To evaluate the performance and hydrological utility of merged precipitation products at the current technical level of integration, a newly developed merged precipitation product, Multi-Source Weighted-Ensemble Precipitation (MSWEP) Version 2.1 was evaluated in this study based on rain gauge observations and the Variable Infiltration Capacity (VIC) model for the upper Huaihe River Basin, China. For comparison, three satellite-based precipitation products (SPPs), including Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) Version 2.0, Climate Prediction Center MORPHing technique (CMORPH) bias-corrected product Version 1.0, and Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) 3B42 Version 7, were evaluated. The error analysis against rain gauge observations reveals that the merged precipitation MSWEP performs best, followed by TMPA and CMORPH, which in turn outperform CHIRPS. Generally, the contribution of the random error in all four quantitative precipitation estimates (QPEs) is larger than the systematic error. Additionally, QPEs show large uncertainty in the mountainous regions, with larger systematic errors, and tend to underestimate the precipitation. Under two parameterization scenarios, the MSWEP provides the best streamflow simulation results and TMPA forced simulation ranks second. Unfortunately, the CHIRPS and CMORPH forced simulations produce unsatisfactory results. The relative error (RE) of QPEs is the main factor affecting the RE of simulated streamflow, especially for the results of Scenario I (model parameters calibrated by rain gauge observations). However, its influence on the simulated streamflow can be greatly reduced by recalibration of the parameters using the corresponding QPEs (Scenario II). All QPEs forced simulations underestimate the streamflow with exceedance probabilities below 5.0%, while they overestimate the streamflow with exceedance probabilities above 30.0%. The results of the soil moisture simulation indicate that the influence of the precipitation input on the RE of the simulated soil moisture is insignificant. However, the dynamic variation of soil moisture, simulated by precipitation with higher precision, is more consistent with the measured results. The simulation results at a depth of 0-10 cm are more sensitive to the accuracy of precipitation estimates than that for depths of 0-40 cm. In summary, there are notable advantages of MSWEP and TMPA with respect to hydrological applicability compared with CHIRPS and CMORPH. The MSWEP has a greater potential for basin-scale hydrological modeling than TMPA.
Abstract:Water vapor transport (WVT) is an important element in drought development. In this study, we examined the geographical and vertical anomalies of WVT during severe summer and early fall drought processes and their occurrence, persistence and recovery phases in Southwest China (SWC) by using the method of standardized anomalies (SA) and composite analysis. The SA-based indices of WVT were built up to quantify composited anomalous WVT channels objectively. Essentially, we further explored the synchronous and lagged correlations between drought processes and these channels. Key points and limitations include: (1) Two drought-related WVT channels were geographically identified with composited SA below −0.2, based on the composite of severe drought processes. The Somali channel is characterized by zonally less-than-normal African-Asian continental WVT anomalies originating from Somalia, whereas the IndoChina-Peninsula channel represents meridionally less-than-normal WVT anomalies from the IndoChina-Peninsula; (2) Both geographical and vertical WVT anomalies were intensified and concentrated at the time of drought occurrence, and then weakened and became scattered at drought recovery; (3) Most drought-related WVT anomalies were distinguishable from those of wetter events; (4) The IndoChina-Peninsula channel performs better in correlations with these drought and wetter processes than the Somali channel. Therefore, dynamic and thermodynamic anomalies need to be investigated, which are important for exploring the drought mechanism.
Field capacity is one of the most important soil hydraulic properties in water cycle, agricultural irrigation, and drought monitoring. It is difficult to obtain the distribution of field capacity on a large scale using manual measurements that are both time-consuming and labor-intensive. In this study, the field capacity ensemble members were established using existing pedotransfer functions (PTFs) and multiple linear regression (MLR) based on three soil datasets and 2388 in situ field capacity measurements in China. After evaluating the accuracy of each ensemble member, an integration approach was proposed for estimating the field capacity distribution and development of a 250 m gridded field capacity dataset in China. The spatial correlation coefficient (R) and root mean square error (RMSE) between the in situ field capacity and ensemble field capacity were 0.73 and 0.048 m 3 ·m −3 in region scale, respectively. The ensemble field capacity shows great consistency with practical distribution of field capacity, and the deviation is revised when compared with field capacity datasets provided by previous researchers. It is a potential product for estimating field capacity in hydrological and agricultural practices on both large and fine scales, especially in ungauged regions.
Abstract. Reliable drought prediction is fundamental for water resource managers to develop and implement drought mitigation measures. Considering that drought development is closely related to the spatial-temporal evolution of largescale circulation patterns, we developed a conceptual prediction model of seasonal drought processes based on atmospheric and oceanic standardized anomalies (SAs). Empirical orthogonal function (EOF) analysis is first applied to drought-related SAs at 200 and 500 hPa geopotential height (HGT) and sea surface temperature (SST). Subsequently, SA-based predictors are built based on the spatial pattern of the first EOF modes. This drought prediction model is essentially the synchronous statistical relationship between 90-day-accumulated atmospheric-oceanic SA-based predictors and SPI3 (3-month standardized precipitation index), calibrated using a simple stepwise regression method. Predictor computation is based on forecast atmospheric-oceanic products retrieved from the NCEP Climate Forecast System Version 2 (CFSv2), indicating the lead time of the model depends on that of CFSv2. The model can make seamless drought predictions for operational use after a year-to-year calibration. Model application to four recent severe regional drought processes in China indicates its good performance in predicting seasonal drought development, despite its weakness in predicting drought severity. Overall, the model can be a worthy reference for seasonal water resource management in China.
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