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Hydrologic data has traditionally been collected with permanent installations of sophisticated and accurate but expensive monitoring equipment at limited numbers of sites. Consequently, observation frequency and costs are high, but spatial coverage of the data is limited. Citizen Hydrology can possibly overcome these challenges by leveraging easily scaled mobile technology and local residents to collect hydrologic data at many sites. However, understanding of how decreased observational frequency impacts the accuracy of key streamflow statistics such as minimum flow, maximum flow, and runoff is limited. To evaluate this impact, we randomly selected 50 active United States Geological Survey streamflow gauges in California. We used 7 years of historical 15-min flow data from 2008 to 2014 to develop minimum flow, maximum flow, and runoff values for each gauge. To mimic lower frequency Citizen Hydrology observations, we developed a bootstrap randomized subsampling with replacement procedure. We calculated the same statistics, and their respective distributions, from 50 subsample iterations with four different subsampling frequencies ranging from daily to monthly. Minimum flows were estimated within 10% for half of the subsample iterations at 39 (daily) and 23 (monthly) of the 50 sites. However, maximum flows were estimated within 10% at only 7 (daily) and 0 (monthly) sites. Runoff volumes were estimated within 10% for half of the iterations at 44 (daily) and 12 (monthly) sites. Watershed flashiness most strongly impacted accuracy of minimum flow, maximum flow, and runoff estimates from subsampled data. Depending on the questions being asked, lower frequency Citizen Hydrology observations can provide useful hydrologic information.

When inferring models from hydrological data or calibrating hydrological models, we are interested in the information content of those data to quantify how much can potentially be learned from them. In this work we take a perspective from (algorithmic) information theory, (A)IT, to discuss some underlying issues regarding this question. In the information-theoretical framework, there is a strong link between information content and data compression. We exploit this by using data compression performance as a time series analysis tool and highlight the analogy to information content, prediction and learning (understanding is compression). The analysis is performed on time series of a set of catchments. We discuss both the deeper foundation from algorithmic information theory, some practical results and the inherent difficulties in answering the following question: "How much information is contained in this data set?". The conclusion is that the answer to this question can only be given once the following counter-questions have been answered: (1) information about which unknown quantities? and (2) what is your current state of knowledge/beliefs about those quantities? Quantifying information content of hydrological data is closely linked to the question of separating aleatoric and epistemic uncertainty and quantifying maximum possible model performance, as addressed in the current hydrological literature. The AIT perspective teaches us that it is impossible to answer this question objectively without specifying prior beliefs. [ABSTRACT FROM AUTHOR]

A difference has been detected between the C-band wind scatterometer measurements from the morning (descending) and evening (ascending) passes of the European Remote Sensing (ERS) 1/2 satellite. In the West African savanna, for example, these differences correspond to the onset of vegetation water stress. A literature review of the current state of knowledge regarding the diurnal variation in vegetation dielectric properties and its influence on observed backscatter is presented. A numerical sensitivity study using the Michigan microwave canopy scattering model was performed to investigate whether this difference might be explained by diurnal variation in the dielectric properties of the canopy. For vertically copolarized backscatter, as in the case of the ERS wind scatterometer, the greatest sensitivity is to leaf moisture (and, hence, dielectric constant), but the trunk moisture is significant at low values of leaf moisture content. This suggests that the ERS wind scatterometer may well detect changes in vegetation water status. The impact of leaf, branch, trunk, and soil moisture contents on L-band HH, VV, and HV backscatter was also investigated to explore the implications for the National Aeronautics and Space Administration's upcoming Soil Moisture Active Passive (SMAP) mission. Results suggest that combining the morning and evening passes of the SMAP radar observations might yield valuable insight into water stress in areas otherwise considered too densely vegetated for traditional soil moisture retrieval. [ABSTRACT FROM AUTHOR]

Soil hydrological properties are highly variable in space. Field measurements of these properties are costly and error prone. As spatially distributed approaches become increasingly important in current hydrological and ecological modeling, an appropriate field sampling scheme to effectively capture spatial variability of hydrological processes becomes essential. A terrain-based slope classification system was applied to delineate the hillslope into representative hydrological domains. This model assumes that there are hydrological landscape units (LUs) along the hillslope in which distinct sets of hydrological and pedological processes occur. Possible water and material flows over the hillslope were first interpreted using a continuity equation of mass flow over the surface, and subsequently included in a terrain analysis. The developed terrain index is able to characterize the hydrological processes, accommodating both continuous and discrete concepts. The model was tested against the intensive soil moisture data at the Tarrawarra catchment, Australia [Water Resour. Res. 34 (1998) 2765]. The delineated soil–LUs explain up to 73% of the average soil moisture variation when it is combined with other terrain parameters (surface curvature, upslope contributing area and slope aspect). Soil moisture at each LU shows significantly different variance characteristics when compared with other units, and the delineation procedure reduces the spatial variation of soil moisture within each LU. Random permutation and bootstrapping techniques indicate that stratified random sampling based on the delineated hillslope units significantly reduces the number of samples needed to estimate the average soil moisture and the overall error of estimation. [Copyright &y& Elsevier]

Information regarding biosphere-atmosphere interactions is important in the study of a hydrological cycle. To this purpose, xylem sap flow (SF) using the Granier system and evapotranspiration (ET) using the eddy correlation method were measured during a "wet-to-dry" transition period in a young cashew (Anacardium occidentale L.) plantation. Estimates of half-hourly tree transpiration made from stem sap flow measurements and above-canopy eddy correlation measurements of water vapor flux were compared for a period of 22 days of complete records. Cross-correlation analysis was used to estimate the time lags (τ) between the time courses of SF and ET, and between SF and ET with solar radiation (RS) and vapor pressure deficit (D). Applying a simple functional technique, values of τ = 43 min (November), τ = 46 min (December), and τ = 75 min (January) with an overall τ = 53 min (using all data) between the time courses of ET and SF were estimated. A positive lag indicates that SF lags behind ET. However, both ET and SF were more dependent on RS (r2 > 0.81) than on D, whereas SF was more related to D (r2 = 0.60) compared to ET (r2 = 0.38). An insignificant (p > 0.05) decrease in daily values of both ET and SF over the 22 days of concurrent measurements were observed. Daytime average ET ranged from 2.01 to 3.17 mm day-1 with a mean of 2.7 mm day-1, whereas values of SF ranged from 0.55 to 0.72 mm day-1 with a mean of 0.65 mm day-1. Tree transpiration accounted for about 25% of the evapotranspiration from the orchard. This result may be of help in correctly predicting the diurnal behavior of transpiration from sap flow measurements. [ABSTRACT FROM AUTHOR]

Recent publications from field and simulation studies indicate that runoff per unit area decreases as the length of the slope being observed is increased. This scale effect has been observed and documented for slopes with a uniform infiltration capacity as well as for slopesalong which infiltration capacity is variable. This paper presents the design and testing of a laboratory flume for the study of the processes that lead to this scale effect, particularly for the case of slopes covered with crops. The features of the flume include reliable experimental control of soil water content prior to rainfall, high intensity rainfall without erosion, uniform crop growth along the slope,and the option of varying the infiltration rate independently of rainfall and soil characteristics. [ABSTRACT FROM AUTHOR]

Hortonian runoff was measured from plots with lengths of 1725 and 12m, and at watershed level for rainstorms during the 1996 rainy season in cental Cote d'Ivoire, Africa. A clear reduction in runoff coefficients was found with increasing slope lengths, giving order of magnitude differences between runoff measurements at point level (1 m2: 30-50% of total rain) and watershed level (130 ha: 4% of total rain). Runoff reduction from 1725 and 12 m slopes was reproduced for each major runoff-producing rainstorm at two different sets of plots, but the reduction was erratic for rainfall events which produced little runoff. In addition, runoff reduction varied wildly from one rainstorm to the next. In the analysis, we show that the spatial variability of runoff parameters causes the erratic behaviour during rainstorms with little runoff. During the more important, larger runoff-producing events, which give 78% of total runoff, the temporal dynamicsof the rainfall-runoff process determine the reduction of runoff coefficients from longer slopes. A simple infiltration/runoff model was used to simulate the field results, thereby confirming the importanceof rainfall dynamics as an explanatory factor for measured reductionof runoff coefficients. [ABSTRACT FROM AUTHOR]

Short-term water system operation can be realized using Model Predictive Control (MPC). MPC is a method for operational management of complex dynamic systems. Applied to open water systems, MPC provides integrated, optimal, and proactive management, when forecasts are available. Notwithstanding these properties, if forecast uncertainty is not properly taken into account, the system performance can critically deteriorate. Ensemble forecast is a way to represent short-term forecast uncertainty. An ensemble forecast is a set of possible future trajectories of a meteorological or hydrological system. The growing ensemble forecasts' availability and accuracy raises the question on how to use them for operational management. The theoretical innovation presented here is the use of ensemble forecasts for optimal operation. Specifically, we introduce a tree based approach. We called the new method Tree-Based Model Predictive Control (TB-MPC). In TB-MPC, a tree is used to set up a Multistage Stochastic Programming, which finds a different optimal strategy for each branch and enhances the adaptivity to forecast uncertainty. Adaptivity reduces the sensitivity to wrong forecasts and improves the operational performance. TB-MPC is applied to the operational management of Salto Grande reservoir, located at the border between Argentina and Uruguay, and compared to other methods. [ABSTRACT FROM AUTHOR]