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The purpose of this paper is to develop an algorithm, using PCA‐based neural network, to retrieve the vertical rainfall structure in a precipitating atmosphere. The algorithm is powered by a rigorous solution to the plane parallel radiative transfer equation for the atmosphere with thermodynamically consistent vertical profiles of humidity, temperature and cloud structures, together with “measured” vertical profiles of the rain structure derived from a radar.

The raining atmosphere is considered to be a plane parallel, radiatively participating medium. The atmospheric thermodynamic profiles such as pressure, temperature and relative humidity along with wind speed at sea surface and cloud parameters corresponding to Nargis, a category 4 tropical cyclone that made its landfall on May 2, 2008 at the Republic of Myanmar, are obtained by solving the flux form of Euler's equations in three‐dimensional form. The state‐of‐the‐art community software Weather Research and Forecasting has been used for solving the set of equations. The three‐dimensional rain profiles for the same cyclone at the same instant of time are obtained from National Aeronautics and Space Administration's space borne Tropical Rainfall Measuring Mission's precipitation radar over collocated pixels. An in‐house Micro‐Tropiques code is used to perform radiative transfer simulations for frequencies corresponding to a typical space borne radiometer, and hence to generate the database which is later used for training the neural network. The back propagation‐based neural network is optimized with reduced number of parameters using principal component analysis (PCA).

The results show that neural network is capable of retrieving the vertical rainfall structure with a correlation coefficient of over 0.99. Further, reducing the ill‐posedness in retrieving 56 parameters from just nine measurements using PCA has improved the root mean square error in the retrievals at reduced computational time.

The paper shows that combining numerically generated atmospheric profiles together with radar measurements to serve as input to a radiative transfer model brings in the much‐required synergy between numerical weather prediction, radar measurements and radiative transfer. This strategy can be gainfully used in satellite meteorology. Using principal components to reduce the ill‐posedness, thereby increasing the robustness in retrieving vertical rain structure, has been attempted for the first time. A well‐trained network can be used as one possible option for an operational algorithm for the proposed Indian climate research satellite Megha‐Tropiques, due to be launched in early 2011.

The purpose of this study is to address a highly heterogeneous rift margin environment and exhibit considerable spatiotemporal hydro-climatic variations. In spite of limited, random and inaccurate data retrieved from rainfall gauging stations, the recent advancement of satellite rainfall estimate (SRE) has provided promising alternatives over such remote areas. The aim of this research is to take advantage of the technologies through performance evaluation of the SREs against ground-based-gauge rainfall data sets by incorporating its applicability in calibrating hydrological models.

Selected multi satellite-based rainfall estimates were primarily compared statistically with rain gauge observations using a point-to-pixel approach at different time scales (daily and seasonal). The continuous and categorical indices are used to evaluate the performance of SRE. The simple scaling time-variant bias correction method was further applied to remove the systematic error in satellite rainfall estimates before being used as input for a semi-distributed hydrologic engineering center's hydraulic modeling system (HEC-HMS). Runoff calibration and validation were conducted for consecutive periods ranging from 1999–2010 to 2011–2015, respectively.

The spatial patterns retrieved from climate hazards group infrared precipitation with stations (CHIRPS), multi-source weighted-ensemble precipitation (MSWEP) and tropical rainfall measuring mission (TRMM) rainfall estimates are more or less comparably underestimate the ground-based gauge observation at daily and seasonal scales. In comparison to the others, MSWEP has the best probability of detection followed by TRMM at all observation stations whereas CHIRPS performs the least in the study area. Accordingly, the relative calibration performance of the hydrological model (HEC-HMS) using ground-based gauge observation (Nash and Sutcliffe efficiency criteria [NSE] = 0.71; R² = 0.72) is better as compared to MSWEP (NSE = 0.69; R² = 0.7), TRMM (NSE = 0.67, R² = 0.68) and CHIRPS (NSE = 0.58 and R² = 0.62).

Calibration of hydrological model using the satellite rainfall estimate products have promising results. The results also suggest that products can be a potential alternative source of data sparse complex rift margin having heterogeneous characteristics for various water resource related applications in the study area.

This research is an original work that focuses on all three satellite rainfall estimates forced simulations displaying substantially improved performance after bias correction and recalibration.

The most common methodology to estimate erosivity is using rainfall data obtained from rain monitoring stations. However, the quality of this estimation may be compromised due to low density, operational problems and maintenance cost of rainfall monitoring stations, common problem encountered in developing countries such as Brazil. The objective of this study was to evaluate the applicability of pluviometric data obtained by TRMM satellite images for the spatiotemporal characterization of erosivity in the state of Espírito Santo (Brazil).

For this, rainfall data and annual and monthly erosivities of 71 rainfall stations were statistically compared with those from TRMM images.

For this, rainfall data and annual and monthly erosivities of 71 rainfall stations were statistically compared with those from TRMM images. The estimate proved that TRMM is efficient since the NSE values were higher than 0.70 and the coefficient of determination was higher than 0.77 for monthly and annual erosivities, but in most months and yearly, erosivity was overestimated.

The use of satellite images to estimate rainfall allowed the spatial representation over time (months) of the oscillating degree of erosivity in the state of Espírito Santo (Brazil). The spatialization may provide an identification of areas and periods in which are essential for the implementation of land use management in order to minimize environmental problems related to soil loss.

The technique applied may be an alternative to overcome common problems on rainfall monitoring station, such as low density, low data reliability, high manutention and maintenance cost and operational problems.

The purpose of this study is rangeland biomass estimation and its spatial–temporal dynamics. Remote sensing has been a significant method for estimating biomass in recent years. The connection between vegetation index and field biomass will be used to assign probabilities, but in some cases, it does not provide acceptable results because of soil background and geographical and temporal variability.

In this study, the normalized difference red-edge (NDRE) index was used to calculate the rangeland biomass in comparison to five vegetation indices. Field measurements of biomass of natural rangeland in the West of Iran were taken in 2015, 2018 and 2021, and SENTINEL-2 data were used for analysis.

The results indicated that the overall advantage of NDRE stems from the fact that it adjusts for changes in leaf water content while overcoming the detrimental effects of soil substrate heterogeneity, both of these factors have a significant impact on pasture biomass. These results suggest that an NDRE-based biomass estimation model might be useful for estimating and monitoring biomass in large rangelands with significant geographical and temporal variability.

Identifying the best vegetation index to establish a vegetation-based biomass regression model for rangelands in large areas with different climatic conditions, plant compositions and soil types is the overall aim of this study.

This paper places a college at the centreof a multi-hazard assessment (earthquake, flood and landslide). The college is within a less studied, rural area of Ladakh, North India. Research focusses on a case study (Students Educational and Cultural Movement of Ladakh (SECMOL) College), close to Leh, Ladakh, and extends to incorporate/apply thinking from/to the wider Ladakh region. The approach adopted, centring on the hazard assessment of a single entity/local area, allows a rapid uptake of hazard recommendations within a college environment planning to continue its existence for decades ahead. A sister paper (Petterson et al., 2019) documents the active involvement of college staff and students in the principles of geohazard assessment and the development of student-centric hazard assessments of the college and their home village. SECMOL is a self-sufficient, alternative, college, organised along strong environmentally sustainable principles. The paper aims to discuss these issues.

This work has adopted different strategies for different hazards. Fieldwork involved the collection of quantitative and qualitative data (e.g. shape and size of valleys/river channels/valley sides, estimation of vegetation density, measurement of sediment clasts, angle of slopes, assessment of sediment character, stratigraphy of floodplains and identification of vulnerable elements). These data were combined with satellite image analysis to: define river catchment character and flood vulnerability (e.g. using the methodology of Collier and Fox, 2003), examine catchment connectivity, and examine landslip scars and generic terrain analysis. Literature studies and seismic database interrogation allowed the calculation of potential catchment floodwater volumes, and the collation of epicentre, magnitude, depth and date of seismic events, together with recent thinking on the return period of large Himalayan earthquakes. These data were used to develop geological-seismic and river catchment maps, the identification of vulnerable elements, and disaster scenario analyses.

This research concludes that SECMOL, and much of the Ladakh region, is exposed to significant seismic, flood and landslide hazard risk. High magnitude earthquakes have return periods of 100s to c. 1,000 years in the Himalayas and can produce intense levels of damage. It is prudent to maximise earthquake engineering wherever possible. The 2010 Leh floods demonstrated high levels of devastation: these floods could severely damage the SECMOL campus if storms were centred close by. This study reveals the connectivity of catchments at varying altitudes and the potential interactions of adjacent catchments. Evacuation plans need to be developed for the college. Northern ridges at SECMOL could bury parts of the campus if mobilised by earthquakes/rainfall. Slope angles can be lowered and large boulders moved to reduce risk. This work reinforces recommendations that relate to building quality and urban/rural planning, e.g. using spatial planning to keep people away from high-risk zones.

The frequency of hazards is low, but potential impacts high to very high. Hazard mitigation actions include engineering options for hazardous slopes, buildings to be earthquake-proofed, and evacuation management for large floods.

Methodologies undertaken in this research are well-tested. Linkages between disciplines are ambitious and somewhat original. The application of this work to a specific college centre site with the capacity to rapidly take up recommendations is novel. The identification of catchment inter-connectivity in this part of Ladakh is novel. This work complements a sister paper (Petterson et al., 2019) for community aspects of this study, adding to the novelty value.

Each of the agencies participating in GCDIS will play a role appropriate to its agency mission and consistent with the funds available to it. Descriptions of each agency's resources follow. Each agency will implement the GCDIS at its own pace.

Disaster risk reduction (DRR) technology sharing has emerged as an important global issue across national boundaries. As a result, the DRR technology-sharing environment needs strengthening based on the findings of difficulties in sharing, investment, and commitment. The purpose of this paper is to three fold: first, it will clarify that a web-based platform is the best conduit for linking DRR technology from the supplier to the beneficiary developing countries. Second, it will find the most evolutionary path based on Korean case studies. Finally, it will make recommendations for the efficient implementation of a web-based platform for DRR technology sharing.

This paper posits that DRR technology sharing is a necessary part of enhancing resilience to disasters in the Asia-Pacific region. The evolution of web-based approaches (a merchandise display-type platform, a journal-type platform, an electronic market-type platform, and an architecture and governance-type platform) and an actual case of space technology sharing providing support for an evolution toward community-based technology sharing in DRR. The research will find the most efficient way going forward based on the case study of a four-year R & D project sponsored by the South Korean Government. It will also find the success factors for the way to establish and operate the platform efficiently and effectively through case studies of the four types of web-based platforms.

Sharing of DRR technology through a web-based platform among Asia-Pacific countries is necessary in order to equip all parties with essential technology to mitigate intensifying disasters, to overcome the barriers of technology sharing, to breakdown transfer issues due to language barriers, and to strengthen insufficient DRR budgets. Among the four types of web-based platforms, the architecture and governance-type platform proved to be the most effective and efficient. In addition, four principles for an efficient implementation are identified through case studies, analysis, and research.

This paper reports on the reasons why it is difficult for DRR technology sharing in the Asia and Pacific regions and suggests a web-based DRR technology-sharing platform as a solution for the region. Moreover, it substantiates that the architecture and governance platform type as the most efficient and effective evolution with four principles: develop based on end-user’s needs; take a problem-solving approach; contain interactive communication tools; and provide business opportunity.

Cambodia is considered one of the countries that are most vulnerable to adverse effects of climate change, particularly floods and droughts. Kampong Speu Province is a frequent site of calamitous flash floods. Reliable sources of flash flood information and analysis are critical in efforts to minimize the impact of flooding. Unfortunately, Cambodia does not yet have a comprehensive program for flash flood hazard mapping, with many places such as Kampong Speu Province having no such information resources available. The purpose of this paper is, therefore, to determine flash flood hazard levels across all of Kampong Speu Province using analytical hierarchy process (AHP) and geographical information system (GIS) with satellite information.

The integrated AHP–GIS analysis in this study encompasses ten parameters in the assessment of flash flood hazard levels across the province: rainfall, geology, soil, elevation, slope, stream order, flow direction, distance from drainage, drainage density and land use. The study uses a 10 × 10 pairwise matrix in AHP to compare the relative importance of each parameter and find each parameter’s weight. Finally, a flash flood hazard map is developed displaying all areas of Kampong Speu Province classified into five levels, with Level 5 being the most hazardous.

This study reveals that high and very high flash flood hazard levels are identified in the northwest part of Kampong Speu Province, particularly in Aoral, Phnum Srouch and Thpong districts and along Prek Thnot River and streams.

The flash flood hazard map developed here provides a wealth of information that can be invaluable for implementing effective disaster mitigation, improving disaster preparedness and optimizing land use.

The ecological environment of the Loess Plateau, China, is extremely fragile under the context of global warming. Over the past two decades, the vegetation of the Loess Plateau has undergone great changes. This paper aims to clarify the response mechanisms of vegetation to climate change, to provide support for the restoration and environmental treatment of vegetation on the Loess Plateau.

The Savitsky–Golay (S-G) filtering algorithm was used to reconstruct time series of moderate resolution imaging spectroradiometer (MODIS) 13A2 data. Combined with trend analysis and partial correlation analysis, the influence of climate change on the phenology and enhanced vegetation index (EVI) during the growing season was described.

The S-G filtering algorithm is suitable for EVI reconstruction of the Loess Plateau. The date of start of growing season was found to gradually later along the Southeast–Northwest direction, whereas the date of the end of the growing season showed the opposite pattern and the length of the growing season gradually shortened. Vegetation EVI values decreased gradually from Southeast to Northwest. Vegetation changed significantly and showed clear differentiation according to different topographic factors. Vegetation correlated positively with precipitation from April to July and with temperature from August to November.

This study provides technical support for ecological environmental assessment, restoration of regional vegetation coverage and environmental governance of the Loess Plateau over the past two decades. It also provides theoretical support for the prediction model of vegetation phenology changes based on remote sensing data.