Defining homogeneous precipitation regions is fundamental for hydrologic applications, yet nontrivial, particularly for regions with highly varied spatial–temporal patterns. Traditional approaches ...typically include aspects of subjective delineation around sparsely distributed precipitation stations. Here, hierarchical and nonhierarchical (k means) clustering techniques on a gridded dataset for objective and automatic delineation are evaluated. Using a spatial sensitivity analysis test, the k-means clustering method is found to produce much more stable cluster boundaries. To identify a reasonable optimal k, various performance indicators, including the within-cluster sum of square errors (WSS) metric, intra- and intercluster correlations, and postvisualization are evaluated. Two new objective selection metrics (difference in minimum WSS and difference in difference) are developed based on the elbow method and gap statistics, respectively, to determine k within a desired range. Consequently, eight homogenous regions are defined with relatively clear and smooth boundaries, as well as low intercluster correlations and high intracluster correlations. The underlying physical mechanisms for the regionalization outcomes not only help justify the optimal number of clusters selected, but also prove informative in understanding the local- and large-scale climate factors affecting Ethiopian summertime precipitation. A principal component linear regression model to produce cluster-level seasonal forecasts also proves skillful.
Water rights law and corresponding markets exist to promote economic water resource use efficiency by permitting water rights holders to trade allocations. In some regions, hydrologic uncertainty ...drives annual assignment of per‐water right allocation values. Water rights holders, specifically those involved in agricultural production, may use collaborative water resource decision making to mitigate allocation uncertainty and promote economic and social efficiency. Such is the case in semi‐arid North Chile, where interactions between representative farmer groups, treated as resource competitive growers’ cooperatives, and modeled at water market‐scale, can provide both price and water right allocation distribution signals for unregulated, temporary water right markets. For the range of feasible per‐water right allocation values, a coupled agricultural‐economic model is developed to describe the equilibrium distribution of water, the corresponding market price of water rights, and the net surplus generated by collaboration between competing agricultural uses. A static, demand‐based allocation redistribution ruleset is generated by which the cooperatives are constrained to abide. Water right supply and demand inequality impacts at the market‐scale are used to characterize market performance under existing water rights law, and to evaluate the efficacy of intercooperative collaboration over the period 2000–2015. Exclusive intercooperative water trading, following a demand‐based ruleset, produces joint mean annual expected profits 24%–122% larger than a case of no‐interaction, depending on initial rights distribution. The broader insights of this research suggest that water rights holders engaged in agriculture can achieve enhanced benefits by forming crop‐type cooperatives and implementing demand‐based allocation redistribution rulesets.
Key Points
Demand‐based water right allocation redistribution rulesets can enhance economic outcomes across agricultural cooperatives
Market‐scale socioeconomic efficiency gains can be made through temporary water transfers without institutional policy change or investment
Coupled crop‐water and agro‐economic models reveal water market price signals, and can inform water right redistribution strategies
Excessive algae growth can lead to negative consequences for ecosystem function, economic opportunity, and human and animal health. Due to the cost‐effectiveness and temporal availability of ...satellite imagery, remote sensing has become a powerful tool for water quality monitoring. The use of remotely sensed products to monitor water quality related to algae and cyanobacteria productivity during a bloom event may help inform management strategies for inland waters. To evaluate the ability of satellite imagery to monitor algae pigments and dissolved oxygen conditions in a small inland lake, chlorophyll‐a, phycocyanin, and dissolved oxygen concentrations are measured using a YSI EXO2 sonde during Sentinel‐2 and Sentinel‐3 overpasses from 2019 to 2022 on Lake Mendota, WI. Machine learning methods are implemented with existing algorithms to model chlorophyll‐a, phycocyanin, and Pc:Chla. A novel machine learning‐based dissolved oxygen modeling approach is developed using algae pigment concentrations as predictors. Best model results based on Sentinel‐2 (Sentinel‐3) imagery achieved R2 scores of 0.47 (0.42) for chlorophyll‐a, 0.69 (0.22) for phycocyanin, and 0.70 (0.41) for Pc:Chla. Dissolved oxygen models achieved an R2 of 0.68 (0.36) when applied to Sentinel‐2 (Sentinel‐3) imagery, and Pc:Chla is found to be the most important predictive feature. Random forest models are better suited to water quality estimations in this system given built in methods for feature selection and a relatively small data set. Use of these approaches for estimation of Pc:Chla and dissolved oxygen can increase the water quality information extracted from satellite imagery and improve characterization of algae conditions among inland waters.
Plain Language Summary
Agricultural runoff and wastewater discharge has fueled nutrient pollution in Lake Mendota over the last century. As a result, algae blooms have become a common summertime occurrence on Lake Mendota. Algae blooms are often made up of different algae species. Green algae are typically harmless, but cyanobacteria (blue‐green algae) can produce a range of toxins harmful to human and animal health. The ability to discriminate between cyanobacteria and green algae during a bloom may be useful for lake managers and public health officials in making decisions about closing waterfront areas and communicating with the public. In recent years, satellite imagery has become a powerful tool for monitoring water quality. In this study, we build models that use imagery from two satellites to estimate the abundance of cyanobacteria versus green algae in Lake Mendota. We also find that our algae estimates can be used to model dissolved oxygen, an important water quality indicator that cannot be directly measured from satellite imagery. The methods presented for satellite‐based monitoring of algae pigments, the Pc:Chla ratio, and dissolved oxygen has the potential to increase the water quality information extracted from satellite imagery, better characterize algae blooms, and inform management strategies for Lake Mendota.
Key Points
Chlorophyll‐a and phycocyanin are sampled from 2019 to 2022 on Lake Mendota, WI
Sentinel‐2 and Sentinel‐3 are used to model chlorophyll‐a, phycocyanin, and Pc:Chla
A model based in situ data allows for satellite‐based estimates of dissolved oxygen
Abstract
For countries dependent on rainfed agriculture, failure of the rainy season can lead to serious consequences on the broader economy. Maize, a common staple crop in these countries, often ...expresses significant interannual variability, given its high sensitivity to water stress. It is traditionally planted at rainy season onset to maximize the growing season and potential yield; however, this risks planting during a ‘false onset’ that can damage the crop or require replanting. Rainy season onset forecasts offer some promise in reducing this risk; however, the potential for increasing yield has not been explicitly quantified. This study quantifies the yield gap associated with suboptimal maize planting times using a process-based crop model over a 36 year historical period across Ethiopia. Onset-informed and forecast-informed approaches are compared with a baseline approach, and results indicate a strong potential for yield gains in drier regions as well as reductions in interannual variance countrywide. In contrast, regions with reliably sufficient precipitation illustrate only minimal gains. In general, integration of onset forecasts into agricultural decision-making warrants inclusion in agricultural extension efforts.
•Seasonal forecasting models are built for algae magnitude, severity, and duration in 178 lakes.•Regions of pre-season sea surface temperature and chlorophyll-a show the most predictive power.•> 70% ...of magnitude models and 90% of duration models outperform climatology.•High and severe algae magnitude forecasts perform best in large meso‑ and oligotrophic lakes.
In recent decades, many inland lakes have seen an increase in the prevalence of potentially harmful algae. In many inland lakes, the peak season for algae abundance (summer and early fall in the northern hemisphere) coincides with the peak season for recreational use. Currently, little information regarding expected algae conditions is available prior to the peak season for productivity in inland lakes. Peak season algae conditions are influenced by an array of pre-season (spring and early summer) local and global scale variables; identifying these variables for forecast development may be useful in managing potential public health threats posed by harmful algae. Using the LAGOS-NE dataset, pre-season local and global drivers of peak-season algae metrics (represented by chlorophyll-a) are identified for 178 lakes across the Northeast and Midwest U.S. from readily available gridded datasets. Forecasting models are built for each lake conditioned on relevant pre-season predictors. Forecasts are assessed for the magnitude, severity, and duration of seasonal chlorophyll concentrations. Regions of pre-season sea surface temperature, and pre-season chlorophyll-a demonstrate the most predictive power for peak season algae metrics, and resulting models show significant skill. Based on categorical forecast metrics, more than 70% of magnitude models and 90% of duration models outperform climatology. Forecasts of high and severe algae magnitude perform best in large mesotrophic and oligotrophic lakes, however, high algae duration performance appears less dependent on lake characteristics. The advance notice of elevated algae biomass provided by these models may allow lake managers to better prepare for challenges posed by algae during the high use season for inland lakes.
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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.
•Studies focusing on historical and projected hydrologic extremes are reviewed.•Discrepancy among research outputs is addressed.•Blue Nile flow has not changed in the past five decades.•The need to ...prudently consider sources of uncertainty is emphasized.
The Blue Nile river basin in East Africa.
This review paper presents the current understanding of hydrological extremes in the Blue Nile River basin under historic and future climate conditions, largely drawing on research outputs over the past decade. Characteristics of precipitation and streamflow extremes, including historic trends and future projections, are considered.
The review illustrates some discrepancy among research outputs. For the historical context, this is partially related to the period and length of data analyzed and the failure to consider the influence of multi-decadal oscillations. Consequently, we show that annual cycle of Blue Nile flow has not changed in the past five decades. For the future context, discrepancy is partially attributable to the various and differing climate and hydrological models included and the downscaling techniques applied. The need to prudently consider sources of uncertainty and potential causes of bias in historical trend and climate change impact research is highlighted.
This paper introduces the Nino Index Phase Analysis (NIPA) framework for forecasting hydroclimatic variables on a seasonal time scale. Antecedent Sea Surface Temperatures (SSTs) are commonly used in ...statistical predictive frameworks for seasonal forecasting, however, the typical approach of evaluating all the years on record in one bin (“phase”) does not often provide the level of skill required by decision makers. For many locations around the world, the most influential climate signal on the seasonal time scale is the El Nino Southern Oscillation (ENSO), and there are various indices used to capture the state of ENSO and provide this information. NIPA utilizes the state of ENSO to classify the years of record into four phases, operating under the hypothesis that ENSO itself is affecting the “mean state” of the atmospheric‐oceanic system, and relevant teleconnections depend on and must be selected within these mean states. A case study focused on spring precipitation over the Lower Colorado River Basin (LCRB) in Texas is chosen to illustrate NIPA's potential. Results show that correlations between wintertime SST fields and spring precipitation in the LCRB improve from 0.21 to 0.47 for the typical “one phase” and the NIPA “four‐phase” approach, respectively. Even greater improvements are seen across tercile‐based skill scores such as the Heidke Hit Skill Score and Ranked Probability Skill Score; skill is particularly strong for years exhibiting extreme wet or dry conditions. It also outperforms the North American Multi‐Model Ensemble predictions across the LCRB for the selected seasons. This is encouraging as improved predictability through NIPA may translate to better decision‐making for water managers.
Key Points:
Precipitation predictor variables are evaluated conditioned on the “state” of ENSO
Marked difference in SST fields emerges within these states
Forecasting results improve significantly over evaluating SST fields in only one state
Abstract
In their recent paper in ERL, ‘Egypt’s water budget deficit and suggested mitigation policies for the Grand Ethiopian Renaissance Dam (GERD) filling scenarios,’ Heggy
et al
(2021
Environ. ...Res. Lett.
16
074022) paint an alarming picture of the water deficits and economic impacts for Egypt that will occur as a consequence of the filling of the GERD. Their median estimate is that filling the GERD will result in a water deficit in Egypt of ∼31 billion m
3
yr
−1
. They estimate that under a rapid filling of the GERD over 3 yr, the Egyptian economy would lose US$51 billion and 4.74 million jobs, such that in 2024, Gross Domestic Product (GDP) per capita would be 6% lower than under a counterfactual without the GERD. These and other numbers in Heggy
et al
(2021
Environ. Res. Lett.
16
074022) article are inconsistent with the best scientific and economic knowledge of the Nile Basin and are not a dependable source of information for policy-makers or the general public. In this response to Heggy
et al
(2021
Environ. Res. Lett.
16
074022) we draw on high quality peer-reviewed literature and appropriate modeling methods to identify and analyze many flaws in their article, which include (a) not accounting for the current storage level in the High Aswan Dam reservoir (b) inappropriately using a mass-balance approach that does not account for the Nile’s hydrology or how water is managed in Egypt, Sudan and Ethiopia; (c) extreme and unfounded assumptions of reservoir seepage losses from the GERD; and (d) calculations of the economic implications for Egypt during the period of reservoir filling which are based on unfounded assumptions. In contrast to Heggy
et al
(2021
Environ. Res. Lett.
16
074022), robust scientific analysis has demonstrated that, whilst there is a risk of water shortages in Egypt if a severe drought were to occur at the same time as the GERD reservoir is filling, there is minimal risk of additional water shortages in Egypt during the filling period if flows in the Blue Nile are normal or above average. Moreover, the residual risks could be mitigated by effective and collaborative water management, should a drought occur.
The phase of the El Niño Southern Oscillation (ENSO) has large-ranging effects on streamflow and hydrologic conditions globally. While many studies have evaluated this relationship through ...correlation analysis between annual streamflow and ENSO indices, an assessment of potential asymmetric relationships between ENSO and streamflow is lacking. Here, we evaluate seasonal variations in streamflow by ENSO phase to identify asymmetric (AR) and symmetric (SR) spatial pattern responses globally and further corroborate with local precipitation and hydrological condition. The AR and SR patterns between seasonal precipitation and streamflow are identified at many locations for the first time. Our results identify strong SR patterns in particular regions including northwestern and southern US, northeastern and southeastern South America, northeastern and southern Africa, southwestern Europe, and central-south Russia. The seasonally lagged anomalous streamflow patterns are also identified and attributed to snowmelt, soil moisture, and/or cumulative hydrological processes across river basins. These findings may be useful in water resources management and natural hazards planning by better characterizing the propensity of flood or drought conditions by ENSO phase.
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