Soil Moisture Data Research Articles

Development of forest ecosystems on biota monitoring plots in the area of influence of Gabčíkovo Waterwork

Abstract The construction and operation of waterworks has a significant impact on natural ecosystems. The evaluation of their negative impact and the proposal to minimize their impact, as well as the revitalization, especially of large rivers, have been given great importance in recent decades. The main goal of the study is to present impact of Gabčíkovo Waterwork on forest ecosystems after 30 years of damming. Specially evaluated are monitoring sites where changes are observed in the Slovak part of the so-called inland delta, i.e., between old and new riverbed of the Danube. The assessment of changes in terrestrial vegetation on selected monitoring sites was compared with the assessment of parallel measured soil moisture data. At the same time, data from the National Forestry Centre were also used to monitor changes in the state of forest ecosystems in the whole area of interest. When comparing the species composition from state to 2015 and from the period before GW was put into operation (in 1990) an increase was found in the area share of hardwood floodplain forest by 5.77% and the area shares of softwood floodplain forests decreased by 1.71%. Between 1990 and 2015, 68.43% of the territory remained unchanged at the level of forest type groups. A change in habitat conditions towards drier forest types was recorded on 23.61% of the territory.

Soil Temperature and Moisture Forecasting Using Exponential Smoothing Method Based on Mean Absolute Percentage Error

Soil is an essential medium for almost all plants. Temperature and moisture in the soil become one of the factors that affect plant growth. Unbalanced or unfavorable soil conditions will adversely affect the plant. Therefore, soil conditions, including temperature and moisture, must be monitored to maintain plant health. Forecasting can be used to find out the soil condition in the future so that strategies can be prepared to deal with these conditions. This forecasting is carried out based on existing soil temperature and moisture data, which is then processed using the exponential smoothing method and mean absolute percentage error as a reference for the error rate or accuracy of the forecasting. The application of these methods succeeded in producing forecasting with very good overall accuracy.<em> </em>

Comparative assessment of gridded soil moisture data products (2000–2019) for drought detection: A study over Indian region

Comparative assessment of gridded soil moisture data products (2000–2019) for drought detection: A study over Indian region

Investigating FWI Moisture Codes in Relation to Satellite-Derived Soil Moisture Data across Varied Resolutions

In the Mediterranean region, particularly in Antalya, southern Türkiye, rising forest fire risks due to climate change threaten ecosystems, property, and lives. Reduced soil moisture during the growing season is a key factor increasing fire risk by stressing plants and lowering fuel moisture content. This study assessed soil moisture and fuel moisture content (FMC) in ten fires (2019–2021) affecting over 50 hectares. The Fire Weather Index (FWI) and its components (FFMC, DMC, DC) were calculated using data from the General Directorate of Meteorology, EFFIS (8 km), and ERA5 (≈28 km) satellite sources. Relationships between FMCs, satellite-based soil moisture datasets (SMAP, SMOS), and land surface temperature (LST) data (MODIS, Landsat 8) were analyzed. Strong correlations were found between FWI codes and satellite soil moisture, particularly with SMAP. Positive correlations were observed between LST and FWIs, while negative correlations were evident with soil moisture. Statistical models integrating in situ soil moisture and EFFIS FWI (R: −0.86, −0.84, −0.83 for FFMC, DMC, DC) predicted soil moisture levels during extended fire events effectively, with model accuracy assessed through RMSE (0.60–3.64%). The SMAP surface (0–5 cm) dataset yielded a lower RMSE of 0.60–2.08%, aligning with its higher correlation. This study underlines the critical role of soil moisture in comprehensive fire risk assessments and highlights the necessity of incorporating modeled soil moisture data in fire management strategies, particularly in regions lacking comprehensive in situ monitoring.

Significance of Multi-Variable Model Calibration in Hydrological Simulations within Data-Scarce River Basins: A Case Study in the Dry-Zone of Sri Lanka

Traditional hydrological model calibration using limitedly available streamflow data often becomes inadequate, particularly in dry climates, as the flow regimes may abruptly vary from arid conditions to devastating floods. Newly available remote-sensing-based datasets can be supplemented to overcome such inadequacies in hydrological simulations. To address this shortcoming, we use multi-variable-based calibration by setting up and calibrating a lumped-hydrological model using observed streamflow and remote-sensing-based soil moisture data from Soil Moisture Active Passive Level 4. The proposed method was piloted at the Maduru Oya River Basin, Sri Lanka, as a proof of concept. The relative contributions from streamflow and soil moisture were assessed and optimised via the Kling–Gupta Efficiency (KGE). The Generalized Reduced Gradient non-linear solver function was used to optimise the Tank Model parameters. The findings revealed satisfactory performance in streamflow simulations under single-variable model validation (KGE of 0.85). Model performances were enhanced by incorporating soil moisture data (KGE of 0.89), highlighting the capability of the proposed multi-variable calibration technique for improving the overall model performance. Further, the findings of this study highlighted the instrumental role of remote sensing data in representing the soil moisture dynamics of the study area and the importance of using multi-variable calibration to ensure robust hydrological simulations of river basins in dry climates.

Soil moisture prediction based on integrating the CEEMDAN decomposition technique with LSTM in the proximity of Prosopis Juliflora

Human-made adaptations in plant diversity, particularly the introduction of alien invasive plant species, can result in problems such as depletion of groundwater, extraction of natural water resources, and modifications in the local biodiversity. In addition to regulating ecosystems and accurate farming, precise forecasting of soil moisture in the vicinity of these exotic plant species can greatly assist in various other agricultural applications. In this study, we present an improved approach for predicting soil moisture levels at different depths in the vicinity of Prosopis Juliflora (IAPS) by integrating long short-term memory (LSTM) and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). CEEMDAN is employed as a preprocessing and denoising technique to decompose the initial soil moisture data into several intrinsic mode functions (IMF). After that, LSTM is used to predict all the IMF (Intrinsic Mode Function) parts from the CEEMDAN process. The individual prediction outcomes of each IMF are combined to obtain the final prediction results. Soil moisture measurements were collected continuously in the Coimbatore area of Tamil Nadu at multi-layer soil depths of 10 cm, 30 cm, 60 cm, and 100 cm. The provided data was utilized for training models and achieving optimal accuracy in forecasting soil moisture levels one month prior. The proposed CEEMDAN-LSTM model achieved the highest R2, ranging from 0.985 to 0.995 at various depths when compared to RNN, LSTM, EMD-LSTM, EEMD-LSTM models, and existing literature models. The results show that the proposed method works well at predicting soil moisture at different depths, which can help improve the planting pattern and schedule of native species to increase the area’s green cover.

Generating surface soil moisture at the 30 m resolution in grape-growing areas based on stacked ensemble learning

ABSTRACT Accurate and timely monitoring of drought conditions in grape-producing regions is crucial for achieving healthy growth of grapes. Current soil moisture (SM) products are primarily available at coarse resolutions (e.g. several to tens of kilometres), constraining its applications at fine scales. Here, we trained a weighted stacking ensemble model including three tree-based models (categorical boosting, random forest, and gradient boosting decision tree), using seven forcing parameters related to spectral reflectance (SR), land surface temperature (LST), and evapotranspiration (ET), in conjunction with the digital elevation model (DEM) feature. The weighted stacking ensemble model exhibited an average R2 of 0.86 and an average RMSE of 0.021 m3/m3 in simulating SM in the vegetive stage and the mid-ripening stage of grape. Then we generated high spatiotemporal downscaled SM (HSM) data at a grape growing area at high spatiotemporal resolutions (30 m, 8-day) from 2009 to 2018. Our HSM dataset demonstrated strong spatial, seasonal and interannual dynamics that align with 500 m SM dataset derived from single MODIS data, and the HSM dataset shows more details in SM distribution. Additionally, the SM time series in the HSM is consistently correlated with drought events, offering intricate spatiotemporal information for drought monitoring. The application of downscaled SM results identified a concentration of drought events in the eastern foothills of the Helan Mountains, particularly severe drought conditions were observed in the Hongsipu production area. Drought occurrences in the Hongsipu production area ranged from 90% to 91% during May and June, decreasing to 73% and 41% in July and August, respectively. These findings significantly contribute to enhancing high spatiotemporal SM monitoring capabilities, offering valuable guidance for timely water management in grape-growing regions.

Estimating the soil water retention curve by the HYPROP-WP4C system, HYPROP-based PCNN-PTF and inverse modeling using HYDRUS-1D

Due to the difficulty of direct field measurement, soil hydraulic properties are often obtained in laboratory settings using small undisturbed soil samples or estimated indirectly through pedotransfer functions (PTFs). The pseudo-continuous pedotransfer function (PCNN-PTF) is a neural network-based approach for estimating soil hydraulic properties. The main objective of this study was to use field soil moisture and tension data to assess soil water retention curves obtained from the HYPROP-WP4C system and the HYPROP-based PCNN-PTF. The in-situ soil water retention data were simultaneously acquired using Acclima TDT and MeterGroup MPS-6 sensors (every 30 min, May to September 2020) from 11 hybrid bermudagrass plots under different irrigation treatments in Riverside, California. We utilized extended evaporation and dewpoint methods using HYPROP-WP4C (Meter Group Inc., USA) devices to obtain lab-measured SWRCs. In addition, SWRCs were estimated from Rosetta (Schaap et al., 2001) and by inverse modeling in HYDRUS-1D utilizing in-situ moisture retention data. Although the hysteresis impacted field data, overall, there was a good agreement between the in-situ and lab water retention data for most samples, especially within the pF range of 2–3.5. The PCNN-PTF outperformed Rosetta in estimating laboratory (RMSE=0.034 cm3 cm−3 vs 0.063 cm3 cm−3) and in-situ soil moisture data (RMSE=0.048 cm3 cm−3 vs 0.082 cm3 cm−3). Inverse modeling of in-situ data also performed well in estimating the SWRC (RMSE=0.043 cm3 cm−3); however, further attention is required in dry and saturated soil conditions. We developed a simple, free, and easy-to-access tool called PC-PTF for estimating the SWRC using the PCNN-PTF model evaluated in this study. The PC-PTF can be accessed from the Verdi Water Management Group website: http://www.ucrwater.com/software-and-tools.html.

Analysis of Flash Drought and Its Impact on Forest Normalized Difference Vegetation Index (NDVI) in Northeast China from 2000 to 2020

Flash drought is characterized by rapid onset and short-duration drought conditions caused by a combination of factors, including high evaporation, high temperature, and prolonged periods of little to no precipitation, leading to a sudden and severe decrease in soil moisture levels. In comparison to conventional drought, it is more susceptible to the effects of global warming and has the potential to become a common drought phenomenon in the coming years, necessitating further research. In this paper, we focused on flash drought events, specifically in forest parts of northeastern China that are included within the Greater Khingan Mountains (GKM), Lesser Khingan Mountains (LKM), and Changbai Mountains (CM), using daily soil moisture data as well as SPOT- VEGETATION NDVI satellite data from 2000 to 2020 and determined their impact on the forest NDVI. Our major findings are as follows. (1) The forest within GKM had the maximum area being affected by flash drought events. (2) The frequency ranged from 1 to 2 times, whereas the total duration varied between 20 and 55 days over the study area in a 21-year period. (3) Flash drought was most common in the plant-growing seasons. (4) The flash drought events had a negative influence on the forest NDVI. Our study contributes to a deeper understanding of the flash drought dynamics in forest areas of northeast China for flash drought monitoring, prediction, and management strategies in this region.

Atmospheric water demand dominates terrestrial ecosystem productivity in China

Vegetation production is critical for carbon sequestration in terrestrial ecosystems. However, the relative contribution of soil moisture (SM) and vapor pressure deficit (VPD) to vegetation production in ecosystems remains debated. This study combined SM and VPD data with satellite observations of solar-induced fluorescence (SIF) from 2003 to 2018 to examine the contribution of SM and VPD to long-term variations in vegetation production using binning approach. Constraint lines based on restricted cubic splines were established to investigate the threshold for the responses of vegetation production potential to VPD and SM. The results revealed that VPD had greater impacts on vegetation production in Chinese terrestrial ecosystems than SM, with VPD-dominated and SM-dominated areas accounting for approximately 84 % and 16 % of the total national area, respectively. The mean values of SIF changes under the independent influence of SM and VPD were 0.034 mWm−2nm−1sr−1 and 0.130 mWm−2nm−1sr−1, respectively, indicating higher sensitivity of SIF to VPD. The effects of SM and VPD on ecosystem production varied across aridity gradients, with VPD making a significant contribution to forest ecosystem production. However, determining the relative effects of SM and VPD in ecosystems with low vegetation cover was difficult. Further exploration revealed that the maximum potential for terrestrial ecosystem production increased with VPD, peaking at a VPD of about 1.275 ± 0.068 kPa. Beyond this range, the maximum production potential declined. Our study confirms that VPD has a stronger and more extensive influence on the trend and variability of ecosystem production in China than SM.

20-year permafrost evolution documented through petrophysical joint inversion, thermal and soil moisture data

This study investigates the ground characteristics of the high altitude (3410 m a.s.l.) permafrost site Stockhorn in the Swiss Alps using a combination of surface and subsurface temperature, soil moisture, electrical resistivity and P-wave velocity time series data including a novel approach to explicitly quantify changes in ground ice content. This study was motivated by the clear signal of permafrost degradation visible in the full dataset at this long-term monitoring site within the PERMOS (Permafrost Monitoring Switzerland) network. Firstly, we assess the spatio-temporal evolution of the ground ice and water content by a combined analysis of all available in situ thermal (borehole and ground surface temperature), hydrological (soil moisture) and geophysical (geoelectric and seismic refraction) data over two decades (2002–2022) regarding the driving factors for the spatially different warming. Secondly, we explicitly quantify the volumetric water and ice content and their changes in the subsurface from 2015 to 2022 using a time-consistent petrophysical joint inversion scheme within the open-source library pyGIMLi. The petrophysical joint inversion scheme has been improved by constraining the rock content to be constant in time for six subsequent inversions to obtain consistent changes in ice and water content over the monitoring period based on jointly inverted resistivity and traveltime data. All different data show a warming trend of the permafrost. The ice content modeled from the petrophysical joint inversion has decreased by about 15 vol.% between 2015 and 2022. Changes in ice content are first observed in the lower, south-facing part of the profile. As a result, resistivity and P-wave velocity have been decreasing significantly. Permafrost temperatures measured in the boreholes have increased between 0.5 °C and 1 °C in 20 years. Our study shows the high value of joint and quantitative analysis of datasets comprising complementary subsurface variables for long-term permafrost monitoring.

Learning Constitutive Relations From Soil Moisture Data via Physically Constrained Neural Networks

AbstractThe constitutive relations of the Richardson‐Richards equation encode the macroscopic properties of soil water retention and conductivity. These soil hydraulic functions are commonly represented by models with a handful of parameters. The limited degrees of freedom of such soil hydraulic models constrain our ability to extract soil hydraulic properties from soil moisture data via inverse modeling. We present a new free‐form approach to learning the constitutive relations using physically constrained neural networks. We implemented the inverse modeling framework in a differentiable modeling framework, JAX, to ensure scalability and extensibility. For efficient gradient computations, we implemented implicit differentiation through a nonlinear solver for the Richardson‐Richards equation. We tested the framework against synthetic noisy data and demonstrated its robustness against varying magnitudes of noise and degrees of freedom of the neural networks. We applied the framework to soil moisture data from an upward infiltration experiment and demonstrated that the neural network‐based approach was better fitted to the experimental data than a parametric model and that the framework can learn the constitutive relations.

Variable soil moisture responses to rainfall events in fields under different management practices

AbstractSoil moisture response to rainfall is a key factor that dictates how well a landscape can support crop growth as well as its susceptibility to water runoff and leaching, however, few studies have investigated how agricultural management impacts this important soil function. This study compares two common agricultural soil treatments (cover crops and soil compaction) and their soil moisture response to rainfall in comparison to a control. In a humid temperate climate during March to November, individual rainfall events were delineated over two growing seasons and corresponding soil moisture responses were identified using in situ soil moisture sensors at four soil depths (20, 30, 40, and 60 cm). Results suggest that hydrological responses differed with both event type and management treatment. Not all rainfall events triggered a response: those that triggered responses at shallower soil depths were typically characterized by higher total event rainfall, and higher maximum and average rainfall intensity. In contrast, rainfall events triggering responses at deeper soil depths were characterized by longer event duration as well as higher 10‐day antecedent rainfall (AR). Soil moisture responses for the cover crop treatment were characterized by relatively lower initial and peak soil moisture at shallower depths but higher values at 60 cm depth, whereas soil moisture responses for the control and compacted soil treatments demonstrated the opposite. Matrix flow was most often generated for rainfall events with high magnitude and was not preferentially associated with any particular soil treatment. However, specific conditions were needed to generate vertical preferential flow, namely high total event rainfall for both horizons, or high AR for preferential flow in the Ap horizon, or high rainfall intensity for preferential flow in the Bt horizon. Our findings demonstrate the potential for detailed event‐based soil water process analysis using high‐frequency, multi‐depth soil moisture data.

Dimensionality and scales of preferential flow in soils of Shale Hills hillslope simulated using HYDRUS

AbstractPreferential flow (PF) processes are governed by subsurface soil structures at various scales. Still, model validation and mechanistic understanding of PF are very lacking. We hypothesize that PF at hillslope and larger scales cannot be described and quantified when neglecting small‐scaled spatially variable processes and simplifying the model dimensionality. The objective was to learn from comparing simulation results of multidimensional (1D, 2D, and 3D) and multiscale (pedon, catena, and catchment) modeling approaches with comprehensive datasets, and so as to evaluate PF simulations based on the Richards’ equation (solved by the HYDRUS software). Results showed limited alignment between 1D simulations and soil moisture data, mainly affected by vertical changes in porosity, permeability, and precipitation features. 2D and 3D simulations outperformed 1D models. 3D simulations provided satisfactory description of PF dynamics at the pedon scale, considering accurate representations of soil and bedrock structures for three dimensions (vertical, horizontal, and surrounding area). In 2D simulations at the pedon scale, models incorporating dual‐porosity and anisotropy of soils yielded more accurate predictions of water dynamics than single‐porosity and isotropic models. Furthermore, the application of 2D simulation at the catena scale identify PF pathways owing to the enhanced representation of the hydraulic connectivity between different locations along the slope. The results confirmed the significance of multidimensional and multiscale modeling approaches for PF simulations in hillslope hydrology. Considering the complexity and parameterization of 2D and 3D “bottom‐up” physically based models in representing spatial variability within and between soil profiles and/or underlying bedrock geology, the results contribute to creating a modeling framework applicable to identify the PF processes and thus their implications in managing water resources.

Field measurement of entrapped pore‐air pressure and the effect of rising groundwater level in the soil layer

AbstractUnsaturated soil contains pore air, some of which becomes entrapped in the soil layer during the infiltration process. Such entrapped pore air in the soil layer inhibits infiltration, decreases hydraulic conductivity, and causes erroneous estimates of groundwater response. Experimental studies suggest an effect of compressed entrapped pore air on stream discharge. However, few studies have investigated the behaviour of pore air in the field, and no method for measurement of pore‐air behaviour has been established. We measured pore‐air pressure (Pair) with a simple handmade probe, combined with atmospheric pressure (Patm) and groundwater level. The entrapment and compression of pore air were detected based on the pressure difference (ΔP = Pair – Patm). Observations were conducted for approximately 1 year in two small headwater catchments (TC and HA, which have differences in soil depth and groundwater level dynamics) in Ibaraki Prefecture, Japan. Positive responses of ΔP during some rainfall events were confirmed at both TC and HA. Pore‐air entrapment occurred even during weak rainfall events with rainfall intensity of <10 mm/h. During a typhoon event in October 2019, a maximum ΔP of 3.9 kPa was observed at HA. The pore pressure increase recorded by the tensiometer was mostly explained by the increase in ΔP, which was supported by the soil moisture data. Therefore, we concluded that our handmade measurement system could successfully measure Pair and detect pore‐air entrapment in the field. This showed that Pair is not negligible. Total event rainfall was related to the maximum ΔP. It also showed that the rise in the groundwater level plays a significant role in pressurization of the entrapped pore air. We present the first field data demonstrating the effect of groundwater level rise on the pressurization of entrapped Pair.

Characterizing Satellite Soil Moisture Drydown: A Bivariate Filtering Approach

AbstractDrying of soil impacts land energy and water balance, influences the sustainability of vegetation growth, and modulates hydrological extremes including floods. While satellite soil moisture data are widely used for a range of environmental applications, systematic differences from regional in‐situ data prevent their optimal use as key physical signatures (such as soil moisture recession, also termed drydown) are represented differently. This study investigates differences in drydowns from the Soil Moisture Active Passive (SMAP) level 4 product with reference to in‐situ observations. A bivariate filtering alternative is proposed to minimize the disparity noted by modeling the relationship between the rate of drying and initial soil wetness and representing the same as in‐situ. Considerable improvements are observed in the resulting SMAP soil moisture filtered estimates. Although the algorithm assumes spatial stationarity, improvements exist across different soil properties and climatic conditions, providing a parsimonious alternative to better capture the dynamics of soil moisture loss.

Comparative analysis of satellite and reanalysis data with ground‐based observations in Northern Ghana

AbstractAccurate predictions of streamflow and flood events are contingent upon the availability of reliable hydrometeorological data. In regions characterized by scarcity of ground‐based hydrometeorological observations, satellite and reanalysis data assume prominence as alternative predictors. Floods and droughts have emerged as a significant concern in Northern Ghana, yet the scarcity of ground‐based hydrometeorological data impedes effective prediction of these hydrological events. Consequently, the identification of suitable surrogate hydrometeorological data holds paramount importance in addressing these challenges. This study, therefore, assessed the accuracy of satellite and reanalysis data against ground‐based data in Northern Ghana. Rainfall and mean temperature spanning from 1998 to 2019 and soil moisture datasets from 2019 to 2022 were collected from GMet, ISMN (ground‐based), CHIRPS, PERSIANN‐CDR, ERA5, ARC2, MERRA‐2, TRMM and CFSR (satellite and reanalysis). Employing rigorous statistical measures, namely standard deviation, mean absolute error (MAE) and mean bias error (MBE), the accuracy of these datasets was thoroughly evaluated. The results revealed that CHIRPS and PERSIANN‐CDR exhibited superior accuracy in rainfall simulation, with CHIRPS demonstrating particularly consistent congruence with observed data. In terms of mean temperature prediction, ERA5 surpassed MERRA‐2 and CFSR. Regarding soil moisture assessments, both ERA5 and CFSR offered satisfactory simulations. Hence, our findings advocate for the preference of CHIRPS (for rainfall data), ERA5 (for temperature data) and a combination of CFSR/ERA5 (for soil moisture data) as dependable primary data sources for streamflow modelling, drought analysis, flood prediction and water resource management in the context of Northern Ghana.

Soil Moisture Information Improves Drought Risk Protection Provided by the USDA Livestock Forage Disaster Program

Abstract The USDA Livestock Forage Disaster Program (LFP) offers financial assistance to farmers and ranchers with grazed forage losses caused by fire or drought. Payments for drought losses are based on the U.S. Drought Monitor (USDM), which is designed to integrate meteorological, agricultural, hydrological, ecological, and socioeconomic droughts. Because soil moisture deficit is a more specific measure of agricultural drought, we hypothesized that basing LFP payments on soil moisture observations could better reduce producers’ risk. Therefore, our objectives were to 1) quantify relationships of forage yield with USDM-based LFP payment multipliers and with in situ soil moisture, 2) develop an alternative LFP payment multiplier structure based on in situ soil moisture, and 3) quantify risk reduction using the current and alternative payment structures. We focused on Oklahoma, United States, which has led the nation in LFP payments received and has >25 years of in situ soil moisture observations statewide. Using non-alfalfa hay yield as a surrogate for forage production, we found that LFP payment multiplier values and soil moisture anomaly were each related to yield, and soil moisture anomaly explained 54% of yield variability. However, the USDM-based LFP payment structure sometimes resulted in payments for above-average yield, and higher payments did not always correspond with greater yield losses. We developed an alternative soil-moisture-based payment structure that reduced financial risk by >20% compared with the current USDM-based structure. Our study identifies an improved LFP payment structure for Oklahoma that can be evaluated and refined in other states or nationwide using other soil moisture data sources.

Kontrol Motor Berbasis PID untuk Pompa Air di Miniatur Rumah Kaca

Greenhouses are crucial in agriculture for enhancing crop yields by providing controlled environmental conditions. Efficient water management is a key challenge in conventional greenhouse systems, often leading to water wastage due to unmeasured soil moisture levels. This research aimed to design and implement a PID control system for water pumps in a miniature greenhouse to optimize water usage and maintain stable soil moisture and temperature levels. The methodology involved integrating PID control with sensors to monitor and adjust the water pump operation based on real-time soil moisture and temperature data. The study found that the PID-controlled system reduced the time to reach steady-state conditions from 650 ms to 600 ms, with no overshoot compared to systems without control. Additionally, the error in the PID system ranged from 0.89% to 2.32%. These findings suggest that PID control significantly enhances the efficiency and effectiveness of water usage in greenhouses, contributing to better crop management and resource conservation. The implications of this research are substantial for agricultural practices, as it demonstrates a viable solution for improving irrigation precision and overall greenhouse management.

Flash droughts and their impacts—using newspaper articles to assess the perceived consequences of rapidly emerging droughts

Flash droughts (FDs) have attracted increasing attention in the past decade. They are characterised by a rapid depletion of soil moisture resulting from interactions between the soil and atmospheric conditions. To date, there is a lack of consistent FD definitions and an understanding of their socio-economic impacts. Here, we explore the relationship between biophysical FD conditions and the perceived impacts of FDs in Germany between 2000 and 2022. We measured perceived impacts by analysing consequences reported in newspaper articles (2000–2022) and online search behaviour using Google trends data (2004–2022). To characterise the physical process, we considered root zone soil moisture data. Our results show that FDs are becoming increasingly frequent in Germany, occurring once every two years on average. Despite the lack of knowledge from the general public regarding the phenomenon of FDs, the peaks of interest in drought impacts correspond to the physical occurrence of FDs across the country. We identified an average time gap of four weeks between FD onset and the reporting of perceived impacts. This gap is longer than the average duration of FDs’ onset. Consequently, our findings highlight that consistent monitoring of FD conditions and drivers is necessary to guarantee effective preparedness. As impact perception is too slow to allow the adoption of mitigation measures, FDs require new schemes for response measures compared with slowly emerging (conventional) drought events. The novel method also allows the consistent and impact-based validation of FD identification methods.