The Utility of Land-Surface Model Simulations to Provide Drought Information in a Water Management Context Using Global and Local Forcing Datasets

Abstract

Drought diagnosis and forecasting are fundamental issues regarding hydrological management in Spain, where recurrent water scarcity periods are normal. Land-surface models (LSMs) could provide relevant information for water managers on how drought conditions evolve. Here, we explore the usefulness of LSMs driven by atmospheric analyses with different resolutions and accuracies in simulating drought and its propagation to precipitation, soil moisture and streamflow through the system. We perform simulations for the 1980-2014 period with SASER (5 km resolution) and LEAFHYDRO (2.5 km resolution), which are forced by the Spanish SAFRAN dataset (at 5km and 30km resolutions), and the global eartH2Observe datasets at 0.25 degrees (including the MSWEP precipitation dataset). We produce standardized indices for precipitation (SPI), soil moisture (SSMI) and streamflow (SSI). The results show that the model structure uncertainty remains an important issue in current generation large-scale hydrological simulations based on LSMs. This is true for both the SSMI and SSI. The differences between the simulated SSMI and SSI are large, and the propagation scales for drought regarding both soil moisture and streamflow are overly dependent on the model structure. Forcing datasets have an impact on the uncertainty of the results but, in general, this impact is not as large as the uncertainty due to model formulation. Concerning the global products, the precipitation product that includes satellite observations (MSWEP) represents a large improvement compared with the product that does not.

Global characterization of hydrological and meteorological droughts under future climate change: The importance of timescales, vegetation‐CO2feedbacks and changes to distribution functions

“…Although climate models have some uncertainty in reproducing specific drought characteristics (e.g., drought propagation, Barella‐Ortiz and Quintana‐Seguí, ; Quintana‐Seguí et al ., ), they show better skill in identification of drought variability and trends, particularly when comparing observation‐ and model‐based drought indices (Orlowsky and Seneviratne, ; Zhao and Dai, ). This has been shown for metrics that employ precipitation data only (e.g., Standardized precipitation index [SPI]; Orlowsky and Seneviratne, ; Preethi et al ., ), as well as metrics that combine precipitation and AED (e.g., PDSI; Zhao and Dai, ).…”

Unraveling the influence of atmospheric evaporative demand on drought and its response to climate change

“…AED is also an input in crop and vegetation growth models (Jones et al, ; Stöckle, Donatelli, & Nelson, ), involved in the modeling of NPP and total biomass production. Ecohydrological models still show several limitations and uncertainties including: (a) strong bias in ET estimates (Ukkola et al, ); (b) uncertainties in modeled hydrological variables (Beck et al, ; Liu & Gupta, ); and (c) the reproduction of key processes such as drought propagation throughout the hydrological cycle (Barella‐Ortiz & Quintana‐Seguí, ; Quintana‐Seguí, Barella‐Ortiz, Regueiro‐Sanfiz, & Miguez‐Macho, ). Acknowledgement of these three issues supports the use of simple conceptual models based on climate data that are strongly related to observed hydrological variables (Barker et al, ; Cook et al, ; Scaini, Sánchez, Vicente‐Serrano, & Martínez‐Fernández, ), and that show great capacity to identify drought impacts (Bachmair et al, ; Peña‐Gallardo et al, ).…”

Modeling the response of soil moisture to climate variability in the Mediterranean region

“…Assuming that the changes in temperature and precipitation are stochastic variables, the first-order Sobol indices are computed using the state-dependent parameter modeling proposed by (Ratto et al, 2007). For the GSA, a different set of 1000 sets of temperature and precipitation changes, generated randomly in the range of values presented in Sect.…”