Evaluation of Gridded Multi-Satellite Precipitation (TRMM-3B42-V7) Estimation Performance in the Upper Indus Basin (UIB)

The present study aims to evaluate the capability of the TRMM-3B42-(V7) precipitation product to estimate appropriate precipitation rates in the Upper Indus basin (UIB) and the analysis of the dependency of the estimates’ accuracies on the time scale. To that avail statistical analyses and comparison of the TMPA- products with gauge measurements in the UIB are carried out. The dependency of the TMPA estimates’ quality on the time scale is analysed by comparisons of daily, monthly, seasonal and annual sums for the UIB. The results show considerable biases in the TMPA- (TRMM) precipitation estimates for the UIB, as well as high false alarms and miss ratios. The correlation of the TMPA- estimates with ground-based gauge data increases considerably and almost in a linear fashion with increasing temporal aggregation, i.e. time scale. The BIAS is mostly positive for the summer season, while for the winter season it is predominantly negative, thereby showing a slight over-estimation of the precipitation in summer and under-estimation in winter. The results of the study suggest that, in spite of these discrepancies between TMPA- estimates and gauge data, the use of the former in hydrological watershed modelling, endeavoured presently by the authors, may be a valuable alternative in data- scarce regions, like the UIB, but still must be taken with a grain of salt.

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Evaluation of Gridded Multi-Satellite Precipitation Estimation (TRMM-3B42-V7) Performance in the Upper Indus Basin (UIB)

Climate ◽

10.3390/cli6030076 ◽

2018 ◽

Vol 6 (3) ◽

pp. 76 ◽

Cited By ~ 8

Author(s):

Asim Khan ◽

Manfred Koch ◽

Karen Chinchilla

Keyword(s):

Time Scale ◽

Winter Season ◽

Indus Basin ◽

Tropical Rainfall Measurement Mission ◽

Satellite Precipitation ◽

Tropical Rainfall ◽

Upper Indus Basin ◽

Gauge Data ◽

Precipitation Analysis ◽

Trmm 3B42

The present study aims to evaluate the capability of the Tropical Rainfall Measurement Mission (TRMM), Multi-satellite Precipitation Analysis (TMPA), version 7 (TRMM-3B42-V7) precipitation product to estimate appropriate precipitation rates in the Upper Indus Basin (UIB) by analyzing the dependency of the estimates’ accuracies on the time scale. To that avail, various statistical analyses and comparison of Multisatellite Precipitation Analysis (TMPA) products with gauge measurements in the UIB are carried out. The dependency of the TMPA estimates’ quality on the aggregation time scale is analyzed by comparisons of daily, monthly, seasonal and annual sums for the UIB. The results show considerable biases in the TMPA Tropical Rainfall Measurement Mission (TRMM) precipitation estimates for the UIB, as well as high numbers of false alarms and miss ratios. The correlation of the TMPA estimates with ground-based gauge data increases considerably and almost in a linear fashion with increasing temporal aggregation, i.e., time scale. There is a predominant trend of underestimation of the TRMM product across the UIB at most of the gauge stations, i.e., TRMM-estimated rainfall is generally lower than the gauge-measured rainfall. For the seasonal aggregates, the bias is mostly positive for the summer but predominantly negative for the winter season, thereby showing a slight overestimation of the precipitation in summer and underestimation in winter. The results of the study suggest that, in spite of these discrepancies between TMPA estimates and gauge data, the use of the former in hydrological watershed modeling undertaken by the authors may be a valuable alternative in data-scarce regions like the UIB, but still must be taken with a grain of salt.

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Prevailing climatic trends and runoff response from Hindukush–Karakoram–Himalaya, upper Indus basin

Earth System Dynamics Discussions ◽

10.5194/esdd-6-579-2015 ◽

2015 ◽

Vol 6 (1) ◽

pp. 579-653 ◽

Cited By ~ 17

Author(s):

S. Hasson ◽

J. Böhner ◽

V. Lucarini

Keyword(s):

High Altitude ◽

Water Availability ◽

Winter Season ◽

Indus Basin ◽

Climatic Trend ◽

Upper Indus Basin ◽

Climatic Trends ◽

Mixed Response ◽

Long Term Trends

Abstract. Largely depending on meltwater from the Hindukush–Karakoram–Himalaya, withdrawals from the upper Indus basin (UIB) contribute to half of the surface water availability in Pakistan, indispensable for agricultural production systems, industrial and domestic use and hydropower generation. Despite such importance, a comprehensive assessment of prevailing state of relevant climatic variables determining the water availability is largely missing. Against this background, we present a comprehensive hydro-climatic trend analysis over the UIB, including for the first time observations from high-altitude automated weather stations. We analyze trends in maximum, minimum and mean temperatures (Tx, Tn, and Tavg, respectively), diurnal temperature range (DTR) and precipitation from 18 stations (1250–4500 m a.s.l.) for their overlapping period of record (1995–2012), and separately, from six stations of their long term record (1961–2012). We apply Mann–Kendall test on serially independent time series to assess existence of a trend while true slope is estimated using Sen's slope method. Further, we statistically assess the spatial scale (field) significance of local climatic trends within ten identified sub-regions of UIB and analyze whether the spatially significant (field significant) climatic trends qualitatively agree with a trend in discharge out of corresponding sub-region. Over the recent period (1995–2012), we find a well agreed and mostly field significant cooling (warming) during monsoon season i.e. July–October (March–May and November), which is higher in magnitude relative to long term trends (1961–2012). We also find general cooling in Tx and a mixed response in Tavg during the winter season and a year round decrease in DTR, which are in direct contrast to their long term trends. The observed decrease in DTR is stronger and more significant at high altitude stations (above 2200 m a.s.l.), and mostly due to higher cooling in Tx than in Tn. Moreover, we find a field significant decrease (increase) in late-monsoonal precipitation for lower (higher) latitudinal regions of Himalayas (Karakoram and Hindukush), whereas an increase in winter precipitation for Hindukush, western- and whole Karakoram, UIB-Central, UIB-West, UIB-West-upper and whole UIB regions. We find a spring warming (field significant in March) and drying (except for Karakoram and its sub-regions), and subsequent rise in early-melt season flows. Such early melt response together with effective cooling during monsoon period subsequently resulted in a substantial drop (weaker increase) in discharge out of higher (lower) latitudinal regions (Himalaya and UIB-West-lower) during late-melt season, particularly during July. These discharge tendencies qualitatively differ to their long term trends for all regions, except for UIB-West-upper, western-Karakorum and Astore. The observed hydroclimatic trends, being driven by certain changes in the monsoonal system and westerly disturbances, indicate dominance (suppression) of nival (glacial) runoff regime, altering substantially the overall hydrology of UIB in future. These findings largely contribute to address the hydroclimatic explanation of the "Karakoram Anomaly".

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Characterization of Interannual and Seasonal Variability of Hydro-Climatic Trends in the Upper Indus Basin

10.21203/rs.3.rs-376764/v1 ◽

2021 ◽

Author(s):

Muhammad Usman Liaqat ◽

Giovanna Grossi ◽

Shabeh ul Hasson ◽

Roberto Ranzi

Keyword(s):

Trend Analysis ◽

Hydrological Cycle ◽

Winter Season ◽

Rain Gauge ◽

Indus Basin ◽

Annual Precipitation ◽

Monthly Precipitation ◽

Catchment Scale ◽

Upper Indus Basin ◽

Annual Scale

Abstract A high resolution seasonal and annual precipitation climatology of the Upper Indus Basin was developed, based on 1995-2017 precipitation normals obtained from four different gridded datasets (Aphrodite, CHIRPS, PERSIANN-CDR and ERA5) and quality-controlled high and mid elevation ground observations. Monthly precipitation values were estimated through the anomaly method at the catchment scale and compared with runoff data (1975-2017) for verification and detection of changes in the hydrological cycle. The gridded dataset is then analysed using running trends and spectral analysis and the Mann–Kendall test was employed to detect significant trends. The nonparametric Pettitt test was also used to identify the change point in precipitation and runoff time series. The results indicated that bias corrected CHIRPS precipitation dataset, followed by ERA5, performed better in terms of RMSE, MAE, MAPE and BIAS in simulating rain gauge-observed precipitation. The running trend analysis of annual precipitation exhibited a very slight increase whereas a more significant increase was found in the winter season (DJF). A runoff coefficient value greater than one, especially in glacierized catchments (Shigar, Shyok and Gilgit) indicate that precipitation was likely underestimated and glacial melt in a warming climate provides excess runoff volumes. As far as the streamflow is concerned, variabilities are more pronounced at the seasonal rather than at the annual scale. At the annual scale, trend analysis of discharge shows slightly significant increasing trend for the Indus River at the downstream Kachura, Shyok and Gilgit stations. Seasonal flow analysis reveals more complex regimes and its comparison with the variability of precipitation favours a deeper understanding of precipitation, snow- and ice-melt runoff dynamics, addressing the hydroclimatic behaviour of the Karakoram region.

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Characterization of Interannual and Seasonal Variability of Hydro-Climatic Trends in the Upper Indus Basin

10.5194/egusphere-egu2020-5867 ◽

2020 ◽

Author(s):

Muhammad Usman Liaqat ◽

Roberto Ranzi ◽

Giovanna Grossi ◽

Talha Mahmood

Keyword(s):

Trend Analysis ◽

Change Point ◽

Winter Season ◽

Kendall Test ◽

Indus Basin ◽

Percentage Error ◽

Monsoon Period ◽

Upper Indus Basin ◽

Climatic Trends ◽

Basin Scale

<p>A major part of Pakistan&#8217;s economy is dependent upon agriculture which is irrigated from the water resources of the Upper Indus Basin (UIB). Therefore the human impact of hydroclimatic variability in this area is of paramount importance. The Upper Indus Basin is characterized by uncertain hydro-climatic behaviour with changing patterns in different sub-basins. Many studies have worked on hydro-climatic trends at basin scale but only few studies focused on the hydroclimate, precipitation dynamics and their magnitude at sub-basin level. Based upon this scenario, high resolution seasonal and annual climatology of UIB was developed. It is based on precipitation normals 1995-2017 obtained from four different gridded satellite datasets (Aphrodite, Chirps, PERSIANN-CDR and GPCC) as well as quality- controlled high and mid elevation ground observations (1250&#8211;4500 m a.s.l.). The quality-control of the gridded dataset is computed by the anomaly method. In order to, evaluate the data quality of the gridded rainfall, four statistics i.e., BIAS, Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) are used in this study. Using running trends and spectral analysis with multi-gauge based anomaly, the study analyses the precipitation and runoff &#160; seasonal and annual temporal variability at sub-basin scale. For this, Mann&#8211;Kendall test was employed to detect the presence of any trend while their slope is calculated by Theil Sen&#8217;s slope method. The nonparametric Pettitt Test was also used in this study to eventually identify the change point in hydro-climatic time series. The results indicated that bias corrected CHIRPS precipitation datasets performed better in simulating precipitation with RMSE, MAE, MAPE [%] and BIAS followed by APHRODITE. The annual and seasonal precipitation climatology exhibited higher precipitation in the lower side of the basin. The comparison between short and long duration climatologies is being investigated as well. The annual running trend analysis of precipitation exhibited a very slight change whereas a more significant increase was found in the winter season (DJF) and most of sub-basins feature a significant decreasing rate in precipitation and constant change point within the monsoon period (JJA). Similarly, trend analysis for runoff in main rivers of Upper Indus Basin at Gilgat, Indus (Besham Qila, Bunji) exhibit nonsignificant increase except Hunza and Indus at Kharmong which are showed decrease annual trends and will be further investigated for seasonal patterns. Overall, these findings would assist to better understand precipitation, snow- and ice-melt runoff dynamics, addressing the hydroclimatic behaviour of the Karakoram region.</p>

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Precipitation Identification with Bispectral Satellite Information Using Deep Learning Approaches

Journal of Hydrometeorology ◽

10.1175/jhm-d-16-0176.1 ◽

2017 ◽

Vol 18 (5) ◽

pp. 1271-1283 ◽

Cited By ~ 21

Author(s):

Yumeng Tao ◽

Xiaogang Gao ◽

Alexander Ihler ◽

Soroosh Sorooshian ◽

Kuolin Hsu

Keyword(s):

Deep Learning ◽

Winter Season ◽

Stage Iv ◽

False Alarms ◽

Learning Approaches ◽

Critical Success ◽

Satellite Information ◽

Precipitation Estimation ◽

Precipitation Estimates ◽

Performance Gains

Abstract In the development of a satellite-based precipitation product, two important aspects are sufficient precipitation information in the satellite-input data and proper methodologies, which are used to extract such information and connect it to precipitation estimates. In this study, the effectiveness of the state-of-the-art deep learning (DL) approaches to extract useful features from bispectral satellite information, infrared (IR), and water vapor (WV) channels, and to produce rain/no-rain (R/NR) detection is explored. To verify the methodologies, two models are designed and evaluated: the first model, referred to as the DL-IR only method, applies deep learning approaches to the IR data only; the second model, referred to as the DL-IR+WV method, incorporates WV data to further improve the precipitation identification performance. The radar stage IV data are the reference data used as ground observation. The operational product, Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Cloud Classification System (PERSIANN-CCS), serves as a baseline model with which to compare the performances. The experiments show significant improvement for both models in R/NR detection. The overall performance gains in the critical success index (CSI) are 21.60% and 43.66% over the verification periods for the DL-IR only model and the DL-IR+WV model compared to PERSIANN-CCS, respectively. In particular, the performance gains in CSI are as high as 46.51% and 94.57% for the models for the winter season. Moreover, specific case studies show that the deep learning techniques and the WV channel information effectively help recover a large number of missing precipitation pixels under warm clouds while reducing false alarms under cold clouds.

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Rainfall forecasting in upper Indus basin using various artificial intelligence techniques

Stochastic Environmental Research and Risk Assessment ◽

10.1007/s00477-021-02013-0 ◽

2021 ◽

Author(s):

Muhammad Hammad ◽

Muhammad Shoaib ◽

Hamza Salahudin ◽

Muhammad Azhar Inam Baig ◽

Mudasser Muneer Khan ◽

...

Keyword(s):

Artificial Intelligence ◽

Indus Basin ◽

Rainfall Forecasting ◽

Artificial Intelligence Techniques ◽

Upper Indus Basin

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Temperature Projections over the Indus River Basin of Pakistan Using Statistical Downscaling

Atmosphere ◽

10.3390/atmos12020195 ◽

2021 ◽

Vol 12 (2) ◽

pp. 195

Author(s):

Muhammad Saleem Pomee ◽

Elke Hertig

Keyword(s):

21St Century ◽

Statistical Downscaling ◽

General Circulation ◽

Winter Season ◽

Circulation Model ◽

Indus Basin ◽

Indus River ◽

Temperature Changes ◽

Basin Scale ◽

Maximum Heating

We assessed maximum (Tmax) and minimum (Tmin) temperatures over Pakistan’s Indus basin during the 21st century using statistical downscaling. A particular focus was given to spatiotemporal heterogeneity, reference and General Circulation Model (GCM) uncertainties, and statistical skills of regression models using an observational profile that could significantly be improved by recent high-altitude observatories. First, we characterized the basin into homogeneous climate regions using K-means clustering. Predictors from ERA-Interim reanalysis were then used to model observed temperatures skillfully and quantify reference and GCM uncertainties. Thermodynamical (dynamical) variables mainly governed reference (GCM) uncertainties. The GCM predictors under RCP4.5 and RCP8.5 scenarios were used as “new” predictors in statistical models to project ensemble temperature changes. Our analysis projected non-uniform warming but could not validate elevation-dependent warming (EDW) at the basin scale. We obtained more significant warming during the westerly-dominated seasons, with maximum heating during the winter season through Tmin changes. The most striking feature is a low-warming monsoon (with the possibility of no change to slight cooling) over the Upper Indus Basin (UIB). Therefore, the likelihood of continuing the anomalous UIB behavior during the primary melt season may not entirely be ruled out at the end of the 21st century under RCP8.5.

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