Spatio-temporal variability of temperature and potential evapotranspiration over India

2016 ◽  
Vol 7 (4) ◽  
pp. 810-822 ◽  
Author(s):  
P. Sonali ◽  
D. Nagesh Kumar
Keyword(s):  
Hydrological Cycle ◽  
Potential Evapotranspiration ◽  
Grid Point ◽  
Rank Correlation ◽  
Trend Detection ◽  
Detection Analysis ◽  
Change Impact ◽  
Spatio Temporal ◽  
Grid Points ◽  
Significant Factors

Worldwide, major changes in the climate are expected due to global warming, which leads to temperature variations. To assess the climate change impact on the hydrological cycle, a spatio-temporal change detection study of potential evapotranspiration (PET) along with maximum and minimum temperatures (Tmax and Tmin) over India have been performed for the second half of the 20th century (1950–2005) both at monthly and seasonal scale. From the observed monthly climatology of PET over India, high values of PET are envisioned during the months of March, April, May and June. Temperature is one of the significant factors in explaining changes in PET. Hence seasonal correlations of PET with Tmax and Tmin were analyzed using Spearman rank correlation. Correlation of PET with Tmax was found to be higher compared to that with Tmin. Seasonal variability of trend at each grid point over India was studied for Tmax, Tmin and PET separately. Trend Free Pre-Whitening and Modified Mann Kendall approaches, which consider the effect of serial correlation, were employed for the trend detection analysis. A significant trend was observed in Tmin compared to Tmax and PET. Significant upward trends in Tmax, Tmin and PET were observed over most of the grid points in the interior peninsular region.

scholarly journals Spatio-temporal calibration of Hargreaves–Samani model in the Northern Region of Nigeria

2021 ◽  
Author(s):  
Akinwale Temitope Ogunrinde ◽  
Israel Emmanuel ◽  
Mike A. Enaboifo ◽  
Taiwo Adedayo Ajayi ◽  
Quoc Bao Pham
Keyword(s):  
Hydrological Cycle ◽  
Potential Evapotranspiration ◽  
Statistical Tests ◽  
Correlation Coefficients ◽  
Absolute Error ◽  
Northern Region ◽  
Average Value ◽  
Climatic Regions ◽  
Spatio Temporal ◽  
Semi Arid

Abstract One of the significant components of the hydrological cycle is evapotranspiration. Monthly meteorological parameters of 35 years from 19 meteorological stations across the Northern Region of Nigeria (NRN) were obtained and utilized for the calibration of Hargreaves–Samani (HS) model by comparing between potential evapotranspiration (ETo) values estimated from the original HS and the Penman–Monteith (FAO-56 PM) models. The calibrated HS equation was assessed using trend patterns and some statistical indices. The average value of root mean square error (RMSE) and the mean absolute error (MAE) decreased by 37.1 and 40%, respectively, after the calibration of the model. Also, the correlation coefficients (R) of stations that had values > 0.8 increased from 6 to 11 and the minimum R value increased by 12% above that of the original HS equation. The trend and spatial map of the statistical tests conducted also indicate better performance in most climatic regions after calibration. The precision of the HS equation improved significantly after calibration for semi-arid, humid, and sub-humid regions. However, few stations in the semi-arid, humid, and sub-humid regions did not show drastic improvement due to the peculiarity of the location and high variations in the wind speed and relative humidity parameters.

scholarly journals Image-based textile decoding

2020 ◽  
pp. 1-14
Author(s):  
Siqiang Chen ◽  
Masahiro Toyoura ◽  
Takamasa Terada ◽  
Xiaoyang Mao ◽  
Gang Xu
Keyword(s):  
Deep Learning ◽  
Grid Point ◽  
Intermediate Representation ◽  
Training Set ◽  
Binary Matrix ◽  
The Matrix ◽  
Correct Pattern ◽  
Grid Points ◽  
Textile Fabric ◽  
Direct Correspondence

A textile fabric consists of countless parallel vertical yarns (warps) and horizontal yarns (wefts). While common looms can weave repetitive patterns, Jacquard looms can weave the patterns without repetition restrictions. A pattern in which the warps and wefts cross on a grid is defined in a binary matrix. The binary matrix can define which warp and weft is on top at each grid point of the Jacquard fabric. The process can be regarded as encoding from pattern to textile. In this work, we propose a decoding method that generates a binary pattern from a textile fabric that has been already woven. We could not use a deep neural network to learn the process based solely on the training set of patterns and observed fabric images. The crossing points in the observed image were not completely located on the grid points, so it was difficult to take a direct correspondence between the fabric images and the pattern represented by the matrix in the framework of deep learning. Therefore, we propose a method that can apply the framework of deep learning viau the intermediate representation of patterns and images. We show how to convert a pattern into an intermediate representation and how to reconvert the output into a pattern and confirm its effectiveness. In this experiment, we confirmed that 93% of correct pattern was obtained by decoding the pattern from the actual fabric images and weaving them again.

scholarly journals Hydro-Meteorological Trends in an Austrian Low-Mountain Catchment

Climate ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 122
Author(s):  
Gerald Krebs ◽  
David Camhy ◽  
Dirk Muschalla
Keyword(s):  
Hydrological Cycle ◽  
Potential Evapotranspiration ◽  
Remote Sensing Data ◽  
Kendall Test ◽  
Adaptation Measures ◽  
Mitigation And Adaptation ◽  
Regional Impacts ◽  
Long Term Trends ◽  
Annual Scale

While ongoing climate change is well documented, the impacts exhibit a substantial variability, both in direction and magnitude, visible even at regional and local scales. However, the knowledge of regional impacts is crucial for the design of mitigation and adaptation measures, particularly when changes in the hydrological cycle are concerned. In this paper, we present hydro-meteorological trends based on observations from a hydrological research basin in Eastern Austria between 1979 and 2019. The analyzed variables include air temperature, precipitation, and catchment runoff. Additionally, the number of wet days, trends for catchment evapotranspiration, and computed potential evapotranspiration were derived. Long-term trends were computed using a non-parametric Mann–Kendall test. The analysis shows that while mean annual temperatures were decreasing and annual temperature minima remained constant, annual maxima were rising. Long-term trends indicate a shift of precipitation to the summer, with minor variations observed for the remaining seasons and at an annual scale. Observed precipitation intensities mainly increased in spring and summer between 1979 and 2019. Catchment actual evapotranspiration, computed based on catchment precipitation and outflow, showed no significant trend for the observed time period, while potential evapotranspiration rates based on remote sensing data increased between 1981 and 2019.

Climate and land use change impacts on water resources in Sardinia (Italy)

2021 ◽  
Author(s):  
Dario Ruggiu ◽  
Salvatore Urru ◽  
Roberto Deidda ◽  
Francesco Viola
Keyword(s):  
Land Use ◽  
Water Resources ◽  
Land Use Change ◽  
Hydrological Cycle ◽  
Potential Evapotranspiration ◽  
Regional Scale ◽  
Annual Runoff ◽  
Annual Rainfall ◽  
Land Use Scenarios ◽  
Near Future

<p>The assessment of climate change and land use modifications effects on hydrological cycle is challenging. We propose an approach based on Budyko theory to investigate the relative importance of natural and anthropogenic drivers on water resources availability. As an example of application, the proposed approach is implemented in the island of Sardinia (Italy), which is affected by important processes of both climate and land use modifications. In details, the proposed methodology assumes the Fu’s equation to describe the mechanisms of water partitioning at regional scale and uses the probability distributions of annual runoff (Q) in a closed form. The latter is parametrized by considering simple long-term climatic info (namely first orders statistics of annual rainfall and potential evapotranspiration) and land use properties of basins.</p><p>In order to investigate the possible near future water availability of Sardinia, several climate and land use scenarios have been considered, referring to 2006-2050 and 2051-2100 periods. Climate scenarios have been generated considering fourteen bias corrected outputs of climatic models from EUROCORDEX’s project (RCP 8.5), while three land use scenarios have been created following the last century tendencies.</p><p>Results show that the distribution of annual runoff in Sardinia could be significantly affected by both climate and land use change. The near future distribution of Q generally displayed a decrease in mean and variance compared to the baseline.   </p><p>The reduction of  Q is more critical moving from 2006-2050 to 2051-2100 period, according with climatic trends, namely due to the reduction of annual rainfall and the increase of potential evapotranspiration. The effect of LU change on Q distribution is weaker than the climatic one, but not negligible.</p>

scholarly journals A Review of Case Studies on Climate Change Impact on Hydrologic Cycle: An Indian Perspective

2021 ◽  
pp. 249-266
Author(s):  
P K Bhunya ◽  
Sanjay Kumar ◽  
Sunil Gurrapu ◽  
M K Bhuyan
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Hydrological Cycle ◽  
Groundwater Resources ◽  
Rainfall Events ◽  
Groundwater Levels ◽  
Groundwater Level Fluctuations ◽  
Change Impact ◽  
Heavy Rainfall Events ◽  
Flood Characteristics

In recent times, several studies focused on the global warming that may affect the hydrological cycle due to intensification of temporal and spatial variations in precipitation. Such climatic change is likely to impact significantly upon freshwater resources availability. In India, demand for water has already increased manifold over the years due to urbanization, agriculture expansion, increasing population, rapid industrialization and economic development. Numerous scientific studies also report increases in the intensity, duration, and spatial extents of floods, higher atmospheric temperatures, warmer sea, changes in precipitation patterns, and changing groundwater levels. This work briefly discusses about the present scenario regarding impact of climate change on water resources in India. Due to the insufficient resolution of climate models and their generally crude representation of sub-grid scale and convective processes, little confidence can be placed in any definite predictions of such effects, although a tendency for more heavy rainfall events seems likely, and a modest increase in frequency in floods. Thus to analyses this effect, this work considers real problems about the changing flood characteristics pattern in two river regions, and the effect of spatial and temporal pattern in rainfall. In addition to these, the work also examines the trend of groundwater level fluctuations in few blocks of Ganga–Yamuna and Sutlej-Yamuna Link interfluves region. As a whole, it examines the potential for sustainable development of surface water and groundwater resources within the constraints imposed by climate change.

Exact Subthreshold Integration with Continuous Spike Times in Discrete-Time Neural Network Simulations

2007 ◽  
Vol 19 (1) ◽  
pp. 47-79 ◽  
Author(s):  
Abigail Morrison ◽  
Sirko Straube ◽  
Hans Ekkehard Plesser ◽  
Markus Diesmann
Keyword(s):  
Grid Point ◽  
Propagation Delay ◽  
Integration Scheme ◽  
The Novel ◽  
Neuron Models ◽  
Exact Integration ◽  
Wide Range ◽  
Network Simulations ◽  
Adequate Representation ◽  
Grid Points

Very large networks of spiking neurons can be simulated efficiently in parallel under the constraint that spike times are bound to an equidistant time grid. Within this scheme, the subthreshold dynamics of a wide class of integrate-and-fire-type neuron models can be integrated exactly from one grid point to the next. However, the loss in accuracy caused by restricting spike times to the grid can have undesirable consequences, which has led to interest in interpolating spike times between the grid points to retrieve an adequate representation of network dynamics. We demonstrate that the exact integration scheme can be combined naturally with off-grid spike events found by interpolation. We show that by exploiting the existence of a minimal synaptic propagation delay, the need for a central event queue is removed, so that the precision of event-driven simulation on the level of single neurons is combined with the efficiency of time-driven global scheduling. Further, for neuron models with linear subthreshold dynamics, even local event queuing can be avoided, resulting in much greater efficiency on the single-neuron level. These ideas are exemplified by two implementations of a widely used neuron model. We present a measure for the efficiency of network simulations in terms of their integration error and show that for a wide range of input spike rates, the novel techniques we present are both more accurate and faster than standard techniques.

Meteorological and geographical control on stable isotope signature of precipitation in a Western Mediterranean area (Tuscany, Central Italy)

2021 ◽  
Author(s):  
Stefano Natali ◽  
Giovanni Zanchetta ◽  
Ilaria Baneschi ◽  
Marco Doveri ◽  
Roberto Giannecchini
Keyword(s):  
Stable Isotope ◽  
Hydrological Cycle ◽  
Central Italy ◽  
Precipitation Amount ◽  
Rank Correlation ◽  
Western Mediterranean ◽  
Meteorological Variables ◽  
Stable Water Isotopes ◽  
Fractionation Factor ◽  
Monthly Data

<p>Stable water isotopes of precipitation are widely used to track processes occurring within the hydrological cycle and to understand regional atmospheric patterns that influence a specific area. Moreover, the use of the oxygen isotopic composition in continental carbonates (e.g. speleothems) is a well-established practice to reconstruct climatic variations in the recent past. In the Mediterranean basin, the continental carbonate δ<sup>18</sup>O is generally used as a proxy of paleo-precipitation since the water-calcite fractionation factor is able to compensate the δ<sup>18</sup>O-T gradient of about 0.2‰/°C typical of rainfall in this area. However, few comprehensive investigations were performed in the Western Mediterranean in order to analyze the statistical relationships between measured stable isotopes in precipitation and meteorological variables, and none of them accounted for the possible seasonality in these relationships. Understanding the degree of dependence of the rainfall isotopic signature from precipitation amount and temperature at present day is of primary importance in Tuscany (Central-Western Italy), where many performed palaeohydrological studies require a more precise and quantitative interpretation. To this end, in the present study 560 isotope monthly data (δ<sup>18</sup>O, δ<sup>2</sup>H, and deuterium excess) of precipitation collected in 11 sites through Tuscany from 1971 to 2018 were gathered in a database. A large part of dataset was extracted from GNIP database (and integrated with new data) or derived from local hydrogeological studies, whereas 83 new measurements were produced at two novel sites. Then, only sites whose monthly data covered almost one year were considered for processing, resulting in 474 precipitation samples archived along with monthly mean temperature and rainfall amount. In this framework, a LMWL for Tuscany Region was determined for the first time by applying different regression techniques. A Spearman’s rank correlation analysis was performed to summarize the strength and direction of the relationship between stable isotope signatures of precipitation and meteorological variables, both at monthly and annual timescale. The monthly correlation was also investigated on seasonal basis. Finally, the influence of local geographical effects (altitude, distance to the coast, etc.) on the isotopic signals registered at different sites was evaluated.</p>

scholarly journals Appropriateness of Potential Evapotranspiration Models for Climate Change Impact Analysis in Yarlung Zangbo River Basin, China

Atmosphere ◽  
2019 ◽  
Vol 10 (8) ◽  
pp. 453 ◽  
Author(s):  
Pan ◽  
Xu ◽  
Xuan ◽  
Gu ◽  
Bai
Keyword(s):  
Climate Change ◽  
River Basin ◽  
Climate Change Impact ◽  
Impact Analysis ◽  
Potential Evapotranspiration ◽  
Global Climate Models ◽  
Historical Period ◽  
Yarlung Zangbo River ◽  
The Future ◽  
Change Impact

Evapotranspiration (ET) is an important element in the water and energy cycle. Potential evapotranspiration (PET) is an important measurement of ET. Its accuracy has significant influence on agricultural water management, irrigation planning, and hydrological modelling. However, whether current PET models are applicable under climate change or not, is still a question. In this study, five frequently used PET models were chosen, including one combination model (the FAO Penman-Monteith model, FAO-PM), two temperature-based models (the Blaney-Criddle and the Hargreaves models) and two radiation-based models (the Makkink and the Priestley-Taylor models), to estimate their appropriateness in the historical and future periods under climate change impact on the Yarlung Zangbo river basin, China. Bias correction methods were not only applied to the temperature output of Global Climate Models (GCMs), but also for radiation, humidity, and wind speed. It was demonstrated that the results from the Blaney-Criddle and Makkink models provided better agreement with the PET obtained by the FAO-PM model in the historical period. In the future period, monthly PET estimated by all five models show positive trends. The changes of PET under RCP8.5 are much higher than under RCP2.6. The radiation-based models show better appropriateness than the temperature-based models in the future, as the root mean square error (RMSE) value of the former models is almost half of the latter models. The radiation-based models are recommended for use to estimate PET under climate change in the Yarlung Zangbo river basin.

scholarly journals The recent developments in cloud removal approaches of MODIS snow cover product

2019 ◽  
Vol 23 (5) ◽  
pp. 2401-2416 ◽  
Author(s):  
Xinghua Li ◽  
Yinghong Jing ◽  
Huanfeng Shen ◽  
Liangpei Zhang
Keyword(s):  
Snow Cover ◽  
Cloud Cover ◽  
Hydrological Cycle ◽  
Global Climate ◽  
Optical Observations ◽  
Optical Remote Sensing ◽  
Spatial Methods ◽  
Fusion Methods ◽  
Spatio Temporal ◽  
Cloud Removal

Abstract. The snow cover products of optical remote sensing systems play an important role in research into global climate change, the hydrological cycle, and the energy balance. Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover products are the most popular datasets used in the community. However, for MODIS, cloud cover results in spatial and temporal discontinuity for long-term snow monitoring. In the last few decades, a large number of cloud removal methods for MODIS snow cover products have been proposed. In this paper, our goal is to make a comprehensive summarization of the existing algorithms for generating cloud-free MODIS snow cover products and to expose the development trends. The methods of generating cloud-free MODIS snow cover products are classified into spatial methods, temporal methods, spatio-temporal methods, and multi-source fusion methods. The spatial methods and temporal methods remove the cloud cover of the snow product based on the spatial patterns and temporal changing correlation of the snowpack, respectively. The spatio-temporal methods utilize the spatial and temporal features of snow jointly. The multi-source fusion methods utilize the complementary information among different sources among optical observations, microwave observations, and station observations.

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