Simulation of Rainwater Harvesting Potential to Satisfy Domestic Water Demand Based on Observed Precipitation Data in Jakarta

Jakarta as the most populous urban center of Indonesia has a major problem related to clean water availability for the domestic needs of its residents, who mostly depend on the extraction of groundwater. The rooftop rainwater harvesting (RRWH) system is a solution to reduce the use of groundwater to satisfy domestic water needs. This study used demographic data and precipitation observation data from the rain gauge network in Jakarta to simulate the water supply from rainwater harvesting from 2010 to 2019 in each municipality. Three simulations were carried out to calculate the percentage of domestic water demand (DS) satisfied by RRWH based on the proportion of residential areas installed with RRWH (RA). The results showed that an RA value of 0.2 produced the lowest DS (approximately 11% to 18.7%), while an RA value of 0.3 produced a higher DS (approximately 16.3% to 28%). An RA value of 0.4 resulted in a DS of around 21.8% to 37.4%. Overall, the RRWH system could provide up to 30% of domestic water demand on average, with South Jakarta having the highest fulfillment of water needs with an average of 28% based on the three simulations, while Central Jakarta had the lowest with 16.4%.

Improving GSMaP V06 Precipitation Products Over the Upper Tocantins River Basin in the Brazilian Cerrado, Based on Local Rain-Gauge Network

This study aimed to evaluate the performance of GSMaP (Global Satellite Mapping of Precipitation) in estimating rainfall in central Brazil, using the Upper Tocantins River sub-basin as a specific area of ​​analysis. GSMaP data were compared with data from a rain gauge network between 2000 and 2019. Evaluations were made at daily and monthly temporal scales. In general, GSMaP products show an overestimate bias for drizzle (0.1~1 mm day−1) and underestimate for rainfalls above 10 mm day−1. The use of monthly scale data significantly reduces the bias observed in the daily scale, but with an underestimation trend of -28.3% and -39.7% for the dry and rainy periods, respectively. Categorical indices showed that the GSMaP system had better hit rates for rain detection in the rainy season (October-April) than in the dry season (May-September). For the studied region, the use of GSMaP data on daily and monthly scales should be preceded by a bias analysis as a function of rain gauge network data. The use of bias coefficient corrected observed rainfall data underestimation on daily and monthly scales, improved correlation between GSMaP and observed rainfall data and reduced errors associated with rainfall network data within the basin influence area.

Precipitation Estimates from Commercial Microwave Links: Practical Approaches to Wet-antenna Correction

An inadequate correction for wet antenna attenuation (WAA) often causes a notable bias in quantitative precipitation estimates (QPEs) from commercial microwave links (CMLs) limiting the usability of these rainfall data in hydrological applications. This paper analyzes how WAA can be corrected without dedicated rainfall monitoring for a set of 16 CMLs. Using data collected over 53 rainfall events, the performance of six empirical WAA models was studied, both when calibrated to rainfall observations from a permanent municipal rain gauge network and when using model parameters from the literature. The transferability of WAA model parameters among CMLs of various characteristics has also been addressed. The results show that high-quality QPEs with a bias below 5% and RMSE of 1 mm/h in the median could be retrieved, even from sub-kilometer CMLs where WAA is relatively large compared to raindrop attenuation. Models in which WAA is proportional to rainfall intensity provide better WAA estimates than constant and time-dependent models. It is also shown that the parameters of models deriving WAA explicitly from rainfall intensity are independent of CML frequency and path length and, thus, transferable to other locations with CMLs of similar antenna properties.

Evaluation of Integrated Nowcasting through Comprehensive Analysis (INCA) precipitation analysis using a dense rain-gauge network in southeastern Austria

An accurate estimate of precipitation is essential to improve the reliability of hydrological models and helps in decision making in agriculture and economy. Merged radar–rain-gauge products provide precipitation estimates at high spatial and temporal resolution. In this study, we assess the ability of the INCA (Integrated Nowcasting through Comprehensive Analysis) precipitation analysis product provided by ZAMG (the Austrian Central Institute for Meteorology and Geodynamics) in detecting and estimating precipitation for 12 years in southeastern Austria. The blended radar–rain-gauge INCA precipitation analyses are evaluated using WegenerNet – a very dense rain-gauge network with about one station per 2 km2 – as “true precipitation”. We analyze annual, seasonal, and extreme precipitation of the 1 km × 1 km INCA product and its development from 2007 to 2018. From 2007 to 2011, the annual area-mean precipitation in INCA was slightly higher than WegenerNet, except in 2009. However, INCA underestimates precipitation in grid cells farther away from the two ZAMG meteorological stations in the study area (which are used as input for INCA), especially from May to September (“wet season”). From 2012 to 2014, INCA's overestimation of the annual-mean precipitation amount is even higher, with an average of 25 %, but INCA performs better close to the two ZAMG stations. Since new radars were installed during this period, we conclude that this increase in the overestimation is due to new radars' systematic errors. From 2015 onwards, the overestimation is still dominant in most cells but less pronounced than during the second period, with an average of 12.5 %. Regarding precipitation detection, INCA performs better during the wet seasons. Generally, false events in INCA happen less frequently in the cells closer to the ZAMG stations than in other cells. The number of true events, however, is comparably low closer to the ZAMG stations. The difference between INCA and WegenerNet estimates is more noticeable for extremes. We separate individual events using a 1 h minimum inter-event time (MIT) and demonstrate that INCA underestimates the events' peak intensity until 2012 and overestimates this value after mid-2012 in most cases. In general, the precipitation rate and the number of grid cells with precipitation are higher in INCA. Considering four extreme convective short-duration events, there is a time shift in peak intensity detection. The relative differences in the peak intensity in these events can change from approximately −40 % to 40 %. The results show that the INCA analysis product has been improving; nevertheless, the errors and uncertainties of INCA to estimate short-duration convective rainfall events and the peak of extreme events should be considered for future studies. The results of this study can be used for further improvements of INCA products as well as for future hydrological studies in regions with moderately hilly topography and convective dominance in summer.

The Development of a Combined Satellite-Based Precipitation Dataset across Bolivia from 2000 to 2015

This study proposes the use of satellite-based precipitation (SBP) products in combination with local rain gauges in Bolivia. Using this approach, the country was divided into three major hydrographic basins: the Altiplano, La Plata, and Amazon. The selected SBP products were Global Satellite Mapping of Precipitation (GSMaP) and Climate Hazards Group Infrared Precipitations with Stations (CHIRPS). The correlation coefficients of SBP were found to be from 0.94 to 0.98 at monthly temporal scale. The applied methodology iterates correction factors, taking advantage of surface measurements from the national rain gauge network; five iterations showed stability in the convergence. Once the improved SBP product was obtained, validation was performed by reducing ten percent the number of rain gauges randomly. After applying the correction factors, the combined products improved their correlation coefficient values by up to 0.99. The validation of the methodology showed that with a combination of products using 90% of the rain gauges, correlation coefficients ranged from 0.98 to 0.99. Among the three basins, the Amazon basin presented the poorest results; this fact may be related to low rain gauge density compared to the other two basins. The validation approach shows that the methodology has an acceptable performance. The database generated in this study, now open to the public, is ready to be used for different hydrological applications such as precipitation time-series analysis, water balance, and water assessment at the sub-basin scale within Bolivia.