The potential of using satellite-related precipitation data sources in arid regions

ABSTRACT Accurate estimates of precipitation amounts are necessary to evaluate river flows, assess water-related risks (floods and drought) and quantify water availability for a broad range of water uses, such as water supply, agriculture, navigation and energy production. Especially in the context of operations in the Brazilian electricity sector, where the electrical system is essentially hydrothermal and more than 65% of its production comes from hydroelectric generation, real-time observed precipitation plays a key role as a primary input for hydrological models and river flow forecasting. It is thus crucial to build knowledge on and quantify river basin precipitation and its uncertainties. In this paper, we evaluate two sources of real-time (or near real-time) precipitation data, the TRMM-MERGE dataset from the CPETC and the CPC dataset, distributed by NOAA. Our assessment is based on 41 river basins in South America and covers the period 1997-2017. We investigated differences for different time resolutions (daily, monthly and annual precipitation) and their impact on the simulation of streamflows. Substantial differences were found between the two data sources, which seem to be amplified in the second decade. A spatial trend was found towards higher TRMM-MERGE precipitation values than CPC values when moving from north and west in the study area. We also found evidence that differences in precipitation propagate to simulated flows, with large percent differences in precipitation resulting in even larger percent differences in streamflow.

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The CAMELS-CL dataset: catchment attributes and meteorology for large sample studies – Chile dataset

Hydrology and Earth System Sciences ◽

10.5194/hess-22-5817-2018 ◽

2018 ◽

Vol 22 (11) ◽

pp. 5817-5846 ◽

Cited By ~ 47

Author(s):

Camila Alvarez-Garreton ◽

Pablo A. Mendoza ◽

Juan Pablo Boisier ◽

Nans Addor ◽

Mauricio Galleguillos ◽

...

Keyword(s):

Time Series ◽

Arid Regions ◽

Snow Water Equivalent ◽

Potential Evapotranspiration ◽

Data Sources ◽

Large Sample ◽

Daily Streamflow ◽

Multiple Data ◽

Diverse Data ◽

High Elevations

Abstract. We introduce the first catchment dataset for large sample studies in Chile. This dataset includes 516 catchments; it covers particularly wide latitude (17.8 to 55.0∘ S) and elevation (0 to 6993 m a.s.l.) ranges, and it relies on multiple data sources (including ground data, remote-sensed products and reanalyses) to characterise the hydroclimatic conditions and landscape of a region where in situ measurements are scarce. For each catchment, the dataset provides boundaries, daily streamflow records and basin-averaged daily time series of precipitation (from one national and three global datasets), maximum, minimum and mean temperatures, potential evapotranspiration (PET; from two datasets), and snow water equivalent. We calculated hydro-climatological indices using these time series, and leveraged diverse data sources to extract topographic, geological and land cover features. Relying on publicly available reservoirs and water rights data for the country, we estimated the degree of anthropic intervention within the catchments. To facilitate the use of this dataset and promote common standards in large sample studies, we computed most catchment attributes introduced by Addor et al. (2017) in their Catchment Attributes and MEteorology for Large-sample Studies (CAMELS) dataset, and added several others. We used the dataset presented here (named CAMELS-CL) to characterise regional variations in hydroclimatic conditions over Chile and to explore how basin behaviour is influenced by catchment attributes and water extractions. Further, CAMELS-CL enabled us to analyse biases and uncertainties in basin-wide precipitation and PET. The characterisation of catchment water balances revealed large discrepancies between precipitation products in arid regions and a systematic precipitation underestimation in headwater mountain catchments (high elevations and steep slopes) over humid regions. We evaluated PET products based on ground data and found a fairly good performance of both products in humid regions (r>0.91) and lower correlation (r<0.76) in hyper-arid regions. Further, the satellite-based PET showed a consistent overestimation of observation-based PET. Finally, we explored local anomalies in catchment response by analysing the relationship between hydrological signatures and an attribute characterising the level of anthropic interventions. We showed that larger anthropic interventions are correlated with lower than normal annual flows, runoff ratios, elasticity of runoff with respect to precipitation, and flashiness of runoff, especially in arid catchments. CAMELS-CL provides unprecedented information on catchments in a region largely underrepresented in large sample studies. This effort is part of an international initiative to create multi-national large sample datasets freely available for the community. CAMELS-CL can be visualised from http://camels.cr2.cl and downloaded from https://doi.pangaea.de/10.1594/PANGAEA.894885.

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Impacts of different precipitation data sources on water budgets

Journal of Hydrology ◽

10.1016/j.jhydrol.2003.08.020 ◽

2004 ◽

Vol 298 (1-4) ◽

pp. 311-334 ◽

Cited By ~ 32

Author(s):

Jianzhong Guo ◽

Xu Liang ◽

L. Ruby Leung

Keyword(s):

Data Sources ◽

Precipitation Data ◽

Water Budgets

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Suitability of different precipitation data sources for hydrological analysis: a study from Western Ghats, India

Environmental Monitoring and Assessment ◽

10.1007/s10661-021-09745-0 ◽

2022 ◽

Vol 194 (2) ◽

Author(s):

Beeram Satya Narayana Reddy ◽

Shahanas P. V. ◽

S. K. Pramada

Keyword(s):

Western Ghats ◽

Data Sources ◽

Precipitation Data

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Evaluation of Surface and Radar-Estimated Precipitation Data Sources Over the Lower Mississippi River Alluvial Plain

Physical Geography ◽

10.2747/0272-3646.30.5.430 ◽

2009 ◽

Vol 30 (5) ◽

pp. 430-452 ◽

Cited By ~ 10

Author(s):

Jamie Dyer

Keyword(s):

Mississippi River ◽

Alluvial Plain ◽

Data Sources ◽

Precipitation Data ◽

Lower Mississippi River

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Usability of the BLRP model for hydrological applications in arid and semi-arid regions with limited precipitation data

Modeling Earth Systems and Environment ◽

10.1007/s40808-017-0312-1 ◽

2017 ◽

Vol 3 (2) ◽

pp. 539-555 ◽

Cited By ~ 4

Author(s):

Esmaeel Dodangeh ◽

Kaka Shahedi ◽

Karim Solaimani ◽

Panagiotis Kossieris

Keyword(s):

Arid Regions ◽

Precipitation Data ◽

Semi Arid ◽

Hydrological Applications

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Inter-Comparison of Rain-Gauge, Radar, and Satellite (IMERG GPM) Precipitation Estimates Performance for Rainfall-Runoff Modeling in a Mountainous Catchment in Poland

Water ◽

10.3390/w10111665 ◽

2018 ◽

Vol 10 (11) ◽

pp. 1665 ◽

Cited By ~ 14

Author(s):

Paweł Gilewski ◽

Marek Nawalany

Keyword(s):

Rain Gauge ◽

Data Sources ◽

Efficiency Coefficient ◽

Precipitation Data ◽

Rainfall Runoff ◽

Satellite Precipitation ◽

Mountainous Catchment ◽

Runoff Modeling ◽

Precipitation Estimates ◽

Event Based

Precipitation is one of the essential variables in rainfall-runoff modeling. For hydrological purposes, the most commonly used data sources of precipitation are rain gauges and weather radars. Recently, multi-satellite precipitation estimates have gained importance thanks to the emergence of Integrated Multisatellite Retrievals for Global Precipitation Measurement (IMERG GPM), a successor of a very successful Tropical Rainfall Measuring Mission (TRMM) mission which has been providing high-quality precipitation estimates for almost two decades. Hydrological modeling of mountainous catchment requires reliable precipitation inputs in both time and space as the hydrological response of such a catchment is very quick. This paper presents an inter-comparison of event-based rainfall-runoff simulations using precipitation data originating from three different sources. For semi-distributed modeling of discharge in the mountainous river, the Hydrologic Engineering Center-Hydrologic Modelling System (HEC-HMS) is applied. The model was calibrated and validated for the period 2014–2016 using measurement data from the Upper Skawa catchment a small mountainous catchment in southern Poland. The performance of the model was assessed using the Nash–Sutcliffe efficiency coefficient (NSE), Pearson’s correlation coefficient (r), Percent bias (PBias) and Relative peak flow difference (rPFD). The results show that for the event-based modeling adjusted radar rainfall estimates and IMERG GPM satellite precipitation estimates are the most reliable precipitation data sources. For each source of the precipitation data the model was calibrated separately as the spatial and temporal distributions of rainfall significantly impact the estimated values of model parameters. It has been found that the applied Soil Conservation Service (SCS) Curve Number loss method performs best for flood events having a unimodal time distribution. The analysis of the simulation time-steps indicates that time aggregation of precipitation data from 1 to 2 h (not exceeding the response time of the catchment) provide a significant improvement of flow simulation results for all the models while further aggregation, up to 4 h, seems to be valuable only for model based on rain gauge precipitation data.

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Evaluation of Gridded Precipitation Datasets over Arid Regions of Pakistan

Water ◽

10.3390/w11020210 ◽

2019 ◽

Vol 11 (2) ◽

pp. 210 ◽

Cited By ~ 33

Author(s):

Kamal Ahmed ◽

Shamsuddin Shahid ◽

Xiaojun Wang ◽

Nadeem Nawaz ◽

Khan Najeebullah

Keyword(s):

Temporal Variability ◽

Arid Regions ◽

Research University ◽

Distribution Patterns ◽

Absolute Error ◽

Research Unit ◽

Climatic Research Unit ◽

Mean Bias Error ◽

Bias Error ◽

Precipitation Data

The rough topography, harsh climate, and sparse monitoring stations have limited hydro-climatological studies in arid regions of Pakistan. Gauge-based gridded precipitation datasets provide an opportunity to assess the climate where stations are sparsely located. Though, the reliability of these datasets heavily depends on their ability to replicate the observed temporal variability and distribution patterns. Conventional correlation or error analyses are often not enough to justify the variability and distribution of precipitation. In the present study, mean bias error, mean absolute error, modified index of agreement, and Anderson–Darling test have been used to evaluate the performance of four widely used gauge-based gridded precipitation data products, namely, Global Precipitation Climatology Centre (GPCC), Climatic Research Unit (CRU); Asian Precipitation Highly Resolved Observational Data Integration towards Evaluation (APHRODITE), Center for Climatic Research—University of Delaware (UDel) at stations located in semi-arid, arid, and hyper-arid regions in the Balochistan province of Pakistan. The result revealed that the performance of different products varies with climate. However, GPCC precipitation data was found to perform much better in all climatic regions in terms of most of the statistical assessments conducted. As the temporal variability and distribution of precipitation are very important in many hydrological and climatic applications, it can be expected that the methods used in this study can be useful for the better assessment of gauge-based data for various applications.

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