Hydrostreamer v1.0 – improved streamflow predictions for local applications from an ensemble of downscaled global runoff products

An increasing number of different types of hydrological, land surface, and rainfall–runoff models exist to estimate streamflow in river networks. Results from various model runs from global to local scales are readily available online. However, the usability of these products is often limited, as they often come aggregated in spatial units which are not compatible with the desired analysis purpose. We present here an R package, a software library Hydrostreamer v1.0, which aims to improve the usability of existing runoff products by addressing the modifiable area unit problem and allows non-experts with little knowledge of hydrology-specific modelling issues and methods to use them for their analyses. Hydrostreamer workflow includes (1) interpolation from source zones to target zones, (2) river routing, and (3) data assimilation via model averaging, given multiple input runoff and observation data. The software implements advanced areal interpolation methods and area-to-line interpolation not available in other products and is the first R package to provide vector-based routing. Hydrostreamer is kept as simple as possible – intuitive with minimal data requirements – and minimises the need for calibration. We tested the performance of Hydrostreamer by downscaling freely available coarse-resolution global runoff products from the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) in an application in 3S Basin in Southeast Asia. Results are compared to observed discharges as well as two benchmark streamflow data products, finding comparable or improved performance. Hydrostreamer v1.0 is open source and is available from http://github.com/mkkallio/hydrostreamer/ (last access: 5 May 2021) under the MIT licence.

Post-LGM evolution of the Dora Baltea glacial system and paleoclimatic implications in the Western Italian Alps

<p>Alpine glaciers repeatedly advanced and retreated from the high Alps to the forelands during the Quaternary and most recently reached their maximum extent and thickness during the Last Glacial Maximum (LGM, 26.5-19.0 ka ago)<sup> [1]</sup>. After the LGM, glaciers abandoned the Alpine foreland and retreated within the internal valleys. However, post-LGM withdrawal was not continuous but interrupted by stages of ice stasis or re-advance (stadials<sup> [2]</sup>), related to episodes of temporary climatic cooling. Glacial landforms and deposits associated to post-LGM ice stadials have been recognised across the Alps <sup>[2]</sup>. Our study contributes to this line of research by quantitatively reconstructing the age and configuration of several ice stages from the LGM to the Holocene, within the Dora Baltea (DB) catchment (SW Alps, Italy).</p><p>Following a detailed geomorphological mapping of glacial landforms and deposits, sixteen erratic boulders and two glacially-polished bedrocks were sampled along the DB valley for <em>in-situ</em><sup><em> </em>10</sup>Be surface-exposure dating, and five samples for luminescence dating were collected from fluvio-lacustrine and fluvio-glacial deposits. The obtained chronologies, combined with recalculated <sup>10</sup>Be surface-exposure ages from previous works in the study area <sup>[1, 3, 4, 5]</sup>, constrain seven post-LGM ice stages in the DB valley. The first three retreat stages occurred between the end of the LGM and the early Lateglacial, probably with rapid ice decay. The following three stages correspond to the well-known Gschnitz, Daun and Egesen Alpine Lateglacial stadials <sup>[2]</sup>, while we also identified a late-Holocene ice re-advance in the upstream DB catchment.</p><p>Paleo-ice configurations of each stage (including the LGM) were obtained with a semi-automatic ArcGIS routine (similar approach to GlaRe ArcGIS toolbox <sup>[6]</sup>), based on the areal interpolation of 2D ice surface profiles generated through Profiler v.2 <sup>[7]</sup>. Glacier equilibrium-line altitudes (ELAs) were computed for the eight 3D ice surface reconstructions <sup>[8]</sup>, with the aim of deriving potential paleoclimatic implications of the different reconstructed ice stages in comparison to other paleoclimatic proxies.</p><p> </p><p><strong>References</strong></p><p><sup>[1] </sup>Wirsig, C. et al., 2016, Quaternary Science Reviews.</p><p><sup>[2] </sup>Ivy-ochs, S., 2015, Cuadernos de Investigación Geográfica.</p><p><sup>[3] </sup>Gianotti, F. et al., 2015, Alpine and Mediterranean Quaternary.</p><p><sup>[4] </sup>Deline, P. et al., 2015, Quaternary Science Reviews.</p><p><sup>[5] </sup>Le Roy, M., 2012. Université Grenoble Alpes.</p><p><sup>[6] </sup>Pellitero, R. et al., 2016, Computers and Geosciences.</p><p><sup>[7] </sup>Benn, D., Hulton, N., 2010, Quaternary Science Reviews.</p><p><sup>[8] </sup>Pellitero, R. et al., 2015, Computers and Geosciences.</p>

Application of areal interpolation methods when determining zones of potential fertility of soils of agricultural lands

The article presents one of the possible options for improving the methodology for identifying zones of potential soil fertility. The necessity of using areal interpolation as the only method of geostatistical analysis that takes into account the area of input objects is proved. To check the data for a Gaussian normal distribution, it is necessary to use several verification methods, since when evaluating only statistical parameters, significant (in the case of phosphorus, abnormal) deviations were found, however, when evaluating histograms and quartile-quartile plots, it is necessary to bring the data to a normal distribution was relevant only for humus and phosphorus. The main advantages and disadvantages of the areal interpolation method are shown. With a significant deviation from the normal distribution, in the absence of built-in functions for automated reduction of data to the Gaussian distribution, one of the few ways can be the logarithm of the data. After zoning, it is necessary to perform a reverse translation to the original values for a representative visualization of the results. As a result of the selection of theoretical semivariograms-deconvolutions, the degrees of spatial dependence and optimal distances for the studied properties are determined. It is clear that the lag of acidity and potassium content is 1000 m and 1050 m, respectively. For phosphorus, it is 1300 m. For the humus content, the lag is much lower—440 m. The maximum autocorrelation distance is typical for potassium and humus—2330 and 1528 m; the minimum for phosphorus is 637. The reliability of the cartograms of agrochemical properties is confirmed by the calculated root-mean-square errors. The deviations of pH values are in the range of up to 0.15 units. The highest mean square error of interpolation is observed in weakly acidic soils. The error in the interpolated values of humus from the initial data is inherent in anthropogenically transformed soils. The root-mean-square error of phosphorus values can be estimated as insignificant. The largest errors in K2O—in isolated cases, they reach 120 mg/ha in the central and eastern parts of the region. The resulting map of potential soil fertility was used to determine the relationship with the granulometric composition of soils. A low level is observed on sandy and sandy loam soils, a high level—on loams. Also, the productivity is affected by the relief of the territory—in the dissected areas, productivity is lower than on the plains.

Hydrostreamer v1.0 – improved streamflow predictions for local applications from an ensemble of downscaled global runoff products

An increasing number of different types of hydrological, land surface, and rainfall-runoff models exist to estimate streamflow in river networks. Results from various model runs from global to local scale are readily available online. However, the usability of these products is often limited, as they often come aggregated in spatial units which are not compatible with the desired analysis purpose. We present here an R package, a software library hydrostreamer v1.0 which aims to improve the usability of existing runoff products by addressing the Modifiable Area Unit Problem, and allows non-experts with little knowledge of hydrology-specific modelling issues and methods to use them for their analyses. Hydrostreamer workflow includes 1) interpolation from source zones to target zones, 2) river routing, and 3) data assimilation via model averaging, given multiple input runoff and observation data. The software implements advanced areal interpolation methods and area-to-line interpolation not available in other products, and is the first R package to provide vector-based routing. Hydrostreamer is kept as simple as possible – intuitive with minimal data requirements – and minimizes need for calibration. We tested the performance of hydrostreamer by downscaling freely available coarse-resolution global runoff products from the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) in an application in 3S Basin in Southeast Asia. Results are compared to observed discharges as well as two benchmark streamflow data products, finding comparable or improved performance. Hydrostreamer v1.0 is open source and is available from http://github.com/mkkallio/hydrostreamer/ under MIT licence.

Using Areal Interpolation to Deal with Differing Regional Structures in International Research

When working with regional data from different countries, issues concerning data comparability need to be solved, including regional comparability. Differing regional unit size is a common issue which influences the results of socio-economic analyses. In this paper, we introduce a strategy to deal with the regional incomparability of administrative data in international research. We propose a methodological approach based on the areal interpolation method, which facilitates the usage of advanced spatial analyses. To illustrate, we analyze spatial patterns of unemployment in seven Central European countries. We use a very detailed spatial (municipal) level to reveal local tendencies. To have comparable units across the whole region, we apply the areal interpolation method, a process of projecting data from source administrative units to the target structure of a grid. After choosing the most suitable grid structure and projecting the data onto the grid, we perform a hot spot analysis to show the benefits of the grid structure for socio-economic analyses. The proposed approach has great potential in international research for its methodological correctness and the ability to interpret results.

Areal Interpolation Using Parcel and Census Data in Highly Developed Urban Environments

Areal interpolation is routinely used when spatial data are unavailable at desired geographical units. While many methods are available, few of them were developed specifically for and tested in highly developed urban cores. Even fewer studied subpopulation or population characteristics. This paper explores both issues using parcel map and decennial census data as ancillary information. Using census blocks as intermediate zones, the method first disaggregates source-zone data to intermediate zones, then disaggregates data to parcel level in intermediate zones intersecting target zones, and finally aggregates intermediate-zone and parcel-level estimates to obtain target-zone estimates. Compared to areal weighting and residential proportion, the proposed method is significantly more accurate. All three methods perform the best on population count, and worst on spatially clustered subpopulations such as black/African American population. Quotient variables are more difficult to interpolate than count variables. The research demonstrates the utility of parcel and decennial census data for areal interpolation in highly developed urban cores, and calls for future research on subpopulation and population characteristics.

Estimating changes in urban land and urban population using refined areal interpolation techniques

The analysis of changes in urban land and population is important because the majority of future population growth will take place in urban areas. U.S. Census historically classifies urban land using population density and various land-use criteria. This study analyzes the reliability of census-defined urban lands for delineating the spatial distribution of urban population and estimating its changes over time. To overcome the problem of incompatible enumeration units between censuses, regular areal interpolation methods including Areal Weighting (AW) and Target Density Weighting (TDW), with and without spatial refinement, are implemented. The goal in this study is to estimate urban population in Massachusetts in 1990 and 2000 (source zones), within tract boundaries of the 2010 census (target zones), respectively, to create a consistent time series of comparable urban population estimates from 1990 to 2010. Spatial refinement is done using ancillary variables such as census-defined urban areas, the National Land Cover Database (NLCD) and the Global Human Settlement Layer (GHSL) as well as different combinations of them. The study results suggest that census-defined urban areas alone are not necessarily the most meaningful delineation of urban land. Instead, it appears that alternative combinations of the above-mentioned ancillary variables can better depict the spatial distribution of urban land, and thus make it possible to reduce the estimation error in transferring the urban population from source zones to target zones when running spatially-refined temporal areal interpolation.