Improving Mean Annual Precipitation Prediction Incorporating Elevation and Taking into Account Support Size

Accounting for secondary exhaustive variables (such as elevation) in modelling the spatial distribution of precipitation can improve their estimate accuracy. However, elevation and precipitation data are associated with different support sizes and it is necessary to define methods to combine such different spatial data. The paper was aimed to compare block ordinary cokriging and block kriging with an external drift in estimating the annual precipitation using elevation as covariate. Block ordinary kriging was used as reference of a univariate geostatistical approach. In addition, the different support sizes associated with precipitation and elevation data were also taken into account. The study area was the Calabria region (southern Italy), which has a spatially variable Mediterranean climate because of its high orographic variability. Block kriging with elevation as external drift, compared to block ordinary kriging and block ordinary cokriging, was the most accurate approach for modelling the spatial distribution of annual mean precipitation. The three measures of accuracy (MAE, mean absolute error; RMSEP, root-mean-squared error of prediction; MRE, mean relative error) have the lowest values (MAE = 112.80 mm; RMSEP = 144.89 mm, and MRE = 0.11), whereas the goodness of prediction (G) has the highest value (75.67). The results clearly indicated that the use of an exhaustive secondary variable always improves the precipitation estimate, but in the case of areas with elevations below 120 m, block cokriging makes better use of secondary information in precipitation estimation than block kriging with external drift. At higher elevations, the opposite is always true: block kriging with external drift performs better than block cokriging. This approach takes into account the support size associated with precipitation and elevation data. Accounting for elevation allowed to obtain more detailed maps than using block ordinary kriging. However, block kriging with external drift produced a map with more local details than that of block ordinary cokriging because of the local re-evaluation of the linear regression of precipitation on block estimates.

Space-time characteristics of areal reduction factors and precipitation mechanisms

<p>We link areal reduction factors (ARFs, the ratio of annual maxima catchment precipitation and point precipitation) to the dominating precipitation mechanisms in Austria (84,000km²), using a new efficient method of estimating ARFs based on block kriging. A better understanding of the precipitation mechanisms help assess the plausibility of the ARFs estimated, but ARFs likewise contribute to a better understanding of the precipitation mechanisms as they are a fingerprint of the spatial statistical behavior of extreme precipitation. Our main focus is on two sub-regions in the West and East of Austria, dominated by stratiform and convective precipitation, respectively. ARFs are estimated using rain gauge data with hourly resolution across five durations. ARFs decay faster with increasing area in regions of pronounced convective activity than in regions dominated by stratiform processes. Low ARF values are linked to increased lightening activity (as a proxy for convective activity), but low ARFs can likewise occur in areas of reduced lightning activity as, in summer, convective precipitation can occur everywhere in the country. ARFs tend to decrease with increasing return period, possibly because the contribution of convective precipitation is higher. Our analysis is a key component towards a better understanding of the hydrometeorology in the region, as the process links of the ARFs relate to the space-time scaling of floods.</p>

SEBARAN SPASIAL LOGAM Pb, Cd, Cu, Zn DAN FRAKSI GEOKIMIA DI SEDIMEN PERAIRAN PANTAI KOTA MAKASSAR

Perairan pantai Kota Makassar, termasuk dua muara sungai yang mengapit, yaitu Sungai Jeneberang dan Sungai Tallo banyak mendapat inputan logam dari badan sungai dan dari daratan utama, berupa limbah industri dan limbah perkotaan. Penelitian ini bertujuan untuk menentukan distribusi spasial konsentrasi Pb, Cd, Cu, dan Zn serta fraksi bioavailable di sedimen perairan pantai Kota Makassar. Penelitian dilakukan di wilayah perairan pantai Kota Makassar, mulai dari muara Sungai Jeneberang hingga muara Sungai Tallo. Pengukuran logam dilakukan pada sedimen berukuran < 63 μm. Spesiasi logam pada fraksi sedimen ditentukan dengan metode Community Bureau of Reference (BCR) Three-steps Sequential method yang menghasilkan fraksi exchangeable dan acid soluble, reducible, serta oxidisable. Interpolasi sebaran spasial logam di sedimen dilakukan dengan menggunakan teknik Sistem Informasi Geografis (SIG) yaitu block kriging (BK) dengan program Arc View. Sebaran logam sangat ditentukan oleh input atau sumber dari daratan dimana sebaran spasial logam Pb, Cd, Cu, dan Zn di sedimen meningkat ke arah utara pantai Kota Makassar. Sebaran spasial fraksi 1 (terlarut dalam asam, acid reducible) logam Pb dan Cu tidak berbanding lurus dengan sebaran konsentrasi totalnya di sedimen. Sedangkan sebaran spasial fraksi 1 logam Cd dan Zn berbanding lurus dengan sebaran konsentrasi totalnya di sedimen.

Spatio-temporal approach to moving window block kriging of satellite data v1.0

Numerous existing satellites observe physical or environmental properties of the Earth system. Many of these satellites provide global-scale observations, but these observations are often sparse and noisy. By contrast, contiguous, global maps are often most useful to the scientific community (i.e., Level 3 products). We develop a spatio-temporal moving window block kriging method to create contiguous maps from sparse and/or noisy satellite observations. This approach exhibits several advantages over existing methods: (1) it allows for flexibility in setting the spatial resolution of the Level 3 map, (2) it is applicable to observations with variable density, (3) it produces a rigorous uncertainty estimate, (4) it exploits both spatial and temporal correlations in the data, and (5) it facilitates estimation in real time. Moreover, this approach only requires the assumption that the observable quantity exhibits spatial and temporal correlations that are inferable from the data. We test this method by creating Level 3 products from satellite observations of CO2 (XCO2) from the Greenhouse Gases Observing Satellite (GOSAT), CH4 (XCH4) from the Infrared Atmospheric Sounding Interferometer (IASI) and solar-induced chlorophyll fluorescence (SIF) from the Global Ozone Monitoring Experiment-2 (GOME-2). We evaluate and analyze the difference in performance of spatio-temporal vs. recently developed spatial kriging methods.