Generation of monthly irrigation maps for India using spatial interpolation techniques

High nutrient concentrations despite mitigation measures and reduced inputs are a common problem in anthropogenically impacted catchments. To investigate how water and solutes of different ages are mixed and released from catchment storage to the stream, catchment-scale models based on water transit time from StorAge Selection functions (SAS) are a promising tool. Tracking fluxes of environmental tracers, such as stable water isotopes, allows to calibrate and validate these models. However, this requires collection of water samples with an adequate temporal and spatial resolution, while sampling in catchments at the management scale is often limited by the high costs of the instruments, maintenance and chemical analysis. Therefore, temporal and spatial interpolation techniques are needed. This study demonstrates how to deal with sparse tracer data in space and time, and evaluates if these data are valuable to constrain the subsurface mixing dynamics and transit time with SAS modelling. We simulated water isotope data in diverse sub-basins of the Bode catchment (Germany) and calibrated the SAS function parameters against the measured streamflow isotope data. We tested four different combinations of spatial and temporal interpolation of the measured precipitation isotope data. In terms of temporal interpolation, monthly oxygen isotopes in precipitation (&#948;18OP) collected between 2012 and 2015 were converted to a daily time step with a step function and sinusoidal interpolation. In terms of spatial interpolation, the model was tested with raw values of &#948;18OP&#160;collected at a specific sampling point and with &#948;18OP&#160;interpolated using kriging to gain the spatial pattern of precipitation. The effect of the spatial and temporal interpolation techniques on the modeled SAS functions was analyzed using different parameterizations of the SAS function (i.e., power law time-invariant, power law time-variant and beta law). The results show how tracer input data with different distribution in time and space affect the SAS parameterization and water transit time. Moreover, they reveal preference of the sub-basins to mobilize either younger or older water, which has implications on how water flows through a catchment and on the fate of solutes.

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Estimation of the distribution of the total net radiative flux from satellite and automatic weather station data in the Upper Blue Nile basin, Ethiopia

Theoretical and Applied Climatology ◽

10.1007/s00704-020-03397-9 ◽

2020 ◽

Vol 143 (1-2) ◽

pp. 587-602

Author(s):

Eyale Bayable Tegegne ◽

Yaoming Ma ◽

Xuelong Chen ◽

Weiqiang Ma ◽

Bingbing Wang ◽

...

Keyword(s):

Spatial Interpolation ◽

Weather Prediction ◽

Weather Station ◽

Automatic Weather Station ◽

Net Radiation ◽

Nile Basin ◽

Blue Nile ◽

Radiation Fluxes ◽

Residual Kriging ◽

Interpolation Techniques

AbstractNet radiation is an important factor in studies of land–atmosphere processes, water resource management, and global climate change. This is particularly true for the Upper Blue Nile (UBN) basin, where significant parts of the basin are dry and evapotranspiration (ET) is a major mechanism for water loss. However, net radiation has not yet been appropriately parameterized in the basin. In this study, we estimated the instantaneous distribution of the net radiation flux in the basin using data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard the Terra satellite and Automatic Weather Station (AWS) data. Downward shortwave radiation and air temperature usually vary with topography, so we applied residual kriging spatial interpolation techniques to convert AWS data for point locations into gridded surface data. Simulated net radiation outputs were validated through comparison with independent field measurements. Validation results show that our method successfully reproduced the downward shortwave, upward shortwave, and net radiation fluxes. Using AWS data and residual kriging spatial interpolation techniques makes our results robust and comparable to previous works that used satellite data at a finer spatial resolution than MODIS. The estimated net shortwave, longwave, and total radiation fluxes were in close agreement with ground truth measurements, with mean bias (MB) values of − 14.84, 5.7, and 20.53 W m−2 and root mean square error (RMSE) values 83.43, 32.54, and 78.07 W m−2, respectively. The method presented here has potential applications in research focused on energy balance, ET estimation, and weather prediction for regions with similar physiographic features to those of the Nile basin.

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Smooth regional estimation of low-flow indices: physiographical space based interpolation and top-kriging

Hydrology and Earth System Sciences ◽

10.5194/hess-15-715-2011 ◽

2011 ◽

Vol 15 (3) ◽

pp. 715-727 ◽

Cited By ~ 43

Author(s):

S. Castiglioni ◽

A. Castellarin ◽

A. Montanari ◽

J. O. Skøien ◽

G. Laaha ◽

...

Keyword(s):

Spatial Interpolation ◽

Central Italy ◽

Low Flow ◽

Flow Index ◽

Ungauged Basins ◽

Low Flows ◽

Interpolation Techniques ◽

Point Data ◽

Leave One Out ◽

Low Flow Index

Abstract. Recent studies highlight that spatial interpolation techniques of point data can be effectively applied to the problem of regionalization of hydrometric information. This study compares two innovative interpolation techniques for the prediction of low-flows in ungauged basins. The first one, named Physiographical-Space Based Interpolation (PSBI), performs the spatial interpolation of the desired streamflow index (e.g., annual streamflow, low-flow index, flood quantile, etc.) in the space of catchment descriptors. The second technique, named Topological kriging or Top-kriging, predicts the variable of interest along river networks taking both the area and nested nature of catchments into account. PSBI and Top-kriging are applied for the regionalization of Q355 (i.e., a low-flow index that indicates the streamflow that is equalled or exceeded 355 days in a year, on average) over a broad geographical region in central Italy, which contains 51 gauged catchments. The two techniques are cross-validated through a leave-one-out procedure at all available gauges and applied to a subregion to produce a continuous estimation of Q355 along the river network extracted from a 90m elevation model. The results of the study show that Top-kriging and PSBI present complementary features. Top-kriging outperforms PSBI at larger river branches while PSBI outperforms Top-kriging for headwater catchments. Overall, they have comparable performances (Nash-Sutcliffe efficiencies in cross-validation of 0.89 and 0.83, respectively). Both techniques provide plausible and accurate predictions of Q355 in ungauged basins and represent promising opportunities for regionalization of low-flows.

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Comparison of spatial interpolation techniques to generate high-resolution climate surfaces for Nigeria

International Journal of Climatology ◽

10.1002/joc.4990 ◽

2017 ◽

Vol 37 ◽

pp. 179-192 ◽

Cited By ~ 14

Author(s):

Aisha Olushola Arowolo ◽

Avit Kumar Bhowmik ◽

Wei Qi ◽

Xiangzheng Deng

Keyword(s):

High Resolution ◽

Spatial Interpolation ◽

Interpolation Techniques

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Assessment of Air Pollution over Baghdad City Using Fixed Annual Stations and GIS Techniques

Journal of Southwest Jiaotong University ◽

10.35741/issn.0258-2724.54.6.36 ◽

2019 ◽

Vol 54 (6) ◽

Author(s):

Zainab B. Mohammed ◽

Ali Abdul Khaliq Kamal ◽

Ali S. Resheq ◽

Waleed M. Sh. Alabdraba

Keyword(s):

Air Pollution ◽

Air Pollutants ◽

Spatial Interpolation ◽

Nitrogen Monoxide ◽

World Health ◽

Distribution Maps ◽

Pollution Levels ◽

Who Standards ◽

Main Observation ◽

Interpolation Techniques

Baghdad, considered one of the most polluted and populated cities in Iraq, waschoosen for mapping the distribution of air pollutants and the overall pollution levels by using the ArcGIS techniques. Six of main observation stations werechoosen in a particular location. Then, the recorded data from these stations were spatially interpolated using two types of ArcGIS interpolation techniques. The spatial interpolation techniques used in this work were Inverse distance weighting (IDW) and fuzzy logic. This study includes measuring the main air pollutants, which were nitrogen dioxide (NO2), sulfur dioxide (SO2), carbon monoxide (CO), nitrogen oxide (NOx), and nitrogen monoxide (NO) during the period from January 2018 to December 2018. The data recorded by the stations during the work period and the distribution maps of air pollutants, which resulted from spatial interpolation (IDW) method, showed that the concentration of NO2 was within the International limits of World Health Origination (WHO) which is about 0.11 ppm. SO2 concentrations were exceeding the WHO limits in all stations for the study area. The concentrations of CO ranged from 0.484 ppm to 7.027 ppm that were within acceptable limits of WHO standards that is 9 ppm. NOx concentrations ranged between 0.01506 ppm – 0.214 ppm, which were exceeding acceptable limits of WHO standards (0.01 ppm). The concentrations of NO did not exceed the WHO standard limits, which are 0.08 ppm. Finally, the fuzzsy logic method of spatial interpolation in ArcGIS was applied to evaluate the air pollution over Baghdad city.

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