Areal Precipitation Coverage Ratio for Enhanced AI Modelling of Monthly Runoff: A New Satellite Data-Driven Scheme for Semi-Arid Mountainous Climate

Satellite remote sensing provides useful gridded data for the conceptual modelling of hydrological processes such as precipitation–runoff relationship. Structurally flexible and computationally advanced AI-assisted data-driven (DD) models foster these applications. However, without linking concepts between variables from many grids, the DD models can be too large to be calibrated efficiently. Therefore, effectively formulized, collective input variables and robust verification of the calibrated models are desired to leverage satellite data for the strategic DD modelling of catchment runoff. This study formulates new satellite-based input variables, namely, catchment- and event-specific areal precipitation coverage ratios (CCOVs and ECOVs, respectively) from the Global Precipitation Mission (GPM) and evaluates their usefulness for monthly runoff modelling from five mountainous Karkheh sub-catchments of 5000–43,000 km2 size in west Iran. Accordingly, 12 different input combinations from GPM and MODIS products were introduced to a generalized deep learning scheme using artificial neural networks (ANNs). Using an adjusted five-fold cross-validation process, 420 different ANN configurations per fold choice and 10 different random initial parameterizations per configuration were tested. Runoff estimates from five hybrid models, each an average of six top-ranked ANNs based on six statistical criteria in calibration, indicated obvious improvements for all sub-catchments using the new variables. Particularly, ECOVs were most efficient for the most challenging sub-catchment, Kashkan, having the highest spacetime precipitation variability. However, better performance criteria were found for sub-catchments with lower precipitation variability. The modelling performance for Kashkan indicated a higher dependency on data partitioning, suggesting that long-term data representativity is important for modelling reliability.

Accuracy of precipitation network in different geophysical regions in India

For any type of hydro meteorological studies it Is imperative that an optimum design of network of raingauge stations is determined taking into consideration various factors influencing specific purpose for which such designs are envisaged. In the present paper an attempt has been made to determine the relative accuracy of the precipitation network designed for estimation of normal areal precipitation in comparison to the standard prescribed by World Meteorological Orgamsatlon. It is observed that III the present case the proposed network is fairly accurate for the purpose for which it has been designed.

Some hydrometeorological studies over Teesta basin in north Bengal

Based on data of 40 rainfall stations located within and in the neighbourhood of Teesta basin in north Bengal for period ranging between 7 & 23 years, hydrometeorological informations of the spatial distribution of monthly rainfall, umber of rainy days and extreme rainfall distribution over Teesta basin have been determined and presented on basin maps for the months of May to October. The average monthly areal precipitation depth as wi1l as extreme areal precipitation depth for a day have been discussed for 6 sectors of the basin. The pentads rainfall for 22 selected stations in the catchment during May to October have also been evaluated and discussed.

QPF model for Sabarmati basin based on Synoptic analogue method

The paper formulates a synoptic analogue model for issuing Quantitative Precipitation Forecast (QPF) for Sabarmati basin based on 10 years data (2000-2009) during southwest monsoon period. The model was verified with the actual Average Areal Precipitation (AAP) for the corresponding synoptic situations during 2010.The performance of the model were observed Percentage Correct (PC) up to 71%. The cases out by one or two stage were due to variation in the intensity of the system especially upper air circulation (S3) over the basin. The synoptic analogue model was able to generate accurate QPF 24 hrs in advance to facilitate flood forecasters of Central Water Commission.

Quantitative Precipitation Forecast (QPF) for Teesta basin and heavy rainfall warning over Teesta basin & adjoining areas in north Bengal & Sikkim using synoptic analog method

This article presents the method to issue Quantitative Precipitation Forecast (QPF) for Teesta catchment. A synoptic analog model has been developed analyzing 10 years (1998-2007) data for Teesta catchment. The outcomes are then validated with the realized Average Areal Precipitation (AAP) for the corresponding synoptic situations during south-west monsoon season 2008 (1st June to 30th September) over Teesta basin and results revealed that there exists a good agreement between day-to-day QPF with corresponding realized AAP calculated over this basin next day. In addition, occurrence of heavy rainfall has also been studied in this paper.

Anticipated maximum scale precipitation for calculating the worst-case floods

Against increasing number of unprecedented heavy rains and typhoons reflecting climate change, the Japanese Government decided saving life as the top priority considering a ‘worst-case’ scenario. Accordingly, the Flood Risk Management Act was amended in 2015 to use the anticipated maximum scale precipitation (AMSP) for flood inundation calculation. In order to estimate the AMSP, the Ministry of Land, Infrastructure, Transport and Tourism (MLIT) chose historical maximum areal precipitation in the form of duration–area–depth (DAD) curves rather than climate change projections' dataset d4PDF. In this paper, policy development and detailed estimation procedures for the AMSP were reviewed and discussed. It was concluded that the current climate change projections are still not accurate enough to be used as the basis for real local operations, while long accumulated ground observations and ground-based radars are available in good quality all over Japan. But at the same time, historical maximum should always be updated as past records are renewed. Also, regional partitioning should not be done at too coarse of scale for proper regionalization of DAD. Such strategy would serve as a useful reference for other nations.

Evaluation of Areal Monthly Average Precipitation Estimates from MERRA2 and ERA5 Reanalysis in a Colombian Caribbean Basin

Global reanalysis dataset estimations of climate variables constitute an alternative for overcoming data scarcity associated with sparsely and unevenly distributed hydrometeorological networks often found in developing countries. However, reanalysis datasets require detailed validation to determine their accuracy and reliability. This paper evaluates the performance of MERRA2 and ERA5 regarding their monthly rainfall products, comparing their areal precipitation averages with estimates based on ground measurement records from 49 rain gauges managed by the Institute of Hydrology, Meteorology, and Environmental Studies (IDEAM) and the Thiessen polygons method in the Sinu River basin, Colombia. The performance metrics employed in this research are the correlation coefficient, the bias, the normalized root mean square error (NRMSE), and the Nash–Sutcliffe efficiency (NSE). The results show that ERA5 generally outperforms MERRA2 in the study area. However, both reanalyses consistently overestimate the monthly averages calculated from IDEAM records at all time and spatial scales. The negative NSE values indicate that historical monthly averages from IDEAM records are better predictors than both MERRA2 and ERA5 rainfall products.

Areal Precipitation Estimation Using Satellite Derived Rainfall Data over an Irrigation Area

The average precipitation on the irrigation field can be estimated from the Meteorology Observation Stations by using spatial interpolation methods such as Thiessen polygon and isohyetal curves. However, the fact that precipitation doesn't occur homogenous in spatial scales, spatial interpolation methodologies need a large number of meteorology stations for more accurate results. In recent years, remote sensing methods have diversified to estimate precipitation. In this study, performance of the satellite-based precipitation data was assessed to determine areal precipitation over an irrigation area. This study was conducted over left bank irrigation area located in the Çukurova Plain of Turkey. Relationship between CHIRPS satellite based on monthly precipitation data and 4 meteorology stations’ data were analyzed. Determination coefficients (R2) of the stations were found between 0.64 and 0.77, for point based comparison, R2 was calculated as 0.84 with Thiessen polygon method. It is concluded that the precipitation amount in the irrigated area can be estimated as accurately as classical methods such as Thiessen polygon with satellite-based precipitation data.

Terrestrial Water Storage Change Retrieved by GRACE and Its Implication in the Tibetan Plateau: Estimating Areal Precipitation in Ungauged Region

The Tibetan Plateau (TP) is referred to as the water tower of Asia, where water storage and precipitation have huge impacts on most major Asian rivers. Based on gravity recovery and climate experiment data, this study analyzed the terrestrial water storage (TWS) changes and estimated areal precipitation based on the water balance equation in four different basins, namely, the upper Yellow River (UYE), the upper Yangtze River (UYA), the Yarlung Zangbo River (YZ), and the Qiangtang Plateau (QT). The results show that the TWS change exhibits different patterns in the four basins and varies from −13 to 2 mm/year from 2003 to 2017. The estimated mean annual precipitation was 260 ± 19 mm/year (QT), 697 ± 26 mm/year (UYA), 541 ± 36 mm/year (UYE), and 1160 ± 39 mm/year (YZ) which performed better than other precipitation products in the TP. It indicates a potential method for estimating basin-scale precipitation through integrating basin average precipitation from the water balance equation in the poorly gauged and ungauged regions.