The newly merged satellite remotely sensed, gauge and reanalysis-based Multi-Source Weighted-Ensemble Precipitation: Evaluation over Australia and Africa (1981-2016)

Integrating satellite and reanalysis precipitation products for SWAT hydrological simulation in the Jing River Basin, China

In hydrological studies, satellite and reanalysis precipitation products are increasingly being used to supplement gauge observation data. This study designed the composite simulation index (COSI), considering two factors: F1 (data accuracy assessment) and F2 (hydrological simulation performance), to compare the performance of the latest satellite-based and reanalysis-based precipitation products (IMERG, ERA5, ERA5-Land), the prior precipitation products (TRMM, CMADS), and the multi-source weighted-ensemble precipitation (MSWEP). The Soil and Water Assessment Tool (SWAT) model was then applied to compare and analyze the hydrological simulation performance of four preferred products using three data fusion methods including simple model averaging, variance-based weighted averaging, and the latest quantile-based Bayesian model averaging (QBMA). The results can be summarized as follows: (1) Reanalysis products are superior to satellite-based products in terms of F1. However, the satellite-based precipitation products exhibit less BIAS and relatively higher F2, while the MSWEP has relatively high performance on both F1 and F2. (2) Among reanalysis-based precipitation products, CMADS has the best COSI value of 0.53. Although ERA5-Land shows good performance for individual parameters, the comprehensive assessment reveals that ERA5 outperforms ERA5-Land in terms of both F1 and F2. (3) IMERG and TRMM exhibit similar spatiotemporal patterns and similar F1, but IMERG is superior in F2. (4) QBMA outperformed traditional methods in F2, improving the NS coefficient of SWAT model from 0.74 to 0.85. These findings provide a useful reference for analyzing the strengths and limitations of satellite-based and reanalysis precipitation products, and also provide valuable ideas for the combined application of multi-source precipitation products in hydrological studies.

The Contribution of Transpiration to Precipitation Over African Watersheds

The redistribution of biological (transpiration) and non-biological (interception loss, soil evaporation) fluxes of terrestrial evaporation via atmospheric circulation and precipitation is an important Earth system process. In vegetated ecosystems, transpiration dominates terrestrial evaporation and is thought to be crucial for regional moisture recycling and ecosystem functioning. However, the spatial and temporal variability in the dependency of precipitation on transpiration remains understudied, particularly in sparsely sampled regions like Africa. Here, we investigate how biological and non-biological sources of evaporation in Africa contribute to rainfall over the major watersheds in the continent. Our study is based on simulated atmospheric moisture trajectories derived from the Lagrangian model FLEXPART, driven by 1° resolution reanalysis data over 1981-2016. Using daily satellite-based fractions of transpiration over terrestrial evaporation, we isolate the contribution of vegetation to monthly rainfall. Furthermore, we highlight two watersheds (Congo and Senegal) for which we explore intra- and interannual variability of different precipitation sources, and where we find contrasting patterns of vegetation-sourced precipitation within and between years. Overall, our results show that almost 50% of the annual rainfall in Africa originates from transpiration, although the variability between watersheds is large (5%-68%). We conclude that, considering the current and projected patterns of land use change in Africa, a better understanding of the implications for continental-scale water availability is needed.

Inference of the spatio-temporal variability and storage potential of groundwater in data-deficient regions through groundwater models and inversion of impact factors on groundwater, as exemplified by the Lake Victoria Basin

Groundwater is an important resource for supporting domestic water use for people's livelihoods and for maintaining ecosystems. Borehole observations provide the first-hand data that characterise the fluctuation, depth, and aquifer conditions of the groundwater. Unfortunately, such observations are not available or are insufficient for scientific use in many regions. Taking the Lake Victoria Basin (LVB) as an example of data-deficient regions, this study proposes a simple knowledge-based approach that uses the Global Land Data Assimilation System (GLDAS) Catchment Land Surface Model (CLSM) for the main data, with rainfall, hydrological, topographical and geological datasets as supports, by which to infer the spatio-temporal variability and storage potential of groundwater. The method is based on analysis and inversion of impact factors on groundwater, and the feasibility of such a method is proven by showing that the groundwater results from GLDAS CLSM can correctly indicate the seasonality, as well as the link to topographical and geological features. For example, both results from the water balance equation (WBE) and GLDAS CLSM indicate that there are two groundwater recharge seasons in the basin, e.g., March to May and September to November. Compared to the eastern side of the LVB, the western side has mountains blocking surface runoff, and thus, reasonably, has larger storage potential estimates in GLDAS CLSM. Due to the low degree of weathering of the basement rocks, it is expected that there is only small storage potential and variation of groundwater in the southeastern parts of the LVB. GLDAS CLSM also correctly reflects this behaviour. Additionally, the largest groundwater storage potential over the LVB is found in regions near the Kagera River and the western shoreline, since it associates with unconsolidated rocks and behaviours of large groundwater recharge from GLDAS CSLM during the wet year of 2006. The major limitation of this knowledge-based method is that the uncertainty in terms of magnitude on GLDAS CLSM groundwater changes cannot be assessed, in addition to the fact that the reliability of the results cannot be quantified in terms of specific numbers. Therefore, the results and interpretation of groundwater behaviours using such methods can only be a guide for 'where' and 'when' to find groundwater.

Study of uncertainty of satellite and reanalysis precipitation products and their impact on hydrological simulation

Satellite and reanalysis precipitation products are potential alternatives in hydrological studies, and it is very important to evaluate their accuracy and potential use for reliable simulations. In this study, three precipitation products (Tropical Rainfall Measuring Mission 3B43 Version 7 (TRMM 3B43), spatial interpolation grid data based on 2472 national meteorological observation stations in China (GRID_0.5), and National Centers for Environmental Prediction-Climate Forecast System Reanalysis (NCEP-CFSR)) were evaluated against gauge observations in the Xiangxi River watershed of Hubei Province. The performance results indicated that the results of the three precipitation products were correlated with those of the rain gauges; however, there were differences among the three products. TRMM 3B43 tended to overestimate precipitation with the highest correlation coefficient, while NCEP-CFSR tended to underestimate precipitation with the least satisfactory performance, and the performance of GRID_0.5 ranked between them. However, the annual and monthly mean errors differed, as the errors of most of the results driven by NCEP-CFSR were lowest. The errors varied at different time scales. During years with high precipitation, the results were often underestimated, while the results are often overestimated during years with low precipitation. According to the average monthly results, the GRID_0.5 results were closest to the gauge observations for most months. During the wet season, TRMM 3B43 performed better, while NCEP-CFSR precipitation performed better during the dry season. The errors from precipitation to streamflow, NPS pollution, and water environmental capacity (WEC) driven by the three precipitation products increased gradually, ranging from 10% for precipitation to over 20% for NPS pollution and almost 100% for WEC. The error increase for NCEP-CFSR was lower than that of the other two products. Although the simulation error from precipitation to the WEC results driven by the three precipitation products gradually increased, the degree of overestimation and underestimation became smaller.

Impacts of extreme climate on Australia's green cover (2003-2018): A MODIS and mascon probe

Australia as a continent represents a semi-arid environment that is generally water-limited. Changes in rainfall pattern will inevitably occur due to rising temperatures caused by climate change, which has a direct impact on the distribution of Australia's vegetation (green cover). As variability in rainfall continues to increase, i.e., in frequency and/or magnitude, due to climate change, extreme climate events such as droughts are predicted to become more pervasive and severe that will have an adverse effect on vegetation. This study investigates the effects of extreme climate on Australia's green cover during 2003-2018 for the end of rainy seasons of April and October in the northern and southern parts, respectively, to (i) determine the state of vegetation and its changes, (ii) identify "hotspots", i.e., regions that constantly experienced statistically significant decrease in NDVI, and (iii), relate changes in the identified hotspots to GRACE-hydrological changes. These are achieved through the exploitation of the statistical tools of Principal Component Analysis (PCA) and Mann-Kendel Test on Gravity Recovery and Climate Experiment (GRACE) hydrological products on the one hand, and the utilization of Australia's rainfall product and Moderate Resolution Imaging Spectroradiometer Normalized Difference Vegetation Index (MODIS-NDVI) used here with its native spatial resolution of 0.002413^∘ × 0.002413^∘ on the other hand. Differences between 3-year intervals from 2003 to 2018 for both April and October datasets are used to quantify vegetation variations. Through area change analysis, the vegetation differences (2003-2018) indicate that April exhibited larger increase (13.77% of total vegetation area) than decrease (7.83%) compared to October, which experienced slightly larger decrease (9.41%) than increase (8.71%). South Australia and Western Australia emerge as "hotspots" in which vegetation statistically decreased in October, with no noticeable change in April. GRACE-based hydrological changes in both hotspots reflect a decreasing trend (2003-2009) and increasing trend (2009-2012) that peaks in 2011, which then transitions towards a gradually decreasing trend after 2012. Australia-wide climate variability (ENSO and IOD) influenced vegetation variations during the data period 2003 to 2018.

Altimetry-derived surface water data assimilation over the Nile Basin

Global hydrological models facilitate studying of water resources and their variations over time. The accuracies of these models are enhanced when combined with ever-increasing satellite remotely sensed data. Traditionally, these combinations are done via data assimilation approach, which permits the use of improved hydrological outputs to study regions with limited in-situ measurements such as the Nile Basin. This study aims at using the state-of-art satellite radar altimetry data to enhance a land-based hydrological model for studying water storage changes over the Nile Basin. Altimetry-derived surface water storage, for the first time, is assimilated into the model using the ensemble Kalman filter (EnKF) for the period of 2003 to 2016. Multiple datasets from ground measurements, as well as space observations, are used to evaluate the performance of the assimilated satellite altimetry data. Results indicate that the assimilation successfully improves model outputs, especially the surface water component. The process increases the correlation between surface water storage changes and water level variations from satellite radar altimetry by 0.44 and reduces the surface water discharge root-mean-square errors (RMSE) by approximately 33%.

A framework for developing a spatial high-resolution daily precipitation dataset over a data-sparse region

This paper outlines a framework in order to provide a reliable and up-to date local precipitation dataset over Sistan and Baluchestan province, one of the poorly rain gauged areas in Iran. Initially, the accuracy of GPCC data, as the reference dataset, was evaluated. Next, the performance of eight gridded precipitation products (namely, CHIRPS, CMORPH-RAW, ERA5, ERA-Interim, GPM-IMERG, GSMaP-MVK, PERSIANN and TRMM3B42) were compared based on the GPCC observations during 1982-2016 over the study area. The evaluation was done by using eight commonly used statistical and categorical metrics. Then, among the products, the most suitable ones on the basis of their better performance and least time delay in providing data, were utilized as the constituent members of the proposed hybrid dataset. Using several statistical/machine learning approaches (namely, NSGA II, ETROPY and TOPSIS), daily weights of the chosen datasets were estimated, while the correlation coefficient and the estimation error of the data were maximized and minimized, respectively. Finally, the efficiency of the proposed hybrid precipitation dataset was investigated. Results indicate that the developed hybrid dataset (2014-present), using the estimates of the chosen ensemble members (GPM-IMERG, GSMaP-MVK and PERSIANN) and their respective weighting coefficients, provides accurate local daily precipitation data with a spatial resolution of 0.25°, representing the minimum time delay, compared to the other available datasets.

Precipitation Characteristic Analysis of the Zhoushan Archipelago: From the View of MSWEP and Rainfall Merging

Based on the long series of gauge rainfall data from 1979 to 2015, the performance of Multi-Source Weighted-Ensemble Precipitation (MSWEP) precipitation dataset in the Zhoushan Archipelago and its surrounding sea area in Southeast China was evaluated from a variety of perspectives, and then the Cressman scheme was used to merge MSWEP with surface gauge measurements. It was found that at the spatial scale of 0.1° × 0.1°, MSWEP correctly detected most of the daily rainfall events in the study area. The surface precipitation was generally underestimated, with a relative deviation no more than 10%, but there was a fairly high miss reporting on heavy precipitation. The performance of MSWEP is also obviously characterized with seasonal fluctuation. Compared with the gauge records interpolation results, the accuracy statistics of rainfall dataset generated by merging MSWEP with gauge observations is improved to a certain degree. Especially its comprehensive identification ability of the dry and wet state for daily precipitation has been obviously raised. In addition, the merged data has the mixed characteristics of rain gauge observations and MSWEP in spatial structure. This paper has deepened the understanding of the performance of MSWEP in islands and sea areas, and also strengthened the understanding of the marginal effect of merging gauge data with MSWEP, even other global precipitation datasets.

Evaluation of Remotely Sensed and Interpolated Environmental Datasets for Vector-Borne Disease Monitoring Using In Situ Observations Over the Amhara Region, Ethiopia

Despite the sparse distribution of meteorological stations and issues with missing data, vector-borne disease studies in Ethiopia have been commonly conducted based on the relationships between these diseases and ground-based in situ measurements of climate variation. High temporal and spatial resolution satellite-based remote-sensing data is a potential alternative to address this problem. In this study, we evaluated the accuracy of daily gridded temperature and rainfall datasets obtained from satellite remote sensing or spatial interpolation of ground-based observations in relation to data from 22 meteorological stations in Amhara Region, Ethiopia, for 2003-2016. Famine Early Warning Systems Network (FEWS-Net) Land Data Assimilation System (FLDAS) interpolated temperature showed the lowest bias (mean error (ME) ≈1-3 °C), and error (mean absolute error (MAE) ≈1-3 °C), and the highest correlation with day-to-day variability of station temperature (COR ≈0.7-0.8). In contrast, temperature retrievals from the blended Advanced Microwave Scanning Radiometer on Earth Observing Satellite (AMSR-E) and Advanced Microwave Scanning Radiometer 2 (AMSR2) passive microwave and Moderate-resolution Imaging Spectroradiometer (MODIS) land-surface temperature data had higher bias and error. Climate Hazards group InfraRed Precipitation with Stations (CHIRPS) rainfall showed the least bias and error (ME ≈-0.2-0.2 mm, MAE ≈0.5-2 mm), and the best agreement (COR ≈0.8), with station rainfall data. In contrast FLDAS had the higher bias and error and the lowest agreement and Global Precipitation Mission/Tropical Rainfall Measurement Mission (GPM/TRMM) data were intermediate. This information can inform the selection of geospatial data products for use in climate and disease research and applications.

Analysis of the Lake-Effect on Precipitation in the Taihu Lake Basin Based on the GWR Merged Precipitation

Based on the high-density gauged rainfall, the geographically weighted regression (GWR) was used to fuse the daily precipitation of rain gauges with those of Multi-source Weighted-Ensemble Precipitation V2.1 (MSWEP V2.1) and a new merged daily precipitation was generated (referred to as GWR merged precipitation, denoted by GWRMP). Then, the precipitation accuracy at 0.1° × 0.1° grid scale and the lake-effect on precipitation in the Taihu Lake Basin were investigated. Results show that GWRMP is characterized with higher precision and stronger spatial recognition ability compared with MSWEP in the whole basin at 0.1° × 0.1° grid scale, and lake area with a relatively sparse network of rain gauges is no exception. Topography is the most important influencing factor of rainfall in the Taihu Lake Basin, the Pearson correlation coefficient (r) between DEM and the main precipitation type (EOF-1) in the whole basin is 0.64, resulting in a rainy area in the southwestern mountain, and less rain at plain and lake area based on the GWRMP. The multi-year average precipitation in the lake upwind area is 8.31% lower than that in the downwind area. Different with the influence mechanism of precipitation in the southwestern mountainous area characterized by high consistency between the spatial distribution of precipitation and the climatic elements derive from the ERA5 meteorological reanalysis data (|r| > 0.6), there is a lower consistency in the lake downwind area (|r| < 0.5) and no consistency in the lake upwind area at the 0.25° × 0.25° grid scale. The southeast monsoon is deduced as the most important factor affecting the procedure of lake-effect on precipitation in the Taihu Lake Basin. The distribution of wind direction and wind speed determines the dynamic changes of surface water vapor to a certain extent, and the lake-effect on precipitation is most likely occurs in July.

Spatio-temporal groundwater variations associated with climatic and anthropogenic impacts in South-West Western Australia

South-West Western Australia (SWWA) is a critical agricultural region that heavily relies on groundwater for domestic, agricultural and industrial use. However, the behaviours of groundwater associated with climate variability/change and anthropogenic impacts within this region are not well understood. This study investigates the spatio-temporal variability of groundwater in SWWA based on 2997 boreholes over the past 36 years (1980-2015). Results identify the decline in groundwater level (13 mm/month) located in the central coastal region of SWWA (i.e., north and south of Perth) to be caused by anthropogenic impacts (primary factor) and climate variability/change (secondary). In detail, anthropogenic impacts are mainly attributed to substantial groundwater abstraction, e.g., hotspots (identified by above 7 m/month groundwater level change) mostly occur in the central coastal region, as well as close to dams and mines. Impacts of climate variability/change indicate that coupled ENSO and positive IOD cause low-level rainfall in the coastal regions, subsequently, affecting groundwater recharge. In addition, correlation between groundwater and rainfall is significant at 0.748 over entire SWWA (at 95% confidence level). However, groundwater in northeastern mountainous regions hardly changes with rainfall because of very small amounts of rainfall (average 20-30 mm/month) in this region, potentially coupled with terrain and geological impacts. A marked division for groundwater bounded by the Darling and Gingin Scarps is found. This is likely due to the effects of the Darling fault, dams, central mountainous terrain and geology. For the region south of Perth and southern coastal regions, a hypothesis through multi-year analysis is postulated that rainfall of at least 60 and 65-70 mm/month, respectively, are required during the March-October rainfall period to recharge groundwater.