Linking Long-Term Water Balances and Statistical Scaling to Estimate River Flows along the Drainage Network of Colombia

Poveda, Germán; Vélez, Jaime Ignacio; Sánchez, Óscar José Mesa; Cuartas, Adriana; Barco, Janet; Mantilla, Ricardo; Mejía, John F.; Hoyos, Carlos D.; Ramı́rez, Jorge; Ceballos, Lina I.; Zuluaga, Manuel D.; Arias, Paola A.; Botero, B. A.; Montoya, María Isabel Villa; Giraldo, Juan D.; Quevedo, Diana I.

doi:10.1061/(asce)1084-0699(2007)12:1(4)

Cited by 57 publications

(56 citation statements)

References 55 publications

(70 reference statements)

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“…We used the indicator approach to model the local (at-a-pixel) uncertainty and to estimate mean annual precipitation, as well as conditional variance and coefficient of variation fields using the ICCKM modelled pixel's CCFDs. The estimated precipitation fields exhibit some of the most relevant previously identified features, widely commented by Oster (1979), DNP (1984), Vélez et al (2000) and Poveda et al (2007), along with some new features that improve those previous estimations. Particularly, the new fields adequately preserve (1) the spatial behaviour of rainfall in Caribbean region, (2) the PO over the intraAndean valleys, (3) the spatial distribution of rainfall and location of the pluviometric maximum at the Pacific region, the rainy belts at the eastern piedmont of the eastern Andes, and the middle Magdalena River valley, and (4) the spatial variability of precipitation at the southeastern (Orinoco and Amazonia) and oceanic regions, of very limited or nonexistent raingauge coverage.…”

Section: Discussionsupporting

confidence: 57%

“…Comparing the newly developed long-term average precipitation fields with some of the previously estimated rainfall fields over Colombia (Snow, 1976;Oster, 1979;Mesa et al, 1997;Mejía et al, 1999;Poveda and Mesa, 2000;Vélez et al, 2000;Poveda et al, 2007), the following results are worth noting: (a) All interpolation algorithms capture the most recognizable features of rainfall over the Caribbean region, with average rainfall estimates below 1500 mm year −1 , including three well-known extreme precipitation spots: (1) the extremely dry Guajira region in northeastern Colombia (labelled as 1 in precipitation fields of Figure 6), with average rainfall estimates around 300-400 mm year −1 , and (2) two rainy regions, one near Sierra Nevada de Santa Marta (labeled as 2 in precipitation fields of Figure 6), with estimates around 1900 mm year −1 , and the southern region of Bolivar's department (labelled as 3 in precipitation fields of Figure 6), with estimates around 3500 mm year −1 , (b) With the only exception of SCK (Figure 6(b)), all interpolation algorithms were able to capture the presence of the PO within the intra-Andean (Cauca and Magdalena River) valleys. Figure 8 contains four longitudinal profiles of both topography and mean annual precipitation.…”

Section: Quality Control Of the Estimated Rainfall Fieldsmentioning

confidence: 99%

“…On seasonal time scales, the displacement of the intertropical convergence zone (ITCZ) exerts a strong control on the annual cycle of Colombia's hydroclimatology (Eslava, 1993;Mesa et al, 1997;Mejía et al, 1999;León et al, 2000;Pabón et al, 2001;Montoya et al, 2001b;Poveda, 2004;Poveda et al, 2006Poveda et al, , 2007. Central and western Colombia experience a bimodal annual cycle of precipitation with marked high rain seasons (April-May and September-November) and low rain seasons (December-February and June-August), resulting from the double passage of the ITCZ over the region.…”

Section: Introductionmentioning

confidence: 99%

“…More recently, the governmental Institute for Hydrology, Meteorology and Environmental Studies (IDEAM) has produced several mean annual rainfall maps for Colombia (IDEAM, 2001; see map 3.6 on p. 69), although they lack information on the data and interpolation algorithms used and do not estimate the associated uncertainties. Further detailed annual and monthly mean precipitation maps were estimated for the period 1966-1987 at 5 arcmin resolution by Mejía et al (1999), Vélez et al (2000) and Poveda et al (2007). These latter studies used measurements from near 700 raingauges inland and diverse variants of kriging as interpolation algorithms, using digitized versions of Oster's and Snow's isohyets maps and topography as secondary drift functions.…”

Section: Introductionmentioning

confidence: 99%

“…Since soil and groundwater storage, S, is finite as T is increased, the quantity [S(t) − S(0)]/T goes to 0. Thus, the long-term approximation for the water balance gives R = P − E. The study by Poveda et al (2007) combined the longterm water balance equation along with statistical scaling to estimate mean and extreme river discharges through the entire river network of Colombia. Here, we aim to improve the estimated long-term mean annual precipitation field, which in turn may help to improve the longterm water balances estimation, with ample scientific and applied benefits for hydropower generation, agriculture, risks planning and management, human health, etc.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Improved long‐term mean annual rainfall fields for Colombia

Álvarez-Villa¹,

Vélez²,

Poveda³

2010

Intl Journal of Climatology

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ABSTRACT:With the aim of improving the long-term mean annual surface water balance of Colombia, four new annual average precipitation fields are estimated at 4 km spatial resolution. To put in context, a concise literature review of rainfall in Colombia is presented. For estimation purposes, diverse multivariate geostatistical methods are implemented by combining information from 1180 raingauges covering the period 1950-2005, and satellite data from the tropical rainfall measuring mission (TRMM) for the period 1999-2005, used as a drift for the following geostatistical methods: (1) kriging with an external drift (KED), (2) standardized cokriging (SCK), (3) colocated cokriging (CCK), and (4) Markov regionalization CCK (CCKM). To ensure the reliability of the estimated precipitation fields, a detailed cross-validation procedure is performed, including univariate and bivariate analyses of residuals, which allows us to conclude that the best estimated rainfall field is obtained with KED, and the worst with SCK. Visual analyses are also performed in the search for consistency of the resulting precipitation fields. Furthermore, local (at-a-pixel) uncertainty modelling analysis is performed using the indicator approach. Conditional cumulative distribution functions (CCDF) are estimated using indicator CCK with Bayes-Markov hypothesis. Statistical descriptors for the pixel's CCDFs are estimated based on the resulting precipitation fields, including long-term mean, conditional variance and the coefficient of variation. These improved precipitation fields along with their estimated uncertainties are available (http://cancerbero.unalmed.edu.co/∼hidrosig/index.php) for the scientific community and constitute useful basic information for diverse applications in water resources, agriculture, hydropower generation, human health, risks and disaster prevention, and many other applied sectors in Colombia.

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Section: Discussionsupporting

confidence: 57%

Section: Quality Control Of the Estimated Rainfall Fieldsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Improved long‐term mean annual rainfall fields for Colombia

Álvarez-Villa¹,

Vélez²,

Poveda³

2010

Intl Journal of Climatology

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Seasonal precipitation patterns along pathways of South American low-level jets and aerial rivers

2014

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[1] We study the seasonal dynamics of the eastern Pacific (CHOCO) and Caribbean lowlevel jets (LLJ), and aerial rivers (AR) acting on tropical and subtropical South America. Using the ERA-Interim reanalysis , we show that the convergence of both LLJs over the eastern Pacific-western Colombia contributes to the explanation of the region's world-record rainfall. Diverse variables involved in the transport and storage of moisture permit the identification of an AR over northern South America involving a midtropospheric easterly jet that connects the Atlantic and Pacific Oceans across the Andes, with stronger activity in April to August. Other major seasonal AR pathways constitute part of a large gyre originating over the tropical North Atlantic, veering to the southeast over the eastern Andes and reaching regions of northern Argentina and southeastern Brazil. We illustrate the distribution of average seasonal precipitation along the LLJs and AR pathways with data from the Tropical Rainfall Measuring Mission (1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011), combined with considerations of CAPE, topography, and land cover. In addition, the theory of the biotic pump of atmospheric moisture (BiPAM) is tested at seasonal time scales, and found to hold in 8 out of 12 ARs, and 22 out of 32 forest-covered tracks (64% in distance) along the ARs. Deviations from BiPAM's predictions of rainfall distribution are explained by the effects of topography, orography, and land cover types different from forests. Our results lend a strong observational support to the BiPAM theory at seasonal time scales over South American forested flat lands.

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Ground validation of TRMM 3B43 V7 precipitation estimates over Colombia. Part I: Monthly and seasonal timescales

Vallejo-Bernal

Urrea

Bedoya-Soto

et al. 2020

Intl Journal of Climatology

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In this study, we validate precipitation estimates remotely sensed by the Tropical Rainfall Measuring Mission (TRMM) at monthly and seasonal timescales, during the period 1998-2015, by calculating and analyzing diverse error metrics between the 3B43 V7 product and in situ measurements from 1,180 rain gauges over Colombia, of which at least 987 are fully independent of TRMM. We explore the existence of spatiotemporal patterns to assess the performance of 3B43 V7 over the five major natural regions of Colombia: Caribbean, Pacific, Andes, Orinoco and Amazon. The results show that 3B43 V7 product is able to capture the phase of the annual cycle of monthly mean precipitation, but the performance is not good for the amplitude, in particular over the Andes and Pacific regions owing to complex climatic and topographic conditions. In general, 3B43 V7 exhibits good performance in the low-lying and plain Amazon, Orinoco and Caribbean regions. Over the Andes region, characterized by complex topography, overestimation errors are identified [root mean squared error (RMSE) ≥83.59 mm•month −1 and relative bias (BIAS) ≥4.69%], whereas the extremely wet rainfall regime of the Pacific region is largely underestimated (RMSE ≥253.52 mm •month −1 and BIAS ≤−11.75%). These errors are greater during the wet seasons when the metrics reach worse scores than those reported in similar studies worldwide. Occurrence analyses showed that 3B43 V7 misses very frequent light rainfall events and less frequent but very heavy storms, which contribute to the overall underestimation (overestimation) observed over the Pacific (Andes) region. The error characteristics identified and quantified in this study confirm the well-documented limitations of remote precipitation sensing and constitute a warning about major challenges that complex climatic and physiographic features can impose on satellite rainfall missions.

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Linking Long-Term Water Balances and Statistical Scaling to Estimate River Flows along the Drainage Network of Colombia

Abstract: Colombia using regionalization and scaling concepts with the long-term flow field as the scaling variable. All data sets, methods, and results are included in HidroSIG, an available interactive hydrologic atlas of Colombia.

Cited by 57 publications

References 55 publications

Improved long‐term mean annual rainfall fields for Colombia

Improved long‐term mean annual rainfall fields for Colombia

Seasonal precipitation patterns along pathways of South American low-level jets and aerial rivers

Ground validation of TRMM 3B43 V7 precipitation estimates over Colombia. Part I: Monthly and seasonal timescales

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