Framework for rainfall-triggered landslide-prone critical infrastructure zonation.

Rainfall-induced landslides cause frequent disruptions to critical infrastructure in mountainous countries. Climate change is altering rainfall patterns and localizing extreme rainfall events, increasing the occurrence of landslides. For planning climate-resilient critical infrastructure in landslide-prone regions, it is urgent to understand the changing landslide susceptibility in relation to changing rainfall extremes and spatially overlay them with critical infrastructure to determine risk zones. As such, areas requiring financial reinforcements can be prioritized. In this paper, we develop a framework linking changing rainfall extremes to landslide susceptibility and intensity of critical infrastructure - exemplified on a national scale using Nepal as a case study. First, we define a set of 21 different unique rainfall indices that describe extreme and localized rainfall. Second, we prepare a new annual (2016-2020) inventory of 107,900 landslides in Nepal mapped on PlanetScope satellite imagery. Next, we prepare a landslide susceptibility map by training a random forest model using the collected extreme rainfall indices and landslide locations in combination with spatial data on topography. Fourth, we construct a gridded critical infrastructure spatial density map that quantifies the intensity of infrastructure (i.e., transportation, energy, telecommunication, waste, water, health, and education) at each grid location using OpenStreetMap. The landslide susceptibility map classified Nepal's topography into low (36 %), medium (33 %), and (32 %) high rainfall-triggered landslide susceptibility zones. The landslide susceptibility map had an average area under the receiver characteristic curve value of 0.94. Finally, we overlay the landslide susceptibility map with the critical infrastructure intensity to identify areas needing financial reinforcement. Our framework reasonably mapped critical infrastructure hotspots in Nepal prone to landslides on a 1 km grid. The hotspots are mainly concentrated along major national highways and in provinces 4, 3, and 1, highlighting the need for improved land management practices. These hotspots need spatial prioritization regarding climate-resilient critical infrastructure financing and slope conservation policies. The research data, output maps, and code are publicly released via an ArcGIS WebApp and GitHub repository. The framework is scalable and can be used for developing infrastructure financing strategies for landslide mountain regions and countries.

Measuring the unseen: mobilizing citizen scientists to monitor groundwater in Nepal.

Groundwater-level monitoring provides crucial information on the nature and status of aquifers and their response to stressors like climate change, groundwater extraction, and land use changes. Therefore, the development of a spatially distributed long-term monitoring network is indispensable for sustainable groundwater resource management. Despite being one of our greatest unseen resources, groundwater systems are too often poorly understood, ineffectively managed, and unsustainably used. This study investigates the feasibility of establishing a groundwater monitoring network mobilizing citizen scientists. We established a network of 45 shallow monitoring wells in the Kathmandu Valley using existing wells. We recruited 75% of the citizen scientists through personal connections and the rest through outreach programs at academic institutes and site visits. We used various methods to encourage citizen scientists to complete regular measurements and solicited feedback from them based on their experiences. Citizen scientists were more consistent during the monsoon season (June through September) than non-monsoon seasons. The depth-to-water below the ground surface varied from - 0.11 m (negative sign represents a groundwater level higher than the ground surface) to 11.5 m, with a mean of 4.07 m and standard deviation of 2.63 m. Groundwater levels began to rise abruptly with the onset of monsoon season and the shallowest and the deepest groundwater levels were recorded in peak rainfall months and dry months respectively. Citizen science-based groundwater monitoring using existing wells would be an economic and sustainable approach for groundwater monitoring. Improved groundwater-level data will provide essential information for understanding the shallow groundwater system of the valley, which will assist concerned authorities in planning and formulating evidence-based policy on sustainable groundwater management.

Modelling the impact of past and future climate scenarios on streamflow in a highly mountainous watershed: A case study in the West Seti River Basin, Nepal.

Hydrological model parameters are important during representation of the hydrological characteristics of a watershed. The West Seti River Basin (WSRB), a prominent Himalayan Basin of Nepal, is a major source of fresh water in the western region of the country. We used the Soil and Water Assessment Tool (SWAT) for hydrological modelling and identified the most sensitive hydrological parameters, while the Sequential Uncertainty Fitting (SUFI-2) technique was employed for model calibration. The model was calibrated for the study period (1999-2005) with a three-year warm-up period (1996-1998). Subsequently, it was validated for three years (2006-2008). The results show that the large number of Hydrological Response Units (HRUs) for model simulation took a considerable time, without improving the performance statistics. Importantly, significant improvements were observed during both calibration and validation periods when elevation bands (EBs) were taken into consideration. The p-factor, r-factor, coefficient of determination (R2), Nash-Sutcliffe efficiency (NSE), percent bias (PBIAS), Root mean square error (RMSE)-observations, and standard deviation (STDEV) ratio (RSR) were used to measure the performance between observed and simulated values. The values of p-factor, r-factor, R2, NSE, PBIAS, and RSR during the calibration were 0.82, 0.80, 0.84, 0.82, 7.2, and 0.42, respectively, whereas during validation they were 0.79, 0.72, 0.83, 0.82, 11.8, and 0.42, respectively. The calibrated model was then used to assess the anticipated river discharge. This study used four regional climate models (RCMs) for precipitation and six for temperature, together with their arithmetical average as multi-model ensembles (MMEs) under two representative concentration pathways (RCPs). We analysed the changes in precipitation, temperature, and river discharge for three future time frames: Future1 (F1: 2020-2044), Future2 (F2: 2045-2069), and Future3 (F3: 2075-2099) with respect to the baseline (1996-2005). The magnitude of changes varied according to the different climate models and warming scenarios. In general, the MMEs showed slightly increasing precipitation (higher during the F2 period), significantly increasing temperature (continuous rising trend), and moderately increasing river discharge (higher during the F2 period). Information such as the anticipated shift in the flow duration curve may be helpful to stakeholders across different water sectors for effective water resource management in the future. From the modelling perspective, the results show greater significance for EBs than HRUs during the modelling of high mountain basins with SWAT. This take-home message would be useful to hydrologists and other stakeholders in evaluating different scenarios over a short duration, without iteratively spending higher computational time.

The potential of Tidal River Management for flood alleviation in South Western Bangladesh.

Reduced sediment deposition, land subsidence, channel siltation, and salinity intrusion has been an unintended consequence of the construction of polders in the south western delta of Bangladesh in the 1960s. Tidal River Management (TRM) is a process that is intended to temporarily reverse these processes and restore sediment deposition and land elevation at the low-lying sites, known as 'beels', where TRM is carried out. However, there is limited evidence to prioritise sites for TRM on the basis of its potential effectiveness at alleviating flooding. In this study, the south western delta of Bangladesh was classified according to different flood susceptible zones. In south western Bangladesh, the major portion of agricultural and aquaculture land is located within flood susceptible zones (65% and 81%, respectively). 44.5% of the total population in embanked regions live in areas classified as being flood susceptible. This study identified 106 'beels' suitable for TRM. Modelling of potential sediment deposition predicted that the consequent increase in land elevation could be up to 1.4 m in five years, which would alleviate land subsidence and modify several geomorphological factors such as aspect, slope, curvature, and Stream Power Index (SPI). Implementation of TRM at these sites could potentially reduce the probability of annual flooding from 0.86 (on average) to 0.57 (on average). Therefore, TRM could lower the flood susceptible area by 35% in suitable 'beels'. Whilst during the implementation of TRM agriculture has to cease for a few years, a systematic programme of TRM could result in a long-term increase in agricultural production by reducing flood susceptibility of agricultural lands in delta regions.

Assessment of rainfall erosivity (R-factor) during 1986-2015 across Nepal: a step towards soil loss estimation.

Rainfall is a main cause of soil erosion which varies spatially and temporarily. R-factor is an erosive power of the rainfall that is responsible for soil detachment and subsequent displacement. Mathematically, it is expressed as a sum of the product of kinetic energy and maximum 30-min rain intensity. A precise assessment of R-factor needs higher temporal resolution rainfall data (sub-hourly) for a period of several years, which is rarely available. Many empirical approaches are used to predict R-factor as a function of mean monthly and annual rainfall amount. In this study, we used Loureiro and Countinho (Journal of Hydrology 250:12-18, 2001) approximation approach to estimate R-factor and explore its intra-annual variability using 30 years (1986-2015) of daily rainfall data from 280 stations distributed across Nepal. This study employs different intra-annual variability indices and calculates erosivity density (ED) and weighted erosivity density (WED). The country average mean annual R-factor (MAR), annual ED, and WED are found to be 9434.8 MJ mm ha-1 h-1 year-1, 4.39 MJ ha-1 h-1,and 1.61 MJ ha-1 h-1, respectively. On a monthly scale, July is the highest erosive month followed by August (> 2000 MJ mm ha-1 h-1 month-1). Likewise, November is the lowest erosive month followed by December (~ 50 MJ mm ha-1 h-1 month-1). Spatial distributions of these indices show clear delineations of areas with different erosivity patterns at different time of the year. In addition, this study explores inter-annual variation, temporal evolution, and trend estimation of R-factors over the country (for the first time). Significant rising trends are observed in the western region of the country. We found that the mean soil erosion for Nepal is estimated at 21.01 ton ha-1 year-1. The smallest R-factors are observed in the north-western region of the country and the maximum values are observed at mid hills and southern plains of the country. Our study could be an initial but important step for effective soil conservation, land use planning, and agricultural production.

Correction to: Assessing climate boundary shifting under climate change scenarios across Nepal.

The original version of this article unfortunately contained an error. All "50s" and "70s" were replaced by "1950s" and "1970s" throughout the published paper.

Assessing climate boundary shifting under climate change scenarios across Nepal.

This study assesses the climate boundary shifts from the historical time to near/mid future by using a slightly modified Köppen-Geiger (KG) classification scheme and presents comprehensive pictures of historical (1960-1990) and projected near/mid future (1950s: 2040-2060/1970s: 2060-2080) climate classes across Nepal. Ensembles of three selected general circulation models (GCMs) under two Representative Concentration Pathways (RCP 4.5 and RCP 8.5) were used for projected future analysis. During the 1950s, annual average temperature is expected to increase by 2.5 °C under RCP 8.5. Similarly, during the 1970s, it is even anticipated to rise by 3.6 °C under RCP 8.5. The rate of temperature rise is higher in the non-monsoon period than in monsoon period. During the 1970s, annual precipitation is projected to increase by 8.1% under RCP 8.5. Even though the precipitation is anticipated to increase in the future in annual scale, winter seasons are estimated to be drier by more than 15%. This study shows significant increments of tropical (Am and Aw) and arid (BSk) climate types and reductions of temperate (Cwa and Cwb) and polar (ET and EF). Noticeably, the reduction of the areal coverage of polar frost (EF) is considerably high. In general, about 50% of the country's area is covered by the temperate climate (Cwa and Cwb) in baseline scenario and it is expected to reduce to 45% under RCP 4.5 and 42.5% under RCP 8.5 during the 1950s, and 42% under RCP 4.5 and 39% under RCP 8.5 during the 1970s. Importantly, the degree of climate boundary shifts is quite higher under RCP 8.5 than RCP 4.5, and likewise, the degree is higher during the 1970s than the 1950s. We believe this study to facilitate the identification of regions in which impacts of climate change are notable for crop production, soil management, and disaster risk reduction, requiring a more detailed assessment of adaptation measures. The assessment of climate boundary shifting can serve as valuable information for stakeholders of many disciplines like water, climate, transport, energy, environment, disaster, development, agriculture, and tourism.