Search Results (86)

As global populations grow and environmental constraints intensify, improving agricultural water management is essential for sustainable food production. Traditional irrigation methods often lack adaptability, leading to inefficient water use. Reinforcement learning (RL) offers a promising solution for developing dynamic irrigation strategies that balance productivity and resource conservation. However, agricultural RL tasks are characterized by sparse actions—irrigation only when necessary—and delayed rewards realized at the end of the growing season. This study integrates RL with AquaCrop-OSPy simulations in the Gymnasium framework to develop adaptive irrigation policies for maize. We introduce a reward mechanism that penalizes incremental water usage while rewarding end-of-season yields, encouraging resource-efficient decisions. Using the Proximal Policy Optimization (PPO) algorithm, our RL-driven approach outperforms fixed-threshold irrigation strategies, reducing water use by 29% and increasing profitability by 9%. It achieves a water use efficiency of 76.76 kg/ha/mm, a 40% improvement over optimized soil moisture threshold methods. These findings highlight RL’s potential to address the challenges of sparse actions and delayed rewards in agricultural management, delivering significant environmental and economic benefits. Full article

In the context of smart agriculture, accurate soil moisture monitoring is crucial to optimise irrigation, improve water usage efficiency and increase crop yields. Although low-cost capacitive sensors are used to make monitoring affordable, these sensors face accuracy challenges that often result in inefficient irrigation practices. This paper presents a method for calibrating capacitive soil moisture sensors through data assimilation. The method was validated using data collected from a farm in Dos Hermanas, Seville, Spain, which utilises a drip irrigation system. The proposed solution integrates the Hydrus 1D model with particle filter (PF) and the Iterative Ensemble Smoother (IES) to continuously update and refine the model and sensor calibration parameters. The methodology includes the implementation of physical constraints, ensuring that the updated parameters remain within physically plausible ranges. Soil moisture was measured using low-cost SoilWatch 10 capacitive sensors and ThetaProbe ML3 high-precision sensors as a reference. Furthermore, a comparison was carried out between the PF and IES methods. The results demonstrate that the data assimilation approach markedly enhances the precision of sensor readings, aligning them closely with reference measurements and model simulations. The PF method demonstrated superior performance, achieving an 84.8% improvement in accuracy compared to the raw sensor readings. This substantial improvement was measured against high-precision reference sensors, confirming the effectiveness of the PF method in calibrating low-cost capacitive sensors. In contrast, the IES method showed a 68% improvement in accuracy, which, while still considerable, was outperformed by the PF. By effectively mitigating observation noise and sensor biases, this approach proves robust and practical for large-scale implementations in precision agriculture. Full article

The impact of fertilization and irrigation on heavy metal accumulation in saline–alkali soil and its underlying mechanisms are critical issues given the constraints that soil salinization places on agricultural development and crop quality. This study addressed these issues by investigating the effects of adjusting organic fertilizer types, proportions, and irrigation volumes on the physicochemical properties of lightly to moderately saline–alkali soils and analyzing the interaction mechanisms between microorganisms and heavy metals. The results indicate that the rational application of organic fertilizers combined with supplemental irrigation can mitigate soil salinity accumulation and water deficits, and reduce the soil pH, thereby enhancing soil oxidation, promoting nitrogen transformation and increasing nitrate–nitrogen levels. As the proportion of organic fertilizers increased, heavy metal residues, enrichment, and risk indices in the crop grains also increased. Compared to no irrigation, supplemental irrigation of 22 mm during the grain-filling stage increased soil surface Cd content, Zn content, and the potential ecological risk index (HRI) by 10.2%, 3.1%, and 8%, respectively, while simultaneously reducing the heavy metal content in grains by 12–13.5% and decreasing heavy metal enrichment. Principal component analysis revealed the primary factors influencing Cu and Zn residues and Cd accumulation in the crop grains. Soil salinity was significantly negatively correlated with soil pH, organic matter, total nitrogen, and ammonium nitrogen, whereas soil organic matter, total nitrogen, ammonium nitrogen, soil pH, oxidation–reduction potential, soluble nitrogen, and microbial biomass nitrogen were positively correlated. The accumulation and residues of Zn and Cu in the soil were more closely correlated with the soil properties compared to those of Cd. Specifically, Zn accumulation on the soil surface was primarily related to aliphatic organic functional groups, followed by soil salinity. Residual Zn in the crop grains was primarily associated with soil oxidation–reduction properties, followed by soil moisture content. The accumulation of Cu on the soil surface was mainly correlated with the microbial biomass carbon (MBC), whereas the residual Cu in the crop grains was primarily linked to the soil moisture content. These findings provide theoretical insights for improving saline–alkali soils and managing heavy metal contamination, with implications for sustainable agriculture and environmental protection. Full article

E’bian Yi Autonomous County is a mineral-rich area located in a complex geological structure zone. The region experiences frequent geological disasters due to concentrated rainfall, steep terrain, and uneven vegetation cover. In particular, during the rainy season, large amounts of rainwater rapidly accumulate, increasing soil moisture and slope pressure, making landslides and debris flows more likely. Additionally, human activities such as mining, road construction, and building can alter the original geological structure, exacerbating the risk of geological disasters. According to publicly available data from the Leshan government, various types of geological disasters occurred in 2019, 2020, 2022, and 2023, resulting in economic losses and casualties. Although some studies have focused on geological disaster issues in E’bian, these studies are often limited to specific areas or types of disasters and lack comprehensive spatial and temporal analysis. Furthermore, due to constraints in technology, funding, and manpower, geophysical exploration, field geological exploration, and environmental ecological investigations have been challenging to carry out comprehensively, leading to insufficient and unsystematic data collection. To provide data support and monitoring for regional territorial spatial planning and geological disaster prevention and control, this paper proposes a new method to study the correlation between soil moisture changes and geological disasters. Six high-resolution Landsat remote sensing images were used as the main data sources to process the image band data, and terrain factors were extracted and classified using a digital elevation model (DEM). Meanwhile, a Normalized Difference Vegetation Index–Land Surface Temperature (NDVI-LST) feature space was constructed. The Temperature Vegetation Drought Index (TVDI) was calculated to analyze the variation trend and influencing factors of soil moisture in the study area. The research results showed that the variation in soil moisture in the study area was relatively stable, and the overall soil moisture content was high (0.18 < TVDI < 0.33). However, due to the large variation in topographic relief, it could provide power and be a source basis for geological disasters such as landslide and collapse, so the inversion value of TVDI was small. The minimum and maximum values of the correlation coefficient (R²) were 0.60 and 0.72, respectively, indicating that the surface water content was relatively large, which was in good agreement with the calculated results of vegetation coverage and conducive to the restoration of ecological stability. In general, based on the characteristics of remote sensing technology and the division of soil moisture critical values, the promoting and hindering effects of soil moisture on geological hazards can be accurately described, and the research results can provide effective guidance for the prevention and control of geological hazards in this region. Full article

The Priestley–Taylor model of the Jet Propulsion Laboratory (PT-JPL) evapotranspiration (ET) model is relatively simple and has been widely used based on meteorological and satellite data. However, soil moisture (SM) constraints include a vapor pressure deficit (VPD) that causes large uncertainty. In this study, we proposed a PT-SinRH model by introducing a sine function of air relative humidity (RH) to replace RH^VPD to characterize SM constraints, which can improve the accuracy of ET estimations. The PT-SinRH model is validated by eddy covariance (EC) data from 2000–2020. These data were collected by AmeriFlux at 28 sites on the conterminous United States (CONUS), and the land cover types of the sites vary from croplands to wetlands, grasslands, shrub lands and forests. The validation results from daily scale-based on-site and satellite data inputs showed that the PT-SinRH model estimates fit the observations with a coefficient of determination (R²) of 0.55, root-mean-square error (RMSE) of 17.5 W/m², bias of −1.2 W/m² and Kling–Gupta efficiency (KGE) of 0.70. Additionally, the PT-SinRH model based on reanalysis and satellite data inputs has an R² of 0.49, an RMSE of 20.3 W/m², a bias of −8.6 W/m² and a KGE of 0.55. The PT-SinRH model showed better accuracy when using the site-measured meteorological data than when using reanalysis meteorological data as inputs. Additionally, compared with the PT-JPL model, the results demonstrate that our approach, i.e., PT-SinRH, improved ET estimates, increasing the R² and KGE by 0.02 and decreasing the RMSE by about 0.6 W/m². This simple but accurate method permits us to investigate the decadal variation in regional ET over the land. Full article

Climate change severely impacts sub-Saharan Africa, which relies heavily on rainfed agriculture for food production. Variable and insufficient rainfall exacerbates food insecurity across the region. Traditional in situ water harvesting (IS_WH) practices enhance soil water-holding capacity, improve infiltration, and promote soil conservation. This meta-analysis of the peer-reviewed literature examines IS_WH practices’ effects on crop yield, soil moisture, runoff, and soil loss reduction across various rainfall conditions in sub-Saharan Africa. The analysis reveals that IS_WH practices significantly boost agricultural productivity, with a combined effect size showing a 71% increase in total crop yield. IS_WH practices also improve soil moisture retention by 59% and effectively reduce runoff by 53% and soil loss by 58.66%, demonstrating their robust water and soil conservation benefits. Despite their proven benefits, the adoption of IS_WH practices in sub-Saharan Africa is hindered by socioeconomic and institutional barriers, including limited technical knowledge, resource constraints, and inadequate extension services. By addressing these barriers, there is significant potential to scale up IS_WH practices, enhancing agricultural productivity and sustainability across the region. Such efforts are crucial for mitigating the impacts of climate change, ensuring food security, and promoting sustainable development in sub-Saharan Africa. Full article

In recent years, with the escalation of food waste generation, stringent legal constraints on landfill usage and incineration have necessitated the exploration of alternative disposal methods, augmenting interest in diverse recycling strategies. Notably, carbonized food waste (CFW), a byproduct of food waste carbonization, has emerged as an efficacious adsorbent for pollutant removal. This study focuses on the application of in situ remediation techniques, specifically electrokinetic (EK) remediation combined with enhancers, to decontaminate soil afflicted with single or multiple heavy metals. The utilization of a permeable reactive barrier (PRB) infused with CFW aims to mitigate secondary environmental repercussions, including the propagation of contaminants in soil and groundwater. Experiments were conducted on clay samples contaminated with copper, lead, or a combination thereof. Observations revealed that the current density peaked during the initial 1–2 days of the experiment, experienced a resurgence post-electrode exchange, and subsequently diminished. The efficacy of metal removal was predominantly pronounced for copper, with remediation rates ranging from 85% to 92% in singly contaminated samples and 75% to 89% in dually contaminated samples after a 10-day treatment period, incorporating an electrode exchange on the eighth day. Conversely, the efficacy of lead removal was markedly lower, with rates of 0.6% to 33% in singly contaminated samples and 14% to 25% in combined contamination scenarios, suggesting the necessity for extended treatment durations. The post-experimental moisture content indicated successful enhancer injection. Additionally, pH measurements affirmed that heavy metals migrated effectively within the sample matrix, facilitated by the EK phenomenon after the electrode exchange. This study highlights the potential of CFW within PRBs for the remediation of heavy metal-contaminated soils, although the removal efficiencies between different metals is variable, emphasizing the need for tailored approaches in the treatment of lead-contaminated environments. Full article

Water scarcity is one of the significant constraints on sustainable agricultural development in arid and semi-arid regions. The challenges faced in forage production are even more severe than those encountered with general crops. The industry still struggles to achieve water-efficient, high-yield quality forage in water-scarce pastoral areas. This study focuses on alfalfa, a high-quality forage crop, employing a combination of “subsurface drip irrigation (SDI) + alternate partial root-zone irrigation (APRI)” and establishing three water supply gradients (full irrigation, 75% deficit, 50% deficit), in comparison with the widely used subsurface drip irrigation, to study the effects of two irrigation methods and three moisture gradients on alfalfa. The aim is to provide some theoretical basis and data support for achieving water-saving and high-yield quality forage in water-scarce pastoral areas. The main findings are as follows: First, compared with SDI, the two-year alternate dry and wet environment provided by alternate partial root-zone drip irrigation (ARDI) significantly increased the specific root length, specific surface area, and root length density of alfalfa at 20~40 cm depth, increasing by 33.3~76.8%, 6.4~32.97%, and 15.2~93.9%, respectively, compared to SDI. Under ARDI irrigation, the alfalfa root system has a greater contact area with the soil, which lays a solid foundation for the water and nutrient supply needed for the accumulation of its above-ground biomass. Secondly, over the two-year production process, the plant height of alfalfa under ARDI treatment was 12~14.5% higher than that under SDI, the total fresh forage yield was 43.5~64% higher, and the total dry forage yield was 23.2~33.8% higher than SDI. Under ARDI, the 75% water deficit treatment could still maintain the plant height and stem thickness of alfalfa compared to full irrigation with SDI and increased the dry forage yield by 6.6% without significantly reducing the quality, significantly enhancing the productive performance of alfalfa. Moreover, during the two years of production and utilization, the nutritional quality of alfalfa under the ARDI irrigation mode did not significantly decrease compared to SDI, maintaining the stable nutritional quality of alfalfa over multiple years of production. Lastly, thanks to the improved root system and increased yield of alfalfa under ARDI irrigation, and based on this, its water evapotranspiration did not significantly increase compared to SDI; the annual average Alfalfa Water Productivity Index (AWP_I) and Alfalfa Water Productivity of Crop (AWP_C) under ARDI irrigation increased by 28.8% and 37.2%, respectively, improving the water use efficiency of alfalfa production. In summary, in the production of alfalfa in water-scarce pastoral areas, ARDI and its water deficit treatment have more potential for water-saving than SDI as a water-saving irrigation strategy. Full article

Drone multispectral technology enables the real-time monitoring and analysis of soil moisture across vast agricultural lands. overcoming the time-consuming, labor-intensive, and spatial discontinuity constraints of traditional methods. This study establishes a rapid inversion model for deep soil moisture (0–200 cm) in dryland agriculture using data from drone-based multispectral remote sensing. Maize, millet, sorghum, and potatoes were selected for this study, with multispectral data, canopy leaf, and soil moisture content at various depths collected every 3 to 6 days. Vegetation indices highly correlated with crop canopy leaf moisture content (p < 0.01) and were identified using Pearson correlation analysis, leading to the development of linear and nonlinear regression models for predicting moisture content in canopy leaves and soil. The results show a significant linear correlation between the predicted and actual canopy leaf moisture levels for the four crops, according to the chosen vegetation indices. The use of canopy leaf moisture content to predict surface soil moisture (0–20 cm) demonstrated enhanced accuracy. The models designed for the top 20 cm of soil moisture successfully estimated deep soil moisture levels (up to 200 cm) for all four crops. The 20 cm range soil moisture model showed improvements over the 10 cm range model, with increases in Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Coefficient of Determination (R²), and Nash–Sutcliffe Efficiency Coefficient (NSE) by 0.4, 0.8, 0.73, and 0.34, respectively, in the corn area; 0.28, 0.69, 0.48, and 0.25 in the millet area; 0.4, 0.48, 0.22, and 0.52 in the sorghum area; and 1.14, 0.81, 0.73, and 0.56 in the potato area, all with an average Relative Error (RE) of less than 10% across the crops. Using drone-based multispectral technology, this study forecasts leaf water content via vegetation index analysis, facilitating swift and effective soil moisture inversion. This research introduces a novel method for monitoring and managing agricultural water resources, providing a scientific basis for precision farming and moisture variation monitoring in dryland areas. Full article

Sustainable fruit orchard development in arid areas is severely affected by the scarcity of fresh water. To mitigate the lack of fresh water, the use of low-quality water for irrigation is becoming a common practice in several margin areas. However, salinity is considered one of the most important environmental constraints limiting the successful crop production. Therefore, the effects of deficit irrigation strategies using saline water (3.1 dS m⁻¹) on soil water content, soil salinity, and yield of commercial peach orchard were investigated. Three irrigation treatments were considered: a Control, full irrigated (FI); and partial root-zone drying (PRD₅₀); and deficit irrigation (DI) strategies irrigated at 50% ETc. These levels of water supply allowed for contrasting watering conditions with clear distinction between irrigation treatments. The differential pattern in soil moisture was accompanied by that of soil salinity with an increase in all FI treatments (16–25%). The results indicated that soil salinity increased with increasing water supply and evaporative demand during the growing season from January (3.2 dS m⁻¹) to August (6.6 dS m⁻¹). Deficit irrigation strategies (DI, PRD₅₀) induced more soil salinity along the row emitter compared to the Control due to insufficient leaching fractions. By the end of the growing season, the soil salinity under long-term saline drip irrigation remained stable (5.3–5.7 dS m⁻¹). An efficient leaching action seemed to be guaranteed by rainfall and facilitated by sandy soil texture, as well as the high evaporative demand and the important salt quantity supplied, which maintain the deficit irrigation strategies as valuable tools for water saving and improving water productivity. The significant water saving of 50% of water requirements induced a fruit yield loss of 20%. For this reason, DI and PRD₅₀ could be reasonable irrigation management tools for saving water and controlling soil salinity in arid areas and on deep sandy soil. Full article

In the field of agricultural machinery, various empirical field tests are conducted to measure design loads for the optimal design and implementation of tractors. However, conducting field tests is costly and time-consuming, with many constraints on weather and field soil conditions, and research utilizing simulations has been proposed as an alternative to overcome these shortcomings. The objective of this study is to develop a DEM-based draft force prediction model that reflects differences in soil properties. For this, soil property measurements were conducted in two fields (Field A in Daejeon, Republic of Korea, and Field B in Chuncheon, Republic of Korea). The measured properties were used as parameters for DEM-based particle modeling. For the interparticle contact model, the EEPA contact model was used to reflect the compressibility and stickiness of cohesive soils. To generate an environment similar to real soil, particle mass and surface energy were calibrated based on bulk density and shear torque. The soil property measurements showed that Field B had a higher shear strength and lower cone index and moisture content compared to Field A. The actual measured draft force was 19.47% higher in Field B than in Field A. In this study, this demonstrates the uncertainty in predicting draft force by correlating only one soil property and suggests the need for a comprehensive consideration of soil properties. The simulation results of the tillage operation demonstrated the accuracy of the predicted shedding force compared to the actual field experiment and the existing theoretical calculation method (ASABE D497.4). Compared to the measured draft force in the actual field test, the predictions were 86.75% accurate in Field A and 74.51% accurate in Field B, which is 84% more accurate in Field A and 37.32% more accurate in Field B than the theoretical calculation method. This result shows that load prediction should reflect the soil properties of the working environment, and is expected to be used as an indicator of soil–tool interaction for digital twin modeling processes in the research field of bio-industrial machinery. Full article

The interaction between soil moisture (SM) and evaporative fraction (EF), which reflects the degree of exchange of water and energy between the land and the atmosphere, is an important component of the theory of land–atmosphere coupling. Exploring the relationship between SM and EF in the transitional climate zone of China can help deepen our understanding of the characteristics of water and energy exchange in this region of strong land–atmosphere coupling. Data on observations in fluxes in the transitional climate zone revealed that fluxes in the energy on the surface of the land in this region exhibited significant inter-annual variations. The sensible heat flux (SH) exhibited the largest fluctuations in July and August, while the latent heat flux (LE) varied the most from June to August. The EF was found to exhibit weak correlations with indicators of vegetation growth such as the leaf area index, Normalized Difference Vegetation Index, and gross primary productivity in the transitional zone of the East Asian summer monsoon. By contrast, the relationship of land–atmosphere coupling between EF and SM in the transitional climate zone was stronger. Based on an analysis of the consistency of the relationship of SM-EF coupling, when the SMP reached 35%, there was a significant transition in the linear relationship between the SMP and EF that was consistent between the shallower and deeper layers of soil (0–40 and 40–80 cm). However, neither level had SM that reached saturation during the six-year observational period (2007–2012), and the mean values of its probability density function showed that the deep soil was drier than the shallow soil. This characteristic shows that SM plays a dominant role in variations in the EF in the transitional climate zone, which in turn indicates that constraints on the moisture govern the SM–EF relationship. The results of this study provide a better understanding of the mechanisms of land–atmosphere coupling in the transitional climate zone of China. Full article

The North China Plain plays a pivotal role in China’s crop production, contributing to 30% of the maize yield. Nevertheless, summer maize in this region faces challenges due to climatic constraints characterized by concurrent high temperatures and rainfall during the growing season, resulting in a relatively high evapotranspiration rate. In this study, we explored eight soil moisture-based threshold irrigation strategies, consisting of two upper limits and four lower limits, along with a rainfed mode (E). The upper and lower irrigation limits are expressed as a percentage of the field’s water-holding capacity (s_fc). For the four full irrigation modes (A1, A2, A3, A4), the lower limits were set at 0.6 s_fc, 0.6 s_fc, 0.5 s_fc, and 0.5 s_fc, respectively. The upper limits were defined at two levels: 0.8 s_fc for A1 and A2 and s_fc for A3 and A4. Similarly, for the four deficit irrigation modes (B1, B2, B3, B4), the lower limits were established at 0.4 s_fc, 0.4 s_fc, 0.3 s_fc, and 0.3 s_fc, respectively, with the upper limits set at two levels: 0.8 s_fc for B1 and B2 and the full s_fc for B3 and B4. To investigate the impact of rainfall and potential evapotranspiration on these irrigation modes under long-term fluctuations, we employed a stochastic framework that probabilistically linked rainfall events and irrigation applications. The Monte Carlo method was employed to simulate a long-term series (4000a) of rainfall parameters and evapotranspiration using 62 years of meteorological data from the Xinxiang region, situated in the southern part of the North China Plain. Results showed that the relative yield and net irrigation water requirement of summer maize decreased with decreasing irrigation lower limits. Additionally, the interannual variation of rainfall parameters and evapotranspiration during the growing season were remarkable, which led to the lowest relative yield of the rainfed mode (E) aligned with a larger interannual difference. According to the simulation results, mode A4 (irrigation lower limit equals 0.5 s_fc, irrigation upper limit equals 0.8 s_fc) could be adopted for adequate water resources. Conversely, mode B2 is more suitable for a lack of water resources. Full article

Wireless Sensor Networks (WSNs) and the Internet of Things (IoT) have emerged as transforming technologies, bringing the potential to revolutionize a wide range of industries such as environmental monitoring, agriculture, manufacturing, smart health, home automation, wildlife monitoring, and surveillance. Population expansion, changes in the climate, and resource constraints all offer problems to modern IoT applications. To solve these issues, the integration of Wireless Sensor Networks (WSNs) and the Internet of Things (IoT) has come forth as a game-changing solution. For example, in agricultural environment, IoT-based WSN has been utilized to monitor yield conditions and automate agriculture precision through different sensors. These sensors are used in agriculture environments to boost productivity through intelligent agricultural decisions and to collect data on crop health, soil moisture, temperature monitoring, and irrigation. However, sensors have finite and non-rechargeable batteries, and memory capabilities, which might have a negative impact on network performance. When a network is distributed over a vast area, the performance of WSN-assisted IoT suffers. As a result, building a stable and energy-efficient routing infrastructure is quite challenging in order to extend network lifetime. To address energy-related issues in scalable WSN-IoT environments for future IoT applications, this research proposes EEDC: An Energy Efficient Data Communication scheme by utilizing “Region based Hierarchical Clustering for Efficient Routing (RHCER)”—a multi-tier clustering framework for energy-aware routing decisions. The sensors deployed for IoT application data collection acquire important data and select cluster heads based on a multi-criteria decision function. Further, to ensure efficient long-distance communication along with even load distribution across all network nodes, a subdivision technique was employed in each tier of the proposed framework. The proposed routing protocol aims to provide network load balancing and convert communicating over long distances into shortened multi-hop distance communications, hence enhancing network lifetime.The performance of EEDC is compared to that of some existing energy-efficient protocols for various parameters. The simulation results show that the suggested methodology reduces energy usage by almost 31% in sensor nodes and provides almost 38% improved packet drop ratio. Full article

Spatiotemporal climate variability is a leading environmental constraint to the rain-fed agricultural productivity and food security of communities in the Abbay basin and elsewhere in Ethiopia. The previous one-size-fits-all approach to soil and water management technology targeting did not effectively address climate-induced risks to rain-fed agriculture. This study, therefore, delineates homogenous climatic regions and identifies climate-induced risks to rain-fed agriculture that are important to guide decisions and the selection of site-specific technologies for green water management in the Abbay basin. The k-means spatial clustering method was employed to identify homogenous climatic regions in the study area, while the Elbow method was used to determine an optimal number of climate clusters. The k-means clustering used the Enhancing National Climate Services (ENACTS) daily rainfall, minimum and maximum temperatures, and other derived climate variables that include daily rainfall amount, length of growing period (LGP), rainfall onset and cessation dates, rainfall intensity, temperature, potential evapotranspiration (PET), soil moisture, and AsterDEM to define climate regions. Accordingly, 12 climate clusters or regions were identified and mapped for the basin. Clustering a given geographic region into homogenous climate classes is useful to accurately identify and target locally relevant green water management technologies to effectively address local-scale climate-induced risks. This study also provided a methodological framework that can be used in the other river basins of Ethiopia and, indeed, elsewhere. Full article