ESTIMATION OF SNOW DEPLETION CURVE FOR GANGOTRI BASIN USING MULTI-SOURCE REMOTE SENSING DATA

Snow Depletion Curve derived from satellite images is a key parameter in Snowmelt Runoff Model. The fixed temporal resolution of a satellite and presence of cloud cover in Himalayas restricts accuracy of generated SDC. This study presents an effective approach of reducing temporal interval between two consecutive dates by integrating normalized Snow Cover Area estimated from multiple sources of satellite data. SCA is extracted by using Normalized Difference Snow Index for six snowmelt seasons from 2013 to 2018 for Gangotri basin situated in Indian Himalayas. This work also explores potential of recently launched Sentinel-3A for estimating SCA. Normalized SCA is utilized to eliminate the effect of difference in spatial resolution of various satellites. The result develops an important linear relation between SDC and time with a decrease in snow cover of 0.005/day that may be further refined by increasing the number of snowmelt seasons. This relationship may help scientific community in understanding hydrological response of glaciers to climate change.

SIMULATION OF THE AUTUMN-WINTER RUNOFF OF THE ANADYR RIVER

. Runoff during the autumn-winter low-water period is of great importance in the life of river ecosystems. In this work, the goal is to construct a mathematical model of the curves of daily water discharge during the autumn-winter low-water period (September-April) on the Anadyr River near the village Snezhny (catchment area 106 000 km2 ). Anadyr River is the largest waterway of the Chukotka. The river is used by public utilities, river transport in summer and road transport in winter. Pacific salmon spawn in this river every year. The conditions for the survival of salmon eggs in winter are influenced by the water content of the rivers. The basin under consideration is located in a zone of subarctic climate and continuous permafrost. Therefore, the autumn-winter low-water period on the Anadyr River lasts from September to April during this period, 4–6 % of the annual runoff occurs. Based on the average annual monthly water discharges for September-April, the averaged runoff depletion curve was calculated in relative ordinates, which is approximated by an exponential function. Based on this curve, you can predict daily water discharges from September 15th to April 15th of the following year. Verification calculations for the years of winters with different water content showed that the mathematical model works satisfactorily. Based on the data on the runoff of other hydrological stations in the Anadyr catchment, the study can be continued to derive a basin formula, which can be used to predict the daily water discharges of the autumn-winter low-water period on any unexplored river.

Spatial and temporal variation of snow cover in the Himalayan and Karakorum region using MODIS data (2000-2019)

<p>The Himalayan and Karakorum (H-K) region comprise the highest amount of snow and ice cover outside the Polar Regions. The H-K region is grouped into four-part, i.e., the Karakorum (KK), Western (WH), Central (CH), and Eastern Himalayas (EH), based on climate and geographic location. The EH and CH mainly feed by summer-monsoon snowfall, whereas the KK and WH are winters accumulated. This regional variability of climate will affect the water availability for hydropower generation, agriculture, and ecosystem. Therefore, the mapping and monitoring of snow cover change over the study area played an essential role in the context of climate change. The snow cover area (SCA) was observed using Moderate-resolution Imaging Spectroradiometer (MODIS) daily snow cover products version 6 during 2000-2019. Different cloud removal techniques (e.g., multi-sensor, temporal, spatial, regional snow line, multiday backward) are applied to reduce the cloud cover pixels over snow pixels of the MODIS data. The mean annual SCA of the H-K region is ∼26.4% of the total geographical area during the study period. The statistical trend analysis of mean monthly, seasonal, and annual SCA is examined using Mann-Kendal and Sen’s slope test. The mean yearly SCA of the H-K region shows an increasing trend during 2000-2009 and start decreasing significantly during 2009-2019. Similar results are observed in the KK, WH, CH, and EH, which shows a decreasing trend of mean annual SCA since 2009. The mean seasonal SCA shows a significant decreasing trend in summer (June to September) and winter (December to February) since 2009, suggesting a seasonal shift or change in snow cover. Overall, the winter shows an insignificant decreasing trend in comparison to the other seasons during 19 hydrological years (2000-01 to 2018-19). The mean monthly minimum SCA observed in August for the KK and WH, July for the CH, and June for the EH. However, the mean maximum SCA in February for the KK, WH, CH, and March for the EH. The snow cover depletion curve suggests that the maximum SCA in February and minimum in August of the entire region during the study period. The seasonal variation of SCA can be highly related to the influence of monsoonal patterns in the region.</p>

Monod-based ‘single-data’ strategy for biodegradation screening tests

Environmental contextObtaining biodegradation data over time can be difficult, especially when dealing with environmental compartments of increasing complexity. We evaluated the possibility of obtaining a full biodegradation depletion curve from a single biodegradation-time experimental measurement, and found that environmental information related to potential chemical persistence can be derived. The applicability of this ‘single-data’ strategy is illustrated using simulated and experimental data for several compounds. AbstractInformation obtained from biodegradability tests, e.g. half-life (t50) or kinetics parameters, is relevant in environmental risk assessment of new chemicals. In these tests, the removal of the tested compound is measured over a prefixed period of time (e.g. 28 days in ready biodegradability tests) to derive a substrate depletion curve. The implementation can be time-consuming, costly and difficult, especially when the complexity of the environmental compartment increases. In this work, the possibility of obtaining a full biodegradation depletion curve from a single biodegradation-time experimental data point (‘single-data’ strategy) was evaluated. Monod kinetics are assumed to avoid the limitations related to first-order kinetics (only valid for very low substrate concentrations). Experimental and simulated data were used to illustrate the potential of the proposed strategy. The effects on the estimates of several variables (e.g. Monod kinetics parameters, compound concentration or variability in biodegradation data) and the errors introduced to some of the variables were also evaluated. The results suggest that the proposed strategy can be used as a rapid (based on data measured at day 7) and low-cost screening approach to anticipate the result of a biodegradability test for new chemicals. The applicability and practical limitations of the ‘single-data’ strategy have been illustrated using experimental data for several compounds ranging from readily biodegradable (e.g. benzoic acid, acetylsalicylic acid, p-toluic acid) to potentially persistent compounds (e.g. bupivacaine, p-phenitidine, phtadinitrile).

A High-Resolution Data Assimilation Framework for Snow Water Equivalent Estimation across the Western United States and Validation with the Airborne Snow Observatory

Numerical simulations of snow water equivalent (SWE) in mountain systems can be biased, and few SWE observations have existed over large domains. New approaches for measuring SWE, like NASA’s ultra-high-resolution Airborne Snow Observatory (ASO), offer an opportunity to improve model estimates by providing a high-quality validation target. In this study, a computationally efficient snow data assimilation (DA) approach over the western United States at 1.75-km spatial resolution for water years (WYs) 2001–17 is presented. A local ensemble transform Kalman filter implemented as a batch smoother is used with the VIC hydrology model to assimilate the remotely sensed daily MODIS fractional snow-covered area (SCA). Validation of the high-resolution SWE estimates is done against ASO SWE data in the Tuolumne basin (California), Uncompahgre basin (Colorado), and Olympic Peninsula (Washington). Results indicate good performance in dry years and during melt, with DA reducing Tuolumne basin-average SWE percent differences from −68%, −92%, and −84% in open loop to 0.6%, 25%, and 3% after DA for WYs 2013–15, respectively, for ASO dates and spatial extent. DA also improved SWE percent difference over the Uncompahgre basin (−84% open loop, −65% DA) and Olympic Peninsula (26% open loop, −0.2% DA). However, in anomalously wet years DA underestimates SWE, likely due to an inadequate snow depletion curve parameterization. Despite potential shortcomings due to VIC model setup (e.g., water balance mode) or parameterization (snow depletion curve), the DA framework implemented in this study shows promise in overcoming some of these limitations and improving estimated SWE, in particular during drier years or at higher elevations, when most in situ observations cannot capture high-elevation snowpack due to lack of stations there.

REGIONALIZAÇÃO DE VAZÕES MÍNIMAS: BREVE REVISÃO TEÓRICA [Regionalization of minimum flows: brief theoretical review]

RESUMO: A ocorrência de fenômenos de déficit hídrico depende da variabilidade temporal da precipitação pluvial e, consequentemente, da vazão. Assim, o conhecimento dessa variabilidade se torna necessária para a previsão da quantidade hídrica disponível que pode estar abaixo do volume necessário para determinado uso, sendo fundamental para a gestão dos recursos hídricos. No entanto, os dados necessários para a estimativa de vazões nem sempre estão disponíveis. Diante da ausência desses dados e da dificuldade em prever a variabilidade, utiliza-se da técnica de regionalização, a qual visa transferir as informações hidrológicas de uma região com dados para outra com ausência de dados. Diante disso, o presente artigo teve por objetivo realizar uma revisão teórica acerca da regionalização de vazões mínimas. Para tal, a metodologia, classificada como exploratória, descritiva, bibliográfica e documental, consistiu em análise das produções bibliográficas científicas da área utilizando bases de pesquisa como o Scielo, Scopus, Web of Science, Science Direct além de teses e dissertações. A regionalização de vazões mínimas vem sendo aplicada em diversos estudos e é desenvolvida a partir da análise dos seguintes dados: análise de frequência, curva de duração e curva de depleção. As vazões mínimas indicam a disponibilidade hídrica de uma bacia hidrográfica, sendo assim, conhecê-las é importante para projetos de barragens e usinas hidrelétricas, avaliação de disponibilidade hídrica para irrigação, dentre outros projetos hidrológicos.ABSTRACT: The occurrence of water deficit phenomena depends on the temporal variability of the pluvial precipitation and, consequently, the flow. Thus, the knowledge of this variability becomes fundamental for management of water resources. However, the data required for flow estimation are not always available. Given the lack of data and the difficulty in predicting variability, the regionalization technique is used to transfer the hydrological information from one region with data to another with no data. Therefore, the aim of this paper was to carry out a theoretical review about the regionalization of minimum flows. For this, the methodology, classified as exploratory, descriptive, bibliographical and documentary, consisted of an analysis of the scientific bibliographic productions of the area using research bases such as Scielo, Scopus, Web of Science, Science Direct as well as theses and dissertations. The regionalization of minimum flows has been applied in several studies and is developed from the analysis of the following data: frequency analysis, duration curve and depletion curve. The minimum flows indicate the water availability of a river basin, so knowing them is important for dam projects and hydroelectric plants, water availability assessment for irrigation, among other hydrological projects.

Assessment of MERRA-2 Land Surface Hydrology Estimates

The MERRA-2 atmospheric reanalysis product provides global, 1-hourly estimates of land surface conditions for 1980–present at ~50-km resolution. MERRA-2 uses observations-based precipitation to force the land (unlike its predecessor, MERRA). This paper evaluates MERRA-2 and MERRA land hydrology estimates, along with those of the land-only MERRA-Land and ERA-Interim/Land products, which also use observations-based precipitation. Overall, MERRA-2 land hydrology estimates are better than those of MERRA-Land and MERRA. A comparison against GRACE satellite observations of terrestrial water storage demonstrates clear improvements in MERRA-2 over MERRA in South America and Africa but also reflects known errors in the observations used to correct the MERRA-2 precipitation. Validation against in situ measurements from 220–320 stations in North America, Europe, and Australia shows that MERRA-2 and MERRA-Land have the highest surface and root zone soil moisture skill, slightly higher than that of ERA-Interim/Land and higher than that of MERRA (significantly for surface soil moisture). Snow amounts from MERRA-2 have lower bias and correlate better against reference data from the Canadian Meteorological Centre than do those of MERRA-Land and MERRA, with MERRA-2 skill roughly matching that of ERA-Interim/Land. Validation with MODIS satellite observations shows that MERRA-2 has a lower snow cover probability of detection and probability of false detection than MERRA, owing partly to MERRA-2’s lower midwinter, midlatitude snow amounts and partly to MERRA-2’s revised snow depletion curve parameter compared to MERRA. Finally, seasonal anomaly R values against naturalized streamflow measurements in the United States are, on balance, highest for MERRA-2 and ERA-Interim/Land, somewhat lower for MERRA-Land, and lower still for MERRA (significantly in four basins).

Change in snow depletion pattern in a river basin of Arunachal Pradesh under projected climatic scenarios

<p>Snow depletion curves (SDCs) are important in hydrological studies for predicting snowmelt generated runoff in high mountain catchments. The present study deals with the derivation of the average snow depletion pattern in the Mago basin of Arunachal Pradesh, which falls in the eastern Himalayan region and the generation of climate affected SDCs in future years (2020, 2030, 2040, and 2050) under different projected climatic scenarios. The MODIS daily snow cover product at 500m resolution from both the Aqua and Terra satellites was used to obtain daily snow cover maps. MOD10A1 and MYD10A1 images were compared to select cloud free or minimum cloud image to obtain the temporal distribution of snow cover area (SCA). Snow accumulation and depletion patterns were obtained by analysing SCA at different days. For most of the years, two peaks were observed in the SCA analysis. The conventional depletion curve (CDC) representing present climate was derived by determining and interpolating the SCA from cloud-free (cloud&lt;5%) images for the selected hydrological year 2007. The investigation shows that the SCA was highest in February and lowest in May. Ten years meteorological data were used to normalize the temperature and precipitation data of the selected hydrological year (2007) to eliminate the impact of their yearly fluctuations on the snow cover depletion. The temperature and precipitation changes under four different projected climatic scenarios (A1B, A2, B1, and IPCC Commitment) were analysed for future years. Changes in the cumulative snowmelt depth with respect to the present climate for different future years were studied by a degree-day approach and were found to be highest under A1B, followed by A2, B1, and IPCC Commitment scenarios. It was observed that the A1B climatic scenario affected the depletion pattern most, making the depletion of snow to start and complete faster than under different scenarios. Advancing of depletion curve for different future years was found to be highest under A1B and lowest under IPCC Commitment scenarios with A2 and B1 in-between them.</p>

Improvement of the Snow Depth in the Common Land Model by Coupling a Two-Dimensional Deterministic Ensemble Model with a Variational Hybrid Snow Cover Fraction Data Assimilation Scheme and a New Observation Operator

During snow cover fraction (SCF) data assimilation (DA), the simplified observation operator and presence of cloud cover cause large errors in the assimilation results. To reduce these errors, a new snow cover depletion curve (SDC), known as an observation operator in the DA system, is statistically fitted to in situ snow depth (SD) observations and Moderate Resolution Imaging Spectroradiometer (MODIS) SCF data from January 2004 to October 2008. Using this new SDC, a two-dimensional deterministic ensemble–variational hybrid DA (2DEnVar) method of integrating the deterministic ensemble Kalman filter (DEnKF) and a two-dimensional variational DA (2DVar) is proposed. The proposed 2DEnVar is then used to assimilate the MODIS SCF into the Common Land Model (CoLM) at five sites in the Altay region of China for data from November 2008 to March 2009. The analysis performance of the 2DEnVar is compared with that of the DEnKF. The results show that the 2DEnVar outperforms the DEnKF as it effectively reduces the bias and root-mean-square error during the snow accumulation and ablation periods at all sites except for the Qinghe site. In addition, the 2DEnVar, with more assimilated MODIS SCF observations, produces more innovations (observation minus forecast) than the DEnKF, with only one assimilated MODIS SCF observation. The problems of cloud cover and overestimation are addressed by the 2DEnVar.