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Rainfall monitoring network design using conditioned Latin Hypercube Sampling and satellite precipitation estimates: An application in the ungauged Ecuadorian Amazon

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Abstract

Rain gauge networks are crucial for enhancing the spatio‐temporal characterization of precipitation. In tropical regions, scarcity of rain gauge data, climatic variability, and variable spatial accessibility make conventional approaches to design rain gauge networks inadequate and impractical. In this study, we propose the use of conditioned Latin Hypercube Sampling (cLHS) method with multi‐temporal layers of remotely sensed precipitation measurements for capturing the spatio‐temporal precipitation patterns in ungauged areas. The study was conducted in the Amazon Region of Ecuador, for which monthly precipitation averages were derived based on a 16‐year period of Tropical Rainfall Measuring Mission (TRMM 3B43 V7) data which were used as prior information to select representative sampling points through cLHS. Two scenarios for the sampling design were considered and evaluated, one without and one with restrictions on accessible sites according to the proximity to roads and settlements. Results showed that both optimized networks captured the variability of precipitation according to the TRMM climatology. Furthermore, evaluation against an independent satellite precipitation dataset showed that the optimized networks support mapping precipitation based on ordinary kriging (OK). Comparison with regular and random sampling methods showed that, particularly when a practical scenario is considered, the optimized network provided more reliable results over time, highlighting the suitability of the network to capture temporal changes and map precipitation with high accuracy. The proposed approach could be easily adopted in other ungauged and poorly accessible regions for rain gauge network design as well as to the design of multi‐objective monitoring networks.

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... In terms of the RMSE, the SPPs presented errors that ranged between 0.6 and 3.3 mm/h. Compared to previous studies [75][76][77], these values could be considered acceptable. However, CORR and KGE metrics were below 0.4, indicating a poor agreement between SPPs and observed precipitation data. ...
... Similarly, detection performances (POD < 0.6, FAR > 0.5, CSI < 0.4) suggested that SSPs cannot correctly capture the hourly precipitation occurrence. These results agreed with several authors [67][68][69][70][71][72][73][74][75][76][77][78][79] who previously found important limitations in precipitation estimates of SSPs at fine temporal scales over complex topography regions, as in the case of the study area. ...
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... While several studies (e.g. Schmidt et al., 2014;Contreras et al., 2019) showed that using RF in combination with cLHS gives the most accurate prediction, we showed that in our case cLHS performed worse than other sampling designs exploiting covariates for mapping with RF. While the results obtained by Schmidt et al. (2014) and Contreras et al. (2019) are possible outcomes (as shown by Fig. 2), these could have been incidental results if their validation sample size was small. ...
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Chapter
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... Since it was proposed in 1979, LHS has been an important method in the field of the "Filling a Space" design, and it is also a widely used method at present [20], [21], [22]. The sampling result is a NK  matrix. ...
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Mountain regions worldwide present a pronounced spatiotemporal precipitation variability, which added to scarce monitoring networks limits our understanding of the generation processes involved. To improve our understanding of clouds and precipitation dynamics and cross-scale generation processes in mountain regions, we analyzed spatiotemporal rainfall patterns using satellite cloud products (SCP) in the Paute basin (900–4200 m a.s.l. and 6481 km2) in the Andes of Ecuador. Precipitation models, using SCP and GIS data, reveal the spatial extension of three regimes: a three-modal (TM) regime present across the basin, a bimodal (BM) regime, along sheltered valleys, and a unimodal (UM) regime at windward slopes of the eastern cordillera. Subsequently, the spatiotemporal analysis using synoptic information shows that the dry season of the BM regime during boreal summer is caused by strong subsidence inhibiting convective clouds formation. Meanwhile, in UM regions, low advective shallow cap clouds mainly cause precipitation, influenced by water vapor from the Amazon and enhanced easterlies during boreal summer. TM regions are transition zones from UM to BM and zones on the windward slopes of the western cordillera. These results highlight the suitability of satellite and GIS data-driven statistical models to study spatiotemporal rainfall seasonality and generation processes in complex terrain, as the Andes.
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This study detects climate trends and variability from precipitation and temperature observations in Ecuador and assesses their links to the El Niño Southern Oscillation (ENSO) for the period 1966–2011, using the El Niño 1+2 and El Niño 3.4 indices. Excluding the Amazonian region (for which there is a lack of data), two main regions were distinguishable in terms of variability and trends among climate variables, especially for precipitation. In general, there was no trend in precipitation for the coastal region, and a very close relationship between the magnitude and seasonal distribution of precipitation and the El Niño 1+2 variability was found. In contrast, for the mountainous region (the Andes), there was an increase of precipitation during the study period, and a signal of El Niño 3.4 influence was detected. Temperatures were spatially homogeneous and showed an intense warming trend, except for maximum temperatures in the coastal region. The El Niño 1+2 influence on temperature was large from January to July. The results provide evidence of the close control exerted by the ENSO, especially in the coast of Ecuador, as well as for the occurrence of significant warming across the country independent of the ENSO phenomenon.
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Precipitation stations are important components of a hydrological monitoring network. Given their critical role in rainfall forecasting and flood warnings, along with limited observation resources, determining the optimal locations to deploy precipitation stations presents an important problem. In this paper, we use a maximal covering location problem to identify the best precipitation station sites. Considering the terrain conditions and the characteristics of a rainfall network, the original maximal covering location model is modified with the introduction of a set of additional constraints. The minimum density requirement is used to determine a precipitation station’s coverage range, and three weighting schemes are used to evaluate each demand object’s covering priority. As a typical mountainous watershed with high annual precipitation, the Jinsha River Basin is selected as the study area to test the applicability of the proposed method. Results show that the proposed method is effective for precipitation station configuration optimization, and the model solution achieves higher coverage than the real-world deployment. Compared with the commercial solver CPLEX, a genetic algorithm-based heuristic can significantly reduce the computation time when the problem size is large. Several deployment strategies are also discussed for establishing the optimal configuration of precipitation stations.
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In mountainous regions, rainfall plays a key role in water supply for millions of people. However, rainfall data for these sites are limited and generally of low quality, making it difficult to evaluate the nature, amount, and timing of rainfall. This is particularly true for the Páramo, a high-elevation grassland in the northern Andes that is a primary source of water for large populations in Ecuador, Colombia, and Venezuela. In this study, high-resolution laser disdrometer data and standard tipping-bucket rain gauge data were used to improve our knowledge of rainfall in the Páramo. For 36 months, rainfall was monitored in a high-elevation (3780 m a.s.l.) headwater catchment in southern Ecuador. Average annual rainfall during this period was 1345 mm. Results indicate that (i) when input from very low intensity events (drizzle) is taken into account, rainfall is 15% higher than previously thought; (ii) rainfall occurs throughout the year (only approximately 12% of the days are dry); (iii) rainfall occurs primarily as drizzle (80% of rainfall duration), which accounts for 29% of total rainfall amount; and (iv) the timing and average intensity of rainfall varies throughout the year (shorter afternoon events are common from October to May, whereas longer night events—with lower intensities—are more frequent from June to September). Although some of these numbers may vary regionally, the results contribute to a better understanding of rainfall in the wet Andean Páramo.
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Rainfall data is a fundamental input for effective planning, design and operation of water resources projects. A well-designed rain gauge network is capable of providing accurate estimates of necessary areal average and/or point rainfall estimates at any desired ungauged location in a catchment. Increasing network density with additional rain gauge stations has been the main underlying criterion in the past to reduce error and uncertainty in rainfall estimates. However, installing and operation of additional stations in a network involves large cost and manpower. Hence, the objective of this study is to design an optimal rain gauge network in the Middle Yarra River catchment in Victoria, Australia. The optimal positioning of additional stations as well as optimally relocating of existing redundant stations using the kriging-based geostatistical approach was undertaken in this study. Reduction of kriging error was considered as an indicator for optimal spatial positioning of the stations. Daily rainfall records of 1997 (an El Niño year) and 2010 (a La Niña year) were used for the analysis. Ordinary kriging was applied for rainfall data interpolation to estimate the kriging error for the network. The results indicate that significant reduction in the kriging error can be achieved by the optimal spatial positioning of the additional as well as redundant stations. Thus, the obtained optimal rain gauge network is expected to be appropriate for providing high quality rainfall estimates over the catchment. The concept proposed in this study for optimal rain gauge network design through combined use of additional and redundant stations together is equally applicable to any other catchment. This article is protected by copyright. All rights reserved.
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hydrol-earth-syst-sci.net/18/3179/2014/ doi:10.5194/hess-18-3179-2014 © Author(s) 2014. CC Attribution 3.0 License. Abstract. The Pacific–Andean region in western South America suffers from rainfall data scarcity, as is the case for many regions in the South. An important research question is whether the latest satellite-based and numerical weather pre-diction (NWP) model outputs capture well the temporal and spatial patterns of rainfall over the region, and hence have the potential to compensate for the data scarcity. Based on an interpolated gauge-based rainfall data set, the performance of the Tropical Rainfall Measuring Mission (TRMM) 3B42 V7 and its predecessor V6, and the North Western South America Retrospective Simulation (OA-NOSA30) are eval-uated over 21 sub-catchments in the Pacific–Andean region of Ecuador and Peru (PAEP). In general, precipitation estimates from TRMM and OA-NOSA30 capture the seasonal features of precipitation in the study area. Quantitatively, only the southern sub-catchments of Ecuador and northern Peru (3.6–6 • S) are relatively well estimated by both products. The accuracy is considerably less in the northern and central basins of Ecuador (0–3.6 • S). It is shown that the probability of detection (POD) is better for light precipitation (POD decreases from 0.6 for rates less than 5 mm day −1 to 0.2 for rates higher than 20 mm day −1). Compared to its predecessor, 3B42 V7 shows modest region-wide improvements in reducing biases. The improvement is specific to the coastal and open ocean sub-catchments. In view of hydrological applications, the correlation of TRMM and OA-NOSA30 estimates with observations increases with time aggregation. The correlation is higher for the monthly time aggregation in comparison with the daily, weekly, and 15-day time scales. Furthermore, it is found that TRMM per-forms better than OA-NOSA30 in generating the spatial dis-tribution of mean annual precipitation.
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A total of 21 gauges across the mountainous leeward portion of the island of Oʻahu, Hawaiʻi, were used to compare rainfall interpolation methods and assess rainfall spatial variability over a 34-month monitoring period from 2005 to 2008. Traditional and geo-statistical interpolation methods, including Thiessen polygon, inverse distance weighting (IDW), linear regression, ordinary kriging (OK), and simple kriging with varying local means (SKlm), were used to estimate wet and dry season rainfall. The linear regression and SKlm methods were used to incorporate two types of exhaustive secondary information: (1) elevation extracted from a digital elevation model (DEM), and (2) distance to a regional rainfall maximum. The Thiessen method produced the highest error, whereas OK produced the lowest error in all but one period. The OK method produced more accurate predictions than linear regression of rainfall against elevation when the correlation between rainfall and elevation is moderate (R < 0:82). The SKlm method produced lower error than linear regression and IDW methods in all periods. Comparison of the OK interpolation map with gridded isohyet data indicate that the areas of greatest rainfall deficit were confined to the mountainous region of west Oʻahu.
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In the past decade, substantial progress has been made in model-based optimization of sampling designs for mapping. This paper is an update of the overview of sampling designs for mapping presented by de Gruijter et al. (2006). For model-based estimation of values at unobserved points (mapping), probability sampling is not required, which opens up the possibility of optimized non-probability sampling. Non-probability sampling designs for mapping are regular grid sampling, spatial coverage sampling, k-means sampling, conditioned Latin hypercube sampling, response surface sampling, Kennard-Stone sampling and model-based sampling. In model-based sampling a preliminary model of the spatial variation of the soil variable of interest is used for optimizing the sample size and or the spatial coordinates of the sampling locations. Kriging requires knowledge of the variogram. Sampling designs for variogram estimation are nested sampling, independent random sampling of pairs of points, and model-based designs in which either the uncertainty about the variogram parameters, or the uncertainty about the kriging variance is minimized. Various minimization criteria have been proposed for designing a single sample that is suitable both for estimating the variogram and for mapping. For map validation, additional probability sampling is recommended, so that unbiased estimates of map quality indices and their standard errors can be obtained. For all sampling designs, R scripts are available in the supplement. Further research is recommended on sampling designs for mapping with machine learning techniques, designs that are robust against deviations of modeling assumptions, designs tailored at mapping multiple soil variables of interest and soil classes or fuzzy memberships, and probability sampling designs that are efficient both for design-based estimation of populations means and for model-based mapping.
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The spatial distribution of droughts is a key factor for designing water management policies at basin scale in arid and semi-arid regions. Ground hydro-meteorological data in neo-tropical areas are scarce; therefore, the merging of ground and satellite datasets is a promissory approach for improving our understanding of water distribution. This paper compares three monthly rainfall interpolation methods for drought evaluation. The ordinary kriging technique based on ground data, and cokriging with elevation as auxiliary variable were compared against cokriging using the Tropical Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA). Twenty rain gauge stations and the 3B42V7 version of the TMPA research dataset were considered. Comparisons were made over the Coello river basin (Colombia) at 3″ spatial resolution covering a period of eight years (1998–2005). The best spatial rainfall estimation was found for cokriging using ground data and elevation. The spatial support of TMPA dataset is very coarse for a merged interpolation with ground data, this spatial scales discrepancy highlight the need to consider scaling rules in the interpolation process.
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The book presents the statistical knowledge and methodology of sampling and data analysis useful for spatial inventory and monitoring of natural resources. The authors omitted all theory not essential for applications or for basic understanding. This presentation is broader than standard statistical texts, as the authors pay much attention to how statistical methodology can be employed and embedded in real-life spatial inventory and monitoring projects. Thus they discuss in detail how efficient sampling schemes and monitoring systems can be designed in view of the aims and constraints of the project.
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Representing land surface spatial heterogeneity when designing observation networks is a critical scientific challenge. Here we present a geospatial approach that utilizes the multivariate spatial heterogeneity of soil-forming factors—namely, climate, topography, land cover types, and surficial geology—to identify observation sites to improve soil organic carbon (SOC) stock estimates across the State of Alaska, USA. Standard deviations in existing SOC samples indicated that 657, 870, and 906 randomly distributed pedons would be required to quantify the average SOC stocks for 0–1 m, 0–2 m, and whole-profile depths, respectively, at a confidence interval of 5 kg Cm−2. Using the spatial correlation range of existing SOC samples, we identified that 309, 446, and 484 new observation sites are needed to estimate current SOC stocks to 1 m, 2 m, and whole-profile depths, respectively. We also investigated whether the identified sites might change under future climate by using eight decadal (2020–2099) projections of precipitation, temperature, and length of growing season for three representative concentration pathway (RCP 4.5, 6.0, and 8.5) scenarios of the Intergovernmental Panel on Climate Change. These analyses determined that 12 to 41 additional sites (906 + 12 to 41; depending upon the emission scenarios) would be needed to capture the impact of future climate on Alaskan whole-profile SOC stocks by 2100. The identified observation sites represent spatially distributed locations across Alaska that captures the multivariate heterogeneity of soil-forming factors under current and future climatic conditions. This information is needed for designing monitoring networks and benchmarking of Earth system model results.
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Spatial rainfall data is an essential input to Distributed Hydrological Models (DHM), and a significant contributor to hydrological model uncertainty. Model uncertainty is higher when rain gauges are sparse, as is often the case in practice. Currently, satellite-based precipitation products increasingly provide an alternative means to ground-based rainfall estimates, in which case a rigorous product assessment is required before implementation. Accordingly, the twofold objective of this work paper was the real-world assessment of both (a) the Tropical Rainfall Measuring Mission (TRMM) rainfall product using gauge data, and (b) the TRMM product’s role in forcing data for hydrologic simulations in the area of the Tiaoxi catchment (Taihu lake basin, China). The TRMM rainfall products used in this study are the Version-7 real-time 3B42RT and the post-real-time 3B42. It was found that the TRMM rainfall data showed a superior performance at the monthly and annual scales, fitting well with surface observation-based frequency rainfall distributions. The Nash-Sutcliffe Coefficient of Efficiency (NSCE) and the relative bias ratio (BIAS) were used to evaluate hydrologic model performance. The satisfactory performance of the monthly runoff simulations in the Tiaoxi study supports the view that the implementation of real-time 3B42RT allows considerable room for improvement. At the same time, post-real-time 3B42 can be a valuable tool of hydrologic modeling, water balance analysis, and basin water resource management, especially in developing countries or at remote locations in which rainfall gauges are scarce.
Article
We have been facing a remarkable decline in the number of raingauges in many areas of the world, as a compromise to the expensive cost of operating and maintaining raingauges. The question of how to effectively deploy new or remove current raingauges in order to create optimal rainfall information is becoming more and more important. On the other hand, larger-scaled remotely-sensed rainfall measurements, although poorer quality compared with traditional raingauge rainfall measurements, provide an insight into the local storm characteristics, which traditional methods for designing a raingauge network sort to seek. Based on these facts, this study proposes a new methodology for raingauge network design using remotely-sensed rainfall data set, which aims to explore how many gauges are essential and where they should be placed. Principal component analysis (PCA) is used to analyse the redundancy of the radar grids network and determine the number of raingauges while the potential locations are determined by cluster analysis (CA) selection. The proposed methodology has been performed on 373 different storm events measured by a weather radar grids network, and compared against an existing dense raingauge network in Southwest England. Due to the simple structure, the proposed scheme could be easily implemented in other study areas. This study provides a new insight into raingauge network design, which is also a preliminary attempt of using remotely-sensed data to solve the traditional raingauge problems.
Article
Detailed digital soil maps showing the spatial heterogeneity of soil properties consistent with the landscape are required for site-specific management of plant nutrients, land use planning and process-based environmental modeling. We characterized the short-scale spatial heterogeneity of soil properties in an Alfisol catena in a tropical landscape of Sri Lanka. The impact of different land-uses (paddy, vegetable and un-cultivated) was examined to assess the impact of anthropogenic activities on the variability of soil properties at the catenary level. Conditioned Latin hypercube sampling was used to collect 58 geo-referenced topsoil samples (0–30 cm) from the study area. Soil samples were analyzed for pH, electrical conductivity (EC), organic carbon (OC), cation exchange capacity (CEC) and texture. The spatial correlation between soil properties was analyzed by computing cross-variograms and subsequent fitting of theoretical model. Spatial distribution maps were developed using ordinary kriging. The range of soil properties, pH: 4.3–7.9; EC: 0.01–0.18 dS m− 1; OC: 0.1–1.37%; CEC: 0.44–11.51 cmol (+) kg− 1; clay: 1.5–25% and sand: 59.1–84.4% and their coefficient of variations indicated a large variability in the study area. Electrical conductivity and pH showed a strong spatial correlation which was reflected by the cross-variogram close to the hull of the perfect correlation. Moreover, cross-variograms calculated for EC and Clay, CEC and OC, CEC and clay and CEC and pH indicated weak positive spatial correlation between these properties. Relative nugget effect (RNE) calculated from variograms showed strongly structured spatial variability for pH, EC and sand content (RNE < 25%) while CEC, organic carbon and clay content showed moderately structured spatial variability (25% < RNE < 75%). Spatial dependencies for examined soil properties ranged from 48 to 984 m. The mixed effects model fitting followed by Tukey's post-hoc test showed significant effect of land use on the spatial variability of EC. Our study revealed a structured variability of topsoil properties in the selected tropical Alfisol catena. Except for EC, observed variability was not modified by the land uses. Investigated soil properties showed distinct spatial structures at different scales and magnitudes of strength. Our results will be useful for digital soil mapping, site specific management of soil properties, developing appropriate land use plans and quantifying anthropogenic impacts on the soil system.
Article
Most calibration sampling designs for Digital Soil Mapping (DSM) demarcate spatially distinct sample sites. In practical applications major challenges are often limited field accessibility and the question on how to integrate legacy soil samples to cope with usually scarce resources for field sampling and laboratory analysis. The study focuses on the development and application of an efficiency improved DSM sampling design that (1) applies an optimized sample set size, (2) compensates for limited field accessibility, and (3) enables the integration of legacy soil samples. The proposed sampling design represents a modification of conditioned Latin Hypercube Sampling (cLHS), which originally returns distinct sample sites to optimally cover a soil related covariate space and to preserve the correlation of the covariates in the sample set. The sample set size was determined by comparing multiple sample set sizes of original cLHS sets according to their representation of the covariate space. Limited field accessibility and the integration of legacy samples were incorporated by providing alternative sample sites to replace the original cLHS sites. We applied the modified cLHS design (cLHSadapt) in a small catchment (4.2 km²) in Central China to model topsoil sand fractions using Random Forest regression (RF). For evaluating the proposed approach, we compared cLHSadapt with the original cLHS design (cLHSorig). With an optimized sample set size n = 30, the results show a similar representation of the cLHS covariate space between cLHSadapt and cLHSorig, while the correlation between the covariates is preserved (r = 0.40 vs. r = 0.39). Furthermore, we doubled the sample set size of cLHSadapt by adding available legacy samples (cLHSadapt+) and compared the prediction accuracies. Based on an external validation set cLHSval (n = 20), the coefficient of determination (R²) of the cLHSadapt predictions range between 0.59 and 0.71 for topsoil sand fractions. The R²-values of the RF predictions based on cLHSadapt+, using additional legacy samples, are marginally increased on average by 5%.
Article
The spatial prediction of point values from areal data of the same attribute is addressed within the general geostatistical framework of change of support; the term support refers to the domain informed by each datum or unknown value. It is demonstrated that the proposed geostatistical framework can explicitly and consistently account for the support differences between the available areal data and the sought-after point predictions. In particular it is proved that appropriate modeling of all area-to-area and area-to-point covariances required by the geostatistical framework yields coherent (mass-p reserving or pycnophylactic) predictions. In other words, the areal average (or areal total) of point predictions within any arbitrary area informed by an areal-average (or areal-total) datum is equal to that particular datum. In addition, the proposed geostatistical framework offers the unique advantage of providing a measure of there liability (standard error) of each point prediction. It is also demonstrated that several existing approaches for area-to-point interpolation can be viewed within this geostatistical framework. More precisely, it is shown that (i) the choropleth map case corresponds to the geostatistical solution under the assumption of spatial independence at the point support level; (ii) several forms of kernel smoothing can be regarded as alternative (albeit sometimes incoherent) implementations of the geostatistical approach; and (iii) Tobler's smooth pycnophylactic interpolation, on a quasi- infinite domain without non-negativity constraints, corresponds to the geostatistical solution when the semivariogram model adopted at the point support level is identified to the free-space Green's functions (linear in 1-D or logarithmic in 2-D) of Poisson's partial differential equation. In lien of a formal case study, several I-D examples are given to illustrate pertinent concepts.
Article
A methodology for the design of a rain gauge network is developed in this study. To the best of the authors' knowledge, this is the first time a combination of geostatistical tools and factor analysis, along with a clustering technique, has been used to prioritize rain gauge stations in terms of information content over the study area. The whole study area is divided into homogeneous subregions and a conventional variance-based approach is implemented in each subregion to rank rain gauge stations. For this purpose, factor analysis coupled with ordinary block kriging is used to identify the number of homogeneous subregions, and then, ordinary point kriging is used to assign rain gauge stations to each subregion. The developed scheme is quite time-efficient as it is not sensitive to initial guesses on cluster centers, there is no need to specify the number of clusters in advance and, above all, it is highly relevant to the overall objective stipulated in rain gauge network design. The proposed methodology is implemented on real data set in the south west of Iran. The results show that the proposed approach compares well with existing paradigms in rain gauge network design and only six rain gauge stations are required to provide the necessary information. In particular, the measure of network accuracy lies somewhere in between the so called time consuming and more simplified approaches used in rain gauge network design. © 2013 Sharif University of Technology. Production and hosting by Elsevier B.V. All rights reserved.
Article
The post-real time product of Day-1 Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) is evaluated over Mainland China from April to December 2014 at the hourly timescale, against data from hourly ground-based observations. In addition, the IMERG product is compared with its predecessor-the Version-7 post-real-time 3B42 (3B42V7) product of Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) at its original 3-hourly and then daily timescales for the same period. All the products are cross-evaluated at gridded, regional, and national scales. Results show that: (1) the Day-1 IMERG shows appreciably better performance than 3B42V7 at both sub-daily and daily timescales, and all the three spatial scales. The gap between the two products is more significant at the sub-daily resolution; (2) Out of the six sub-regions of China, IMERG especially performs better than 3B42V7 at the mid- and high-latitudes, as well as relatively dry climate regions; (3) IMERG can better reproduce the probability density function (PDF) in terms of precipitation intensity, particularly in the low ranges; and (4) although IMERG better captures the precipitation diurnal variability, both products have room to further improve their capability, particularly in the dry climate and high-latitude regions. This study is among the earliest evaluation and comparison of IMERG and 3B42V7 products, which could be valuable in providing reference for the development of IMERG algorithms, associated global products, and various applications as well.
Article
In this study, we investigate the capabilities of the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) and the recently released Integrated Multi-satellitE Retrievals for GPM (IMERG) in detecting and estimating heavy rainfall across India. First, the study analyzes TMPA data products over a 17-year period (1998–2014). While TMPA and reference gauge-based observations show similar mean monthly variations of conditional heavy rainfall events, the multi-satellite product systematically overestimates its inter-annual variations. Categorical as well as volumetric skill scores reveal that TMPA over-detects heavy rainfall events (above 75th percentile of reference data), but it shows reasonable performance in capturing the volume of heavy rain across the country. An initial assessment of the GPM-based multi-satellite IMERG precipitation estimates for the southwest monsoon season shows notable improvements over TMPA in capturing heavy rainfall over India. The recently released IMERG shows promising results to help improve modeling of hydrological extremes (e.g., floods and landslides) using satellite observations.
Article
The goal of this study is to quantitatively inter-compare the standard products of Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) and its successor, Global Precipitation Measurement (GPM) mission Integrated Multi-satellite Retrievals for GPM (IMERG) with a dense gauge network over the mid-latitude Ganjiang River basin in Southeast China. In general, direct comparisons of the TMPA 3B42V7, 3B42RT, and GPM Day-1 IMERG estimates with gauge observations over an extended period of rainy season (from May through September 2014) at 0.25° and daily resolutions show that all the three products demonstrate similarly acceptable (~0.63) and high (0.87) correlation at grid and basin scales, respectively, although 3B42RT shows much higher overestimation. Both the post-real-time corrections effectively reduce the bias of Day-1 IMERG and 3B42V7 to single digits of underestimation from 20+% overestimation of 3B42RT. The Taylor diagram shows that Day-1 IMERG and 3B42V7 are comparable at grid and basin scales. Hydrologic assessment with the Coupled Routing and 42 Excess STorage (CREST) hydrologic model indicates that the Day-1 IMERG product performs comparably to gauge reference data. In many cases, the IMERG product outperforms TMPA standard products, suggesting a promising prospect of hydrologic utility and a desirable hydrologic continuity from TRMM-era product heritages to GPM-era IMERG products. Overall, this early study highlights that the Day-1 IMERG product can adequately substitute TMPA products both statistically and hydrologically even with its limited data availability to date in this well-gauged mid-latitude basin. As more IMERG data are released, more studies to explore the potential of GPM-era IMERG in water, weather, and climate research are urgently needed.
Article
The GPM mission is specifically designed to unify and advance precipitation measurements from a constellation of research and operational microwave sensors. NASA and JAXA have successfully deployed the GPM Core Observatory on February 28, 2014, building upon the success of TRMM launched by NASA of the US and JAXA of Japan in 1997. The observatory carries the first spaceborne dual-frequency phased array precipitation radar, the DPR, operating at Ku and Ka bands and a conical-scanning multi-channel microwave imager known as the GMI. This sensor package is an extension of the TRMM instruments, which focused primarily on heavy to moderate rain over tropical and subtropical oceans. The GPM sensors will extend the measurement range attained by TRMM to include light-intensity precipitation and falling snow, which accounts for a significant fraction of precipitation occurrence in the middle and high latitudes.
Article
Rain gauge networks are used to provide estimates of area average, spatial variability and point rainfalls at catchment scale and provide the most important input for hydrological models. Therefore, it is desired to design the optimal rain gauge networks with a minimal number of rain gauges to provide reliable data with both areal mean values and spatial–temporal variability. Based on a dense rain gauge network of 185 rain gauges in Xiangjiang River Basin, southern China, this study used an entropy theory based multi-criteria method which simultaneously considers the information derived from rainfall series, minimize the bias of areal mean rainfall as well as minimize the information overlapped by different gauges to resample the rain gauge networks with different gauge densities. The optimal networks were examined using two hydrological models: The lumped Xinanjiang Model and the distributed SWAT Model. The results indicate that the performances of the lumped model using different optimal networks are stable while the performances of the distributed model keep on improving as the number of rain gauges increases. The results reveal that the entropy theory based multi-criteria strategy provides an optimal design of rain gauge network which is of vital importance in regional hydrological study and water resources management. http://authors.elsevier.com/a/1QpHp_WGi0hSj
Article
Understanding the soil attributes and types occurring within a region is critical for providing the best land-use decisions. Soils vary in their ability to clean and store water, provide water for plant growth, and many other ecosystem services. Soil variability is dependent on climate, parent material, organisms, time, and topography. When only topography varies within an area, the topography and redistribution of water should be the main drivers for soils differentiation. Digital soil mapping (DSM) has advantages due to computational tools and easily accessible digital elevation models (DEMs) at multiple resolutions. Terrain attributes (e.g., slope, wetness index, and profile curvature) are derived from the DEM and, in association with a soil expert, knowledge-based models can be applied to predict soil variability. The objective of this study was to create and validate a predicted Cambisol (Inceptisol) solum depth map for Lavrinha Creek Watershed (LCW) in Minas Gerais, Brazil, by applying DSM techniques for the Brazilian soil landscapes. The best available 30-m DEM was used to derive the terrain derivatives. A set of rules were formulated according to the terrain attributes, limited data, and expert knowledge to predict the solum depth behavior throughout the watershed. Conditioned Latin hypercube sampling scheme was used for allocating the validation points. In this study, 20 out of the 25 validating samples were correctly classified yielding a Kappa index of 0.616. Soil expert knowledge and Digital Soil Mapping techniques can be employed for mapping areas, especially in countries where there is limited data available, which will provide a useful soil map for planning while saving time and investments.