Trends in Drought over the Northeast United States

The Northeast United States is a generally wet region that has had substantial increases in mean precipitation over the past decades, but also experiences damaging droughts. We evaluated drought frequency, intensity, and duration trends in the region over the period 1901–2015. We used a dataset of Standardized Precipitation Evapotranspiration Index (SPEI), a measure of water balance based on meteorology that is computed at multiple timescales. It was found that the frequency of droughts decreased over this period, but their average intensity and duration did not show consistent changes. There was an increase in mean SPEI, indicating mostly wetter conditions, but also in an increase in SPEI variance, which kept the likelihood of extremely dry conditions from decreasing as much as would be expected from the wetter mean state. The changes in the SPEI mean and variance, as well as the decrease in drought frequency, were most pronounced for longer timescales. These results are consistent with the paradigm of hydrologic intensification under global warming, where both wet and dry extremes may increase in severity alongside changes in mean precipitation

Remote Sensing and Ground-Based Weather Forcing Data Analysis for Streamflow Simulation

Hydrological simulation, based on weather inputs and the physical characterization of the watershed, is a suitable approach to predict the corresponding streamflow. This work, carried out on four different watersheds, analyzed the impacts of using three different meteorological data inputs in the same model to compare the model’s accuracy when simulated and observed streamflow are compared. Meteorological data from the Daily Global Historical Climatology Network (GHCN-D), National Land Data Assimilation Systems (NLDAS) and the National Operation Hydrological Remote Sensing Center’s Interactive Snow Information (NOHRSC-ISI) were used as an input into the Soil and Water Assessment Tool (SWAT) hydrological model and compared as three different scenarios on each watershed. The results showed that meteorological data from an assimilation system like NLDAS achieved better results than simulations performed with ground-based meteorological data, such as GHCN-D. However, further work needs to be done to improve both the datasets and model capabilities, in order to better predict streamflow

Analysis of the Effects of Snowpack Properties on Satellite Microwave Brightness Temperature and Emissivity Data

Spatial variations of snowpack properties are an essential component in flood predictions and water resource management. Satellite microwave remote sensing has shown great potential in retrieving snowpack properties such as: snow depth, snow grain size, and snow density. In this research, we investigate the potential of microwave emissivity which is highly influenced by snowpack properties. Brightness temperature and emissivity data generated from HUT (Helsinki University of Technology) microwave emission of snow model were evaluated with satellite microwave measurements. The comparison of the real measurements (in-situ and satellite) with the modeled results shows that the scattering signature (19GHz-37GHz and 19GHz-85GHz) shows better results in emissivities rather than brightness temperature data. Furthermore, the over the deep snow (\u3e30cm), the emissivities scattering signature of (19GHz- 37GHz) has best performance while over shallow snow

Evaluation of the Snow Thermal Model (SNTHERM) through Continuous in situ Observations of Snow’s Physical Properties at the CREST-SAFE Field Experiment

Snowpack properties like temperature or density are the result of a complex energy and mass balance process in the snowpack that varies temporally and spatially. The Snow Thermal Model (SNTHERM) is a 1-dimensional model, energy and mass balance-driven, that simulates these properties. This article analyzes the simulated snowpack properties using SNTHERM forced with two datasets, namely measured meteorological data at the Cooperative Remote Sensing Science and Technology-Snow Analysis and Field Experiment (CREST-SAFE) site and the National Land Data Assimilation System (NLDAS). The study area is located on the premises of Caribou Municipal Airport at Caribou (ME, USA). The model evaluation is based on properties such as snow depth, snow water equivalent, and snow density, in addition to a layer-by-layer comparison of snowpack properties. The simulations were assessed with precise in situ observations collected at the CREST-SAFE site. The outputs of the SNTHERM model showed very good agreement with observed data in properties like snow depth, snow water equivalent, and average temperature. Conversely, the model was not very efficient when simulating properties like temperature and grain size in different layers of the snowpack

Proof of Concept: Development of Snow Liquid Water Content Profiler Using CS650 Reflectometers at Caribou, ME, USA

The quantity of liquid water in the snowpack defines its wetness. The temporal evolution of snow wetness’s plays a significant role in wet-snow avalanche prediction, meltwater release, and water availability estimations and assessments within a river basin. However, it remains a difficult task and a demanding issue to measure the snowpack’s liquid water content (LWC) and its temporal evolution with conventional in situ techniques. We propose an approach based on the use of time-domain reflectometry (TDR) and CS650 soil water content reflectometers to measure the snowpack’s LWC and temperature profiles. For this purpose, we created an easily-applicable, low-cost, automated, and continuous LWC profiling instrument using reflectometers at the Cooperative Remote Sensing Science and Technology Center-Snow Analysis and Field Experiment (CREST-SAFE) in Caribou, ME, USA, and tested it during the snow melt period (February–April) immediately after installation in 2014. Snow Thermal Model (SNTHERM) LWC simulations forced with CREST-SAFE meteorological data were used to evaluate the accuracy of the instrument. Results showed overall good agreement, but clearly indicated inaccuracy under wet snow conditions. For this reason, we present two (for dry and wet snow) statistical relationships between snow LWC and dielectric permittivity similar to Topp’s equation for the LWC of mineral soils. These equations were validated using CREST-SAFE in situ data from winter 2015. Results displayed high agreement when compared to LWC estimates obtained using empirical formulas developed in previous studies, and minor improvement over wet snow LWC estimates. Additionally, the equations seemed to be able to capture the snowpack state (i.e., onset of melt, medium, and maximum saturation). Lastly, field test results show advantages, such as: automated, continuous measurements, the temperature profiling of the snowpack, and the possible categorization of its state. However, future work should focus on improving the instrument’s capability to measure the snowpack’s LWC profile by properly calibrating it with in situ LWC measurements. Acceptable validation agreement indicates that the developed snow LWC, temperature, and wetness profiler offers a promising new tool for snow hydrology research

Drought risk assessment in central Nepal: temporal and spatial analysis

This paper presents temporal and spatial pattern of drought phenomena in central Nepal using standardized precipitation index (SPI) at multiple time scales. The study is based on 32 years of monthly precipitation data from 40 meteorological stations from 1981 to 2012. Results indicate that, while there is no distinct trend in regional precipitation, interannual variation is large. Trend analysis of drought index shows that most stations are characterized by increases in both severity and frequency of drought and trend is stronger for longer drought time scales. Over the study period, the summer season of 2004, 2005, 2006, 2009 and winters 2006, 2008 and 2009 were the worst widespread droughts. These dry periods have a serious impact on agriculture–livestock production of central Nepal. Better understanding of these SPI dynamics could help in understanding the characteristics of droughts and also to develop effective mitigation strategies

The Impact of Climate Change on Biodiversity in Nepal: Current Knowledge, Lacunae, and Opportunities

Nepal has an extreme altitudinal range from 60–8850m with heterogeneous topography and distinct climatic zones. The country is considered a biodiversity hotspot, with nearly a quarter of the land area located in protected areas. Nepal and the surrounding Himalayan region are particularly vulnerable to climate change because of their abrupt ecological and climatic transitions. Tens of millions of people rely on the region’s ecosystem services, and observed and modeled warming trends predict increased climate extremes in the Himalayas. To study the ecological impacts of climate change in Nepal and inform adaptation planning, we review the literature on past, present, and predicted future climatic changes and their impacts on ecological diversity in Nepal. We found few studies focusing on organisms, while research on species and communities was more common. Most studies document or predict species range shifts and changes in community composition. Results of these few investigations highlight major lacunae in research regarding the effects of changing climate on species comprising the Himalayan biota. Further empirical work is needed at all levels of biological organization to build on information regarding direct ecological impacts of climatic changes in the region. Countries face an ever-increasing threat of climate change, and Nepal has strong physiographic, elevational, and climatic gradients that could provide a useful model for studying the effects of climate change on a mountainous, and highly biodiverse, area

Analysis of Large Scale Spatial Variability of Soil Moisture Using a Geostatistical Method

Spatial and temporal soil moisture dynamics are critically needed to improve the parameterization for hydrological and meteorological modeling processes. This study evaluates the statistical spatial structure of large-scale observed and simulated estimates of soil moisture under pre- and post-precipitation event conditions. This large scale variability is a crucial in calibration and validation of large-scale satellite based data assimilation systems. Spatial analysis using geostatistical approaches was used to validate modeled soil moisture by the Agriculture Meteorological (AGRMET) model using in situ measurements of soil moisture from a state-wide environmental monitoring network (Oklahoma Mesonet). The results show that AGRMET data produces larger spatial decorrelation compared to in situ based soil moisture data. The precipitation storms drive the soil moisture spatial structures at large scale, found smaller decorrelation length after precipitation. This study also evaluates the geostatistical approach for mitigation for quality control issues within in situ soil moisture network to estimates at soil moisture at unsampled stations