Variability Of Water Resources Research Articles

Analysis of Spatial and Temporal Variation in Sustainable Water Resources and Their Use Based on Improved Combination Weights

The sustainable use of water resources has become increasingly crucial given the present water supply and demand situation. In this study, the degree of sustainable water resource utilization in Harbin City from 2014 to 2021 was calculated using a fuzzy identification model with a combination of the “sequential relationship analysis method (G1) and coefficient of variation method (CVM)” and 18 evaluation indicators retrieved for water resources, reflecting social, economic, and ecological aspects. The study shows that (1) in terms of the research method, the combined weighting of “G1-CVM” is a feasible approach to avoid the shortcomings of single weighting and (2) in terms of the evaluation of water resources sustainable utilization, the spatial distribution of water resources in each district (county) of Harbin City has been stable over the past 8 years. The spatial distribution pattern is relatively stable, with the three regions of Binxian, Bayan, and Shuangcheng showing better sustainable water resource utilization and the three regions of Tonghe County, including the main urban area and Wuchang City, showing deteriorating sustainable water resource utilization. As a whole, the spatial distribution of sustainable water resources in the 13 districts (counties) of Harbin City from 2014 to 2021 shows a negative correlation, with the main urban area, Wuchang City, Hulan District, Bayan County, Shuangcheng District, and Yilan County showing a clustering type in the local spatial autocorrelation analysis. Based on the evaluation results, the spatial and temporal distribution characteristics of the sustainable use of water resources in Harbin are identified and found to be conducive to the timely adjustment of water resources allocation and the rational use of water resources in each district (county). Meanwhile, the research ideas and methods used in this paper can be applied to research on the sustainable use of water resources in other regions.

Spatiotemporal dynamics of blue and green water resources in a mountainous watershed: A case study of the Wujiang River Basin, China

Study regionWujiang River Basin, China. Study focusBlue and green water play key roles in water management and sustainable development in the Wujiang River Basin. It is essential to conduct a comprehensive study on the availability of blue and green water resources due to the uneven distribution of water resources in the basin. In this study, the monthly, seasonal, and interannual variations in blue and green water resources were analyzed during 1985–2014, and the changes in blue and green water resources were predicted for the period 2015–2100 based on the 5 Coupled Model Intercomparison Project Phase 6 (CMIP6). New hydrological insights for the regionFrom 1985–2014, blue water resources were dominant in the Wujiang River basin, accounting for 53.6% of the total water resources. Blue water resources exhibited an increasing trend, while green water resources exhibited a decreasing trend. Precipitation exhibited a much more significant impact on blue water resources than on green water resources. The spatial distribution of blue and green water resources in the basin is uneven, where the blue water resource with an annual average of 629.5 mm, indicating an increase followed by a decreasing trend from upstream to downstream. The green water resources had an annual average of 544.7 mm, with a decrease followed by an increasing trend from upstream to downstream. Delta and multi-model ensemble (MME) can effectively reduce the uncertainties associated with CMIP6 model output. Annual precipitation and mean annual temperature increase from 2015 to 2100 in SSP1–2.6, SSP2–4.5, SSP3–7.0, and SSP5–8.5. Compared to the baseline period, blue water resources show the highest growth in the recent (2015–2040) and intermediate (2041–2070) periods in SSP1–2.6, i.e., by 4.9% and 13.7%, respectively. In the final period (2071–2100), blue water resources exhibit the highest growth in SSP5–8.5, i.e., by 21.5%. The future projections indicated that the uneven spatial distribution of blue water resources in the basin will persist. This study improves our understanding of the blue and green water resources in the Wujiang River Basin under climate change scenarios and can provide valuable insights for integrated water resources management in the Wujiang River Basin.

Characterizing hydraulic heterogeneity of Bayin River Basin using river stage tomography

The drastic spatial and temporal variations of water resources are one of the causes of the fragile ecological environment in the Bayin River Basin. While the temporal variation is less predictable, a detailed spatial distribution of hydrological parameters of the basin is invaluable for developing guidance for allocating and utilizing water resources in the Bayin River Basin. This study estimates the two-dimensional spatial distribution of hydraulic diffusivity (D) of the Bayin River Basin through the relationship between river flow and groundwater using limited available river stages data along the river. It first conducted a wavelet analysis to illustrate strong correlations between river flow and groundwater during the flood season in the basin. Subsequently, it used a simple linear model to estimate temporal and spatial variations of the river stage along the river due to flood. Then, the study applied a recently developed river stage tomography (RST) to the alluvial fan to estimate the spatial distribution of the D values. The RST treats the flood river stage as a signal pressure transmitter, and the groundwater fluctuation recorded in the observation wells as the images of the aquifer heterogeneity between the river and observation wells. The RST then synthesizes these images to characterize the heterogeneous distribution of D in the alluvial fan in the Bayin River Basin. The results show that the northeast area of the alluvial fan has a relatively high D, while the D is relatively low in the tail and the southwest area of the fan. These findings are consistent with the general concept of alluvial fan evolution.Moreover, the predicted groundwater levels during the flood event in 2019 based on the RST-estimated D is more accurate than the preliminary stratigraphic survey, confirming that capturing groundwater responses at various parts of a basin caused by some disturbances could reveal hydraulic connectivity, which the prior geologic information cannot. This result is essential to advancing aquifer characterization technologies and affirming the contribution of RST and the view toward the future subsurface sciences advocated by Yeh et al. (2008).

Scrutinizing the Hydrological Responses of Chennai, India Using Coupled SWAT-FEM Model under Land Use Land Cover and Climate Change Scenarios

This study implemented a coupled SWAT-FEM simulation model to evaluate the impacts of land use land cover and climate change scenarios (LCS) on the water resources of river catchments in Chennai, India. The land use land cover data were obtained by merging the source data from National Remote Sensing Centre (NRSC) and International Water Management Institute (IWMI). Climate change simulations were obtained from four global climate models (GCM), including GFDL Baseline Scenario (1981–2000), GFDL A1B Scenario (2081–2100), CCSM4 Baseline Scenario (1986–2005), and CCSM4 A1B Scenario (2081–2100). The LCS predicted temperature increases of 2.32 °C and 1.74 °C for GFDL and CCSM4, respectively, by the end of the century. The water use predictions suggested increases above 20% in the utilization of water by 2100, inferring the noticeable dynamics of inter-annual as well as inter-month variability in water resources in the river basins of Chennai soon. The study is novel through its implementation of a coupled modeling approach to improve the practicality of the SWAT-FEM model and to deliver useful projections of land and climate change impacts on hydrological responses. The results provide useful insights into how the variability in climate conditions alters the spatiotemporal water responses in catchment scales.

Using the Global Navigation Satellite System and Precipitation Data to Establish the Propagation Characteristics of Meteorological and Hydrological Drought in Yunnan, China

AbstractAnalyzing the spatiotemporal characteristics and evolution of meteorological and hydrological droughts can reproduce the process of drought propagation, which helps reduce the impact of droughts and improve water resources management. While emerging studies have attempted to build drought severity index based on the cutting‐edge space geodetic observations, few have focused the propagation characteristics of meteorological to hydrological drought using geodetic data. In this paper, using global navigation satellite systems (GNSS) observations together with precipitation data, we systematically investigated drought propagation in the Yunnan province, Southwest China. We first identified seven meteorological and seven hydrological droughts in Yunnan from January 2011 to May 2021, and the meteorological drought was mainly concentrated in northern Yunnan lasting for 1–11 months. By contrast, hydrological droughts were more severe and larger than meteorological droughts, lasting for 2–16 months. The drought propagation time was 2–7 months, short in the southwest but long in the northeast. Water vapor, precipitation, and water storage demonstrated a spatiotemporal pattern of uneven distribution, with the red river fault (RRF) as the boundary, and their phase difference also presented notable regional differences, indicating that the RRF not only influences spatial variation of water resources but also affects drought propagation.

Impacts of Water Resources Management on Land Water Storage in the Lower Lancang River Basin: Insights from Multi-Mission Earth Observations

Climate change and heavy reservoir regulation in the lower Lancang River basin (LLRB) have caused significant impacts on terrestrial water storage (TWS) in several ways, including changes in surface water storage (SWS), soil moisture storage (SMS), and groundwater storage (GWS). Understanding these impacts is crucial for promoting comprehensive cooperation in managing and utilizing water resources within the basin. This study utilized multi-mission Earth observation (EO) datasets, i.e., gravimetry (GRACE/-FO), altimetry (Jason-2, Sentinel-3, and Cryosat-2), imagery (Sentinel-1/2), and microwave sensors (IMERG), as well as gauged meteorological, hydrological data and reanalysis products, to investigate the spatial-temporal variation of water resources in the LLRB. The study shows that the fluctuations in precipitation and the construction of reservoirs are the primary drivers of changes in the TWS anomaly (TWSA) in the region. Precipitation decreased significantly from 2010 to 2019 (−34.68 cm/yr), but the TWSA showed a significant increase (8.96 cm/yr) due to enhanced water storage capacity in the Xiaowan and Nuozhadu reservoirs. SWS and GWS were also analyzed, with SWS showing a decrease (−5.48 cm/yr) from 2010 to 2019 due to declining precipitation and increasing evaporation. GWS exhibited a steady rise (9.73 cm/yr) due to the maintenance of groundwater levels by the reservoirs. This study provides valuable insights into the potential of EO data for monitoring water resources at a regional scale.

Blue and green water re-distribution dependency on precipitation datasets for a tropical Indian River basin

Study regionThe Damodar River basin, India. Study focusWater resource assessment at the river basin scale is crucial for human well-being and ecosystem health, and it can be performed quantitatively through green water (GW) and blue water (BW) flow evaluation. However, the quantification of BW and GW through land surface models (LSMs) is significantly influenced by the accuracy of precipitation datasets. In this present study, the spatiotemporal variation of blue and green water resources under the influence of six secondary precipitation products (SPPs) [APHRODITE; IMDAA; WFDEI; PRINCETON; CHIRPS, PERSIANN-CDR] are investigated using JULES LSM, considering India Meteorological Department (IMD) precipitation product as an observed precipitation dataset. This will help in examining the dependencies of precipitation datasets on partitioning between BW and GW distribution in a tropical River basin. New hydrological insights for the study regionThe results suggest significant differences in BW and GW simulation with the use of six aforementioned SPPs. In comparison to the reference IMD-based JULES model simulation, the annual average BW and GW estimates varied significantly: from 9.05% to − 40% and 15.24% to − 11.79%, respectively due to changes in the SPPs. Furthermore, the higher correlation between BW and precipitation datasets (R2 = 0.84–0.96) suggested that BW is more sensitive to precipitation changes in comparison to GW. Overall, our study emphasizes the importance of carefully considering the specification of precipitation databases for estimating BW and GW in a tropical River basin.

Interactions between large‐scale and mesoscale processes define long‐term rainfall variability and availability of water resources in Western Bahia, Brazil

AbstractWestern Bahia, a region in Northeast Brazil highly dependent on water availability due to the large concentration of irrigated lands, has been suffering from decreasing precipitation in the last decades. The mean climate of Northeast Brazil and its variability have been linked to teleconnections with the Atlantic and Pacific sea surface temperatures, including the Atlantic Multidecadal Oscillation (AMO). This study investigates how large‐scale (AMO) and mesoscale (orographic) interactions are related to precipitation patterns in the region. Time series of precipitation and naturalized flows (corresponding to the flow that would occur in the river section if there were no anthropogenic actions) for the period 1981–2020 are analysed, indicating an average reduction of 11.5% in rainfall since the 1980s. The analyses show a significant (α = 0.05) rainfall reduction in the wettest months, the December and January bimester (DJ). A strong and significant negative correlation between AMO and precipitation (R = −0.62, α = 0.01) indicates a decrease in DJ rainfall when the AMO index is positive. The five rainiest and five driest DJ bimesters between 1981 and 2020 were selected to investigate the changes in atmospheric circulation. In the rainiest years, large‐scale mechanisms interact with the local topography to promote a major convection centre over Western Bahia. The driest years show suppressed convection over the topographic gradient and a change in the convection centre several hundred kilometres to the west. The continued increase in temperatures in the North Atlantic may perpetuate the decline in Western Bahia precipitation, putting pressure on water availability and impacting economic activities in the region.

Dynamics of the water environment in a water quantity-quality-soil model of China's Yellow River basin: imbalance and driving factors.

Dynamic assessment of the water environment reflects variations in water resources in a basin under the combined influence of nature and humans and is a prerequisite for rational water management. This study provides an integrated assessment of the water environment in a water quantity-quality-soil model. Using the long-term monthly data from hydrological monitoring stations, the water environment of the Yellow River basin is assessed from the year 2006 to 2019. The kernel density estimation and the Dagum Gini coefficient are used to analyze the spatial and temporal imbalances of the water environment. Geographic detectors are used to extract external driving factors of the unbalanced evolution. The study results reveal that (1) the water environment in the basin shows a fluctuating downward trend, which mainly depends on the organic pollution control indicators, with a contribution of 22.85%. Scores of the water environment in the midstream are lower than those in the upstream and downstream due to the heavy pollutant discharges. (2) The spatial imbalance shows a fluctuating downward trend. Inter-regional variation is the primary source of regional variation in the water environment, with an average contribution of 56.02%. (3) The temporal imbalance of the water environment is on the rise, with a degree of multipolarity. The significant left trailing feature of the kernel density curve suggests that there are areas within the basin where the water environment is extremely poor. (4) For the overall basin and upstream, economic development and technological innovation are the main external driving factors influencing the spatial and temporal imbalances of the water environment. For the midstream and downstream, population density and environmental regulations are the main drivers. The interaction of any two factors has a greater impact than the single one.

Removal efficiency prediction model based on the artificial neural network for pollution prevention in wastewater treatment plants

PurposeArtificial intelligence (AI) models are demonstrating day by day that they can find long-term solutions to improve wastewater treatment efficiency. Artificial neural networks (ANNs) are one of the most important of these models, and they are increasingly being used to forecast water resource variables. The goal of this study was to create an ANN model to estimate the removal efficiency of biological oxygen demand (BOD), total nitrogen (TN), total phosphorus (TP) and total suspended solids (TSS) at the effluent of various primary and secondary treatment methods in a wastewater treatment plant (WWTP).Design/methodology/approachThe MATLAB App Designer model was used to generate the data set. Various combinations of wastewater quality data, such as temperature(T), TN, TP and hydraulic retention time (HRT) are used as inputs into the ANN to assess the degree of effect of each of these variables on BOD, TN, TP and TSS removal efficiency. Two of the models reflect two different types of primary treatment, while the other nine models represent different types of subsequent treatment. The ANN model’s findings are compared to the MATLAB App Designer model. For evaluating model performance, mean square error (MSE) and coefficient of determination statistics (R2) are utilized as comparative metrics.FindingsFor both training and testing, the R values for the ANN models were greater than 0.99. Based on the comparisons, it was discovered that the ANN model can be used to estimate the removal efficiency of BOD, TN, TP and TSS in WWTP and that the ANN model produces very similar and satisfying results to the APPDESIGNER model. The R-value (Correlation coefficient) of 0.9909 and the MSE of 5.962 indicate that the model is accurate. Because of the many benefits of the ANN models used in this study, it has a lot of potential as a general modeling tool for a range of other complicated process systems that are difficult to solve using conventional modeling techniques.Originality/valueThe objective of this study was to develop an ANN model that could be used to estimate the removal efficiency of pollutants such as BOD, TN, TP and TSS at the effluent of various primary and secondary treatment methods in a WWTP. In the future, the ANN could be used to design a new WWTP and forecast the removal efficiency of pollutants.

Genetic engineering to enhance microalgal-based produced water treatment with emphasis on CRISPR/Cas9: A review.

In recent years, the increased demand for and regional variability of available water resources, along with sustainable water supply planning, have driven interest in the reuse of produced water. Reusing produced water can provide important economic, social, and environmental benefits, particularly in water-scarce regions. Therefore, efficient wastewater treatment is a crucial step prior to reuse to meet the requirements for use within the oil and gas industry or by external users. Bioremediation using microalgae has received increased interest as a method for produced water treatment for removing not only major contaminants such as nitrogen and phosphorus, but also heavy metals and hydrocarbons. Some research publications reported nearly 100% removal of total hydrocarbons, total nitrogen, ammonium nitrogen, and iron when using microalgae to treat produced water. Enhancing microalgal removal efficiency as well as growth rate, in the presence of such relevant contaminants is of great interest to many industries to further optimize the process. One novel approach to further enhancing algal capabilities and phytoremediation of wastewater is genetic modification. A comprehensive description of using genetically engineered microalgae for wastewater bioremediation is discussed in this review. This article also reviews random and targeted mutations as a method to alter microalgal traits to produce strains capable of tolerating various stressors related to wastewater. Other methods of genetic engineering are discussed, with sympathy for CRISPR/Cas9 technology. This is accompanied by the opportunities, as well as the challenges of using genetically engineered microalgae for this purpose.

Effects of ENSO on hydrological process and hydropower across the Lancang‐Mekong River Basin

AbstractRapid growth in energy demand and accompanying hydropower development has been observed in the Lancang‐Mekong River Basin (LMRB) in the last two decades. However, climate‐induced variations in water resources have brought uncertainties and challenges to energy security across the LMRB, especially for the hydropower sector. In this study, we propose a multiscale approach by linking two spatially distributed models to simulate and evaluate the effects of climatic drivers both on the water resources and hydropower generations in the LMRB. Specifically, we evaluate the effects of midterm large‐scale climatic drivers (i.e., ENSO) on the local hydrometeorological process and energy production. During ENSO events, the midstream to downstream LMRB is more impacted than the upstream LMRB, leading to hydroelectricity anomalies in the LMRB varying within a range of ±11,000 GWh. However, basin‐wide drought conditions caused by El Niño events in the LMRB do not always result in a reduction in hydropower generation. The proposed approach provides a methodological basis for characterizing power system responses to climatic drivers, potentially informing the future energy strategy and infrastructure development in the LMRB.

Spatial and temporal dynamics of water resources in typical ecosystems of the Dongjiang River Basin, China

In hydrology research, how to quantitatively analyze the spatial and temporal changes of water resources in the ecosystem of a watershed is one of the focuses of attention. Especially in the strategically important Dongjiang River Basin, which is the source of water for three world-class cities of Hong Kong, Shenzhen and Guangzhou. Hence, it is important to quantitatively analyze the spatial and temporal variation of water resources at the basin and ecosystem scale of Dongjiang river basin. The Soil and Water Assessment Tool (SWAT) was used in this study to analyze the spatial and temporal dynamics of typical ecosystem water resources in the Dongjiang River basin over the last 50 years. The objectives of the study were to: (1) identify the distribution of water resources in typical ecosystems; (2) determine the quantity of water in the forest ecosystem; and (3) characterize the spatial and temporal changes. The findings revealed that: (1) the average water resources in the Dongjiang River Basin from 1971 to 2020 were 37.944 Bm3, and there were obvious changes in the basin’s total quantity of water resources. The most important climatic factors influencing water resources were precipitation and temperature. (2) From 1971 to 2020, the total water resources of typical Donjiang River Basin ecosystems exhibited a declining trend. The total average water resources of the forest ecosystem were 27.532 Bm3, which first increased and then decreased from upstream to downstream. The total quantity of water resources in the grassland ecosystem increases with the amount of water per unit area. (3) The change in total water resources has been decreasing over the last decade, which is consistent with the change in the water resources of the forest ecosystem in the Dongjiang River Basin. Changes in water resources were greater in the lower and middle reaches than in the upper reaches. Changes in land use were a significant factor in the evolution of water resources in typical ecosystems. This study can provide theoretical support for the comprehensive management of typical ecosystems and water resources in the Dongjiang River Basin.

A multicriteria analysis of groundwater development pathways in three river basins in Sub-Saharan Africa

Reliance on groundwater in Sub-Saharan Africa is growing and expected to rise as surface water resource variability increases under climate change. Major questions remain about how groundwater will be used, and who informs these decisions. We represent different visions of groundwater use by ‘pathways’: politically and environmentally embedded socio-technological regimes for governing and managing groundwater systems. We presented policy actors (9 sets), development and research stakeholders (4 sets), and water users (6 sets) in three river basins in Ethiopia, Niger and Tanzania with information on the social and environmental impacts of six ‘Groundwater Development Pathways’, before gathering their opinions on each, through Multicriteria Mapping (MCM). Participants preferred pathways of low-intensity use, incorporating multiple agricultural, pastoral and domestic purposes, to high-intensity single-use pathways. Water availability and environmental sustainability, including water quality, were central concerns. Participants recognised that all groundwater uses potentially impinge upon one another affecting both the quantity and quality of abstracted water. Across participant groups there was ambiguity about what the most important water use was; each expressed demands for more detailed, certain modelling data. Water users preferred community or municipal-scale management regimes, perceiving that water quality was more likely to be safeguarded by institutions at these levels, whereas policy and development actors preferred individual-scale management, viewed as more efficient in terms of operation and maintenance. We conclude that MCM, combined with more detailed modelling, can provide an effective framework for policy actors to understand other stakeholders’ perspectives on groundwater development futures, enabling equitable, inclusive decision-making and governance.

A multivariate-driven approach for disentangling the reduction in near-natural Iberian water resources post-1980

Abstract. Although the literature still debates how several anthropogenic and natural factors have contributed to the recent streamflow decline in the Iberian Peninsula, a continuing decrease in winter precipitation (WP) has been noticed in this area since the 1980s and has been associated with large-scale atmospheric drivers. This contribution assesses the potential propagation of this WP deficit into water resource variability. For this purpose, the novel “NEar-Natural Water Inflows to REservoirs of Spain” (NENWIRES) dataset was created. The results highlight that higher decreases in winter water inflows (WWIs) are always related to WP reductions. However, while the decline in WP was strongly provoked by the enhancement of the North Atlantic Oscillation index (NAOi) during the study period, WWI reductions could not be essentially linked to the NAOi behaviour in several NENWIRES catchments. Instead, the intensification of drought conditions and forest extension promoted WWI decreases over the target area and aided in understanding why WWI reductions were generally higher than WP decreases. In summary, most humid catchments registered a WWI decline that was mainly promoted by NAOi enhancement, whereas the extension of forest and evapotranspiration increases seem to explain WWI losses in semi-arid environments. This contribution sheds light on the recent debate regarding the magnitude and drivers of water resource decline over southern European regions.

Evaluation of spatial variation of water resources carrying capacity using optimal method: a case study of Fujian, China.

Studying regional water resources carrying capacity (WRCC) is an important guarantee for realizing regional sustainable development. Previous evaluation of WRCC mainly focused on the construction of the index system and the optimization of multiattribute decision analysis methods (MDAMs). The selection of the weighting assignment method (WAM) is a key link in the evaluation of WRCC, but different WAMs may bring different results, which brings challenges to the actual WRCC evaluation. Therefore, this paper proposes a WAM selection framework, under which WRCC evaluation index system is firstly constructed. Secondly, from the four objective WAMs, the most suitable WAM is identified and selected to evaluate WRCC in Fujian Province in terms of the reliability and stability of the evaluation results. In addition, the main factors affecting regional WRCC are discussed using the obstacle degree model. The results show that (1) under the same evaluation index system, the CRITIC method was the suitable method with more reliability and stability than the coefficient of variation method, the entropy method, and the equal weight method. (2) The higher water resources carrying capacity (level I) is mainly located in the west of the study area, and the lower one (level V) is located in the east coast. (3) The superimposition effects ecological water use rate, water production modulus, acreage water consumption of farmland irrigation, per capita water resources, and COD emissions hindering the level of regional WRCC. The framework proposed in this study can provide methodological options for quantitative evaluation of WRCC. The results of this paper provide favorable support for decision makers to carry out rational planning of resources.

Prescriptive drought policy and water supplier compliance

Governments often cannot use prices to induce water conservation, and the need to understand the impacts of alternate methods is growing due to increased variability in water resources. During the 2012–2016 drought in California, a period that may presage the future of water management in a warmer climate, the state attempted to manage water use through a set of mandatory restrictions that assigned each of California's 412 largest urban water suppliers to one of nine conservation tiers; those with greater historic usage needed to conserve more. I find that even though significant statewide savings occurred, only half of all suppliers complied with their conservation target. Moreover, the increased savings were not caused by the tiered design of the mandate: evidence from a regression discontinuity design shows that suppliers that just missed a stricter conservation tier actually conserved more. Additionally, water use rebounded after the regulation was removed, implying that variable adjustments in demand contributed more to water use savings than fixed cost household investments. Given the significant costs of water regulation and the high probability of future droughts, the policy implication is that both governments and water suppliers may benefit from investments in water supply reliability and less complex prescriptive policies.

Integration of seasonal precipitation forecast information into local-level agricultural decision-making using an agent-based model to support community adaptation

Accessibility and variability of water resources can have profound impacts on social, political, and economic security. In regions with pronounced climate variability (e.g., seasonal and inter-annual variability in precipitation), seasonal climate forecasts issued in advance may enhance sectoral planning and management decisions to benefit vulnerable communities. Yet, as the development and communication of seasonal climate forecasts continue to advance, integration of forecasts into decision-making remains sparse. This work investigates the integration of a locally-tailored seasonal precipitation forecast into agricultural decision-making using a simple agent-based model designed to resemble a stylized local Ethiopian community, to understand factors that may influence adoption. We do not make claims on representativeness, yet results from our model indicate that forecasts improve gross benefit to farmer agents across different climate series, with potential for improved profit, yields, and nutritional outcomes. Accuracy of a seasonal forecast seems to correlate with increased adoption and therefore benefit; yet, the sequence of precipitation conditions, risk preference and heuristics for building trust nuance this relationship. Further, similar to well-established literature in economics and sociology, our stylized model suggests that community-level social dynamics (e.g., peer interaction, sensing others’ trust in the forecast, and the ability to learn from peers) seem to have a large impact on patterns of forecast adoption. Ultimately, if the motivation for seasonal forecast development is to enhance water and food security for adaptation to climate variability in vulnerable regions, then interdisciplinary collaborations that connect local-scale forecasts with public engagement and attention to community-level social dynamics are critical.

Exploring the temporal and spatial variability of water and land resources carrying capacity based on ecological footprint: A case study of the Beijing-Tianjin-Hebei urban agglomeration, China

In recent years, with the rapid social and economic development of the Beijing-Tianjin-Hebei region (the Jing-Jin-Ji region), ecological and environmental problems have become increasingly prominent. The shortage of water and land resources continues to affect the sustainable development of the ecological carrying capacity. Thus, a comprehensive evaluation method of water and land resource carrying capacity based on ecological footprint theory and spatial analysis with geographic information system (GIS) is proposed. The method aims to correct the following flaws in the evaluation of carrying capacity of water and land resources: (1) less consideration of the coupling performance of water and land resources, and (2) the lack of objective analysis on the weights of different water and land resource types. The method is applied in a case study of the Jing-Jin-Ji region, China. The results show that half of the cities in the Jing-Jin-Ji region are under uncoordinated and unsustainable status on water and land resources. Especially in Beijing and Tianjin, water and land resources are running an ecological deficit under “extremely uncoordinated” (less than 0.5 in the study years). And for the sustainable level, the ecological carrying capacity shows a deficit under unsustainable status in Beijing, Tianjin and the central and south part of Hebei province (−0.05 hm2 for water resources in Xingtai, Handan and Hengshui, less than −0.2 hm2 for land resources in Baoding and Cangzhou). Therefore, improving the management system of water and land resources and changing the way of production and consumption to promote sustainable and coordinated ecological development is recommended.

Advancing the representation of reservoir hydropower in energy systems modelling: The case of Zambesi River Basin.

In state-of-the-art energy systems modelling, reservoir hydropower is represented as any other thermal power plant: energy production is constrained by the plant's installed capacity and a capacity factor calibrated on the energy produced in previous years. Natural water resource variability across different temporal scales and the subsequent filtering effect of water storage mass balances are not accounted for, leading to biased optimal power dispatch strategies. In this work, we aim at introducing a novelty in the field by advancing the representation of reservoir hydropower generation in energy systems modelling by explicitly including the most relevant hydrological constraints, such as time-dependent water availability, hydraulic head, evaporation losses, and cascade releases. This advanced characterization is implemented in an open-source energy modelling framework. The improved model is then demonstrated on the Zambezi River Basin in the South Africa Power Pool. The basin has an estimated hydropower potential of 20,000 megawatts (MW) of which about 5,000 MW has been already developed. Results show a better alignment of electricity production with observed data, with a reduction of estimated hydropower production up to 35% with respect to the baseline Calliope implementation. These improvements are useful to support hydropower management and planning capacity expansion in countries richly endowed with water resource or that are already strongly relying on hydropower for electricity production.