequential Feature Selection (SFS) were applied to select the optimum input features to be used in the SVR model. The results showed that the salinity of Lake Urmia was estimated with a relatively reliable accuracy using GA along with the SVR model, where the R2 of 65.7% and the Root Mean Square Error (RMSE) of 11.5 PSU were obtained when the results were compared with in-situ data. Overall, this study showed that Sentinel-2 provides valuable high spatial-temporal datasets for continuous monitoring of water salinity over Lake Urmia.
Technological innovations during the recent centuries have enabled us to significantly boost agricultural production to feed the rapidly increasing global population. While advances in digital technologies enabled the onset of the fourth digital revolution in agriculture, we also have several challenges such as limited cropland, diminishing water resources, and climate change, underscoring the need for unprecedented measures to achieve agricultural resilience to support the world population. Geographic information system (GIS), along with other partner technologies such as remote sensing, global positioning system, artificial intelligence, computational systems , and data analytics, has been playing a pivotal role in monitoring crops and in implementing optimal and targeted management practices towards improving crop productivity. Here we have reviewed the diverse applications of GIS in agriculture that cover the entire pipeline from land-use planning to crop-soil-yield monitoring to post-harvest operations. GIS, in combination with digital technologies and through new and emerging areas of applications, is enabling the realization of precision farming and sustainable food production goals.
Landsat-8 spectral values have been used to map the earth’s surface information for decades. However, forest types and other land-use/land-cover (LULC) in the mountain terrains exist on different altitudes and climatic conditions. Hence, spectral information alone cannot be sufficient to accurately classify the forest types and other LULC, especially in high mountain complex. In this study, the suitability of Landsat-8 spectral bands and ancillary variables to discriminate forest types, and other LULC, using random forest (RF) classification algorithm for the Hindu Kush mountain ranges of northern Pakistan, was discussed. After prior-examination (multicollinearity) of spectral bands and ancillary variables, three out of six spectral bands and five out of eight ancillary variables were selected with threshold correlation coefficients r2<0.7. The selected datasets were stepwise stacked together and six Input Datasets (ID) were created. The first ID-1 includes only the Surface Reflectance (SR) of spectral bands, and then in each ID, the extra one ancillary variable including Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Normalized Difference Snow Index (NDSI), Land Surface Temperature (LST), and Digital Elevation Model (DEM) was added. We found an overall accuracy (OA) = 72.8% and kappa coefficient (KC) =61.9% for the classification of forest types, and other LULC classes by using the only SR bands of Landsat-8. The OA = 81.5% and KC=73.7% was improved by the addition of NDVI, NDWI, and NDSI to the spectral bands of Landsat-8. However, the addition of LST and DEM further increased the OA, and Kappa coefficient (KC) by 87.5% and 82.6%, respectively. This indicates that ancillary variables play an important role in the classification, especially in the mountain terrain, and should be adopted in addition to spectral bands. The output of the study will be useful for the protection and conservation, analysis, climate change research, and other mountains forest-related management information.
Water on the earth is in abundance but its distribution is very much uneven on the land surface. Only 2% of the total water is available for use. Due to its distribution and quality, scarcity of portable water will be the major challenge at global level as most of the water available in surface reservoirs and groundwater are affected by various kind of contaminations of various sources. The situation is more aggravated in arid and semi-arid areas. Rajasthan state of India is located in arid & semi-climatic region with poor water quality. Same is the situation in the Bhilwara district located in the central part of the Rajasthan state where availability of water resource is very poor because of quality, quantity, and distribution issues. At the same time demand for potable water is increasing day by day for irrigation, industrial & domestic purposes. The present study is focused on spatial variability of groundwater qualityGroundwater Quality for the Bhilwara district of Rajasthan, India using geospatial techniques. Four important water quality parameters that is Total dissolved solidsTotal Dissolved Solids (TDS), Chloride, Nitrate, and Fluoride (TDS, Cl, NO3, and F) has been taken into consideration for assessment of water quality. Data on these parameters have been collected and classified with the standard parameter values as suggested by the BIS standards (ISI 10,500:2012). After data normalization appropriate weights have been given according to the contribution of individual parameter in water quality and a ground Water Quality Index (WQI)Water Quality Index (WQI) is generated. The scale of WQI is categorized into (1) Very Good, (2) Good, (3) Average, and (4) Poor. The analysis indicates that good water quality is associated with high water level, more thickness of alluvium, deep bedrock, more water-saturated strata, good groundwater recharge areas, nearness from the river, etc. The results are verified in the field at appropriate locations supported by interviews of local farmers. Status of water quality shows that the 24.65 and 20.18% area of district cover by the “Very Good” and “Good” quality of water and 33.72% area show the “Average” quality of water while the 21.45% of area is covered by the “Poor” quality of water.
Accurate soil moisture indicator is critically important for hydrological applications such as water resource management and hydrological modelling. Modern satellite remote sensing has shown a huge potential for providing soil moisture measurements at a large scale. However its effective utilisation in the aforementioned areas still needs comprehensive research. This chapter focuses on exploring the advances and potential issues in the current application of satellite soil moisture observations in hydrological modelling. It has been proposed that hydrological application of soil moisture data requires the data relevant to hydrology. In order to meet the requirement, the following two research tasks are suggested: the first is to carry out comprehensive assessments of satellite soil moisture observations for hydrological modelling, not merely based on evaluations against point-based in situ measurements; the second is that a soil moisture product (e.g. soil moisture deficit) directly applicable to hydrological modelling should be developed. Only fully accomplishing these two steps will push forward the utilisation of satellite soil moisture in hydrological modelling to a greater extent.
The article presents a new method for an assessment of past water–human interactions. The method is based on a tool developed by space syntax pioneers Bill Hillier and Julienne Hanson: a gamma graph analysis representing ‘permeability’ of buildings. The article discusses how this tool was altered in order to model a water management system. Subsequently, the method is tested on a case study – an ancient oasis‐city of Miran in north‐western China. The positive verification makes the author argue that the introduced method can provide new insight into the relations of power interlinked with water management.
An important consequence of global population and food preference pressures is the need to increase water productivity in agriculture. Irrigation remains an important part of the strategy to feed the global population in the future, and may often be the only option in
some arid and semiarid countries, such as Pakistan. In terms of attempts to map and use estimates of irrigated area, the calculation of water productivity is a second-level set of questions after learning crop area and crop intensity. In practice, it is often difficult to access sufficient data on water supply and water use to determine crop water
consumption, and it is even more challenging to get spatially disaggregated yield data to allow mapping of water productivity.
Energy balance techniques using remote sensing data have been developed by various researchers over the last 20 years, and they can be used as a tool to directly estimate evapotranspiration. Better estimates of water consumption result in better estimates of
water productivity, even if production data are aggregated from secondary agricultural statistics. A principal benefit is to allow identification of where agricultural performance is below potential and to understand where and how irrigation systems can be managed
better to improve overall performance and broad-scale water productivity.
This chapter provides a contrast to the presentation of methods to estimate crop area to more direct estimates of irrigation performance and management of water productivity using a combination of remote-sensed data and secondary crop statistics to provide
estimates of agricultural water consumption and water productivity in the Rechna Doab, Punjab Province in Pakistan. It also provides direct estimates of equity, adequacy, reliability and sustainability of the irrigation system.
The scarcity of water encourages scientists to develop new analytical tools
to enhance water resource management. Water accounting and distributed
hydrological models are examples of such tools. Water accounting needs
accurate input data for adequate descriptions of water distribution and
water depletion in river basins. Ground-based observatories are decreasing,
and not generally accessible. Remote sensing data is a suitable alternative
to measure the required input variables. This paper reviews the reliability
of remote sensing algorithms to accurately determine the spatial
distribution of actual evapotranspiration, rainfall and land use. For our
validation we used only those papers that covered study periods of
seasonal to annual cycles because the accumulated water balance is the primary
concern. Review papers covering shorter periods only (days, weeks) were not
included in our review. Our review shows that by using remote sensing, the
absolute values of evapotranspiration can be estimated with an overall
accuracy of 95% (SD 5%) and rainfall with an overall absolute
accuracy of 82% (SD 15%). Land use can be identified with an overall
accuracy of 85% (SD 7%). Hence, more scientific work is needed to
improve the spatial mapping of rainfall and land use using multiple
space-borne sensors. While not always perfect at all spatial and temporal
scales, seasonally accumulated actual evapotranspiration maps can be used
with confidence in water accounting and hydrological modeling.
Water is the most basic need of life since the existence of living things. Physicochemical monitoring of water quality is important for public health.In this study, we aimed to research the quality of drinking and domestic waters in dams, sources, wells and water tanks,located in Çorum in terms of several physicochemical parameters. In winter, spring and summer seasons we collected a total of 2894 water samples from 3 dams, 10 well-sources, and water tanks on a monthly basis; water tanks in the 80 different locations in the Center six days a week,and lastly domestic waters five days a week in 250 ml sterile water sampling bottles.We used the nephelometric method for determining the amount of turbidity, the electrometric method for analyzing conductivity, pH and, dissolved oxygen the photometric method for measuring free chlorine, the spectrophotometric method for determining the amount of sulphate, iron, nitrite, ammonia and, manganese and lastly TS 266 volumetric titration for analyzing total alkalinity, organic matter, magnesium, calcium and total hardness. We monitored water height, filling rate, turbidity, pH and free chlorine control in each drinking water storage tanks in Çorum with the Scada System and free chlorine with both the Scada System and manually. Detected in the range of Ammonium and Iron (mg/L): <0.5; Nitrite (mg/L): <0.02; Manganese (mg/L): <0.01; Turbidity (NTU): between 0.1 and 0.4; Conductivity (μS / cm): between 400 and 600; pH: 7.5 to 8.0; Sulfate (mg/L): 10 to 45; Total Alkalinity (mg/L): between 180 and 250; Organic matter (mg/L): 1 to 3. In accordance with Turkish Regulation on Water Intended for Human Consumption, the several physicochemical parameters in drinking and domestic waters in Çorum are in between stated values.
Hydrological model calibration combining Earth observations and in situ measurements is a promising solution to overcome the limitations of the traditional streamflow‐only calibration. However, combining multiple data sources in model calibration requires a meaningful integration of the data sets, which should harness their most reliable contents to avoid accumulation of their uncertainties and mislead the parameter estimation procedure. This study analyzes the improvement of model parameter selection by using only the spatial patterns of satellite remote sensing data, thereby ignoring their absolute values. Although satellite products are characterized by uncertainties, their most reliable key feature is the representation of spatial patterns, which is a unique and relevant source of information for distributed hydrological models. We propose a novel multivariate calibration framework exploiting spatial patterns and simultaneously incorporating streamflow and three satellite products (i.e., Global Land Evaporation Amsterdam Model [GLEAM] evaporation, European Space Agency Climate Change Initiative [ESA CCI] soil moisture, and Gravity Recovery and Climate Experiment [GRACE] terrestrial water storage). The Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature data set is used for model evaluation. A bias‐insensitive and multicomponent spatial pattern matching metric is developed to formulate a multiobjective function. The proposed multivariate calibration framework is tested with the mesoscale Hydrologic Model (mHM) and applied to the poorly gauged Volta River basin located in a predominantly semiarid climate in West Africa. Results of the multivariate calibration show that the decrease in performance for streamflow (−7%) and terrestrial water storage (−6%) is counterbalanced with an increase in performance for soil moisture (+105%) and evaporation (+26%). These results demonstrate that there are benefits in using satellite data sets, when suitably integrated in a robust model parametrization scheme.
Delineation of the groundwater potential zones (GWPZs) is one of the most vital practices for the sustainable management of groundwater resources. In the present study, the delineation of GWPZs is done using an integrated approach of geospatial technique and fuzzy-analytic hierarchy process (a hybrid method that combines fuzzy set theory and AHP). Seven major thematic layers like geology, slope, rainfall, landuse/landcover, soil type, lineament density, and drainage density are used to generate GWPZs map of the area. The final map is delineated into three different GWPZs, namely, high (20.44% of total study region), moderate and low zones both covering more than 79.55% of the total study region. Validation was done by overlaying the existing well yield (WY) and groundwater depth (GD) data on the final map. Results show that an accuracy of 81.81% and 75.75% was found between WY and GD data, respectively.
This chapter attempts to develop an integrated approach of remote sensing and geographic information systems for examining the effects of urban growth on surface runoff at local level by using the Zhujiang Delta of South China as a case. The model used for estimating surface runoff in the chapter was developed by the United States Soil Conservation Service. It has been widely applied to estimate storm runoff depth for every patch within a watershed based on runoff curve numbers (CN). The chapter summarizes a method to estimate composite CN which was developed with the vegetation–impervious surface–soil model, normalized difference vegetation index (NDVI), and soil types. First, soil types and NDVI are classified into several classes, respectively. Second, each class of soil type and NDVI are given an initial CN of soil and initial CN of vegetation, respectively. Lastly, the CN is calculated by computing the percentages of impervious surface, NDVI class, and soil class.
Sentinel-1 and Landsat-8 data were used to retrieve soil moisture from top soil surface (0–5 cm depth) at agricultural land (area under wheat crop). After pre-processing of satellite data and removal of vegetation influence (σ°veg) using Water Cloud Model (WCM), total backscattering coefficient (σ°total) and Normalized Difference Vegetation Index (NDVI) were used to simulate backscattering from soil (σ°soil). Modified Dubois Model (MDM) and Topp's Model were used to retrieve soil moisture using ε. Further, modelled soil moisture was evaluated using in situ soil moisture measurements and a Time Domain Reflectometer during Sentinel-1 overpass (24 January, 25 February and 13 March 2018). Statistical tests showed that an integrated approach has potential to improve soil moisture estimates over the vegetated/cropped area for agricultural and hydrological studies.
This study evaluated the Advanced Microwave Scanning Radiometer 2 (AMSR2) L2 soil moisture product (ver. 3) using in situ hydrological observational data, acquired over 7 years (2012–2018), from a 50 × 50 km flat area of the Mongolian Plateau covered with bare soil, pasture and shrubs. Although AMSR2 slightly underestimated soil moisture content at 3-cm depth, satisfactory timing was observed in both the response patterns and the in situ soil moisture data, and the differences between these factors were not large. In terms of the relationship between AMSR2 soil moisture from descending orbits and in situ measured soil moisture at 3-cm depth, the values of the RMSE (m3/m3) and the bias (m3/m3) varied from 0.028 to 0.063 and from 0.011 to − 0.001 m3/m3, respectively. The values of the RMSE and bias depended on rainfall condition. The mean value of the RMSE for the 7-year period was 0.042 m3/m3, i.e., lower than the target accuracy 0.050 m3/m3. The validation results for descending orbits were found slightly better than for ascending orbits. Comparison of the Soil Moisture and Ocean Salinity (SMOS) soil moisture product with the AMSR2 L2 soil moisture product showed that AMSR2 could observe surface soil moisture with nearly same accuracy and stability. However, the bias of the AMSR2 soil moisture measurement was slightly negative and poorer than that of SMOS with deeper soil moisture measurement. It means that AMSR2 cannot effectively measure soil moisture at 3-cm depth. In situ soil temperature at 3-cm depth and surface vegetation (normalized difference vegetation index) did not influence the underestimation of AMSR2 soil moisture measurements. These results suggest that a possible cause of the underestimation of AMSR2 soil moisture measurements is the difference between the depth of the AMSR2 observations and in situ soil moisture measurements. Overall, this study proved the AMSR2 L2 soil moisture product has been useful for monitoring daily surface soil moisture over large grassland areas and it clearly demonstrated the high-performance capability of AMSR2 since 2012.
Water scarcity is a major problem for villagers’ survival as it determines the population density and affects the migration pattern in Bara region. In the present study, groundwater resource endowment is studied and correlated with the distributional pattern of population, settlements and economic activity for analysing regional development in the study area. The study has been carried out using geospatial platforms, i.e. Erdas Imagine 2014 and ArcGis 10.2.2 software. Sentinel-2 satellite imagery and Cartosat-1 DEM data were the major data sources for extracting factor layers. Geomorphology and lineament maps of NRSC, District Resource Map of GSI, topographic maps and Google Earth images along with field surveys were ancillary database. Saaty’s 9-point rating scale of analytical hierarchy process was used to extract the GWPI by integrating factor layers of geomorphology, lineament density, slope, geology, rainfall, drainage density and land use land cover according to their relative influence. Final map shows different zones of groundwater prospects in the study region, which is validated from aquifer thickness data. Result shows that 39.19% (291.41 km²) of the total area (743.64 km²) is classified as high-to-excellent GWP, whereas 27.96% (207.89 km²) of the area is under very poor-to-poor GWP. Areas having poor-to-poorest groundwater storage impact on population distribution, as 14% of the total population is lying over these zones. The descriptive statistical analysis showed that CGWPI and built-up area are significantly correlated (F = 18.024 > 4.41*, t = − 4.245 > 2.101, R² = 50.03%). CGWPI is also correlated significantly with population density (F = 18.855 > 4.41*, t = -4.342 > 2.101, R² = 50.16%). However, the relationship is not very high on linear regression model as expected since only 50% variations in population distribution can be attributed to CGWPI, for example, the Shankargarh town having the highest population density and the second highest built-up percentage in the whole study area in spite of being endowed with the lowest groundwater potential.
This article is aimed at demonstrating the feasibility of combining water quality observations with modeling using data fusion techniques for efficient nutrients monitoring in the Shenandoah River (SR). It explores the hypothesis; “Sensitivity and uncertainty from water quality modeling and field observation can be improved through data fusion for a better prediction of water quality.” It models water quality using water quality simulation programs and combines the results with field observation, using a Kalman filter (KF). The results show that the analysis can be improved by using more observations in watersheds where minor variations to the analysis result in large differences in the subsequent forecast. Analyses also show that while data fusion was an invaluable tool to reduce uncertainty, an improvement in the temporal scales would also enhance results and reduce uncertainty. To examine how changes in the field observation affects the final KF analysis, the fusion and lab analysis cross-validation showed some improvement in the results with a very high coefficient of determination.
Briefly tracing the history of hydrologic modeling, this paper discusses the progress that has been achieved in hydrologic modeling since the advent of computer and what the future may have in store for hydrologic modeling. Hydrologic progress can be described through the developments in data collection and processing, concepts and theories, integration with allied sciences, computational and analysis tools, and models and model results. It is argued that with the aid of new information gathering and computational tools, hydrology will witness greater integration with both technical and non-technical areas and increasing applications of information technology tools. Furthermore, hydrology will play an increasingly important role in meeting grand challenges of the twenty-first century, such as food security, water security, energy security, health security, ecosystem security, and sustainable development.
This article is aimed at demonstrating the feasibility of combining water quality observations with modeling using data fusion techniques for efficient nutrients monitoring in the Shenandoah River (SR). It explores the hypothesis; “Sensitivity and uncertainty from water quality modeling and field observation can be improved through data fusion for a better prediction of water quality.” It models water quality using water quality simulation programs and combines the results with field observation, using a Kalman filter (KF). The results show that the analysis can be improved by using more observations in watersheds where minor variations to the analysis result in large differences in the subsequent forecast. Analyses also show that while data fusion was an invaluable tool to reduce uncertainty, an improvement in the temporal scales would also enhance results and reduce uncertainty. To examine how changes in the field observation affects the final KF analysis, the fusion and lab analysis cross-validation showed some improvement in the results with a very high coefficient of determination.
Unprecedented outbreaks of defoliating insects severely damaged blueberry crops near Port Graham on the Kenai Peninsula in Alaska from 2008-2012. The Native people in this region rely heavily on gathered blueberries and other foods for sustenance and nourishment. Influences of topography and stand structure on blueberry abundance and fruiting were examined and used to develop spatial models to predict abundance and productivity of blueberry plants. Fruiting was associated with decreased canopy density, a low basal area and southwesterly aspects. Stands with relatively high site indices have greater abundance of blueberry plants, while the opposite trend was observed with productivity. Results demonstrate the feasibility of modeling the abundance and productivity of blueberry plants using easily obtained satellite imagery in conjunction with a well-organized field data collection system.
Evaluation of groundwater resources in dry areas without enough data is a challenging task in many parts of the world, including Tehran–Karaj plain in Iran, which includes Tehran, the capital city of Iran and Karaj, one of Iran’s biggest cities. Water demand due to increasing agricultural and industrial activities caused many problems in the field of water resources management. In this study, the potential of groundwater resources was evaluated using remote sensing, geographic information system (GIS), and analytic hierarchy process (AHP) for the first time. Digital Elevation Model from Shuttle Radar Topography Mission was used to generate a slope map and drainage density map. Three Landsat-8 satellite images were utilized to provide lineament density and land cover/land use maps. Geological and soil type maps were provided from the Geological Survey and Mineral Explorations of Iran (GSI). Tropical Rainfall Measuring Mission data were used to prepare average annual precipitation map. Discharge values from 102 pumping wells in the time period of 2002–2014 were used to evaluate the results. Seven data layers were prepared, and the geodatabase was made in GIS. The layers and their classes were assigned weights using AHP method. Finally, the layers were overlaid based on their weights, and the potential map of groundwater resources was generated. The area was classified into five zones with very high, high, moderate, low, and very low potentials. The zones covered 5.95, 32.90, 22.70, 10.20, and 28.25% of the study area, respectively. The results showed good agreement with the field data obtained from discharge wells.
Reckoning precipitation by means of customary methods has many breaks and various slips due to manual noticing which is the most unintended aspects for precipitation analysis. These errors in records rises the inconsistency and grounds uncertainty while using it in simulation modeling, sediment modeling and other such type of works. TMPA 3B42, a research product of the world’s foremost satellite in precipitation study is used in this work for rainfall variability analysis with good accuracy level. The integration of GIS and Remote Sensing acts as an operative tool for extracting and analyzing this precipitation product with spatially interpolating it over region. The objective of this work is to analyze the variability in rainfall over Shipra catchment between 1998 and 2012, and extraction of precipitation data from network common form data files. The study proves that the catchment has an exceedingly variable drift of rainfall in downstream portion. Also, the work results that there is high inconsistency in the year 2008 with fall in rainfall all over the catchment while precipitation was found very less in 2000 and high in 2006 followed by the year 2011. This work also states that downstream portion of the study area leftovers in dearth rainfall condition most of the time but with a tad high runoff affecting the groundwater state in the area. Precipitation shortfall, diminishing water controlling structures and increasing land use is disturbing the water availability in the region.
Plant primary production is a key driver of several ecosystem functions in seasonal marshes, such as water purification and secondary production by wildlife and domestic animals. Knowledge of the spatio-temporal dynamics of biomass production is therefore essential for the management of resources—particularly in seasonal wetlands with variable flooding regimes. We propose a method to estimate standing aboveground plant biomass using NDVI Land Surface Phenology (LSP) derived from MODIS, which we calibrate and validate in the Doñana National Park’s marsh vegetation. Out of the different estimators tested, the Land Surface Phenology maximum NDVI (LSP-Maximum-NDVI) correlated best with ground-truth data of biomass production at five locations from 2001–2015 used to calibrate the models (R ² = 0.65). Estimators based on a single MODIS NDVI image performed worse (R ² ≤ 0.41). The LSP-Maximum-NDVI estimator was robust to environmental variation in precipitation and hydroperiod, and to spatial variation in the productivity and composition of the plant community. The determination of plant biomass using remote-sensing techniques, adequately supported by ground-truth data, may represent a key tool for the long-term monitoring and management of seasonal marsh ecosystems. View Full-Text
Experience in the estimation of rainfall using thermal infra-red data over tropical Africa has given insight into the problems of making and validating satellite rainfall estimates. The spatial and temporal variations in the rainfall mechanisms over Africa are demonstrated to be large. Rainfall algorithms that are mainly empirical and are tuned to perform in a particular climatic regime may be unsuitable for monitoring climate change. The high spatial variability of tropical rainfall combined with the sparse gauge network makes the validation of rainfall estimates difficult. Validation must take account of the non representative nature of gauge measurements and should be on the basis of identifying changes from climatologically expected rainfall. It is suggested that a robust multispectral rainfall algorithm will require physically based relationships between different cloud structures and observed radiances to be applied, probably in conjunction with GCM outputs which should define the weightings to be given to the different channels.
Soil moisture estimation from satellite earth observation has emerged effectively advantageous due to the high temporal resolution, spatial resolution, coverage, and processing convenience it affords. In this paper, we present a study carried out to estimate soil moisture level at every location within Enugu State Nigeria from satellite earth observation. Comparative analysis of multiple indices for soil moisture estimation was carried out with a view to evaluating the robustness, correlation, appropriateness and accuracy of the indices in estimating the spatial distribution of soil moisture level in Enugu State. Results were correlated and validated with In-Situ soil moisture observations from multi-sample points. To achieve this, the Topographic Wetness Index (TWI), based on digital elevation data, the Temperature Vegetation Dryness Index (TVDI) and an improved TVDI (iTVDI) incorporating air temperature and a Digital Elevation Model (DEM) were calculated from ASTER global DEM and Landsat images. Possible dependencies of the indices on land cover type, topography, and precipitation were explored. In-Situ soil moisture data were used to validate the derived indices. The results showed that there was a positive significant relationship between iTVDI versus TVDI (R = 0.53, P value < 0.05), while in iTVDI versus TWI (R = 0.00, P value > 0.05) and TVDI versus TWI (R = −0.01, P value > 0.05) no significant relationship existed. There was a strong relationship between iTVDI and topography, land cover type, and precipitation than other indices (TVDI, TWI). In situ measured soil moisture values showed negative significant relationship with TVDI (R = −0.52, P value < 0.05) and iTVDI (R = −0.63, P value < 0.05) but not with TWI (R = −0.10, P value > 0.05). The iTVDI outperformed the other two index; having a stronger relationship with topography, precipitation, land cover classes and soil moisture. It concludes that although iTVDI outperformed other indices (TVDI, TWI) in soil moisture estimation, the decision of which index to apply is dependent on available data, the intent of usage and spatial scale.
Small reservoirs are prevalent landscape features that affect the physical, chemical, and biological characteristics of headwater streams. Tens of thousands of small reservoirs, often less than a hectare in size, were constructed over the past century within the United States. While remote-sensing and geographic-mapping technologies assist in identifying and quantifying these features, their localized influence on water quality is uncertain. We report a year-long physicochemical study of nine small reservoirs (0.15–2.17 ha) within the Oconee and Broad River Watersheds in the Georgia Piedmont. Study sites were selected along an urban-rural gradient with differing amounts of agricultural, forested, and developed land covers. Sites were sampled monthly for discharge and inflow/outflow water quality parameters (temperature, specific conductance, pH, dissolved oxygen, turbidity, alkalinity, total phosphorus, total nitrogen, nitrate, ammonium). While the proportion of developed land cover within watersheds had positive correlations with reservoir specific conductivity values, agricultural and forested land covers showed correlations (positive and negative, respectively) with reservoir alkalinity, total nitrogen, nitrate, and specific conductivity. The majority of outflow temperatures were warmer than inflows for all land uses throughout the year, especially in the summer. Outflows had lower nitrate concentrations, but higher ammonium. The type of outflow structure was also influential; top-release dams showed higher dissolved oxygen and pH than bottom-release dams. Water quality effects were still evident 250 m below the dam, albeit reduced.
ResearchGate has not been able to resolve any references for this publication.