Assessment of 13 Gridded Precipitation Datasets for Hydrological Modeling in a Mountainous Basin

Precipitation measurement with high spatial and temporal resolution over highly elevated and complex terrain in the eastern part of Turkey is an essential task to manage the water structures in an optimum manner. The objective of this study is to evaluate the consistency and hydrologic utility of 13 Gridded Precipitation Datasets (GPDs) (CPCv1, MSWEPv2.8, ERA5, CHIRPSv2.0, CHIRPv2.0, IMERGHHFv06, IMERGHHEv06, IMERGHHLv06, TMPA-3B42v7, TMPA-3B42RTv7, PERSIANN-CDR, PERSIANN-CCS, and PERSIANN) over a mountainous test basin (Karasu) at a daily time step. The Kling-Gupta Efficiency (KGE), including its three components (correlation, bias, and variability ratio), and the Nash-Sutcliffe Efficiency (NSE) are used for GPD evaluation. Moreover, the Hanssen-Kuiper (HK) score is considered to evaluate the detectability strength of selected GPDs for different precipitation events. Precipitation frequencies are evaluated considering the Probability Density Function (PDF). Daily precipitation data from 23 meteorological stations are provided as a reference for the period of 2015–2019. The TUW model is used for hydrological simulations regarding observed discharge located at the outlet of the basin. The model is calibrated in two ways, with observed precipitation only and by each GPD individually. Overall, CPCv1 shows the highest performance (median KGE; 0.46) over time and space. MSWEPv2.8 and CHIRPSv2.0 deliver the best performance among multi-source merging datasets, followed by CHIRPv2.0, whereas IMERGHHFv06, PERSIANN-CDR, and TMPA-3B42v7 show poor performance. IMERGHHLv06 is able to present the best performance (median KGE; 0.17) compared to other satellite-based GPDs (PERSIANN-CCS, PERSIANN, IMERGHHEv06, and TMPA-3B42RTv7). ERA5 performs well both in spatial and temporal validation compared to satellite-based GPDs, though it shows low performance in producing a streamflow simulation. Overall, all gridded precipitation datasets show better performance in generating streamflow when the model is calibrated by each GPD separately.

Spatiotemporal Changes in Precipitation Concentration Indicators Over Iran

This study was conducted to investigate the spatiotemporal variability in precipitation concentration over Iran. For that purpose, daily precipitation data with a spatial resolution of 0.25° × 0.25° from the Asfazari database for the period from 01/01/1962 to 31/12/2019 were used. Three indices including the precipitation concentration index (PCI), precipitation concentration period (PCP), and precipitation concentration degree (PCD) were utilized for examination of the variability in precipitation concentration over the country. The results demonstrated that the central, south-eastern, and eastern parts of the country exhibited maximum temporal precipitation concentration, while the least precipitation concentration could be observed over the Caspian coasts and the northern half of the country. The year 1998 was selected as the change point due to the considerable difference in the values of the examined indices, and the long-term statistical period was divided into two sub-periods before and after the change. During the sub-period after the change point (1999-2019), precipitation concentration has increased in the western, central, eastern, and south-eastern parts of Iran, according to PCI and PCD, and has decreased in the North and Northeast and along the northern coastline of Oman Sea. Furthermore, there have been great spatial differences in the period of occurrence of precipitation along the Northern coasts, according to PCP, varying from November, along the Caspian coasts, to August, along the northern foothills of Alborz Mountains. The PCP index has increased during the sub-period after the change point along the northern coastlines of Persian Gulf and Oman Sea and in parts of the North (along Alborz Mountains), indicating a shift in the period of precipitation from winter to the warm seasons of spring and summer. Moreover, the decrease in PCP in the Northwest and Northeast suggested that the period of occurrence of precipitation has shifted from the second half of winter toward early winter and late fall. After the year of change point, the frequency of rainy days and precipitation have decreased, and PCI and PCD have increased.

Chains of Spatial and Temporal Precipitation Occurrence Predictability Across the Continental U.S.

Both spatial and temporal information sources contribute to the predictability of precipitation occurrence at a given location. These sources, and the level of predictability they provide, are relevant to forecasting and understanding precipitation processes at different time scales. We use information theory-based measures to construct connected “chains of influence” of spatial extents and timescales of precipitation occurrence predictability across the continental U.S, based on gridded daily precipitation data. These regions can also be thought of as “footprints” or regions where precipitation states tend to be most synchronized. We compute these chains of precipitation influence for grid cells in the continental US, and study metrics regarding their lengths, extents, and curvature for different seasons. We find distinct geographic and seasonal patterns, particularly longer chain lengths during the summer that are indicative of larger spatial extents for storms. While synchronous, or instantaneous, relationships are strongest for grid cells in the same region, lagged relationships arise as chains reach areas farther from the original cell. While this study focuses on precipitation occurrence predictability given only information about precipitation, it could be extended to study spatial and temporal properties of other driving factors.

Temporal Monitoring and Predicting of the Abundance of Malaria Vectors Using Time Series Analysis of Remote Sensing Data through Google Earth Engine

Background: In many studies in the field of malaria, environmental factors have been acquired in single-time, multi-time or a short time series using remote sensing and meteorological data. Selecting the best periods of the year to monitor the habitats of Anopheles larvae can be effective in better and faster control of malaria outbreak.Methods: In this article, high-risk times for three regions in Iran, including Qaleh-Ganj, Sarbaz and Bashagard counties with history of malaria prevalence had been estimated. For this purpose, a series of environmental factors affecting the growth and survival of Anopheles had been used over a seven-year period through the GEE. Environmental factors used in this study include NDVI and LST extracted from Landsat-8 satellite images, daily precipitation data from PERSIANN-CDR, soil moisture data from NASA-USDA Enhanced SMAP, ET data from MODIS sensor, and vegetation health indices included TCI and VCI extracted from MODIS sensors. All these parameters were extracted on a monthly average for seven years and, their results were fused at the decision level using majority voting method to estimate high-risk time in a year.Results: The results of this study indicated that there were two high-risk times for all three study areas in a year to increase the abundance of Anopheles mosquitoes. The first peak occurred from late winter to late spring and the second peak from late summer to mid-autumn. If there is a malaria patient in the area, after the end of the Anopheles larvae growth period, the disease will spread throughout the region. Further evaluation of the results against the entomological data available in previous studies showed that the high-risk times predicted in this study were consistent with the increase in the abundance of Anopheles mosquitoes in the study areas. Conclusions: The proposed method is very useful for temporal prediction of the increase of the abundance of Anopheles mosquitoes and also the use of optimal data with the aim of monitoring the exact location of Anopheles habitats. This study extracted high-risk time based on the analysis of the time series of remote sensing data.

Identifying and correcting biases in localized downscaling estimates of daily precipitation return values

Extreme precipitation events are a major cause of economic damage and disruption, and need to be addressed for increasing resilience to a changing climate, particularly at the local scale. Practitioners typically want to understand local changes at spatial scales much smaller than the native resolution of most Global Climate Models, for which downscaling techniques are used to translate planetary-to-regional scale change information to local scales. However, users of statistically downscaled outputs should be aware that how the observational data used to train the statistical models is constructed determines key properties of the downscaled solutions. Specifically for one such downscaling approach, when considering seasonal return values of extreme daily precipitation, we find that the Localized Constructed Analogs (LOCA) method produces a significant low bias in return values due to choices made in building the observational data set used to train LOCA. The LOCA low biases in daily extremes are consistent across event extremity, but do not degrade the overall performance of LOCA-derived changes in extreme daily precipitation. We show that the low (negative) bias in daily extremes is a function of a time-of-day adjustment applied to the training data and the manner of gridding daily precipitation data. The effects of these choices are likely to affect other downscaling methods trained with observations made in the same way. The results developed here show that efforts to improve resilience at the local level using extreme precipitation projections can benefit from using products specifically created to properly capture the statistics of extreme daily precipitation events.

Assessment of gridded precipitation products in the hydrological modeling of a flood-prone mesoscale basin

Precipitation plays a critical role in hydrometeorological studies. A predictive analysis of gridded rainfall datasets may provide a cost-effective alternative to conventional rain gauge observations. Here, our objective is to evaluate the performance of satellite and reanalysis precipitation products in the hydrological modeling of a mesoscale watershed. The research also examines the accuracy of hydrological simulations in a sizeable flood-prone watershed in the absence of observed data associated with the myriad water retaining structures present in the catchment. We use three precipitation products, namely Tropical Rainfall Measurement Missions (TRMM) 3B42 Version 7, Climate Forecast System Reanalysis (CFSR), and daily precipitation data recorded at multiple rain gauges in the upper Huai River Basin to simulate streamflow. The Soil & Water Assessment Tool (SWAT) is utilized for runoff modeling, while SWAT-CUP is used to perform sensitivity analysis and to calibrate and validate the simulation results. Nash–Sutcliffe efficiency, percent bias, and Kling-Gupta efficiency (KGE) are employed to evaluate modeling efficiency for three precipitation datasets on different temporal scales. The results indicate that TRMM and CFSR datasets provide satisfactory results on both daily and monthly scales. Specifically, the SWAT model performs better at monthly simulations than daily simulations for all precipitation datasets used.

Rainfall-Induced Landslide Early Warning System based on corrected mesoscale numerical models: an application for the Southern Andes

Rainfall-Induced Landslide Early Warning Systems (RILEWS) are critical tools for reducing and mitigating economic and social damages related to landslides. Despite this critical need, the Southern Andes does not yet possess an operational-scale system to support decision-makers. We propose RILEWS using a logistic regression system in the Southern Andes. The models were forced by corrected simulations of precipitation and geomorphological features. We evaluated the precipitation using the Weather and Research Forecast (WRF) model on an hourly scale. The precipitation was corrected using bias correction approaches with daily data from 12 meteorological stations. Four logistic and probabilistic models were then calibrated using Logit and Probit distributions. The predictor variables used were combinations of the slope, corrected daily precipitation and data preceding the events (7 and 30 days previous) for 57 Rainfall-Induced Landslides (RIL); validation was by ROC analysis. Our results showed that WRF does not represent the spatial variability of the precipitation. This situation was resolved by bias correcting. Specifically, the PP_M4a method with Bernoulli distribution for the occurrence and Gamma for the intensity produced lower MAE and RMSE values and higher correlation values. Finally, our RILEWS had a high predicting capacity with an AUC of 0.80 using daily precipitation data and slope. We conclude that our methodology is suitable at an operational level in the Southern Andes. Our contribution could become a useful tool in the mitigation of impacts related to climate change.

Preliminary assessment for sub-seasonal to seasonal precipitation model on four specific conditions over western Indonesia

Preliminary assessment of sub-seasonal to seasonal reforecast precipitation model (S2S) was conducted to analyze the model's performance over western Indonesia on four conditions. The ECMWF S2S model was compared to quality controlled daily precipitation data from 645 observation points over the region. The control and perturbed model for the first three time steps and the last three were utilized to obtain the best performance comparison. The analysis was conducted in monthly period, MJO events, NCS events, and when both of them were active during period of November-December-January-February (NDJF) from 1998 to 2017. The results show that the first three time steps perform much better than the last one with a slightly higher correlation coefficient from the control model with relatively similar RMSE in Natuna Islands. Spatial analysis indicates that both of the control and perturbed models can catch the variation brought by the wet season in the NDJF period, by the MJO, show a hint of NCS effect, and the combination when MJO and NCS were active at the same time. The models can depict the precipitation pattern pretty well with the tendency to overestimate low rainfall intensity and underestimate the high one. The models relatively overestimate the intensity in Sumatra for the whole period. Meanwhile, consistently good spatial performance is shown by the models over Java, both in NDJF periods or MJO events.

High-Resolution North Sulawesi Drought Hazzard Mapping Based on Consecutive Dry Days (CDD)

North Sulawesi is one of the Province in northern Indonesia with high spatial annual rainfall variations and influenced by global climate anomaly that can lead to extreme events and disaster occurrence, such as flood, landslide, drought, etc. The purpose of this study is to generate high-resolution meteorological hazard map based on long-term historical consecutive dry days (CDD) over the North Sulawesi region. CDD was calculated based on observed daily precipitation data from Indonesia Agency for Meteorology, Climatology, and Geophysics (BMKG) surface observation station network (CDDobs) and the daily-improved Climate Hazards group Infrared Precipitation with Stations (CHIRPS) version 2.0 (CDDCHIRPS) during 1981 – 2010 period. The Japanese 55-year Reanalysis (JRA-55) data obtained from iTacs (Interactive Tool for Analysis of the Climate System) with the same time scale period also used to explain physical – dynamical atmospheric properties related to drought hazard over this region. The Geostatistical approach using regression kriging method was applied as spatial interpolation technique to generate high resolution gridded (0.05° × 0.05°) drought hazard map. This method combines a regression of CDDobs as dependent variable (target variable) on CDDCHIRPS as predictors with kriging of the prediction residuals. The results show that most of the areas were categorized as medium drought hazard level with CDD values ranging from 80-100 days. Meanwhile, small islands around main Sulawesi island such as Sangihe and Karakelong island are dominated by low drought hazard levels with CDD values ranging from 50-60 days. The highest levels of drought hazard area are located in South Bolaang Mongondow Regency.