Unraveling the impacts of droughts and agricultural intensification on the Altiplano water resources

Satgé, Frederic; Hussain, Yawar; Xavier, Alvaro; Zolá, Ramiro Pillco; Salles, Leandro de Almeida; Timouk, Franck; Seyler, Frédérique; Garnier, Jérémie; Frappart, Frédéric; Bonnet, Marie‐Paule

doi:10.1016/j.agrformet.2019.107710

Cited by 34 publications

(25 citation statements)

References 86 publications

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“…These datasets differ in terms of spatial coverage, spatial resolution and forcing data. The GLEAM v.3.a dataset was previously validated in the Altiplano (Satgé et al ., 2019b) and was therefore selected for this study. GLEAM v.3.a uses the Priestley and Taylor equation to calculate potential evapotranspiration (ETp) using air temperature and net surface radiation from the ERA‐Interim reanalysis dataset.…”

Section: Methodsmentioning

confidence: 99%

“…IMERG (‐Early, ‐Late and ‐Final), MSWEP v.2.2 and CHIRPS v.2 P‐datasets for a common period of data availability were used to assess the potential improvement of SM2RAIN P‐datasets over the most efficient and currently available SPPs over the considered regions (Satgé et al ., 2017b; Satgé et al ., 2019a; Satgé et al ., 2019b). It is also an opportunity to assess the potential benefits of the ‘bottom‐up’ (SM2RAIN‐CCI and ‐ASCAT) compared to the ‘top‐down’ approach using gauge‐based (CHIRPS, MSWEP and IMERG‐Final) or satellite‐only (IMERG‐Early and ‐Late) products.…”

Section: Introductionmentioning

confidence: 99%

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Reliability of SM2RAIN precipitation datasets in comparison to gauge observations and hydrological modelling over arid regions

Satgé

Hussain

Molina‐Carpio

et al. 2020

Intl Journal of Climatology

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Numerous satellite-based precipitation datasets have been successively made available. Their precipitation estimates rely on clouds properties derived from microwave and thermal sensors in a so-named 'top-down' approach. Recently, a 'bottom-up' approach to infer precipitation from soil moisture (SM) estimates has resulted in the release of two new precipitation datasets (P-datasets). One uses satellite-based SM estimates from the European Spatial Agency (ESA) Climate Change Initiative (CCI) (SM2RAIN-CCI) while the other uses satellitebased SM from European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Advanced SCATterometer (ASCAT) (SM2RAIN-ASCAT). This study assesses SM2RAIN-ASCAT and-CCI reliability over two arid regions: Bolivian and Peruvian Altiplano and Pakistan (South Asia) using (a) direct comparisons with rain gauges and (b) testing the sensitivity of streamflow modelling to the P-datasets. Selecting two different regions and different indicators helps to assess whether the P-dataset reliability varies depending on the assessment method and location. For comparison purposes, the most reliable P-datasets from the literature are also considered (IMERG-E v.6, IMERG-L v.6, IMERG-F v.6, CHIRPS v.2 and MSWEP v.2.2). Compared to rain gauge observations and based on the modified Kling-Gupta Efficiency (KGE) values, the SM2RAIN-ASCAT and-CCI are more accurate in the Altiplano than in Pakistan. This difference is explained by a more favourable physical context for satellite-based SM estimates in the Altiplano. Over the Altiplano and despite an overall positive bias, SM2RAIN-ASCAT describes rain gauges temporal dynamics as well as IMERG-F v.6, CHIRPS v.2 and MSWEP v.2.2 and provides streamflow simulations very close to those obtained when using IMERG-F v.6, CHIRPS v.2 and MSWEP v.2.2 as forcing data.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Reliability of SM2RAIN precipitation datasets in comparison to gauge observations and hydrological modelling over arid regions

Satgé

Hussain

Molina‐Carpio

et al. 2020

Intl Journal of Climatology

Self Cite

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“…For centuries, mining has been one of the most important economic activities in the Bolivian Andean region, severely impacting water resources [ 21 , 22 ]. This region also faces water scarcity due to the adverse effects of climate change on glaciers [ 23 ]. Besides, urban wastewater is directly released into the environment, polluting the water with enteric pathogens, antibiotics, and antibiotic-resistant bacteria [ 24 , 25 , 26 ].…”

Section: Introductionmentioning

confidence: 99%

Fecal Pollution Drives Antibiotic Resistance and Class 1 Integron Abundance in Aquatic Environments of the Bolivian Andes Impacted by Mining and Wastewater

et al. 2020

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An increased abundance of antibiotic resistance genes (ARGs) in aquatic environments has been linked to environmental pollution. Mining polluted sites with high concentration of metals could favor the in situ coselection of ARGs, whereas wastewater discharges release fecal antibiotic resistant bacteria in the environment. To study the effect of human fecal contamination and mining pollution, water and sediment samples affected by mining activities and sewage discharges were collected from three lakes in Bolivia, the pristine Andean lake Pata Khota, the Milluni Chico lake directly impacted by acid mine drainage, and the Uru-Uru lake located close to Oruro city and highly polluted by mining activities and human wastewater discharges. Physicochemical parameters, including metal composition, were analyzed in water and sediment samples. ARGs were screened for and verified by quantitative polymerase chain reaction (PCR) together with the mobile element class 1 integron (intl1), as well as crAssphage, a marker of human fecal pollution. The gene intl1 was positively correlated with sul1, sul2, tetA, and blaOXA-2. CrAssphage was only detected in the Uru-Uru lake, and its tributaries and significantly higher abundance of ARGs were found in these sites. Multivariate analysis showed that crAssphage abundance, electrical conductivity, and pH were positively correlated with higher levels of intl1 and ARGs. Taken together, our results suggest that fecal pollution is the major driver of higher levels of ARGs and intl1 in environments contaminated by wastewater and mining activities.

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“…As a result, the spatiotemporal trends of precipitation and temperature greatly affect environmental processes, control natural hazards such as floods and droughts, and influence water quality and availability (Alidoost et al, 2019;Ribeiro et al, 2019). Therefore, future socio-economic development and sustainable use of water resources is highly dependent on reliable representations and modeling of these two variables (Alexandrov and Hoogenboom, 2000;Phiri et al, 2019;Satgé et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Copulas for hydroclimatic applications – A practical note on common misconceptions and pitfalls

Tootoonchi

Haerter

Räty

et al. 2020

Preprint

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Abstract. For most hydroclimatic applications, precipitation and temperature are of particular interest as they strongly affect the water cycle, can easily be measured and are often readily available from many meteorological stations worldwide. To account for precipitation and temperature variability, their co-dependence and their correlation, several multivariate analysis methods have been adopted in the hydroclimatic literature in recent years. In line with the steadily rising number of publications on this topic, the notion of copula-based probability distribution has also attracted tremendous interest to deal with the complexity of compound events in the multidimensional context. A copula is a function that connects a multivariate distribution to its one-dimensional margins, which allows for a joint distribution of random variables with great flexibility for the marginal distribution. However, there seems to be a lack of comprehensive understanding of the fundamental requirements of the copula concept such as the strength and significance of correlation between variables, autocorrelation effects and the choice of representative copula families, which potentially compromises the robustness of projections of future environmental processes and natural hazards. Therefore, by combining a systematic literature review with a specific hydroclimatic case study in Sweden, we illustrate a practical approach to copula-based modeling, which (1) provides end-users with an overview of necessary requirements, statistical assumptions and consequential limitations of copulas, (2) highlights possible pitfalls and misconceptions, and (3) offers a decision support framework for the application of copulas to support researchers and decision makers in addressing climatological hazards and sustainable development, thereby demystifying what is currently an area of great confusion.

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Unraveling the impacts of droughts and agricultural intensification on the Altiplano water resources

Cited by 34 publications

References 86 publications

Reliability of SM2RAIN precipitation datasets in comparison to gauge observations and hydrological modelling over arid regions

Reliability of SM2RAIN precipitation datasets in comparison to gauge observations and hydrological modelling over arid regions

Fecal Pollution Drives Antibiotic Resistance and Class 1 Integron Abundance in Aquatic Environments of the Bolivian Andes Impacted by Mining and Wastewater

Copulas for hydroclimatic applications – A practical note on common misconceptions and pitfalls

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