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Communities in regions with seasonal rainfall face annual dry seasons, during which water shortages and conflicts between different water use sectors may erupt. These difficulties increase following wet seasons with low rainfall, such as in relation to El Nino events in the wet-dry tropics of Central America. Hydrologic data are typically scarce in this region, making the development of drought adaptation strategies challenging. Many communities in the region depend on groundwater as their primary water source. For instance in the Province of Guanacaste in Costa Rica, groundwater supplied 78% of the total domestic water demand in 2015 and groundwater recharge from the wet season provides the primary water supply for the subsequent dry season. In this research we present a ‘groundwater recharge indicator’ that can support water managers in preparing for seasonal droughts. We developed this tool for an aquifer in northwestern Costa Rica where we conducted hydrological monitoring of streams and groundwater levels for 2.5 years, obtained further hydrological data (groundwater levels from 2005 to 2016), and worked with communities to assemble water use data. We combined these datasets in a hydrologic model (the Water Evaluation And Planning system, WEAP) and modelled groundwater recharge from 2005 to 2016, permitting a characterization of the relationship between rainfall and groundwater recharge. The groundwater recharge indicator is based on this relationship, and allows estimating total groundwater recharge for a wet season from cumulative rainfall measured to date. The indicator permits water managers to assess if the current year will likely fall into a low recharge category prior to the end of the wet season. This information can then be used to trigger short-term adaptation strategies with the goal to ‘bank’ groundwater while surface water sources are still available in the wet season. This indicator-based tool was refined through feedback provided in local stakeholder workshops. We also assessed the overall accuracy of predicting end-of-wet-season groundwater recharge with cumulative rainfall to date, and found that after the first 2–3 months of the wet season, prediction accuracies are high, leaving 5–6 months of wet season to respond adaptively to the prediction. The indicator can help water managers to plan ahead, and communicate the need for water conservation (demand management) and consideration of other water sources such as rain and surface water (supply management). This idea is transferable to other communities in regions with seasonal rainfall, and can support decision makers in increasing the socio-hydrological resilience of communities to seasonal droughts.

The recent paper by Morillas et al. [Morillas, L. et al. Using radiometric surface temperature for surface energy flux estimation in Mediterranean drylands from a two-source perspective, Remote Sens. Environ. 136, 234–246, 2013] evaluates the two-source model (TSM) of Norman et al. (1995) with revisions by Kustas and Norman (1999) over a semiarid tussock grassland site in southeastern Spain. The TSM - in its current incarnation, the two-source energy balance model (TSEB) - was applied to this landscape using ground-based infrared radiometer sensors to estimate both the composite surface radiometric temperature and component soil and canopy temperatures. Morillas et al. (2013) found the TSEB model substantially underestimated the sensible H (and overestimated the latent heat LE) fluxes. Using the same data set from Morillas et al. (2013), we were able to confirm their results. We also found energy transport and exchange behavior derived from primarily the observations themselves to differ significantly from a number of prior studies using land surface temperature for estimating heat fluxes with one-source modeling approaches in semi-arid landscapes. However, revisions to key vegetation inputs to TSEB and the soil resistance formulation resulted in a significant reduction in the bias and root mean square error (RMSE) between model output of H and LE and the measurements compared to the prior results from Morillas et al. (2013). These included more representative ground-based vegetation greenness and local leaf area index values as well as modifications to the coefficients of the soil resistance formulation to account for the very rough (rocky) soil surface conditions with a clumped canopy. This indicates that both limitations in remote estimates of biophysical indicators of the canopy at the site and the lack of adjustment in soil resistance formulation to account for site specific characteristics, contributed to the earlier findings of Morillas et al. (2013). This suggests further studies need to be conducted to reduce the uncertainties in the vegetation and land surface temperature input data in order to more accurately assess the effects of the transport exchange processes of this Mediterranean landscape on TSEB formulations.

The temperature-based two-source model (TSM) of Norman et al. (1995) has not been properly evaluated under the water stress conditions that are typical in natural Mediterranean drylands. In such areas, the asynchrony between precipitation and energy supply strongly reduces evapotranspiration, E (or latent heat flux, LE , if expressed in energy terms), making sensible heat flux ( H ) the dominant turbulent heat flux. In this study, we present a detailed analysis of the main environmental factors affecting the TSM effectiveness under such challenging conditions. The accuracy of the TSM, evaluated via errors in 15-min H estimates, was shown to have a diurnal variation. Accuracy was clearly reduced for solar elevation angles lower than 25° and during marginal hours of daytime, before 10 am and after 3 pm. The surface to air temperature difference ( T R − T a ) and the wind speed were the two environmental factors showing the strongest effect on the TSM accuracy. In contrast with results observed in other ecosystems, in this Mediterranean tussock grassland the TSM accuracy was not clearly reduced by cloudiness and it was improved under highly stressed vegetation conditions. The parallel resistances scheme of the TSM ( TSM P ) showed overall lower errors and a lower tendency to underestimate at high H values, but the series resistances scheme of the TSM ( TSM S ) increased the model accuracy under some specific circumstances such as low energy supply and atmospheric neutral conditions. Finally, two extrapolation methods to obtain daytime ( Rn > 55 W m −2 ) turbulent fluxes from the 15-min estimates of TSM were compared: (i) assuming the self-preservation of the evaporative and the non-evaporative fraction ( EF and NEF method ) and (ii) averaging the total daytime instantaneous fluxes ( Averaging method ). Despite the assumption of daytime self-preservation of EF and NEF was showed consistent, this method retrieved less accurate daytime estimates of H , and E than the Averaging method as a result of inaccuracies affecting estimates of EF and NEF from the TSM at our site. Moreover, better daytime estimates of H and E were obtained when using instantaneous fluxes from the TSM P than from the TSM S . Thus, reliable daytime estimates of H were obtained from the TSM P in a Mediterranean dryland, with mean errors of 20% and high correlations ( R 2 = 0.85). However, daytime E was strongly overestimated (125%) using the TSM by both methods, although a good correlation with eddy covariance measurements was found ( R 2 = 0.84).