Groundwater development will provide a more stable water source and enhance food security. Sustainable groundwater development requires collecting and analyzing data produced at global and national levels and disseminating that data and knowledge to end users such as States, NGOs, municipalities, businesses, and agropastoralists in a format that is useful for planning and decision-making. In developing countries, analyzing in situ measurements to de can be challenging due to sparsity of data and lack of tools and expertise. To address these problems we have developed a web-based geospatial tool that ingests in situ water level measurements and performs temporal and spatial interpolation to build interactive animated maps, time series plots, and long-term aquifer depletion curves. We use machine learning to find correlations among Earth observation data, such as precipitation or soil moisture, with water level data and perform more accurate interpolation. This approach ensures that scarce in situ data are used as effectively and accurately as possible. This tool helps water managers gain a better understanding of groundwater resources and determine how aquifers are responding to groundwater development, droughts, and climate change.

The isotopic composition of surface water vapor flux (δ) is a quantity frequently used to investigate the local and regional water cycle. In this study, the δ determined with the Keeling method was evaluated against the flux-gradient method and the Craig-Gordon model prediction. Previous studies have shown that the choice of regression fitting methods can bias the δ intercept results and precision of the Keeling method. Here, the Keeling method was applied to high-frequency (0.2 to 1 Hz) data measured at a cropland and a lake site to test different regression methods. Results show that the Keeling method with the York’s solution (YS) and the ordinary least squares (OLS) regression produced robust estimates of δ when compared with the flux-gradient method. Increasing concentration range reduced the standard error of estimate but did not bring obvious improvement to the bias error for the YS and OLS regression. The Keeling result was better using data from two sampling heights than only one. There was evidence that the Keeling method with the OLS regression slightly outperformed the flux-gradient method during periods with small vertical vapor gradient. Results also show that the Keeling method with the geometric mean regression gave highly biased estimate of δ for the types of isotope ratio infrared spectroscopy analyzer deployed in this study. These results can inform δ calculations and future experimental designs.

From inside the stemflow research community, the past decade’s progress might look great: 1) the number of papers published on stemflow per year has doubled; 2) citations of stemflow publications have more than doubled; and 3) the number of research sites monitoring stemflow is on the rise. However, from a broader perspective, a brief Web of Science bibliometric analysis of the past decade reveals issues with these trends: 1) annual publication numbers have increased year-to-year for most topics in natural science, but stemflow publication trends are lower than related and broader disciplines; 2) self-citation is significantly higher for stemflow research than other disciplines (e.g., 26% compared to 2% for all hydrology); and, most importantly, 3) we may have more stemflow data, but we still lack a clear understanding of stemflow’s mechanistic importance. This creates ambiguities as to whether and how stemflow processes can be incorporated into hydrological models and concepts. In this presentation, I argue that we should progress from using metrics that are exclusively used by those who work on stemflow (e.g., unitless metrics such as funneling and enrichment ratios) towards using scaled flux-per-unit-area metrics that may support better integration into hydrological and ecological models (e.g., water or chemical yield per unit canopy area). While the magnitudes of funneling and enrichment ratios from individual plants have effectively conveyed to broader audiences the possibility for stemflow to play important roles in ecosystem functioning, I argue that we need to now move onto mechanistic investigations of stemflow’s impact on specific processes at ecohydrologically relevant scales. Dimensionless (often individual plant-scale) funneling-type metrics may not be useful in this regard, as they tell us nothing about where stemflow goes or what roles stemflow may play in the ecosystem. They also rely on an estimate of infiltration area, which has rarely been observed to date. Their use is further limited to falling liquid-phase rain, which prevents comparison of stemflow observations/processes under occult precipitation (fog, condensation) or mixed and solid-phase precipitation (snow, rime, etc). Please view the “Make Stemflow Unit-ed Again” companion video on YouTube: https://youtu.be/4vPk9m45V0c

Recent, record-breaking discharge in the Yenisei River, Siberia, is part of a larger trend of increasing river flow in the Arctic driven by Arctic Amplification (AA). These changes in magnitude and timing of discharge can lead to increased risk of extreme flood events, with implications for infrastructure, ecosystems, and climate. To better understand the changes taking place, it is useful to have records that help place recent hydrological changes in context. In addition to an existing network of river gauges, wood anatomical features in riparian trees have been shown to record extreme flooding. Along the lower reaches of the Yenisei River we collected white willow (Salix alba) samples from a fluvial fill flat terrace that occasionally floods when water levels are extremely high. At the end of certain annual growth rings these samples displayed terminal white bands, a type of intra-annual density fluctuation (IADF). To identify the characteristics and causes of these features we use an approach known as quantitative wood anatomy (QWA) to measure variation in fiber cell dimensions across tree rings, particularly fiber lumen area (LA) and cell wall thickness (CWT). We investigate (1) which cell parameters and method to extract intra-annual data from annual tree rings best capture terminal white bands identified in Salix, and (2) if these patterns are related to flood magnitude and/or duration. We find that fiber CWT best captures the IADFs found in Salix rings. For some trees, time series of normalized CWT correlate with July flood durations, which have changed since the 1980s. Understanding how riparian vegetation responds to extreme flood events can help us better manage riparian ecosystems and understand changes to the Arctic hydrological regime.

Due to the effects of climate change, the USA Southwest is expected to experience increased temperatures and decreased water availability. Extended droughts will likely have important consequences for riparian trees whose growth and habitat are strongly limited by water availability. One consequence of extended water table declines on riparian trees is the development of wood anatomical anomalies. Using techniques developed for quantitative wood anatomy (QWA) it may be possible to quantify these anomalies and reconstruct water table variability through time. We explore this possibility using tree rings from netleaf hackberry (Celtis laevigata var. reticulata) collected along the Upper Santa Cruz River, Arizona. We suggest hackberry has the potential to be used 1) as an indicator of riparian ecosystem health, and 2) to provide improved reconstructions of water availability. Understanding how hackberry responds to periods of low and high flows can provide insight into how to better manage these ecosystems into the future.

Three estimates of soil moisture from National Oceanic and Atmospheric Administration (NOAA) programs are compared. The estimates are from a high-resolution atmospheric model with a land surface model, a hydrologic model and in situ observations. Both models demonstrate wetter soil moisture in dry regions and drier soil moistures in wet regions, as compared to the in situ observations. These soil moisture differences occur at most soil depths but are larger at the deeper depths below the surface (100 cm). In terms of soil moisture variance, both models generally have lower standard deviations as compared to the in situ observations, except for near the surface where the in situ and high-resolution, land surface model compare well. These NOAA soil moisture estimates are used for a variety of forecasting and societal applications, and understanding their differences provides important context for their applications and can lead to model improvements.

Chapter 25 of the Fourth National Climate Assessment (NCA4) is an assessment of climate change and variability, climate-related risks, impacts and adaptation in the U.S. Southwest. The chapter builds on assessments of climate change in the Southwest from the three previous U.S. National Climate Assessments. Each assessment has consistently identified drought, water resource reliability, and loss of ecosystem integrity as climate change challenges for the Southwest region. Chapter 25 further examines interconnections among water, ecosystems, coastal and marine systems, food, and human health and adds new key messages concerning energy and Indigenous peoples. The Southwest chapter is one of 29 chapters in Volume II of the Fourth National Climate Assessment - Impacts, Risks, and Adaptation in the United States. The National Climate Assessment fulfills the mandate of the Global Change Research Act (GCRA) of 1990 to provide the nation with a timely assessment and analysis of scientific findings of the effects of global change on multiple economic and natural resource sectors in the United States, and an analysis of observed and projected trends in global change. Chapter 25, Southwest, was written by a team of scientists and practitioners with expertise spanning areas specified in the GCRA, after extensive stakeholder engagement that involved the collection of input on key climate-related challenges, impacts, and opportunities in the Southwest region. The chapter went through multiple rounds of public and governmental review, during 2017 and 2018. This poster will focus on the findings from Chapter 25.

Abstract Gas relative permeability, krg, is a key parameter to determine gas production in unconventional reservoirs. Several theoretical approaches were proposed to study gas relative permeability in tight and ultra-tight porous rocks. Some models are based on a “bundle of capillary tubes” concept. Some others were developed based upon a combination of universal scaling laws from percolation theory and the effective-medium approximation (EMA). Although applications from the EMA have been successfully used to estimate single-phase permeability in permeable media (Ghanbarian et al., 2017; Ghanbarian and Javadpour, 2017), non-universal scaling from the EMA has never been invoked to model gas relative permeability in tight and/or ultra-tight porous rocks. In this study, it was assumed that pore-throat sizes follow the log-normal distribution. It was further assumed that gas transport in shales is mainly controlled by molecular and hydraulic flow, two mechanisms contributing in parallel. Using the EMA, effective pore-throat radii, effective conductances, and gas relative permeabilities were determined at various gas saturations. Comparison with three-dimensional pore-network simulations showed that the proposed krg model estimated gas relative permeability accurately. We also compared our model with experimental data reported in Yassin et al. (2016) including three Montney tight gas siltstone samples from the Western Canadian Sedimentary Basin. Results showed that our model estimated krg reasonably well, although it slightly overestimated krg. This might be because the fitted log-normal probability density function underestimated the probability of small pore-throat sizes. References Ghanbarian, B., & Javadpour, F. (2017). Upscaling pore pressure‐dependent gas permeability in shales. Journal of Geophysical Research: Solid Earth, 122(4), 2541-2552. Ghanbarian, B., Torres-Verdin, C., Lake, L. W., & Marder, M. P. (2017). Upscaling gas permeability in tight-gas sandstones. AGU Fall Meeting Abstracts. New Orleans LA. Yassin, M. R., Dehghanpour, H., Wood, J., & Lan, Q. (2016). A theory for relative permeability of unconventional rocks with dual-wettability pore network. SPE Journal, 21(06), 1970-1980.

In long-established cities such as Detroit, MI, stormwater runoff from impervious surfaces (roads, roofs, etc.) is combined with sanitary waste and piped beneath the city to a wastewater treatment plant. These engineered methods of water management, termed gray infrastructure, currently dominate management practices but have drawbacks due to issues of cost, capacity, reliability, and maintenance requirements. In addition, combined sewer systems in cities like Detroit can become overwhelmed during extreme storm events, requiring the diversion of untreated sewage into local bodies of water. In a world of growing urbanization and intensifying storm events, the development of innovative approaches to stormwater management is expected to play a large role in mitigating negative environmental impacts from polluted urban runoff and flooding. The eco-friendly counterpart of gray infrastructure, known as green infrastructure (green roofs, rain gardens, bioswales, etc.), is an integrated approach to stormwater management that utilizes decentralized infiltration systems to mimic the natural processes of water filtration and deceleration. Correspondingly, our research focuses on a bioswale that was retrofitted into the center island of a previously designed parking lot in the post-industrial urban ecosystem of Detroit. The main goals of our work are to: 1) quantify potential pollutant removal, 2) quantify runoff infiltration capacity, and 3) provide qualitative insight into best management practices for future bioswale retrofits. We collected soil samples from 2 depths (30 and 45 cm) at 9 points from the bioswale for characterization (pH, organic matter, electric conductivity, and texture). We also collected and analyzed undisturbed soil cores with and without added metal contaminants common to Detroit and with a surface addition of biochar to test infiltration and determine the potential of increased contaminant sorption. The results will allow us to advance with an improved design and methodology and convey a strong argument for the expansion of bioswales and other forms of green infrastructure across the greater Detroit area, potentially leading to a more robust system of controlling stormwater that brings aesthetics, health, and a wide array of ecosystem services to a redeveloping concrete jungle.

The frontier of wildfire-related risk assessment is moving into data science territory, and with good reason. Computational statistics, built on a foundation of high resolution remote sensing data, ground data, and theory, forms the basis of powerful risk assessment tools. The need for data based risk assessment has increased in past years, in view of longer wildfire seasons in the U.S., associated with more frequent droughts, more human ignitions and accumulating fuel loads. We present an application of machine learning (ML), which makes it possible to analyze complex data without a priori definition of interactions—this is a major advantage because these interactions are not known beforehand. Specifically, we build a stochastic gradient boosting machine (GBM) toolkit to assess the change in river flow after wildfire in the contiguous United States (CONUS) over a 5-year period. The GBM accounts for nonlinear relationships and interactions between wildland fire characteristics, watershed geometry, climate variability, topography and land cover. Building the GBM is a sequential process where a loss function is minimized at each fold, along a gradient defined by pseudo-residuals. This process allows the program to progressively learn more about how the variables in the large dataset interact to result in the response (i.e., river flow). Our results show that wildfires increase annual river flow in the CONUS when more than 20% of a gaged basin is burned. Data science tools like the GBM presented here, are essential in generating practical knowledge on how wildfire impacts on ecohydrology can ultimately affect hydrological services, socio-hydrosystems and water security in fire-affected regions.

Irrigation parameterizations in land surface models have been advanced over the past decade, but the newly available data from the Soil Moisture Active Passive (SMAP) satellite has seldom been used to improve irrigation modeling. Here, we investigate the potential of assimilating SMAP soil moisture (SM) data into the Community Land Model (CLM) to improve irrigation representation. Simulations are conducted at 3 arc-minute resolution over the highly irrigated region in the central US, fully enclosing the upstream areas of the river basins draining over the High Plains Aquifer (i.e., the Missouri and Arkansas), and Colorado River basins. We test the original CLM4.5 irrigation scheme and two new irrigation parameterizations using SMAP data assimilation by: (1) directly integrating raw SMAP data, and (2) integrating SMAP data using 1-D Kalman Filter (KF) smoother. An a priori scaling approach is also used to account for bias correction of the shortly-recorded SMAP data based on the ground observations, enabling us to use SMAP for out-of-sample tests (i.e., assessment of the new parameterizations during a non-SMAP period). The ground-based SM observations from three monitoring networks, namely Soil Climate Analysis Network (SCAN), US Climate Reference Network (USCRN), and SNOwpack TELemetry (SNOTEL) are employed for bias correcting SMAP data and validating SM simulations. Results show that SMAP data assimilation using 1-D KF significantly improves irrigation simulations. Bias correction of SMAP data further improves results from KF assimilation in some regions. However, the improvements are small compared to those achieved from 1-D KF application alone, indicating the robustness of using SMAP data and KF globally even for the regions where ground-based data are not available for bias correction. The data assimilation also improves the accuracy of the temporal dynamics and vertical profile of simulated SM. These results are expected to provide a basis for improved modeling of irrigation water use and land-atmosphere interactions.

High-resolution topographic data are used in geomorphic and hydrologic research for many purposes, including topographic change detection, development of computational meshes for hydraulic models, characterizing channel and hillslope geometry, measuring vegetation structure and density. These data can be collected in a variety of ways, ranging from manual surveying with a Total Station or GPS system, airborne LiDAR, terrestrial laser scanning (TLS), and Structure-from-Motion (SfM) photogrammetry using images collected from drones or pole-mounted cameras. These methods can be very time consuming to collect, and the equipment they require can be very costly. With the release of the 2020 iPad Pro and iPhone 12 Pro, Apple added a LiDAR sensor to their devices, enabling them to be used as hand-held 3D scanners. This new technology has the potential to enable very rapid collection of high-resolution topographic data at low cost. Here, we investigate how well iPad-based LiDAR characterizes topography and topographic change in hillslope and fluvial environments. A 2020 iPad Pro using two apps (3D Scanner and Polycam) was used to collect topographic data over areas ranging from about 100 – 600 m2. These same areas were scanned with a Topcon GLS-2000 TLS system, and aerial imagery were collected with a UAV and processed with Agisoft Metashape to create SfM point clouds. Ground-based targets visible in the datasets were surveyed with an RTK-GNSS system and used to register and scale the datasets. The datasets were aligned using the ICP algorithm in CloudCompare, and cross-sections and topographic differences were extracted from each dataset and compared. Our analysis indicates that transects collected with the iPad LiDAR have mean absolute differences with TLS and SfM data within 3 cm, making these data comparable to other high-resolution topographic data collection methods.

Regional crop production estimates are important in both public and private sectors to ensure the adequacy of a food supply and aid policymakers and farmers in managing harvest, storage, import/export, transportation, and anticipate market fluctuations. Food security will be progressively challenged by population growth and climate change. Thus, the prediction of accurate regional crop yield is essential for national food security and the sustainable development of the Indian agriculture sector. In this study, we have selected Punjab, the highest wheat yielding state in India. The district-wise wheat yield data were available for the year 2000 – 2019. We have used several covariates for crop health viz. normalized difference vegetation index (NDVI), leaf area index (LAI), fraction of absorbed photosynthetically active radiation (fAPAR); meteorological indicators viz. land surface temperature (LST), and evapotranspiration (ET); and surface characteristics viz. protrusion coefficient (PC). These indicators were generated at 250 m spatial resolution from the MODIS data using Google Earth Engine. The whole data was divided into two groups for training (2000 – 2009, 2011, 2013, 2014, 2016 - 2019) and testing (2010, 2012, 2015), which were randomly selected. This study uses the random forest (RF) regression method to create a wheat yield prediction model. We created several combinations of covariates and found that fAPAR and ET are highly correlated with NDVI and do not have much influence on the model’s prediction accuracy. Hence, only four out of six covariates were selected for final training. The coefficient of determination between district-level yield vs. (NDVI/LAI/PC/LST) was 0.37/0.31/0.15/0.13 respectively. We used randomized search cross-validation as well as grid search cross-validation for hyper-parameter tuning. Furthermore, we used mean absolute error (MAE) and accuracy as quality metrics. The MAE for training was 0.1870 t/Ha with 95.81% accuracy, whereas the MAE on test data was obtained as 0.4293 t/Ha with 90.02% accuracy. The results of this study are within acceptable error limits of the published research articles. Overall, this study demonstrates that covariates derived from coarse resolution satellite data can predict district-level crop yield with reasonable accuracy.

With observational data becoming widely available, researchers struggle to find information enabling assessment for its reliable use. A small first-step toward enabling data quality assessment of observational data is to associate the data with the sensor used to make the observations and to have the sensor description machine-harvestable. In the latest additions to the X-DOMES (Cross-Domain Observational Metadata for Enviromental Sensing) toolset, we have created targeted editors for creating SensorML documents to describe sensor models. The team has adjusted its delivery to enable integration of the X-DOMES content with the GEOCODES (JSON-LD/schema.org) EarthCube project. At our poster-session, we will highlight the new changes and capabilities and demonstrate the use of new X-DOMES tools.

A simple and efficient method was developed to improve soil moisture representation in an operational water balance model through satellite data assimilation. The proposed method exploits temporal covariance statistics between modelled and satellite-derived soil moisture to produce analysed estimates, as a weighted combination of all data sources. We demonstrate the application of the method to the Australian Water Resources Assessment (AWRA) model and evaluate the accuracy of the approach against in-situ observations across the water balance. The correlation between simulated surface soil moisture and in-situ observation is increased from 0.54 (open-loop) to 0.77 (data assimilation). We suggest an approach to use analysed surface moisture estimates to impart mass conservation constraints on related states and fluxes of the AWRA model in a post-analysis adjustment. The improvements gained from data assimilation can persist for more than one week in surface soil moisture estimates and one month in root-zone soil moisture estimates.

We investigate hydrology during a past climate slightly warmer than the present: the Last Interglacial (LIG). With daily output of pre-industrial and LIG simulations from eight new climate models we force hydrological model PCR-GLOBWB, and in turn hydrodynamic model CaMa-Flood. Compared to pre-industrial, annual mean LIG runoff, discharge, and 100-year flood volume are considerably larger in the Northern Hemisphere, by 14%, 25% and 82%, respectively. Anomalies are negative in the Southern Hemisphere. In some boreal regions, LIG runoff and discharge are lower despite higher precipitation, due the higher temperatures and evaporation. LIG discharge is much higher for the Niger, Congo, Nile, Ganges, Irrawaddy, Pearl, and lower for the Mississippi, Saint Lawrence, Amazon, Paraná, Orange, Zambesi, Danube, Ob. Discharge is seasonally postponed in tropical rivers affected by monsoon changes. Results agree with published proxies on the sign of discharge anomaly in 15 of 23 sites where comparison is possible.

ENSO events are the most significant interannual perturbation of the climate system. Previous attempts to link ENSO with volcanic eruptions failed because only large eruptions across the globe, which typically eject tephra into the stratosphere, were considered. I have analysed all volcanic eruptions in South Eastern (SE) Asia, about 10ºS to 10ºN and 90ºE to 160ºE (4d), the most volcanically active area in the world with over 23% of all eruptions in the Global Volcanism Program database since 1500 occurring here and with 5 volcanoes stated in the literature to have erupted nearly continuously for 30 years. SE Asia is also the region where the convective arm of the thermally direct Walker Circulation occurs driven by the intense equatorial solar radiation which creates the high surface temperature. The volcanic tephra plume intercepts some of the solar radiation by absorption/reflection which cools the surface and heats the atmosphere creating a temperature inversion compared to periods without the plume. This reduces convection and causes the Walker Circulation and Trade Winds to weaken. This reduced wind speed causes the central Pacific Ocean to warm creating convection there and further weakening the Walker Circulation. With the reduced wind stress the western Pacific warm pool migrates east. This creates an ENSO event which continues until the tephra plume reduces, typically when the SE Asian monsoon commences, and convection is re-established over SE Asia and the Pacific warm pool migrates back to the west. Correlations of SE Asian tephra and the ENSO indices are typically over 0.80 at ρ < 0.02 at 5c below. In recent decades the anthropogenic SE Asian aerosol Plume (SEAP) has intensified the volcanic plume in some years from September to November (SON). Using NASA satellite data and the NASA MERRA-2 reanalysis dataset I show correlation coefficients typically over 0.70 and up to 0.99 at ρ < 0.01 between the aerosol optical depth (AOD) or aerosol index (AI) and the ENSO indices on a detrended basis in SON at 5a. If two events A and B correlate 5 options are possible: (1) A causes B; (2) B causes A; (3) C, another event, causes A & B simultaneously; (4) It’s a coincidence; and (5) The relationship is complex with feedback. The volcanic results: only allow options 1 or 4 as ENSO cannot cause volcanic eruptions; and are backed up by 4 independent satellite datasets and NASA’s MERRA-2 reanalysis which assimilates aerosol observations. I conclude volcanic and anthropogenic aerosols over SE Asia are the sole cause of all ENSO events.

The present study aims to quantify (estimate) the impact of human water consumption—as for irrigation, livestock, domestic, manufacturing, and thermal energy production—versus (natural) climatic variability on the water balance and storage of the Lake Urmia (LU) basin and consequently the lake desiccation during the past decades. This is indeed a curious question with high practical relevance, given the ongoing drying of the lake and scientific debates around possible causes and viable remedies. One of the strength of the study is incorporating multiple input data (both ground and remote sensing) in developing the basin’s hydrologic model. The authors have also attempted to include the groundwater data which is highly important in this basin, and has been ignored in many (not all) of the previous studies. I enjoyed reading the paper, however, as the other reviewers have already pointed out there are major shortcomings that call for a major revision. In the spirit of helping the authors to improve the manuscript, I’d like to further comment on a number of—I believe—major deficiencies and questionable assumptions of the study that undermine the reliability of their results and discussion, given my own (limited) knowledge/experience in studying the lake’s dynamics and desiccation [Khatami, 2013; Khatami and Berndtsson, 2013; Khazaei et al., in review]. I hope the authors would find my comments useful in highlighting the new insights and contribution of their study.

Equifinality is understood as one of the fundamental difficulties in the study of open complex systems, including catchment hydrology. A review of the hydrologic literature reveals that the term equifinality has been widely used, but in many cases inconsistently and without coherent recognition of the various facets of equifinality, which can lead to ambiguity but also methodological fallacies. Therefore, in this study we first characterise the term equifinality within the context of hydrological modelling by reviewing the genesis of the concept of equifinality and then presenting a theoretical framework. During past decades, equifinality has mainly been studied as a subset of aleatory (arising due to randomness) uncertainty and for the assessment of model parameter uncertainty. Although the connection between parameter uncertainty and equifinality is undeniable, we argue there is more to equifinality than just aleatory parameter uncertainty. That is, the importance of equifinality and epistemic uncertainty (arising due to lack of knowledge) and their implications is overlooked in our current practice of model evaluation. Equifinality and epistemic uncertainty in studying, modelling, and evaluating hydrologic processes are treated as if they can be simply discussed in (or often reduced to) probabilistic terms (as for aleatory uncertainty). The deficiencies of this approach to conceptual rainfall-runoff modelling are demonstrated for selected Australian catchments by examination of parameter and internal flux distributions and interactions within SIMHYD. On this basis, we present a new approach that expands equifinality concept beyond model parameters to inform epistemic uncertainty. The new approach potentially facilitates the identification and development of more physically plausible models and model evaluation schemes particularly within the multiple working hypotheses framework, and is generalisable to other fields of environmental modelling as well.

Scarcity of available records is a major hindrance in hurricane hazard assessment. In addition, frequency analysis on maximum intensities of all historical storms is incapable of analyzing very rare phenomena. Ensemble generation is crucial for circumventing these difficulties, targeted at this study. We will show here that ensembles like Sandy can be statistically generated even by removing its trajectory from historical records. We began with historical compilations of NOAA National Climatic Data Center (NCDC) tropical cyclone (TC) database. TC reaching a hurricane strength and making landfall in or passing close to the United States were identified. The geographical area influenced by these hurricanes was discretized and the parameters of Markov chains and multivariate distributions were derived for each discretized area. Synthetic tracks were generated using repetitive random draws from the spatiotemporal distribution of historical genesis and storm motion, conditioned by Markov chains for each 6-hour displacement. The proposed algorithm is validated in macro and micro scales. In macro scale, tracks coming within the specified radius of an area of interest were counted for a given hurricane scale. The results revealed that the general pattern of hits conforms well to historical observations. In micro scale, the model was evaluated for Miami and New York City with quite different hurricane climatology. The track generator produces a history of potential wind and translational speeds for both of these regions as well.

The Congo basin is one of the most hydrologically active and pristine locations with limited understanding of how precipitation changes impacts on stream flow dynamics and variations in catchment stores. Given that the basin is among the three prominent convective regions that dominates global rainfall climatology during transition seasons, historical space-time variability of rainfall (1901-2014) over the basin in relation to river discharge is analyzed in order to understand significant hydro-climatic shift. Based on advance multivariate analyses, the total variability of the leading modes (annual variations) of rainfall increased during the 1931-1960 (56.3%) and 1961-1990 (57.3%) periods compared to the 1901-1930 baseline period (51.3%). It varied less between 1991 and 2014 (55.4%) as opposed to the two climatological periods between 1931 and 1990. Furthermore, the total variability in the multi-annual rainfall signals declined from 16.5% at the start of the century (1901-1930) to 13.6% in the 1991-2014 period while the total variability accounted for by other short-term meteorological signals oscillated between 4.0% and 2.7% during the entire period. Between 1995 and 2010 there seems to be a change in the hydrological regimes of the Congo river as the cumulative departures of rainfall and discharge were in opposite directions. The considerable association of discharge with rainfall in catchments characterized by strong annual and seasonal amplitudes in rainfall implies that the wetland hydrology of the basin is largely nourished by rainfall, in addition to possible exchange of fluxes within the Congo floodplain wetlands. Notably, a significant proportion of changes in the dominant rainfall patterns is still not explained by those of river discharge. This information signals the threshold of complex hydrological processes in the region, and perhaps suggest the influence of anthropogenic contributions (e.g., deforestation) and strong multi-scale ocean-atmosphere phenomena as key secondary drivers of hydrologic variability.

Groundwater is critical in sustaining streamflow, especially in mountain catchments, because of its ability to supply baseflow in the absence of precipitation. In water-limited arid and semi-arid mountain environments, the need to characterize groundwater recharge and discharge has grown in tandem with demands to effectively manage current and future water resources. However, studying groundwater is challenging in complex terrain due to limited field measurements. Nearly a decade of monitoring data collection at Gordon Gulch in the Colorado Front Range provides a unique opportunity to study such an environment. The field data is used to parameterize and calibrate a groundwater flow model (MODFLOW-NWT). Model results reveal spatial and temporal patterns in groundwater recharge and discharge to the stream. Groundwater is recharged primarily by one to two recharge events each year, driven by spring snowmelt and rain. The majority of groundwater recharge occurs in upper Gordon Gulch and is stored in saprolite and weathered bedrock. Groundwater is discharged to the stream via long, deep flowpaths sourced from upper Gordon Gulch and short, shallow flowpaths from soil and saprolite in lower Gordon Gulch. Using Gordon Gulch as a case study, this model and data analysis contribute to a larger effort to understand and constrain the mechanisms driving groundwater recharge and groundwater-stream exchanges in semi-arid, montane environments.

Understanding the impacts of climate on surface water hydrology is required to predict consequences and implications on freshwater habitats, ecological assets, and wetland functions. Although the Congo basin is considerably a freshwater-rich region, largely characterised by numerous water resources after the similitude of the Amazon basin, recent accounts of droughts in the basin are indications that even the most humid regions of the world can be affected by droughts and its impacts. Given the scarcity and limited availability of hydrological data in the region, GRACE (Gravity Recovery and Climate Experiment) observations are combined with model and SPEI (standardized precipitation evapotranspiration index) data to investigate the likelihood of such impacts on the Congo basin’s surface water hydrology. By integrating multivariate analysis with support vector machine regression (SVMR), this study provides some highlights on the characteristics (intensity and variability) of drought events and GRACE-derived terrestrial water storage (TWS) and the influence of global climate on the Congo river discharge. The southern section of the basin shows considerable variability in the spatial and temporal patterns of SPEI and extreme droughts over the Congo basin appear to have persisted with more than 40% coverage in 1994. However, there has been a considerable fall in drought intensities since 2007 and coincides with periods of strong positive anomalies in discharge (i.e., 2007-010). GRACE-derived TWS over the Congo basin is driven by annual fluctuations in rainfall (r = 0.81 at three months phase lag) and strong inter-annual variations of river discharge (r = 0.88, α= 0.05). Generally, results show that changes in the surface water variations (from gauge and model output) of the Congo basin is a key component of the GRACE water column. The outputs of the SVMR scheme indicate that global climate through sea surface temperature anomalies of the Atlantic (r = 0.79, α= 0.05), Pacific (r = 0.79, α= 0.05), and Indian (r = 0.74, α= 0.05) oceans are associated with fluctuations in the Congo river discharge, and confirm the importance of climatic influence on surface water hydrology in the Congo basin.

Human activities affecting hydrological processes in a catchment have many forms and the effect of those activities on the propagation of drought in a catchment depends on the relevant scales of the processes involved. The research presented focuses on the influence of reservoir and land use on drought dynamics. As a case study, we use the River Kamienna, Swietokrzyskie mountains, Poland. The Kamienna River has a mountainous character, with several water retention reservoirs and a history of industrial activities in the region. Annual water balance is also affected by water withdrawals in the catchment. Modeling tools in the form of lumped and semi-distributed hydrological models are applied to analyze hydrological processes in the catchment and to separate climatological and human-related factors affecting them. Two main goals of the modeling can be summarized as an investigation of the effects of reservoir operation on hydrological processes, especially dry season runoff, and an analysis of impacts of land-use change on the spatial-temporal characteristics of hydrological drought propagation. We apply three different models to simulate the catchment hydrological processes: two semi-distributed models, SWAT, and TOPMODEL, and the lumped hydrological model HBV. We present the calibration and verification of the models applied and a comparison of results using the goodness of fit criteria. The simulated flow at the gauging station along the Kamienna River is compared with the observed flow. The study applies several flow-related indices to understand how the climate and human-induced changes are affecting flow patterns in the region. The flow regime is described using a baseflow index and the runoff coefficient. The standardized runoff index (SRI) and runoff coefficients are also derived for the catchment. The results indicate that human activities dominated the decrease in runoff over the Kamienna River. The main finding shows that man-made activities such as the construction of reservoirs, land-use changes, mining, etc. have led to a more severe hydrological drought than under natural conditions. The research is part of the project “Human and climate impacts on drought dynamics and vulnerability” HUMDROUGHT (http://HUMDROUGHT.igf.edu.pl).