Hydrological Assessment of Daily Satellite Precipitation Products over a Basin in Iran

In order to measure precipitation as the main variable for estimating the runoff and designing hydraulic structures, the satellite algorithm products that have the proper spatial and temporal coverage, can be used. In this study, at first, the daily streamflow simulation of Sarough-Cahy River from the Zarinehroud basin was conducted through the artificial neural network (ANN) and ground data of daily precipitation, temperature and discharge for the years of 1988 to 2008. The developed network was trained, validated and tested. Then, in order to evaluate the products of satellite precipitation algorithms in streamflow simulation which is the aim of this study, daily satellite rainfall data of PERSIANN, TMPA-3B42V7, TMPA-3B42RT and CMORPH between 2003 and 2008 were used as an input data to the trained ANN model. Considering indices of R2, RMSE and MAE implemented for evaluations, the results indicated that satellite rainfall algorithms are able to simulate runoff efficiently over the study area

Evaluasi Kesesuaian Data Satelit Global Precipitation Measurement (GPM) terhadap Stasiun Curah Hujan Disekitar Kawasan Inti Pusat Pemerintahan (KIPP) di Kabupaten Penajam Paser Utara

Pada tanggal 26 Agustus 2019, Bapak Presiden Indonesia, Ir. H. Joko Widodo menyampaikan pernyataan resmi perihal Pemindahan Ibukota Indonesia yang baru. Dikatakan bahwa sebutan Ibu Kota Negara Baru yaitu Ibu Kota Nusantara yang terbagi kedalam beberapa Kawasan. Menurut Perpres No. 64 Tahun 2022 Tentang Rencana Tata Ruang Kawasan Strategis Nasional Ibu Kota Nusantara Tahun 2022-2024, terdapat 2 (dua) kawasan inti yaitu Kawasan Inti Pusat Pemerintahan (KIPP) dan Kawasan Ibukota Negara (K-IKN). Pada wilayah KIPP banyak perencanaan dan pembangunan infrastruktur yang sedang berjalan. Namun pos curah hujan yang tersedia di lokasi pada umumnya sangat minim. Dalam mengatasi permasalahan tersebut dapat digunakan data hujan satelit GPM. Pos hujan yang berada di sekitar KIPP berjumlah 10 pos curah hujan. Data satelit GPM yang digunakan dalam penelitian ini adalah Daily accumulate precipitation estimate-final run (GPM-3IMERGDF v06). Hasil penelitian menunjukkan analisis dilakukan pada 6 pos curah hujan dikarenakan panjang data pos curah hujan yang lain kurang dari 3 tahun. Dari hasil korelasi bulanan, pos curah hujan BMKG Sepinggan memiliki korelasi paling tinggi yaitu 0,8. Kemudian dilakukan koreksi data harian didapatkan Root Mean Square Error (RMSE) sebesar 0,64 %. Pada koreksi hujan harian maksimum tahunan (HHMT) didapatkan error sebelum dan sesudah koreksi 0,22 – 0,03. Berdasarkan hasil koreksi data GPM tersebut dengan pos hujan BMKG Sepinggan, data GPM tersebut memenuhi uji kualitas data dan dapat digunakan dalam analisis perhitungan hidrologi

Global-scale evaluation of 22 precipitation datasets using gauge observations and hydrological modeling

Abstract. We undertook a comprehensive evaluation of 22 gridded (quasi-)global (sub-)daily precipitation (P) datasets for the period 2000–2016. Thirteen non-gauge-corrected P datasets were evaluated using daily P gauge observations from 76 086 gauges worldwide. Another nine gauge-corrected datasets were evaluated using hydrological modeling, by calibrating the HBV conceptual model against streamflow records for each of 9053 small to medium-sized ( <  50 000 km2) catchments worldwide, and comparing the resulting performance. Marked differences in spatio-temporal patterns and accuracy were found among the datasets. Among the uncorrected P datasets, the satellite- and reanalysis-based MSWEP-ng V1.2 and V2.0 datasets generally showed the best temporal correlations with the gauge observations, followed by the reanalyses (ERA-Interim, JRA-55, and NCEP-CFSR) and the satellite- and reanalysis-based CHIRP V2.0 dataset, the estimates based primarily on passive microwave remote sensing of rainfall (CMORPH V1.0, GSMaP V5/6, and TMPA 3B42RT V7) or near-surface soil moisture (SM2RAIN-ASCAT), and finally, estimates based primarily on thermal infrared imagery (GridSat V1.0, PERSIANN, and PERSIANN-CCS). Two of the three reanalyses (ERA-Interim and JRA-55) unexpectedly obtained lower trend errors than the satellite datasets. Among the corrected P datasets, the ones directly incorporating daily gauge data (CPC Unified, and MSWEP V1.2 and V2.0) generally provided the best calibration scores, although the good performance of the fully gauge-based CPC Unified is unlikely to translate to sparsely or ungauged regions. Next best results were obtained with P estimates directly incorporating temporally coarser gauge data (CHIRPS V2.0, GPCP-1DD V1.2, TMPA 3B42 V7, and WFDEI-CRU), which in turn outperformed the one indirectly incorporating gauge data through another multi-source dataset (PERSIANN-CDR V1R1). Our results highlight large differences in estimation accuracy, and hence the importance of P dataset selection in both research and operational applications. The good performance of MSWEP emphasizes that careful data merging can exploit the complementary strengths of gauge-, satellite-, and reanalysis-based P estimates

Merging high-resolution satellite-based precipitation fields and point-scale rain gauge measurements-A case study in Chile

With high spatial-temporal resolution, Satellite-based Precipitation Estimates (SPE) are becoming valuable alternative rainfall data for hydrologic and climatic studies but are subject to considerable uncertainty. Effective merging of SPE and ground-based gauge measurements may help to improve precipitation estimation in both better resolution and accuracy. In this study, a framework for merging satellite and gauge precipitation data is developed based on three steps, including SPE bias adjustment, gauge observation gridding, and data merging, with the objective to produce high-quality precipitation estimates. An inverse-root-mean-square-error weighting approach is proposed to combine the satellite and gauge estimates that are in advance adjusted and gridded, respectively. The model is applied and tested with the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS) estimates (daily, 0.04° × 0.04°) over Chile, for the 6 year period of 2009-2014. Daily observations from about 90% of collected gauges over the study area are used for model calibration; the rest of the gauged data are regarded as ground “truth” for validation. Evaluation results indicate high effectiveness of the model in producing high-resolution-precision precipitation data. Compared to reference data, the merged data (daily) show correlation coefficients, probabilities of detection, root-mean-square errors, and absolute mean biases that were consistently improved from the original PERSIANN-CCS estimates. The cross-validation evidences that the framework is effective in providing high-quality estimates even over nongauged satellite pixels. The same method can be applied globally and is expected to produce precipitation products in near real time by integrating gauge observations with satellite estimates

Estimation of TRMM rainfall for landslide occurrences based on rainfall threshold analysis

Landslide can be triggered by intense or prolonged rainfall. Precipitation data obtained from ground-based observation is very accurate and commonly used to do analysis and landslide prediction. However, this approach is costly with its own limitation due to lack of density of ground station, especially in mountain area. As an alternative, satellite derived rainfall techniques have become more favorable to overcome these limitations. Moreover, the satellite derived rainfall estimation needs to be validated on its accuracy and its capability to predict landslide which presumably triggered by rainfall. This paper presents the investigation of using the TRMM-3B42V7 data in comparison to the available rain-gauge data in Ulu Kelang, Selangor. The monthly average rainfall, cumulative rainfall and rainfall threshold analysis from 1998 to 2011 is compared using quantitative statistical criteria (Pearson correlation, bias, root mean square error, mean different and mean). The results from analysis showed that there is a significant and strong positive correlation between the TRMM 3B42V7 and rain gauge data. The threshold derivative from the satellite products is lower than the rain gauge measurement. The findings indicated that the proposed method can be applied using TRMM satellite estimates products to derive rainfall threshold for the possible landslide occurrence

Evaluating the Contribution of Remote Sensing Data Products for Regional Simulations of Hydrological Processes in West Africa using a Multi-Model Ensemble

Water is a crucial resource for human health, agricultural production and economic development. This holds especially true in West Africa, where large parts of the population work as self-sustaining farmers. Accurate knowledge of available water resources is therefore essential to properly manage this valuable commodity. Hydrologic modeling is seen as a key aspect in generating predictions of available resources. However, the overall availability of in situ data for model parametrization in West Africa has been steadily declining since the 1990s. When observations are available, they often contain errors and gaps. This lack of data severely hinders the application of hydrologic models in the region. Nowadays, many global and regional remote sensing and reanalysis data products exist which may be used to overcome these problems. A thorough analysis of the contribution of these products to regional simulations of hydrologic processes in West Africa has so far not been conducted. The purpose of this study is to close this gap. The study area spans from 3 to 24° latitude and -18 to 16° longitude and encompasses, among others, the Niger, Volta, and Senegal river basins. This study focuses on three key aspects, namely how the performance of remotely sensed and reanalyzed products can be validated without the availability of in situ data for the region; to what extent semi-distributed hydrologic models of the region can be parameterized and validated using these data; and how a fully distributed, grid-based model can be set up, calibrated and validated for sparsely-gauged river basins using multivariate data inputs. Comparisons of remote sensing and reanalysis precipitation products for the region show strong variability. A hydrologic evaluation was conducted, during which the skill of each precipitation dataset to accurately reproduce observed streamflow in HBV-light simulations was tested. Best results are achieved by products which include satellite infrared and microwave measurements as well as bias-correction based on in situ observations. Averaged Nash-Sutcliffe Efficiencies (NSE) of 0.66 were reached during the calibration of the CMORPH CRT and PERSIANN CDR products over six subbasins. In a next step, three SWAT models were set up for the region using multiple remote sensing and reanalysis data products and then calibrated and validated against observed river discharge with global and local approaches. While streamflow results differ within models and model regions, they are mostly satisfactory with coefficient of determination (R2) values of 0.52 and 0.51 for calibrations and 0.63 and 0.61 for validations. In a multivariate validation framework, the skill of the model in simulating variables not included in the calibration is further evaluated against remote sensing observations of actual evapotranspiration, soil moisture dynamics, and total water storage anomaly. Here, it has been shown that the models perform robustly and reach a good agreement in relation to observations. Furthermore, the grid-based mHM model was applied to several river basins in the south of the study area. After the quality of precipitation and evapotranspiration inputs was tested, a multivariate calibration was conducted. Models were calibrated using discharge observations (Q) and, to further constrain model boundary conditions, discharge in combination with remote sensing actual evapotranspiration observations (Q/ET). Finally, the quality of the simulations was tested against streamflow data as well as against remote sensing actual evapotranspiration, soil moisture, and total water storage anomaly data. Streamflow simulations performed well with averaged Kling-Gupta Efficiencies (KGE) of 0.53 for the first (Q) and 0.49 for the second (Q/ET) calibration. Further variables tested during the multiobjective validation were within good predictive ranges, especially during the Q/ET calibration. When SWAT and mHM model results are compared against each other and against external data products, results show that while both models perform robustly, mHM predictions outperform SWAT results. This study furthers the understanding of the contribution of remote sensing, reanalysis and global data products in regional simulations of hydrologic processes in West Africa. Specific modeling strategies and routines were developed to further increase predictive capabilities of hydrologic models of the region using these freely-available datasets