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Summary The Palmer drought model that was developed half a century ago has often been used for monitoring and decision making. It has gained wide-spread familiarity and acceptance, together with numerous complaints, criticisms and improvements. This study first introduces a recent variation of Palmer’s model, i.e. the standardized Palmer drought index (SPDI) as a hybrid of a Palmer drought index (PI) and a standardized drought index (SI). Then, a more complicated methodology is employed to propose an SPDI-based joint drought index (SPDI-JDI), which is also physically based on the Palmer drought model and is a multivariate SI in nature. Using meteorological observations from twelve WMO gauges from all around the globe, empirical and parametric copula-based approaches are adopted and compared for the construction of SPDI-JDI. The findings in this study indicate that the SPDI-JDI derived from parametric copulas can provide an overall measure of the joint probability-based drought status by combining the probabilistic properties and dependence structure of multi-temporal scale marginals of SPDI. Meanwhile, the SPDI-JDI shows high coherence and correlation with individual PIs and performs well in drought detection when compared to the integration of Palmer drought severity index, Palmer modified drought index, Palmer hydrologic drought index and Palmer Z index as well as to the U.S. Drought Monitor observations. Taken together, new variations of the SPDI-JDI and/or SPDI are expected to bring new vigor and vitality into Palmer’s drought model, making it more up-to-date for drought monitoring and prediction. [ABSTRACT FROM AUTHOR]

Water scarcity causes conflicts among natural resources, society and economy and reinforces the need for optimal allocation of irrigation water resources in a sustainable way. Uncertainties caused by natural conditions and human activities make optimal allocation more complex. An intuitionistic fuzzy multi-objective non-linear programming (IFMONLP) model for irrigation water allocation under the combination of dry and wet conditions is developed to help decision makers mitigate water scarcity. The model is capable of quantitatively solving multiple problems including crop yield increase, blue water saving, and water supply cost reduction to obtain a balanced water allocation scheme using a multi-objective non-linear programming technique. Moreover, it can deal with uncertainty as well as hesitation based on the introduction of intuitionistic fuzzy numbers. Consideration of the combination of dry and wet conditions for water availability and precipitation makes it possible to gain insights into the various irrigation water allocations, and joint probabilities based on copula functions provide decision makers an average standard for irrigation. A case study on optimally allocating both surface water and groundwater to different growth periods of rice in different subareas in Heping irrigation area, Qing’an County, northeast China shows the potential and applicability of the developed model. Results show that the crop yield increase target especially in tillering and elongation stages is a prevailing concern when more water is available, and trading schemes can mitigate water supply cost and save water with an increased grain output. Results also reveal that the water allocation schemes are sensitive to the variation of water availability and precipitation with uncertain characteristics. The IFMONLP model is applicable for most irrigation areas with limited water supplies to determine irrigation water strategies under a fuzzy environment. [ABSTRACT FROM AUTHOR]

Summary: This study first focuses on comprehensive evaluating three widely used satellite precipitation products (TMPA 3B42V6, TMPA 3B42RT, and CMORPH) with a dense rain gauge network in the Mishui basin (9972km2) in South China and then optimally merge their simulated hydrologic flows with the semi-distributed Xinanjiang model using the Bayesian model averaging method. The initial satellite precipitation data comparisons show that the reanalyzed 3B42V6, with a bias of −4.54%, matched best with the rain gauge observations, while the two near real-time satellite datasets (3B42RT and CMORPH) largely underestimated precipitation by 42.72% and 40.81% respectively. With the model parameters first benchmarked by the rain gauge data, the behavior of the streamflow simulation from the 3B42V6 was also the most optimal amongst the three products, while the two near real-time satellite datasets produced deteriorated biases and Nash–Sutcliffe coefficients (NSCEs). Still, when the model parameters were recalibrated by each individual satellite data, the performance of the streamflow simulations from the two near real-time satellite products were significantly improved, thus demonstrating the need for specific calibrations of the hydrological models for the near real-time satellite inputs. Moreover, when optimally merged with respect to the streamflows forced by the two near real-time satellite precipitation products and all the three satellite precipitation products using the Bayesian model averaging method, the resulted streamflow series further improved and became more robust. In summary, the three current state-of-the-art satellite precipitation products have demonstrated potential in hydrological research and applications. The benchmarking, recalibration, and optimal merging schemes for streamflow simulation at a basin scale described in the present work will hopefully be a reference for future utilizations of satellite precipitation products in global and regional hydrological applications. [Copyright &y& Elsevier]