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The use of Geographic Information Systems (GIS) and remote sensing technologies for the development of water quality management programs and for post-implementation assessments has increased dramatically in the past decade. This increase in adoption has been made more accessible through the interfaces of many popular software tools used in the regulation and assessment of water quality. Customized applications of these tools will increase, as ease of access and affordability of directly monitored and remotely sensed datasets improve over time. Concurrently, there is a need for inclusive participatory engagement with stakeholders to achieve solutions to current watershed management challenges. This paper explores the potential of these GIS and remote sensing datasets, tools, models, and immersive engagement technologies from other domains, for improving public participation and stakeholder engagement throughout the watershed planning process. To do so, an initial review is presented about the use of GIS and remote sensing in watershed management and its role in impairment identification, model development, and planning and implementation. Then, ways in which GIS and remote sensing can be integrated with stakeholder engagement through (1) leveraging GIS and remote sensing datasets, and (2) stakeholder engagement approaches including outreach and education, modeler-led development, and stakeholder-led involvement and feedback, are discussed. Finally, future perspectives on the potential for transforming public participation and stakeholder engagement in the watershed management process through applications of GIS and remote sensing are presented.

Surface water reservoirs are increasingly being relied upon to meet rising demands in the context of growing population and changing climate. However, the amount of water available in reservoirs (and the corresponding trends) have not been well quantified at the global scale. Here we use satellite observations to estimate the storage variations of 7245 global reservoirs from 1999 to 2018. Total global reservoir storage has increased at a rate of 27.82 ± 0.08 km3/yr, which is mainly attributed to the construction of new dams. However, the normalized reservoir storage (NS)—the ratio of the actual storage to the storage capacity—has declined by 0.82 ± 0.01%. The decline of NS values is especially pronounced in the global south, while the global north mainly exhibits an NS increase. With predicted decreasing runoff and increasing water demand, these observed diminishing storage returns of reservoir construction will likely persist into the future.

We present a simple methods to leverage the table content for the BERT-based model to solve the text-to-SQL problem. Based on the observation that some of the table content match some words in question string and some of the table header also match some words in question string, we encode two addition feature vector for the deep model. Our methods also benefit the model inference in testing time as the tables are almost the same in training and testing time. We test our model on the WikiSQL dataset and outperform the BERT-based baseline by 3.7% in logic form and 3.7% in execution accuracy and achieve state-of-the-art., Comment: working in progress

This paper studies the performances of BERT combined with tree structure in short sentence ranking task. In retrieval-based question answering system, we retrieve the most similar question of the query question by ranking all the questions in datasets. If we want to rank all the sentences by neural rankers, we need to score all the sentence pairs. However it consumes large amount of time. So we design a specific tree for searching and combine deep model to solve this problem. We fine-tune BERT on the training data to get semantic vector or sentence embeddings on the test data. We use all the sentence embeddings of test data to build our tree based on k-means and do beam search at predicting time when given a sentence as query. We do the experiments on the semantic textual similarity dataset, Quora Question Pairs, and process the dataset for sentence ranking. Experimental results show that our methods outperform the strong baseline. Our tree accelerate the predicting speed by 500%-1000% without losing too much ranking accuracy.

We present a simple way to do the task of text-to-SQL problem with weak supervision. We call it Rule-SQL. Given the question and the answer from the database table without the SQL logic form, Rule-SQL use the rules based on table column names and question string for the SQL exploration first and then use the explored SQL for supervised training. We design several rules for reducing the exploration search space. For the deep model, we leverage BERT for the representation layer and separate the model to SELECT, AGG and WHERE parts. The experiment result on WikiSQL outperforms the strong baseline of full supervision and is comparable to the start-of-the-art weak supervised mothods.

Semantic parsing is the task of mapping natural language to logic form. In question answering, semantic parsing can be used to map the question to logic form and execute the logic form to get the answer. One key problem for semantic parsing is the hard label work. We study this problem in another way: we do not use the logic form any more. Instead we only use the schema and answer info. We think that the logic form step can be injected into the deep model. The reason why we think removing the logic form step is possible is that human can do the task without explicit logic form. We use BERT-based model and do the experiment in the WikiSQL dataset, which is a large natural language to SQL dataset. Our experimental evaluations that show that our model can achieves the baseline results in WikiSQL dataset.

Freshwater availability and agricultural production are key factors for sustaining the fast growing population and economy in the state of Texas, which is the third largest state in terms of agricultural production in the United States. This paper describes a long-term (1918-2011) grid-based (1/8°) surface hydrological dataset for Texas at a daily time step based on simulations from the Variable Infiltration Capacity (VIC) hydrological model. The model was calibrated and validated against observed streamflow over 10 Texas river basins. The simulated soil moisture was also evaluated using in situ observations. Results suggest that there is a decreasing trend in precipitation and an increasing trend in temperature in most of the basins. Droughts and floods were reconstructed and analyzed. In particular, the spatially distributed severity and duration of major Texas droughts were compared to identify new characteristics. The modeled flood recurrence interval and the return period were also compared with observations. Results suggest the performance of extreme flood simulations needs further improvement. This dataset is expected to serve as a benchmark which may contribute to water resources management and to mitigating agricultural drought, especially in the context of understanding the effects of climate change on crop yield in Texas.

In just the past 5 years, the field of Earth observation has progressed beyond the offerings of conventional space-agency-based platforms to include a plethora of sensing opportunities afforded by CubeSats, unmanned aerial vehicles (UAVs), and smartphone technologies that are being embraced by both for-profit companies and individual researchers. Over the previous decades, space agency efforts have brought forth well-known and immensely useful satellites such as the Landsat series and the Gravity Research and Climate Experiment (GRACE) system, with costs typically of the order of 1 billion dollars per satellite and with concept-to-launch timelines of the order of 2 decades (for new missions). More recently, the proliferation of smartphones has helped to miniaturize sensors and energy requirements, facilitating advances in the use of CubeSats that can be launched by the dozens, while providing ultra-high (3– 5 m) resolution sensing of the Earth on a daily basis. Startup companies that did not exist a decade ago now operate more satellites in orbit than any space agency, and at costs that are a mere fraction of traditional satellite missions. With these advances come new space-borne measurements, such as real-time high-definition video for tracking air pollution, storm-cell development, flood propagation, precipitation monitoring, or even for constructing digital surfaces using structure-from-motion techniques. Closer to the surface, measurements from small unmanned drones and tethered balloons have mapped snow depths, floods, and estimated evaporation at sub-metre resolutions, pushing back on spatiotemporal constraints and delivering new process insights. At ground level, precipitation has been measured using signal attenuation between antennae mounted on cell phone towers, while the proliferation of mobile devices has enabled citizen scientists to catalogue photos of environmental conditions, estimate daily average temperatures from battery state, and sense other hy

Generating structural query language (SQL) queries from natural language is a long-standing open problem. Answering a natural language question about a database table requires modeling complex interactions between the columns of the table and the question. In this paper, we apply the synthesizing approach to solve this problem. Based on the structure of SQL queries, we break down the model to three sub-modules and design specific deep neural networks for each of them. Taking inspiration from the similar machine reading task, we employ the bidirectional attention mechanisms and character-level embedding with convolutional neural networks (CNNs) to improve the result. Experimental evaluations show that our model achieves the state-of-the-art results in WikiSQL dataset., Comment: 7 pages 3 figures

With its headwaters in the water towers of the western Cordillera of North America, the Fraser River is one of the continent’s mightiest rivers by annual flows, supplies vital freshwater resources to populous downstream locations, and sustains the world’s largest stocks of sockeye salmon along with four other salmon species. Here we show the Variable Infiltration Capacity (VIC) model’s ability to reproduce accurately observed trends in daily streamflow for the Fraser River’s main stem and six of its major tributaries over 1949-2006 when air temperatures rose by 1.4 °C while annual precipitation amounts remained stable. Rapidly declining mountain snowpacks and earlier melt onsets result in a 10-day advance of the Fraser River’s spring freshet with subsequent reductions in summer flows when up-river salmon migrations occur. Identification of the sub-basins driving the Fraser River’s most significant changes provides a measure of seasonal predictability of future floods or droughts in a changing climate.

With its headwaters in the water towers of the western Cordillera of North America, the Fraser River is one of the continent’s mightiest rivers by annual flows, supplies vital freshwater resources to populous downstream locations, and sustains the world’s largest stocks of sockeye salmon along with four other salmon species. Here we show the Variable Infiltration Capacity (VIC) model’s ability to reproduce accurately observed trends in daily streamflow for the Fraser River’s main stem and six of its major tributaries over 1949-2006 when air temperatures rose by 1.4 °C while annual precipitation amounts remained stable. Rapidly declining mountain snowpacks and earlier melt onsets result in a 10-day advance of the Fraser River’s spring freshet with subsequent reductions in summer flows when up-river salmon migrations occur. Identification of the sub-basins driving the Fraser River’s most significant changes provides a measure of seasonal predictability of future floods or droughts in a changing climate.

Image Classification based on BOW (Bag-of-words) has broad application prospect in pattern recognition field but the shortcomings are existed because of single feature and low classification accuracy. To this end we combine three ingredients: (i) Three features with functions of mutual complementation are adopted to describe the images, including PHOW (Pyramid Histogram of Words), PHOC (Pyramid Histogram of Color) and PHOG (Pyramid Histogram of Orientated Gradients). (ii) The improvement of traditional BOW model is presented by using dense sample and an improved K-means clustering method for constructing the visual dictionary. (iii) An adaptive feature-weight adjusted image categorization algorithm based on the SVM and the fusion of multiple features is adopted. Experiments carried out on Caltech 101 database confirm the validity of the proposed approach. From the experimental results can be seen that the classification accuracy rate of the proposed method is improved by 7%-17% higher than that of the traditional BOW methods. This algorithm makes full use of global, local and spatial information and has significant improvements to the classification accuracy., Comment: 22 pages,4 figures

This paper presents an application of the Variable Infiltration Capacity (VIC) model to the Fraser River basin (FRB) of British Columbia (BC), Canada, over the latter half of the twentieth century. The Fraser River is the longest waterway in BC and supports the world’s most abundant Pacific Ocean salmon populations. Previous modeling and observational studies have demonstrated that the FRB is a snow-dominated system, but with climate change, it may evolve to a pluvial regime. Thus, the goal of this study is to evaluate the changing contribution of snow to the hydrology of the FRB over the latter half of the twentieth century. To this end, a 0.25° atmospheric forcing dataset is used to drive the VIC model from 1949 to 2006 (water years) at a daily time step over a domain covering the entire FRB. A model evaluation is first conducted over 11 major subwatersheds of the FRB to quantitatively assess the spatial variations of snow water equivalent (SWE) and runoff (R). The ratio of the spatially averaged maximum SWE to R (RSR) is used to quantify the contribution of snow to the runoff in the 11 subwatersheds of interest. From 1949 to 2006, RSR exhibits a significant decline in 9 of the 11 subwatersheds (with p < 0.05 according to the Mann–Kendall test statistics). To determine the sensitivity of RSR, the air temperature and precipitation in the forcing dataset are then perturbed. The ratio RSR decreases more significantly, especially during the 1990s and 2000s, when air temperatures have warmed considerably compared to the 1950s. On the other hand, increasing precipitation by a multiplicative factor of 1.1 causes RSR to decrease. As the climate continues to warm, ecological processes and human usage of natural resources in the FRB may be substantially affected by its transition from a snow to a hybrid (nival/pluvial) and even a rain-dominated system.

This paper presents an application of the Variable Infiltration Capacity (VIC) model to the Fraser River basin (FRB) of British Columbia (BC), Canada, over the latter half of the twentieth century. The Fraser River is the longest waterway in BC and supports the world’s most abundant Pacific Ocean salmon populations. Previous modeling and observational studies have demonstrated that the FRB is a snow-dominated system, but with climate change, it may evolve to a pluvial regime. Thus, the goal of this study is to evaluate the changing contribution of snow to the hydrology of the FRB over the latter half of the twentieth century. To this end, a 0.25° atmospheric forcing dataset is used to drive the VIC model from 1949 to 2006 (water years) at a daily time step over a domain covering the entire FRB. A model evaluation is first conducted over 11 major subwatersheds of the FRB to quantitatively assess the spatial variations of snow water equivalent (SWE) and runoff (R). The ratio of the spatially averaged maximum SWE to R (RSR) is used to quantify the contribution of snow to the runoff in the 11 subwatersheds of interest. From 1949 to 2006, RSR exhibits a significant decline in 9 of the 11 subwatersheds (with p < 0.05 according to the Mann–Kendall test statistics). To determine the sensitivity of RSR, the air temperature and precipitation in the forcing dataset are then perturbed. The ratio RSR decreases more significantly, especially during the 1990s and 2000s, when air temperatures have warmed considerably compared to the 1950s. On the other hand, increasing precipitation by a multiplicative factor of 1.1 causes RSR to decrease. As the climate continues to warm, ecological processes and human usage of natural resources in the FRB may be substantially affected by its transition from a snow to a hybrid (nival/pluvial) and even a rain-dominated system.