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Impact of Historical Land Cover Changes on Land Surface Characteristics over the Indian Region Using Land Information System
The present study has employed a regional Land Surface Model (LSM) to investigate the impact of historical land cover changes on land surface characteristics over the Indian subcontinent for the period of 1930–2013. Four simulations that include a control run and three experiment runs are performed with the Noah 3.6 LSM within the Land Information System (LIS). In the present study, the Noah LSM is driven by meteorological forcings, with radiation data obtained from the Global Data Assimilation System (GDAS) and the rainfall data obtained from IMD gridded rainfall data. The control run is performed with a MODIS-IGBP land cover map, while the three experimental runs are performed with three different potential land cover maps for the years 1930, 1975, and 2013. The potential land cover maps for the above three simulations are developed by blending the MODIS-IGBP data set with the fractional forest cover data set; the latter data is available for the years 1930, 1975, and 2013. Results indicate that the historical land cover change (1930 to 2013) has reduced the annual mean of latent heat flux and net surface heat flux over the Indian domain by -24.74 W/m2 and -14.18 W/m2 respectively, while the sensible heat flux and the soil temperature has increased by 4.97 W/m2 and 2.78 K. The annual mean change in latent heat flux, sensible heat flux, and soil temperature demonstrate that the largest changes occur when the land cover changes from forest to urban land as compared to forest to cropland, forest to grassland and forest to open shrubland. The annual mean change in latent heat flux is moderately large for the land cover change from forest to open shrubland when compared to forest to grassland and forest to cropland. The above is attributed to the effects of evapotranspiration, which has high values for the cropland followed by grassland and open shrubland. Furthermore, the triple collocation method is employed to assess the impact of historical land cover change on soil moisture. Results indicate that the triple collocation method effectively demonstrates the impact of land cover change on soil moisture.
Journal Article
QGIS in remote sensing set. Volume 3, QGIS and applications in territorial planning
These four volumes present innovative thematic applications implemented using the open source software QGIS. These are applications that use remote sensing over continental surfaces. The volumes detail applications of remote sensing over continental surfaces, with a first one discussing applications for agriculture.
BOOK
Rainfall-induced landslide hazard analyses using spatiotemporal retrievals of soil moisture and geomorphologic data
Rainfall-induced landslides threaten residential and civil infrastructure. As extreme rainfall events increase with climatological variability, so does the need to effectively monitor these occurrences. However, physical monitoring of landslide occurrence requires costly instrumentation over vast areas. Therefore, a means for large scale spatial monitoring is desired. This study conducts infinite slope stability analyses on known spatially distributed rainfall-induced shallow colluvial landslides. Infinite slope analyses were chosen due to applicability to the investigated shallow landslides. These analyses were investigated as functions of spatial geomorphologic and spatiotemporal soil moisture data. The underlying assumption of these analyses was that soil moisture would act as a hydro-mechanical precursor for rainfall-induced landslides. A majority of geomorphologic data for these analyses was obtained via web databases. Contrarily, it was observed that measurements of friction angle were not spatially available. To remedy this, an Artificial Neural Network (ANN) machine learning workflow was developed to yield these requisite measurements. For spatiotemporal soil moisture, the Land Information System (LIS) was utilized to conduct assimilation of NOAH 3.6 LSM and NASA SMAP L3SMP_E moisture estimates. The LIS workflow yielded soil moisture estimates at various depths and fine resolutions. With spatial geomorphologic and spatiotemporal soil moisture available, this study moved towards the associated stability analyses. These analyses were focused upon a region of Eastern Kentucky, USA, which experienced an extreme rainfall and subsequent landslide event. Through these analyses, a majority of occurred landslides were able to be detected in areas observed to experience increases in soil moisture. Therefore, this study confirmed the underlying assumption that soil moisture can serve as a hydro-mechanical precursor for rainfall-induced landslide occurrence.
Journal Article
Exploring the Utility of Machine Learning-Based Passive Microwave Brightness Temperature Data Assimilation over Terrestrial Snow in High Mountain Asia
This study explores the use of a support vector machine (SVM) as the observation operator within a passive microwave brightness temperature data assimilation framework (herein SVM-DA) to enhance the characterization of snow water equivalent (SWE) over High Mountain Asia (HMA). A series of synthetic twin experiments were conducted with the NASA Land Information System (LIS) at a number of locations across HMA. Overall, the SVM-DA framework is effective at improving SWE estimates (~70% reduction in RMSE relative to the Open Loop) for SWE depths less than 200 mm during dry snowpack conditions. The SVM-DA framework also improves SWE estimates in deep, wet snow (~45% reduction in RMSE) when snow liquid water is well estimated by the land surface model, but can lead to model degradation when snow liquid water estimates diverge from values used during SVM training. In particular, two key challenges of using the SVM-DA framework were observed over deep, wet snowpacks. First, variations in snow liquid water content dominate the brightness temperature spectral difference (TB) signal associated with emission from a wet snowpack, which can lead to abrupt changes in SWE during the analysis update. Second, the ensemble of SVM-based predictions can collapse (i.e., yield a near-zero standard deviation across the ensemble) when prior estimates of snow are outside the range of snow inputs used during the SVM training procedure. Such a scenario can lead to the presence of spurious error correlations between SWE and TB, and as a consequence, can result in degraded SWE estimates from the analysis update. These degraded analysis updates can be largely mitigated by applying rule-based approaches. For example, restricting the SWE update when the standard deviation of the predicted TB is greater than 0.05 K helps prevent the occurrence of filter divergence. Similarly, adding a thin layer (i.e., 5 mm) of SWE when the synthetic TB is larger than 5 K can improve SVM-DA performance in the presence of a precipitation dry bias. The study demonstrates that a carefully constructed SVM-DA framework cognizant of the inherent limitations of passive microwave-based SWE estimation holds promise for snow mass data assimilation.
Journal Article
Navigating the Terrain of Digital Transition: Ghana’s Journey of Developing a Digital Land Information System
This paper explores Ghana’s two-decade journey in developing a digital land information system (LIS), exemplified by Ghana Enterprise Land Information System (GELIS) and Enterprise Land Information System (ELIS). Despite advancements by Ghana’s Lands Commission, manual processes persist and coexist with digital ones. Our study uses a qualitative approach to assess the effectiveness of the LIS implemented in Accra. This study found that Accra’s LIS is highly aligned with clear institutional mandates, roles, and responsibilities, as well as the availability of laws and policies to support analogue-to-digital conversion. Furthermore, a robust approach to protecting the data, operating system, and software underpins the system. Subsequently, the integration of a digital LIS has enhanced service delivery and accountability. Nonetheless, Accra’s LIS is still at the beginning of a comprehensive learning and development curve. Sustaining the LIS requires furtherance in the implementation plan, funding, law implementation, ICT strategy, divisional integration, work processes, data quality, and communication strategy. These findings will inform the next steps of improvement for Accra’s LIS and guide its nationwide scaling, contributing to discussions on technology acceptance dynamics in predominantly analogue environments.
Journal Article
Impacts of Different Rainfall Forcings on Soil Moisture Distribution Over India: Assessment Using the Land Information System
Precipitation is an important forcing for land surface models (LSMs). Various types of precipitation data sets are available based on satellites, rain gauges, merged data sets, as well as analysis products. This study evaluates the uncertainty in soil moisture estimates using the five different forcing precipitation data sets from the: Global Data Assimilation System (GDAS), Tropical Rainfall Measurement Mission (TRMM)-Multi-satellite Precipitation Analysis (TMPA), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), Global Precipitation Mission (GPM), and Indian Meteorological Department (IMD) gridded data over the Indian domain using the Noah land surface model within the NASA Land Information System (LIS). The simulations are conducted using the five different precipitation data sets over the Indian subcontinent for 3 years from 2012–2014. Except for the rainfall forcing, the simulation environment was retained identical for each of the five simulations in terms of model configuration and physics. The simulation results are compared with the weekly soil moisture station data available from the IMD for different depths. Results indicate that the LIS-Noah soil moisture estimates forced with IMD rainfall agreed better among the five simulations with IMD in situ data. The simulated output soil moisture using IMD as precipitation data has the lowest soil moisture RMSE for all 3 years as compared to other simulations, while the GPM forced simulation has a higher RMSE value for all 3 years. The correlation coefficients of simulated soil moisture outputs with respect to different in situ stations show that, among the five simulations, IMD forced simulation has a higher correlation coefficient for the majority of stations for the years 2012 and 2013, while for the year 2014, GPM forced simulation shows better results. The correlation coefficient between the PERSIANN-CDR forced simulation output and in situ stations shows poor results as compared to other products. IMD gridded rainfall forced simulation is superior to the other four precipitation forced simulations in all study areas and could be used in the future for hydrological and meteorological models.
Journal Article
Evaluating a Fit-For-Purpose Integrated Service-Oriented Land and Climate Change Information System for Mountain Community Adaptation
Climate change challenges mountain communities to prepare themselves via Community-Based Adaptation (CBA) plans that reduce vulnerability. This paper outlines the evaluation of a developed web-based information system to support CBA, referred to as a Mountain Community Adaptive System (MCAS). The web-based user interface visualizes collated data from data providers, integrating it with near real-time climate and weather datasets. The interface provides more up-to-date information than was previously available on the environment, particularly on land and climate. MCAS, a cloud-based Land Information System (LIS), was developed using an Agile-inspired approach offering system creation based on bare minimum system requirements and iterative development. The system was tested against Fit-For-Purpose Land Administration (FFP LA) criteria to assess the effectiveness in a case from Nepal. The results illustrate that an MCAS-style system can provide useful information such as land use status, adaptation options, near real-time rainfall and temperature details, amongst others, to enable services that can enhance CBA activities. The information can facilitate improved CBA planning and implementation at the mountain community level. Despite the mentioned benefits of MCAS, ensuring system access was identified as a key limitation: smartphones and mobile technologies still remain prohibitively expensive for members of mountain communities, and underlying information communication technology (ICT) infrastructures remain under-developed in the assessed mountain communities. The results of the evaluation further suggest that the land-related aspects of climate change should be added to CBA initiatives. Similarly, existing LIS could have functionalities extended to include climate-related variables that impact on land use, tenure, and development.
Journal Article
Global Reach-Level 3-Hourly River Flood Reanalysis (1980–2019)
Better understanding and quantification of river floods for very local and “flashy” events calls for modeling capability at fine spatial and temporal scales. However, long-term discharge records with a global coverage suitable for extreme events analysis are still lacking. Here, grounded on recent breakthroughs in global runoff hydrology, river modeling, high-resolution hydrography, and climate reanalysis, we developed a 3-hourly river discharge record globally for 2.94 million river reaches during the 40-yr period of 1980–2019. The underlying modeling chain consists of the VIC land surface model (0.05°, 3-hourly) that is well calibrated and bias corrected and the RAPID routing model (2.94 million river and catchment vectors), with precipitation input from MSWEP and other meteorological fields downscaled from ERA5. Flood events (above 2-yr return) and their characteristics (number, spatial distribution, and seasonality) were extracted and studied. Validations against 3-hourly flow records from 6,000+ gauges in CONUS and daily records from 14,000+ gauges globally show good modeling performance across all flow ranges, good skills in reconstructing flood events (high extremes), and the benefit of (and need for) subdaily modeling. This data record, referred as Global Reach-Level Flood Reanalysis (GRFR), is publicly available at https://www.reachhydro.org/home/records/grfr.
Journal Article