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AbstractBased on three rainfall run‐off‐induced sediment transport data for bare surface experimental plots, the generalized regression neural network (GRNN) and empirical models were developed to predict sediment load. Rainfall intensity, slope, rainfall duration, soil particle median diameter, clay content of the soil, rill density and soil particle mass density constituted the input variables of the models while sediment load was the target output. The GRNN model was trained and tested. The GRNN model was found successful in predicting sediment load. Sensitivity analysis by the GRNN model revealed that slope and rainfall duration were the most sensitive parameters. In addition to the GRNN model, two empirical models were proposed: (1) in the first empirical model, all the input variables were related to the sediment load, and (2) in the second empirical model, only rainfall intensity, slope and rainfall duration were related to the sediment load. The empirical models were calibrated and validated. At the calibration stage, the coefficients and the exponents of the empirical models were obtained using the genetic algorithm optimization method. The validated empirical models were also applied to two more experimental data sets: (1) one data set was from a field experiment, and (2) one set was from a laboratory experiment. The results indicated the success of the empirical models in predicting sediment load from bare land surfaces.

We explore how to address the challenges of adaptation of water resources systems under changing conditions by supporting flexible, resilient and low-regret solutions, coupled with on-going monitoring and evaluation. This will require improved understanding of the linkages between biophysical and social aspects in order to better anticipate the possible future co-evolution of water systems and society. We also present a call to enhance the dialogue and foster the actions of governments, the international scientific community, research funding agencies and additional stakeholders in order to develop effective solutions to support water resources systems adaptation. Finally, we call the scientific community to a renewed and unified effort to deliver an innovative message to stakeholders. Water science is essential to resolve the water crisis, but the effectiveness of solutions depends, inter alia, on the capability of scientists to deliver a new, coherent and technical vision for the future development of water systems.

We explore how to address the challenges of adaptation of water resources systems under changing conditions by supporting flexible, resilient and low-regret solutions, coupled with on-going monitoring and evaluation. This will require improved understanding of the linkages between biophysical and social aspects in order to better anticipate the possible future co-evolution of water systems and society. We also present a call to enhance the dialogue and foster the actions of governments, the international scientific community, research funding agencies and additional stakeholders in order to develop effective solutions to support water resources systems adaptation. Finally, we call the scientific community to a renewed and unified effort to deliver an innovative message to stakeholders. Water science is essential to resolve the water crisis, but the effectiveness of solutions depends, inter alia, on the capability of scientists to deliver a new, coherent and technical vision for the future development of water systems.