Assessing rainfall global products reliability for water resource management in a tropical volcanic mountainous catchment

Dumont, Marc; Saadi, Mohamed; Oudin, Ludovic; Lachassagne, Patrick; Nugraha, Bayu; Fadillah, Arif; Bonjour, J.-L.; Muhammad, A.; Hendarmawan, Hendarmawan; Dörfliger, Nathalie; Plagnes, Valérie

doi:10.1016/j.ejrh.2022.101037

Cited by 14 publications

(11 citation statements)

References 47 publications

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“…This limitation can partly explain the difference between the trends derived from the gauges and GPP's over space and time. However, in a recent evaluation of the three GPP's over mountainous terrain in Java, the TC emerged as the most accurate and reliable in representing the temporal dynamics of the precipitation compared to the CHIRPS-v2 and CHELSA-v2.1 (Dumont et al, 2022). Similarly, our results suggest remarkable improvements in TC's statistical downscaling.…”

Section: Trend Analysis Of Climatic Variablessupporting

confidence: 51%

Assessing the skill of gridded satellite and reanalysis precipitation products over in East and Southern Africa

Muthoni¹,

Kigosi²

2023

Atm.

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Validation of gridded precipitation products (GPP’s) increase confidence of the users and highlights possible improvements of the algorithms to handle complex rainfall forming processes. We evaluated the skill of three gridded precipitation products (GPP’s) in estimating the gauge observations and compared the precipitation trends derived from these products across the east and southern Africa region. Taylor diagrams and Kling-Gupta Efficiency (KGE) was used to assess the accuracy. A modified Mann-Kendal test and the Sen’s slope estimator were utilized to determine the significance and the magnitude of the trends respectively. The three GPP’s had varied performance over temporal and altitudinal ranges. The skill of the three GPP’s at monthly scale, was generally high but showed lower performance at elevations over 1500 m especially during the OND season. The three GPP’s performed equally well between 1001 – 1500 m elevation range. CHELSA-v2.1 was most accurate at 0-500m but had the lowest skill at 501 – 1000 m and above 1500 m elevations that caused over-estimation of the annual and seasonal precipitation trends over mountainous terrain and over large inland waterbodies. The quantified precipitation trends revealed high spatial-temporal variability. Generally, the skill and precipitation trends derived from CHIRPS-v2 and TC data showed substantial convergence except in Tanzania. Our results emphasize the importance of validation of climate datasets to avoid error propagation in different models and applications. Our results demonstrates that new or higher resolution precipitation data is not always the most accurate since update of the algorithms can introduce artifacts or biases.

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Section: Trend Analysis Of Climatic Variablessupporting

confidence: 51%

Assessing the skill of gridded satellite and reanalysis precipitation products over in East and Southern Africa

Muthoni¹,

Kigosi²

2023

Atm.

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“…Where n is the daily timestep and PET is the potential evapotranspiration estimated using the Penman-Monteith equations [Monteith, 1965], which explicitly accounts for climatic and vegetation characteristics: Weather data used for the water cycle simulation come from the Stamet Citeko station of BMKG network (Badan Meteorologi, Klimatologi, dan Geofisika-http://www.bmkg.go.id) located at 14 km north of the watershed. While the station is not within the watershed, the rainfall analysis of Dumont et al [2022] demonstrated the representativity and accuracy of this weather station monitored from 2000 to 2021 for the studied area (Figure 2). For the estimation of potential evapotranspiration, the Stamet Citeko station is the only station providing long-term climate data (temperature, radiation, wind, sun exposure time per day and relative air humidity) without long gaps.…”

Section: Lumped Model Structurementioning

confidence: 89%

“…The study site is located in Java Island characterized by a tropical climate. The year is divided in two seasons: the wet season characterized by strong moonson rainfall (October-March); the dry season with less or no rainfall at all [April-September;Dumont et al, 2022]. The annual rainfall varies from 2 to 5 m/year, while the daily average temperature ranges from 18 to 25 °C.…”

Section: Hydrology and Geochemistrymentioning

confidence: 99%

Water cycle modelling strengthened by probabilistic integration of field data for groundwater management of a quite unknown tropical volcanic hydrosystem

Dumont¹,

Plagnes²,

Lachassagne³

et al. 2024

Comptes Rendus. Géoscience

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Andesitic volcanic hydrosystems in Indonesia are mostly hydrogeologically unknown despite their socio-economic importance. The development of robust and easy-to-implement methodologies to conceptualize and quantify the water cycle components becomes a prerequisite for their sustainable management.We developed a lumped hydrological model to mimic the structure and functioning of a previously unknown hydrosystem located on the flanks of the Salak volcano (West Java). The structure of the aquifers was revealed with electrical resistivity tomography. The distinction between springs fed by the extensive artesian aquifer and others fed by shallow perched aquifers was obtained mostly using hydrochemistry. The elevation of the recharge area was identified using isotopic analysis of spring water.After designing the hydrological model structure, we carried out a probabilistic parameters exploration using the multiple-try differential evolution adaptive Metropolis algorithm to calibrate aquifer discharge. Multiple Markov chains allow a better exploration of the parameter values. The Bayesian approach provides the best water cycle simulation with a parameter uncertainty analysis, improving the accuracy and representation of the water cycle appropriate for previously unknown hydrosystems.

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“…Rain gauges are often used as a reference source of quantitative precipitation estimates (QPEs; Boushaki et al, 2009;Derin et al, 2019;Dumont et al, 2022;Schleiss et al, 2020). However, they are sparse and may miss the spatial variability in precipitation, especially of convective precipitation fields that can generate extreme flooding events in high-elevation, complex terrain configurations (Anquetin et al, 2005;Emmanuel et al, 2017;Sokol et al, 2021;Tetzlaff and Uhlenbrook, 2005).…”

Section: Precipitation Estimates and Hydrological Modeling Approachesmentioning

confidence: 99%

How uncertain are precipitation and peak flow estimates for the July 2021 flooding event?

Saadi

Furusho

Belleflamme³

et al. 2023

Nat. Hazards Earth Syst. Sci.

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Abstract. The disastrous July 2021 flooding event made us question the ability of current hydrometeorological tools in providing timely and reliable flood forecasts for unprecedented events. This is an urgent concern since extreme events are increasing due to global warming, and existing methods are usually limited to more frequently observed events with the usual flood generation processes. For the July 2021 event, we simulated the hourly streamflows of seven catchments located in western Germany by combining seven partly polarimetric, radar-based quantitative precipitation estimates (QPEs) with two hydrological models: a conceptual lumped model (GR4H) and a physically based, 3D distributed model (ParFlowCLM). GR4H parameters were calibrated with an emphasis on high flows using historical discharge observations, whereas ParFlowCLM parameters were estimated based on landscape and soil properties. The key results are as follows. (1) With no correction of the vertical profiles of radar variables, radar-based QPE products underestimated the total precipitation depth relative to rain gauges due to intense collision–coalescence processes near the surface, i.e., below the height levels monitored by the radars. (2) Correcting the vertical profiles of radar variables led to substantial improvements. (3) The probability of exceeding the highest measured peak flow before July 2021 was highly impacted by the QPE product, and this impact depended on the catchment for both models. (4) The estimation of model parameters had a larger impact than the choice of QPE product, but simulated peak flows of ParFlowCLM agreed with those of GR4H for five of the seven catchments. This study highlights the need for the correction of vertical profiles of reflectivity and other polarimetric variables near the surface to improve radar-based QPEs for extreme flooding events. It also underlines the large uncertainty in peak flow estimates due to model parameter estimation.

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Assessing rainfall global products reliability for water resource management in a tropical volcanic mountainous catchment

Cited by 14 publications

References 47 publications

Assessing the skill of gridded satellite and reanalysis precipitation products over in East and Southern Africa

Assessing the skill of gridded satellite and reanalysis precipitation products over in East and Southern Africa

Water cycle modelling strengthened by probabilistic integration of field data for groundwater management of a quite unknown tropical volcanic hydrosystem

How uncertain are precipitation and peak flow estimates for the July 2021 flooding event?

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