On the role of spatial resolution on snow estimates using a process‐based snow model across a range of climatology and elevation

Hydrological processes in mountainous settings depend on snow distribution, whose prediction accuracy is a function of model spatial scale. Although model accuracy is expected to improve with finer spatial resolution, an increase in resolution comes with modelling costs related to increased computational time and greater input data and parameter information. This computational and data collection expense is still a limiting factor for many large watersheds. Thus, this work's main objective is to question which physical processes lead to loss in model accuracy with regard to input spatial resolution under different climatic conditions and elevation ranges. To address this objective, a spatially distributed snow model, iSnobal, was run with inputs distributed at 50‐m—our benchmark for comparison—and 100‐m resolutions and with aggregated (averaged from the fine to the large resolution) inputs from the 50‐m model to 100‐, 250‐, 500‐, and 750‐m resolution for wet, average, and dry years over the Upper Boise River Basin (6,963 km²), which spans four elevation bands: rain dominated, rain–snow transition, and snow dominated below treeline and above treeline. Residuals, defined as differences between values quantified with high resolution (>50 m) models minus the benchmark model (50 m), of simulated snow‐covered area (SCA) and snow water equivalent (SWE) were generally slight in the aggregated scenarios. This was due to transferring the effects of topography on meteorological variables from the 50‐m model to the coarser scales through aggregation. Residuals in SCA and SWE in the distributed 100‐m simulation were greater than those of the aggregated 750 m. Topographic features such as slope and aspect were simplified, and their gradient was reduced due to coarsening the topography from the 50‐ to 100‐m resolution. Therefore, solar radiation was overestimated, and snow drifting was modified and caused substantial SCA and SWE underestimation in the distributed 100‐m model relative to the 50‐m model. Large residuals were observed in the wet year and at the highest elevation band when and where snow mass was large. These results support that model accuracy is substantially reduced with model scales coarser than 50 m.     

Probabilistic seismic hazard map of NW Himalaya and its adjoining area,India

The seismically active Northwest (NW) Himalaya falls within Seismic Zone IV and V of the hazard zonation map of India. The region has suffered several moderate (~25), large-to-great earthquakes (~4) since Assam earthquake of 1897. In view of the major advancement made in understanding the seismicity and seismotectonics of this region during the last two decades, an updated probabilistic seismic hazard map of NW Himalaya and its adjoining areas covering 28–34°N and 74–82°E is prepared. The northwest Himalaya and its adjoining area is divided into nineteen different seismogenic source zones; and two different region-specific attenuation relationships have been used for seismic hazard assessment. The peak ground acceleration (PGA) estimated for 10% probability of exceedance in 50 and 10 years at locations defined in the grid of 0.25 × 0.25°. The computed seismic hazard map reveals longitudinal variation in hazard level along the NW Himalayan arc. The high hazard potential zones are centred around Kashmir region (0.70 g/0.35 g), Kangra region (0.50 g/0.020 g), Kaurik-Spitti region (0.45 g/0.20 g), Garhwal region (0.50 g/0.20 g) and Darchula region (0.50 g/0.20 g) with intervening low hazard area of the order of 0.25 g/0.02 g for 10% probability in 50 and 10 years in each region respectively.     

Impact of gully incision on hillslope hydrology

The Southern U.S. Piedmont ranging from Virginia to Georgia underwent severe gully erosion over a century of farming mainly for cotton (1800s–1930s). Although tree succession blanketed much of this region by the middle 20th century, gully erosion still occurs, especially during wet seasons. While many studies on gully erosion have focused on soil loss, soil carbon exchange, and stormwater response, the impacts on soil moisture, groundwater, and transpiration remain under-studied. Using a newly developed 2D hydrologic model, this study analyzes the impacts of gully erosion on hillslope hydrologic states and fluxes. Results indicate that increases in gully incision lead to reduction in groundwater table, root zone soil moisture, and transpiration. These reductions show seasonal variations, but the season when the reduction is maximum differs among the hydrologic variables. Spatially, the impacts are generally the greatest near the toe of the hillslope and reduce further away from it, although the reductions are sometimes non-monotonic. Overall, the impacts are larger for shallow gully depths and diminish as the incision goes deeper. Lastly, the extent of impacts on a heterogeneous hillslope is found to be very different with respect to a homogeneous surrogate made of dominant soil properties. These results show that through gully erosion, the landscape not only loses soil but also a large amount of water from the subsurface. The magnitude of water loss is, however, dependent on hydrogeologic and topographic configuration of the hillslope. The results will facilitate (a) mapping of relative susceptibility of landscapes to gullying, (b) understanding of the impacts of stream manipulations such as due to dredging on hillslope eco-hydrology, (c) prioritization of mitigation measures to prevent gullying, and (d) design of observation campaigns to assess the impacts of gullying on hydrologic response.     

Role of temporal resolution of meteorological inputs for process‐based snow modelling

Accurate snow accumulation and melt simulations are crucial for understanding and predicting hydrological dynamics in mountainous settings. As snow models require temporally varying meteorological inputs, time resolution of these inputs is likely to play an important role on the model accuracy. Because meteorological data at a fine temporal resolution (~1 hr) are generally not available in many snow‐dominated settings, it is important to evaluate the role of meteorological inputs temporal resolution on the performance of process‐based snow models. The objective of this work is to assess the loss in model accuracy with temporal resolution of meteorological inputs, for a range of climatic conditions and topographic elevations. To this end, a process‐based snow model was run using 1‐, 3‐, and 6‐hourly inputs for wet, average, and dry years over Boise River Basin (6,963 km²), which spans rain dominated (≤1,400 m), rain–snow transition (>1,400 and ≤1,900 m), snow dominated below tree line (>1,900 and ≤2,400 m), and above tree line (>2,400 m) elevations. The results show that sensitivity of the model accuracy to the inputs time step generally decreases with increasing elevation from rain dominated to snow dominated above tree line. Using longer than hourly inputs causes substantial underestimation of snow cover area (SCA) and snow water equivalent (SWE) in rain‐dominated and rain–snow transition elevations, due to the precipitation phase mischaracterization. In snow‐dominated elevations, the melt rate is underestimated due to errors in estimation of net snow cover energy input. In addition, the errors in SCA and SWE estimates generally decrease toward years with low snow mass, that is, dry years. The results indicate significant increases in errors in estimates of SCA and SWE as the temporal resolution of meteorological inputs becomes coarser than an hour. However, use of 3‐hourly inputs can provide accurate estimates at snow‐dominated elevations. The study underscores the need to record meteorological variables at an hourly time step for accurate process‐based snow modelling.     

Radioactivity monitoring in the vicinity of Jawalamukhi thrust NW Himalaya,India for tectonic study

Natural Hazards - This work reports radon-thoron monitoring at two depths (60 and 90 cm) and at 82 sites around Jawalamukhi thrust of NW Himalaya, India using Solid State Nuclear Track...     

Hydrogeochemical evaluation of groundwater quality and seasonal variation in East Bokaro coalfield region,Jharkhand

Groundwater samples collected from the East Bokaro coalfield of Jharkhand state, India during the dry and rainy seasons of the year 2012. Samples were analyzed for the assessment of groundwater quality in the study area. The results of the chemical analysis indicate that the pH values were found alkaline in nature during both the season. The major cations in groundwater was in the order of Na⁺>Ca²⁺>Mg²⁺>K⁺ during the dry season while Ca²⁺>Na⁺>Mg²⁺>K⁺ during the rainy season. The abundance of the major anions was of HCO₃^->SO₄^2->Cl^->NO₃^->F^- did not change on the seasonal basis. The average NO₃^-concentration was exceeded the desirable limit for drinking water as per Indian standard in the rainy season. Silicate weathering was inferred to be a dominant process, controlling the groundwater chemistry in both seasons, with lesser contributions by carbonate weathering and ion exchange. Leaching of salts from the unsaturated zone also has a major impact on groundwater quality during the rainy season. The water quality data indicate that groundwater is generally suitable for irrigation. However, higher salinity and residual sodium carbonate values at some sites may limit groundwater use and therefore an adequate drainage and water management plan for the study area is required.     

Modifications in aerosol physical,optical and radiative properties during heavy aerosol events over Dushanbe,Central Asia

The location of Central Asia,almost at the center of the global dust belt region,makes it susceptible for dust events.The studies on atmospheric impact of dust over the region are very limited despite the large area occupied by the region and its proximity to the mountain regions(Tianshan,Hindu Kush-Karakoram-Himalayas,and Tibetan Plateau).In this study,we analyse and explain the modification in aerosols'phys-ical,optical and radiative properties during various levels of aerosol loading observed over Central Asia utilizing the data collected during 2010-2018 at the AERONET station in Dushanbe,Tajikistan.Aerosol epi-sodes were classified as strong anthropogenic,strong dust and extreme dust.The mean aerosol optical depth(AOD)during these three types of events was observed a factor of～3,3.5 and 6.6,respectively,higher than the mean AOD for the period 2010-2018.The corresponding mean fine-mode fraction was 0.94,0.20 and 0.16,respectively,clearly indicating the dominance of fine-mode anthropogenic aerosol during the first type of events,whereas coarse-mode dust aerosol dominated during the other two types of events.This was corroborated by the relationships among various aerosol parameters(AOD vs.AE,and EAE vs.AAE,SSA and RRI).The mean aerosol radiative forcing(ARF)at the top of the atmosphere(ARFTOA),the bottom of the atmosphere(ARFBOA),and in the atmosphere(ARFATM)were-35±7,-73±16,and 38±17 Wm-2 during strong anthropogenic events,-48±12,-85±24,and 37±15 Wm-2 during strong dust event,and-68±19,-117±38,and 49±21 Wm-2 during extreme dust events.Increase in aerosol loading enhanced the aerosol-induced atmospheric heating rate to 0.5-1.6 K day-1(strong anthropogenic events),0.4-1.9 K day-1(strong dust events)and 0.8-2.7 K day 1(extreme dust events).The source regions of air masses to Dushanbe during the onset of such events are also identified.Our study con-tributes to the understanding of dust and anthropogenic aerosols,in particular the extreme events and their disproportionally high radiative impacts over Central Asia.     

Forecasting effective drought index using a wavelet extreme learning machine (W-ELM) model

A drought forecasting model is a practical tool for drought-risk management. Drought models are used to forecast drought indices (DIs) that quantify drought by its onset, termination, and subsequent properties such as the severity, duration, and peak intensity in order to monitor and evaluate the impacts of future drought. In this study, a wavelet-based drought model using the extreme learning machine (W-ELM) algorithm where the input data are first screened through the wavelet pre-processing technique for better accuracy is developed to forecast the monthly effective DI (EDI). The EDI is an intensive index that considers water accumulation with a weighting function applied to rainfall data with the passage of time in order to analyze the drought-risk. Determined by the autocorrelation function (ACF) and partial ACFs, the lagged EDI signals for the current and past months are used as significant inputs for 1 month lead-time EDI forecasting. For drought model development, 97 years of data for three hydrological stations (Bathurst Agricultural, Wilsons Promontory and Merredin in Australia) are partitioned in approximately 90:5:5 ratios for training, cross-validation and test purposes, respectively. The discrete wavelet transformation (DWT) is applied to the predictor datasets to decompose inputs into their time–frequency components that capture important information on periodicities. DWT sub-series are used to develop new EDI sub-series as inputs for the W-ELM model. The forecasting capability of W-ELM is benchmarked with ELM, artificial neural network (ANN), least squares support vector regression (LSSVR) and their wavelet-equivalent (W-ANN, W-LSSVR) models. Statistical metrics based on agreement between the forecasted and observed EDI, including the coefficient of determination, Willmott’s index, Nash–Sutcliffe coefficient, percentage peak deviation, root-mean-square error, mean absolute error, and model execution time are used to assess the effectiveness of the models. The results demonstrate enhanced forecast skill of the drought models that use wavelet pre-processing of the predictor dataset. Based on statistical measures, W-ELM outperformed traditional ELM, LSSVR, ANN and their wavelet-equivalent counterparts (W-ANN, W-LSSVR). It is found that the W-ELM model is computationally efficient as shown by a faster running time with the majority of forecasting errors in lower frequency bands. The results demonstrate the usefulness of W-ELM over W-ANN and W-LSSVR models and the benefits of wavelet transformation of input data to improve the performance of drought forecasting models.     

Mortality risk from heat stress expected to hit poorest nations the hardest

Climatic Change - Anthropogenic climate warming has increased the likelihood of extreme hot summers. To facilitate mitigation and adaptation planning, it is essential to quantify and synthesize...