The Role of Satellite-Based Remote Sensing in Improving Simulated Streamflow: A Review

Jiang, Dejuan; Wang, Kun

doi:10.3390/w11081615

Cited by 97 publications

(53 citation statements)

References 200 publications

(382 reference statements)

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“…In China, most studies analyzing the climate change impact on extreme floods have focused on several major rivers in the southern and southeastern parts of the country [23]. Recently, the annual and seasonal changes of extreme hydrological events in inland rivers of northwestern China have attracted increasing attention as well.…”

Section: Introductionmentioning

confidence: 99%

Climate Change Impacts on Extreme Flows Under IPCC RCP Scenarios in the Mountainous Kaidu Watershed, Tarim River Basin

Huang

et al. 2020

Sustainability

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In the 21st century, heavier rainfall events and warmer temperatures in mountainous regions have significant impacts on hydrological processes and the occurrence of flood/drought extremes. Long-term modeling and peak flow detection of streamflow series are crucial in understanding the behavior of flood and drought. This study was conducted to analyze the impacts of future climate change on extreme flows in the Kaidu River Basin, northwestern China. The soil water assessment tool (SWAT) was used for hydrological modeling. The projected future precipitation and temperature under Intergovernmental Panel on Climate Change (IPCC) representative concentration pathway (RCP) scenarios were downscaled and used to drive the validated SWAT model. A generalized extreme value (GEV) distribution was employed to assess the probability distribution of flood events. The modeling results showed that the simulated discharge well matched the observed ones both in the calibration and validation periods. Comparing with the historical period, the ensemble with 15 general circulation models (GCMs) showed that the annual precipitation will increase by 7.9-16.1% in the future, and extreme precipitation events will increase in winter months. Future temperature will increase from 0.42 • C/10 a to 0.70 • C/10 a. However, with respect to the hydrological response to climate change, annual mean runoff will decrease by 21.5-40.0% under the mean conditions of the four RCP scenarios. A reduction in streamflow will occur in winter, while significantly increased discharge will occur from April to May. In addition, designed floods for return periods of five, 10 and 20 years in the future, as predicted by the GEV distribution, will decrease by 3-20% over the entire Kaidu watershed compared to those in the historical period. The results will be used to help local water resource management with hazard warning and flood control.

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Section: Introductionmentioning

confidence: 99%

Climate Change Impacts on Extreme Flows Under IPCC RCP Scenarios in the Mountainous Kaidu Watershed, Tarim River Basin

Huang

et al. 2020

Sustainability

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“…Elevation-dependent biases are widely reported in the literature with larger biases in GPMderived estimates at elevations exceeding 4000 m [106]. However, the current work shows that for narrow ranges between 0 to 1800 m in the UAE, the GPM IMERG daily product performs better over the northeastern highlands.…”

Section: Discussionmentioning

confidence: 47%

“…The overland ANN-based correction framework proposed here can be used to generate more reliable inputs for hydrological studies over ungauged areas across the UAE. These include hydrological assessments from the catchment-scale and beyond (e.g., the macro-and regional-scale) [106]. While the developed ANN configuration is set up locally for the UAE, the methodology followed is applicable to other arid and hyper-arid regions requiring improved precipitation monitoring.…”

Section: Discussionmentioning

confidence: 99%

Enhancing Precipitation Estimates Through the Fusion of Weather Radar, Satellite Retrievals, and Surface Parameters

2020

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Accurate and timely monitoring of precipitation remains a challenge, particularly in hyper-arid regions such as the United Arab Emirates (UAE). The aim of this study is to improve the accuracy of the Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (GPM) mission’s latest product release (IMERG V06B) locally over the UAE. Two distinct approaches, namely, geographically weighted regression (GWR), and artificial neural networks (ANNs) are tested. Daily soil moisture retrievals from the Soil Moisture Active Passive (SMAP) mission (9 km), terrain elevations from the Advanced Spaceborne Thermal Emission and Reflection digital elevation model (ASTER DEM, 30 m) and precipitation estimates (0.5 km) from a weather radar network are incorporated as explanatory variables in the proposed GWR and ANN model frameworks. First, the performances of the daily GPM and weather radar estimates are assessed using a network of 65 rain gauges from 1 January 2015 to 31 December 2018. Next, the GWR and ANN models are developed with 52 gauges used for training and 13 gauges reserved for model testing and seasonal inter-comparisons. GPM estimates record higher Pearson correlation coefficients (PCC) at rain gauges with increasing elevation (z) and higher rainfall amounts (PCC = 0.29 z0.12), while weather radar estimates perform better for lower elevations and light rain conditions (PCC = 0.81 z−0.18). Taylor diagrams indicate that both the GWR- and the ANN-adjusted precipitation products outperform the original GPM and radar estimates, with the poorest correction obtained by GWR during the summer period. The incorporation of soil moisture resulted in improved corrections by the ANN model compared to the GWR, with relative increases in Nash–Sutcliffe efficiency (NSE) coefficients of 56% (and 25%) for GPM estimates, and 34% (and 53%) for radar estimates during summer (and winter) periods. The ANN-derived precipitation estimates can be used to force hydrological models over ungauged areas across the UAE. The methodology is expandable to other arid and hyper-arid regions requiring improved precipitation monitoring.

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“…In this context, it is not trivial to draw general guidelines about which SRPs should be favoured or which error metric(s) should be used to identify the best performing rainfall product for flood forecasting (Qi et al, 2016;Hossain and Huffman, 2008). The only largely accepted suggestion is about SRP bias, recognized as a major issue for a reliable flood forecast across several basins around the world (Maggioni et al, 2013;Thiemig et al 2013;Shah and Mishra 2016;Jiang and Wang, 2019). Based on that, bias correction methods have shown to significantly reduce streamflow errors (e. g, Yilmaz et al 2005;Bitew et al, 2012;Valdes-Pined et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Which rainfall metric is more informative about the flood simulation performance? A comprehensive assessment on 1318 basins over Europe

Camici¹,

Massari²,

Ciabatta³

et al. 2020

Preprint

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Abstract. The global availability of satellite rainfall products (SRPs) at an increasingly high temporal/spatial resolution has made possible their exploitation in hydrological applications, especially over in-situ data scarce regions. In this context, understand how uncertainties transfer from SRPs into flood simulation, through the hydrological model, is a main research question. SRPs accuracy is normally characterized by comparing them with ground observations via the calculation of categorical (e.g., threat score, false alarm ratio, probability of detection) and/or continuous (e.g., bias, root mean square error, Nash-Sutcliffe index, Kling-Gupta efficiency index, correlation coefficient) metrics. However, whether these metrics are informative about the associated performance in flood simulations (when the SRP is used as input to an hydrological model) is an underdiscussed research topic. This study aims to relate the accuracy of different SRPs both in terms of rainfall and in terms of flood simulation. That is, the following research question are addressed: is (are) there appropriate performance metric (s) to drive the choice of the best performing rainfall product for flood simulation? To answer this question three SRPs, namely the Tropical Rainfall Measurement Mission Multi-satellite Precipitation Analysis, TMPA; the Climate Prediction Center Morphing algorithm, CMORPH, and the SM2RAIN algorithm applied to the ASCAT (Advanced SCATterometer) soil moisture product, SM2RAIN-ASCAT, have been used as input into a lumped hydrologic model (MISDc, “Modello Idrologico Semi-Distribuito in continuo”) on 1318 basins over Europe with different physiographic characteristics. Results have suggested that, among the continuous metrics, correlation coefficient and Kling-Gupta efficiency index are not reliable scores to select rainfall product performing best for hydrological modelling whereas bias and root mean square error seem more appropriate. In particular, by constraining the relative bias to values lower than 0.2 and the relative root mean square error to values lower than 2, good hydrological performances (Kling-Gupta efficiency index on discharge greater than 0.5) are ensured for almost 75 % of the basins fulfilling these criteria. Conversely, the categorical scores have not provided suitable information to address the SRPs selection for hydrological modelling.

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The Role of Satellite-Based Remote Sensing in Improving Simulated Streamflow: A Review

Cited by 97 publications

References 200 publications

Climate Change Impacts on Extreme Flows Under IPCC RCP Scenarios in the Mountainous Kaidu Watershed, Tarim River Basin

Climate Change Impacts on Extreme Flows Under IPCC RCP Scenarios in the Mountainous Kaidu Watershed, Tarim River Basin

Enhancing Precipitation Estimates Through the Fusion of Weather Radar, Satellite Retrievals, and Surface Parameters

Which rainfall metric is more informative about the flood simulation performance? A comprehensive assessment on 1318 basins over Europe

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