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Analyzing landslide susceptibility, health vulnerability and risk using multi criteria decision-making analysis in Arunachal Pradesh, India

Rehman Sufa, Azhoni Adani

Acta Geophysica

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2023

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Vol. 71, no. 1

101--128

EN

Landslides being a widespread disaster are associated with susceptibility, vulnerability and risk. The physical factors inducing landslides are relatively well-known. However, how landslide susceptibility will be exacerbated by climate change, impede the attainment of the sustainable development goals and increase health vulnerability is relatively less explored. We present an integrated assessment of landslide susceptibility, health vulnerability and overall risk to understand these interconnected dimensions using Arunachal Pradesh, India, as a case study, which is susceptible to landslides due to its topography and climate conditions. Landslide susceptibility was examined using twenty landslide conditioning parameters through the fuzzy analytical hierarchy process (FAHP). The susceptibility map was validated using the area under the ROC curve (AUC). National Family Health Survey (NFHS 4) data were used to analyze the health vulnerability, while the overall risk was computed through the integration of susceptibility and vulnerability. Landslide susceptibility analysis indicated that nearly 22% area of the state is characterized by moderate susceptibility followed by high (17%) and very high susceptibility (13%). High elevation, slope, rainfall, SPI, drainage density and complex geology were identified as the causative factors of landslides. In the case of health vulnerability, East Kameng and Lohit districts were found to be very highly vulnerable, while Papum Pare, Changlang and Tirap districts experience high health vulnerability due to high degree of exposure and sensitivity. Overall risk analysis revealed over 16.8% area of the state is under moderate risk followed by high (9.8%) and very high (4.2%) risk. Linking this analysis with the climate change projections and SDG goals attainment revealed that Papum Pare, Upper Subansiri, Tirap and West Kameng require priority for lessening susceptibility, vulnerability and risk for achieving sustainable development. A strong correlation (99%) between HVI and risk further demonstrates the need for lessening health vulnerability and risk in the study area. Furthermore, our study contributes additional insights into landslide susceptibility by considering heal vulnerability and risk which may help in planning sustainable development strategies in a changing climate.

2

Climate analysis as a basis for a sustainable water management at the Lusatian Neisse

Pluntke T., Schwarzak S., Kuhn K., Lünich K., Adynkiewicz-Piragas M., Otop I., Miszuk B.

Meteorology Hydrology and Water Management. Research and Operational Applications

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2016

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Vol. 4, Iss. 1

3--11

EN

Current and future climate conditions and their impact on water balance, ecosystems, air quality and bio and agro-climatology were investigated in the region of the Lusatian Neisse within the two EU -projects – NEYMO and KLAPS. This work focuses on the climate analysis of the region at the German-Polish border as a preliminary step for a hydrological analysis of current and future conditions. Observed climatological data were processed and analysed using the indicators air temperature, precipitation, sunshine duration, potential evapotranspiration and the climatic water balance (CWB). The latter defines the difference between precipitation and potential evapotranspiration and is a measure for the climatological water availability in the region. Observations were used to statistically downscale data from Global Circulation Models under various scenarios regarding greenhouse gas emissions (A1B, RCP 2.6, RCP 8.5) and applying the WETTREG-method for regionalization. In total, 50 climate projections for periods up until the end of the 21st century were analysed, with the application of the mentioned indicators. For the period 1971-2010, increasing trends of temperature, precipitation, sunshine duration and potential evapotranspiration were found. This leads to a reduced CWB in the summer half-year (SHY), which could be partly compensated by an increase in the winter half-year (WHY). Trends of temperature, sunshine duration and potential evapotranspiration remain positive for the far future (2071-2100), but precipitation decreases. These climatic conditions aggravate water availability, especially in the SHY. Impacts on water management are very probable and were therefore further investigated in the NEY MO project that applied hydrological models.

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Classification by multiple regression - a new approach towards the classification of extremes

Enke W., Spekat A., Kreienkamp F.

Meteorology Hydrology and Water Management. Research and Operational Applications

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2016

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Vol. 4, Iss. 1

25--39

EN

There are numerous algorithmic classification methods that attempt to address the connections between different scales of the atmosphere, such as EOFs, clustering, and neural nets. However, their relative strength lies in the description of the mean conditions, whereas extremes are poorly covered by them. A novel approach towards the identification of linkages between large-scale atmospheric fields and local extremes of meteorological parameters is presented in this paper. The principle is that a small number of objectively selected fields can be used to circumscribe a local meteorological parameter by way of regression. For each day, the regression coefficients form a kind of pattern which is used for a classification based on similarity. As it turns out, several classes are generated which contain days that constitute extreme atmospheric conditions and from which local meteorological parameters can be computed, yielding an indirect way of determining these local extremes just from large-scale information. The range of applications is large. (i) Not only local meteorological parameters can be subjected to such a regression based classification procedure. It can be extended to extreme indicators, such as threshold exceedances, yielding on the one hand the relevant atmospheric fields to describe those indicators, and on the other hand grouping days with “favourable atmospheric conditions”. This approach can be further extended by investigating networks of measurement stations from a region and describing, e.g., the probability for threshold exceedances at a given percentage of the network. (ii) The method can not only be used as a filtering tool to supply days in the current climate with extreme conditions, identified in an objective way. The method can be applied to climate model projections, using the previously found parameter-specific combinations of atmospheric fields. From those fields, as they constitute the modelled future climate, local time series can be generated which are then analysed with respect to the frequency and magnitude of future extremes. The method has sensitivities (i) due to the degree to which there are connections between large-scale fields and local meteorological parameters (measured, e.g., by the correlation) and (ii) due to the varying quality of the different fields (geopotential, temperature, humidity etc.) projected by the climate model.