Sustainable Urban Drainage Systems (SUDS) represent a paradigm shift in stormwater management, offering holistic solutions to urban water challenges. This review examines SUDS principles, design strategies, effectiveness and barriers to implementation. SUDS prioritize infiltration and mimic natural hydrological processes to reduce flood risk, improve water quality and support ecosystems in urban environments. Effective SUDS design integrates different components such as permeable pavements, green roofs, and rain gardens, tailored to the local context. Evidence suggests that well-designed SUDS can mitigate peak flows, reduce runoff volumes, and purify water. However, barriers to widespread adoption include lack of awareness, upfront costs, and regulatory complexity. Overcoming these will require collaborative stakeholder action to prioritize education, policy support, and funding opportunities. Future research should focus on optimizing SUDS design, assessing long-term performance, and quantifying socio-economic benefits. By integrating SUDS into urban landscapes, cities can strengthen hydrological resilience, promote sustainability, and enrich urban life.

Drought is a significant challenge to semiarid Mediterranean grasslands, Increasing the accuracy of monitoring allows improving the conservation and management of these vital ecosystems. Meteorological drought is commonly monitored by the Standard Precipitation Index (SPI) or the Standard Precipitation Evapotranspiration Index (SPEI). On the other hand, agriculture drought is estimated by the Vegetation Health Index (VHI). This work aims to optimise the correlation between both drought types using the best transformation of VHI and the most appropriate time scale. Two drought-vulnerable Mediterranean grasslands were selected to evaluate the performance of the drought indexes. The SPI and the SPEI were calculated using data obtained from nearby weather stations. MODIS data were used to calculate the VHI. This index was standardised, naming it as SVHI. Our results revealed that SPEI was better correlated with VHI compared to SPI. In addition, SVHI obtained better results in the critical vegetation phases than VHI. Overall, SPEI and SVHI were the best correlated indexes. The quarterly scale showed stronger relationships than the monthly scale and the most correlated time frame were Mediterranean spring and autumn. This fact suggests that SPEI and SVHI could provide a plus point for increasing the precision of vegetation monitoring during these periods.

The dynamic of rangelands results from complex interactions between vegetation, soil, climate, and human activity. This scenario makes rangeland’s condition challenging to monitor, and degradation assessment should be carefully considered when studying grazing pressures. In the present work, we study the interaction of vegetation and soil moisture in semiarid rangelands using vegetation and soil moisture indices. We aim to study the feasibility of using soil moisture negative anomalies as a warning index for vegetation or agricultural drought. Two semiarid agricultural regions were selected in Spain for this study: Los Vélez (Almería) and Bajo Aragón (Teruel). MODIS images, with 250 m and 500 m spatial resolution, from 2002 to 2019, were acquired to calculate the Vegetation Condition Index (VCI) and the Water Condition Index (WCI) based on the Normalised Difference Vegetation Index (NDVI) and soil moisture component (W), respectively. The Optical Trapezoid Model (OPTRAM) estimated this latter W index. From them, the anomaly (Z-score) for each index was calculated, being $Z_{VCI}$ and $Z_{WCI}$, respectively. The probability of coincidence of their negative anomalies was calculated every 10 days (10-day periods). The results show that for specific months, the $Z_{WCI}$ had a strong probability of informing in advance, where the negative $Z_{VCI}$ will decrease. Soil moisture content and vegetation indices show more similar dynamics in the months with lower temperatures (from autumn to spring). In these months, given the low temperatures, precipitation leads to vegetation growth. In the following months, water availability depends on evapotranspiration and vegetation type as the temperature rises and the precipitation falls. The stronger relationship between vegetation and precipitation from autumn to the beginning of spring is reflected in the feasibility of $Z_{WCI}$ to aid the prediction of $Z_{VCI}$. During these months, using $Z_{WCI}$ as a warning index is possible for both areas studied. Notably, November to the beginning of February showed an average increase of 20–30% in the predictability of vegetation anomalies, knowing moisture soil anomalies four lags in advance. We found other periods of relevant increment in the predictability, such as March and April for Los Vélez, and from July to September for Bajo Aragón.

Multiple studies revealed that pasture grasslands are a time-varying complex ecological system. Climate variables regulate vegetation growing, being precipitation and temperature the most critical driver factors. This work aims to assess the response of two different Vegetation Indices (VIs) to the temporal dynamics of temperature and precipitation in a semiarid area. Two Mediterranean grasslands zones situated in the center of Spain were selected to accomplish this goal. Correlations and cross-correlations between VI and each climatic variable were computed. Different lagged responses of each VIs series were detected, varying in zones, the year’s season, and the climatic variable. Recurrence Plots (RPs) and Cross Recurrence Plots (CRPs) analyses were applied to characterise and quantify the system’s complexity showed in the cross-correlation analysis. RPs pointed out that short-term predictability and high dimensionality of VIs series, as well as precipitation, characterised this dynamic. Meanwhile, temperature showed a more regular pattern and lower dimensionality. CRPs revealed that precipitation was a critical variable to distinguish between zones due to their complex pattern and influence on the soil’s water balance that the VI reflects. Overall, we prove RP and CRP’s potential as adequate tools for analysing vegetation dynamics characterised by complexity.